system

Abstract

We provide the system. [Solution] Means for collecting project progress data and planning data, Means for preprocessing the aforementioned data and converting it into an analyzable format, A method for training generative models using past project data and learning risks, A means of identifying potential risks in ongoing projects, A means for automatically generating risk countermeasures for the identified risks, Means for notifying the user of the aforementioned risks and the results of countermeasures, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In project management, there is a problem that it is difficult to foresee risks during progress and appropriately respond to them. In particular, since there is no mechanism to efficiently identify risks latent in the progress status and plan of a project and promptly formulate countermeasures, risk response is often delayed, leading to project failure, delay, and cost increase. Improvement of such a situation and provision of a system that enables real-time risk management are desired.

Means for Solving the Problems

[0005] This invention provides a system that collects project progress and planning data in real time, learns risks using a generative model based on historical project data, and identifies potential risks in ongoing projects. The system automatically generates countermeasures for identified risks and includes a dashboard that visualizes and notifies the user of the analysis results. This allows project managers to address risks quickly and improve the success rate of projects.

[0006] A "project" is a set of activities or tasks planned to achieve a specific goal, and it has a set start and end date.

[0007] "Progress data" refers to data that shows information about the status and degree of completion of each task and activity in a project.

[0008] "Planning data" refers to data that shows information about the schedule, work content, and resource allocation that have been set in advance for a project.

[0009] A "generative model" is an algorithm that uses machine learning to learn patterns from large amounts of data and perform data-based predictions and classifications.

[0010] "Potential risks" refer to uncertain factors or events that have not yet materialized but could potentially become problems in the future.

[0011] "Risk response measures" refer to specific actions or strategies taken to avoid, mitigate, or accept identified risks.

[0012] "Notification means" refers to functions and methods for providing information and data generated within a system to users in an appropriate format.

[0013] A "dashboard" is an interface or platform that visually displays system information, allowing users to grasp the situation at a glance. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined. [[Embodiments for Carrying Out the Invention]]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] 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, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention provides a system to support risk identification and response in project management. The embodiments thereof are described in detail below.

[0036] This system begins by collecting project progress and planning data and formatting this data into a parseable format. The server accesses project management tools and external databases to integrate and collect the necessary data. The collected data is then made consistent, and any missing data is filled in.

[0037] The formatted data is input into a generative model and trained using historical project data. This allows the system to learn patterns of past successes and failures, improving the accuracy of risk predictions. After analysis, the server analyzes the status of ongoing projects and identifies potential risks. For identified risks, it generates optimal risk mitigation strategies based on historical data.

[0038] The generated risk mitigation measures are visualized to facilitate user interaction. The terminal provides a dashboard as a user interface, displaying risk information and detailed mitigation measures to the user. The user can review the proposed mitigation measures and adjust the project plan as needed.

[0039] As a concrete example, consider a software development project. The server collects and analyzes data such as the development schedule, member working hours, and the number of bugs. If this analysis predicts development delays, the server proposes countermeasures such as increasing resources or revising the schedule. Subsequently, the server presents these proposals to the user via a terminal, allowing the user to make decisions based on established information.

[0040] The system according to the present invention thus streamlines project risk management and contributes to improving the success rate.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server automatically collects progress and planning data from project management tools and external databases. This includes data retrieval via APIs and periodic data extraction tasks.

[0044] Step 2:

[0045] The server preprocesses the collected data, fills in missing data, and detects and corrects outliers. This process also includes standardizing data formats and removing noise.

[0046] Step 3:

[0047] The server converts pre-processed data into features and inputs them into the generative model. This feature generation process creates metrics such as resource utilization, task completion status, and schedule progress.

[0048] Step 4:

[0049] The server uses a generative model to learn from historical data and identify potential risks inherent in the project. This is where the risk prediction algorithm is implemented.

[0050] Step 5:

[0051] The server automatically generates risk mitigation measures based on past success stories for identified risks. These measures include adjusting resource allocations and replanning schedules.

[0052] Step 6:

[0053] The terminal displays a dashboard that visually presents the analysis results and risk mitigation measures to the user. This allows the user to see the risk status of the project at a glance.

[0054] Step 7:

[0055] Based on the information presented, users consider risk mitigation measures and make necessary adjustments to the project's progress via the dashboard. This process efficiently optimizes the project.

[0056] (Example 1)

[0057] 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."

[0058] In project management, identifying potential risks early and promptly providing appropriate countermeasures is crucial for improving project success rates. Traditional methods often involve time-consuming data collection and analysis, leading to delays in risk response. Therefore, a system that efficiently processes information and manages risks is needed.

[0059] 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.

[0060] In this invention, the server includes means for collecting information progress data and planning data, means for preprocessing the collected data and converting it into an analyzable format, and means for training an estimation model using historical information data and learning hazards. This enables the rapid identification of potential hazards and the automatic generation of appropriate countermeasures.

[0061] "Information" refers to the collective term for events and figures related to progress and plans that are the subject of data collection and analysis.

[0062] "Progress data" refers to a series of data that indicates the progress of a project, including the degree of task completion and adherence to deadlines.

[0063] "Planning data" refers to information based on the assumption of future implementation, such as project schedules and resource allocation.

[0064] "Means of collection" refers to methods and technologies for acquiring necessary data from information management systems and external information infrastructure.

[0065] "Means of preprocessing and converting into an analyzable format" refers to techniques and methods for shaping collected data into a format and structure suitable for analysis.

[0066] An "estimated model" is an algorithm or mathematical model that learns from past data and uses that data to predict specific events or outcomes.

[0067] "Risk" refers to potential problems or challenges that could affect the achievement of the project's goals.

[0068] "Countermeasures" refer to actions or policies to be taken in response to identified risks, and their purpose is to mitigate or avoid risks.

[0069] "Users" refer to individuals or organizations that operate and monitor the system and make decisions based on the results obtained.

[0070] "Means of notifying and supporting decision-making" refer to technologies and methods that provide information to users and support them in making appropriate decisions.

[0071] A "display infrastructure" refers to a user interface or tool for visually presenting information, representing data in a way that is easy to see and understand.

[0072] This invention is a system aimed at quickly identifying potential risks in project management-related information and generating appropriate countermeasures. Specific embodiments are described below.

[0073] The server retrieves progress and planning data from project management systems and external data infrastructure. First, it uses APIs to access these data sources and collect all necessary information related to the project.

[0074] The collected data is not suitable for analysis in its raw state, so the server preprocesses the data. The data is formatted, and any missing parts are filled in. For example, if the progress of a task over time is not recorded, it is estimated based on similar data from past projects.

[0075] The formatted data is input into a generative AI model. This model functions as the estimation model mentioned earlier, performing pattern recognition based on past project data. This allows it to predict potential risks in the current project and calculate their probabilities. Machine learning libraries such as Scikit-learn and TENSORFLOW® are used in the generative AI model.

[0076] Once a risk is identified, the server generates countermeasures tailored to that risk. For example, if a delay is predicted for a particular task, it will suggest specific measures such as increasing resources or readjusting the schedule.

[0077] The terminal uses a dashboard as its user interface to visualize risk information and countermeasures sent from the server. This dashboard uses visualization tools such as Charts.js and D3.js to display information in a way that is easy for the user to understand.

[0078] Users can make decisions based on this information. They may accept the proposed solutions or make modifications. Changes to the project plan will result in new data being sent to the server for further analysis.

[0079] As a concrete example, in a software development project, the server collects data on the development schedule, the working hours of team members, and the number of bugs in the generated product. If the analysis determines that there is a risk of development delays, it proposes to the project manager that additional members be added. This proposal is displayed to the user via a terminal, and the user makes a decision.

[0080] In this way, the system efficiently manages project risks and contributes to improving the project's success rate. Examples of prompts for the generated AI model include, "Please provide recommendations for the next steps based on the current progress."

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server retrieves progress and planning data from project management systems and external information infrastructure. Specifically, it uses APIs to collect necessary information and stores this data in its raw state in data storage. Inputs include project progress, deadlines, and resource allocation information, while output is an unprocessed dataset.

[0084] Step 2:

[0085] The server preprocesses the collected data. This includes checking data integrity and filling in missing parts based on data from similar past projects. This process uses programming languages ​​such as Python to execute data cleaning algorithms. The input is the raw data, and the output is data formatted for analysis.

[0086] Step 3:

[0087] The server inputs pre-processed data into a generating AI model to predict potential risks. This model learns from past successes and failures and recognizes patterns that could lead to failure. Specifically, the model is built using Scikit-learn and TensorFlow, with pre-formatted data as input and risk assessment results as output.

[0088] Step 4:

[0089] The server generates optimal countermeasures for identified risks based on the analysis results from the generated AI model. For example, it creates suggestions for adding resources or revising the schedule for tasks deemed high-risk. This makes it possible to proactively address potential problems in a project. The input is the risk assessment result, and the output is a list of specific countermeasures.

[0090] Step 5:

[0091] The terminal visualizes risk information and countermeasures sent from the server. For example, using Charts.js or D3.js, it displays the risk level as a color-coded graph on the dashboard. This allows the user to intuitively understand the information. The input is a list of countermeasures, and the output is a visualized dashboard.

[0092] Step 6:

[0093] Users review the risk information and countermeasures displayed on their device and make adjustments to the project plan. They can approve proposed countermeasures or customize them as needed. The input is a visualized dashboard, and the output is an updated project plan.

[0094] (Application Example 1)

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

[0096] In factory production management, there is a need to identify production risks in real time and quickly provide effective countermeasures. However, conventional methods often involve manual data collection and analysis, resulting in a lack of immediacy and efficiency. Furthermore, the insufficient use of data analysis and its results for optimizing production processes hinders improvements in production efficiency. This invention aims to solve these problems.

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

[0098] In this invention, the server includes means for collecting project progress data and planning data, means for preprocessing the data and converting it into an analyzable format, and means for aggregating and analyzing production data from the factory in real time. This makes it possible to efficiently identify potential risks and propose countermeasures in real time in factory production management.

[0099] "Project progress data" refers to information that shows the degree of completion of each task in a project and its progress according to the schedule.

[0100] "Planning data" refers to information that outlines the goals set before the project is executed, the planned work schedule, and the necessary resources.

[0101] "Means for preprocessing data and converting it into an analyzable format" refers to techniques or devices for organizing collected data into a consistent form and converting it into a format suitable for analysis.

[0102] "Past project data" refers to various historical information and record data related to projects that have been carried out in the past.

[0103] "Methods for training generative models and learning risks" refers to technologies or devices that use machine learning models to derive patterns of failure and success from past project data in order to predict risks.

[0104] "Means of identifying potential risks" refers to techniques or devices for analyzing ongoing project data and predicting where problems may occur.

[0105] "Means for automatically generating risk response measures" refers to technology or equipment that automatically proposes appropriate solutions for identified risks.

[0106] "Factory production data" refers to information that shows various performance indicators and the progress of processes in the manufacturing process.

[0107] "Means for proposing optimization of production processes based on analysis results" refers to technologies or devices for proposing methods to improve the efficiency and quality of production processes based on results obtained through data analysis.

[0108] The system based on this invention improves production efficiency by identifying risks in factory production management in real time and proposing appropriate countermeasures. The server continuously collects production data from various sensors and production management systems within the factory. This includes information on machine operating status and production line progress. This data is pre-processed within the server and formatted into an analyzable format.

[0109] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to run a generative model trained on historical project data. This generative AI model detects patterns in the data and predicts potential risks. Specific examples include frequent failures on a particular production line or production bottlenecks learned from historical data.

[0110] Based on the analysis, the server automatically generates optimal risk mitigation measures based on potential risks. This includes suggestions such as rearranging production processes and reallocating resources. Terminal devices visualize these analysis results and mitigation measures as a user interface and provide them to production managers. Production managers (users) use this information to make quick decisions and adjust production plans.

[0111] Specifically, prompts such as, "Based on current production data, list the risks that may occur within the next three months and propose countermeasures," can be used to set guidelines for activities. This makes it possible to improve the efficiency and quality of the factory's production processes.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server collects production data in real time from sensors and production management systems within the factory. It takes machine operation data and production line progress data as input. This data is temporarily stored on the server to prepare the data needed for the next processing step.

[0115] Step 2:

[0116] The server preprocesses the collected data and formats it into an analyzable format. It receives collected production data as input, performs data imputation and format conversion, and generates a dataset suitable for analysis as output.

[0117] Step 3:

[0118] The server inputs the formatted data into a generative AI model to predict potential risks. At this stage, a pre-processed dataset is used as input. The generative AI model refers to past data patterns to estimate the probability of risk occurrence. The output is the identified risk.

[0119] Step 4:

[0120] The server automatically generates risk mitigation measures for identified risks. It uses the risk information obtained as output of the generative model as input to calculate the risk mitigation measures. The output is a list of specific mitigation measures.

[0121] Step 5:

[0122] The server sends the generated risk mitigation measures and analysis results to the terminal. It takes risk mitigation measures and analysis results as input and generates visualization data as output.

[0123] Step 6:

[0124] The terminal presents results to the user using a visualized dashboard. Using data received from the server as input, it displays risk information on the user interface by applying past prompts. Based on the results, the user makes quick decisions using prompts, such as "Based on the current production data, list the risks and propose countermeasures."

[0125] 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.

[0126] This invention combines an emotion engine with a system for project management that identifies risks and proposes countermeasures, thereby enabling risk response that takes user emotions into consideration. The embodiments are described in detail below.

[0127] This system first collects project progress and planning data in real time and uses it to identify potential project risks. The server aggregates data from project management tools and databases, checks for consistency, and then performs analysis. This process helps to understand the current state of the project and identify areas where problems may occur.

[0128] Next, the emotion engine built into the device analyzes the user's emotional state in real time. This engine has the ability to analyze the user's input behavior and interaction data to infer their current emotional state. For example, it analyzes data that suggests the user's emotions may be negative, such as when the user frequently postpones tasks or when the number of characters typed decreases.

[0129] The results of this emotion analysis are directly used to generate risk mitigation strategies. In particular, the server incorporates communication methods and project management techniques tailored to the user's emotional state to efficiently address risks while reducing stress. When emotions are negative, attempts are made to maintain the user's motivation by providing suggestions that include more positive feedback.

[0130] For example, if the system determines that a user is prone to experiencing project-related stress, it will automatically suggest changing task priorities or adjusting the schedule to allow for more flexibility. In this way, the system improves the probability of project success by addressing risks while also being sensitive to the user's emotions.

[0131] This invention thus provides a system that promotes not only risk management in project management but also project optimization through improvements to the user interface.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server collects project progress and planning data from project management tools and external databases. This integrates important information such as task progress and resource usage.

[0135] Step 2:

[0136] The server preprocesses the collected data, including imputing missing values ​​and standardizing the format. Converting the data into an analyzable format facilitates subsequent analysis.

[0137] Step 3:

[0138] The server inputs pre-processed data into a generative model and leverages historical project data to identify risks. This involves using machine learning algorithms for pattern recognition and prediction.

[0139] Step 4:

[0140] The device analyzes the user's emotional state using an emotion engine. It monitors the user's input and usage to generate an emotional state index. This index is obtained from user interface operation logs and feedback activities.

[0141] Step 5:

[0142] The server automatically generates the optimal risk mitigation strategy, taking into account the identified risks and the user's emotional state. This process customizes the suggestions and presentation methods depending on whether the user's emotions are positive or negative.

[0143] Step 6:

[0144] The device displays the generated risk mitigation measures and sentiment analysis results on a visually clear dashboard. This allows users to clearly understand the current project status and the optimal course of action.

[0145] Step 7:

[0146] Users can take action to address risks based on the displayed information. By applying the suggested adjustments to the project, it is possible to reduce risks and stress during its progress.

[0147] (Example 2)

[0148] 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".

[0149] In project management, if progress information and plans are not properly managed, and the identification of potential risks and the development of countermeasures are delayed, the project's success rate will decrease. Furthermore, if risk management does not take into account the emotional state of individual users, stress and decreased motivation can negatively impact the project's progress.

[0150] 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.

[0151] In this invention, the server includes means for collecting project progress information and planning information, means for converting the information into an analyzable format, and means for training a model using past project information to learn risks. This makes it possible to identify potential risks in a project and automatically generate appropriate risk mitigation measures according to the user's emotional state.

[0152] "Project progress information" refers to information that shows what stage a project is currently in and how far along it is in relation to the plan.

[0153] "Planning information" refers to information related to the planning necessary for project execution, including project goals, milestones, schedules, and resource allocation.

[0154] An "analyzable format" is a format that makes it easy to interpret and perform calculations on collected data.

[0155] "Past project information" refers to data about previously executed projects, including information such as progress, issues, and results.

[0156] "Training a model" is the process of using machine learning techniques to learn patterns and rules from data.

[0157] "Potential risks" are problems that have not yet materialized but could potentially hinder the progress of the project in the future.

[0158] "User behavior data" refers to records of operations and inputs performed by users on the system.

[0159] "Emotional state" refers to information about the user's mood and psychological state, including stress levels and satisfaction levels.

[0160] "Risk mitigation measures" refer to the means or measures taken to mitigate or eliminate the impact of identified risks.

[0161] "Visual presentation methods" refer to methods of displaying data and information in an easy-to-understand format, such as graphs and charts.

[0162] This invention provides a system in which servers and terminals work together to aggregate progress and planning information and identify potential risks in project management. The software and hardware used include project management tools, a database management system, machine learning models, and a sentiment analysis engine. All of these are built on existing computer graphics and communication technologies.

[0163] First, the server retrieves progress and planning information from the project management tool. The server accesses the database via an API to collect necessary information in real time. This data is then used to check project integrity and convert it into a parseable format. Specifically, it calculates data representing progress and metrics indicating whether the project is on track.

[0164] Next, the sentiment analysis engine installed in the device collects user behavior data and infers their emotional state. For example, the frequency of user operations and the number of characters entered are observed, and their stress and motivation levels are analyzed.

[0165] The results of this emotional state analysis are used to generate risk mitigation measures based on the risks identified by the server. The server uses an AI model to formulate risk mitigation measures tailored to the user's emotional state and proposes them to the user. For example, if the user shows a decrease in motivation, the server will promptly suggest rescheduling or provide positive feedback.

[0166] As a concrete example, when a user inputs the prompt "Task delays are beginning to affect the entire project" into the AI ​​model, the system automatically assesses the risks and proposes an appropriate action plan. In this way, the invention can improve the efficiency of risk management and increase the probability of project success.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects progress and planning information from project management tools and external databases. It uses raw data obtained through the project management tool's API as input. The server converts this data into a parseable format and checks for inconsistencies. The output of this step is clean, well-structured data.

[0170] Step 2:

[0171] The server analyzes consistent data to identify potential risks. The data obtained in Step 1 is used as input. The server uses machine learning algorithms to calculate potential deviations from the plan and budget and time risks. The output is a risk assessment list.

[0172] Step 3:

[0173] The device collects user behavior data in real time and infers their emotional state. The input for this process consists of data related to user actions and inputs. The emotion analysis engine uses this data to infer stress levels and motivation levels. The output of this step is the user's inferred emotional state.

[0174] Step 4:

[0175] The server uses a generative AI model to generate risk mitigation strategies based on a risk assessment list and the user's emotional state. This prompt serves as input, providing specific instructions to the generative AI model. The server utilizes the data to automatically generate risk mitigation strategies tailored to the user. The output provides a detailed action plan that is specific to the user's situation.

[0176] Step 5:

[0177] Users receive risk mitigation measures from the server and apply them to their actual projects. The output manifests as updates to the project plan after application and, if necessary, adjustments to resource allocation. As a result, smooth project progress can be expected.

[0178] (Application Example 2)

[0179] 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".

[0180] Existing project management systems often fail to consider the user's emotional state when identifying risks and generating countermeasures, which can lead to increased stress and decreased efficiency. Furthermore, they lack automated workload adjustments within factories, making productivity optimization difficult.

[0181] 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.

[0182] In this invention, the server includes means for collecting project progress and planning information, means for analyzing the emotional state of users and generating countermeasures for stress reduction, and means for monitoring activities within the factory and appropriately adjusting the workload. This enables risk management that takes user emotions into consideration and allows for the efficient optimization of work within the factory.

[0183] "Project progress information" refers to data that shows the status of project implementation and the progress of its stages.

[0184] "Planning information" refers to data that shows the details of a plan that has been established in advance, such as project goal setting and work schedule.

[0185] A "generative model" is a model that uses artificial intelligence trained on past data to perform data prediction and decision-making.

[0186] "Risk response measures" refer to specific countermeasures implemented in response to identified risks.

[0187] "User emotional state" refers to information that indicates the emotional reactions and mental state of the individuals involved in the project.

[0188] "Stress reduction measures" refer to a series of actions taken to alleviate the burden on users and maintain or improve productivity.

[0189] "Internal factory activities" refers to various tasks performed within the factory, such as manufacturing, processing, and inspection.

[0190] "Workload adjustment" is a process for efficiently performing tasks by optimizing the amount of work and tasks assigned to employees and machines.

[0191] In this system, a server collects project progress and planning information and converts it into an analyzable format. This is achieved by acquiring data from sensors within the factory and from the project management system. The server uses Apache® Kafka to collect data in real time and stores the data using PostgreSQL.

[0192] The server trains a generative model based on the collected data to identify potential risks. This makes it possible to automatically generate risks and countermeasures for ongoing projects. Furthermore, the server utilizes the emotion engine installed on the terminal to analyze the user's emotional state. This emotion engine has the ability to analyze the user's input data and infer their emotional state.

[0193] The terminal receives notifications from the server and presents the user with risks and countermeasures. Notifications are delivered through a visualized dashboard, providing users with easy-to-understand risk information. If the user's emotions are judged to be negative, specific countermeasures to reduce stress are proposed, such as optimizing the reallocation of workload.

[0194] For example, suppose a project falls behind schedule, and the system determines that the user is experiencing stress. In this case, the system will re-evaluate the task priorities and propose a schedule with more leeway.

[0195] An example of a prompt would be, "When progress is behind schedule on a factory production line, what risk mitigation measures would you propose after analyzing employee sentiment?" This prompt forms the basis for deriving appropriate risk mitigation measures through a generative AI model.

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

[0197] Step 1:

[0198] The server collects progress and planning information from sensors and project management systems within the factory. Inputs include real-time data from sensors and data from the project management system. The server utilizes Apache Kafka to aggregate this data in real time and store it in PostgreSQL, outputting an analyzable dataset.

[0199] Step 2:

[0200] The server trains a generative model based on the collected data to identify potential risks. The input for this step is the data collected in step 1. The server uses machine learning algorithms to identify risks and outputs the results. This organizes information about the project's potential risks.

[0201] Step 3:

[0202] The terminal receives risk information and generated countermeasures from the server. The input consists of the risk information and countermeasures generated by the server. The terminal visualizes this information on a dashboard and presents it to the user. The output includes graphical risk notifications to aid user understanding.

[0203] Step 4:

[0204] The server utilizes the terminal's emotion engine to analyze the user's emotional state. The input for this step is user input data and interaction data. The server processes this data and infers the user's emotional state. The output provides information about the user's emotional state.

[0205] Step 5:

[0206] The server generates specific countermeasures for stress reduction based on the user's emotional state. The inputs are risk information and the user's emotional state. The server uses an AI model to determine the optimal countermeasure and outputs the result. This result includes suggestions such as reallocating workloads or adjusting schedules, as needed.

[0207] Step 6:

[0208] The user receives notifications from the server via their device and implements the proposed countermeasures. The input is the specific countermeasures generated in step 5. The user puts these into action, improving the efficiency of the project. The output is the smooth progress of the project and reduced user stress.

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

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

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

[0212] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0225] This invention provides a system to support risk identification and response in project management. The embodiments thereof are described in detail below.

[0226] This system begins by collecting project progress and planning data and formatting this data into a parseable format. The server accesses project management tools and external databases to integrate and collect the necessary data. The collected data is then made consistent, and any missing data is filled in.

[0227] The formatted data is input into a generative model and trained using historical project data. This allows the system to learn patterns of past successes and failures, improving the accuracy of risk predictions. After analysis, the server analyzes the status of ongoing projects and identifies potential risks. For identified risks, it generates optimal risk mitigation strategies based on historical data.

[0228] The generated risk mitigation measures are visualized to facilitate user interaction. The terminal provides a dashboard as a user interface, displaying risk information and detailed mitigation measures to the user. The user can review the proposed mitigation measures and adjust the project plan as needed.

[0229] As a concrete example, consider a software development project. The server collects and analyzes data such as the development schedule, member working hours, and the number of bugs. If this analysis predicts development delays, the server proposes countermeasures such as increasing resources or revising the schedule. Subsequently, the server presents these proposals to the user via a terminal, allowing the user to make decisions based on established information.

[0230] The system according to the present invention thus streamlines project risk management and contributes to improving the success rate.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The server automatically collects progress and planning data from project management tools and external databases. This includes data retrieval via APIs and periodic data extraction tasks.

[0234] Step 2:

[0235] The server preprocesses the collected data, fills in missing data, and detects and corrects outliers. This process also includes standardizing data formats and removing noise.

[0236] Step 3:

[0237] The server converts pre-processed data into features and inputs them into the generative model. This feature generation process creates metrics such as resource utilization, task completion status, and schedule progress.

[0238] Step 4:

[0239] The server uses a generative model to learn from historical data and identify potential risks inherent in the project. This is where the risk prediction algorithm is implemented.

[0240] Step 5:

[0241] The server automatically generates risk mitigation measures based on past success stories for identified risks. These measures include adjusting resource allocations and replanning schedules.

[0242] Step 6:

[0243] The terminal displays a dashboard that visually presents the analysis results and risk mitigation measures to the user. This allows the user to see the risk status of the project at a glance.

[0244] Step 7:

[0245] Based on the information presented, users consider risk mitigation measures and make necessary adjustments to the project's progress via the dashboard. This process efficiently optimizes the project.

[0246] (Example 1)

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

[0248] In project management, identifying potential risks early and promptly providing appropriate countermeasures is crucial for improving project success rates. Traditional methods often involve time-consuming data collection and analysis, leading to delays in risk response. Therefore, a system that efficiently processes information and manages risks is needed.

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

[0250] In this invention, the server includes means for collecting information progress data and planning data, means for preprocessing the collected data and converting it into an analyzable format, and means for training an estimation model using historical information data and learning hazards. This enables the rapid identification of potential hazards and the automatic generation of appropriate countermeasures.

[0251] "Information" refers to the collective term for events and figures related to progress and plans that are the subject of data collection and analysis.

[0252] "Progress data" refers to a series of data that indicates the progress of a project, including the degree of task completion and adherence to deadlines.

[0253] "Planning data" refers to information based on the assumption of future implementation, such as project schedules and resource allocation.

[0254] "Means of collection" refers to methods and technologies for acquiring necessary data from information management systems and external information infrastructure.

[0255] "Means of preprocessing and converting into an analyzable format" refers to techniques and methods for shaping collected data into a format and structure suitable for analysis.

[0256] An "estimated model" is an algorithm or mathematical model that learns from past data and uses that data to predict specific events or outcomes.

[0257] "Risk" refers to potential problems or challenges that could affect the achievement of the project's goals.

[0258] "Countermeasures" refer to actions or policies to be taken in response to identified risks, and their purpose is to mitigate or avoid risks.

[0259] "Users" refer to individuals or organizations that operate and monitor the system and make decisions based on the results obtained.

[0260] "Means of notifying and supporting decision-making" refer to technologies and methods that provide information to users and support them in making appropriate decisions.

[0261] A "display infrastructure" refers to a user interface or tool for visually presenting information, representing data in a way that is easy to see and understand.

[0262] This invention is a system aimed at quickly identifying potential risks in project management-related information and generating appropriate countermeasures. Specific embodiments are described below.

[0263] The server retrieves progress and planning data from project management systems and external data infrastructure. First, it uses APIs to access these data sources and collect all necessary information related to the project.

[0264] The collected data is not suitable for analysis in its raw state, so the server preprocesses the data. The data is formatted, and any missing parts are filled in. For example, if the progress of a task over time is not recorded, it is estimated based on similar data from past projects.

[0265] The formatted data is input into a generative AI model. This model functions as the estimation model mentioned earlier, performing pattern recognition based on past project data. This allows it to predict potential risks in the current project and calculate their probabilities. Machine learning libraries such as Scikit-learn and TensorFlow are used in the generative AI model.

[0266] Once a risk is identified, the server generates countermeasures tailored to that risk. For example, if a delay is predicted for a particular task, it will suggest specific measures such as increasing resources or readjusting the schedule.

[0267] The terminal uses a dashboard as its user interface to visualize risk information and countermeasures sent from the server. This dashboard uses visualization tools such as Charts.js and D3.js to display information in a way that is easy for the user to understand.

[0268] Users can make decisions based on this information. They may accept the proposed solutions or make modifications. Changes to the project plan will result in new data being sent to the server for further analysis.

[0269] As a concrete example, in a software development project, the server collects data on the development schedule, the working hours of team members, and the number of bugs in the generated product. If the analysis determines that there is a risk of development delays, it proposes to the project manager that additional members be added. This proposal is displayed to the user via a terminal, and the user makes a decision.

[0270] In this way, the system efficiently manages project risks and contributes to improving the project's success rate. Examples of prompts for the generated AI model include, "Please provide recommendations for the next steps based on the current progress."

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The server retrieves progress and planning data from project management systems and external information infrastructure. Specifically, it uses APIs to collect necessary information and stores this data in its raw state in data storage. Inputs include project progress, deadlines, and resource allocation information, while output is an unprocessed dataset.

[0274] Step 2:

[0275] The server preprocesses the collected data. This includes checking data integrity and filling in missing parts based on data from similar past projects. This process uses programming languages ​​such as Python to execute data cleaning algorithms. The input is the raw data, and the output is data formatted for analysis.

[0276] Step 3:

[0277] The server inputs pre-processed data into a generating AI model to predict potential risks. This model learns from past successes and failures and recognizes patterns that could lead to failure. Specifically, the model is built using Scikit-learn and TensorFlow, with pre-formatted data as input and risk assessment results as output.

[0278] Step 4:

[0279] The server generates optimal countermeasures for identified risks based on the analysis results from the generated AI model. For example, it creates suggestions for adding resources or revising the schedule for tasks deemed high-risk. This makes it possible to proactively address potential problems in a project. The input is the risk assessment result, and the output is a list of specific countermeasures.

[0280] Step 5:

[0281] The terminal visualizes the risk information and countermeasures sent from the server. For example, using Charts.js or D3.js, it is displayed on the dashboard as a graph with the risk levels color-coded. This enables the user to intuitively understand the information. The input is the list of countermeasures, and the output is the visualized dashboard.

[0282] Step 6:

[0283] The user examines the risk information and countermeasures displayed on the terminal and makes adjustments to the project plan. The user approves the proposed countermeasures or customizes them as necessary. The input is the visualized dashboard, and the output is the updated project plan.

[0284] (Application Example 1)

[0285] 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".

[0286] In the production management of a factory, it is required to identify production risks in real time and promptly present effective countermeasures. However, in conventional methods, data collection and analysis are often performed manually, lacking immediacy and efficiency. Also, there is a problem that data analysis for optimizing the production process and the utilization of its results are not sufficiently carried out, thus hindering the improvement of production efficiency. The object of this invention is to solve these problems.

[0287] 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.

[0288] In this invention, the server includes means for collecting project progress data and planning data, means for preprocessing the data and converting it into an analyzable format, and means for aggregating and analyzing production data from the factory in real time. This makes it possible to efficiently identify potential risks and propose countermeasures in real time in factory production management.

[0289] "Project progress data" refers to information that shows the degree of completion of each task in a project and its progress according to the schedule.

[0290] "Planning data" refers to information that outlines the goals set before the project is executed, the planned work schedule, and the necessary resources.

[0291] "Means for preprocessing data and converting it into an analyzable format" refers to techniques or devices for organizing collected data into a consistent form and converting it into a format suitable for analysis.

[0292] "Past project data" refers to various historical information and record data related to projects that have been carried out in the past.

[0293] "Methods for training generative models and learning risks" refers to technologies or devices that use machine learning models to derive patterns of failure and success from past project data in order to predict risks.

[0294] "Means of identifying potential risks" refers to techniques or devices for analyzing ongoing project data and predicting where problems may occur.

[0295] "Means for automatically generating risk response measures" refers to technology or equipment that automatically proposes appropriate solutions for identified risks.

[0296] "Factory production data" refers to information that shows various performance indicators and the progress of processes in the manufacturing process.

[0297] "Means for proposing optimization of production processes based on analysis results" refers to technologies or devices for proposing methods to improve the efficiency and quality of production processes based on results obtained through data analysis.

[0298] The system based on this invention improves production efficiency by identifying risks in factory production management in real time and proposing appropriate countermeasures. The server continuously collects production data from various sensors and production management systems within the factory. This includes information on machine operating status and production line progress. This data is pre-processed within the server and formatted into an analyzable format.

[0299] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to run a generative model trained on historical project data. This generative AI model detects patterns in the data and predicts potential risks. Specific examples include frequent failures on a particular production line or production bottlenecks learned from historical data.

[0300] Based on the analysis, the server automatically generates optimal risk mitigation measures based on potential risks. This includes suggestions such as rearranging production processes and reallocating resources. Terminal devices visualize these analysis results and mitigation measures as a user interface and provide them to production managers. Production managers (users) use this information to make quick decisions and adjust production plans.

[0301] Specifically, prompts such as, "Based on current production data, list the risks that may occur within the next three months and propose countermeasures," can be used to set guidelines for activities. This makes it possible to improve the efficiency and quality of the factory's production processes.

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The server collects production data in real time from sensors and production management systems within the factory. As input, it obtains the operating data of machines and the progress data of production lines. By temporarily storing this data within the server, it prepares the data necessary for the next process.

[0305] Step 2:

[0306] The server preprocesses the collected data and formats it into an analyzable form. As input, it receives the collected production data and performs tasks such as complementing missing data and converting the data format. As output, it generates a dataset in a form suitable for analysis.

[0307] Step 3:

[0308] The server inputs the formatted data into a generation AI model to predict potential risks. At this stage, it uses the preprocessed dataset as input. The generation AI model refers to past data patterns and estimates the probability of risk occurrence. As output, it obtains the specific results of the risk.

[0309] Step 4:

[0310] The server automatically generates risk response measures for the identified risks. It uses the risk information obtained as the output of the generation model as input to calculate the risk response measures. As output, it creates a list of specific response measures.

[0311] Step 5:

[0312] The server transmits the generated risk response measures and analysis results to the terminal. It has the risk response measures and analysis results as input and generates data for visualization as output.

[0313] Step 6:

[0314] The terminal presents results to the user using a visualized dashboard. Using data received from the server as input, it displays risk information on the user interface by applying past prompts. Based on the results, the user makes quick decisions using prompts, such as "Based on the current production data, list the risks and propose countermeasures."

[0315] 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.

[0316] This invention combines an emotion engine with a system for project management that identifies risks and proposes countermeasures, thereby enabling risk response that takes user emotions into consideration. The embodiments are described in detail below.

[0317] This system first collects project progress and planning data in real time and uses it to identify potential project risks. The server aggregates data from project management tools and databases, checks for consistency, and then performs analysis. This process helps to understand the current state of the project and identify areas where problems may occur.

[0318] Next, the emotion engine built into the device analyzes the user's emotional state in real time. This engine has the ability to analyze the user's input behavior and interaction data to infer their current emotional state. For example, it analyzes data that suggests the user's emotions may be negative, such as when the user frequently postpones tasks or when the number of characters typed decreases.

[0319] The results of this emotion analysis are directly used to generate risk mitigation strategies. In particular, the server incorporates communication methods and project management techniques tailored to the user's emotional state to efficiently address risks while reducing stress. When emotions are negative, attempts are made to maintain the user's motivation by providing suggestions that include more positive feedback.

[0320] For example, if the system determines that a user is prone to experiencing project-related stress, it will automatically suggest changing task priorities or adjusting the schedule to allow for more flexibility. In this way, the system improves the probability of project success by addressing risks while also being sensitive to the user's emotions.

[0321] This invention thus provides a system that promotes not only risk management in project management but also project optimization through improvements to the user interface.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The server collects project progress and planning data from project management tools and external databases. This integrates important information such as task progress and resource usage.

[0325] Step 2:

[0326] The server preprocesses the collected data, including imputing missing values ​​and standardizing the format. Converting the data into an analyzable format facilitates subsequent analysis.

[0327] Step 3:

[0328] The server inputs pre-processed data into a generative model and leverages historical project data to identify risks. This involves using machine learning algorithms for pattern recognition and prediction.

[0329] Step 4:

[0330] The device analyzes the user's emotional state using an emotion engine. It monitors the user's input and usage to generate an emotional state index. This index is obtained from user interface operation logs and feedback activities.

[0331] Step 5:

[0332] The server automatically generates the optimal risk mitigation strategy, taking into account the identified risks and the user's emotional state. This process customizes the suggestions and presentation methods depending on whether the user's emotions are positive or negative.

[0333] Step 6:

[0334] The device displays the generated risk mitigation measures and sentiment analysis results on a visually clear dashboard. This allows users to clearly understand the current project status and the optimal course of action.

[0335] Step 7:

[0336] Users can take action to address risks based on the displayed information. By applying the suggested adjustments to the project, it is possible to reduce risks and stress during its progress.

[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] In project management, if progress information and plans are not properly managed, and the identification of potential risks and the development of countermeasures are delayed, the project's success rate will decrease. Furthermore, if risk management does not take into account the emotional state of individual users, stress and decreased motivation can negatively impact the project's progress.

[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 means for collecting project progress information and planning information, means for converting the information into an analyzable format, and means for training a model using past project information to learn risks. This makes it possible to identify potential risks in a project and automatically generate appropriate risk mitigation measures according to the user's emotional state.

[0342] "Project progress information" refers to information that shows what stage a project is currently in and how far along it is in relation to the plan.

[0343] "Planning information" refers to information related to the planning necessary for project execution, including project goals, milestones, schedules, and resource allocation.

[0344] An "analyzable format" is a format that makes it easy to interpret and perform calculations on collected data.

[0345] "Past project information" refers to data about previously executed projects, including information such as progress, issues, and results.

[0346] "Training a model" is the process of using machine learning techniques to learn patterns and rules from data.

[0347] "Potential risks" are problems that have not yet materialized but could potentially hinder the progress of the project in the future.

[0348] "User behavior data" refers to records of operations and inputs performed by users on the system.

[0349] "Emotional state" refers to information about the user's mood and psychological state, including stress levels and satisfaction levels.

[0350] "Risk mitigation measures" refer to the means or measures taken to mitigate or eliminate the impact of identified risks.

[0351] "Visual presentation methods" refer to methods of displaying data and information in an easy-to-understand format, such as graphs and charts.

[0352] This invention provides a system in which servers and terminals work together to aggregate progress and planning information and identify potential risks in project management. The software and hardware used include project management tools, a database management system, machine learning models, and a sentiment analysis engine. All of these are built on existing computer graphics and communication technologies.

[0353] First, the server retrieves progress and planning information from the project management tool. The server accesses the database via an API to collect necessary information in real time. This data is then used to check project integrity and convert it into a parseable format. Specifically, it calculates data representing progress and metrics indicating whether the project is on track.

[0354] Next, the sentiment analysis engine installed in the device collects user behavior data and infers their emotional state. For example, the frequency of user operations and the number of characters entered are observed, and their stress and motivation levels are analyzed.

[0355] The results of this emotional state analysis are used to generate risk mitigation measures based on the risks identified by the server. The server uses an AI model to formulate risk mitigation measures tailored to the user's emotional state and proposes them to the user. For example, if the user shows a decrease in motivation, the server will promptly suggest rescheduling or provide positive feedback.

[0356] As a concrete example, when a user inputs the prompt "Task delays are beginning to affect the entire project" into the AI ​​model, the system automatically assesses the risks and proposes an appropriate action plan. In this way, the invention can improve the efficiency of risk management and increase the probability of project success.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1:

[0359] The server collects progress and planning information from project management tools and external databases. It uses raw data obtained through the project management tool's API as input. The server converts this data into a parseable format and checks for inconsistencies. The output of this step is clean, well-structured data.

[0360] Step 2:

[0361] The server analyzes consistent data to identify potential risks. The data obtained in Step 1 is used as input. The server uses machine learning algorithms to calculate potential deviations from the plan and budget and time risks. The output is a risk assessment list.

[0362] Step 3:

[0363] The device collects user behavior data in real time and infers their emotional state. The input for this process consists of data related to user actions and inputs. The emotion analysis engine uses this data to infer stress levels and motivation levels. The output of this step is the user's inferred emotional state.

[0364] Step 4:

[0365] The server uses a generative AI model to generate risk mitigation strategies based on a risk assessment list and the user's emotional state. This prompt serves as input, providing specific instructions to the generative AI model. The server utilizes the data to automatically generate risk mitigation strategies tailored to the user. The output provides a detailed action plan that is specific to the user's situation.

[0366] Step 5:

[0367] Users receive risk mitigation measures from the server and apply them to their actual projects. The output manifests as updates to the project plan after application and, if necessary, adjustments to resource allocation. As a result, smooth project progress can be expected.

[0368] (Application Example 2)

[0369] 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."

[0370] Existing project management systems often fail to consider the user's emotional state when identifying risks and generating countermeasures, which can lead to increased stress and decreased efficiency. Furthermore, they lack automated workload adjustments within factories, making productivity optimization difficult.

[0371] 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.

[0372] In this invention, the server includes means for collecting project progress and planning information, means for analyzing the emotional state of users and generating countermeasures for stress reduction, and means for monitoring activities within the factory and appropriately adjusting the workload. This enables risk management that takes user emotions into consideration and allows for the efficient optimization of work within the factory.

[0373] "Project progress information" refers to data that shows the status of project implementation and the progress of its stages.

[0374] "Planning information" refers to data that shows the details of a plan that has been established in advance, such as project goal setting and work schedule.

[0375] A "generative model" is a model that uses artificial intelligence trained on past data to perform data prediction and decision-making.

[0376] "Risk response measures" refer to specific countermeasures implemented in response to identified risks.

[0377] "User emotional state" refers to information that indicates the emotional reactions and mental state of the individuals involved in the project.

[0378] "Stress reduction measures" refer to a series of actions taken to alleviate the burden on users and maintain or improve productivity.

[0379] "Internal factory activities" refers to various tasks performed within the factory, such as manufacturing, processing, and inspection.

[0380] "Workload adjustment" is a process for efficiently performing tasks by optimizing the amount of work and tasks assigned to employees and machines.

[0381] In this system, a server collects project progress and planning information and converts it into an analyzable format. This is achieved by acquiring data from sensors within the factory and from the project management system. The server uses Apache Kafka to collect data in real time and PostgreSQL to store the data.

[0382] The server trains a generative model based on the collected data to identify potential risks. This makes it possible to automatically generate risks and countermeasures for ongoing projects. Furthermore, the server utilizes the emotion engine installed on the terminal to analyze the user's emotional state. This emotion engine has the ability to analyze the user's input data and infer their emotional state.

[0383] The terminal receives notifications from the server and presents the user with risks and countermeasures. Notifications are delivered through a visualized dashboard, providing users with easy-to-understand risk information. If the user's emotions are judged to be negative, specific countermeasures to reduce stress are proposed, such as optimizing the reallocation of workload.

[0384] For example, suppose a project falls behind schedule, and the system determines that the user is experiencing stress. In this case, the system will re-evaluate the task priorities and propose a schedule with more leeway.

[0385] An example of a prompt would be, "When progress is behind schedule on a factory production line, what risk mitigation measures would you propose after analyzing employee sentiment?" This prompt forms the basis for deriving appropriate risk mitigation measures through a generative AI model.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The server collects progress and planning information from sensors and project management systems within the factory. Inputs include real-time data from sensors and data from the project management system. The server utilizes Apache Kafka to aggregate this data in real time and store it in PostgreSQL, outputting an analyzable dataset.

[0389] Step 2:

[0390] The server trains a generative model based on the collected data to identify potential risks. The input for this step is the data collected in step 1. The server uses machine learning algorithms to identify risks and outputs the results. This organizes information about the project's potential risks.

[0391] Step 3:

[0392] The terminal receives risk information and generated countermeasures from the server. The input consists of the risk information and countermeasures generated by the server. The terminal visualizes this information on a dashboard and presents it to the user. The output includes graphical risk notifications to aid user understanding.

[0393] Step 4:

[0394] The server utilizes the terminal's emotion engine to analyze the user's emotional state. The input for this step is user input data and interaction data. The server processes this data and infers the user's emotional state. The output provides information about the user's emotional state.

[0395] Step 5:

[0396] The server generates specific countermeasures for stress reduction based on the user's emotional state. The inputs are risk information and the user's emotional state. The server uses an AI model to determine the optimal countermeasure and outputs the result. This result includes suggestions such as reallocating workloads or adjusting schedules, as needed.

[0397] Step 6:

[0398] The user receives notifications from the server via their device and implements the proposed countermeasures. The input is the specific countermeasures generated in step 5. The user puts these into action, improving the efficiency of the project. The output is the smooth progress of the project and reduced user stress.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] [Third Embodiment]

[0403] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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).

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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".

[0415] This invention provides a system to support risk identification and response in project management. The embodiments thereof are described in detail below.

[0416] This system begins by collecting project progress and planning data and formatting this data into a parseable format. The server accesses project management tools and external databases to integrate and collect the necessary data. The collected data is then made consistent, and any missing data is filled in.

[0417] The formatted data is input into a generative model and trained using historical project data. This allows the system to learn patterns of past successes and failures, improving the accuracy of risk predictions. After analysis, the server analyzes the status of ongoing projects and identifies potential risks. For identified risks, it generates optimal risk mitigation strategies based on historical data.

[0418] The generated risk mitigation measures are visualized to facilitate user interaction. The terminal provides a dashboard as a user interface, displaying risk information and detailed mitigation measures to the user. The user can review the proposed mitigation measures and adjust the project plan as needed.

[0419] As a concrete example, consider a software development project. The server collects and analyzes data such as the development schedule, member working hours, and the number of bugs. If this analysis predicts development delays, the server proposes countermeasures such as increasing resources or revising the schedule. Subsequently, the server presents these proposals to the user via a terminal, allowing the user to make decisions based on established information.

[0420] The system according to the present invention thus streamlines project risk management and contributes to improving the success rate.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The server automatically collects progress and planning data from project management tools and external databases. This includes data retrieval via APIs and periodic data extraction tasks.

[0424] Step 2:

[0425] The server preprocesses the collected data, fills in missing data, and detects and corrects outliers. This process also includes standardizing data formats and removing noise.

[0426] Step 3:

[0427] The server converts pre-processed data into features and inputs them into the generative model. This feature generation process creates metrics such as resource utilization, task completion status, and schedule progress.

[0428] Step 4:

[0429] The server uses a generative model to learn from historical data and identify potential risks inherent in the project. This is where the risk prediction algorithm is implemented.

[0430] Step 5:

[0431] The server automatically generates risk mitigation measures based on past success stories for identified risks. These measures include adjusting resource allocations and replanning schedules.

[0432] Step 6:

[0433] The terminal displays a dashboard that visually presents the analysis results and risk mitigation measures to the user. This allows the user to see the risk status of the project at a glance.

[0434] Step 7:

[0435] Based on the information presented, users consider risk mitigation measures and make necessary adjustments to the project's progress via the dashboard. This process efficiently optimizes the project.

[0436] (Example 1)

[0437] 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."

[0438] In project management, identifying potential risks early and promptly providing appropriate countermeasures is crucial for improving project success rates. Traditional methods often involve time-consuming data collection and analysis, leading to delays in risk response. Therefore, a system that efficiently processes information and manages risks is needed.

[0439] 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.

[0440] In this invention, the server includes means for collecting information progress data and planning data, means for preprocessing the collected data and converting it into an analyzable format, and means for training an estimation model using historical information data and learning hazards. This enables the rapid identification of potential hazards and the automatic generation of appropriate countermeasures.

[0441] "Information" refers to the collective term for events and figures related to progress and plans that are the subject of data collection and analysis.

[0442] "Progress data" refers to a series of data that indicates the progress of a project, including the degree of task completion and adherence to deadlines.

[0443] "Planning data" refers to information based on the assumption of future implementation, such as project schedules and resource allocation.

[0444] "Means of collection" refers to methods and technologies for acquiring necessary data from information management systems and external information infrastructure.

[0445] "Means of preprocessing and converting into an analyzable format" refers to techniques and methods for shaping collected data into a format and structure suitable for analysis.

[0446] An "estimated model" is an algorithm or mathematical model that learns from past data and uses that data to predict specific events or outcomes.

[0447] "Risk" refers to potential problems or challenges that could affect the achievement of the project's goals.

[0448] "Countermeasures" refer to actions or policies to be taken in response to identified risks, and their purpose is to mitigate or avoid risks.

[0449] "Users" refer to individuals or organizations that operate and monitor the system and make decisions based on the results obtained.

[0450] "Means of notifying and supporting decision-making" refer to technologies and methods that provide information to users and support them in making appropriate decisions.

[0451] A "display infrastructure" refers to a user interface or tool for visually presenting information, representing data in a way that is easy to see and understand.

[0452] This invention is a system aimed at quickly identifying potential risks in project management-related information and generating appropriate countermeasures. Specific embodiments are described below.

[0453] The server retrieves progress and planning data from project management systems and external data infrastructure. First, it uses APIs to access these data sources and collect all necessary information related to the project.

[0454] The collected data is not suitable for analysis in its raw state, so the server preprocesses the data. The data is formatted, and any missing parts are filled in. For example, if the progress of a task over time is not recorded, it is estimated based on similar data from past projects.

[0455] The formatted data is input into a generative AI model. This model functions as the estimation model mentioned earlier, performing pattern recognition based on past project data. This allows it to predict potential risks in the current project and calculate their probabilities. Machine learning libraries such as Scikit-learn and TensorFlow are used in the generative AI model.

[0456] Once a risk is identified, the server generates countermeasures tailored to that risk. For example, if a delay is predicted for a particular task, it will suggest specific measures such as increasing resources or readjusting the schedule.

[0457] The terminal uses a dashboard as its user interface to visualize risk information and countermeasures sent from the server. This dashboard uses visualization tools such as Charts.js and D3.js to display information in a way that is easy for the user to understand.

[0458] Users can make decisions based on this information. They may accept the proposed solutions or make modifications. Changes to the project plan will result in new data being sent to the server for further analysis.

[0459] As a concrete example, in a software development project, the server collects data on the development schedule, the working hours of team members, and the number of bugs in the generated product. If the analysis determines that there is a risk of development delays, it proposes to the project manager that additional members be added. This proposal is displayed to the user via a terminal, and the user makes a decision.

[0460] In this way, the system efficiently manages project risks and contributes to improving the project's success rate. Examples of prompts for the generated AI model include, "Please provide recommendations for the next steps based on the current progress."

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The server retrieves progress and planning data from project management systems and external information infrastructure. Specifically, it uses APIs to collect necessary information and stores this data in its raw state in data storage. Inputs include project progress, deadlines, and resource allocation information, while output is an unprocessed dataset.

[0464] Step 2:

[0465] The server preprocesses the collected data. This includes checking data integrity and filling in missing parts based on data from similar past projects. This process uses programming languages ​​such as Python to execute data cleaning algorithms. The input is the raw data, and the output is data formatted for analysis.

[0466] Step 3:

[0467] The server inputs pre-processed data into a generating AI model to predict potential risks. This model learns from past successes and failures and recognizes patterns that could lead to failure. Specifically, the model is built using Scikit-learn and TensorFlow, with pre-formatted data as input and risk assessment results as output.

[0468] Step 4:

[0469] The server generates optimal countermeasures for identified risks based on the analysis results from the generated AI model. For example, it creates suggestions for adding resources or revising the schedule for tasks deemed high-risk. This makes it possible to proactively address potential problems in a project. The input is the risk assessment result, and the output is a list of specific countermeasures.

[0470] Step 5:

[0471] The terminal visualizes risk information and countermeasures sent from the server. For example, using Charts.js or D3.js, it displays the risk level as a color-coded graph on the dashboard. This allows the user to intuitively understand the information. The input is a list of countermeasures, and the output is a visualized dashboard.

[0472] Step 6:

[0473] Users review the risk information and countermeasures displayed on their device and make adjustments to the project plan. They can approve proposed countermeasures or customize them as needed. The input is a visualized dashboard, and the output is an updated project plan.

[0474] (Application Example 1)

[0475] 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."

[0476] In factory production management, there is a need to identify production risks in real time and quickly provide effective countermeasures. However, conventional methods often involve manual data collection and analysis, resulting in a lack of immediacy and efficiency. Furthermore, the insufficient use of data analysis and its results for optimizing production processes hinders improvements in production efficiency. This invention aims to solve these problems.

[0477] 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.

[0478] In this invention, the server includes means for collecting project progress data and planning data, means for preprocessing the data and converting it into an analyzable format, and means for aggregating and analyzing production data from the factory in real time. This makes it possible to efficiently identify potential risks and propose countermeasures in real time in factory production management.

[0479] "Project progress data" refers to information that shows the degree of completion of each task in a project and its progress according to the schedule.

[0480] "Planning data" refers to information that outlines the goals set before the project is executed, the planned work schedule, and the necessary resources.

[0481] "Means for preprocessing data and converting it into an analyzable format" refers to techniques or devices for organizing collected data into a consistent form and converting it into a format suitable for analysis.

[0482] "Past project data" refers to various historical information and record data related to projects that have been carried out in the past.

[0483] "Methods for training generative models and learning risks" refers to technologies or devices that use machine learning models to derive patterns of failure and success from past project data in order to predict risks.

[0484] "Means of identifying potential risks" refers to techniques or devices for analyzing ongoing project data and predicting where problems may occur.

[0485] "Means for automatically generating risk response measures" refers to technology or equipment that automatically proposes appropriate solutions for identified risks.

[0486] "Factory production data" refers to information that shows various performance indicators and the progress of processes in the manufacturing process.

[0487] "Means for proposing optimization of production processes based on analysis results" refers to technologies or devices for proposing methods to improve the efficiency and quality of production processes based on results obtained through data analysis.

[0488] The system based on this invention improves production efficiency by identifying risks in factory production management in real time and proposing appropriate countermeasures. The server continuously collects production data from various sensors and production management systems within the factory. This includes information on machine operating status and production line progress. This data is pre-processed within the server and formatted into an analyzable format.

[0489] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to run a generative model trained on historical project data. This generative AI model detects patterns in the data and predicts potential risks. Specific examples include frequent failures on a particular production line or production bottlenecks learned from historical data.

[0490] Based on the analysis, the server automatically generates optimal risk mitigation measures based on potential risks. This includes suggestions such as rearranging production processes and reallocating resources. Terminal devices visualize these analysis results and mitigation measures as a user interface and provide them to production managers. Production managers (users) use this information to make quick decisions and adjust production plans.

[0491] Specifically, prompts such as, "Based on current production data, list the risks that may occur within the next three months and propose countermeasures," can be used to set guidelines for activities. This makes it possible to improve the efficiency and quality of the factory's production processes.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The server collects production data in real time from sensors and production management systems within the factory. It takes machine operation data and production line progress data as input. This data is temporarily stored on the server to prepare the data needed for the next processing step.

[0495] Step 2:

[0496] The server preprocesses the collected data and formats it into an analyzable format. It receives collected production data as input, performs data imputation and format conversion, and generates a dataset suitable for analysis as output.

[0497] Step 3:

[0498] The server inputs the formatted data into a generative AI model to predict potential risks. At this stage, a pre-processed dataset is used as input. The generative AI model refers to past data patterns to estimate the probability of risk occurrence. The output is the identified risk.

[0499] Step 4:

[0500] The server automatically generates risk mitigation measures for identified risks. It uses the risk information obtained as output of the generative model as input to calculate the risk mitigation measures. The output is a list of specific mitigation measures.

[0501] Step 5:

[0502] The server sends the generated risk mitigation measures and analysis results to the terminal. It takes risk mitigation measures and analysis results as input and generates visualization data as output.

[0503] Step 6:

[0504] The terminal presents results to the user using a visualized dashboard. Using data received from the server as input, it displays risk information on the user interface by applying past prompts. Based on the results, the user makes quick decisions using prompts, such as "Based on the current production data, list the risks and propose countermeasures."

[0505] 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.

[0506] This invention combines an emotion engine with a system for project management that identifies risks and proposes countermeasures, thereby enabling risk response that takes user emotions into consideration. The embodiments are described in detail below.

[0507] This system first collects project progress and planning data in real time and uses it to identify potential project risks. The server aggregates data from project management tools and databases, checks for consistency, and then performs analysis. This process helps to understand the current state of the project and identify areas where problems may occur.

[0508] Next, the emotion engine built into the device analyzes the user's emotional state in real time. This engine has the ability to analyze the user's input behavior and interaction data to infer their current emotional state. For example, it analyzes data that suggests the user's emotions may be negative, such as when the user frequently postpones tasks or when the number of characters typed decreases.

[0509] The results of this emotion analysis are directly used to generate risk mitigation strategies. In particular, the server incorporates communication methods and project management techniques tailored to the user's emotional state to efficiently address risks while reducing stress. When emotions are negative, attempts are made to maintain the user's motivation by providing suggestions that include more positive feedback.

[0510] For example, if the system determines that a user is prone to experiencing project-related stress, it will automatically suggest changing task priorities or adjusting the schedule to allow for more flexibility. In this way, the system improves the probability of project success by addressing risks while also being sensitive to the user's emotions.

[0511] This invention thus provides a system that promotes not only risk management in project management but also project optimization through improvements to the user interface.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The server collects project progress and planning data from project management tools and external databases. This integrates important information such as task progress and resource usage.

[0515] Step 2:

[0516] The server preprocesses the collected data, including imputing missing values ​​and standardizing the format. Converting the data into an analyzable format facilitates subsequent analysis.

[0517] Step 3:

[0518] The server inputs pre-processed data into a generative model and leverages historical project data to identify risks. This involves using machine learning algorithms for pattern recognition and prediction.

[0519] Step 4:

[0520] The device analyzes the user's emotional state using an emotion engine. It monitors the user's input and usage to generate an emotional state index. This index is obtained from user interface operation logs and feedback activities.

[0521] Step 5:

[0522] The server automatically generates the optimal risk mitigation strategy, taking into account the identified risks and the user's emotional state. This process customizes the suggestions and presentation methods depending on whether the user's emotions are positive or negative.

[0523] Step 6:

[0524] The device displays the generated risk mitigation measures and sentiment analysis results on a visually clear dashboard. This allows users to clearly understand the current project status and the optimal course of action.

[0525] Step 7:

[0526] Users can take action to address risks based on the displayed information. By applying the suggested adjustments to the project, it is possible to reduce risks and stress during its progress.

[0527] (Example 2)

[0528] 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."

[0529] In project management, if progress information and plans are not properly managed, and the identification of potential risks and the development of countermeasures are delayed, the project's success rate will decrease. Furthermore, if risk management does not take into account the emotional state of individual users, stress and decreased motivation can negatively impact the project's progress.

[0530] 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.

[0531] In this invention, the server includes means for collecting project progress information and planning information, means for converting the information into an analyzable format, and means for training a model using past project information to learn risks. This makes it possible to identify potential risks in a project and automatically generate appropriate risk mitigation measures according to the user's emotional state.

[0532] "Project progress information" refers to information that shows what stage a project is currently in and how far along it is in relation to the plan.

[0533] "Planning information" refers to information related to the planning necessary for project execution, including project goals, milestones, schedules, and resource allocation.

[0534] An "analyzable format" is a format that makes it easy to interpret and perform calculations on collected data.

[0535] "Past project information" refers to data about previously executed projects, including information such as progress, issues, and results.

[0536] "Training a model" is the process of using machine learning techniques to learn patterns and rules from data.

[0537] "Potential risks" are problems that have not yet materialized but could potentially hinder the progress of the project in the future.

[0538] "User behavior data" refers to records of operations and inputs performed by users on the system.

[0539] "Emotional state" refers to information about the user's mood and psychological state, including stress levels and satisfaction levels.

[0540] "Risk mitigation measures" refer to the means or measures taken to mitigate or eliminate the impact of identified risks.

[0541] "Visual presentation methods" refer to methods of displaying data and information in an easy-to-understand format, such as graphs and charts.

[0542] This invention provides a system in which servers and terminals work together to aggregate progress and planning information and identify potential risks in project management. The software and hardware used include project management tools, a database management system, machine learning models, and a sentiment analysis engine. All of these are built on existing computer graphics and communication technologies.

[0543] First, the server retrieves progress and planning information from the project management tool. The server accesses the database via an API to collect necessary information in real time. This data is then used to check project integrity and convert it into a parseable format. Specifically, it calculates data representing progress and metrics indicating whether the project is on track.

[0544] Next, the sentiment analysis engine installed in the device collects user behavior data and infers their emotional state. For example, the frequency of user operations and the number of characters entered are observed, and their stress and motivation levels are analyzed.

[0545] The results of this emotional state analysis are used to generate risk mitigation measures based on the risks identified by the server. The server uses an AI model to formulate risk mitigation measures tailored to the user's emotional state and proposes them to the user. For example, if the user shows a decrease in motivation, the server will promptly suggest rescheduling or provide positive feedback.

[0546] As a concrete example, when a user inputs the prompt "Task delays are beginning to affect the entire project" into the AI ​​model, the system automatically assesses the risks and proposes an appropriate action plan. In this way, the invention can improve the efficiency of risk management and increase the probability of project success.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The server collects progress and planning information from project management tools and external databases. It uses raw data obtained through the project management tool's API as input. The server converts this data into a parseable format and checks for inconsistencies. The output of this step is clean, well-structured data.

[0550] Step 2:

[0551] The server analyzes consistent data to identify potential risks. The data obtained in Step 1 is used as input. The server uses machine learning algorithms to calculate potential deviations from the plan and budget and time risks. The output is a risk assessment list.

[0552] Step 3:

[0553] The device collects user behavior data in real time and infers their emotional state. The input for this process consists of data related to user actions and inputs. The emotion analysis engine uses this data to infer stress levels and motivation levels. The output of this step is the user's inferred emotional state.

[0554] Step 4:

[0555] The server uses a generative AI model to generate risk mitigation strategies based on a risk assessment list and the user's emotional state. This prompt serves as input, providing specific instructions to the generative AI model. The server utilizes the data to automatically generate risk mitigation strategies tailored to the user. The output provides a detailed action plan that is specific to the user's situation.

[0556] Step 5:

[0557] Users receive risk mitigation measures from the server and apply them to their actual projects. The output manifests as updates to the project plan after application and, if necessary, adjustments to resource allocation. As a result, smooth project progress can be expected.

[0558] (Application Example 2)

[0559] 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."

[0560] Existing project management systems often fail to consider the user's emotional state when identifying risks and generating countermeasures, which can lead to increased stress and decreased efficiency. Furthermore, they lack automated workload adjustments within factories, making productivity optimization difficult.

[0561] 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.

[0562] In this invention, the server includes means for collecting project progress and planning information, means for analyzing the emotional state of users and generating countermeasures for stress reduction, and means for monitoring activities within the factory and appropriately adjusting the workload. This enables risk management that takes user emotions into consideration and allows for the efficient optimization of work within the factory.

[0563] "Project progress information" refers to data that shows the status of project implementation and the progress of its stages.

[0564] "Planning information" refers to data that shows the details of a plan that has been established in advance, such as project goal setting and work schedule.

[0565] A "generative model" is a model that uses artificial intelligence trained on past data to perform data prediction and decision-making.

[0566] "Risk response measures" refer to specific countermeasures implemented in response to identified risks.

[0567] "User emotional state" refers to information that indicates the emotional reactions and mental state of the individuals involved in the project.

[0568] "Stress reduction measures" refer to a series of actions taken to alleviate the burden on users and maintain or improve productivity.

[0569] "Internal factory activities" refers to various tasks performed within the factory, such as manufacturing, processing, and inspection.

[0570] "Workload adjustment" is a process for efficiently performing tasks by optimizing the amount of work and tasks assigned to employees and machines.

[0571] In this system, a server collects project progress and planning information and converts it into an analyzable format. This is achieved by acquiring data from sensors within the factory and from the project management system. The server uses Apache Kafka to collect data in real time and PostgreSQL to store the data.

[0572] The server trains a generative model based on the collected data to identify potential risks. This makes it possible to automatically generate risks and countermeasures for ongoing projects. Furthermore, the server utilizes the emotion engine installed on the terminal to analyze the user's emotional state. This emotion engine has the ability to analyze the user's input data and infer their emotional state.

[0573] The terminal receives notifications from the server and presents the user with risks and countermeasures. Notifications are delivered through a visualized dashboard, providing users with easy-to-understand risk information. If the user's emotions are judged to be negative, specific countermeasures to reduce stress are proposed, such as optimizing the reallocation of workload.

[0574] For example, suppose a project falls behind schedule, and the system determines that the user is experiencing stress. In this case, the system will re-evaluate the task priorities and propose a schedule with more leeway.

[0575] An example of a prompt would be, "When progress is behind schedule on a factory production line, what risk mitigation measures would you propose after analyzing employee sentiment?" This prompt forms the basis for deriving appropriate risk mitigation measures through a generative AI model.

[0576] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0577] Step 1:

[0578] The server collects progress and planning information from sensors and project management systems within the factory. Inputs include real-time data from sensors and data from the project management system. The server utilizes Apache Kafka to aggregate this data in real time and store it in PostgreSQL, outputting an analyzable dataset.

[0579] Step 2:

[0580] The server trains a generative model based on the collected data to identify potential risks. The input for this step is the data collected in step 1. The server uses machine learning algorithms to identify risks and outputs the results. This organizes information about the project's potential risks.

[0581] Step 3:

[0582] The terminal receives risk information and generated countermeasures from the server. The input consists of the risk information and countermeasures generated by the server. The terminal visualizes this information on a dashboard and presents it to the user. The output includes graphical risk notifications to aid user understanding.

[0583] Step 4:

[0584] The server utilizes the terminal's emotion engine to analyze the user's emotional state. The input for this step is user input data and interaction data. The server processes this data and infers the user's emotional state. The output provides information about the user's emotional state.

[0585] Step 5:

[0586] The server generates specific countermeasures for stress reduction based on the user's emotional state. The inputs are risk information and the user's emotional state. The server uses an AI model to determine the optimal countermeasure and outputs the result. This result includes suggestions such as reallocating workloads or adjusting schedules, as needed.

[0587] Step 6:

[0588] The user receives notifications from the server via their device and implements the proposed countermeasures. The input is the specific countermeasures generated in step 5. The user puts these into action, improving the efficiency of the project. The output is the smooth progress of the project and reduced user stress.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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).

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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".

[0606] This invention provides a system to support risk identification and response in project management. The embodiments thereof are described in detail below.

[0607] This system begins by collecting project progress and planning data and formatting this data into a parseable format. The server accesses project management tools and external databases to integrate and collect the necessary data. The collected data is then made consistent, and any missing data is filled in.

[0608] The formatted data is input into a generative model and trained using historical project data. This allows the system to learn patterns of past successes and failures, improving the accuracy of risk predictions. After analysis, the server analyzes the status of ongoing projects and identifies potential risks. For identified risks, it generates optimal risk mitigation strategies based on historical data.

[0609] The generated risk mitigation measures are visualized to facilitate user interaction. The terminal provides a dashboard as a user interface, displaying risk information and detailed mitigation measures to the user. The user can review the proposed mitigation measures and adjust the project plan as needed.

[0610] As a concrete example, consider a software development project. The server collects and analyzes data such as the development schedule, member working hours, and the number of bugs. If this analysis predicts development delays, the server proposes countermeasures such as increasing resources or revising the schedule. Subsequently, the server presents these proposals to the user via a terminal, allowing the user to make decisions based on established information.

[0611] The system according to the present invention thus streamlines project risk management and contributes to improving the success rate.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The server automatically collects progress and planning data from project management tools and external databases. This includes data retrieval via APIs and periodic data extraction tasks.

[0615] Step 2:

[0616] The server preprocesses the collected data, fills in missing data, and detects and corrects outliers. This process also includes standardizing data formats and removing noise.

[0617] Step 3:

[0618] The server converts pre-processed data into features and inputs them into the generative model. This feature generation process creates metrics such as resource utilization, task completion status, and schedule progress.

[0619] Step 4:

[0620] The server uses a generative model to learn from historical data and identify potential risks inherent in the project. This is where the risk prediction algorithm is implemented.

[0621] Step 5:

[0622] The server automatically generates risk mitigation measures based on past success stories for identified risks. These measures include adjusting resource allocations and replanning schedules.

[0623] Step 6:

[0624] The terminal displays a dashboard that visually presents the analysis results and risk mitigation measures to the user. This allows the user to see the risk status of the project at a glance.

[0625] Step 7:

[0626] Based on the information presented, users consider risk mitigation measures and make necessary adjustments to the project's progress via the dashboard. This process efficiently optimizes the project.

[0627] (Example 1)

[0628] 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".

[0629] In project management, identifying potential risks early and promptly providing appropriate countermeasures is crucial for improving project success rates. Traditional methods often involve time-consuming data collection and analysis, leading to delays in risk response. Therefore, a system that efficiently processes information and manages risks is needed.

[0630] 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.

[0631] In this invention, the server includes means for collecting information progress data and planning data, means for preprocessing the collected data and converting it into an analyzable format, and means for training an estimation model using historical information data and learning hazards. This enables the rapid identification of potential hazards and the automatic generation of appropriate countermeasures.

[0632] "Information" refers to the collective term for events and figures related to progress and plans that are the subject of data collection and analysis.

[0633] "Progress data" refers to a series of data that indicates the progress of a project, including the degree of task completion and adherence to deadlines.

[0634] "Planning data" refers to information based on the assumption of future implementation, such as project schedules and resource allocation.

[0635] "Means of collection" refers to methods and technologies for acquiring necessary data from information management systems and external information infrastructure.

[0636] "Means of preprocessing and converting into an analyzable format" refers to techniques and methods for shaping collected data into a format and structure suitable for analysis.

[0637] An "estimated model" is an algorithm or mathematical model that learns from past data and uses that data to predict specific events or outcomes.

[0638] "Risk" refers to potential problems or challenges that could affect the achievement of the project's goals.

[0639] "Countermeasures" refer to actions or policies to be taken in response to identified risks, and their purpose is to mitigate or avoid risks.

[0640] "Users" refer to individuals or organizations that operate and monitor the system and make decisions based on the results obtained.

[0641] "Means of notifying and supporting decision-making" refer to technologies and methods that provide information to users and support them in making appropriate decisions.

[0642] A "display infrastructure" refers to a user interface or tool for visually presenting information, representing data in a way that is easy to see and understand.

[0643] This invention is a system aimed at quickly identifying potential risks in project management-related information and generating appropriate countermeasures. Specific embodiments are described below.

[0644] The server retrieves progress and planning data from project management systems and external data infrastructure. First, it uses APIs to access these data sources and collect all necessary information related to the project.

[0645] The collected data is not suitable for analysis in its raw state, so the server preprocesses the data. The data is formatted, and any missing parts are filled in. For example, if the progress of a task over time is not recorded, it is estimated based on similar data from past projects.

[0646] The formatted data is input into a generative AI model. This model functions as the estimation model mentioned earlier, performing pattern recognition based on past project data. This allows it to predict potential risks in the current project and calculate their probabilities. Machine learning libraries such as Scikit-learn and TensorFlow are used in the generative AI model.

[0647] Once a risk is identified, the server generates countermeasures tailored to that risk. For example, if a delay is predicted for a particular task, it will suggest specific measures such as increasing resources or readjusting the schedule.

[0648] The terminal uses a dashboard as its user interface to visualize risk information and countermeasures sent from the server. This dashboard uses visualization tools such as Charts.js and D3.js to display information in a way that is easy for the user to understand.

[0649] Users can make decisions based on this information. They may accept the proposed solutions or make modifications. Changes to the project plan will result in new data being sent to the server for further analysis.

[0650] As a concrete example, in a software development project, the server collects data on the development schedule, the working hours of team members, and the number of bugs in the generated product. If the analysis determines that there is a risk of development delays, it proposes to the project manager that additional members be added. This proposal is displayed to the user via a terminal, and the user makes a decision.

[0651] In this way, the system efficiently manages project risks and contributes to improving the project's success rate. Examples of prompts for the generated AI model include, "Please provide recommendations for the next steps based on the current progress."

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The server retrieves progress and planning data from project management systems and external information infrastructure. Specifically, it uses APIs to collect necessary information and stores this data in its raw state in data storage. Inputs include project progress, deadlines, and resource allocation information, while output is an unprocessed dataset.

[0655] Step 2:

[0656] The server preprocesses the collected data. This includes checking data integrity and filling in missing parts based on data from similar past projects. This process uses programming languages ​​such as Python to execute data cleaning algorithms. The input is the raw data, and the output is data formatted for analysis.

[0657] Step 3:

[0658] The server inputs pre-processed data into a generating AI model to predict potential risks. This model learns from past successes and failures and recognizes patterns that could lead to failure. Specifically, the model is built using Scikit-learn and TensorFlow, with pre-formatted data as input and risk assessment results as output.

[0659] Step 4:

[0660] The server generates optimal countermeasures for identified risks based on the analysis results from the generated AI model. For example, it creates suggestions for adding resources or revising the schedule for tasks deemed high-risk. This makes it possible to proactively address potential problems in a project. The input is the risk assessment result, and the output is a list of specific countermeasures.

[0661] Step 5:

[0662] The terminal visualizes risk information and countermeasures sent from the server. For example, using Charts.js or D3.js, it displays the risk level as a color-coded graph on the dashboard. This allows the user to intuitively understand the information. The input is a list of countermeasures, and the output is a visualized dashboard.

[0663] Step 6:

[0664] Users review the risk information and countermeasures displayed on their device and make adjustments to the project plan. They can approve proposed countermeasures or customize them as needed. The input is a visualized dashboard, and the output is an updated project plan.

[0665] (Application Example 1)

[0666] 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".

[0667] In factory production management, there is a need to identify production risks in real time and quickly provide effective countermeasures. However, conventional methods often involve manual data collection and analysis, resulting in a lack of immediacy and efficiency. Furthermore, the insufficient use of data analysis and its results for optimizing production processes hinders improvements in production efficiency. This invention aims to solve these problems.

[0668] 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.

[0669] In this invention, the server includes means for collecting project progress data and planning data, means for preprocessing the data and converting it into an analyzable format, and means for aggregating and analyzing production data from the factory in real time. This makes it possible to efficiently identify potential risks and propose countermeasures in real time in factory production management.

[0670] "Project progress data" refers to information that shows the degree of completion of each task in a project and its progress according to the schedule.

[0671] "Planning data" refers to information that outlines the goals set before the project is executed, the planned work schedule, and the necessary resources.

[0672] "Means for preprocessing data and converting it into an analyzable format" refers to techniques or devices for organizing collected data into a consistent form and converting it into a format suitable for analysis.

[0673] "Past project data" refers to various historical information and record data related to projects that have been carried out in the past.

[0674] "Methods for training generative models and learning risks" refers to technologies or devices that use machine learning models to derive patterns of failure and success from past project data in order to predict risks.

[0675] "Means of identifying potential risks" refers to techniques or devices for analyzing ongoing project data and predicting where problems may occur.

[0676] "Means for automatically generating risk response measures" refers to technology or equipment that automatically proposes appropriate solutions for identified risks.

[0677] "Factory production data" refers to information that shows various performance indicators and the progress of processes in the manufacturing process.

[0678] "Means for proposing optimization of production processes based on analysis results" refers to technologies or devices for proposing methods to improve the efficiency and quality of production processes based on results obtained through data analysis.

[0679] The system based on this invention improves production efficiency by identifying risks in factory production management in real time and proposing appropriate countermeasures. The server continuously collects production data from various sensors and production management systems within the factory. This includes information on machine operating status and production line progress. This data is pre-processed within the server and formatted into an analyzable format.

[0680] The server uses a machine learning platform (e.g., TensorFlow or PyTorch) to run a generative model trained on historical project data. This generative AI model detects patterns in the data and predicts potential risks. Specific examples include frequent failures on a particular production line or production bottlenecks learned from historical data.

[0681] Based on the analysis, the server automatically generates optimal risk mitigation measures based on potential risks. This includes suggestions such as rearranging production processes and reallocating resources. Terminal devices visualize these analysis results and mitigation measures as a user interface and provide them to production managers. Production managers (users) use this information to make quick decisions and adjust production plans.

[0682] Specifically, prompts such as, "Based on current production data, list the risks that may occur within the next three months and propose countermeasures," can be used to set guidelines for activities. This makes it possible to improve the efficiency and quality of the factory's production processes.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The server collects production data in real time from sensors and production management systems within the factory. It takes machine operation data and production line progress data as input. This data is temporarily stored on the server to prepare the data needed for the next processing step.

[0686] Step 2:

[0687] The server preprocesses the collected data and formats it into an analyzable format. It receives collected production data as input, performs data imputation and format conversion, and generates a dataset suitable for analysis as output.

[0688] Step 3:

[0689] The server inputs the formatted data into a generative AI model to predict potential risks. At this stage, a pre-processed dataset is used as input. The generative AI model refers to past data patterns to estimate the probability of risk occurrence. The output is the identified risk.

[0690] Step 4:

[0691] The server automatically generates risk mitigation measures for identified risks. It uses the risk information obtained as output of the generative model as input to calculate the risk mitigation measures. The output is a list of specific mitigation measures.

[0692] Step 5:

[0693] The server sends the generated risk mitigation measures and analysis results to the terminal. It takes risk mitigation measures and analysis results as input and generates visualization data as output.

[0694] Step 6:

[0695] The terminal presents results to the user using a visualized dashboard. Using data received from the server as input, it displays risk information on the user interface by applying past prompts. Based on the results, the user makes quick decisions using prompts, such as "Based on the current production data, list the risks and propose countermeasures."

[0696] 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.

[0697] This invention combines an emotion engine with a system for project management that identifies risks and proposes countermeasures, thereby enabling risk response that takes user emotions into consideration. The embodiments are described in detail below.

[0698] This system first collects project progress and planning data in real time and uses it to identify potential project risks. The server aggregates data from project management tools and databases, checks for consistency, and then performs analysis. This process helps to understand the current state of the project and identify areas where problems may occur.

[0699] Next, the emotion engine built into the device analyzes the user's emotional state in real time. This engine has the ability to analyze the user's input behavior and interaction data to infer their current emotional state. For example, it analyzes data that suggests the user's emotions may be negative, such as when the user frequently postpones tasks or when the number of characters typed decreases.

[0700] The results of this emotion analysis are directly used to generate risk mitigation strategies. In particular, the server incorporates communication methods and project management techniques tailored to the user's emotional state to efficiently address risks while reducing stress. When emotions are negative, attempts are made to maintain the user's motivation by providing suggestions that include more positive feedback.

[0701] For example, if the system determines that a user is prone to experiencing project-related stress, it will automatically suggest changing task priorities or adjusting the schedule to allow for more flexibility. In this way, the system improves the probability of project success by addressing risks while also being sensitive to the user's emotions.

[0702] This invention thus provides a system that promotes not only risk management in project management but also project optimization through improvements to the user interface.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The server collects project progress and planning data from project management tools and external databases. This integrates important information such as task progress and resource usage.

[0706] Step 2:

[0707] The server preprocesses the collected data, including imputing missing values ​​and standardizing the format. Converting the data into an analyzable format facilitates subsequent analysis.

[0708] Step 3:

[0709] The server inputs pre-processed data into a generative model and leverages historical project data to identify risks. This involves using machine learning algorithms for pattern recognition and prediction.

[0710] Step 4:

[0711] The device analyzes the user's emotional state using an emotion engine. It monitors the user's input and usage to generate an emotional state index. This index is obtained from user interface operation logs and feedback activities.

[0712] Step 5:

[0713] The server automatically generates the optimal risk mitigation strategy, taking into account the identified risks and the user's emotional state. This process customizes the suggestions and presentation methods depending on whether the user's emotions are positive or negative.

[0714] Step 6:

[0715] The device displays the generated risk mitigation measures and sentiment analysis results on a visually clear dashboard. This allows users to clearly understand the current project status and the optimal course of action.

[0716] Step 7:

[0717] Users can take action to address risks based on the displayed information. By applying the suggested adjustments to the project, it is possible to reduce risks and stress during its progress.

[0718] (Example 2)

[0719] 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".

[0720] In project management, if progress information and plans are not properly managed, and the identification of potential risks and the development of countermeasures are delayed, the project's success rate will decrease. Furthermore, if risk management does not take into account the emotional state of individual users, stress and decreased motivation can negatively impact the project's progress.

[0721] 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.

[0722] In this invention, the server includes means for collecting project progress information and planning information, means for converting the information into an analyzable format, and means for training a model using past project information to learn risks. This makes it possible to identify potential risks in a project and automatically generate appropriate risk mitigation measures according to the user's emotional state.

[0723] "Project progress information" refers to information that shows what stage a project is currently in and how far along it is in relation to the plan.

[0724] "Planning information" refers to information related to the planning necessary for project execution, including project goals, milestones, schedules, and resource allocation.

[0725] An "analyzable format" is a format that makes it easy to interpret and perform calculations on collected data.

[0726] "Past project information" refers to data about previously executed projects, including information such as progress, issues, and results.

[0727] "Training a model" is the process of using machine learning techniques to learn patterns and rules from data.

[0728] "Potential risks" are problems that have not yet materialized but could potentially hinder the progress of the project in the future.

[0729] "User behavior data" refers to records of operations and inputs performed by users on the system.

[0730] "Emotional state" refers to information about the user's mood and psychological state, including stress levels and satisfaction levels.

[0731] "Risk mitigation measures" refer to the means or measures taken to mitigate or eliminate the impact of identified risks.

[0732] "Visual presentation methods" refer to methods of displaying data and information in an easy-to-understand format, such as graphs and charts.

[0733] This invention provides a system in which servers and terminals work together to aggregate progress and planning information and identify potential risks in project management. The software and hardware used include project management tools, a database management system, machine learning models, and a sentiment analysis engine. All of these are built on existing computer graphics and communication technologies.

[0734] First, the server retrieves progress and planning information from the project management tool. The server accesses the database via an API to collect necessary information in real time. This data is then used to check project integrity and convert it into a parseable format. Specifically, it calculates data representing progress and metrics indicating whether the project is on track.

[0735] Next, the sentiment analysis engine installed in the device collects user behavior data and infers their emotional state. For example, the frequency of user operations and the number of characters entered are observed, and their stress and motivation levels are analyzed.

[0736] The results of this emotional state analysis are used to generate risk mitigation measures based on the risks identified by the server. The server uses an AI model to formulate risk mitigation measures tailored to the user's emotional state and proposes them to the user. For example, if the user shows a decrease in motivation, the server will promptly suggest rescheduling or provide positive feedback.

[0737] As a concrete example, when a user inputs the prompt "Task delays are beginning to affect the entire project" into the AI ​​model, the system automatically assesses the risks and proposes an appropriate action plan. In this way, the invention can improve the efficiency of risk management and increase the probability of project success.

[0738] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0739] Step 1:

[0740] The server collects progress and planning information from project management tools and external databases. It uses raw data obtained through the project management tool's API as input. The server converts this data into a parseable format and checks for inconsistencies. The output of this step is clean, well-structured data.

[0741] Step 2:

[0742] The server analyzes consistent data to identify potential risks. The data obtained in Step 1 is used as input. The server uses machine learning algorithms to calculate potential deviations from the plan and budget and time risks. The output is a risk assessment list.

[0743] Step 3:

[0744] The device collects user behavior data in real time and infers their emotional state. The input for this process consists of data related to user actions and inputs. The emotion analysis engine uses this data to infer stress levels and motivation levels. The output of this step is the user's inferred emotional state.

[0745] Step 4:

[0746] The server uses a generative AI model to generate risk mitigation strategies based on a risk assessment list and the user's emotional state. This prompt serves as input, providing specific instructions to the generative AI model. The server utilizes the data to automatically generate risk mitigation strategies tailored to the user. The output provides a detailed action plan that is specific to the user's situation.

[0747] Step 5:

[0748] Users receive risk mitigation measures from the server and apply them to their actual projects. The output manifests as updates to the project plan after application and, if necessary, adjustments to resource allocation. As a result, smooth project progress can be expected.

[0749] (Application Example 2)

[0750] 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".

[0751] Existing project management systems often fail to consider the user's emotional state when identifying risks and generating countermeasures, which can lead to increased stress and decreased efficiency. Furthermore, they lack automated workload adjustments within factories, making productivity optimization difficult.

[0752] 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.

[0753] In this invention, the server includes means for collecting project progress and planning information, means for analyzing the emotional state of users and generating countermeasures for stress reduction, and means for monitoring activities within the factory and appropriately adjusting the workload. This enables risk management that takes user emotions into consideration and allows for the efficient optimization of work within the factory.

[0754] "Project progress information" refers to data that shows the status of project implementation and the progress of its stages.

[0755] "Planning information" refers to data that shows the details of a plan that has been established in advance, such as project goal setting and work schedule.

[0756] A "generative model" is a model that uses artificial intelligence trained on past data to perform data prediction and decision-making.

[0757] "Risk response measures" refer to specific countermeasures implemented in response to identified risks.

[0758] "User emotional state" refers to information that indicates the emotional reactions and mental state of the individuals involved in the project.

[0759] "Stress reduction measures" refer to a series of actions taken to alleviate the burden on users and maintain or improve productivity.

[0760] "Internal factory activities" refers to various tasks performed within the factory, such as manufacturing, processing, and inspection.

[0761] "Workload adjustment" is a process for efficiently performing tasks by optimizing the amount of work and tasks assigned to employees and machines.

[0762] In this system, a server collects project progress and planning information and converts it into an analyzable format. This is achieved by acquiring data from sensors within the factory and from the project management system. The server uses Apache Kafka to collect data in real time and PostgreSQL to store the data.

[0763] The server trains a generative model based on the collected data to identify potential risks. This makes it possible to automatically generate risks and countermeasures for ongoing projects. Furthermore, the server utilizes the emotion engine installed on the terminal to analyze the user's emotional state. This emotion engine has the ability to analyze the user's input data and infer their emotional state.

[0764] The terminal receives notifications from the server and presents the user with risks and countermeasures. Notifications are delivered through a visualized dashboard, providing users with easy-to-understand risk information. If the user's emotions are judged to be negative, specific countermeasures to reduce stress are proposed, such as optimizing the reallocation of workload.

[0765] For example, suppose a project falls behind schedule, and the system determines that the user is experiencing stress. In this case, the system will re-evaluate the task priorities and propose a schedule with more leeway.

[0766] An example of a prompt would be, "When progress is behind schedule on a factory production line, what risk mitigation measures would you propose after analyzing employee sentiment?" This prompt forms the basis for deriving appropriate risk mitigation measures through a generative AI model.

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] The server collects progress and planning information from sensors and project management systems within the factory. Inputs include real-time data from sensors and data from the project management system. The server utilizes Apache Kafka to aggregate this data in real time and store it in PostgreSQL, outputting an analyzable dataset.

[0770] Step 2:

[0771] The server trains a generative model based on the collected data to identify potential risks. The input for this step is the data collected in step 1. The server uses machine learning algorithms to identify risks and outputs the results. This organizes information about the project's potential risks.

[0772] Step 3:

[0773] The terminal receives risk information and generated countermeasures from the server. The input consists of the risk information and countermeasures generated by the server. The terminal visualizes this information on a dashboard and presents it to the user. The output includes graphical risk notifications to aid user understanding.

[0774] Step 4:

[0775] The server utilizes the terminal's emotion engine to analyze the user's emotional state. The input for this step is user input data and interaction data. The server processes this data and infers the user's emotional state. The output provides information about the user's emotional state.

[0776] Step 5:

[0777] The server generates specific countermeasures for stress reduction based on the user's emotional state. The inputs are risk information and the user's emotional state. The server uses an AI model to determine the optimal countermeasure and outputs the result. This result includes suggestions such as reallocating workloads or adjusting schedules, as needed.

[0778] Step 6:

[0779] The user receives notifications from the server via their device and implements the proposed countermeasures. The input is the specific countermeasures generated in step 5. The user puts these into action, improving the efficiency of the project. The output is the smooth progress of the project and reduced user stress.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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."

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] The following is further disclosed regarding the embodiments described above.

[0802] (Claim 1)

[0803] Means for collecting project progress data and planning data,

[0804] Means for preprocessing the aforementioned data and converting it into an analyzable format,

[0805] A method for training generative models using past project data and learning risks,

[0806] A means of identifying potential risks in ongoing projects,

[0807] A means for automatically generating risk countermeasures for the identified risks,

[0808] Means for notifying the user of the aforementioned risks and the results of countermeasures,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the data collection means includes means for acquiring data from a project management tool and an external database.

[0812] (Claim 3)

[0813] The system according to claim 1, wherein the notification means includes means for providing a dashboard that visualizes the results of the risk analysis.

[0814] "Example 1"

[0815] (Claim 1)

[0816] Means for collecting information progress data and planning data,

[0817] The means for preprocessing the collected data and converting it into an analyzable format,

[0818] A method for training an estimation model using historical information data to learn about risks,

[0819] A means of identifying potential risks in ongoing projects,

[0820] A means for automatically generating countermeasures for the identified risks,

[0821] A means of notifying users of the aforementioned risks and the results of countermeasures, and supporting their decision-making,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the data collection means includes an information management means and means for acquiring data from an external information infrastructure.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the notification means includes means for providing a display base for visualizing the risk analysis results.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] Means for collecting project progress data and planning data,

[0830] Means for preprocessing the aforementioned data and converting it into an analyzable format,

[0831] A method for training a generative model using past project data to learn risk,

[0832] A means of identifying potential risks in ongoing projects,

[0833] A means for automatically generating risk countermeasures for the identified risks,

[0834] Means for notifying the user of the aforementioned risks and the results of countermeasures,

[0835] A means of aggregating and analyzing production data in a factory in real time,

[0836] A means for proposing the optimization of the production process based on the aforementioned analysis results,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, wherein the data collection means includes means for acquiring data from a project management tool and an external information storage device.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the notification means includes means for providing a display device for visualizing the risk analysis results.

[0842] "Example 2 of combining an emotion engine"

[0843] (Claim 1)

[0844] Means for collecting project progress information and planning information,

[0845] Means for converting the aforementioned information into an analyzable format,

[0846] A method for training a model using past project information to learn risk,

[0847] A means of identifying potential risks in ongoing projects,

[0848] A method for analyzing user behavior data obtained from a device to infer emotional state,

[0849] A means for automatically generating risk countermeasures for identified risks based on the aforementioned emotional state,

[0850] Means for notifying the user of the aforementioned risks and the results of countermeasures,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, wherein the data collection means includes means for acquiring information from a project management system and an external information source.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the notification means includes a representation means for visually presenting the results of the risk analysis.

[0856] "Application example 2 when combining with an emotional engine"

[0857] (Claim 1)

[0858] Means for collecting project progress information and planning information,

[0859] Means for preprocessing the aforementioned information and converting it into an analyzable format,

[0860] A method for training a generative model using past project information and learning risks,

[0861] A means of identifying potential risks in ongoing projects,

[0862] A means for automatically generating risk countermeasures for the identified risks,

[0863] Means for notifying users of the aforementioned risks and the results of countermeasures,

[0864] A means of analyzing the emotional state of users and generating countermeasures for stress reduction,

[0865] A means of monitoring activities within the factory and appropriately adjusting the workload,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the information gathering means includes means for acquiring data from a project management system and an external information base.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the notification means includes means for providing a display function for visualizing the risk analysis results. [Explanation of Symbols]

[0871] 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. Means for collecting project progress data and planning data, Means for preprocessing the aforementioned data and converting it into an analyzable format, A method for training generative models using past project data and learning risks, A means of identifying potential risks in ongoing projects, A means for automatically generating risk countermeasures for the identified risks, Means for notifying the user of the aforementioned risks and the results of countermeasures, A system that includes this.

2. The system according to claim 1, wherein the data collection means includes means for acquiring data from a project management tool and an external database.

3. The system according to claim 1, wherein the notification means includes means for providing a dashboard that visualizes the risk analysis results.

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