system

Abstract

We provide the system. [Solution] In project management, a means to automatically analyze requirements and detect ambiguities, A means to monitor the project's progress in real time and detect delays and problems, A means of predicting risks and issuing warnings based on past project data, A means to monitor the progress of each process in real time and to instruct necessary adjustments, A means of analyzing progress data and controlling the robot's movements, 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 persona chatbot control method performed by at least one processor, including 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

[0006] "Requirements" refer to the specifications and functions that a system or product in a project must meet.

[0007] "Automatic analysis" refers to the process by which a computer automatically processes data and understands its meaning without human intervention.

[0008] "Ambiguity" refers to a state in which information is unclear, open to diverse interpretations, and lacks specificity.

[0009] "Project progress" refers to the state of how far along a project is in relation to its plan.

[0010] "Real-time monitoring" refers to the process of immediately understanding the current situation by collecting and analyzing data almost simultaneously.

[0011] "Delay" refers to a situation where progress falls behind the scheduled timeline.

[0012] "Past project data" refers to information such as progress, results, and risks obtained from previously implemented projects.

[0013] "Risk prediction" refers to inferring future uncertainties and potential problems based on past data and current circumstances.

[0014] "Issuing a warning" refers to providing information to prompt attention to predicted problems and risks.

[0015] "Optimal allocation of resources" refers to efficiently managing and operating the resources available in a project to obtain the maximum effect.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides a system for efficiently managing projects, and a specific embodiment thereof is described below.

[0038] This system is primarily composed of three entities: servers, terminals, and users.

[0039] First, the user inputs project requirements information via a terminal. The server receives this information and automatically analyzes the requirements using natural language processing technology. This analysis identifies ambiguous information and missing elements, which are then provided to the user as feedback via the terminal. The user then uses this feedback to make revisions to clarify the requirements.

[0040] Next, regarding progress management, the server monitors the overall project progress in real time. To this end, the server has the ability to retrieve progress data from each phase of the project and detect delays and unexpected problems. The terminal displays this information visually and provides the user with warnings and suggestions for necessary actions.

[0041] Furthermore, for risk management, the server predicts risks based on past project data. In this process, it analyzes data from similar past projects to identify risks relevant to the current project. Identified risks are notified to the user as warnings via the terminal, and the user can take preventative measures based on the information provided.

[0042] As a concrete example, in a software development project, suppose the user enters general requirements for a new feature into the system during the requirements definition phase. The server analyzes the entered data and generates feedback indicating that "the UI design is ambiguous," prompting the terminal to provide more specific design requirements. After the corrections are made, the server tracks the progress and, if progress is behind schedule, notifies the user via the terminal and instructs them to make necessary adjustments. Based on data from similar past projects, if risks are predicted during the testing phase, the server warns the user in advance and recommends securing testing resources.

[0043] In this way, the system can provide efficient and proactive support at each stage of project management, thereby increasing the likelihood of project success.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters project requirements into a terminal. The terminal sends the entered requirements information to the server.

[0047] Step 2:

[0048] The server analyzes the received requirements information using a natural language processing engine and automatically detects ambiguous parts and missing information. The detected information may include vague expressions and insufficient specifications.

[0049] Step 3:

[0050] The server identifies ambiguities and missing information and sends the analysis results as feedback to the terminal. The terminal then displays specific instructions to the user, such as "A more detailed explanation of item X is needed."

[0051] Step 4:

[0052] Users complete or modify the necessary requirement information based on the feedback presented on their device. This resolves any ambiguity in the requirements.

[0053] Step 5:

[0054] The server monitors the project's progress in real time, collecting progress data from each phase. This data includes the number of completed tasks and the schedule completion rate.

[0055] Step 6:

[0056] The server analyzes the collected progress data and determines whether progress is behind schedule compared to the benchmark. If a delay is detected, it sends a warning to the user via the terminal.

[0057] Step 7:

[0058] Users can receive delay alerts on their devices and plan meetings and resource reallocations to adjust project progress.

[0059] Step 8:

[0060] Using past project data, the server predicts risk. Data patterns from similar projects are used in the analysis.

[0061] Step 9:

[0062] The server generates and sends a warning about the predicted risk to the terminal. The terminal then presents the user with risk information such as, "There may be an increased rate of bugs during the testing phase."

[0063] Step 10:

[0064] Users will check risk warnings on their devices and take appropriate measures in advance. These measures include improving the testing process and increasing resources.

[0065] (Example 1)

[0066] 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."

[0067] In project management, it is difficult to identify ambiguities or deficiencies in requirements early on, and it is necessary to monitor project progress in real time and respond quickly when delays or problems occur. Furthermore, it is necessary to leverage insights gained from past projects to predict potential risks and address them proactively.

[0068] 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.

[0069] In this invention, the server includes means for automatically analyzing information and detecting information ambiguity, means for continuously monitoring the progress of work and detecting delays or problems, and means for predicting potential risks based on past data sets and issuing warnings. This enables efficient project management and risk reduction.

[0070] "Methods for automatically analyzing information and detecting information ambiguity" refers to technologies that automatically analyze input information and identify ambiguous expressions or missing elements.

[0071] "Means for continuously monitoring project progress and detecting delays or problems" refers to a system that tracks the progress of each stage of a project in real time and detects delays from the schedule or unexpected problems.

[0072] "Methods for predicting potential risks and issuing warnings based on past data" refers to technologies that analyze data from similar past projects to predict potential risks associated with ongoing projects and warn users in advance.

[0073] This invention is a system for efficiently managing projects by automatically analyzing user requirements information and assisting in the detection and correction of ambiguities. This system mainly consists of three components: a server, a terminal, and a user.

[0074] The server utilizes a natural language processing engine based on Python (e.g., SpaCy or NLTK) to analyze the requirements information entered by the user from their terminal. This allows for the automatic identification of ambiguous parts and missing elements in the information. The user can then receive these analysis results themselves through their terminal, which is equipped with a web browser and project management tools.

[0075] Furthermore, the server retrieves data through APIs of various project management tools (e.g., JIRA and Trello) to monitor the project's progress. Based on this, it detects delays and problems in real time and provides appropriate countermeasures to the user via their terminal.

[0076] The server employs technology that collects past project data and uses a generated AI model (e.g., TENSORFLOW®) to predict potential risks. This predictive information is sent to the terminal, allowing users to take risk mitigation measures in advance.

[0077] For example, when a user enters a general request for a new feature, the server can generate a response indicating that "the UI design is ambiguous" and prompt the user via the device to provide more specific design requirements. Furthermore, if progress is behind schedule, the server can send a notification via the device instructing the user to make necessary adjustments.

[0078] A concrete example of a prompt statement would be, "Identify any ambiguities in the requirements for the new feature and propose ways to clarify them."

[0079] In this way, this system provides efficient support at each stage of project management, thereby improving the success rate of projects.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user enters initial project requirements via a terminal. This input includes a project overview and specific functional requirements. The entered data is retrieved through the terminal's user interface and sent to a server via the internet. The output is the requirements data stored on the server based on the user's input.

[0083] Step 2:

[0084] The server analyzes the received requirements data using a natural language processing engine (e.g., SpaCy or NLTK). The server analyzes the sentence structure of the input data and identifies ambiguous expressions and unclear elements. This process involves data calculations such as syntactic analysis and keyword extraction. The output consists of identified ambiguous requirements and recommended corrective actions.

[0085] Step 3:

[0086] The terminal receives analysis results from the server and displays them to the user as feedback. This feedback includes details of the identified ambiguities and specific correction suggestions. The terminal visualizes this information so that the user can easily identify the areas that need correction. The output is the analysis results displayed on the user interface.

[0087] Step 4:

[0088] The server continuously monitors progress using APIs from project management tools (e.g., JIRA or Trello). The server checks the progress status of each task in real time and detects schedule delays. The data processing performed here involves processing progress data obtained from the API. The output consists of warnings and alerts regarding progress.

[0089] Step 5:

[0090] The device notifies the user of progress alerts. These alerts are presented in a visual format, highlighting tasks that are behind schedule or issues that need resolving. The device displays this information on a dashboard to help the user take quick action. The output is a visualized project progress and recommended actions.

[0091] Step 6:

[0092] The server predicts risk using a generative AI model (e.g., TensorFlow) based on past project data. The server analyzes data from similar projects and deploys a model to assess potential risk factors. The input is past project data, and the output is the risk prediction result.

[0093] Step 7:

[0094] Users receive warnings based on risk predictions via their devices. These warnings include identified risks and proposed countermeasures. Users can use this information to adjust project plans and resource allocations. The output consists of risk notifications and their corresponding countermeasures.

[0095] (Application Example 1)

[0096] 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."

[0097] In factories and similar production environments, the inability to monitor the progress of each process in real time and make necessary adjustments quickly leads to a decrease in overall project efficiency and productivity. This problem needs to be solved to increase the success rate of projects.

[0098] 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.

[0099] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks based on past project data and issuing warnings, means for grasping the progress of each process in real time and instructing necessary adjustments, and means for analyzing progress data and controlling the operation of robots. This enables efficient and rapid project management while managing the progress of each process within the factory.

[0100] "Automatic requirements analysis" is a technology in which a computer automatically analyzes project information entered by a user and detects any ambiguity or omissions in that information.

[0101] "Real-time monitoring of progress" is a technology that tracks the progress of a project sequentially and detects delays and problems immediately if they occur.

[0102] "Risk prediction" is a technique that analyzes past project data to predict and warn about risks in ongoing projects in advance.

[0103] "Understanding the progress of the process" refers to a technology that acquires the progress of each work process in a production environment such as a factory in real time and visualizes the situation.

[0104] "Adjustment instructions" is a technology that adjusts a project to ensure its efficient progress by automatically suggesting appropriate countermeasures in response to detected delays and problems.

[0105] "Progress data analysis and motion control" refers to the technology of analyzing acquired progress data and appropriately controlling the movements of production machinery, especially robots, based on that data.

[0106] The system realizing this invention is composed of three components: a server, a terminal, and a user. The server receives project requirement information entered by the user via the terminal and performs analysis using natural language processing technology. Through this analysis, the server detects ambiguity in the requirements and provides feedback to the user on the terminal. In this way, the user can clarify the project requirements and make modifications as needed.

[0107] In monitoring progress, the server acquires progress data from each stage of the project and analyzes it in real time. The server can automatically detect delays and problems, issue warnings via terminals, and instruct necessary adjustments. This process can also be applied to controlling the operation of factory robots using control systems such as ROS (Robot Operating System).

[0108] Regarding risk management, the server predicts risks related to the current project based on past project data. The server performs this risk assessment and notifies the user of any warnings. This allows the user to take preventative measures and ensure productivity.

[0109] One concrete example is the use of this system to manage production processes on automated lines within a factory. For instance, while a robotic arm is working on the factory floor, a server can monitor the progress of parts assembly in real time and immediately issue corrective instructions if any problems occur.

[0110] Example prompt: "Propose an application that allows factory robots to monitor current process progress in real time and detect unexpected delays or problems early."

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

[0112] Step 1:

[0113] The user enters project requirements information via a terminal. The entered data is sent to the server in text format. This input data includes the user's design intent and requirements.

[0114] Step 2:

[0115] The server analyzes the received requirements information using natural language processing technology. This process automatically detects ambiguity and missing information in the text data and extracts it. The analysis results in a list of ambiguous expressions and requirements that need further clarification.

[0116] Step 3:

[0117] The server provides the analysis results as feedback to the terminal. The terminal then displays information about ambiguity to the user and prompts them to input supplementary information or revise the requirements, thereby helping to clarify the requirements.

[0118] Step 4:

[0119] The server monitors project progress data in real time. Data is collected from key locations in the factory, and progress is visualized. This data is based on production status collected from, for example, sensors and logs.

[0120] Step 5:

[0121] The server analyzes the collected progress data to detect delays and problems. The aggregated data is then compared to a baseline time, and calculations are performed to determine abnormal delays or stagnation. If an anomaly is detected, a warning is generated.

[0122] Step 6:

[0123] When the server detects an anomaly or problem, it instructs the user via the terminal on what needs to be corrected. Specific solutions and readjustment suggestions are also provided.

[0124] Step 7:

[0125] The server learns from past project data and predicts risks associated with the current project. Based on the existing database, a generative AI model performs a risk assessment and calculates the likelihood of the risk. This output is used to suggest preventative measures.

[0126] Step 8:

[0127] Based on the risk assessment, the server sends warnings to the user about predicted risks. This allows the user to take preventative measures early.

[0128] Step 9:

[0129] In the actual process, the server controls the robot's movements based on the acquired progress data. Control signals are transmitted using a control system such as ROS, optimizing specific actions in real time. This entire process improves the efficiency and accuracy of production work.

[0130] 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.

[0131] This invention provides a system for achieving efficient and effective operation in project management, and in particular, employs a form that combines user emotion recognition. The basic system configuration centers around three components: a server, a terminal, and a user. By incorporating an emotion engine, more comprehensive project support is achieved.

[0132] While the system is running, users input project requirements from a terminal. These requirements are sent to a server and automatically analyzed using natural language processing technology. Based on this information, the server detects any ambiguity or missing information in the requirements and provides necessary feedback. Users who receive feedback through their terminal can then supplement their requirements information.

[0133] During project execution, the server monitors progress in real time and detects delays and problems. If delays or problems are found, an alert is sent to the terminal, notifying the user. The user then takes appropriate action based on this information.

[0134] By incorporating an emotion engine, the system can recognize the user's emotions and assess their stress and motivation levels. For example, it can determine the emotional state through facial recognition and voice analysis during user input. The server analyzes this emotion-related data, assesses potential risks to project progress, and provides the user with suggested solutions via the terminal as needed.

[0135] As a concrete example, suppose a project meeting is frequently running late. In this case, the server not only monitors progress data but also assesses the user's emotional state. If the user is experiencing significant stress, the system generates suggestions such as "shorten the meeting" or "increase break time" and notifies the user via their terminal. This approach helps maintain user motivation while supporting the efficient progress of the project.

[0136] In this way, this system, equipped with an emotion engine, integrates technical progress management with human-centered emotional management, providing comprehensive support for project success.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The user enters project requirements information into a terminal. The terminal then sends this information to the server.

[0140] Step 2:

[0141] The server receives the input requirements information and analyzes its content using a natural language processing engine. It automatically detects ambiguous expressions and missing data.

[0142] Step 3:

[0143] The server sends the analysis results regarding the ambiguity of the requirements to the terminal. The terminal displays feedback to the user, prompting them to add specific explanations or supplementary information.

[0144] Step 4:

[0145] Based on feedback provided via the device, the user modifies or adds to the requirements information. The information is then sent back to the server for verification.

[0146] Step 5:

[0147] The server monitors the project's progress in real time and collects progress data. If it detects any delays or unexpected problems, it records them.

[0148] Step 6:

[0149] The server performs analysis based on the progress and generates appropriate warnings if delays occur. These warnings are then communicated to the user via the terminal.

[0150] Step 7:

[0151] Users will check the alerts on their devices and make adjustments to project management as needed. Specifically, this may involve considering rescheduling meetings or reallocating resources.

[0152] Step 8:

[0153] The emotion engine evaluates the user's emotional state. It monitors the user's facial expressions and voice during input to determine stress levels and motivation.

[0154] Step 9:

[0155] The server receives the results from the emotion engine and analyzes the user's emotional data. Based on the analysis results, it considers ways to improve project management.

[0156] Step 10:

[0157] The server generates suggestions based on the user's emotional state and provides them to the user through the terminal. These suggestions include setting break times to reduce stress and reassigning tasks.

[0158] Step 11:

[0159] Users refer to the presented suggestions and make decisions that will help guide the project's progress. This ensures the smooth progress of the project and maintains the motivation of team members.

[0160] (Example 2)

[0161] 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".

[0162] While conventional project management systems are capable of detecting ambiguous requirements and missing information, monitoring progress, and predicting risks, they lack sufficient project support that takes into account the user's emotions and stress levels. As a result, it was difficult to prevent the decline in user motivation and stress that can cause project delays, and they were unable to comprehensively support the success of projects.

[0163] 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.

[0164] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, and means for sensing the user's emotions and evaluating their stress and motivational states. This enables efficient and effective project support by understanding the user's emotional state and providing appropriate feedback and suggestions.

[0165] "Methods for automatically analyzing requirements and detecting ambiguity" refers to techniques for mechanically analyzing project requirements and identifying ambiguous or unclear parts.

[0166] "A means of monitoring project progress in real time and detecting delays and problems" refers to technologies for continuously observing the current progress of a project and immediately detecting delays and anomalies.

[0167] "A means of predicting risks and issuing warnings based on past project data" refers to a method of using previously collected project information to anticipate potential hazards and issue appropriate warnings.

[0168] "Means of sensing a user's emotions and evaluating their stress and motivational state" refers to technology that senses a user's emotional state and uses that to determine their stress and motivation levels.

[0169] "A means of generating suggestions based on emotional data and notifying users" refers to a method of creating suggestions for project improvement based on emotion-related data and informing users of these suggestions.

[0170] "Means of visualizing and presenting the current situation through a device that users interact with" refers to technology that visually displays the progress of a project and shows its current status through a user-operable device.

[0171] This invention is a system for efficiently managing projects, and in particular, it employs a form that supports projects by combining user emotions. This system is mainly composed of three components: a server, a terminal, and a user. The server automatically analyzes project requirements and detects ambiguity using natural language processing technology. One example of a natural language processing library used here is spaCy for Python.

[0172] Users input information about the project's progress using a terminal. The server integrates with the project management tool and monitors the progress in real time. This ensures that if delays or problems occur, an alert is immediately sent to the terminal. The server also has technology to predict risks based on past project data and issue warnings, allowing potential problems to be detected in advance.

[0173] The emotion engine integrated into this system has the function of sensing the user's emotional state and evaluating their stress and motivation levels. During this process, facial and voice data is collected through the camera and microphone connected to the terminal and analyzed using emotion recognition technology. Specifically, the OpenCV image processing library is used for facial recognition, and a general speech recognition API is used for voice analysis.

[0174] Furthermore, it also has a function that generates optimal suggestions based on the user's emotional data and notifies the user via the device. For example, if the system evaluates that the user is experiencing high levels of stress, it will generate specific suggestions such as, "Please consider taking a break."

[0175] As a concrete example, consider a situation where project meetings are frequently delayed. In this case, the server evaluates progress data and user emotions, generates suggestions such as "shorten the meeting" or "increase break time," and notifies the user via their terminal. This makes it possible to maintain user motivation while ensuring the efficient progress of the project.

[0176] Possible prompts to input into the generative AI model include the following:

[0177] "Please tell me the best way to support users in project management while considering their emotional state."

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The user enters the project requirements.

[0181] Through the terminal's user interface, the user enters project requirements as text. This input text is sent to the server as the system's initial data. The server prepares to receive this data and stores it for subsequent processing.

[0182] Step 2:

[0183] The server automatically analyzes the requirements.

[0184] The server analyzes the input requirements using natural language processing techniques. Specifically, it uses the Python spaCy library to analyze words and context within the text, identifying ambiguities and missing information in the requirements. Based on this analysis, the server generates data to clarify the requirements.

[0185] Step 3:

[0186] The server generates feedback.

[0187] Based on the analysis results, the server generates feedback regarding ambiguous elements and missing information. This feedback is compiled into supplementary information and specific questions, and sent to the user via the terminal.

[0188] Step 4:

[0189] Users complete and modify the requirements.

[0190] The user reviews the feedback received from the server on their device. Based on this feedback, the user completes or corrects any unclear parts of the requirements. This allows the user to re-enter more accurate requirements.

[0191] Step 5:

[0192] The server monitors the progress.

[0193] During project execution, the server retrieves and monitors progress data in real time from the project management tool. For example, it retrieves progress data via an API and stores it in a database. This stored data is then analyzed to detect delays or anomalies in the project's progress.

[0194] Step 6:

[0195] The server senses and analyzes the user's emotions.

[0196] The server uses the camera and microphone connected to the device to collect facial and audio data in order to understand the user's emotions. Facial recognition is performed using OpenCV, and the emotional state is inferred from the audio data using a speech analysis API. The results are stored on the server as quantitative data.

[0197] Step 7:

[0198] The server generates the proposal.

[0199] The system analyzes user emotional data and generates suggestions to optimize project progress. For example, it might suggest taking a break if the user is experiencing high stress levels. These suggestions are compiled in text format and communicated to the user via their device.

[0200] Step 8:

[0201] The user will respond based on the suggestion.

[0202] Based on the suggestions received on the device, users adjust the project schedule and resource allocation. For example, by shortening meeting times based on the suggestions, users can manage the project more efficiently.

[0203] (Application Example 2)

[0204] 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".

[0205] Modern project management requires not only understanding progress and risks, but also comprehensively managing the work environment by considering the emotional factors of the people involved. However, traditional systems are limited to project progress and risk assessment, lacking the ability to evaluate and intervene in the emotions and stress levels of workers. This leads to challenges such as decreased work efficiency and reduced motivation due to accumulated stress.

[0206] 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.

[0207] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks and issuing warnings based on past project data, means for recognizing the emotional state of workers in the work environment and evaluating stress, and means for providing suggestions for improving work efficiency based on emotional state. This enables emotional intervention in project management, making it possible to create an efficient and sustainable work environment.

[0208] "Means for automatically analyzing requirements and detecting ambiguity" refers to a function that mechanically analyzes the requirements of a project and identifies unclear information.

[0209] "A means of monitoring project progress in real time and detecting delays and problems" refers to a function that allows for instant confirmation of whether a project is progressing according to schedule and identifies delays or obstacles.

[0210] "A means of predicting risks and issuing warnings based on past project data" refers to a function that uses information from previous projects to foresee potential risks and notify users before problems occur.

[0211] "Means for recognizing the emotional state of workers in the work environment and assessing stress" refers to a function that identifies the emotions of employees in the workplace and uses that to determine their mental burden.

[0212] "A means of providing suggestions for improving work efficiency based on emotional state" refers to a function that presents suggestions for more effective work methods based on the mental state of the worker.

[0213] To implement this invention, a system is needed in which three parties—a server, a terminal, and a user—work together to perform their functions. The server acts as the central processing unit, analyzing various data for project management and providing real-time monitoring capabilities. The server utilizes the following hardware and software. The hardware requires a computer with a high-performance processor and sufficient storage capacity, and natural language processing technology is used for data processing. Specifically, Google® Cloud Natural Language API and Azure® Cognitive Services Face API are used to analyze requirements data and the emotional state of workers.

[0214] The device acts as an interface with the user, displaying various notifications and suggestions. Applications on the device are integrated into smart glasses and mobile devices, allowing users to check project progress and recommended actions based on their own emotional state. For example, if a worker is detected as experiencing high stress levels, the device might suggest, "Taking a 5-minute break may improve your efficiency."

[0215] Users input data about their work on the project into a terminal and receive feedback from the server. The user's emotional state is captured in real time through the camera and microphone of smart glasses, and an emotion engine evaluates stress and motivation levels.

[0216] As a concrete example, suppose a worker at a manufacturing facility notices that many delivery delays are occurring during the project's progress. This system can use emotion recognition to detect that the worker is stressed and suggest measures such as "shortening meetings" or "increasing break times."

[0217] An example of a prompt message is: "Explain the function that uses the camera and microphone of smart glasses to recognize emotions in a factory work environment, evaluates the worker's stress level in real time, and suggests efficient break times and work methods."

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The user enters project requirements using a terminal. The entered requirement data is sent to the server using natural language processing technology. During this process, ambiguity and missing information in the data are detected. The input is the requirement text entered by the user on the terminal, and the output is the ambiguity detection results and requests for supplementary information as needed.

[0221] Step 2:

[0222] The server monitors the project's progress in real time. It retrieves progress data and verifies whether it is proceeding according to schedule. Its input is the project data in progress, and its output is the detection of delays and problems based on the progress. If there are any abnormalities in the progress, it prepares to notify the terminal.

[0223] Step 3:

[0224] Past project data is analyzed on the server to predict potential risks. Using past data as input, a risk model is used to calculate future risks, which are then output as warnings. If a specific project element contains potential risks, the server issues a warning to the user via the terminal.

[0225] Step 4:

[0226] The smart glasses recognize the user's emotional state in real time. This is achieved by combining facial expression analysis using a camera and voice analysis using a microphone. Real-time voice and video data of the user are used as input, and stress and motivation levels are output through emotion recognition software.

[0227] Step 5:

[0228] The server generates suggestions to improve work efficiency based on the user's emotional state. Using emotional state data and current work data as input, it outputs improvement suggestions using a generative AI model. The generated suggestions are displayed to the user via the terminal, such as "Take a 5-minute break."

[0229] 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.

[0230] 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.

[0231] 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.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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".

[0245] This invention provides a system for efficiently managing projects, and a specific embodiment thereof is described below.

[0246] This system is primarily composed of three entities: servers, terminals, and users.

[0247] First, the user inputs project requirements information via a terminal. The server receives this information and automatically analyzes the requirements using natural language processing technology. This analysis identifies ambiguous information and missing elements, which are then provided to the user as feedback via the terminal. The user then uses this feedback to make revisions to clarify the requirements.

[0248] Next, regarding progress management, the server monitors the overall project progress in real time. To this end, the server has the ability to retrieve progress data from each phase of the project and detect delays and unexpected problems. The terminal displays this information visually and provides the user with warnings and suggestions for necessary actions.

[0249] Furthermore, for risk management, the server predicts risks based on past project data. In this process, it analyzes data from similar past projects to identify risks relevant to the current project. Identified risks are notified to the user as warnings via the terminal, and the user can take preventative measures based on the information provided.

[0250] As a concrete example, in a software development project, suppose the user enters general requirements for a new feature into the system during the requirements definition phase. The server analyzes the entered data and generates feedback indicating that "the UI design is ambiguous," prompting the terminal to provide more specific design requirements. After the corrections are made, the server tracks the progress and, if progress is behind schedule, notifies the user via the terminal and instructs them to make necessary adjustments. Based on data from similar past projects, if risks are predicted during the testing phase, the server warns the user in advance and recommends securing testing resources.

[0251] In this way, the system can provide efficient and proactive support at each stage of project management, thereby increasing the likelihood of project success.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The user enters project requirements into a terminal. The terminal sends the entered requirements information to the server.

[0255] Step 2:

[0256] The server analyzes the received requirements information using a natural language processing engine and automatically detects ambiguous parts and missing information. The detected information may include vague expressions and insufficient specifications.

[0257] Step 3:

[0258] The server identifies ambiguities and missing information and sends the analysis results as feedback to the terminal. The terminal then displays specific instructions to the user, such as "A more detailed explanation of item X is needed."

[0259] Step 4:

[0260] Users complete or modify the necessary requirement information based on the feedback presented on their device. This resolves any ambiguity in the requirements.

[0261] Step 5:

[0262] The server monitors the project's progress in real time, collecting progress data from each phase. This data includes the number of completed tasks and the schedule completion rate.

[0263] Step 6:

[0264] The server analyzes the collected progress data and determines whether progress is behind schedule compared to the benchmark. If a delay is detected, it sends a warning to the user via the terminal.

[0265] Step 7:

[0266] Users can receive delay alerts on their devices and plan meetings and resource reallocations to adjust project progress.

[0267] Step 8:

[0268] Using past project data, the server predicts risk. Data patterns from similar projects are used in the analysis.

[0269] Step 9:

[0270] The server generates and sends a warning about the predicted risk to the terminal. The terminal then presents the user with risk information such as, "There may be an increased rate of bugs during the testing phase."

[0271] Step 10:

[0272] Users will check risk warnings on their devices and take appropriate measures in advance. These measures include improving the testing process and increasing resources.

[0273] (Example 1)

[0274] 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."

[0275] In project management, it is difficult to identify ambiguities or deficiencies in requirements early on, and it is necessary to monitor project progress in real time and respond quickly when delays or problems occur. Furthermore, it is necessary to leverage insights gained from past projects to predict potential risks and address them proactively.

[0276] 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.

[0277] In this invention, the server includes means for automatically analyzing information and detecting information ambiguity, means for continuously monitoring the progress of work and detecting delays or problems, and means for predicting potential risks based on past data sets and issuing warnings. This enables efficient project management and risk reduction.

[0278] "Methods for automatically analyzing information and detecting information ambiguity" refers to technologies that automatically analyze input information and identify ambiguous expressions or missing elements.

[0279] "Means for continuously monitoring project progress and detecting delays or problems" refers to a system that tracks the progress of each stage of a project in real time and detects delays from the schedule or unexpected problems.

[0280] "Methods for predicting potential risks and issuing warnings based on past data" refers to technologies that analyze data from similar past projects to predict potential risks associated with ongoing projects and warn users in advance.

[0281] This invention is a system for efficiently managing projects by automatically analyzing user requirements information and assisting in the detection and correction of ambiguities. This system mainly consists of three components: a server, a terminal, and a user.

[0282] The server utilizes a natural language processing engine based on Python (e.g., SpaCy or NLTK) to analyze the requirements information entered by the user from their terminal. This allows for the automatic identification of ambiguous parts and missing elements in the information. The user can then receive these analysis results themselves through their terminal, which is equipped with a web browser and project management tools.

[0283] Furthermore, the server obtains data through the APIs of various project management tools (e.g., JIRA and Trello) to monitor the progress of the project. Based on this, it detects progress delays and problems in real time and provides appropriate countermeasures to the user through the terminal.

[0284] The server employs a technique of collecting past project data and predicting potential risks by leveraging a generative AI model (e.g., TensorFlow). This prediction information is sent to the terminal, enabling the user to take risk countermeasures in advance.

[0285] As a specific example, when the user inputs a general requirement regarding a new feature, the server can generate a measure such as "the UI design is ambiguous" and prompt the user for specific design requirement supplements via the terminal. Also, when the progress is delayed, the server issues a notification through the terminal and instructs the necessary adjustments to be made.

[0286] As a specific example of the prompt text, "Please propose a method for identifying and specifying the ambiguity in the requirements of the new feature" can be cited.

[0287] In this way, this system provides efficient support at each stage of project management and improves the success rate of the project.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The user inputs initial requirements regarding the project via the terminal. This input includes the project overview and specific functional requirements. The input data is obtained through the user interface of the terminal and sent to the server via the Internet. Here, the output is the requirement data in which the user's input is stored on the server.

[0291] Step 2:

[0292] The server analyzes the received requirements data using a natural language processing engine (e.g., SpaCy or NLTK). The server analyzes the sentence structure of the input data and identifies ambiguous expressions and unclear elements. This process involves data calculations such as syntactic analysis and keyword extraction. The output consists of identified ambiguous requirements and recommended corrective actions.

[0293] Step 3:

[0294] The terminal receives analysis results from the server and displays them to the user as feedback. This feedback includes details of the identified ambiguities and specific correction suggestions. The terminal visualizes this information so that the user can easily identify the areas that need correction. The output is the analysis results displayed on the user interface.

[0295] Step 4:

[0296] The server continuously monitors progress using APIs from project management tools (e.g., JIRA or Trello). The server checks the progress status of each task in real time and detects schedule delays. The data processing performed here involves processing progress data obtained from the API. The output consists of warnings and alerts regarding progress.

[0297] Step 5:

[0298] The device notifies the user of progress alerts. These alerts are presented in a visual format, highlighting tasks that are behind schedule or issues that need resolving. The device displays this information on a dashboard to help the user take quick action. The output is a visualized project progress and recommended actions.

[0299] Step 6:

[0300] The server predicts risk using a generative AI model (e.g., TensorFlow) based on past project data. The server analyzes data from similar projects and deploys a model to assess potential risk factors. The input is past project data, and the output is the risk prediction result.

[0301] Step 7:

[0302] Users receive warnings based on risk predictions via their devices. These warnings include identified risks and proposed countermeasures. Users can use this information to adjust project plans and resource allocations. The output consists of risk notifications and their corresponding countermeasures.

[0303] (Application Example 1)

[0304] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0305] In factories and similar production environments, the inability to monitor the progress of each process in real time and make necessary adjustments quickly leads to a decrease in overall project efficiency and productivity. This problem needs to be solved to increase the success rate of projects.

[0306] 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.

[0307] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks based on past project data and issuing warnings, means for grasping the progress of each process in real time and instructing necessary adjustments, and means for analyzing progress data and controlling the operation of robots. This enables efficient and rapid project management while managing the progress of each process within the factory.

[0308] "Automatic analysis of requirements" refers to a technology in which a computer automatically analyzes information about a project input by a user and detects ambiguity or deficiencies in its content.

[0309] "Real-time monitoring of progress" refers to a technology that sequentially tracks the progress of a project and immediately detects delays or problems when they occur.

[0310] "Risk prediction" refers to a technology that analyzes past project data and predicts and warns about risks in the currently ongoing project in advance.

[0311] "Grasping the progress status of processes" refers to a technology that acquires in real time the progress of each work process in a production environment such as a factory and visualizes the situation.

[0312] "Instruction for adjustment" refers to a technology that makes adjustments for the efficient progress of a project by the system automatically presenting appropriate countermeasures for detected delays or problems.

[0313] "Analysis of progress data and operation control" refers to a technology that analyzes the acquired progress data and appropriately controls the operation of production machines, especially robots, based on it.

[0314] The system that realizes this invention is composed based on three entities: a server, a terminal, and a user. The server receives project requirement information input by the user via the terminal and performs analysis using natural language processing technology. Through this analysis, the server detects the ambiguity of the requirements and provides feedback to the user on the terminal. By this method, the user can clarify the requirements of the project and make corrections as necessary.

[0315] In monitoring progress, the server acquires progress data from each stage of the project and analyzes it in real time. The server can automatically detect delays and problems, issue warnings via terminals, and instruct necessary adjustments. This process can also be applied to controlling the operation of factory robots using control systems such as ROS (Robot Operating System).

[0316] Regarding risk management, the server predicts risks related to the current project based on past project data. The server performs this risk assessment and notifies the user of any warnings. This allows the user to take preventative measures and ensure productivity.

[0317] One concrete example is the use of this system to manage production processes on automated lines within a factory. For instance, while a robotic arm is working on the factory floor, a server can monitor the progress of parts assembly in real time and immediately issue corrective instructions if any problems occur.

[0318] Example prompt: "Propose an application that allows factory robots to monitor current process progress in real time and detect unexpected delays or problems early."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user enters project requirements information via a terminal. The entered data is sent to the server in text format. This input data includes the user's design intent and requirements.

[0322] Step 2:

[0323] The server analyzes the received requirements information using natural language processing technology. This process automatically detects ambiguity and missing information in the text data and extracts it. The analysis results in a list of ambiguous expressions and requirements that need further clarification.

[0324] Step 3:

[0325] The server provides the analysis results as feedback to the terminal. The terminal then displays information about ambiguity to the user and prompts them to input supplementary information or revise the requirements, thereby helping to clarify the requirements.

[0326] Step 4:

[0327] The server monitors project progress data in real time. Data is collected from key locations in the factory, and progress is visualized. This data is based on production status collected from, for example, sensors and logs.

[0328] Step 5:

[0329] The server analyzes the collected progress data to detect delays and problems. The aggregated data is then compared to a baseline time, and calculations are performed to determine abnormal delays or stagnation. If an anomaly is detected, a warning is generated.

[0330] Step 6:

[0331] When the server detects an anomaly or problem, it instructs the user via the terminal on what needs to be corrected. Specific solutions and readjustment suggestions are also provided.

[0332] Step 7:

[0333] The server learns from past project data and predicts risks associated with the current project. Based on the existing database, a generative AI model performs a risk assessment and calculates the likelihood of risk. This output is used to suggest preventative measures.

[0334] Step 8:

[0335] Based on the risk assessment, the server sends warnings to the user about predicted risks. This allows the user to take preventative measures early.

[0336] Step 9:

[0337] In the actual process, the server controls the robot's movements based on the acquired progress data. Control signals are transmitted using a control system such as ROS, optimizing specific actions in real time. This series of processes improves the efficiency and accuracy of production work.

[0338] 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.

[0339] This invention provides a system for achieving efficient and effective operation in project management, and in particular, employs a form that combines user emotion recognition. The basic system configuration centers around three components: a server, a terminal, and a user. By incorporating an emotion engine, more comprehensive project support is achieved.

[0340] While the system is running, users input project requirements from a terminal. These requirements are sent to a server and automatically analyzed using natural language processing technology. Based on this information, the server detects any ambiguity or missing information in the requirements and provides necessary feedback. Users who receive feedback through their terminal can then supplement their requirements information.

[0341] During project execution, the server monitors progress in real time and detects delays and problems. If delays or problems are found, an alert is sent to the terminal, notifying the user. The user then takes appropriate action based on this information.

[0342] By incorporating an emotion engine, the system can recognize the user's emotions and assess their stress and motivation levels. For example, it can determine the emotional state through facial recognition and voice analysis during user input. The server analyzes this emotion-related data, assesses potential risks to project progress, and provides the user with suggested solutions via the terminal as needed.

[0343] As a concrete example, suppose a project meeting is frequently running late. In this case, the server not only monitors progress data but also assesses the user's emotional state. If the user is experiencing significant stress, the system generates suggestions such as "shorten the meeting" or "increase break time" and notifies the user via their terminal. This approach helps maintain user motivation while supporting the efficient progress of the project.

[0344] In this way, this system, equipped with an emotion engine, integrates technical progress management with human-centered emotional management, providing comprehensive support for project success.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] The user enters project requirements information into a terminal. The terminal then sends this information to the server.

[0348] Step 2:

[0349] The server receives the input requirements information and analyzes its content using a natural language processing engine. It automatically detects ambiguous expressions and missing data.

[0350] Step 3:

[0351] The server sends the analysis results regarding the ambiguity of the requirements to the terminal. The terminal displays feedback to the user, prompting them to add specific explanations or supplementary information.

[0352] Step 4:

[0353] Based on feedback provided via the device, the user modifies or adds to the requirements information. The information is then sent back to the server for verification.

[0354] Step 5:

[0355] The server monitors the project's progress in real time and collects progress data. If it detects any delays or unexpected problems, it records them.

[0356] Step 6:

[0357] The server performs analysis based on the progress and generates appropriate warnings if delays occur. These warnings are then communicated to the user via the terminal.

[0358] Step 7:

[0359] Users will check the alerts on their devices and make adjustments to project management as needed. Specifically, this may involve considering rescheduling meetings or reallocating resources.

[0360] Step 8:

[0361] The emotion engine evaluates the user's emotional state. It monitors the user's facial expressions and voice during input to determine stress levels and motivation.

[0362] Step 9:

[0363] The server receives the results from the emotion engine and analyzes the user's emotional data. Based on the analysis results, it considers ways to improve project management.

[0364] Step 10:

[0365] The server generates suggestions based on the user's emotional state and provides them to the user through the terminal. These suggestions include setting break times to reduce stress and reassigning tasks.

[0366] Step 11:

[0367] Users refer to the presented suggestions and make decisions that will help guide the project's progress. This ensures the smooth progress of the project and maintains the motivation of team members.

[0368] (Example 2)

[0369] 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".

[0370] While conventional project management systems are capable of detecting ambiguous requirements and missing information, monitoring progress, and predicting risks, they lack sufficient project support that takes into account the user's emotions and stress levels. As a result, it was difficult to prevent the decline in user motivation and stress that can cause project delays, and they were unable to comprehensively support the success of projects.

[0371] 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.

[0372] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, and means for sensing the user's emotions and evaluating their stress and motivational states. This enables efficient and effective project support by understanding the user's emotional state and providing appropriate feedback and suggestions.

[0373] "Methods for automatically analyzing requirements and detecting ambiguity" refers to techniques for mechanically analyzing project requirements and identifying ambiguous or unclear parts.

[0374] "A means of monitoring project progress in real time and detecting delays and problems" refers to technologies for continuously observing the current progress of a project and immediately detecting delays and anomalies.

[0375] "A means of predicting risks and issuing warnings based on past project data" refers to a method of using previously collected project information to anticipate potential hazards and issue appropriate warnings.

[0376] "Means of sensing a user's emotions and evaluating their stress and motivational state" refers to technology that senses a user's emotional state and uses that to determine their stress and motivation levels.

[0377] "A means of generating suggestions based on emotional data and notifying users" refers to a method of creating suggestions for project improvement based on emotion-related data and informing users of these suggestions.

[0378] "Means of visualizing and presenting the current situation through a device that users interact with" refers to technology that visually displays the progress of a project and shows its current status through a user-operable device.

[0379] This invention is a system for efficiently managing projects, and in particular, it employs a form that supports projects by combining user emotions. This system is mainly composed of three components: a server, a terminal, and a user. The server automatically analyzes project requirements and detects ambiguity using natural language processing technology. One example of a natural language processing library used here is spaCy for Python.

[0380] Users input information about the project's progress using a terminal. The server integrates with the project management tool and monitors the progress in real time. This ensures that if delays or problems occur, an alert is immediately sent to the terminal. The server also has technology to predict risks and issue warnings based on past project data, allowing potential problems to be detected in advance.

[0381] The emotion engine integrated into this system has the function of sensing the user's emotional state and evaluating their stress and motivation levels. During this process, facial and voice data is collected through the camera and microphone connected to the terminal and analyzed using emotion recognition technology. Specifically, the OpenCV image processing library is used for facial recognition, and a general speech recognition API is used for voice analysis.

[0382] Furthermore, it has a function that generates optimal suggestions based on the user's emotional data and notifies the user via the device. For example, if the system evaluates that the user is experiencing high levels of stress, it will generate specific suggestions such as, "Please consider taking a break."

[0383] As a concrete example, consider a situation where project meetings are frequently delayed. In this case, the server evaluates progress data and user emotions, generates suggestions such as "shorten the meeting" or "increase break time," and notifies the user via their terminal. This makes it possible to maintain user motivation while ensuring the efficient progress of the project.

[0384] Possible prompts to input into the generative AI model include the following:

[0385] "Please tell me the best way to support users in project management while considering their emotional state."

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The user enters the project requirements.

[0389] Through the terminal's user interface, the user enters project requirements as text. This input text is sent to the server as the system's initial data. The server prepares to receive this data and stores it for subsequent processing.

[0390] Step 2:

[0391] The server automatically analyzes the requirements.

[0392] The server analyzes the input requirements using natural language processing techniques. Specifically, it uses the Python spaCy library to analyze words and context within the text, identifying ambiguities and missing information in the requirements. Based on this analysis, the server generates data to clarify the requirements.

[0393] Step 3:

[0394] The server generates feedback.

[0395] Based on the analysis results, the server generates feedback regarding ambiguous elements and missing information. This feedback is compiled into supplementary information and specific questions, and sent to the user via the terminal.

[0396] Step 4:

[0397] Users complete and modify the requirements.

[0398] The user reviews the feedback received from the server on their device. Based on this feedback, the user completes or corrects any unclear parts of the requirements. This allows the user to re-enter more accurate requirements.

[0399] Step 5:

[0400] The server monitors the progress.

[0401] During project execution, the server retrieves and monitors progress data in real time from the project management tool. For example, it retrieves progress data via an API and stores it in a database. This stored data is then analyzed to detect delays or anomalies in the project's progress.

[0402] Step 6:

[0403] The server senses and analyzes the user's emotions.

[0404] The server uses the camera and microphone connected to the device to collect facial and audio data in order to understand the user's emotions. Facial recognition is performed using OpenCV, and the emotional state is inferred from the audio data using a speech analysis API. The results are stored on the server as quantitative data.

[0405] Step 7:

[0406] The server generates the proposal.

[0407] The system analyzes user emotional data and generates suggestions to optimize project progress. For example, it might suggest taking a break if the user is experiencing high stress levels. These suggestions are compiled in text format and communicated to the user via their device.

[0408] Step 8:

[0409] The user will respond based on the suggestion.

[0410] Based on the suggestions received on the device, users adjust the project schedule and resource allocation. For example, by shortening meeting times based on the suggestions, users can manage the project more efficiently.

[0411] (Application Example 2)

[0412] 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 as the "terminal".

[0413] Modern project management requires not only understanding progress and risks, but also comprehensively managing the work environment by considering the emotional factors of the people involved. However, traditional systems are limited to project progress and risk assessment, lacking the ability to evaluate and intervene in the emotions and stress levels of workers. This leads to challenges such as decreased work efficiency and reduced motivation due to accumulated stress.

[0414] 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.

[0415] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks and issuing warnings based on past project data, means for recognizing the emotional state of workers in the work environment and evaluating stress, and means for providing suggestions for improving work efficiency based on emotional state. This enables emotional intervention in project management, making it possible to create an efficient and sustainable work environment.

[0416] "Means for automatically analyzing requirements and detecting ambiguity" refers to a function that mechanically analyzes the requirements of a project and identifies unclear information.

[0417] "A means of monitoring project progress in real time and detecting delays and problems" refers to a function that instantly checks whether a project is progressing according to schedule and identifies delays or obstacles.

[0418] "A means of predicting risks and issuing warnings based on past project data" refers to a function that uses information from previous projects to foresee potential risks and notify users before problems occur.

[0419] "Means for recognizing the emotional state of workers in the work environment and assessing stress" refers to a function that identifies the emotions of employees in the workplace and uses that to determine their mental burden.

[0420] "A means of providing suggestions for improving work efficiency based on emotional state" refers to a function that presents suggestions for more effective work methods based on the mental state of the worker.

[0421] To implement this invention, a system is needed in which three parties—a server, a terminal, and a user—work together to perform their functions. The server acts as the central processing unit, analyzing various data for project management and providing real-time monitoring capabilities. The server utilizes the following hardware and software. The hardware requires a computer with a high-performance processor and sufficient storage capacity, and natural language processing technology is used for data processing. Specifically, Google Cloud Natural Language API and Azure Cognitive Services Face API are used to analyze requirements data and the emotional state of workers.

[0422] The device acts as an interface with the user, displaying various notifications and suggestions. Applications on the device are integrated into smart glasses and mobile devices, allowing users to check project progress and recommended actions based on their own emotional state. For example, if a worker is detected as experiencing high stress levels, the device might suggest, "Taking a 5-minute break may improve your efficiency."

[0423] Users input data about their work on the project into a terminal and receive feedback from the server. The user's emotional state is captured in real time through the camera and microphone of smart glasses, and an emotion engine evaluates stress and motivation levels.

[0424] As a concrete example, suppose a worker at a manufacturing facility notices that many delivery delays are occurring during the project's progress. This system can use emotion recognition to detect that the worker is stressed and suggest measures such as "shortening meetings" or "increasing break times."

[0425] An example of a prompt message is: "Explain the function that uses the camera and microphone of smart glasses to recognize emotions in a factory work environment, evaluates the worker's stress level in real time, and suggests efficient break times and work methods."

[0426] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0427] Step 1:

[0428] The user enters project requirements using a terminal. The entered requirement data is sent to the server using natural language processing technology. During this process, ambiguity and missing information in the data are detected. The input is the requirement text entered by the user on the terminal, and the output is the ambiguity detection results and requests for supplementary information as needed.

[0429] Step 2:

[0430] The server monitors the project's progress in real time. It retrieves progress data and verifies whether it is proceeding according to schedule. Its input is the project data in progress, and its output is the detection of delays and problems based on the progress. If there are any abnormalities in the progress, it prepares to notify the terminal.

[0431] Step 3:

[0432] Past project data is analyzed on the server to predict potential risks. Using past data as input, a risk model is used to calculate future risks, which are then output as warnings. If a specific project element contains potential risks, the server issues a warning to the user via the terminal.

[0433] Step 4:

[0434] The smart glasses recognize the user's emotional state in real time. This is achieved by combining facial expression analysis using a camera and voice analysis using a microphone. Real-time voice and video data of the user are used as input, and stress and motivation levels are output through emotion recognition software.

[0435] Step 5:

[0436] The server generates suggestions to improve work efficiency based on the user's emotional state. Using emotional state data and current work data as input, it outputs improvement suggestions using a generative AI model. The generated suggestions are displayed to the user via the terminal, such as "Take a 5-minute break."

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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".

[0453] This invention provides a system for efficiently managing projects, and a specific embodiment thereof is described below.

[0454] This system is primarily composed of three entities: servers, terminals, and users.

[0455] First, the user inputs project requirements information via a terminal. The server receives this information and automatically analyzes the requirements using natural language processing technology. This analysis identifies ambiguous information and missing elements, which are then provided to the user as feedback via the terminal. The user then uses this feedback to make revisions to clarify the requirements.

[0456] Next, regarding progress management, the server monitors the overall project progress in real time. To this end, the server has the ability to retrieve progress data from each phase of the project and detect delays and unexpected problems. The terminal displays this information visually and provides the user with warnings and suggestions for necessary actions.

[0457] Furthermore, for risk management, the server predicts risks based on past project data. In this process, it analyzes data from similar past projects to identify risks relevant to the current project. Identified risks are notified to the user as warnings via the terminal, and the user can take preventative measures based on the information provided.

[0458] As a concrete example, in a software development project, suppose the user enters general requirements for a new feature into the system during the requirements definition phase. The server analyzes the entered data and generates feedback indicating that "the UI design is ambiguous," prompting the terminal to provide more specific design requirements. After the corrections are made, the server tracks the progress and, if progress is behind schedule, notifies the user via the terminal and instructs them to make necessary adjustments. