system

Abstract

This system aims to improve employees' self-management skills, reduce the burden on managers, and enhance overall workplace efficiency. [Solution] A system comprising: data collection means connected to an information processing device; analysis means for analyzing task data collected by the data collection means using a machine learning algorithm; prediction means for calculating task priorities and estimated work times based on the analysis results of the analysis means; notification means for notifying the user of the information calculated by the prediction means; feedback means for recording actual work times based on user input and providing feedback to the prediction means; adjustment means for adjusting the estimate of the next work time using the recorded data; and a dashboard display means for showing the progress of members to managers.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern workplaces, it is common for various tasks to proceed in parallel. Among them, it is required to accurately grasp the priority of tasks and perform appropriate time allocation. However, in a situation where individual employees frequently have many tasks and projects, it is difficult to grasp the progress of tasks at a glance, and wasteful time consumption and delivery delays are likely to occur. Such problems are particularly prominent in new projects or inexperienced operations, which may lead to a decrease in work efficiency and an impact on normal operations. In addition, management personnel need to appropriately grasp the task progress of all members and provide guidance and reallocation of tasks in a timely manner, but in some cases, this cannot be effectively achieved by conventional methods.

Means for Solving the Problems

[0005] This invention provides a means for connecting to an information processing device to collect internal task and project data, and a means for analyzing that data using a machine learning algorithm. It also has a function to calculate task priorities and estimated work times based on the analysis results and notify the user. Furthermore, it includes a means for recording the actual work time entered by the user and reflecting that information as feedback in the estimated time for subsequent tasks. For managers, it presents the overall progress of team members in a dashboard format to support the efficient distribution of work. In addition, the notification means monitors task progress and provides reminders as needed to prevent task delays. Through this series of functions, it is possible to improve employees' self-management abilities, reduce the burden on managers, and improve the overall work efficiency of the workplace.

[0006] An "information processing device" is an electronic device used for collecting, analyzing, storing, and transmitting data.

[0007] "Data collection means" refers to technologies that have the ability to automatically or manually acquire information related to internal tasks and projects.

[0008] A "machine learning algorithm" is a computational method that learns patterns from past data and predicts future outcomes.

[0009] "Analytical means" refers to methods or devices for processing collected data and extracting useful information or insights.

[0010] A "predictive tool" is a device or method that has the function of estimating future actions or results from the results of an analysis.

[0011] "Notification methods" refer to communication technologies and functions used to transmit important information and reminders to users.

[0012] A "feedback mechanism" is a function that incorporates user input and results into the system to help improve future work.

[0013] "Adjustment means" refers to a method or apparatus for correcting predictions and evaluations based on collected data to derive optimal results.

[0014] A "dashboard display means" is a display device or interface that visually organizes information and allows users to easily understand and access data. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] 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

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be described.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] 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.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides a system that improves the efficiency of task management by combining an information processing device and a machine learning algorithm. This system primarily consists of a server, terminals, and users. The server automatically collects data on various tasks and projects within the company and analyzes this data using a machine learning algorithm. This allows the system to calculate the priority of current tasks and the estimated work time based on past performance.

[0037] When a user registers a new task using their device, the server receives this information and uses a prediction system to calculate the appropriate work time and priority. By notifying the user of these results, tasks can be managed effectively. In addition, when the user inputs the actual work time spent via their device, this information is sent to the server via a feedback system and used to improve the accuracy of predictions for the next task.

[0038] Furthermore, to allow managers to easily check the progress of their work, the server uses a dashboard display to visualize the task progress of team members. This visualization enables quick identification of task delays and workload imbalances, allowing for necessary work adjustments.

[0039] For example, if a user registers a task called "Create a report," the server predicts the required work time as "8 hours" based on data from similar past tasks and sets a priority for the task until the deadline. This information is immediately notified to the user, allowing them to allocate their time appropriately. After completing the task, the user records the actual work time as "10 hours" and sends it to the server. This data is then used in the next prediction, helping the system provide more accurate time estimates.

[0040] Thus, this system is designed to support users in managing their tasks and to enable efficient operation of the entire organization.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server integrates with the company's information systems to automatically collect task and project data from each department. This data includes the task's start date, planned end date, assigned person, and details of related projects.

[0044] Step 2:

[0045] The server inputs the collected data into a machine learning algorithm to calculate task priorities and estimated work times. During this process, it references past performance data from similar tasks and performs statistical analysis.

[0046] Step 3:

[0047] Users register new tasks in the system via their devices. The information entered includes the task name, details, and deadline.

[0048] Step 4:

[0049] The server receives task information registered by the user and calculates the estimated work time and priority for the task based on the analysis results obtained in the previous step.

[0050] Step 5:

[0051] The server notifies the user of the calculated task priority and estimated work time. This notification is provided via email or in-system message through the terminal.

[0052] Step 6:

[0053] When a user completes a task, the actual time spent is recorded on the device and entered into the system. This information is important for later analysis.

[0054] Step 7:

[0055] The server receives actual work time from users and uses feedback to predict the next task. The prediction algorithm is then adjusted, incorporating the newly acquired data.

[0056] Step 8:

[0057] The server updates a dashboard for managers that displays the task progress and workload of all users in real time. This allows managers to quickly grasp the situation and adjust tasks accordingly.

[0058] (Example 1)

[0059] 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."

[0060] In today's work environment, the burden of managing diverse tasks and projects is increasing, demanding greater efficiency for both individuals and organizations as a whole. In this context, an information processing system capable of efficiently prioritizing tasks and predicting work time is necessary. However, current systems often fail to fully utilize historical data, resulting in insufficient prediction accuracy and management transparency. This leads to problems such as inappropriate task prioritization and decreased work efficiency.

[0061] 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.

[0062] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing business data acquired by the data collection means using a machine learning algorithm, and calculation means for calculating business priorities and estimated work times based on the analysis results of the analysis means. This enables users to efficiently manage their work and to make more accurate predictions of work times and prioritize tasks.

[0063] An "information processing device" is a computer system used for data collection, analysis, and notification, and refers to a wide range of hardware, including servers and individual terminals.

[0064] "Data collection means" refers to a function that automatically acquires various business data within a company by being connected to an information processing device.

[0065] "Analysis method" refers to the process of analyzing collected business data using machine learning algorithms.

[0066] "Calculation method" refers to a function that uses the analysis results of the analysis method to calculate the priority of tasks and the required work time.

[0067] "Notification means" refers to the communication process for transmitting information generated by the calculation means to the user.

[0068] A "feedback mechanism" refers to a function that records the actual work time based on user input and sends that information back to the calculation mechanism to improve prediction accuracy.

[0069] "Adjustment means" refers to the process of using recorded data to improve the estimation of the next work time and enhance the accuracy of the model.

[0070] "Progress display means" refers to display technology that enables administrators to visually check the work progress of their team members.

[0071] This invention is a task management system that combines an information processing device and a machine learning algorithm, and mainly consists of a server, terminals, and users. The server automatically collects and stores various business data within the company, and has the function of analyzing that data to prioritize tasks and calculate predicted work times. For data collection and acquisition, it cooperates with existing business management software via an API and stores the data in a database. Python is used for the program, and the machine learning model is built using TENSORFLOW®. This model performs analysis based on historical data and helps to streamline business management.

[0072] Users use their devices to register new tasks and input the actual time spent on them. The server then feeds back the data received from the user, improving the accuracy of future predictions. The user's device has a web application (e.g., a frontend built with React) implemented, providing a user-friendly interface. When a user registers a task like "taking meeting minutes," the system predicts the work time as "2 hours" based on past data and sets a priority. If the user then inputs the actual time spent on the task as "3 hours," the prediction accuracy for similar tasks in the future will improve.

[0073] The server also provides a dashboard for managers to visualize the progress of team members. This allows them to quickly grasp the work progress of each member on the dashboard and adjust tasks as needed. Tableau and Power BI are used as data visualization tools to support intuitive visualization.

[0074] For example, you can enter the following prompt:

[0075] "Please explain how the system calculates the optimal work time and priority when a user registers 'Create a project plan' as a new task."

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server retrieves business data from the company's internal business management software via API. It receives task and project information retrieved from the API as input. The server then processes the data for storage in a database, saving the details of each task. This creates a foundation for understanding the overall business progress.

[0079] Step 2:

[0080] The server performs analysis using a machine learning model based on the acquired data. It uses historical task data stored in a database as input. Data preprocessing is performed using Python, and analysis is conducted using a TensorFlow model. The output provides the priority and estimated work time for each task. These results are used for efficient task management.

[0081] Step 3:

[0082] Users register new tasks through their device. They input task details, deadlines, requirements, etc., into a web application in their browser. The device uses a React-based interface, and the registered data is immediately sent to the server.

[0083] Step 4:

[0084] The server feeds the received new task data into a predictive model and recalculates the work time and priority. User-provided task information is used as input. Based on the data analysis, prediction results are generated using Python calculations, and the results are sent to the user via a notification system.

[0085] Step 5:

[0086] After completing a task, the user reports the actual time spent on that task via their device. The input consists of the ID of the completed task and the actual time spent, which is entered into a web application. The device then resends this information to the server.

[0087] Step 6:

[0088] The server feeds back actual data and updates the predictive model. It uses actual time submitted by the user as input. The server retrains the machine learning model to improve the accuracy of the next prediction. The updated model is then used for the next task analysis.

[0089] Step 7:

[0090] The server visualizes the task progress of each team member for managers. It uses analyzed progress data as input. Through data visualization tools, the server displays each member's progress on a dashboard, allowing managers to quickly adjust tasks.

[0091] (Application Example 1)

[0092] 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."

[0093] In today's manufacturing industry, there is a demand to maximize the efficiency of each process and improve overall productivity. However, properly managing the prioritization and time required for individual tasks and optimizing the entire process is a challenge that requires considerable effort and cost. Therefore, there is a need to build a system that can efficiently manage production processes in real time while utilizing historical data.

[0094] 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.

[0095] In this invention, the server includes information gathering means, analysis means for analyzing task data, and prediction means for calculating process priorities and estimated times. This makes it possible to optimize the overall process while improving the efficiency of each process in the manufacturing equipment.

[0096] An "information processing device" is a computer system used to collect, analyze, and process data.

[0097] A "data collection method" is a system for automatically acquiring business data.

[0098] A "machine learning algorithm" is a mathematical method that analyzes past data to discover new trends and patterns.

[0099] "Analysis methods" refer to the process of analyzing collected data and extracting meaningful information.

[0100] A "predictive tool" is a system that calculates future work time and priorities based on the analysis results.

[0101] A "notification method" is a means of communication used to inform users of the calculated information.

[0102] A "feedback method" is a way to record actual work time and results to help improve the accuracy of future predictions.

[0103] A "correction mechanism" is a system that uses recorded data to revise the estimate of the next work time.

[0104] A "visualization tool" is a display tool that allows administrators to visually check the progress of a team.

[0105] "Production optimization means" refers to processes and methods for improving the efficiency of each stage in manufacturing equipment.

[0106] The system based on the present invention is centered around an information processing device and includes data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, visualization means, and production optimization means. The server plays a central role, receiving and processing data from terminals and users.

[0107] The server primarily uses Python for data processing and operates on cloud services such as AWS® EC2. Operational data is automatically acquired from manufacturing equipment using data collection methods. This data is analyzed using analytical tools incorporating machine learning algorithms, and trends and patterns are extracted using libraries such as TensorFlow.

[0108] Based on the analysis results, the server's prediction system calculates the priority and estimated time for each task. This information is delivered to the user's terminal via a notification system. The user registers a new task using their terminal and inputs the actual work time, sending data to the server via a feedback system. This information is used to improve the accuracy of future predictions, and the work estimate is revised by an adjustment system.

[0109] Managers can understand the progress through visualization tools and achieve efficiency improvements in manufacturing equipment through production optimization tools. For example, if a factory manages the assembly process of automotive parts with this system, it can predict the installation time of individual parts and derive an efficient process sequence.

[0110] An example of a prompt message would be: "Generating AI system, predict the installation time for part A and create an optimization plan for the production line."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server collects operational data from manufacturing equipment using data collection methods. This data includes the start and end times of each process, the resources used, and so on. It receives data provided in real time from the data collection system as input and stores it in the database.

