system

Abstract

We provide the system. [Solution] An information processing means equipped with the function of collecting activity data and analyzing the workload status based on this data, An optimization method that automatically determines the optimal assignment of tasks based on the analysis results, A progress monitoring means for monitoring the progress of operations and detecting anomalies, A system that includes a display mechanism for showing work progress and analysis results.

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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the management of business projects, accurately grasping the workload status and capabilities of each member and making appropriate task assignments is an important issue in improving management efficiency. However, currently, a great deal of time and resources are required to collect and analyze this information, and as a result, there is a risk of causing delays in business and fatigue among members. Furthermore, manually monitoring progress and evaluating performance involves subjectivity in evaluation and uncertainties due to oversight. To solve these problems, it is essential to develop a new system that can grasp the load status of members in real time and enable optimal task assignment and automatic monitoring of progress.

Means for Solving the Problems

[0005] This invention provides an information processing means for collecting activity data and analyzing workload based on that data. This means allows for real-time understanding of each member's workload and skill set. Furthermore, by incorporating an optimization means that automatically assigns tasks optimally using the analysis results, the cumbersome manual task assignment process is streamlined. Progress monitoring means that continuously monitors the progress of tasks and detects anomalies enable early detection and response to problems. In addition, by providing an evaluation means that evaluates the performance of each member and generates evaluation data, it becomes possible to appropriately judge the contributions and abilities of members and utilize this for personnel evaluation. Finally, dynamic adjustment means that readjust assignments according to the progress of tasks supports the efficient progress of projects. Through these means, a system is constructed that achieves efficient task management and appropriate evaluation of members.

[0006] "Information processing means" refers to technologies and devices used to collect and analyze activity data to evaluate workload and the capabilities of team members.

[0007] "Optimization methods" refer to technologies and algorithms that use collected and analyzed data to efficiently and effectively assign tasks to each member.

[0008] "Progress monitoring means" refers to devices and technologies for continuously monitoring the progress of business projects and detecting anomalies or delays early.

[0009] "Display means" refers to interfaces or devices used to visually display the progress of work or the results of analysis.

[0010] "Evaluation means" refers to methods or systems for quantitatively or qualitatively evaluating the work performance of each member and generating evaluation data.

[0011] "Dynamic adjustment means" refers to technologies and mechanisms for readjusting work assignments in a timely manner in response to changes in circumstances during the progress of work. [Brief explanation of the drawing]

[0012] [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Next, 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This system is designed to streamline business management within companies and organizations, and operates with a server and terminals working in conjunction. The server acts as the central information processing unit, collecting and analyzing activity data, assigning tasks, monitoring progress, and evaluating performance.

[0034] The server first periodically receives activity logs and related information from each member's terminal. This information includes email sending and receiving records, document editing history, and data from project management tools. Based on this, the server analyzes each member's workload and skill set.

[0035] Subsequently, the server monitors the overall project progress and automatically determines the optimal task assignments for the most efficient execution of the work. The server notifies each member of these task orders on their terminal, where they can view the task details.

[0036] During task execution, the server tracks the progress of the tasks in real time and reassigns tasks as needed to match the progress. This process involves progress monitoring algorithms that can quickly detect anomalies and delays.

[0037] Furthermore, the server accumulates activity data from its members and uses this data to evaluate each member's work performance. The evaluation results are reflected in performance evaluation reports generated for each member, which serve as an aid in personnel evaluations.

[0038] As a concrete example, in a development project managed by user A, the server calculates the overall progress and re-evaluates task priorities. As a result, the server assigns new tasks to user B, who has superior development skills, supporting efficient project execution. User A's terminal displays feedback on completed tasks and the next steps, while user B's terminal displays notifications for new tasks. This process prevents project delays and optimizes overall management.

[0039] This structure allows organizations to improve operational efficiency and fairly evaluate their members.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects activity data from each member's terminal. This includes email sending and receiving records, document editing history, and data from project management tools. The server then organizes this data and stores it in a database.

[0043] Step 2:

[0044] The server analyzes the collected activity data to evaluate each member's workload and skill set. This evaluation takes into account past work performance and current work progress, and each member is scored accordingly.

[0045] Step 3:

[0046] The server monitors the overall project progress and identifies unassigned tasks. Based on task priority and skill requirements, it determines the most suitable team member to handle the work.

[0047] Step 4:

[0048] The server notifies each member's terminal of the assigned tasks. The notification includes detailed task information, deadlines, and expected deliverables. Users receive and understand this information on their terminals.

[0049] Step 5:

[0050] Users perform tasks via their devices and periodically provide progress feedback to the server. The server receives this information and updates the progress data in real time.

[0051] Step 6:

[0052] The server continuously analyzes progress data to detect anomalies and delays. If necessary, it displays alerts on the dashboard to help administrators take appropriate action.

[0053] Step 7:

[0054] If circumstances change during the progress of a task, the server re-evaluates the assigned task and readjusts the assignment if necessary. This ensures the efficient execution of the task.

[0055] Step 8:

[0056] The server stores all activity data and measures the work performance of each member. The evaluation data is provided to the organization's HR system as regularly generated reports.

[0057] (Example 1)

[0058] 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."

[0059] In many companies and organizations, efficiently understanding the workload and skill sets of individual members and assigning them optimal tasks is difficult. Furthermore, real-time monitoring of delays and anomalies in work progress and prompt response are necessary, but doing this manually has its limitations. In addition, there is a need to improve overall organizational productivity by accurately evaluating member performance and providing appropriate feedback.

[0060] 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.

[0061] In this invention, the server includes an information processing means for collecting activity data and analyzing the workload and skill sets of individual members based on this data; an optimization means for automatically determining the optimal task assignment based on the analysis results and notifying the member's information terminal via a communication means; and a progress monitoring means for monitoring the progress of tasks in real time and quickly detecting any delays or abnormalities. This enables efficient task allocation according to the skills of the members, as well as real-time progress management and performance evaluation.

[0062] "Activity data" refers to log information about members' work-related operations and actions, including email sending and receiving records, document editing history, and data obtained from project management tools.

[0063] "Workload status" refers to the amount and importance of the tasks that each member is currently responsible for, as well as the time and resources required for them.

[0064] A "skill set" refers to a collection of knowledge, abilities, and skills necessary for performing a specific task, all possessed by the members of a group.

[0065] "Information processing means" refers to a series of processes and tools used to analyze the workload and skill sets of each member based on activity data.

[0066] "Optimization means" refers to decision-making processes and algorithms used to assign the most appropriate tasks to team members based on analysis results.

[0067] "Progress monitoring means" refers to a function that tracks the progress of work in real time and detects delays or anomalies in the progress.

[0068] "Evaluation methods" refer to processes and tools for evaluating the work performance of team members based on activity data and generating individual evaluation data for each member.

[0069] "Dynamic adjustment means" refers to a function that readjusts pre-assigned tasks as needed, according to the progress of the work.

[0070] One embodiment of this invention is to streamline business management for companies and organizations using an information processing system. The server functions as a central information processing unit, working in conjunction with each member's terminal to collect and analyze activity data, assign optimal tasks, monitor progress in real time, and evaluate business performance.

[0071] The server periodically retrieves email sending and receiving records, document editing history, and project management tool data from members' terminals to collect activity data. This process utilizes various APIs and database interfaces. For data analysis, Python libraries such as Pandas and Scikit-learn are used to evaluate members' workload and skill sets.

[0072] When assigning tasks optimally, the server determines the priority of tasks for each member based on a predefined algorithm. The assignment results are notified to the member's information terminal via communication means. In this case, Slack or a dedicated desktop application is used as the notification system.

[0073] The server also monitors work progress in real time, and if delays or anomalies are detected, it automatically reallocates tasks using dynamic adjustment mechanisms. This optimizes work management across the entire organization and ensures efficient execution. In addition, user performance data is accumulated by evaluation mechanisms, and performance evaluations are conducted periodically for each member. These evaluations are used as reports for personnel evaluations.

[0074] As a concrete example, in a project managed by user A, the server retrieves progress data from the project management tool and analyzes the progress in real time based on that data. A new task is then assigned to the highly skilled user B, and feedback regarding the next steps is displayed on user A's terminal. This helps prevent project delays.

[0075] Examples of prompts generated using AI models include: "Design a data analysis method that enables real-time progress monitoring in the business management system. Specifically, analyze each member's activity log and explain the optimal method for assigning and reallocating tasks."

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

[0077] Step 1:

[0078] The server collects activity data from each member's terminal. Specifically, the server connects to the mail server and project management tools via API at regular intervals to retrieve email sending and receiving records, document editing history, task data, etc. As input, log data from various sources is provided and stored in the database. As output, a set of activity data for each member is output.

[0079] Step 2:

[0080] The server analyzes the collected activity data to evaluate the workload and skill sets of the team members. Specifically, it preprocesses the data using the Python Pandas library, and then performs data analysis using the Scikit-learn library. The activity data obtained in step 1 is used as input, and after data processing, a report evaluating the workload and skills of each team member is generated as output.

[0081] Step 3:

[0082] The server determines the optimal task assignment based on the analysis results. Specifically, it uses a known algorithm to determine task priorities and operates a mechanism to notify members' terminals of the task orders. It receives the evaluation results from step 2 as input, outputs a task assignment plan based on that, and sends a notification to the members' terminals.

[0083] Step 4:

[0084] The server monitors the progress of tasks in real time. Specifically, it continuously tracks progress and implements an alert function to detect delays and anomalies. As input, it collects progress data reported in real time from the terminals of the members, and as output, it notifies the administrator of anomaly detection alert information.

[0085] Step 5:

[0086] The server performs performance evaluations based on activity logs and creates evaluation data for each member. Specifically, it aggregates and analyzes past activity history and scores the achievement and contribution levels of each member. Past activity data is used as input, and an evaluation report is generated as output, which is submitted to the HR department as needed.

[0087] (Application Example 1)

[0088] 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."

[0089] In modern manufacturing and service industries, efficient resource management and operational optimization are essential. However, traditional systems often fail to adequately reallocate tasks based on the skill sets of individual workers and machines, as well as real-time progress, hindering productivity improvements.

[0090] 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.

[0091] In this invention, the server includes means for collecting activity data and analyzing the workload based on it, means for automatically determining the optimal allocation of tasks based on the analysis results, means for monitoring the progress of tasks and detecting anomalies, means for an information processing device to monitor the work efficiency and progress of robots and workers in real time, and means for reviewing task priorities and proposing the optimal resource allocation. This makes it possible to optimize work efficiency and prevent work delays.

[0092] "Activity data" refers to information related to the performance of tasks, and includes worker and machine operation logs, progress status, and workload.

[0093] "Workload status" refers to the state that indicates the amount of work and the difficulty level of the tasks that employees or machines are responsible for.

[0094] "Analysis results" refer to specific data related to the evaluation of work performance and optimization of work processes, calculated based on the collected activity data.

[0095] "Optimal task allocation" refers to the distribution of work based on analysis results to maximize the efficiency of personnel and machinery.

[0096] An "abnormality" refers to an irregular event that occurs during the progress of work, or a situation that differs from the expected progress.

[0097] "Progress monitoring" refers to a series of processes that observe the work progress of each member or machine in real time.

[0098] "Resource allocation" refers to the process of assigning tasks to members or machines, with the aim of maximizing efficiency.

[0099] "Business performance evaluation data" refers to data that measures the work performance capabilities of employees or machines and expresses the results as numerical values ​​or indicators.

[0100] The server first collects activity data, which includes work logs and progress data for robots and workers. This data is constantly transmitted from sensors and dedicated terminals deployed as edge devices and aggregated on the server. Next, the server evaluates the workload using a Python®-based data analysis algorithm. This allows the server to perform optimal task assignments that take into account each member's skill set and workload.

[0101] The program running on the terminal receives instructions from the server and notifies the user of work progress and the next task via the UI. It also has the function to monitor ongoing tasks in real time and autonomously send information back to the server.

[0102] As an information processing device, the server monitors the progress of operations using anomaly detection algorithms and automatically re-evaluates task resource allocation if an anomaly occurs. Machine learning models are utilized to quickly identify anomaly patterns.

[0103] As a concrete example, in a certain manufacturing line, robots perform assembly work. In this scenario, a server detects in real time when the assembly speed falls below a certain threshold and assigns auxiliary tasks to other robots to improve efficiency. Through the management and optimization of this entire system, companies can achieve increased productivity and more effective use of resources.

[0104] An example of a prompt message for an application using a generative AI model would be: "Please provide an algorithm for efficient business management. In particular, please explain in detail how to assign optimal tasks according to the robot's work progress and skill set."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server collects activity data from robots and worker terminals. Specifically, it obtains work logs and progress information from sensors and dedicated terminals. Based on this input data, the server converts the data into JSON format and stores it in a database.