[0114] Step 2:

[0115] The server inputs the stored data into a machine learning algorithm and performs analysis. Using the TensorFlow library, it models work time and trends from past data and extracts patterns. This process can identify delays in work and potential opportunities for efficiency improvements. The analysis results are generated as output.

[0116] Step 3:

[0117] Based on the analysis results, the server uses predictive tools to calculate the priority and estimated work time for each process. This process uses a generative AI model to predict the optimal work sequence for new tasks registered by the user. Optimization information is generated as output to notify the user.

[0118] Step 4:

[0119] The server sends the prediction results to the terminal via a notification system. The terminal receives this information and is responsible for notifying the user. This information includes the estimated time required for the task and its priority, serving as a guide for the user to work efficiently.

[0120] Step 5:

[0121] Users input the actual time spent on a task using their device and send it to the server via a feedback mechanism. This input data is stored on the server as training data to improve the accuracy of future predictions.

[0122] Step 6:

[0123] The server uses the feedback data to adjust the next work estimate using a recalibration mechanism. In this step, the model is retrained based on actual work data, resulting in more accurate predictions.

[0124] Step 7:

[0125] Administrators can use visualization tools on their terminals to monitor progress and production efficiency in real time. This allows them to quickly identify delays and workload imbalances and take necessary countermeasures promptly.

[0126] 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.

[0127] The present invention relates to a system connected to an information processing device, comprising data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, and dashboard display means, in addition to an emotion engine that recognizes the user's emotions. This emotion engine is used to understand the user's emotional state based on user input and behavioral data, and to improve the quality of decision-making in task management.

[0128] The server first collects task-related data from internal systems. Then, it uses machine learning algorithms to analyze this data and calculate task priorities and estimated completion times. Users register tasks using their terminals, and the server notifies them of the calculation results based on this information. Notifications are provided at appropriate times, allowing users to take action to prevent task delays.

[0129] Furthermore, the emotion engine monitors the user's keystrokes, input patterns, and frequency of breaks during work to estimate the user's emotional state. If a change in emotion is detected, the information is sent to the server, and the analysis tool can dynamically adjust task priorities and notification content. This reduces user stress while enabling efficient task management.

[0130] As a concrete example, consider a situation where a user has multiple tasks. If this user is detected to be experiencing high stress levels while working on the "writing a report" task, the emotion engine sends this information to the server. The server determines that the user should take a break outside of work and notifies the user accordingly. It also uses predictive tools as needed to adjust task deadlines or suggests considering redistributing tasks with other colleagues.

[0131] By utilizing this emotion engine, flexible task management tailored to the user's emotional state becomes possible, leading to improved overall organizational efficiency and a better work environment.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server connects to the company's information systems and periodically collects data on tasks and related projects. This data includes task deadlines, assigned personnel, and progress.

[0135] Step 2:

[0136] The server inputs the collected data into a machine learning algorithm to analyze task priorities and estimated work times. During this process, statistical methods are used based on past performance data to improve prediction accuracy.

[0137] Step 3:

[0138] Users register new tasks and schedules using their devices. The information registered includes the task name, details, and deadline.

[0139] Step 4:

[0140] The server notifies the user of the optimal work time and priority calculated by a prediction system based on the tasks registered by the user. The notification is provided via email or in-system message.

[0141] Step 5:

[0142] The emotion engine monitors the user's keystrokes, frequency of breaks during work, input speed, etc., to estimate the user's emotional state. This information is sent to the server in real time.

[0143] Step 6:

[0144] The server analyzes emotional data received from the emotion engine and dynamically adjusts task reminders, priorities, and workloads if high stress levels or emotional disturbances are detected. Based on these adjustments, it notifies the user of appropriate actions.

[0145] Step 7:

[0146] After a user completes a task, the actual time spent and feedback are recorded on the device and entered into the system. This information is used to predict and improve future processes.

[0147] Step 8:

[0148] The server updates a dashboard for managers based on recorded work and emotional data, providing a visual overview of all team members' progress and emotional status. This allows managers to make appropriate adjustments to tasks and propose stress reduction measures.

[0149] (Example 2)

[0150] 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".

[0151] Many information processing systems fail to consider the user's emotional state in task management, leading to increased user stress and consequently decreased work efficiency. In particular, the fixed nature of task priorities and estimated work times makes it difficult to respond flexibly to the user's situation.

[0152] 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.

[0153] In this invention, the server includes means for collecting data, means for calculating task priorities and estimated work times based on the analysis results, and means for estimating the user's emotional state. This enables flexible task management that responds to the user's emotional state.

[0154] "Means for collecting data" refers to a process or function for automatically or manually obtaining necessary data from an information processing device.

[0155] "Methods of analysis using machine learning algorithms" refer to the process of analyzing data based on specific models or algorithms using collected datasets to gain insights.

[0156] "Means for calculating task priorities and estimated work time" refers to a function that determines the importance of a task and predicts the time required to complete it, based on analyzed data.

[0157] "Means of notifying users" refers to methods and functions for communicating important information to users, including email, messages, and alerts.

[0158] "A means of recording actual work time based on input and feeding it back into the prediction method" refers to a process that uses user input information to record the actual work time taken and utilizes that information to improve the accuracy of predictions for the next time.

[0159] "Means for adjusting work time estimates" refers to a process for more accurately estimating future work time based on past work performance data.

[0160] "Means for presenting progress" refers to functions that visually display task progress to administrators and users, and are provided in the form of dashboards, etc.

[0161] "Means for estimating emotional state" refers to the process of inferring the user's emotions at a given time based on their operation patterns and behavioral data.

[0162] "Means for dynamic adjustment" refers to a function that automatically readjusts tasks and notifications to the optimal state according to the user's current state and environment.

[0163] This invention provides task management using a system connected to an information processing device, and aims to create an efficient work environment by taking into account the user's emotional state. Specifically, it is composed of a combination of data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, dashboard display means, and an emotion engine.

[0164] The server first collects project-managed data. It utilizes APIs from project management applications (e.g., Jira, Trello) to retrieve information such as task details, deadlines, and assignees.

[0165] Next, the server analyzes the data using machine learning algorithms. Using Python data analysis libraries (e.g., scikit-learn, TensorFlow), it calculates task priorities and estimated work times based on historical data. This allows the user to know the optimal task order at any given time.

[0166] Users register tasks and manage their progress through their devices. A specially designed user interface (e.g., web app, mobile app) is provided for this operation. Users enter new tasks, and this information is sent to the server.

[0167] Furthermore, the device monitors the user's keystrokes and break frequency. This is done to reduce workload and encourage breaks at appropriate times. This is achieved through activity monitoring tools (e.g., RescueTime) that run in the background.

[0168] The emotion engine analyzes this data to estimate the user's emotional state. If high stress is detected, the server suggests the user take a break and sends a message at the appropriate time through a notification system. This uses real-time messaging services (e.g., Slack, MICROSOFT® TEAMS®).

[0169] Furthermore, we will use generative AI models as needed to suggest ways to further improve the efficiency of task management. Specifically, we will input a prompt such as, "What is the optimal response if high stress is detected?" and utilize the modeling results.

[0170] In this way, dynamic task management that responds to the user's emotional state becomes possible, leading to improvements in overall organizational productivity and the work environment.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The server collects data using the API of the project management application connected to the information processing device. Specifically, it retrieves task details, deadlines, and assignee information. This collected information becomes the input data. The server stores this in a database in preparation for subsequent analysis.

[0174] Step 2:

[0175] The server inputs task data collected from the database into a machine learning algorithm. Using a Python data analysis library, it determines task priorities from the data and calculates predicted work time. This outputs the optimal order for each task. In this process, a predictive model is trained using historical data and applied to new tasks.

[0176] Step 3:

[0177] The user enters information to register a new task from their terminal. This process involves entering the task name, details, and deadline through a dedicated application screen. The terminal then sends this information to the server, which adds it to the existing database.

[0178] Step 4:

[0179] The device monitors the user's actions. Specifically, it observes the frequency of keystrokes and breaks, and sends this data to the emotion engine. The user's input patterns become the input data, and the emotional state is obtained as the output. Activity monitoring tools operating in the background are used to collect information in real time.

[0180] Step 5:

[0181] The emotion engine analyzes the received data to estimate the user's emotional state. If a high stress level is detected, it sends that information to the server. Based on this information, the server prepares task reprioritization and break suggestions. You input prompts that utilize a generative AI model to generate suggestions for efficiency improvements.

[0182] Step 6:

[0183] The server receives feedback from the emotion engine and provides appropriate notifications to the user. Specifically, it sends messages to the user via email or real-time notifications. The notification system allows users to be notified when they should take a break and to dynamically adjust task deadlines. As an output, user response behavior is monitored and used to improve the system in the future.

[0184] (Application Example 2)

[0185] 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".

[0186] In today's work environment, efficient task management is essential. However, conventional technologies that take into account the user's emotional state have difficulty predicting security risks that reflect the stress and emotional changes that occur during task execution. Therefore, there is a need to appropriately manage the user's stress level while maintaining work efficiency.

[0187] 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.

[0188] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing task data, and emotion recognition means for recognizing the user's emotional state and predicting security risks. This makes it possible to predict security risks based on the user's emotional state.

[0189] An "information processing device" is a computer system used for data collection, analysis, prediction, and notification.

[0190] "Data collection methods" refer to techniques for acquiring necessary information and recording it in a format usable by the system.

[0191] "Analysis methods" refer to methods for analyzing information using collected data to gain useful insights.

[0192] A "predictive method" is a technique for inferring future events based on past data and current information.

[0193] "Notification methods" refer to methods for informing users of the obtained predictive information and analysis results.

[0194] A "feedback mechanism" is a method of returning information to the system based on user actions and results to improve accuracy.

[0195] "Adjustment methods" refer to methods that utilize feedback to improve the accuracy of future processes.

[0196] A "dashboard display method" is a technology that visually presents data and results to facilitate management.

[0197] An "emotion recognition method" is a method of identifying an emotional state based on the user's behavior and input data, and making that state available to the system.

[0198] The system for carrying out this invention consists of an information processing device. The server acquires user behavior and task-related data using data collection means. This includes data obtained from smartphone sensors and input devices. The analysis means utilizes machine learning libraries such as TensorFlow and Keras to analyze the collected data in real time and identify the user's emotional state.

[0199] The prediction mechanism accurately predicts security risks based on this emotional state. Therefore, it can utilize past behavioral data to provide advance notice of situations where risk is high. The notification mechanism has the function of conveying risk information to the user at the appropriate time, and records actual work results through the feedback mechanism, contributing to improved analysis accuracy.

[0200] Furthermore, the dashboard display method helps optimize overall organizational efficiency by visualizing user emotional states and work performance for managers. The adjustment method enables smooth operation of the entire system by improving future processes based on past feedback.

[0201] For example, if emotion recognition measures detect that a bank employee is working under high stress, the server immediately sends an alert to the security team and notifies the employee to temporarily suspend work and refresh themselves.

[0202] An example of a prompt using a generative AI model is: "Explain in detail how the emotion engine can use this information to provide the user with appropriate action when it detects a high-stress state."

[0203] This configuration enables flexible work management and security measures that respond to emotional states.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server aggregates information using data collection methods based on behavioral data and emotion-related inputs received from the terminal. This includes keystroke data and information from mobile sensors. The input is user behavior data, and the output is the processed dataset.

[0207] Step 2:

[0208] The server passes the collected data to an analysis tool, which uses a machine learning model (e.g., an LSTM model) to analyze the emotional state. In this process, the collected data is taken as input, and the result of the emotion recognition is obtained as output. Specifically, TensorFlow is used to dynamically update the model and obtain the analysis results in real time.

[0209] Step 3:

[0210] The server uses prediction tools based on the analysis results to predict security risks related to emotional states. Here, the results of emotion recognition are used as input, and information indicating the risk level is generated as output. This process includes calculating the probability of risk occurrence based on past data.

[0211] Step 4:

[0212] The device communicates risk information to the user through notification methods, prompting them to review their actions and take measures to reduce stress as needed. The input here is risk prediction information, and the output is a notification message to the user. This allows the user to receive specific instructions and pay attention to safety.

[0213] Step 5:

[0214] The server uses feedback mechanisms to record user response data and actual task completion status, which are then used for future analysis and predictions. The input is user feedback data, and the output is updated information that contributes to improving the accuracy of the analysis model. Specifically, new data is incorporated into the learning model to improve the prediction algorithm.

[0215] Step 6:

[0216] Users access visualized sentiment and work performance information obtained through a dashboard display using their terminals to monitor their work progress. The input is organized sentiment data, and the output is a user-friendly dashboard display. This process involves using a data visualization library to present the data in an engaging way.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention provides a system that improves the efficiency of task management by combining an information processing device and a machine learning algorithm. This system primarily consists of a server, terminals, and users. The server automatically collects data on various tasks and projects within the company and analyzes this data using a machine learning algorithm. This allows the system to calculate the priority of current tasks and the estimated work time based on past performance.

[0234] When a user registers a new task using their device, the server receives this information and uses a prediction system to calculate the appropriate work time and priority. By notifying the user of these results, tasks can be managed effectively. In addition, when the user inputs the actual work time spent via their device, this information is sent to the server via a feedback system and used to improve the accuracy of predictions for the next task.

[0235] Furthermore, to allow managers to easily check the progress of their work, the server uses a dashboard display to visualize the task progress of team members. This visualization enables quick identification of task delays and workload imbalances, allowing for necessary work adjustments.

[0236] For example, if a user registers a task called "Create a report," the server predicts the required work time as "8 hours" based on data from similar past tasks and sets a priority for the task until the deadline. This information is immediately notified to the user, allowing them to allocate their time appropriately. After completing the task, the user records the actual work time as "10 hours" and sends it to the server. This data is then used in the next prediction, helping the system provide more accurate time estimates.

[0237] Thus, this system is designed to support users in managing their tasks and to enable efficient operation of the entire organization.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The server integrates with the company's information systems to automatically collect task and project data from each department. This data includes the task's start date, planned end date, assigned person, and details of related projects.

[0241] Step 2:

[0242] The server inputs the collected data into a machine learning algorithm to calculate task priorities and estimated work times. During this process, it references past performance data from similar tasks and performs statistical analysis.

[0243] Step 3:

[0244] Users register new tasks in the system via their devices. The information entered includes the task name, details, and deadline.

[0245] Step 4:

[0246] The server receives task information registered by the user and calculates the estimated work time and priority for the task based on the analysis results obtained in the previous step.

[0247] Step 5:

[0248] The server notifies the user of the calculated task priority and estimated work time. This notification is provided via email or in-system message through the terminal.

[0249] Step 6:

[0250] When a user completes a task, the actual time spent is recorded on the device and entered into the system. This information is important for later analysis.

[0251] Step 7:

[0252] The server receives actual work time from users and uses feedback to predict the next task. The prediction algorithm is then adjusted, incorporating the newly acquired data.

[0253] Step 8:

[0254] The server updates a dashboard for managers that displays the task progress and workload of all users in real time. This allows managers to quickly grasp the situation and adjust tasks accordingly.

[0255] (Example 1)

[0256] 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."

[0257] In today's work environment, the burden of managing diverse tasks and projects is increasing, demanding greater efficiency for both individuals and organizations as a whole. In this context, an information processing system capable of efficiently prioritizing tasks and predicting work time is necessary. However, current systems often fail to fully utilize historical data, resulting in insufficient prediction accuracy and management transparency. This leads to problems such as inappropriate task prioritization and decreased work efficiency.

[0258] 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.

[0259] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing business data acquired by the data collection means using a machine learning algorithm, and calculation means for calculating business priorities and estimated work times based on the analysis results of the analysis means. This enables users to efficiently manage their work and to make more accurate predictions of work times and prioritize tasks.

[0260] An "information processing device" is a computer system used for data collection, analysis, and notification, and refers to a wide range of hardware, including servers and individual terminals.

[0261] "Data collection means" refers to a function that automatically acquires various business data within a company by being connected to an information processing device.

[0262] "Analysis method" refers to the process of analyzing collected business data using machine learning algorithms.

[0263] "Calculation method" refers to a function that uses the analysis results of the analysis method to calculate the priority of tasks and the required work time.

[0264] "Notification means" refers to the communication process for transmitting information generated by the calculation means to the user.

[0265] A "feedback mechanism" refers to a function that records the actual work time based on user input and sends that information back to the calculation mechanism to improve prediction accuracy.

[0266] "Adjustment means" refers to the process of using recorded data to improve the estimation of the next work time and enhance the accuracy of the model.

[0267] "Progress display means" refers to display technology that enables administrators to visually check the work progress of their team members.

[0268] This invention is a task management system that combines an information processing device and a machine learning algorithm, and mainly consists of a server, terminals, and users. The server automatically collects and stores various business data within the company, and has the function of analyzing that data to prioritize tasks and calculate predicted work times. For data collection and acquisition, it cooperates with existing business management software via an API and stores the data in a database. Python is used for the program, and the machine learning model is built using TensorFlow. This model performs analysis based on historical data and helps to streamline business management.

[0269] Users use their devices to register new tasks and input the actual time spent on them. The server then feeds back the data received from the user, improving the accuracy of future predictions. The user's device has a web application (e.g., a frontend built with React) implemented, providing a user-friendly interface. When a user registers a task like "taking meeting minutes," the system predicts the work time as "2 hours" based on past data and sets a priority. If the user then inputs the actual time spent on the task as "3 hours," the prediction accuracy for similar tasks in the future will improve.

[0270] The server also provides a dashboard for managers to visualize the progress of team members. This allows them to quickly grasp the work progress of each member on the dashboard and adjust tasks as needed. Tableau and Power BI are used as data visualization tools to support intuitive visualization.

[0271] For example, you can enter the following prompt:

[0272] "Please explain how the system calculates the optimal work time and priority when a user registers 'Create a project plan' as a new task."

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The server retrieves business data from the company's internal business management software via API. It receives task and project information retrieved from the API as input. The server then processes the data for storage in a database, saving the details of each task. This creates a foundation for understanding the overall business progress.

[0276] Step 2:

[0277] The server analyzes the acquired data using a machine learning model. As input, it uses the past task data stored in the database. It preprocesses the data using Python and performs data-based analysis using a model based on TensorFlow. As output, the priority and estimated working time of each task are obtained. These results are utilized for the efficient management of tasks.

[0278] Step 3:

[0279] The user registers a new task through the terminal. As input, the user enters the task details, deadline, requirements, etc. into a web application on the browser. The terminal has an interface built with React, and the registered data is immediately sent to the server.

[0280] Step 4:

[0281] The server inputs the received new task data into the prediction model and recalculates the working time and priority. As input, the task information from the user is utilized. Based on data analysis, prediction results are generated through calculations using Python, and the results are sent to the user through the notification means.

[0282] Step 5:

[0283] After the user completes the task, the user reports the actual working time on the terminal. As input, the ID of the completed task and the actual time taken are entered into the web application. Thereby, the terminal resends the information to the server.

[0284] Step 6:

[0285] The server feeds back the actual data and updates the prediction model. As input, it uses the actual time sent from the user. The server performs retraining of the machine learning model to improve the prediction accuracy for the next time. The updated model is utilized for the next task analysis.

[0286] Step 7:

[0287] The server visualizes the task progress of each member for management personnel. It uses the analyzed progress data as input. Through the data visualization tool, the server displays the progress status of each member on the dashboard, enabling management personnel to quickly adjust the operations.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In today's manufacturing industry, it is required to maximize the work efficiency of each process and improve the overall productivity. However, appropriately managing the priority and required time of individual tasks and optimizing the entire process is a task that requires a great deal of effort and cost. Therefore, it is desired to construct a system that can efficiently manage the production process in real time while utilizing past data.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes an information collection means, an analysis means for analyzing task data, and a prediction means for calculating the priority and predicted time of processes. Thereby, while improving the efficiency of each process in the manufacturing equipment, it becomes possible to optimize the entire process.

[0293] An "information processing device" is a computer system for collecting, analyzing, and processing data.

[0294] A "data collection means" is a mechanism for automatically acquiring business data.

[0295] A "machine learning algorithm" is a mathematical method that analyzes past data to discover new trends and patterns.

[0296] "Analysis methods" refer to the process of analyzing collected data and extracting meaningful information.

[0297] A "predictive tool" is a system that calculates future work time and priorities based on the analysis results.

[0298] A "notification method" is a means of communication used to inform users of the calculated information.

[0299] A "feedback method" is a way to record actual work time and results to help improve the accuracy of future predictions.

[0300] A "correction mechanism" is a system that uses recorded data to revise the estimate of the next work time.

[0301] A "visualization tool" is a display tool that allows administrators to visually check the progress of a team.

[0302] "Production optimization means" refers to processes and methods for improving the efficiency of each stage in manufacturing equipment.

[0303] The system based on the present invention is centered around an information processing device and includes data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, visualization means, and production optimization means. The server plays a central role, receiving and processing data from terminals and users.

[0304] The server mainly uses Python for data processing and operates on cloud services such as AWS EC2. Through data collection means, business data is automatically acquired from manufacturing equipment. This data is analyzed by analysis means incorporating machine learning algorithms, and by using libraries such as TensorFlow, trends and patterns in the data are extracted.

[0305] Based on the analysis results, prediction means within the server calculates the priority and required time of the work. This information is delivered to the user's terminal through notification means. The user registers a new task using the terminal and inputs the actual working time, thereby sending data to the server via feedback means. This information is utilized to improve the accuracy of the next prediction, and the work estimate is corrected by adjustment means.

[0306] The administrator can grasp the progress status through visualization means, and production optimization means can achieve efficiency improvement in manufacturing equipment. As a specific example, when a certain factory manages the assembly process of automotive parts with this system, it can predict the installation time of individual parts and derive an efficient process sequence.

[0307] As an example of a prompt sentence, a format such as "For the generation AI system, predict the installation time of part A and create an optimization plan for the line." can be considered.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The server collects business data from manufacturing equipment using data collection means. This data includes the start time, end time, and resources used in each process. It receives data provided in real time from the data collection system as input and stores it in the database.

[0311] Step 2:

[0312] The server inputs the stored data into a machine learning algorithm and performs analysis. Using the TensorFlow library, it models work time and trends from past data and extracts patterns. This process can identify delays in work and potential opportunities for efficiency improvements. The analysis results are generated as output.

[0313] Step 3:

[0314] Based on the analysis results, the server uses predictive tools to calculate the priority and estimated work time for each process. This process uses a generative AI model to predict the optimal work sequence for new tasks registered by the user. Optimization information is generated as output to notify the user.

[0315] Step 4:

[0316] The server sends the prediction results to the terminal via a notification system. The terminal receives this information and is responsible for notifying the user. This information includes the estimated time required for the task and its priority, serving as a guide for the user to work efficiently.

[0317] Step 5:

[0318] Users input the actual time spent on a task using their device and send it to the server via a feedback mechanism. This input data is stored on the server as training data to improve the accuracy of future predictions.

[0319] Step 6:

[0320] The server uses the feedback data to adjust the next work estimate using a recalibration mechanism. In this step, the model is retrained based on actual work data, resulting in more accurate predictions.

[0321] Step 7:

[0322] Administrators can use visualization tools on their terminals to monitor progress and production efficiency in real time. This allows them to quickly identify delays and workload imbalances and take necessary countermeasures promptly.

[0323] 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.

[0324] The present invention relates to a system connected to an information processing device, comprising data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, and dashboard display means, in addition to an emotion engine that recognizes the user's emotions. This emotion engine is used to understand the user's emotional state based on user input and behavioral data, and to improve the quality of decision-making in task management.

[0325] The server first collects task-related data from internal systems. Then, it uses machine learning algorithms to analyze this data and calculate task priorities and estimated completion times. Users register tasks using their terminals, and the server notifies them of the calculation results based on this information. Notifications are provided at appropriate times, allowing users to take action to prevent task delays.

[0326] Furthermore, the emotion engine monitors the user's keystrokes, input patterns, and frequency of breaks during work to estimate the user's emotional state. If a change in emotion is detected, the information is sent to the server, and the analysis tool can dynamically adjust task priorities and notification content. This reduces user stress while enabling efficient task management.

[0327] As a concrete example, consider a situation where a user has multiple tasks. If this user is detected to be experiencing high stress levels while working on the "writing a report" task, the emotion engine sends this information to the server. The server determines that the user should take a break outside of work and notifies the user accordingly. It also uses predictive tools as needed to adjust task deadlines or suggests considering redistributing tasks with other colleagues.

[0328] By utilizing this emotion engine, flexible task management tailored to the user's emotional state becomes possible, leading to improved overall organizational efficiency and a better work environment.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server connects to the company's information systems and periodically collects data on tasks and related projects. This data includes task deadlines, assigned personnel, and progress.

[0332] Step 2:

[0333] The server inputs the collected data into a machine learning algorithm to analyze task priorities and estimated work times. During this process, statistical methods are used based on past performance data to improve prediction accuracy.

[0334] Step 3:

[0335] Users register new tasks and schedules using their devices. The information registered includes the task name, details, and deadline.

[0336] Step 4:

[0337] The server notifies the user of the optimal work time and priority calculated by a prediction system based on the tasks registered by the user. The notification is provided via email or in-system message.

[0338] Step 5:

[0339] The emotion engine monitors the user's keystrokes, frequency of breaks during work, input speed, etc., to estimate the user's emotional state. This information is sent to the server in real time.

[0340] Step 6:

[0341] The server analyzes emotional data received from the emotion engine and dynamically adjusts task reminders, priorities, and workloads if high stress levels or emotional disturbances are detected. Based on these adjustments, it notifies the user of appropriate actions.

[0342] Step 7:

[0343] After a user completes a task, the actual time spent and feedback are recorded on the device and entered into the system. This information is used to predict and improve future processes.

[0344] Step 8:

[0345] The server updates a dashboard for managers based on recorded work and emotional data, providing a visual overview of all team members' progress and emotional status. This allows managers to make appropriate adjustments to tasks and propose stress reduction measures.

[0346] (Example 2)

[0347] 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".

[0348] Many information processing systems fail to consider the user's emotional state in task management, leading to increased user stress and consequently decreased work efficiency. In particular, the fixed nature of task priorities and estimated work times makes it difficult to respond flexibly to the user's situation.

[0349] 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.

[0350] In this invention, the server includes means for collecting data, means for calculating task priorities and estimated work times based on the analysis results, and means for estimating the user's emotional state. This enables flexible task management that responds to the user's emotional state.

[0351] "Means for collecting data" refers to a process or function for automatically or manually obtaining necessary data from an information processing device.

[0352] "Methods of analysis using machine learning algorithms" refer to the process of analyzing data based on specific models or algorithms using collected datasets to gain insights.

[0353] "Means for calculating task priorities and estimated work time" refers to a function that determines the importance of a task and predicts the time required to complete it, based on analyzed data.

[0354] "Means of notifying users" refers to methods and functions for communicating important information to users, including email, messages, and alerts.

[0355] "A means of recording actual work time based on input and feeding it back into the prediction method" refers to a process that uses user input information to record the actual work time taken and utilizes that information to improve the accuracy of predictions for the next time.

[0356] "Means for adjusting work time estimates" refers to a process for more accurately estimating future work time based on past work performance data.

[0357] "Means for presenting progress" refers to functions that visually display task progress to administrators and users, and are provided in the form of dashboards, etc.

[0358] "Means for estimating emotional state" refers to the process of inferring the user's emotions at a given time based on their operation patterns and behavioral data.

[0359] "Means for dynamic adjustment" refers to a function that automatically readjusts tasks and notifications to the optimal state according to the user's current state and environment.

[0360] This invention provides task management using a system connected to an information processing device, and aims to create an efficient work environment by taking into account the user's emotional state. Specifically, it is composed of a combination of data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, dashboard display means, and an emotion engine.

[0361] The server first collects project-managed data. It utilizes APIs from project management applications (e.g., Jira, Trello) to retrieve information such as task details, deadlines, and assignees.

[0362] Next, the server analyzes the data using machine learning algorithms. Using Python data analysis libraries (e.g., scikit-learn, TensorFlow), it calculates task priorities and estimated work times based on historical data. This allows the user to know the optimal task order at any given time.

[0363] Users register tasks and manage their progress through their devices. A specially designed user interface (e.g., web app, mobile app) is provided for this operation. Users enter new tasks, and this information is sent to the server.

[0364] Furthermore, the device monitors the user's keystrokes and break frequency. This is done to reduce workload and encourage breaks at appropriate times. This is achieved through activity monitoring tools (e.g., RescueTime) that run in the background.

[0365] The emotion engine analyzes this data to estimate the user's emotional state. If high stress is detected, the server suggests the user take a break and sends a message at the appropriate time through a notification system. This uses real-time messaging services (e.g., Slack, Microsoft Teams).

[0366] Furthermore, we will use generative AI models as needed to suggest ways to further improve the efficiency of task management. Specifically, we will input a prompt such as, "What is the optimal response if high stress is detected?" and utilize the modeling results.

[0367] In this way, dynamic task management that responds to the user's emotional state becomes possible, leading to improvements in overall organizational productivity and the work environment.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The server collects data using the API of the project management application connected to the information processing device. Specifically, it retrieves task details, deadlines, and assignee information. This collected information becomes the input data. The server stores this in a database in preparation for subsequent analysis.

[0371] Step 2:

[0372] The server inputs task data collected from the database into a machine learning algorithm. Using a Python data analysis library, it determines task priorities from the data and calculates predicted work time. This outputs the optimal order for each task. In this process, a predictive model is trained using historical data and applied to new tasks.

[0373] Step 3:

[0374] The user enters information to register a new task from their terminal. This process involves entering the task name, details, and deadline through a dedicated application screen. The terminal then sends this information to the server, which adds it to the existing database.

[0375] Step 4:

[0376] The device monitors the user's actions. Specifically, it observes the frequency of keystrokes and breaks, and sends this data to the emotion engine. The user's input patterns become the input data, and the emotional state is obtained as the output. Activity monitoring tools operating in the background are used to collect information in real time.

[0377] Step 5:

[0378] The emotion engine analyzes the received data to estimate the user's emotional state. If a high stress level is detected, it sends that information to the server. Based on this information, the server prepares task reprioritization and break suggestions. You input prompts that utilize a generative AI model to generate suggestions for efficiency improvements.

[0379] Step 6:

[0380] The server receives feedback from the emotion engine and provides appropriate notifications to the user. Specifically, it sends messages to the user via email or real-time notifications. The notification system allows users to be notified when they should take a break and to dynamically adjust task deadlines. As an output, user response behavior is monitored and used to improve the system in the future.

[0381] (Application Example 2)

[0382] 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."

[0383] In today's work environment, efficient task management is essential. However, conventional technologies that take into account the user's emotional state have difficulty predicting security risks that reflect the stress and emotional changes that occur during task execution. Therefore, there is a need to appropriately manage the user's stress level while maintaining work efficiency.

[0384] 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.

[0385] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing task data, and emotion recognition means for recognizing the user's emotional state and predicting security risks. This makes it possible to predict security risks based on the user's emotional state.

[0386] An "information processing device" is a computer system used for data collection, analysis, prediction, and notification.

[0387] "Data collection methods" refer to techniques for acquiring necessary information and recording it in a format usable by the system.

[0388] "Analysis methods" refer to methods for analyzing information using collected data to gain useful insights.

[0389] A "predictive method" is a technique for inferring future events based on past data and current information.

[0390] "Notification methods" refer to methods for informing users of the obtained predictive information and analysis results.

[0391] A "feedback mechanism" is a method of returning information to the system based on user actions and results to improve accuracy.

[0392] "Adjustment methods" refer to methods that utilize feedback to improve the accuracy of future processes.

[0393] A "dashboard display method" is a technology that visually presents data and results to facilitate management.

[0394] An "emotion recognition method" is a method of identifying an emotional state based on the user's behavior and input data, and making that state available to the system.

[0395] The system for carrying out this invention consists of an information processing device. The server acquires user behavior and task-related data using data collection means. This includes data obtained from smartphone sensors and input devices. The analysis means utilizes machine learning libraries such as TensorFlow and Keras to analyze the collected data in real time and identify the user's emotional state.

[0396] The prediction mechanism accurately predicts security risks based on this emotional state. Therefore, it can utilize past behavioral data to provide advance notice of situations where risk is high. The notification mechanism has the function of conveying risk information to the user at the appropriate time, and records actual work results through the feedback mechanism, contributing to improved analysis accuracy.

[0397] Furthermore, the dashboard display method helps optimize overall organizational efficiency by visualizing user emotional states and work performance for managers. The adjustment method enables smooth operation of the entire system by improving future processes based on past feedback.

[0398] For example, if emotion recognition measures detect that a bank employee is working under high stress, the server immediately sends an alert to the security team and notifies the employee to temporarily suspend work and refresh themselves.

[0399] An example of a prompt using a generative AI model is: "Explain in detail how the emotion engine can use this information to provide the user with appropriate action when it detects a high-stress state."

[0400] This configuration enables flexible work management and security measures that respond to emotional states.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server aggregates information using data collection methods based on behavioral data and emotion-related inputs received from the terminal. This includes keystroke data and information from mobile sensors. The input is user behavior data, and the output is the processed dataset.

[0404] Step 2:

[0405] The server passes the collected data to an analysis tool, which uses a machine learning model (e.g., an LSTM model) to analyze the emotional state. In this process, the collected data is taken as input, and the result of the emotion recognition is obtained as output. Specifically, TensorFlow is used to dynamically update the model and obtain the analysis results in real time.

[0406] Step 3:

[0407] The server uses prediction tools based on the analysis results to predict security risks related to emotional states. Here, the results of emotion recognition are used as input, and information indicating the risk level is generated as output. This process includes calculating the probability of risk occurrence based on past data.

[0408] Step 4:

[0409] The device communicates risk information to the user through notification methods, prompting them to review their actions and take measures to reduce stress as needed. The input here is risk prediction information, and the output is a notification message to the user. This allows the user to receive specific instructions and pay attention to safety.

[0410] Step 5:

[0411] The server uses feedback mechanisms to record user response data and actual task completion status, which are then used for future analysis and predictions. The input is user feedback data, and the output is updated information that contributes to improving the accuracy of the analysis model. Specifically, new data is incorporated into the learning model to improve the prediction algorithm.

[0412] Step 6:

[0413] Users access visualized sentiment and work performance information obtained through a dashboard display using their terminals to monitor their work progress. The input is organized sentiment data, and the output is a user-friendly dashboard display. This process involves using a data visualization library to present the data in an engaging way.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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".

[0430] This invention provides a system that improves the efficiency of task management by combining an information processing device and a machine learning algorithm. This system primarily consists of a server, terminals, and users. The server automatically collects data on various tasks and projects within the company and analyzes this data using a machine learning algorithm. This allows the system to calculate the priority of current tasks and the estimated work time based on past performance.

[0431] When a user registers a new task using their device, the server receives this information and uses a prediction system to calculate the appropriate work time and priority. By notifying the user of these results, tasks can be managed effectively. In addition, when the user inputs the actual work time spent via their device, this information is sent to the server via a feedback system and used to improve the accuracy of predictions for the next task.

[0432] Furthermore, to allow managers to easily check the progress of their work, the server uses a dashboard display to visualize the task progress of team members. This visualization enables quick identification of task delays and workload imbalances, allowing for necessary work adjustments.

[0433] For example, if a user registers a task called "Create a report," the server predicts the required work time as "8 hours" based on data from similar past tasks and sets a priority for the task until the deadline. This information is immediately notified to the user, allowing them to allocate their time appropriately. After completing the task, the user records the actual work time as "10 hours" and sends it to the server. This data is then used in the next prediction, helping the system provide more accurate time estimates.

[0434] Thus, this system is designed to support users in managing their tasks and to enable efficient operation of the entire organization.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The server integrates with the company's information systems to automatically collect task and project data from each department. This data includes the task's start date, planned end date, assigned person, and details of related projects.

[0438] Step 2:

[0439] The server inputs the collected data into a machine learning algorithm to calculate task priorities and estimated work times. During this process, it references past performance data from similar tasks and performs statistical analysis.

[0440] Step 3:

[0441] Users register new tasks in the system via their devices. The information entered includes the task name, details, and deadline.

[0442] Step 4:

[0443] The server receives task information registered by the user and calculates the estimated work time and priority for the task based on the analysis results obtained in the previous step.

[0444] Step 5:

[0445] The server notifies the user of the calculated task priority and estimated work time. This notification is provided via email or in-system message through the terminal.

[0446] Step 6:

[0447] When a user completes a task, the actual time spent is recorded on the device and entered into the system. This information is important for later analysis.

[0448] Step 7:

[0449] The server receives actual work time from users and uses feedback to predict the next task. The prediction algorithm is then adjusted, incorporating the newly acquired data.

[0450] Step 8:

[0451] The server updates a dashboard for managers that displays the task progress and workload of all users in real time. This allows managers to quickly grasp the situation and adjust tasks accordingly.

[0452] (Example 1)

[0453] 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."

[0454] In today's work environment, the burden of managing diverse tasks and projects is increasing, demanding greater efficiency for both individuals and organizations as a whole. In this context, an information processing system capable of efficiently prioritizing tasks and predicting work time is necessary. However, current systems often fail to fully utilize historical data, resulting in insufficient prediction accuracy and management transparency. This leads to problems such as inappropriate task prioritization and decreased work efficiency.

[0455] 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.

[0456] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing business data acquired by the data collection means using a machine learning algorithm, and calculation means for calculating business priorities and estimated work times based on the analysis results of the analysis means. This enables users to efficiently manage their work and to make more accurate predictions of work times and prioritize tasks.

[0457] An "information processing device" is a computer system used for data collection, analysis, and notification, and refers to a wide range of hardware, including servers and individual terminals.

[0458] "Data collection means" refers to a function that automatically acquires various business data within a company by being connected to an information processing device.

[0459] "Analysis method" refers to the process of analyzing collected business data using machine learning algorithms.

[0460] "Calculation method" refers to a function that uses the analysis results of the analysis method to calculate the priority of tasks and the required work time.

[0461] "Notification means" refers to the communication process for transmitting information generated by the calculation means to the user.

[0462] A "feedback mechanism" refers to a function that records the actual work time based on user input and sends that information back to the calculation mechanism to improve prediction accuracy.

[0463] "Adjustment means" refers to the process of using recorded data to improve the estimation of the next work time and enhance the accuracy of the model.

[0464] "Progress display means" refers to display technology that enables administrators to visually check the work progress of their team members.

[0465] This invention is a task management system that combines an information processing device and a machine learning algorithm, and mainly consists of a server, terminals, and users. The server automatically collects and stores various business data within the company, and has the function of analyzing that data to prioritize tasks and calculate predicted work times. For data collection and acquisition, it cooperates with existing business management software via an API and stores the data in a database. Python is used for the program, and the machine learning model is built using TensorFlow. This model performs analysis based on historical data and helps to streamline business management.

[0466] Users use their devices to register new tasks and input the actual time spent on them. The server then feeds back the data received from the user, improving the accuracy of future predictions. The user's device has a web application (e.g., a frontend built with React) implemented, providing a user-friendly interface. When a user registers a task like "taking meeting minutes," the system predicts the work time as "2 hours" based on past data and sets a priority. If the user then inputs the actual time spent on the task as "3 hours," the prediction accuracy for similar tasks in the future will improve.

[0467] The server also provides a dashboard for managers to visualize the progress of team members. This allows them to quickly grasp the work progress of each member on the dashboard and adjust tasks as needed. Tableau and Power BI are used as data visualization tools to support intuitive visualization.

[0468] For example, you can enter the following prompt:

[0469] "Please explain how the system calculates the optimal work time and priority when a user registers 'Create a project plan' as a new task."

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The server retrieves business data from the company's internal business management software via API. It receives task and project information retrieved from the API as input. The server then processes the data for storage in a database, saving the details of each task. This creates a foundation for understanding the overall business progress.

[0473] Step 2:

[0474] The server performs analysis using a machine learning model based on the acquired data. It uses historical task data stored in a database as input. Data preprocessing is performed using Python, and analysis is conducted using a TensorFlow model. The output provides the priority and estimated work time for each task. These results are used for efficient task management.

[0475] Step 3:

[0476] Users register new tasks through their device. They input task details, deadlines, requirements, etc., into a web application in their browser. The device uses a React-based interface, and the registered data is immediately sent to the server.

[0477] Step 4:

[0478] The server feeds the received new task data into a predictive model and recalculates the work time and priority. User-provided task information is used as input. Based on the data analysis, prediction results are generated using Python calculations, and the results are sent to the user via a notification system.

[0479] Step 5:

[0480] After completing a task, the user reports the actual time spent on that task via their device. The input consists of the ID of the completed task and the actual time spent, which is entered into a web application. The device then resends this information to the server.

[0481] Step 6:

[0482] The server feeds back actual data and updates the predictive model. It uses actual time submitted by the user as input. The server retrains the machine learning model to improve the accuracy of the next prediction. The updated model is then used for the next task analysis.

[0483] Step 7:

[0484] The server visualizes the task progress of each team member for managers. It uses analyzed progress data as input. Through data visualization tools, the server displays each member's progress on a dashboard, allowing managers to quickly adjust tasks.

[0485] (Application Example 1)

[0486] 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."

[0487] In today's manufacturing industry, there is a demand to maximize the efficiency of each process and improve overall productivity. However, properly managing the prioritization and time required for individual tasks and optimizing the entire process is a challenge that requires considerable effort and cost. Therefore, there is a need to build a system that can efficiently manage production processes in real time while utilizing historical data.

[0488] 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.

[0489] In this invention, the server includes information gathering means, analysis means for analyzing task data, and prediction means for calculating process priorities and estimated times. This makes it possible to optimize the overall process while improving the efficiency of each process in the manufacturing equipment.

[0490] An "information processing device" is a computer system used to collect, analyze, and process data.

[0491] A "data collection method" is a system for automatically acquiring business data.

[0492] A "machine learning algorithm" is a mathematical method that analyzes past data to discover new trends and patterns.

[0493] "Analysis methods" refer to the process of analyzing collected data and extracting meaningful information.

[0494] A "predictive tool" is a system that calculates future work time and priorities based on the analysis results.

[0495] A "notification method" is a means of communication used to inform users of the calculated information.

[0496] A "feedback method" is a way to record actual work time and results to help improve the accuracy of future predictions.

[0497] A "correction mechanism" is a system that uses recorded data to revise the estimate of the next work time.

[0498] A "visualization tool" is a display tool that allows administrators to visually check the progress of a team.

[0499] "Production optimization means" refers to processes and methods for improving the efficiency of each stage in manufacturing equipment.

[0500] The system based on the present invention is centered around an information processing device and includes data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, visualization means, and production optimization means. The server plays a central role, receiving and processing data from terminals and users.

[0501] The server primarily uses Python for data processing and operates on cloud services such as AWS EC2. Operational data is automatically acquired from manufacturing equipment using data collection methods. This data is analyzed using analytical tools incorporating machine learning algorithms, and trends and patterns are extracted using libraries such as TensorFlow.

[0502] Based on the analysis results, the server's prediction system calculates the priority and estimated time for each task. This information is delivered to the user's terminal via a notification system. The user registers a new task using their terminal and inputs the actual work time, sending data to the server via a feedback system. This information is used to improve the accuracy of future predictions, and the work estimate is revised by an adjustment system.

[0503] Managers can understand the progress through visualization tools and achieve efficiency improvements in manufacturing equipment through production optimization tools. For example, if a factory manages the assembly process of automotive parts with this system, it can predict the installation time of individual parts and derive an efficient process sequence.

[0504] An example of a prompt message would be: "Generating AI system, predict the installation time for part A and create an optimization plan for the production line."

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The server collects operational data from manufacturing equipment using data collection methods. This data includes the start and end times of each process, the resources used, and so on. It receives data provided in real time from the data collection system as input and stores it in the database.

[0508] Step 2:

[0509] The server inputs the stored data into a machine learning algorithm and performs analysis. Using the TensorFlow library, it models work time and trends from past data and extracts patterns. This process can identify delays in work and potential opportunities for efficiency improvements. The analysis results are generated as output.

[0510] Step 3:

[0511] Based on the analysis results, the server uses predictive tools to calculate the priority and estimated work time for each process. This process uses a generative AI model to predict the optimal work sequence for new tasks registered by the user. Optimization information is generated as output to notify the user.

[0512] Step 4:

[0513] The server sends the prediction results to the terminal via a notification system. The terminal receives this information and is responsible for notifying the user. This information includes the estimated time required for the task and its priority, serving as a guide for the user to work efficiently.

[0514] Step 5:

[0515] Users input the actual time spent on a task using their device and send it to the server via a feedback mechanism. This input data is stored on the server as training data to improve the accuracy of future predictions.

[0516] Step 6:

[0517] The server uses the feedback data to adjust the next work estimate using a recalibration mechanism. In this step, the model is retrained based on actual work data, resulting in more accurate predictions.

[0518] Step 7:

[0519] Administrators can use visualization tools on their terminals to monitor progress and production efficiency in real time. This allows them to quickly identify delays and workload imbalances and take necessary countermeasures promptly.

[0520] 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.

[0521] The present invention relates to a system connected to an information processing device, comprising data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, and dashboard display means, in addition to an emotion engine that recognizes the user's emotions. This emotion engine is used to understand the user's emotional state based on user input and behavioral data, and to improve the quality of decision-making in task management.

[0522] The server first collects task-related data from internal systems. Then, it uses machine learning algorithms to analyze this data and calculate task priorities and estimated completion times. Users register tasks using their terminals, and the server notifies them of the calculation results based on this information. Notifications are provided at appropriate times, allowing users to take action to prevent task delays.

[0523] Furthermore, the emotion engine monitors the user's keystrokes, input patterns, and frequency of breaks during work to estimate the user's emotional state. If a change in emotion is detected, the information is sent to the server, and the analysis tool can dynamically adjust task priorities and notification content. This reduces user stress while enabling efficient task management.

[0524] As a concrete example, consider a situation where a user has multiple tasks. If this user is detected to be experiencing high stress levels while working on the "writing a report" task, the emotion engine sends this information to the server. The server determines that the user should take a break outside of work and notifies the user accordingly. It also uses predictive tools as needed to adjust task deadlines or suggests considering redistributing tasks with other colleagues.

[0525] By utilizing this emotion engine, flexible task management tailored to the user's emotional state becomes possible, leading to improved overall organizational efficiency and a better work environment.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The server connects to the company's information systems and periodically collects data on tasks and related projects. This data includes task deadlines, assigned personnel, and progress.

[0529] Step 2:

[0530] The server inputs the collected data into a machine learning algorithm to analyze task priorities and estimated work times. During this process, statistical methods are used based on past performance data to improve prediction accuracy.

[0531] Step 3:

[0532] Users register new tasks and schedules using their devices. The information registered includes the task name, details, and deadline.

[0533] Step 4:

[0534] The server notifies the user of the optimal work time and priority calculated by a prediction system based on the tasks registered by the user. The notification is provided via email or in-system message.

[0535] Step 5:

[0536] The emotion engine monitors the user's keystrokes, frequency of breaks during work, input speed, etc., to estimate the user's emotional state. This information is sent to the server in real time.

[0537] Step 6:

[0538] The server analyzes emotional data received from the emotion engine and dynamically adjusts task reminders, priorities, and workloads if high stress levels or emotional disturbances are detected. Based on these adjustments, it notifies the user of appropriate actions.

[0539] Step 7:

[0540] After a user completes a task, the actual time spent and feedback are recorded on the device and entered into the system. This information is used to predict and improve future processes.

[0541] Step 8:

[0542] The server updates a dashboard for managers based on recorded work and emotional data, providing a visual overview of all team members' progress and emotional status. This allows managers to make appropriate adjustments to tasks and propose stress reduction measures.

[0543] (Example 2)

[0544] 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."

[0545] Many information processing systems fail to consider the user's emotional state in task management, leading to increased user stress and consequently decreased work efficiency. In particular, the fixed nature of task priorities and estimated work times makes it difficult to respond flexibly to the user's situation.

[0546] 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.

[0547] In this invention, the server includes means for collecting data, means for calculating task priorities and estimated work times based on the analysis results, and means for estimating the user's emotional state. This enables flexible task management that responds to the user's emotional state.

[0548] "Means for collecting data" refers to a process or function for automatically or manually obtaining necessary data from an information processing device.

[0549] "Methods of analysis using machine learning algorithms" refer to the process of analyzing data based on specific models or algorithms using collected datasets to gain insights.

[0550] "Means for calculating task priorities and estimated work time" refers to a function that determines the importance of a task and predicts the time required to complete it, based on analyzed data.

[0551] "Means of notifying users" refers to methods and functions for communicating important information to users, including email, messages, and alerts.

[0552] "A means of recording actual work time based on input and feeding it back into the prediction method" refers to a process that uses user input information to record the actual work time taken and utilizes that information to improve the accuracy of predictions for the next time.

[0553] "Means for adjusting work time estimates" refers to a process for more accurately estimating future work time based on past work performance data.

[0554] "Means for presenting progress" refers to functions that visually display task progress to administrators and users, and are provided in the form of dashboards, etc.

[0555] "Means for estimating emotional state" refers to the process of inferring the user's emotions at a given time based on their operation patterns and behavioral data.

[0556] "Means for dynamic adjustment" refers to a function that automatically readjusts tasks and notifications to the optimal state according to the user's current state and environment.

[0557] This invention provides task management using a system connected to an information processing device, and aims to create an efficient work environment by taking into account the user's emotional state. Specifically, it is composed of a combination of data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, dashboard display means, and an emotion engine.

[0558] The server first collects project-managed data. It utilizes APIs from project management applications (e.g., Jira, Trello) to retrieve information such as task details, deadlines, and assignees.

[0559] Next, the server analyzes the data using machine learning algorithms. Using Python data analysis libraries (e.g., scikit-learn, TensorFlow), it calculates task priorities and estimated work times based on historical data. This allows the user to know the optimal task order at any given time.

[0560] Users register tasks and manage their progress through their devices. A specially designed user interface (e.g., web app, mobile app) is provided for this operation. Users enter new tasks, and this information is sent to the server.

[0561] Furthermore, the device monitors the user's keystrokes and break frequency. This is done to reduce workload and encourage breaks at appropriate times. This is achieved through activity monitoring tools (e.g., RescueTime) that run in the background.

[0562] The emotion engine analyzes this data to estimate the user's emotional state. If high stress is detected, the server suggests the user take a break and sends a message at the appropriate time through a notification system. This uses real-time messaging services (e.g., Slack, Microsoft Teams).

[0563] Furthermore, we will use generative AI models as needed to suggest ways to further improve the efficiency of task management. Specifically, we will input a prompt such as, "What is the optimal response if high stress is detected?" and utilize the modeling results.

[0564] In this way, dynamic task management that responds to the user's emotional state becomes possible, leading to improvements in overall organizational productivity and the work environment.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The server collects data using the API of the project management application connected to the information processing device. Specifically, it retrieves task details, deadlines, and assignee information. This collected information becomes the input data. The server stores this in a database in preparation for subsequent analysis.

[0568] Step 2:

[0569] The server inputs task data collected from the database into a machine learning algorithm. Using a Python data analysis library, it determines task priorities from the data and calculates predicted work time. This outputs the optimal order for each task. In this process, a predictive model is trained using historical data and applied to new tasks.

[0570] Step 3:

[0571] The user enters information to register a new task from their terminal. This process involves entering the task name, details, and deadline through a dedicated application screen. The terminal then sends this information to the server, which adds it to the existing database.

[0572] Step 4:

[0573] The device monitors the user's actions. Specifically, it observes the frequency of keystrokes and breaks, and sends this data to the emotion engine. The user's input patterns become the input data, and the emotional state is obtained as the output. Activity monitoring tools operating in the background are used to collect information in real time.

[0574] Step 5:

[0575] The emotion engine analyzes the received data to estimate the user's emotional state. If a high stress level is detected, it sends that information to the server. Based on this information, the server prepares task reprioritization and break suggestions. You input prompts that utilize a generative AI model to generate suggestions for efficiency improvements.

[0576] Step 6:

[0577] The server receives feedback from the emotion engine and provides appropriate notifications to the user. Specifically, it sends messages to the user via email or real-time notifications. The notification system allows users to be notified when they should take a break and to dynamically adjust task deadlines. As an output, user response behavior is monitored and used to improve the system in the future.

[0578] (Application Example 2)

[0579] 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."

[0580] In today's work environment, efficient task management is essential. However, conventional technologies that take into account the user's emotional state have difficulty predicting security risks that reflect the stress and emotional changes that occur during task execution. Therefore, there is a need to appropriately manage the user's stress level while maintaining work efficiency.

[0581] 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.

[0582] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing task data, and emotion recognition means for recognizing the user's emotional state and predicting security risks. This makes it possible to predict security risks based on the user's emotional state.

[0583] An "information processing device" is a computer system used for data collection, analysis, prediction, and notification.

[0584] "Data collection methods" refer to techniques for acquiring necessary information and recording it in a format usable by the system.

[0585] "Analysis methods" refer to methods for analyzing information using collected data to gain useful insights.

[0586] A "predictive method" is a technique for inferring future events based on past data and current information.

[0587] "Notification methods" refer to methods for informing users of the obtained predictive information and analysis results.

[0588] A "feedback mechanism" is a method of returning information to the system based on user actions and results to improve accuracy.

[0589] "Adjustment methods" refer to methods that utilize feedback to improve the accuracy of future processes.

[0590] A "dashboard display method" is a technology that visually presents data and results to facilitate management.

[0591] An "emotion recognition method" is a method of identifying an emotional state based on the user's behavior and input data, and making that state available to the system.

[0592] The system for carrying out this invention consists of an information processing device. The server acquires user behavior and task-related data using data collection means. This includes data obtained from smartphone sensors and input devices. The analysis means utilizes machine learning libraries such as TensorFlow and Keras to analyze the collected data in real time and identify the user's emotional state.

[0593] The prediction mechanism accurately predicts security risks based on this emotional state. Therefore, it can utilize past behavioral data to provide advance notice of situations where risk is high. The notification mechanism has the function of conveying risk information to the user at the appropriate time, and records actual work results through the feedback mechanism, contributing to improved analysis accuracy.

[0594] Furthermore, the dashboard display method helps optimize overall organizational efficiency by visualizing user emotional states and work performance for managers. The adjustment method enables smooth operation of the entire system by improving future processes based on past feedback.

[0595] For example, if emotion recognition measures detect that a bank employee is working under high stress, the server immediately sends an alert to the security team and notifies the employee to temporarily suspend work and refresh themselves.

[0596] An example of a prompt using a generative AI model is: "Explain in detail how the emotion engine can use this information to provide the user with appropriate action when it detects a high-stress state."

[0597] This configuration enables flexible work management and security measures that respond to emotional states.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The server aggregates information using data collection methods based on behavioral data and emotion-related inputs received from the terminal. This includes keystroke data and information from mobile sensors. The input is user behavior data, and the output is the processed dataset.

[0601] Step 2:

[0602] The server passes the collected data to an analysis tool, which uses a machine learning model (e.g., an LSTM model) to analyze the emotional state. In this process, the collected data is taken as input, and the result of the emotion recognition is obtained as output. Specifically, TensorFlow is used to dynamically update the model and obtain the analysis results in real time.

[0603] Step 3:

[0604] The server uses prediction tools based on the analysis results to predict security risks related to emotional states. Here, the results of emotion recognition are used as input, and information indicating the risk level is generated as output. This process includes calculating the probability of risk occurrence based on past data.

[0605] Step 4:

[0606] The device communicates risk information to the user through notification methods, prompting them to review their actions and take measures to reduce stress as needed. The input here is risk prediction information, and the output is a notification message to the user. This allows the user to receive specific instructions and pay attention to safety.

[0607] Step 5:

[0608] The server uses feedback mechanisms to record user response data and actual task completion status, which are then used for future analysis and predictions. The input is user feedback data, and the output is updated information that contributes to improving the accuracy of the analysis model. Specifically, new data is incorporated into the learning model to improve the prediction algorithm.

[0609] Step 6:

[0610] Users access visualized sentiment and work performance information obtained through a dashboard display using their terminals to monitor their work progress. The input is organized sentiment data, and the output is a user-friendly dashboard display. This process involves using a data visualization library to present the data in an engaging way.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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".

[0628] This invention provides a system that improves the efficiency of task management by combining an information processing device and a machine learning algorithm. This system primarily consists of a server, terminals, and users. The server automatically collects data on various tasks and projects within the company and analyzes this data using a machine learning algorithm. This allows the system to calculate the priority of current tasks and the estimated work time based on past performance.

[0629] When a user registers a new task using their device, the server receives this information and uses a prediction system to calculate the appropriate work time and priority. By notifying the user of these results, tasks can be managed effectively. In addition, when the user inputs the actual work time spent via their device, this information is sent to the server via a feedback system and used to improve the accuracy of predictions for the next task.

[0630] Furthermore, to allow managers to easily check the progress of their work, the server uses a dashboard display to visualize the task progress of team members. This visualization enables quick identification of task delays and workload imbalances, allowing for necessary work adjustments.

[0631] For example, if a user registers a task called "Create a report," the server predicts the required work time as "8 hours" based on data from similar past tasks and sets a priority for the task until the deadline. This information is immediately notified to the user, allowing them to allocate their time appropriately. After completing the task, the user records the actual work time as "10 hours" and sends it to the server. This data is then used in the next prediction, helping the system provide more accurate time estimates.

[0632] Thus, this system is designed to support users in managing their tasks and to enable efficient operation of the entire organization.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The server integrates with the company's information systems to automatically collect task and project data from each department. This data includes the task's start date, planned end date, assigned person, and details of related projects.

[0636] Step 2:

[0637] The server inputs the collected data into a machine learning algorithm to calculate task priorities and estimated work times. During this process, it references past performance data from similar tasks and performs statistical analysis.

[0638] Step 3:

[0639] Users register new tasks in the system via their devices. The information entered includes the task name, details, and deadline.

[0640] Step 4:

[0641] The server receives task information registered by the user and calculates the estimated work time and priority for the task based on the analysis results obtained in the previous step.

[0642] Step 5:

[0643] The server notifies the user of the calculated task priority and estimated work time. This notification is provided via email or in-system message through the terminal.

[0644] Step 6:

[0645] When a user completes a task, the actual time spent is recorded on the device and entered into the system. This information is important for later analysis.

[0646] Step 7:

[0647] The server receives actual work time from users and uses feedback to predict the next task. The prediction algorithm is then adjusted, incorporating the newly acquired data.

[0648] Step 8:

[0649] The server updates a dashboard for managers that displays the task progress and workload of all users in real time. This allows managers to quickly grasp the situation and adjust tasks accordingly.

[0650] (Example 1)

[0651] 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".

[0652] In today's work environment, the burden of managing diverse tasks and projects is increasing, demanding greater efficiency for both individuals and organizations as a whole. In this context, an information processing system capable of efficiently prioritizing tasks and predicting work time is necessary. However, current systems often fail to fully utilize historical data, resulting in insufficient prediction accuracy and management transparency. This leads to problems such as inappropriate task prioritization and decreased work efficiency.

[0653] 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.

[0654] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing business data acquired by the data collection means using a machine learning algorithm, and calculation means for calculating business priorities and estimated work times based on the analysis results of the analysis means. This enables users to efficiently manage their work and to make more accurate predictions of work times and prioritize tasks.

[0655] An "information processing device" is a computer system used for data collection, analysis, and notification, and refers to a wide range of hardware, including servers and individual terminals.

[0656] "Data collection means" refers to a function that automatically acquires various business data within a company by being connected to an information processing device.

[0657] "Analysis method" refers to the process of analyzing collected business data using machine learning algorithms.

[0658] "Calculation method" refers to a function that uses the analysis results of the analysis method to calculate the priority of tasks and the required work time.

[0659] "Notification means" refers to the communication process for transmitting information generated by the calculation means to the user.

[0660] A "feedback mechanism" refers to a function that records the actual work time based on user input and sends that information back to the calculation mechanism to improve prediction accuracy.

[0661] "Adjustment means" refers to the process of using recorded data to improve the estimation of the next work time and enhance the accuracy of the model.

[0662] "Progress display means" refers to display technology that enables administrators to visually check the work progress of their team members.

[0663] This invention is a task management system that combines an information processing device and a machine learning algorithm, and mainly consists of a server, terminals, and users. The server automatically collects and stores various business data within the company, and has the function of analyzing that data to prioritize tasks and calculate predicted work times. For data collection and acquisition, it cooperates with existing business management software via an API and stores the data in a database. Python is used for the program, and the machine learning model is built using TensorFlow. This model performs analysis based on historical data and helps to streamline business management.

[0664] Users use their devices to register new tasks and input the actual time spent on them. The server then feeds back the data received from the user, improving the accuracy of future predictions. The user's device has a web application (e.g., a frontend built with React) implemented, providing a user-friendly interface. When a user registers a task like "taking meeting minutes," the system predicts the work time as "2 hours" based on past data and sets a priority. If the user then inputs the actual time spent on the task as "3 hours," the prediction accuracy for similar tasks in the future will improve.

[0665] The server also provides a dashboard for managers to visualize the progress of team members. This allows them to quickly grasp the work progress of each member on the dashboard and adjust tasks as needed. Tableau and Power BI are used as data visualization tools to support intuitive visualization.

[0666] For example, you can enter the following prompt:

[0667] "Please explain how the system calculates the optimal work time and priority when a user registers 'Create a project plan' as a new task."

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The server retrieves business data from the company's internal business management software via API. It receives task and project information retrieved from the API as input. The server then processes the data for storage in a database, saving the details of each task. This creates a foundation for understanding the overall business progress.

[0671] Step 2:

[0672] The server performs analysis using a machine learning model based on the acquired data. It uses historical task data stored in a database as input. Data preprocessing is performed using Python, and analysis is conducted using a TensorFlow model. The output provides the priority and estimated work time for each task. These results are used for efficient task management.

[0673] Step 3:

[0674] Users register new tasks through their device. They input task details, deadlines, requirements, etc., into a web application in their browser. The device uses a React-based interface, and the registered data is immediately sent to the server.

[0675] Step 4:

[0676] The server feeds the received new task data into a predictive model and recalculates the work time and priority. User-provided task information is used as input. Based on the data analysis, prediction results are generated using Python calculations, and the results are sent to the user via a notification system.

[0677] Step 5:

[0678] After completing a task, the user reports the actual time spent on that task via their device. The input consists of the ID of the completed task and the actual time spent, which is entered into a web application. The device then resends this information to the server.

[0679] Step 6:

[0680] The server feeds back actual data and updates the predictive model. It uses actual time submitted by the user as input. The server retrains the machine learning model to improve the accuracy of the next prediction. The updated model is then used for the next task analysis.

[0681] Step 7:

[0682] The server visualizes the task progress of each team member for managers. It uses analyzed progress data as input. Through data visualization tools, the server displays each member's progress on a dashboard, allowing managers to quickly adjust tasks.

[0683] (Application Example 1)

[0684] 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".

[0685] In today's manufacturing industry, there is a demand to maximize the efficiency of each process and improve overall productivity. However, properly managing the prioritization and time required for individual tasks and optimizing the entire process is a challenge that requires considerable effort and cost. Therefore, there is a need to build a system that can efficiently manage production processes in real time while utilizing historical data.

[0686] 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.

[0687] In this invention, the server includes information gathering means, analysis means for analyzing task data, and prediction means for calculating process priorities and estimated times. This makes it possible to optimize the overall process while improving the efficiency of each process in the manufacturing equipment.

[0688] An "information processing device" is a computer system used to collect, analyze, and process data.

[0689] A "data collection method" is a system for automatically acquiring business data.

[0690] A "machine learning algorithm" is a mathematical method that analyzes past data to discover new trends and patterns.

[0691] "Analysis methods" refer to the process of analyzing collected data and extracting meaningful information.

[0692] A "predictive tool" is a system that calculates future work time and priorities based on the analysis results.

[0693] A "notification method" is a means of communication used to inform users of the calculated information.

[0694] A "feedback method" is a way to record actual work time and results to help improve the accuracy of future predictions.

[0695] A "correction mechanism" is a system that uses recorded data to revise the estimate of the next work time.

[0696] A "visualization tool" is a display tool that allows administrators to visually check the progress of a team.

[0697] "Production optimization means" refers to processes and methods for improving the efficiency of each stage in manufacturing equipment.

[0698] The system based on the present invention is centered around an information processing device and includes data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, visualization means, and production optimization means. The server plays a central role, receiving and processing data from terminals and users.

[0699] The server primarily uses Python for data processing and operates on cloud services such as AWS EC2. Operational data is automatically acquired from manufacturing equipment using data collection methods. This data is analyzed using analytical tools incorporating machine learning algorithms, and trends and patterns are extracted using libraries such as TensorFlow.

[0700] Based on the analysis results, the server's prediction system calculates the priority and estimated time for each task. This information is delivered to the user's terminal via a notification system. The user registers a new task using their terminal and inputs the actual work time, sending data to the server via a feedback system. This information is used to improve the accuracy of future predictions, and the work estimate is revised by an adjustment system.

[0701] Managers can understand the progress through visualization tools and achieve efficiency improvements in manufacturing equipment through production optimization tools. For example, if a factory manages the assembly process of automotive parts with this system, it can predict the installation time of individual parts and derive an efficient process sequence.

[0702] An example of a prompt message would be: "Generating AI system, predict the installation time for part A and create an optimization plan for the production line."

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] The server collects operational data from manufacturing equipment using data collection methods. This data includes the start and end times of each process, the resources used, and so on. It receives data provided in real time from the data collection system as input and stores it in the database.

[0706] Step 2:

[0707] The server inputs the stored data into a machine learning algorithm and performs analysis. Using the TensorFlow library, it models work time and trends from past data and extracts patterns. This process can identify delays in work and potential opportunities for efficiency improvements. The analysis results are generated as output.

[0708] Step 3:

[0709] Based on the analysis results, the server uses predictive tools to calculate the priority and estimated work time for each process. This process uses a generative AI model to predict the optimal work sequence for new tasks registered by the user. Optimization information is generated as output to notify the user.

[0710] Step 4:

[0711] The server sends the prediction results to the terminal via a notification system. The terminal receives this information and is responsible for notifying the user. This information includes the estimated time required for the task and its priority, serving as a guide for the user to work efficiently.

[0712] Step 5:

[0713] Users input the actual time spent on a task using their device and send it to the server via a feedback mechanism. This input data is stored on the server as training data to improve the accuracy of future predictions.

[0714] Step 6:

[0715] The server uses the feedback data to adjust the next work estimate using a recalibration mechanism. In this step, the model is retrained based on actual work data, resulting in more accurate predictions.

[0716] Step 7:

[0717] Administrators can use visualization tools on their terminals to monitor progress and production efficiency in real time. This allows them to quickly identify delays and workload imbalances and take necessary countermeasures promptly.

[0718] 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.

[0719] The present invention relates to a system connected to an information processing device, comprising data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, and dashboard display means, in addition to an emotion engine that recognizes the user's emotions. This emotion engine is used to understand the user's emotional state based on user input and behavioral data, and to improve the quality of decision-making in task management.

[0720] The server first collects task-related data from internal systems. Then, it uses machine learning algorithms to analyze this data and calculate task priorities and estimated completion times. Users register tasks using their terminals, and the server notifies them of the calculation results based on this information. Notifications are provided at appropriate times, allowing users to take action to prevent task delays.

[0721] Furthermore, the emotion engine monitors the user's keystrokes, input patterns, and frequency of breaks during work to estimate the user's emotional state. If a change in emotion is detected, the information is sent to the server, and the analysis tool can dynamically adjust task priorities and notification content. This reduces user stress while enabling efficient task management.

[0722] As a concrete example, consider a situation where a user has multiple tasks. If this user is detected to be experiencing high stress levels while working on the "writing a report" task, the emotion engine sends this information to the server. The server determines that the user should take a break outside of work and notifies the user accordingly. It also uses predictive tools as needed to adjust task deadlines or suggests considering redistributing tasks with other colleagues.

[0723] By utilizing this emotion engine, flexible task management tailored to the user's emotional state becomes possible, leading to improved overall organizational efficiency and a better work environment.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The server connects to the company's information systems and periodically collects data on tasks and related projects. This data includes task deadlines, assigned personnel, and progress.

[0727] Step 2:

[0728] The server inputs the collected data into a machine learning algorithm to analyze task priorities and estimated work times. During this process, statistical methods are used based on past performance data to improve prediction accuracy.

[0729] Step 3:

[0730] Users register new tasks and schedules using their devices. The information registered includes the task name, details, and deadline.

[0731] Step 4:

[0732] The server notifies the user of the optimal work time and priority calculated by a prediction system based on the tasks registered by the user. The notification is provided via email or in-system message.

[0733] Step 5:

[0734] The emotion engine monitors the user's keystrokes, frequency of breaks during work, input speed, etc., to estimate the user's emotional state. This information is sent to the server in real time.

[0735] Step 6:

[0736] The server analyzes emotional data received from the emotion engine and dynamically adjusts task reminders, priorities, and workloads if high stress levels or emotional disturbances are detected. Based on these adjustments, it notifies the user of appropriate actions.

[0737] Step 7:

[0738] After a user completes a task, the actual time spent and feedback are recorded on the device and entered into the system. This information is used to predict and improve future processes.

[0739] Step 8:

[0740] The server updates a dashboard for managers based on recorded work and emotional data, providing a visual overview of all team members' progress and emotional status. This allows managers to make appropriate adjustments to tasks and propose stress reduction measures.

[0741] (Example 2)

[0742] 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".

[0743] Many information processing systems fail to consider the user's emotional state in task management, leading to increased user stress and consequently decreased work efficiency. In particular, the fixed nature of task priorities and estimated work times makes it difficult to respond flexibly to the user's situation.

[0744] 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.

[0745] In this invention, the server includes means for collecting data, means for calculating task priorities and estimated work times based on the analysis results, and means for estimating the user's emotional state. This enables flexible task management that responds to the user's emotional state.

[0746] "Means for collecting data" refers to a process or function for automatically or manually obtaining necessary data from an information processing device.

[0747] "Methods of analysis using machine learning algorithms" refer to the process of analyzing data based on specific models or algorithms using collected datasets to gain insights.

[0748] "Means for calculating task priorities and estimated work time" refers to a function that determines the importance of a task and predicts the time required to complete it, based on analyzed data.

[0749] "Means of notifying users" refers to methods and functions for communicating important information to users, including email, messages, and alerts.

[0750] "A means of recording actual work time based on input and feeding it back into the prediction method" refers to a process that uses user input information to record the actual work time taken and utilizes that information to improve the accuracy of predictions for the next time.

[0751] "Means for adjusting work time estimates" refers to a process for more accurately estimating future work time based on past work performance data.

[0752] "Means for presenting progress" refers to functions that visually display task progress to administrators and users, and are provided in the form of dashboards, etc.

[0753] "Means for estimating emotional state" refers to the process of inferring the user's emotions at a given time based on their operation patterns and behavioral data.

[0754] "Means for dynamic adjustment" refers to a function that automatically readjusts tasks and notifications to the optimal state according to the user's current state and environment.

[0755] This invention provides task management using a system connected to an information processing device, and aims to create an efficient work environment by taking into account the user's emotional state. Specifically, it is composed of a combination of data collection means, analysis means, prediction means, notification means, feedback means, adjustment means, dashboard display means, and an emotion engine.

[0756] The server first collects project-managed data. It utilizes APIs from project management applications (e.g., Jira, Trello) to retrieve information such as task details, deadlines, and assignees.

[0757] Next, the server analyzes the data using machine learning algorithms. Using Python data analysis libraries (e.g., scikit-learn, TensorFlow), it calculates task priorities and estimated work times based on historical data. This allows the user to know the optimal task order at any given time.

[0758] Users register tasks and manage their progress through their devices. A specially designed user interface (e.g., web app, mobile app) is provided for this operation. Users enter new tasks, and this information is sent to the server.

[0759] Furthermore, the device monitors the user's keystrokes and break frequency. This is done to reduce workload and encourage breaks at appropriate times. This is achieved through activity monitoring tools (e.g., RescueTime) that run in the background.

[0760] The emotion engine analyzes this data to estimate the user's emotional state. If high stress is detected, the server suggests the user take a break and sends a message at the appropriate time through a notification system. This uses real-time messaging services (e.g., Slack, Microsoft Teams).

[0761] Furthermore, we will use generative AI models as needed to suggest ways to further improve the efficiency of task management. Specifically, we will input a prompt such as, "What is the optimal response if high stress is detected?" and utilize the modeling results.

[0762] In this way, dynamic task management that responds to the user's emotional state becomes possible, leading to improvements in overall organizational productivity and the work environment.

[0763] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0764] Step 1:

[0765] The server collects data using the API of the project management application connected to the information processing device. Specifically, it retrieves task details, deadlines, and assignee information. This collected information becomes the input data. The server stores this in a database in preparation for subsequent analysis.

[0766] Step 2:

[0767] The server inputs task data collected from the database into a machine learning algorithm. Using a Python data analysis library, it determines task priorities from the data and calculates predicted work time. This outputs the optimal order for each task. In this process, a predictive model is trained using historical data and applied to new tasks.

[0768] Step 3:

[0769] The user enters information to register a new task from their terminal. This process involves entering the task name, details, and deadline through a dedicated application screen. The terminal then sends this information to the server, which adds it to the existing database.

[0770] Step 4:

[0771] The device monitors the user's actions. Specifically, it observes the frequency of keystrokes and breaks, and sends this data to the emotion engine. The user's input patterns become the input data, and the emotional state is obtained as the output. Activity monitoring tools operating in the background are used to collect information in real time.

[0772] Step 5:

[0773] The emotion engine analyzes the received data to estimate the user's emotional state. If a high stress level is detected, it sends that information to the server. Based on this information, the server prepares task reprioritization and break suggestions. You input prompts that utilize a generative AI model to generate suggestions for efficiency improvements.

[0774] Step 6:

[0775] The server receives feedback from the emotion engine and provides appropriate notifications to the user. Specifically, it sends messages to the user via email or real-time notifications. The notification system allows users to be notified when they should take a break and to dynamically adjust task deadlines. As an output, user response behavior is monitored and used to improve the system in the future.

[0776] (Application Example 2)

[0777] 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".

[0778] In today's work environment, efficient task management is essential. However, conventional technologies that take into account the user's emotional state have difficulty predicting security risks that reflect the stress and emotional changes that occur during task execution. Therefore, there is a need to appropriately manage the user's stress level while maintaining work efficiency.

[0779] 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.

[0780] In this invention, the server includes data collection means connected to an information processing device, analysis means for analyzing task data, and emotion recognition means for recognizing the user's emotional state and predicting security risks. This makes it possible to predict security risks based on the user's emotional state.

[0781] An "information processing device" is a computer system used for data collection, analysis, prediction, and notification.

[0782] "Data collection methods" refer to techniques for acquiring necessary information and recording it in a format usable by the system.

[0783] "Analysis methods" refer to methods for analyzing information using collected data to gain useful insights.

[0784] A "predictive method" is a technique for inferring future events based on past data and current information.

[0785] "Notification methods" refer to methods for informing users of the obtained predictive information and analysis results.

[0786] A "feedback mechanism" is a method of returning information to the system based on user actions and results to improve accuracy.

[0787] "Adjustment methods" refer to methods that utilize feedback to improve the accuracy of future processes.

[0788] A "dashboard display method" is a technology that visually presents data and results to facilitate management.

[0789] An "emotion recognition method" is a method of identifying an emotional state based on the user's behavior and input data, and making that state available to the system.

[0790] The system for carrying out this invention consists of an information processing device. The server acquires user behavior and task-related data using data collection means. This includes data obtained from smartphone sensors and input devices. The analysis means utilizes machine learning libraries such as TensorFlow and Keras to analyze the collected data in real time and identify the user's emotional state.

[0791] The prediction mechanism accurately predicts security risks based on this emotional state. Therefore, it can utilize past behavioral data to provide advance notice of situations where risk is high. The notification mechanism has the function of conveying risk information to the user at the appropriate time, and records actual work results through the feedback mechanism, contributing to improved analysis accuracy.

[0792] Furthermore, the dashboard display method helps optimize overall organizational efficiency by visualizing user emotional states and work performance for managers. The adjustment method enables smooth operation of the entire system by improving future processes based on past feedback.

[0793] For example, if emotion recognition measures detect that a bank employee is working under high stress, the server immediately sends an alert to the security team and notifies the employee to temporarily suspend work and refresh themselves.

[0794] An example of a prompt using a generative AI model is: "Explain in detail how the emotion engine can use this information to provide the user with appropriate action when it detects a high-stress state."

[0795] This configuration enables flexible work management and security measures that respond to emotional states.

[0796] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0797] Step 1:

[0798] The server aggregates information using data collection methods based on behavioral data and emotion-related inputs received from the terminal. This includes keystroke data and information from mobile sensors. The input is user behavior data, and the output is the processed dataset.

[0799] Step 2:

[0800] The server passes the collected data to an analysis tool, which uses a machine learning model (e.g., an LSTM model) to analyze the emotional state. In this process, the collected data is taken as input, and the result of the emotion recognition is obtained as output. Specifically, TensorFlow is used to dynamically update the model and obtain the analysis results in real time.

[0801] Step 3:

[0802] The server uses prediction tools based on the analysis results to predict security risks related to emotional states. Here, the results of emotion recognition are used as input, and information indicating the risk level is generated as output. This process includes calculating the probability of risk occurrence based on past data.

[0803] Step 4:

[0804] The device communicates risk information to the user through notification methods, prompting them to review their actions and take measures to reduce stress as needed. The input here is risk prediction information, and the output is a notification message to the user. This allows the user to receive specific instructions and pay attention to safety.

[0805] Step 5:

[0806] The server uses feedback mechanisms to record user response data and actual task completion status, which are then used for future analysis and predictions. The input is user feedback data, and the output is updated information that contributes to improving the accuracy of the analysis model. Specifically, new data is incorporated into the learning model to improve the prediction algorithm.

[0807] Step 6:

[0808] Users access visualized sentiment and work performance information obtained through a dashboard display using their terminals to monitor their work progress. The input is organized sentiment data, and the output is a user-friendly dashboard display. This process involves using a data visualization library to present the data in an engaging way.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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."

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] A data collection means connected to an information processing device,

[0833] An analysis means for analyzing task data collected by the data collection means using a machine learning algorithm,

[0834] A prediction means that calculates task priorities and estimated work time based on the analysis results of the aforementioned analysis means,

[0835] A notification means for notifying the user of the information calculated by the prediction means,

[0836] A feedback means that records the actual work time based on the user input and provides feedback to the prediction means,

[0837] An adjustment means for adjusting the estimate of the next work time using the recorded data,

[0838] A dashboard display method for showing the progress of team members to managers,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the notification means has a function to provide reminder notifications based on the progress of a task.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the analysis means has a function to modify a prediction algorithm based on past task processing data and to provide work suggestions suitable for the next task.

[0844] "Example 1"

[0845] (Claim 1)

[0846] A data collection means connected to an information processing device,

[0847] An analysis means for analyzing business data acquired by the aforementioned data collection means using a machine learning algorithm,

[0848] A calculation means for calculating the priority of tasks and estimated work time based on the analysis results of the aforementioned analysis means,

[0849] A notification means for notifying the user of the information calculated by the calculation means,

[0850] A feedback means that records the actual work time based on the input from the user and provides feedback to the calculation means,

[0851] An adjustment means for adjusting the estimation of the next work time using the recorded data,

[0852] A progress display means for administrators to display the progress status of members,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the notification means has a function to provide reminder notifications based on the progress of the work.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the analysis means has a function to improve the prediction algorithm based on past business processing data and provide work suggestions suitable for the next business.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] A data collection means connected to an information processing device,

[0861] An analysis means for analyzing business data collected by the aforementioned data collection means using a machine learning algorithm,

[0862] A prediction means that calculates the priority of tasks and the estimated time based on the analysis results of the aforementioned analysis means,

[0863] A notification means for notifying the user of the information calculated by the prediction means,

[0864] A feedback means that records the actual time required based on the input from the user and provides this feedback to the prediction means,

[0865] An adjustment means for adjusting the estimate of the next work time using the recorded data,

[0866] A visualization method for showing the progress of team members to managers,

[0867] A production optimization means for calculating the optimal process sequence in order to improve the efficiency of each process in manufacturing equipment,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the notification means has a function to provide reminder notifications based on the progress of the work.

[0871] (Claim 3)

[0872] The system according to claim 1, wherein the analysis means has a function to modify a prediction algorithm based on past work processing data and to provide a process proposal suitable for the next work.

[0873] "Example 2 of combining an emotion engine"

[0874] (Claim 1)

[0875] Means for collecting data,

[0876] A means of analyzing collected data using machine learning algorithms,

[0877] A means for calculating task priorities and estimated work time based on the analysis results,

[0878] A means of notifying the user of the calculated information,

[0879] A means to record actual work time based on user input and to feed that information back into a prediction system,

[0880] A means of adjusting work time estimates using recorded data,

[0881] A means of presenting the progress of members to administrators,

[0882] A means for estimating the user's emotional state,

[0883] A means for dynamically adjusting task priorities and notification content based on estimated emotional states,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, wherein the notification means provides a reminder notification based on the progress of the task.

[0887] (Claim 3)

[0888] The system according to claim 1, wherein the analysis means modifies the prediction algorithm based on past data and provides work suggestions suitable for the task.

[0889] "Application example 2 when combining with an emotional engine"

[0890] (Claim 1)

[0891] A data collection means connected to an information processing device,

[0892] An analysis means for analyzing task data collected by the data collection means using a machine learning algorithm,

[0893] A prediction means that calculates task priorities and estimated work time based on the analysis results of the aforementioned analysis means,

[0894] A notification means for notifying the user of the information calculated by the prediction means,

[0895] A feedback means that records the actual work time based on the user input and provides feedback to the prediction means,

[0896] An adjustment means for adjusting the estimate of the next work time using the recorded data,

[0897] A dashboard display method for showing the progress of team members to managers,

[0898] An emotion recognition means that predicts security risks based on the user's emotional state and provides risk information when a high stress state is detected,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein the notification means has a function to provide reminder notifications based on the progress of a task.

[0902] (Claim 3)

[0903] The system according to claim 1, wherein the analysis means has a function to modify a prediction algorithm based on past task processing data and to provide work suggestions suitable for the next task. [Explanation of Symbols]

[0904] 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 data collection means connected to an information processing device, An analysis means for analyzing task data collected by the data collection means using a machine learning algorithm, A prediction means that calculates task priorities and estimated work time based on the analysis results of the aforementioned analysis means, A notification means for notifying the user of the information calculated by the prediction means, A feedback means that records the actual work time based on the user input and provides feedback to the prediction means, An adjustment means for adjusting the estimate of the next work time using the recorded data, A dashboard display method for showing the progress of team members to managers, A system that includes this.

2. The system according to claim 1, wherein the notification means has a function to provide reminder notifications based on the progress of the task.

3. The system according to claim 1, wherein the analysis means has a function to modify a prediction algorithm based on past task processing data and to provide work suggestions suitable for the next task.

Images