[0108] Step 2:

[0109] The server analyzes the workload using accumulated activity data. A data analysis algorithm using Python is employed for this analysis. The input is the collected activity data, and the output is evaluation data indicating the workload status of each member based on that data. The server then performs optimization calculations based on this data.

[0110] Step 3:

[0111] The server performs optimal task assignment based on the analysis results. It uses the previously mentioned evaluation data as input and generates specific task lists for each robot and worker as output. An AI model participates in this process, predicting work efficiency based on historical data.

[0112] Step 4:

[0113] The terminal receives a task list sent from the server and displays it to the user. Information is communicated via the user interface, allowing the user to confirm the next steps to take. The input is the task list from the server, and the output is a visualized work instruction.

[0114] Step 5:

[0115] The server monitors work progress in real time and receives progress data. Specifically, it operates an anomaly detection algorithm based on feedback from terminals and sensors. This algorithm is supported by a machine learning model and immediately identifies anomalous patterns.

[0116] Step 6:

[0117] The server readjusts task resource allocations if an anomaly is detected. The input is a notification of the detected anomaly, which triggers a recalculation of the next task allocation, generating a readjusted task list as output. This improves overall system efficiency.

[0118] Step 7:

[0119] The server generates a business performance report after all processes are complete. This includes progress and tracking data for each member. The final output is a performance report that helps improve future operations and is shared with users. This output is used for strategic feedback.

[0120] 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.

[0121] This system works by combining activity data and an emotion engine to streamline business management for companies and organizations. Its main components include a server, terminals, and emotion recognition sensors.

[0122] The server continuously collects various activity data from each member's terminal. This data includes project progress, email sending and receiving records, and document editing history. It also acquires emotional data from users' facial expressions and voice via emotion recognition sensors. All of this data is then comprehensively analyzed on the server.

[0123] The server uses activity data and emotional data to comprehensively evaluate each member's workload and skill set. By considering emotional data, such as the member's current stress level and satisfaction level, a more accurate evaluation can be provided. Based on the analysis results, the server automatically assigns and adjusts tasks to the optimal level.

[0124] When a task is assigned, the server notifies the member's terminal of the information. The user checks the task details and deadline on their terminal, and reports on the progress as they work on the task. The server tracks this progress data in real time and reassigns tasks as needed. In addition, if the emotional state changes due to the emotion engine, the task is adjusted accordingly.

[0125] For example, if user A is appointed as the leader of a new project, the server will notify user A's terminal of the project overview and related tasks. At the same time, if the emotion recognition results indicate that user A's stress level is high, the server will consider adding support staff or redistributing tasks and propose appropriate measures to the terminal.

[0126] In this way, this system achieves a form that promotes both the efficiency of work management and the well-being of members by evaluating both their activities and emotions.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server collects activity data from each member's terminal. This includes sending and receiving emails, editing history of various project documents, and data from schedule management tools. It also collects emotional data by acquiring users' facial expressions and voices from emotion recognition sensors.

[0130] Step 2:

[0131] The server analyzes the collected activity data and emotional data. It assesses workload levels from the activity data and evaluates employee stress levels and emotional fluctuations from the emotional data. This allows for an overall understanding of the employees' work performance.

[0132] Step 3:

[0133] Based on the analysis results, the server executes an optimization process to assign the most suitable tasks to each member. If a user's emotional state deteriorates, the server adjusts the tasks or provides support to reduce stress.

[0134] Step 4:

[0135] The server notifies the terminal of the assigned task. The user then checks information such as task details, deadlines, and priorities through the terminal.

[0136] Step 5:

[0137] Users perform tasks on their terminals and report their progress to the server in real time. These reports include completion rates, time taken, and any challenges encountered.

[0138] Step 6:

[0139] The server monitors progress in real time and displays alerts on the terminal if there are delays or if the user's emotional state changes. These alerts include recommended actions and revised work plans.

[0140] Step 7:

[0141] The server reassigns tasks as needed, based on the user's emotional state and project progress requirements. The server makes these adjustments and notifies the terminal again.

[0142] Step 8:

[0143] The server records overall activity and emotional data and periodically evaluates the work performance of each member. The evaluation results are generated as performance reports and distributed to administrators, where they are used for personnel evaluations.

[0144] (Example 2)

[0145] 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".

[0146] Traditional work management systems evaluate employee workload based solely on activity data, failing to consider non-numerical factors such as employee emotional state and stress levels. As a result, work allocation is often suboptimal. Furthermore, ignoring the impact of employee emotional state on work performance poses a risk of undermining employee well-being.

[0147] 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.

[0148] In this invention, the server includes an information processing means that collects activity data and emotional data and analyzes the workload and emotional state of its members based on this data; an optimization means that automatically determines the optimal assignment of tasks considering the analysis results and emotional state; and a monitoring means that monitors the progress of tasks and detects abnormalities and changes in emotional state. This enables highly accurate task assignment and adjustment that takes into account the emotional state of its members.

[0149] "Activity data" refers to information about members' work activities, such as project progress, email sending and receiving records, and document editing history.

[0150] "Emotional data" refers to information about emotional states such as stress levels and satisfaction levels, obtained based on members' facial expressions and voices.

[0151] "Information processing means" refers to devices or programs that collect activity data and emotional data, and analyze the workload and emotional state of members based on that data.

[0152] An "optimization tool" is a device or program that uses analyzed activity data and emotional data to optimally assign tasks to members.

[0153] "Monitoring means" refers to devices or programs used to continuously monitor abnormalities or changes in emotional state based on members' work progress and activity data.

[0154] A "dynamic adjustment mechanism" refers to a device or program that readjusts work assignments as needed in response to monitoring work progress or changes in emotional state.

[0155] "Display means" refers to devices or programs for visually providing members with information on work progress, analysis results, and emotional states.

[0156] This invention is a system that enables efficient management of operations using activity data and emotional data of members within an organization. The main components of the system include a server, terminals, and emotion recognition sensors.

[0157] The server utilizes network communication to collect activity data obtained from multiple member terminals. This activity data includes project progress, email sending and receiving records, and document editing history obtained from project management tools and email clients. This data can be retrieved via API and stored in a database at regular intervals.

[0158] Furthermore, the device acquires emotional data from the user's facial expressions and voice through an emotion recognition sensor. This emotional data is collected using a facial recognition camera and microphone during login and when voice commands are received, and then sent to the server.

[0159] The server integrates collected activity and emotion data and uses a generative AI model to analyze the workload and emotional state of its members. Specifically, it utilizes machine learning libraries such as TENSORFLOW® and PyTorch to perform the analysis. The analysis results are generated as a workload assessment and emotional state score for each member, which are used for assigning tasks.

[0160] Based on these analyses, the server optimally assigns tasks, and tasks are automatically notified to members' terminals via project management tools. The server also monitors task progress in real time and dynamically adjusts tasks in response to changes in emotional state.

[0161] As a concrete example, if user A is appointed as the leader of a new project, the server notifies user A's terminal of the project overview and related tasks. If the emotion recognition results indicate that user A is at a high stress level, the server considers adding support personnel or redistributing tasks as appropriate and proposes these changes to user A's terminal. This process enables highly accurate work management that takes into account the emotional state of team members.

[0162] As a prompt for the generating AI model, one possible prompt would be, "When User A is appointed as project leader, stress management will be performed based on the project overview and emotion recognition data."

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server periodically collects activity data from members' terminals via the network. The input used is information received via APIs from project management tools and email clients. Upon receiving this information, the server stores it in a database in CSV format. Specifically, the server calls the API every hour to retrieve the latest project progress and email sending / receiving records.

[0166] Step 2:

[0167] The user collects emotional data from their facial expressions and voice through an emotion recognition sensor. Inputs include facial capture from a face recognition camera and voice data from a microphone. The device acquires this data in real time and sends it to the server. Specifically, the user's face is captured each time they log in, and their emotions are analyzed along with the reception of voice commands.

[0168] Step 3:

[0169] The server integrates the collected activity and sentiment data and stores it in a database. The input data includes activity and sentiment data, while the output is prepared data for integrated analysis. Specifically, the server uses bulk inserts to efficiently import data into the database.

[0170] Step 4:

[0171] The server analyzes integrated data using a generative AI model. Its inputs include integrated activity data and sentiment data, and its output is an evaluation of each member's workload and emotional state. Specifically, the server uses TensorFlow to input data into the AI ​​model in batches and performs evaluations for each member.

[0172] Step 5:

[0173] The server determines the optimal task assignment for each member based on the evaluation results. The input is the analysis results, and the output is the specific task for each member. In practice, the server automatically assigns tasks to members via the API of the project management tool.

[0174] Step 6:

[0175] The server notifies each member's terminal of the assigned tasks. The input is determined task information, and the output is a notification to the member's terminal. Specifically, it uses email notifications or push notifications to send task details to the members.

[0176] Step 7:

[0177] Users report their work progress to the server. Inputs include daily progress data and task completion reports. Output is an updated project progress status. Specifically, users update their progress using a dedicated application and upload that data to a database on the server.

[0178] Step 8:

[0179] The server continuously monitors work progress and emotional state, readjusting tasks as needed. Its inputs include real-time progress and emotional data, while its output consists of adjusted work assignments and break suggestions. Specifically, when the server detects a change in emotional state, it considers new task assignments and notifies the user's terminal of the update.

[0180] (Application Example 2)

[0181] 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".

[0182] In modern companies and organizations, improving operational efficiency and enhancing the well-being of employees are crucial themes. Especially in environments where diverse tasks proceed simultaneously, it is essential to properly manage the workload of each member and reduce emotional stress. However, traditional systems have struggled to accurately assign tasks and make dynamic adjustments that take emotional data into account, sometimes resulting in excessive stress on employees.

[0183] 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.

[0184] In this invention, the server includes an information processing means that collects activity data and emotional data and has the function of analyzing the workload and emotional state based on this data; an optimization means that automatically determines the optimal assignment of tasks and the adjustment of work support as needed based on the analysis results; and a progress monitoring means that monitors the progress of tasks and changes in emotional state and detects abnormalities. This enables efficient execution of tasks and proper management of the emotional state of members.

[0185] "Activity data" refers to objective work information such as the progress of members' work and the content of their tasks.

[0186] "Emotional data" refers to information that indicates the emotional state of the members, and is subjective data obtained from facial expressions and voices acquired via emotion recognition sensors.

[0187] "Information processing means" refers to a computer system that has the function of analyzing collected activity data and emotional data to evaluate workload and emotional state.

[0188] An "optimization tool" is an algorithm or software that has the function of automatically assigning tasks optimally and adjusting work support as needed, based on the analyzed data.

[0189] A "progress monitoring system" is a system that has the function of monitoring the progress of work and changes in emotional state in real time and detecting anomalies.

[0190] The system implementing this invention is equipped with a function to analyze activity data and emotional data in combination in order to streamline business management for companies and organizations. The server collects activity data such as work progress, work content, email exchanges, and document editing history from members' terminals, and acquires emotional data from the user's facial expressions and voice using an emotion recognition sensor.

[0191] The server processes this data holistically, analyzing workload and emotional states. Data analysis software is used for information processing, and emotional data is analyzed by emotion recognition algorithms. This allows for the identification of overworked or stressed members, enabling automatic adjustment of workload assignments and support through optimization measures.

[0192] This system uses progress monitoring to monitor the progress of tasks and changes in emotions in real time, and the work plan is immediately revised when the situation changes. In addition, a display device is provided on the user's terminal, allowing them to constantly check the progress of tasks and emotional evaluations, and appropriate feedback is provided.

[0193] As a concrete example, when a factory line worker is assigned to a new project, the server assesses the worker's workload and emotional state, and automatically adjusts tasks or provides additional support as needed. Based on the emotional data, if the server determines that the worker is highly stressed, it will assign another worker to provide support or take measures to reduce the workload.

[0194] Example of a prompt:

[0195] "Simulate the actions of a robot that automatically provides support to a worker who is under high stress."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The server collects activity data from each member's terminal. This activity data includes work progress, work details, email sending and receiving history, and document editing history. All work-related data from each terminal is used as input, and this data is integrated and stored in a database. This forms a foundation for understanding the overall work situation within the organization.

[0199] Step 2:

[0200] The server collects data from emotion recognition sensors to obtain emotional data such as members' facial expressions and voices. This input data is then processed by an analysis algorithm to provide the members' emotional state (stress level, satisfaction level, etc.) as output. Machine learning models are used to improve the accuracy of emotion recognition.

[0201] Step 3:

[0202] The server integrates and analyzes the collected activity and emotional data to evaluate workload and emotional state. The input at this stage is the output data from steps 1 and 2, and a comprehensive evaluation is performed by data analysis software. The output generates individual workload and emotional evaluation values ​​for each member.

[0203] Step 4:

[0204] The server adjusts the optimal assignment of tasks and work support based on the generated evaluation values. This is a dynamic adjustment process using optimization methods, and the evaluation values ​​from step 3 are used as input. The server adjusts the workload for members deemed to be overloaded and arranges additional support as needed. The output is the adjusted task assignment.

[0205] Step 5:

[0206] The server monitors the progress of tasks and changes in emotional state in real time, and detects anomalies. Inputs are existing task plans and data from step 3, and outputs are notifications and warnings of anomaly detection. Here, dynamic task management is achieved by making full use of progress monitoring methods.

[0207] Step 6:

[0208] The system notifies users of the latest work progress and emotional assessment results on their devices and displays appropriate feedback. Input is updated information from the server, and output is visually displayed information on the user's device. In this way, users can understand their work status and emotional state in real time.

[0209] This series of steps enables integrated business management and emotional state analysis by the server, improving operational efficiency and member well-being.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] This system is designed to streamline business management within companies and organizations, and operates with a server and terminals working in conjunction. The server acts as the central information processing unit, collecting and analyzing activity data, assigning tasks, monitoring progress, and evaluating performance.

[0227] The server first periodically receives activity logs and related information from each member's terminal. This information includes email sending and receiving records, document editing history, and data from project management tools. Based on this, the server analyzes each member's workload and skill set.

[0228] Subsequently, the server monitors the overall project progress and automatically determines the optimal task assignments for the most efficient execution of the work. The server notifies each member of these task orders on their terminal, where they can view the task details.

[0229] During task execution, the server tracks the progress of the tasks in real time and reassigns tasks as needed to match the progress. This process involves progress monitoring algorithms that can quickly detect anomalies and delays.

[0230] Furthermore, the server accumulates activity data from its members and uses this data to evaluate each member's work performance. The evaluation results are reflected in performance evaluation reports generated for each member, which serve as an aid in personnel evaluations.

[0231] As a concrete example, in a development project managed by user A, the server calculates the overall progress and re-evaluates task priorities. As a result, the server assigns new tasks to user B, who has superior development skills, supporting efficient project execution. User A's terminal displays feedback on completed tasks and the next steps, while user B's terminal displays notifications for new tasks. This process prevents project delays and optimizes overall management.

[0232] This structure allows organizations to improve operational efficiency and fairly evaluate their members.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server collects activity data from each member's terminal. This includes email sending and receiving records, document editing history, and data from project management tools. The server then organizes this data and stores it in a database.

[0236] Step 2:

[0237] The server analyzes the collected activity data to evaluate each member's workload and skill set. This evaluation takes into account past work performance and current work progress, and each member is scored accordingly.

[0238] Step 3:

[0239] The server monitors the overall project progress and identifies unassigned tasks. Based on task priority and skill requirements, it determines the most suitable team member to handle the work.

[0240] Step 4:

[0241] The server notifies each member's terminal of the assigned tasks. The notification includes detailed task information, deadlines, and expected deliverables. Users receive and understand this information on their terminals.

[0242] Step 5:

[0243] Users perform tasks via their devices and periodically provide progress feedback to the server. The server receives this information and updates the progress data in real time.

[0244] Step 6:

[0245] The server continuously analyzes progress data to detect anomalies and delays. If necessary, it displays alerts on the dashboard to help administrators take appropriate action.

[0246] Step 7:

[0247] If circumstances change during the progress of a task, the server re-evaluates the assigned task and readjusts the assignment if necessary. This ensures the efficient execution of the task.

[0248] Step 8:

[0249] The server stores all activity data and measures the work performance of each member. The evaluation data is provided to the organization's HR system as regularly generated reports.

[0250] (Example 1)

[0251] 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."

[0252] In many companies and organizations, efficiently understanding the workload and skill sets of individual members and assigning them optimal tasks is difficult. Furthermore, real-time monitoring of delays and anomalies in work progress and prompt response are necessary, but doing this manually has its limitations. In addition, there is a need to improve overall organizational productivity by accurately evaluating member performance and providing appropriate feedback.

[0253] 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.

[0254] In this invention, the server includes an information processing means for collecting activity data and analyzing the workload and skill sets of individual members based on this data; an optimization means for automatically determining the optimal task assignment based on the analysis results and notifying the member's information terminal via a communication means; and a progress monitoring means for monitoring the progress of tasks in real time and quickly detecting any delays or abnormalities. This enables efficient task allocation according to the skills of the members, as well as real-time progress management and performance evaluation.

[0255] "Activity data" refers to log information about members' work-related operations and actions, including email sending and receiving records, document editing history, and data obtained from project management tools.

[0256] "Workload status" refers to the amount and importance of the tasks that each member is currently responsible for, as well as the time and resources required for them.

[0257] A "skill set" refers to a collection of knowledge, abilities, and skills necessary for performing a specific task, all possessed by the members of a group.

[0258] "Information processing means" refers to a series of processes and tools used to analyze the workload and skill sets of each member based on activity data.

[0259] "Optimization means" refers to decision-making processes and algorithms used to assign the most appropriate tasks to team members based on analysis results.

[0260] "Progress monitoring means" refers to a function that tracks the progress of work in real time and detects delays or anomalies in the progress.

[0261] "Evaluation methods" refer to processes and tools for evaluating the work performance of team members based on activity data and generating individual evaluation data for each member.

[0262] "Dynamic adjustment means" refers to a function that readjusts pre-assigned tasks as needed, according to the progress of the work.

[0263] One embodiment of this invention is to streamline business management for companies and organizations using an information processing system. The server functions as a central information processing unit, working in conjunction with each member's terminal to collect and analyze activity data, assign optimal tasks, monitor progress in real time, and evaluate business performance.

[0264] The server periodically retrieves email sending and receiving records, document editing history, and project management tool data from members' terminals to collect activity data. This process utilizes various APIs and database interfaces. For data analysis, Python libraries such as Pandas and Scikit-learn are used to evaluate members' workload and skill sets.

[0265] When assigning tasks optimally, the server determines the priority of tasks for each member based on a predefined algorithm. The assignment results are notified to the member's information terminal via communication means. In this case, Slack or a dedicated desktop application is used as the notification system.

[0266] The server also monitors work progress in real time, and if delays or anomalies are detected, it automatically reallocates tasks using dynamic adjustment mechanisms. This optimizes work management across the entire organization and ensures efficient execution. In addition, user performance data is accumulated by evaluation mechanisms, and performance evaluations are conducted periodically for each member. These evaluations are used as reports for personnel evaluations.

[0267] As a concrete example, in a project managed by user A, the server retrieves progress data from the project management tool and analyzes the progress in real time based on that data. A new task is then assigned to the highly skilled user B, and feedback regarding the next steps is displayed on user A's terminal. This helps prevent project delays.

[0268] Examples of prompts generated using AI models include: "Design a data analysis method that enables real-time progress monitoring in the business management system. Specifically, analyze each member's activity log and explain the optimal method for assigning and reallocating tasks."

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server collects activity data from each member's terminal. Specifically, the server connects to the mail server and project management tools via API at regular intervals to retrieve email sending and receiving records, document editing history, task data, etc. As input, log data from various sources is provided and stored in the database. As output, a set of activity data for each member is output.

[0272] Step 2:

[0273] The server analyzes the collected activity data to evaluate the workload and skill sets of the team members. Specifically, it preprocesses the data using the Python Pandas library, and then performs data analysis using the Scikit-learn library. The activity data obtained in step 1 is used as input, and after data processing, a report evaluating the workload and skills of each team member is generated as output.

[0274] Step 3:

[0275] The server determines the optimal task assignment based on the analysis results. Specifically, it uses a known algorithm to determine task priorities and operates a mechanism to notify members' terminals of the task orders. It receives the evaluation results from step 2 as input, outputs a task assignment plan based on that, and sends a notification to the members' terminals.

[0276] Step 4:

[0277] The server monitors the progress of tasks in real time. Specifically, it continuously tracks progress and implements an alert function to detect delays and anomalies. As input, it collects progress data reported in real time from the terminals of the members, and as output, it notifies the administrator of anomaly detection alert information.

[0278] Step 5:

[0279] The server performs performance evaluation based on the activity log and creates evaluation data for each member of the organization. As a specific operation, it aggregates and analyzes the past activity history, and scores the achievement and contribution of each member. Past activity data is used as input, an evaluation report is generated as output, and is submitted to the personnel department as needed.

[0280] (Application Example 1)

[0281] 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".

[0282] In modern manufacturing and service industries, efficient resource management and business optimization are essential. However, in conventional systems, flexible task reallocation based on the skill sets and real-time progress of each worker or machine is not sufficiently performed, resulting in a problem that the improvement of productivity is hindered.

[0283] 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.

[0284] In this invention, the server includes means for collecting activity data and analyzing the business load situation based on this, means for automatically determining the optimal allocation of business based on the analysis result, means for monitoring the progress of the business and detecting abnormalities, means for the information processing device to monitor the work efficiency and progress of robots and workers in real time, and means for reviewing the priority of tasks and proposing an optimal resource allocation. Thereby, it becomes possible to optimize work efficiency and prevent business delays.

[0285] "Activity data" refers to information related to business performance, which includes the operation logs of workers and machines, progress status, and business load.

[0286] "Business load status" refers to the state indicating the amount of work and the difficulty level of work borne by members or machines.

[0287] "Analysis result" refers to specific data regarding the evaluation of business performance status calculated based on the collected activity data and the optimization of business.

[0288] "Optimal allocation of business" refers to the distribution of work to maximize the efficiency of members or machines based on the analysis results.

[0289] "Abnormality" refers to irregular events occurring in the progress of business or situations different from the expected progress.

[0290] "Progress monitoring" refers to a series of processes for observing the business progress of each member and machine in real time.

[0291] "Resource allocation" refers to the process of distributing business to members or machines, aiming to maximize efficiency.

[0292] "Business performance evaluation data" refers to the measurement of the work execution ability of members or machines and the representation of the results as numerical values or indicators.

[0293] The server first collects activity data, which includes the work logs of robots and workers and the data on the progress status. This data is constantly transmitted from sensors and dedicated terminals arranged as edge devices and aggregated by the server. Next, the server evaluates the business load status using a Python-based data analysis algorithm. Thereby, the server executes an optimal task allocation considering the skill set and work load of each member.

[0294] The program running on the terminal receives instructions from the server and notifies the user about the business progress and the next task via the UI. It also has the function of monitoring the ongoing business in real time and autonomously returning information to the server.

[0295] As an information processing device, the server monitors the progress of operations using anomaly detection algorithms and automatically re-evaluates task resource allocation if an anomaly occurs. Machine learning models are utilized to quickly identify anomaly patterns.

[0296] As a concrete example, in a certain manufacturing line, robots perform assembly work. In this scenario, a server detects in real time when the assembly speed falls below a certain threshold and assigns auxiliary tasks to other robots to improve efficiency. Through the management and optimization of this entire system, companies can achieve increased productivity and more effective use of resources.

[0297] An example of a prompt message for an application using a generative AI model would be: "Please provide an algorithm for efficient business management. In particular, please explain in detail how to assign optimal tasks according to the robot's work progress and skill set."

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects activity data from robots and worker terminals. Specifically, it obtains work logs and progress information from sensors and dedicated terminals. Based on this input data, the server converts the data into JSON format and stores it in a database.

[0301] Step 2:

[0302] The server analyzes the workload using accumulated activity data. A data analysis algorithm using Python is employed for this analysis. The input is the collected activity data, and the output is evaluation data indicating the workload status of each member based on that data. The server then performs optimization calculations based on this data.

[0303] Step 3:

[0304] The server executes optimal task allocation based on the analysis results. Using the previous evaluation data as input, it generates a specific task list for each robot and worker as output. An AI model participates in this task, predicting work efficiency from past data.

[0305] Step 4:

[0306] The terminal receives the task list sent from the server and displays it to the user. Here, information is transmitted via the user interface, and the user checks the work content to be done next. The input is the task list from the server, and the output is the visualized work instructions.

[0307] Step 5:

[0308] The server monitors the progress of the work in real time and receives progress data. Specifically, it applies an anomaly detection algorithm based on feedback from terminals and sensors. This algorithm is supported by a machine learning model and can immediately identify abnormal patterns.

[0309] Step 6:

[0310] If an anomaly is detected, the server readjusts the resource allocation of the task. The input is the notification of anomaly detection. Based on this, it recalculates the next task allocation and generates a readjusted task list as output. This improves the efficiency of the entire system.

[0311] Step 7:

[0312] After all processes are completed, the server generates a report on work performance. This includes the progress data and tracking data of each member. As the final output, a performance report useful for future work improvement is created and shared with the user. This output is used for strategic feedback.

[0313] 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.

[0314] This system works by combining activity data and an emotion engine to streamline business management for companies and organizations. Its main components include a server, terminals, and emotion recognition sensors.

[0315] The server continuously collects various activity data from each member's terminal. This data includes project progress, email sending and receiving records, and document editing history. It also acquires emotional data from users' facial expressions and voice via emotion recognition sensors. All of this data is then comprehensively analyzed on the server.

[0316] The server uses activity data and emotional data to comprehensively evaluate each member's workload and skill set. By considering emotional data, such as the member's current stress level and satisfaction level, a more accurate evaluation can be provided. Based on the analysis results, the server automatically assigns and adjusts tasks to the optimal level.

[0317] When a task is assigned, the server notifies the member's terminal of the information. The user checks the task details and deadline on their terminal, and reports on the progress as they work on the task. The server tracks this progress data in real time and reassigns tasks as needed. In addition, if the emotional state changes due to the emotion engine, the task is adjusted accordingly.

[0318] For example, if user A is appointed as the leader of a new project, the server will notify user A's terminal of the project overview and related tasks. At the same time, if the emotion recognition results indicate that user A's stress level is high, the server will consider adding support staff or redistributing tasks and propose appropriate measures to the terminal.

[0319] In this way, this system achieves a form that promotes both the efficiency of work management and the well-being of members by evaluating both their activities and emotions.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The server collects activity data from each member's terminal. This includes sending and receiving emails, editing history of various project documents, and data from schedule management tools. It also collects emotional data by acquiring users' facial expressions and voices from emotion recognition sensors.

[0323] Step 2:

[0324] The server analyzes the collected activity data and emotional data. It assesses workload levels from the activity data and evaluates employee stress levels and emotional fluctuations from the emotional data. This allows for an overall understanding of the employees' work performance.

[0325] Step 3:

[0326] Based on the analysis results, the server executes an optimization process to assign the most suitable tasks to each member. If a user's emotional state deteriorates, the server adjusts the tasks or provides support to reduce stress.

[0327] Step 4:

[0328] The server notifies the terminal of the assigned task. The user then checks information such as task details, deadlines, and priorities through the terminal.

[0329] Step 5:

[0330] Users perform tasks on their terminals and report their progress to the server in real time. These reports include completion rates, time taken, and any challenges encountered.

[0331] Step 6:

[0332] The server monitors progress in real time and displays alerts on the terminal if there are delays or if the user's emotional state changes. These alerts include recommended actions and revised work plans.

[0333] Step 7:

[0334] The server reassigns tasks as needed, based on the user's emotional state and project progress requirements. The server makes these adjustments and notifies the terminal again.

[0335] Step 8:

[0336] The server records overall activity and emotional data and periodically evaluates the work performance of each member. The evaluation results are generated as performance reports and distributed to administrators, where they are used for personnel evaluations.

[0337] (Example 2)

[0338] 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".

[0339] Traditional work management systems evaluate employee workload based solely on activity data, failing to consider non-numerical factors such as employee emotional state and stress levels. As a result, work allocation is often suboptimal. Furthermore, ignoring the impact of employee emotional state on work performance poses a risk of undermining employee well-being.

[0340] 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.

[0341] In this invention, the server includes an information processing means that collects activity data and emotional data and analyzes the workload and emotional state of its members based on this data; an optimization means that automatically determines the optimal assignment of tasks considering the analysis results and emotional state; and a monitoring means that monitors the progress of tasks and detects abnormalities and changes in emotional state. This enables highly accurate task assignment and adjustment that takes into account the emotional state of its members.

[0342] "Activity data" refers to information about members' work activities, such as project progress, email sending and receiving records, and document editing history.

[0343] "Emotional data" refers to information about emotional states such as stress levels and satisfaction levels, obtained based on members' facial expressions and voices.

[0344] "Information processing means" refers to devices or programs that collect activity data and emotional data, and analyze the workload and emotional state of members based on that data.

[0345] An "optimization tool" is a device or program that uses analyzed activity data and emotional data to optimally assign tasks to members.

[0346] "Monitoring means" refers to devices or programs used to continuously monitor abnormalities or changes in emotional state based on members' work progress and activity data.

[0347] A "dynamic adjustment mechanism" refers to a device or program that readjusts work assignments as needed in response to monitoring work progress or changes in emotional state.

[0348] "Display means" refers to devices or programs for visually providing members with information on work progress, analysis results, and emotional states.

[0349] This invention is a system that enables efficient management of operations using activity data and emotional data of members within an organization. The main components of the system include a server, terminals, and emotion recognition sensors.

[0350] The server utilizes network communication to collect activity data obtained from multiple member terminals. This activity data includes project progress, email sending and receiving records, and document editing history obtained from project management tools and email clients. This data can be retrieved via API and stored in a database at regular intervals.

[0351] Furthermore, the device acquires emotional data from the user's facial expressions and voice through an emotion recognition sensor. This emotional data is collected using a facial recognition camera and microphone during login and when voice commands are received, and then sent to the server.

[0352] The server integrates collected activity and emotion data and uses a generative AI model to analyze the workload and emotional state of team members. Specifically, it utilizes machine learning libraries such as TensorFlow and PyTorch to perform the analysis. The analysis results are generated as a workload assessment and emotional state score for each team member, which are then used for assigning tasks.

[0353] Based on these analyses, the server optimally assigns tasks, and tasks are automatically notified to members' terminals via project management tools. The server also monitors task progress in real time and dynamically adjusts tasks in response to changes in emotional state.

[0354] As a concrete example, if user A is appointed as the leader of a new project, the server notifies user A's terminal of the project overview and related tasks. If the emotion recognition results indicate that user A is at a high stress level, the server considers adding support personnel or redistributing tasks as appropriate and proposes these changes to user A's terminal. This process enables highly accurate work management that takes into account the emotional state of team members.

[0355] As a prompt for the generating AI model, one possible prompt would be, "When User A is appointed as project leader, stress management will be performed based on the project overview and emotion recognition data."

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The server periodically collects activity data from members' terminals via the network. The input used is information received via APIs from project management tools and email clients. Upon receiving this information, the server stores it in a database in CSV format. Specifically, the server calls the API every hour to retrieve the latest project progress and email sending / receiving records.

[0359] Step 2:

[0360] The user collects emotional data from their facial expressions and voice through an emotion recognition sensor. Inputs include facial capture from a face recognition camera and voice data from a microphone. The device acquires this data in real time and sends it to the server. Specifically, the user's face is captured each time they log in, and their emotions are analyzed along with the reception of voice commands.

[0361] Step 3:

[0362] The server integrates the collected activity and sentiment data and stores it in a database. The input data includes activity and sentiment data, while the output is prepared data for integrated analysis. Specifically, the server uses bulk inserts to efficiently import data into the database.

[0363] Step 4:

[0364] The server analyzes integrated data using a generative AI model. Its inputs include integrated activity data and sentiment data, and its output is an evaluation of each member's workload and emotional state. Specifically, the server uses TensorFlow to input data into the AI ​​model in batches and performs evaluations for each member.

[0365] Step 5:

[0366] The server determines the optimal task assignment for each member based on the evaluation results. The input is the analysis results, and the output is the specific task for each member. In practice, the server automatically assigns tasks to members via the API of the project management tool.

[0367] Step 6:

[0368] The server notifies each member's terminal of the assigned tasks. The input is determined task information, and the output is a notification to the member's terminal. Specifically, it uses email notifications or push notifications to send task details to the members.

[0369] Step 7:

[0370] Users report their work progress to the server. Inputs include daily progress data and task completion reports. Output is an updated project progress status. Specifically, users update their progress using a dedicated application and upload that data to a database on the server.

[0371] Step 8:

[0372] The server continuously monitors work progress and emotional state, readjusting tasks as needed. Its inputs include real-time progress and emotional data, while its output consists of adjusted work assignments and break suggestions. Specifically, when the server detects a change in emotional state, it considers new task assignments and notifies the user's terminal of the update.

[0373] (Application Example 2)

[0374] 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."

[0375] In modern companies and organizations, improving operational efficiency and enhancing the well-being of employees are crucial themes. Especially in environments where diverse tasks proceed simultaneously, it is essential to properly manage the workload of each member and reduce emotional stress. However, traditional systems have struggled to accurately assign tasks and make dynamic adjustments that take emotional data into account, sometimes resulting in excessive stress on employees.

[0376] 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.

[0377] In this invention, the server includes an information processing means that collects activity data and emotional data and has the function of analyzing the workload and emotional state based on this data; an optimization means that automatically determines the optimal assignment of tasks and the adjustment of work support as needed based on the analysis results; and a progress monitoring means that monitors the progress of tasks and changes in emotional state and detects abnormalities. This enables efficient execution of tasks and proper management of the emotional state of members.

[0378] "Activity data" refers to objective work information such as the progress of members' work and the content of their tasks.

[0379] "Emotional data" refers to information that indicates the emotional state of the members, and is subjective data obtained from facial expressions and voices acquired via emotion recognition sensors.

[0380] "Information processing means" refers to a computer system that has the function of analyzing collected activity data and emotional data to evaluate workload and emotional state.

[0381] An "optimization tool" is an algorithm or software that has the function of automatically assigning tasks optimally and adjusting work support as needed, based on the analyzed data.

[0382] A "progress monitoring system" is a system that has the function of monitoring the progress of work and changes in emotional state in real time and detecting anomalies.

[0383] The system implementing this invention is equipped with a function to analyze activity data and emotional data in combination in order to streamline business management for companies and organizations. The server collects activity data such as work progress, work content, email exchanges, and document editing history from members' terminals, and acquires emotional data from the user's facial expressions and voice using an emotion recognition sensor.

[0384] The server processes this data holistically, analyzing workload and emotional states. Data analysis software is used for information processing, and emotional data is analyzed by emotion recognition algorithms. This allows for the identification of overworked or stressed members, enabling automatic adjustment of workload assignments and support through optimization measures.

[0385] This system uses progress monitoring to monitor the progress of tasks and changes in emotions in real time, and the work plan is immediately revised when the situation changes. In addition, a display device is provided on the user's terminal, allowing them to constantly check the progress of tasks and emotional evaluations, and appropriate feedback is provided.

[0386] As a concrete example, when a factory line worker is assigned to a new project, the server assesses the worker's workload and emotional state, and automatically adjusts tasks or provides additional support as needed. Based on the emotional data, if the server determines that the worker is highly stressed, it will assign another worker to provide support or take measures to reduce the workload.

[0387] Example of a prompt:

[0388] "Simulate the actions of a robot that automatically provides support to a worker who is under high stress."

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] The server collects activity data from each member's terminal. This activity data includes work progress, work details, email sending and receiving history, and document editing history. All work-related data from each terminal is used as input, and this data is integrated and stored in a database. This forms a foundation for understanding the overall work situation within the organization.

[0392] Step 2:

[0393] The server collects data from emotion recognition sensors to obtain emotional data such as members' facial expressions and voices. This input data is then processed by an analysis algorithm to provide the members' emotional state (stress level, satisfaction level, etc.) as output. Machine learning models are used to improve the accuracy of emotion recognition.

[0394] Step 3:

[0395] The server integrates and analyzes the collected activity and emotional data to evaluate workload and emotional state. The input at this stage is the output data from steps 1 and 2, and a comprehensive evaluation is performed by data analysis software. The output generates individual workload and emotional evaluation values ​​for each member.

[0396] Step 4:

[0397] The server adjusts the optimal assignment of tasks and work support based on the generated evaluation values. This is a dynamic adjustment process using optimization methods, and the evaluation values ​​from step 3 are used as input. The server adjusts the workload for members deemed to be overloaded and arranges additional support as needed. The output is the adjusted task assignment.

[0398] Step 5:

[0399] The server monitors the progress of tasks and changes in emotional state in real time, and detects anomalies. Inputs are existing task plans and data from step 3, and outputs are notifications and warnings of anomaly detection. Here, dynamic task management is achieved by making full use of progress monitoring methods.

[0400] Step 6:

[0401] The system notifies users of the latest work progress and emotional assessment results on their devices and displays appropriate feedback. Input is updated information from the server, and output is visually displayed information on the user's device. In this way, users can understand their work status and emotional state in real time.

[0402] This series of steps enables integrated business management and emotional state analysis by the server, improving operational efficiency and member well-being.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] [Third Embodiment]

[0407] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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).

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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".

[0419] This system is designed to streamline business management within companies and organizations, and operates with a server and terminals working in conjunction. The server acts as the central information processing unit, collecting and analyzing activity data, assigning tasks, monitoring progress, and evaluating performance.

[0420] The server first periodically receives activity logs and related information from each member's terminal. This information includes email sending and receiving records, document editing history, and data from project management tools. Based on this, the server analyzes each member's workload and skill set.

[0421] Subsequently, the server monitors the overall project progress and automatically determines the optimal task assignments for the most efficient execution of the work. The server notifies each member of these task orders on their terminal, where they can view the task details.

[0422] During task execution, the server tracks the progress of the tasks in real time and reassigns tasks as needed to match the progress. This process involves progress monitoring algorithms that can quickly detect anomalies and delays.

[0423] Furthermore, the server accumulates activity data from its members and uses this data to evaluate each member's work performance. The evaluation results are reflected in performance evaluation reports generated for each member, which serve as an aid in personnel evaluations.

[0424] As a concrete example, in a development project managed by user A, the server calculates the overall progress and re-evaluates task priorities. As a result, the server assigns new tasks to user B, who has superior development skills, supporting efficient project execution. User A's terminal displays feedback on completed tasks and the next steps, while user B's terminal displays notifications for new tasks. This process prevents project delays and optimizes overall management.

[0425] This structure allows organizations to improve operational efficiency and fairly evaluate their members.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The server collects activity data from each member's terminal. This includes email sending and receiving records, document editing history, and data from project management tools. The server then organizes this data and stores it in a database.

[0429] Step 2:

[0430] The server analyzes the collected activity data to evaluate each member's workload and skill set. This evaluation takes into account past work performance and current work progress, and each member is scored accordingly.

[0431] Step 3:

[0432] The server monitors the overall project progress and identifies unassigned tasks. Based on task priority and skill requirements, it determines the most suitable team member to handle the work.

[0433] Step 4:

[0434] The server notifies each member's terminal of the assigned tasks. The notification includes detailed task information, deadlines, and expected deliverables. Users receive and understand this information on their terminals.

[0435] Step 5:

[0436] Users perform tasks via their devices and periodically provide progress feedback to the server. The server receives this information and updates the progress data in real time.

[0437] Step 6:

[0438] The server continuously analyzes progress data to detect anomalies and delays. If necessary, it displays alerts on the dashboard to help administrators take appropriate action.

[0439] Step 7:

[0440] If circumstances change during the progress of a task, the server re-evaluates the assigned task and readjusts the assignment if necessary. This ensures the efficient execution of the task.

[0441] Step 8:

[0442] The server stores all activity data and measures the work performance of each member. The evaluation data is provided to the organization's HR system as regularly generated reports.

[0443] (Example 1)

[0444] 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."

[0445] In many companies and organizations, efficiently understanding the workload and skill sets of individual members and assigning them optimal tasks is difficult. Furthermore, real-time monitoring of delays and anomalies in work progress and prompt response are necessary, but doing this manually has its limitations. In addition, there is a need to improve overall organizational productivity by accurately evaluating member performance and providing appropriate feedback.

[0446] 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.

[0447] In this invention, the server includes an information processing means for collecting activity data and analyzing the workload and skill sets of individual members based on this data; an optimization means for automatically determining the optimal task assignment based on the analysis results and notifying the member's information terminal via a communication means; and a progress monitoring means for monitoring the progress of tasks in real time and quickly detecting any delays or abnormalities. This enables efficient task allocation according to the skills of the members, as well as real-time progress management and performance evaluation.

[0448] "Activity data" refers to log information about members' work-related operations and actions, including email sending and receiving records, document editing history, and data obtained from project management tools.

[0449] "Workload status" refers to the amount and importance of the tasks that each member is currently responsible for, as well as the time and resources required for them.

[0450] A "skill set" refers to a collection of knowledge, abilities, and skills necessary for performing a specific task, all possessed by the members of a group.

[0451] "Information processing means" refers to a series of processes and tools used to analyze the workload and skill sets of each member based on activity data.

[0452] "Optimization means" refers to decision-making processes and algorithms used to assign the most appropriate tasks to team members based on analysis results.

[0453] "Progress monitoring means" refers to a function that tracks the progress of work in real time and detects delays or anomalies in the progress.

[0454] "Evaluation methods" refer to processes and tools for evaluating the work performance of team members based on activity data and generating individual evaluation data for each member.

[0455] "Dynamic adjustment means" refers to a function that readjusts pre-assigned tasks as needed, according to the progress of the work.

[0456] One embodiment of this invention is to streamline business management for companies and organizations using an information processing system. The server functions as a central information processing unit, working in conjunction with each member's terminal to collect and analyze activity data, assign optimal tasks, monitor progress in real time, and evaluate business performance.

[0457] The server periodically retrieves email sending and receiving records, document editing history, and project management tool data from members' terminals to collect activity data. This process utilizes various APIs and database interfaces. For data analysis, Python libraries such as Pandas and Scikit-learn are used to evaluate members' workload and skill sets.

[0458] When assigning tasks optimally, the server determines the priority of tasks for each member based on a predefined algorithm. The assignment results are notified to the member's information terminal via communication means. In this case, Slack or a dedicated desktop application is used as the notification system.

[0459] The server also monitors work progress in real time, and if delays or anomalies are detected, it automatically reallocates tasks using dynamic adjustment mechanisms. This optimizes work management across the entire organization and ensures efficient execution. In addition, user performance data is accumulated by evaluation mechanisms, and performance evaluations are conducted periodically for each member. These evaluations are used as reports for personnel evaluations.

[0460] As a concrete example, in a project managed by user A, the server retrieves progress data from the project management tool and analyzes the progress in real time based on that data. A new task is then assigned to the highly skilled user B, and feedback regarding the next steps is displayed on user A's terminal. This helps prevent project delays.

[0461] Examples of prompts generated using AI models include: "Design a data analysis method that enables real-time progress monitoring in the business management system. Specifically, analyze each member's activity log and explain the optimal method for assigning and reallocating tasks."

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The server collects activity data from each member's terminal. Specifically, the server connects to the mail server and project management tools via API at regular intervals to retrieve email sending and receiving records, document editing history, task data, etc. As input, log data from various sources is provided and stored in the database. As output, a set of activity data for each member is output.

[0465] Step 2:

[0466] The server analyzes the collected activity data to evaluate the workload and skill sets of the team members. Specifically, it preprocesses the data using the Python Pandas library, and then performs data analysis using the Scikit-learn library. The activity data obtained in step 1 is used as input, and after data processing, a report evaluating the workload and skills of each team member is generated as output.

[0467] Step 3:

[0468] The server determines the optimal task assignment based on the analysis results. Specifically, it uses a known algorithm to determine task priorities and operates a mechanism to notify members' terminals of the task orders. It receives the evaluation results from step 2 as input, outputs a task assignment plan based on that, and sends a notification to the members' terminals.

[0469] Step 4:

[0470] The server monitors the progress of tasks in real time. Specifically, it continuously tracks progress and implements an alert function to detect delays and anomalies. As input, it collects progress data reported in real time from the terminals of the members, and as output, it notifies the administrator of anomaly detection alert information.

[0471] Step 5:

[0472] The server performs performance evaluations based on activity logs and creates evaluation data for each member. Specifically, it aggregates and analyzes past activity history and scores the achievement and contribution levels of each member. Past activity data is used as input, and an evaluation report is generated as output, which is submitted to the HR department as needed.

[0473] (Application Example 1)

[0474] 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."

[0475] In modern manufacturing and service industries, efficient resource management and operational optimization are essential. However, traditional systems often fail to adequately reallocate tasks based on the skill sets of individual workers and machines, as well as real-time progress, hindering productivity improvements.

[0476] 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.

[0477] In this invention, the server includes means for collecting activity data and analyzing the workload based on it, means for automatically determining the optimal allocation of tasks based on the analysis results, means for monitoring the progress of tasks and detecting anomalies, means for an information processing device to monitor the work efficiency and progress of robots and workers in real time, and means for reviewing task priorities and proposing the optimal resource allocation. This makes it possible to optimize work efficiency and prevent work delays.

[0478] "Activity data" refers to information related to the performance of tasks, and includes worker and machine operation logs, progress status, and workload.

[0479] "Workload status" refers to the state that indicates the amount of work and the difficulty level of the tasks that employees or machines are responsible for.

[0480] "Analysis results" refer to specific data related to the evaluation of work performance and optimization of work processes, calculated based on the collected activity data.

[0481] "Optimal task allocation" refers to the distribution of work based on analysis results to maximize the efficiency of personnel and machinery.

[0482] An "abnormality" refers to an irregular event that occurs during the progress of work, or a situation that differs from the expected progress.

[0483] "Progress monitoring" refers to a series of processes that observe the work progress of each member or machine in real time.

[0484] "Resource allocation" refers to the process of assigning tasks to members or machines, with the aim of maximizing efficiency.

[0485] "Business performance evaluation data" refers to data that measures the work performance capabilities of employees or machines and expresses the results as numerical values ​​or indicators.

[0486] The server first collects activity data, which includes work logs and progress data for robots and workers. This data is constantly transmitted from sensors and dedicated terminals deployed as edge devices and aggregated on the server. Next, the server uses a Python-based data analysis algorithm to evaluate the workload. This allows the server to perform optimal task assignments, taking into account each member's skill set and workload.

[0487] The program running on the terminal receives instructions from the server and notifies the user of work progress and the next task via the UI. It also has the function to monitor ongoing tasks in real time and autonomously send information back to the server.

[0488] As an information processing device, the server monitors the progress of operations using anomaly detection algorithms and automatically re-evaluates task resource allocation if an anomaly occurs. Machine learning models are utilized to quickly identify anomaly patterns.

[0489] As a concrete example, in a certain manufacturing line, robots perform assembly work. In this scenario, a server detects in real time when the assembly speed falls below a certain threshold and assigns auxiliary tasks to other robots to improve efficiency. Through the management and optimization of this entire system, companies can achieve increased productivity and more effective use of resources.

[0490] An example of a prompt message for an application using a generative AI model would be: "Please provide an algorithm for efficient business management. In particular, please explain in detail how to assign optimal tasks according to the robot's work progress and skill set."

[0491] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0492] Step 1:

[0493] The server collects activity data from robots and worker terminals. Specifically, it obtains work logs and progress information from sensors and dedicated terminals. Based on this input data, the server converts the data into JSON format and stores it in a database.

[0494] Step 2:

[0495] The server analyzes the workload using accumulated activity data. A data analysis algorithm using Python is employed for this analysis. The input is the collected activity data, and the output is evaluation data indicating the workload status of each member based on that data. The server then performs optimization calculations based on this data.

[0496] Step 3:

[0497] The server performs optimal task assignment based on the analysis results. It uses the previously mentioned evaluation data as input and generates specific task lists for each robot and worker as output. An AI model participates in this process, predicting work efficiency based on historical data.

[0498] Step 4:

[0499] The terminal receives a task list sent from the server and displays it to the user. Information is communicated via the user interface, allowing the user to confirm the next steps to take. The input is the task list from the server, and the output is a visualized work instruction.

[0500] Step 5:

[0501] The server monitors work progress in real time and receives progress data. Specifically, it operates an anomaly detection algorithm based on feedback from terminals and sensors. This algorithm is supported by a machine learning model and immediately identifies anomalous patterns.

[0502] Step 6:

[0503] The server readjusts task resource allocations if an anomaly is detected. The input is a notification of the detected anomaly, which triggers a recalculation of the next task allocation, generating a readjusted task list as output. This improves overall system efficiency.

[0504] Step 7:

[0505] The server generates a business performance report after all processes are complete. This includes progress and tracking data for each member. The final output is a performance report that helps improve future operations and is shared with users. This output is used for strategic feedback.

[0506] 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.

[0507] This system works by combining activity data and an emotion engine to streamline business management for companies and organizations. Its main components include a server, terminals, and emotion recognition sensors.

[0508] The server continuously collects various activity data from each member's terminal. This data includes project progress, email sending and receiving records, and document editing history. It also acquires emotional data from users' facial expressions and voice via emotion recognition sensors. All of this data is then comprehensively analyzed on the server.

[0509] The server uses activity data and emotional data to comprehensively evaluate each member's workload and skill set. By considering emotional data, such as the member's current stress level and satisfaction level, a more accurate evaluation can be provided. Based on the analysis results, the server automatically assigns and adjusts tasks to the optimal level.

[0510] When a task is assigned, the server notifies the member's terminal of the information. The user checks the task details and deadline on their terminal, and reports on the progress as they work on the task. The server tracks this progress data in real time and reassigns tasks as needed. In addition, if the emotional state changes due to the emotion engine, the task is adjusted accordingly.

[0511] For example, if user A is appointed as the leader of a new project, the server will notify user A's terminal of the project overview and related tasks. At the same time, if the emotion recognition results indicate that user A's stress level is high, the server will consider adding support staff or redistributing tasks and propose appropriate measures to the terminal.

[0512] In this way, this system achieves a form that promotes both the efficiency of work management and the well-being of members by evaluating both their activities and emotions.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The server collects activity data from each member's terminal. This includes sending and receiving emails, editing history of various project documents, and data from schedule management tools. It also collects emotional data by acquiring users' facial expressions and voices from emotion recognition sensors.

[0516] Step 2:

[0517] The server analyzes the collected activity data and emotional data. It assesses workload levels from the activity data and evaluates employee stress levels and emotional fluctuations from the emotional data. This allows for an overall understanding of the employees' work performance.

[0518] Step 3:

[0519] Based on the analysis results, the server executes an optimization process to assign the most suitable tasks to each member. If a user's emotional state deteriorates, the server adjusts the tasks or provides support to reduce stress.

[0520] Step 4:

[0521] The server notifies the terminal of the assigned task. The user then checks information such as task details, deadlines, and priorities through the terminal.

[0522] Step 5:

[0523] Users perform tasks on their terminals and report their progress to the server in real time. These reports include completion rates, time taken, and any challenges encountered.

[0524] Step 6:

[0525] The server monitors progress in real time and displays alerts on the terminal if there are delays or if the user's emotional state changes. These alerts include recommended actions and revised work plans.

[0526] Step 7:

[0527] The server reassigns tasks as needed, based on the user's emotional state and project progress requirements. The server makes these adjustments and notifies the terminal again.

[0528] Step 8:

[0529] The server records overall activity and emotional data and periodically evaluates the work performance of each member. The evaluation results are generated as performance reports and distributed to administrators, where they are used for personnel evaluations.

[0530] (Example 2)

[0531] 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."

[0532] Traditional work management systems evaluate employee workload based solely on activity data, failing to consider non-numerical factors such as employee emotional state and stress levels. As a result, work allocation is often suboptimal. Furthermore, ignoring the impact of employee emotional state on work performance poses a risk of undermining employee well-being.

[0533] 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.

[0534] In this invention, the server includes an information processing means that collects activity data and emotional data and analyzes the workload and emotional state of its members based on this data; an optimization means that automatically determines the optimal assignment of tasks considering the analysis results and emotional state; and a monitoring means that monitors the progress of tasks and detects abnormalities and changes in emotional state. This enables highly accurate task assignment and adjustment that takes into account the emotional state of its members.

[0535] "Activity data" refers to information about members' work activities, such as project progress, email sending and receiving records, and document editing history.

[0536] "Emotional data" refers to information about emotional states such as stress levels and satisfaction levels, obtained based on members' facial expressions and voices.

[0537] "Information processing means" refers to devices or programs that collect activity data and emotional data, and analyze the workload and emotional state of members based on that data.

[0538] An "optimization tool" is a device or program that uses analyzed activity data and emotional data to optimally assign tasks to members.

[0539] "Monitoring means" refers to devices or programs used to continuously monitor abnormalities or changes in emotional state based on members' work progress and activity data.

[0540] A "dynamic adjustment mechanism" refers to a device or program that readjusts work assignments as needed in response to monitoring work progress or changes in emotional state.

[0541] "Display means" refers to devices or programs for visually providing members with information on work progress, analysis results, and emotional states.

[0542] This invention is a system that enables efficient management of operations using activity data and emotional data of members within an organization. The main components of the system include a server, terminals, and emotion recognition sensors.

[0543] The server utilizes network communication to collect activity data obtained from multiple member terminals. This activity data includes project progress, email sending and receiving records, and document editing history obtained from project management tools and email clients. This data can be retrieved via API and stored in a database at regular intervals.

[0544] Furthermore, the device acquires emotional data from the user's facial expressions and voice through an emotion recognition sensor. This emotional data is collected using a facial recognition camera and microphone during login and when voice commands are received, and then sent to the server.

[0545] The server integrates collected activity and emotion data and uses a generative AI model to analyze the workload and emotional state of team members. Specifically, it utilizes machine learning libraries such as TensorFlow and PyTorch to perform the analysis. The analysis results are generated as a workload assessment and emotional state score for each team member, which are then used for assigning tasks.

[0546] Based on these analyses, the server optimally assigns tasks, and tasks are automatically notified to members' terminals via project management tools. The server also monitors task progress in real time and dynamically adjusts tasks in response to changes in emotional state.

[0547] As a concrete example, if user A is appointed as the leader of a new project, the server notifies user A's terminal of the project overview and related tasks. If the emotion recognition results indicate that user A is at a high stress level, the server considers adding support personnel or redistributing tasks as appropriate and proposes these changes to user A's terminal. This process enables highly accurate work management that takes into account the emotional state of team members.

[0548] As a prompt for the generating AI model, one possible prompt would be, "When User A is appointed as project leader, stress management will be performed based on the project overview and emotion recognition data."

[0549] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0550] Step 1:

[0551] The server periodically collects activity data from members' terminals via the network. The input used is information received via APIs from project management tools and email clients. Upon receiving this information, the server stores it in a database in CSV format. Specifically, the server calls the API every hour to retrieve the latest project progress and email sending / receiving records.

[0552] Step 2:

[0553] The user collects emotional data from their facial expressions and voice through an emotion recognition sensor. Inputs include facial capture from a face recognition camera and voice data from a microphone. The device acquires this data in real time and sends it to the server. Specifically, the user's face is captured each time they log in, and their emotions are analyzed along with the reception of voice commands.

[0554] Step 3:

[0555] The server integrates the collected activity and sentiment data and stores it in a database. The input data includes activity and sentiment data, while the output is prepared data for integrated analysis. Specifically, the server uses bulk inserts to efficiently import data into the database.

[0556] Step 4:

[0557] The server analyzes integrated data using a generative AI model. Its inputs include integrated activity data and sentiment data, and its output is an evaluation of each member's workload and emotional state. Specifically, the server uses TensorFlow to input data into the AI ​​model in batches and performs evaluations for each member.

[0558] Step 5:

[0559] The server determines the optimal task assignment for each member based on the evaluation results. The input is the analysis results, and the output is the specific task for each member. In practice, the server automatically assigns tasks to members via the API of the project management tool.

[0560] Step 6:

[0561] The server notifies each member's terminal of the assigned tasks. The input is determined task information, and the output is a notification to the member's terminal. Specifically, it uses email notifications or push notifications to send task details to the members.

[0562] Step 7:

[0563] Users report their work progress to the server. Inputs include daily progress data and task completion reports. Output is an updated project progress status. Specifically, users update their progress using a dedicated application and upload that data to a database on the server.

[0564] Step 8:

[0565] The server continuously monitors work progress and emotional state, readjusting tasks as needed. Its inputs include real-time progress and emotional data, while its output consists of adjusted work assignments and break suggestions. Specifically, when the server detects a change in emotional state, it considers new task assignments and notifies the user's terminal of the update.

[0566] (Application Example 2)

[0567] 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."

[0568] In modern companies and organizations, improving operational efficiency and enhancing the well-being of employees are crucial themes. Especially in environments where diverse tasks proceed simultaneously, it is essential to properly manage the workload of each member and reduce emotional stress. However, traditional systems have struggled to accurately assign tasks and make dynamic adjustments that take emotional data into account, sometimes resulting in excessive stress on employees.

[0569] 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.

[0570] In this invention, the server includes an information processing means that collects activity data and emotional data and has the function of analyzing the workload and emotional state based on this data; an optimization means that automatically determines the optimal assignment of tasks and the adjustment of work support as needed based on the analysis results; and a progress monitoring means that monitors the progress of tasks and changes in emotional state and detects abnormalities. This enables efficient execution of tasks and proper management of the emotional state of members.

[0571] "Activity data" refers to objective work information such as the progress of members' work and the content of their tasks.

[0572] "Emotional data" refers to information that indicates the emotional state of the members, and is subjective data obtained from facial expressions and voices acquired via emotion recognition sensors.

[0573] "Information processing means" refers to a computer system that has the function of analyzing collected activity data and emotional data to evaluate workload and emotional state.

[0574] An "optimization tool" is an algorithm or software that has the function of automatically assigning tasks optimally and adjusting work support as needed, based on the analyzed data.

[0575] A "progress monitoring system" is a system that has the function of monitoring the progress of work and changes in emotional state in real time and detecting anomalies.

[0576] The system implementing this invention is equipped with a function to analyze activity data and emotional data in combination in order to streamline business management for companies and organizations. The server collects activity data such as work progress, work content, email exchanges, and document editing history from members' terminals, and acquires emotional data from the user's facial expressions and voice using an emotion recognition sensor.

[0577] The server processes this data holistically, analyzing workload and emotional states. Data analysis software is used for information processing, and emotional data is analyzed by emotion recognition algorithms. This allows for the identification of overworked or stressed members, enabling automatic adjustment of workload assignments and support through optimization measures.

[0578] This system uses progress monitoring to monitor the progress of tasks and changes in emotions in real time, and the work plan is immediately revised when the situation changes. In addition, a display device is provided on the user's terminal, allowing them to constantly check the progress of tasks and emotional evaluations, and appropriate feedback is provided.

[0579] As a concrete example, when a factory line worker is assigned to a new project, the server assesses the worker's workload and emotional state, and automatically adjusts tasks or provides additional support as needed. Based on the emotional data, if the server determines that the worker is highly stressed, it will assign another worker to provide support or take measures to reduce the workload.

[0580] Example of a prompt:

[0581] "Simulate the actions of a robot that automatically provides support to a worker who is under high stress."

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The server collects activity data from each member's terminal. This activity data includes work progress, work details, email sending and receiving history, and document editing history. All work-related data from each terminal is used as input, and this data is integrated and stored in a database. This forms a foundation for understanding the overall work situation within the organization.

[0585] Step 2:

[0586] The server collects data from emotion recognition sensors to obtain emotional data such as members' facial expressions and voices. This input data is then processed by an analysis algorithm to provide the members' emotional state (stress level, satisfaction level, etc.) as output. Machine learning models are used to improve the accuracy of emotion recognition.

[0587] Step 3:

[0588] The server integrates and analyzes the collected activity and emotional data to evaluate workload and emotional state. The input at this stage is the output data from steps 1 and 2, and a comprehensive evaluation is performed by data analysis software. The output generates individual workload and emotional evaluation values ​​for each member.

[0589] Step 4:

[0590] The server adjusts the optimal assignment of tasks and work support based on the generated evaluation values. This is a dynamic adjustment process using optimization methods, and the evaluation values ​​from step 3 are used as input. The server adjusts the workload for members deemed to be overloaded and arranges additional support as needed. The output is the adjusted task assignment.

[0591] Step 5:

[0592] The server monitors the progress of tasks and changes in emotional state in real time, and detects anomalies. Inputs are existing task plans and data from step 3, and outputs are notifications and warnings of anomaly detection. Here, dynamic task management is achieved by making full use of progress monitoring methods.

[0593] Step 6:

[0594] The system notifies users of the latest work progress and emotional assessment results on their devices and displays appropriate feedback. Input is updated information from the server, and output is visually displayed information on the user's device. In this way, users can understand their work status and emotional state in real time.

[0595] This series of steps enables integrated business management and emotional state analysis by the server, improving operational efficiency and member well-being.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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".

[0613] This system is designed to streamline business management within companies and organizations, and operates with a server and terminals working in conjunction. The server acts as the central information processing unit, collecting and analyzing activity data, assigning tasks, monitoring progress, and evaluating performance.

[0614] The server first periodically receives activity logs and related information from each member's terminal. This information includes email sending and receiving records, document editing history, and data from project management tools. Based on this, the server analyzes each member's workload and skill set.

[0615] Subsequently, the server monitors the overall project progress and automatically determines the optimal task assignments for the most efficient execution of the work. The server notifies each member of these task orders on their terminal, where they can view the task details.

[0616] During task execution, the server tracks the progress of the tasks in real time and reassigns tasks as needed to match the progress. This process involves progress monitoring algorithms that can quickly detect anomalies and delays.

[0617] Furthermore, the server accumulates activity data from its members and uses this data to evaluate each member's work performance. The evaluation results are reflected in performance evaluation reports generated for each member, which serve as an aid in personnel evaluations.

[0618] As a concrete example, in a development project managed by user A, the server calculates the overall progress and re-evaluates task priorities. As a result, the server assigns new tasks to user B, who has superior development skills, supporting efficient project execution. User A's terminal displays feedback on completed tasks and the next steps, while user B's terminal displays notifications for new tasks. This process prevents project delays and optimizes overall management.

[0619] This structure allows organizations to improve operational efficiency and fairly evaluate their members.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] The server collects activity data from each member's terminal. This includes email sending and receiving records, document editing history, and data from project management tools. The server then organizes this data and stores it in a database.

[0623] Step 2:

[0624] The server analyzes the collected activity data to evaluate each member's workload and skill set. This evaluation takes into account past work performance and current work progress, and each member is scored accordingly.

[0625] Step 3:

[0626] The server monitors the overall project progress and identifies unassigned tasks. Based on task priority and skill requirements, it determines the most suitable team member to handle the work.

[0627] Step 4:

[0628] The server notifies each member's terminal of the assigned tasks. The notification includes detailed task information, deadlines, and expected deliverables. Users receive and understand this information on their terminals.

[0629] Step 5:

[0630] Users perform tasks via their devices and periodically provide progress feedback to the server. The server receives this information and updates the progress data in real time.

[0631] Step 6:

[0632] The server continuously analyzes progress data to detect anomalies and delays. If necessary, it displays alerts on the dashboard to help administrators take appropriate action.

[0633] Step 7:

[0634] If circumstances change during the progress of a task, the server re-evaluates the assigned task and readjusts the assignment if necessary. This ensures the efficient execution of the task.

[0635] Step 8:

[0636] The server stores all activity data and measures the work performance of each member. The evaluation data is provided to the organization's HR system as regularly generated reports.

[0637] (Example 1)

[0638] 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".

[0639] In many companies and organizations, efficiently understanding the workload and skill sets of individual members and assigning them optimal tasks is difficult. Furthermore, real-time monitoring of delays and anomalies in work progress and prompt response are necessary, but doing this manually has its limitations. In addition, there is a need to improve overall organizational productivity by accurately evaluating member performance and providing appropriate feedback.

[0640] 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.

[0641] In this invention, the server includes an information processing means for collecting activity data and analyzing the workload and skill sets of individual members based on this data; an optimization means for automatically determining the optimal task assignment based on the analysis results and notifying the member's information terminal via a communication means; and a progress monitoring means for monitoring the progress of tasks in real time and quickly detecting any delays or abnormalities. This enables efficient task allocation according to the skills of the members, as well as real-time progress management and performance evaluation.

[0642] "Activity data" refers to log information about members' work-related operations and actions, including email sending and receiving records, document editing history, and data obtained from project management tools.

[0643] "Workload status" refers to the amount and importance of the tasks that each member is currently responsible for, as well as the time and resources required for them.

[0644] A "skill set" refers to a collection of knowledge, abilities, and skills necessary for performing a specific task, all possessed by the members of a group.

[0645] "Information processing means" refers to a series of processes and tools used to analyze the workload and skill sets of each member based on activity data.

[0646] "Optimization means" refers to decision-making processes and algorithms used to assign the most appropriate tasks to team members based on analysis results.

[0647] "Progress monitoring means" refers to a function that tracks the progress of work in real time and detects delays or anomalies in the progress.

[0648] "Evaluation methods" refer to processes and tools for evaluating the work performance of team members based on activity data and generating individual evaluation data for each member.

[0649] "Dynamic adjustment means" refers to a function that readjusts pre-assigned tasks as needed, according to the progress of the work.

[0650] One embodiment of this invention is to streamline business management for companies and organizations using an information processing system. The server functions as a central information processing unit, working in conjunction with each member's terminal to collect and analyze activity data, assign optimal tasks, monitor progress in real time, and evaluate business performance.

[0651] The server periodically retrieves email sending and receiving records, document editing history, and project management tool data from members' terminals to collect activity data. This process utilizes various APIs and database interfaces. For data analysis, Python libraries such as Pandas and Scikit-learn are used to evaluate members' workload and skill sets.

[0652] When assigning tasks optimally, the server determines the priority of tasks for each member based on a predefined algorithm. The assignment results are notified to the member's information terminal via communication means. In this case, Slack or a dedicated desktop application is used as the notification system.

[0653] The server also monitors work progress in real time, and if delays or anomalies are detected, it automatically reallocates tasks using dynamic adjustment mechanisms. This optimizes work management across the entire organization and ensures efficient execution. In addition, user performance data is accumulated by evaluation mechanisms, and performance evaluations are conducted periodically for each member. These evaluations are used as reports for personnel evaluations.

[0654] As a concrete example, in a project managed by user A, the server retrieves progress data from the project management tool and analyzes the progress in real time based on that data. A new task is then assigned to the highly skilled user B, and feedback regarding the next steps is displayed on user A's terminal. This helps prevent project delays.

[0655] Examples of prompts generated using AI models include: "Design a data analysis method that enables real-time progress monitoring in the business management system. Specifically, analyze each member's activity log and explain the optimal method for assigning and reallocating tasks."

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The server collects activity data from each member's terminal. Specifically, the server connects to the mail server and project management tools via API at regular intervals to retrieve email sending and receiving records, document editing history, task data, etc. As input, log data from various sources is provided and stored in the database. As output, a set of activity data for each member is output.

[0659] Step 2:

[0660] The server analyzes the collected activity data to evaluate the workload and skill sets of the team members. Specifically, it preprocesses the data using the Python Pandas library, and then performs data analysis using the Scikit-learn library. The activity data obtained in step 1 is used as input, and after data processing, a report evaluating the workload and skills of each team member is generated as output.

[0661] Step 3:

[0662] The server determines the optimal task assignment based on the analysis results. Specifically, it uses a known algorithm to determine task priorities and operates a mechanism to notify members' terminals of the task orders. It receives the evaluation results from step 2 as input, outputs a task assignment plan based on that, and sends a notification to the members' terminals.

[0663] Step 4:

[0664] The server monitors the progress of tasks in real time. Specifically, it continuously tracks progress and implements an alert function to detect delays and anomalies. As input, it collects progress data reported in real time from the terminals of the members, and as output, it notifies the administrator of anomaly detection alert information.

[0665] Step 5:

[0666] The server performs performance evaluations based on activity logs and creates evaluation data for each member. Specifically, it aggregates and analyzes past activity history and scores the achievement and contribution levels of each member. Past activity data is used as input, and an evaluation report is generated as output, which is submitted to the HR department as needed.

[0667] (Application Example 1)

[0668] 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".

[0669] In modern manufacturing and service industries, efficient resource management and operational optimization are essential. However, traditional systems often fail to adequately reallocate tasks based on the skill sets of individual workers and machines, as well as real-time progress, hindering productivity improvements.

[0670] 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.

[0671] In this invention, the server includes means for collecting activity data and analyzing the workload based on it, means for automatically determining the optimal allocation of tasks based on the analysis results, means for monitoring the progress of tasks and detecting anomalies, means for an information processing device to monitor the work efficiency and progress of robots and workers in real time, and means for reviewing task priorities and proposing the optimal resource allocation. This makes it possible to optimize work efficiency and prevent work delays.

[0672] "Activity data" refers to information related to the performance of tasks, and includes worker and machine operation logs, progress status, and workload.

[0673] "Workload status" refers to the state that indicates the amount of work and the difficulty level of the tasks that employees or machines are responsible for.

[0674] "Analysis results" refer to specific data related to the evaluation of work performance and optimization of work processes, calculated based on the collected activity data.

[0675] "Optimal task allocation" refers to the distribution of work based on analysis results to maximize the efficiency of personnel and machinery.

[0676] An "abnormality" refers to an irregular event that occurs during the progress of work, or a situation that differs from the expected progress.

[0677] "Progress monitoring" refers to a series of processes that observe the work progress of each member or machine in real time.

[0678] "Resource allocation" refers to the process of assigning tasks to members or machines, with the aim of maximizing efficiency.

[0679] "Business performance evaluation data" refers to data that measures the work performance capabilities of employees or machines and expresses the results as numerical values ​​or indicators.

[0680] The server first collects activity data, which includes work logs and progress data for robots and workers. This data is constantly transmitted from sensors and dedicated terminals deployed as edge devices and aggregated on the server. Next, the server uses a Python-based data analysis algorithm to evaluate the workload. This allows the server to perform optimal task assignments, taking into account each member's skill set and workload.

[0681] The program running on the terminal receives instructions from the server and notifies the user of work progress and the next task via the UI. It also has the function to monitor ongoing tasks in real time and autonomously send information back to the server.

[0682] As an information processing device, the server monitors the progress of operations using anomaly detection algorithms and automatically re-evaluates task resource allocation if an anomaly occurs. Machine learning models are utilized to quickly identify anomaly patterns.

[0683] As a concrete example, in a certain manufacturing line, robots perform assembly work. In this scenario, a server detects in real time when the assembly speed falls below a certain threshold and assigns auxiliary tasks to other robots to improve efficiency. Through the management and optimization of this entire system, companies can achieve increased productivity and more effective use of resources.

[0684] An example of a prompt message for an application using a generative AI model would be: "Please provide an algorithm for efficient business management. In particular, please explain in detail how to assign optimal tasks according to the robot's work progress and skill set."

[0685] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0686] Step 1:

[0687] The server collects activity data from robots and worker terminals. Specifically, it obtains work logs and progress information from sensors and dedicated terminals. Based on this input data, the server converts the data into JSON format and stores it in a database.

[0688] Step 2:

[0689] The server analyzes the workload using accumulated activity data. A data analysis algorithm using Python is employed for this analysis. The input is the collected activity data, and the output is evaluation data indicating the workload status of each member based on that data. The server then performs optimization calculations based on this data.

[0690] Step 3:

[0691] The server performs optimal task assignment based on the analysis results. It uses the previously mentioned evaluation data as input and generates specific task lists for each robot and worker as output. An AI model participates in this process, predicting work efficiency based on historical data.

[0692] Step 4:

[0693] The terminal receives a task list sent from the server and displays it to the user. Information is communicated via the user interface, allowing the user to confirm the next steps to take. The input is the task list from the server, and the output is a visualized work instruction.

[0694] Step 5:

[0695] The server monitors work progress in real time and receives progress data. Specifically, it operates an anomaly detection algorithm based on feedback from terminals and sensors. This algorithm is supported by a machine learning model and immediately identifies anomalous patterns.

[0696] Step 6:

[0697] The server readjusts task resource allocations if an anomaly is detected. The input is a notification of the detected anomaly, which triggers a recalculation of the next task allocation, generating a readjusted task list as output. This improves overall system efficiency.

[0698] Step 7:

[0699] The server generates a business performance report after all processes are complete. This includes progress and tracking data for each member. The final output is a performance report that helps improve future operations and is shared with users. This output is used for strategic feedback.

[0700] 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.

[0701] This system works by combining activity data and an emotion engine to streamline business management for companies and organizations. Its main components include a server, terminals, and emotion recognition sensors.

[0702] The server continuously collects various activity data from each member's terminal. This data includes project progress, email sending and receiving records, and document editing history. It also acquires emotional data from users' facial expressions and voice via emotion recognition sensors. All of this data is then comprehensively analyzed on the server.

[0703] The server uses activity data and emotional data to comprehensively evaluate each member's workload and skill set. By considering emotional data, such as the member's current stress level and satisfaction level, a more accurate evaluation can be provided. Based on the analysis results, the server automatically assigns and adjusts tasks to the optimal level.

[0704] When a task is assigned, the server notifies the member's terminal of the information. The user checks the task details and deadline on their terminal, and reports on the progress as they work on the task. The server tracks this progress data in real time and reassigns tasks as needed. In addition, if the emotional state changes due to the emotion engine, the task is adjusted accordingly.

[0705] For example, if user A is appointed as the leader of a new project, the server will notify user A's terminal of the project overview and related tasks. At the same time, if the emotion recognition results indicate that user A's stress level is high, the server will consider adding support staff or redistributing tasks and propose appropriate measures to the terminal.

[0706] In this way, this system achieves a form that promotes both the efficiency of work management and the well-being of members by evaluating both their activities and emotions.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The server collects activity data from each member's terminal. This includes sending and receiving emails, editing history of various project documents, and data from schedule management tools. It also collects emotional data by acquiring users' facial expressions and voices from emotion recognition sensors.

[0710] Step 2:

[0711] The server analyzes the collected activity data and emotional data. It assesses workload levels from the activity data and evaluates employee stress levels and emotional fluctuations from the emotional data. This allows for an overall understanding of the employees' work performance.

[0712] Step 3:

[0713] Based on the analysis results, the server executes an optimization process to assign the most suitable tasks to each member. If a user's emotional state deteriorates, the server adjusts the tasks or provides support to reduce stress.

[0714] Step 4:

[0715] The server notifies the terminal of the assigned task. The user then checks information such as task details, deadlines, and priorities through the terminal.

[0716] Step 5:

[0717] Users perform tasks on their terminals and report their progress to the server in real time. These reports include completion rates, time taken, and any challenges encountered.

[0718] Step 6:

[0719] The server monitors progress in real time and displays alerts on the terminal if there are delays or if the user's emotional state changes. These alerts include recommended actions and revised work plans.

[0720] Step 7:

[0721] The server reassigns tasks as needed, based on the user's emotional state and project progress requirements. The server makes these adjustments and notifies the terminal again.

[0722] Step 8:

[0723] The server records overall activity and emotional data and periodically evaluates the work performance of each member. The evaluation results are generated as performance reports and distributed to administrators, where they are used for personnel evaluations.

[0724] (Example 2)

[0725] 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".

[0726] Traditional work management systems evaluate employee workload based solely on activity data, failing to consider non-numerical factors such as employee emotional state and stress levels. As a result, work allocation is often suboptimal. Furthermore, ignoring the impact of employee emotional state on work performance poses a risk of undermining employee well-being.

[0727] 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.

[0728] In this invention, the server includes an information processing means that collects activity data and emotional data and analyzes the workload and emotional state of its members based on this data; an optimization means that automatically determines the optimal assignment of tasks considering the analysis results and emotional state; and a monitoring means that monitors the progress of tasks and detects abnormalities and changes in emotional state. This enables highly accurate task assignment and adjustment that takes into account the emotional state of its members.

[0729] "Activity data" refers to information about members' work activities, such as project progress, email sending and receiving records, and document editing history.

[0730] "Emotional data" refers to information about emotional states such as stress levels and satisfaction levels, obtained based on members' facial expressions and voices.

[0731] "Information processing means" refers to devices or programs that collect activity data and emotional data, and analyze the workload and emotional state of members based on that data.

[0732] An "optimization tool" is a device or program that uses analyzed activity data and emotional data to optimally assign tasks to members.

[0733] "Monitoring means" refers to devices or programs used to continuously monitor abnormalities or changes in emotional state based on members' work progress and activity data.

[0734] A "dynamic adjustment mechanism" refers to a device or program that readjusts work assignments as needed in response to monitoring work progress or changes in emotional state.

[0735] "Display means" refers to devices or programs for visually providing members with information on work progress, analysis results, and emotional states.

[0736] This invention is a system that enables efficient management of operations using activity data and emotional data of members within an organization. The main components of the system include a server, terminals, and emotion recognition sensors.

[0737] The server utilizes network communication to collect activity data obtained from multiple member terminals. This activity data includes project progress, email sending and receiving records, and document editing history obtained from project management tools and email clients. This data can be retrieved via API and stored in a database at regular intervals.

[0738] Furthermore, the device acquires emotional data from the user's facial expressions and voice through an emotion recognition sensor. This emotional data is collected using a facial recognition camera and microphone during login and when voice commands are received, and then sent to the server.

[0739] The server integrates collected activity and emotion data and uses a generative AI model to analyze the workload and emotional state of team members. Specifically, it utilizes machine learning libraries such as TensorFlow and PyTorch to perform the analysis. The analysis results are generated as a workload assessment and emotional state score for each team member, which are then used for assigning tasks.

[0740] Based on these analyses, the server optimally assigns tasks, and tasks are automatically notified to members' terminals via project management tools. The server also monitors task progress in real time and dynamically adjusts tasks in response to changes in emotional state.

[0741] As a concrete example, if user A is appointed as the leader of a new project, the server notifies user A's terminal of the project overview and related tasks. If the emotion recognition results indicate that user A is at a high stress level, the server considers adding support personnel or redistributing tasks as appropriate and proposes these changes to user A's terminal. This process enables highly accurate work management that takes into account the emotional state of team members.

[0742] As a prompt for the generating AI model, one possible prompt would be, "When User A is appointed as project leader, stress management will be performed based on the project overview and emotion recognition data."

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The server periodically collects activity data from members' terminals via the network. The input used is information received via APIs from project management tools and email clients. Upon receiving this information, the server stores it in a database in CSV format. Specifically, the server calls the API every hour to retrieve the latest project progress and email sending / receiving records.

[0746] Step 2:

[0747] The user collects emotional data from their facial expressions and voice through an emotion recognition sensor. Inputs include facial capture from a face recognition camera and voice data from a microphone. The device acquires this data in real time and sends it to the server. Specifically, the user's face is captured each time they log in, and their emotions are analyzed along with the reception of voice commands.

[0748] Step 3:

[0749] The server integrates the collected activity and sentiment data and stores it in a database. The input data includes activity and sentiment data, while the output is prepared data for integrated analysis. Specifically, the server uses bulk inserts to efficiently import data into the database.

[0750] Step 4:

[0751] The server analyzes integrated data using a generative AI model. Its inputs include integrated activity data and sentiment data, and its output is an evaluation of each member's workload and emotional state. Specifically, the server uses TensorFlow to input data into the AI ​​model in batches and performs evaluations for each member.

[0752] Step 5:

[0753] The server determines the optimal task assignment for each member based on the evaluation results. The input is the analysis results, and the output is the specific task for each member. In practice, the server automatically assigns tasks to members via the API of the project management tool.

[0754] Step 6:

[0755] The server notifies each member's terminal of the assigned tasks. The input is determined task information, and the output is a notification to the member's terminal. Specifically, it uses email notifications or push notifications to send task details to the members.

[0756] Step 7:

[0757] Users report their work progress to the server. Inputs include daily progress data and task completion reports. Output is an updated project progress status. Specifically, users update their progress using a dedicated application and upload that data to a database on the server.

[0758] Step 8:

[0759] The server continuously monitors work progress and emotional state, readjusting tasks as needed. Its inputs include real-time progress and emotional data, while its output consists of adjusted work assignments and break suggestions. Specifically, when the server detects a change in emotional state, it considers new task assignments and notifies the user's terminal of the update.

[0760] (Application Example 2)

[0761] 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".

[0762] In modern companies and organizations, improving operational efficiency and enhancing the well-being of employees are crucial themes. Especially in environments where diverse tasks proceed simultaneously, it is essential to properly manage the workload of each member and reduce emotional stress. However, traditional systems have struggled to accurately assign tasks and make dynamic adjustments that take emotional data into account, sometimes resulting in excessive stress on employees.

[0763] 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.

[0764] In this invention, the server includes an information processing means that collects activity data and emotional data and has the function of analyzing the workload and emotional state based on this data; an optimization means that automatically determines the optimal assignment of tasks and the adjustment of work support as needed based on the analysis results; and a progress monitoring means that monitors the progress of tasks and changes in emotional state and detects abnormalities. This enables efficient execution of tasks and proper management of the emotional state of members.

[0765] "Activity data" refers to objective work information such as the progress of members' work and the content of their tasks.

[0766] "Emotional data" refers to information that indicates the emotional state of the members, and is subjective data obtained from facial expressions and voices acquired via emotion recognition sensors.

[0767] "Information processing means" refers to a computer system that has the function of analyzing collected activity data and emotional data to evaluate workload and emotional state.

[0768] An "optimization tool" is an algorithm or software that has the function of automatically assigning tasks optimally and adjusting work support as needed, based on the analyzed data.

[0769] A "progress monitoring system" is a system that has the function of monitoring the progress of work and changes in emotional state in real time and detecting anomalies.

[0770] The system implementing this invention is equipped with a function to analyze activity data and emotional data in combination in order to streamline business management for companies and organizations. The server collects activity data such as work progress, work content, email exchanges, and document editing history from members' terminals, and acquires emotional data from the user's facial expressions and voice using an emotion recognition sensor.

[0771] The server processes this data holistically, analyzing workload and emotional states. Data analysis software is used for information processing, and emotional data is analyzed by emotion recognition algorithms. This allows for the identification of overworked or stressed members, enabling automatic adjustment of workload assignments and support through optimization measures.

[0772] This system uses progress monitoring to monitor the progress of tasks and changes in emotions in real time, and the work plan is immediately revised when the situation changes. In addition, a display device is provided on the user's terminal, allowing them to constantly check the progress of tasks and emotional evaluations, and appropriate feedback is provided.

[0773] As a concrete example, when a factory line worker is assigned to a new project, the server assesses the worker's workload and emotional state, and automatically adjusts tasks or provides additional support as needed. Based on the emotional data, if the server determines that the worker is highly stressed, it will assign another worker to provide support or take measures to reduce the workload.

[0774] Example of a prompt:

[0775] "Simulate the actions of a robot that automatically provides support to a worker who is under high stress."

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The server collects activity data from each member's terminal. This activity data includes work progress, work details, email sending and receiving history, and document editing history. All work-related data from each terminal is used as input, and this data is integrated and stored in a database. This forms a foundation for understanding the overall work situation within the organization.

[0779] Step 2:

[0780] The server collects data from emotion recognition sensors to obtain emotional data such as members' facial expressions and voices. This input data is then processed by an analysis algorithm to provide the members' emotional state (stress level, satisfaction level, etc.) as output. Machine learning models are used to improve the accuracy of emotion recognition.

[0781] Step 3:

[0782] The server integrates and analyzes the collected activity and emotional data to evaluate workload and emotional state. The input at this stage is the output data from steps 1 and 2, and a comprehensive evaluation is performed by data analysis software. The output generates individual workload and emotional evaluation values ​​for each member.

[0783] Step 4:

[0784] The server adjusts the optimal assignment of tasks and work support based on the generated evaluation values. This is a dynamic adjustment process using optimization methods, and the evaluation values ​​from step 3 are used as input. The server adjusts the workload for members deemed to be overloaded and arranges additional support as needed. The output is the adjusted task assignment.

[0785] Step 5:

[0786] The server monitors the progress of tasks and changes in emotional state in real time, and detects anomalies. Inputs are existing task plans and data from step 3, and outputs are notifications and warnings of anomaly detection. Here, dynamic task management is achieved by making full use of progress monitoring methods.

[0787] Step 6:

[0788] The system notifies users of the latest work progress and emotional assessment results on their devices and displays appropriate feedback. Input is updated information from the server, and output is visually displayed information on the user's device. In this way, users can understand their work status and emotional state in real time.

[0789] This series of steps enables integrated business management and emotional state analysis by the server, improving operational efficiency and member well-being.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] An information processing means equipped with the function of collecting activity data and analyzing the workload status based on this data,

[0814] An optimization method that automatically determines the optimal assignment of tasks based on the analysis results,

[0815] A progress monitoring means for monitoring the progress of operations and detecting anomalies,

[0816] A system that includes a display mechanism for showing work progress and analysis results.

[0817] (Claim 2)

[0818] The system according to claim 1, comprising an evaluation means for evaluating the capabilities of individual members from activity data and generating work performance evaluation data for each member.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising a dynamic adjustment means that has a function to readjust the assignment of tasks as needed based on monitoring the progress of the tasks.

[0821] "Example 1"

[0822] (Claim 1)

[0823] An information processing means that collects activity data and analyzes the workload and skill sets of individual members based on this data,

[0824] An optimization means that automatically determines the optimal task assignment based on the analysis results and notifies the member's information terminal via a communication means,

[0825] A progress monitoring system that monitors the progress of work in real time and quickly detects delays or anomalies,

[0826] A display means for displaying work progress and analysis results on the information terminals of the members,

[0827] An evaluation method that accumulates activity logs and generates work performance evaluation data for each member,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, comprising dynamic adjustment means that has a function to dynamically readjust work assignments as needed based on monitoring of work progress.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising a function to provide feedback for optimizing business management by creating prompt statements using a generative AI model.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A means of collecting activity data and analyzing the workload situation based on it,

[0836] A means to automatically determine the optimal assignment of tasks based on the analysis results,

[0837] A means of monitoring the progress of operations and detecting anomalies,

[0838] A means of displaying work progress and analysis results,

[0839] Information processing equipment provides a means for monitoring the work efficiency and progress of robots and workers in real time,

[0840] A means of reviewing task priorities and proposing optimal resource allocation,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, comprising means for evaluating the capabilities of individual members from activity data and generating work performance evaluation data for each member.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising means for readjusting work assignments as necessary based on monitoring of work progress.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] An information processing means that collects activity data and emotional data, and analyzes the workload and emotional state of members based on this data,

[0849] An optimization method that automatically determines the optimal assignment of tasks considering analysis results and emotional state,

[0850] A monitoring means for monitoring the progress of work and detecting abnormalities and changes in emotional state,

[0851] A dynamic adjustment mechanism that performs work adjustments based on the emotional state of each member,

[0852] A system including a display mechanism for showing work progress, analysis results, and emotional state.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising an evaluation means for evaluating the capabilities of individual members from activity data and emotional data, and for generating work performance and emotional state evaluation data for each member.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising a dynamic adjustment means that has a function to readjust work assignments as necessary based on monitoring of work progress and emotional state.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] An information processing means equipped with the function of collecting activity data and emotional data, and analyzing work load and emotional state based on this data,

[0860] An optimization means that automatically determines the optimal assignment of tasks and adjusts work support as needed based on the analysis results,

[0861] A progress monitoring system that monitors the progress of work and changes in emotional state, and detects anomalies,

[0862] A system that includes display means for showing work progress, analysis results, and emotional states.

[0863] (Claim 2)

[0864] The system according to claim 1, comprising an evaluation means for evaluating the abilities and emotional state of individual members from activity data and emotional data, and for generating work performance and emotional state evaluation data for each member.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising a dynamic adjustment means that has a function to dynamically readjust work assignments and work support as needed based on monitoring of work progress and changes in emotional state. [Explanation of Symbols]

[0867] 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. An information processing means equipped with the function of collecting activity data and analyzing the workload status based on this data, An optimization method that automatically determines the optimal assignment of tasks based on the analysis results, A progress monitoring means for monitoring the progress of operations and detecting anomalies, A system that includes a display mechanism for showing work progress and analysis results.

2. The system according to claim 1, comprising an evaluation means for evaluating the capabilities of individual members from activity data and generating work performance evaluation data for each member.

3. The system according to claim 1, comprising a dynamic adjustment means that has a function to readjust the assignment of tasks as needed based on monitoring the progress of the tasks.

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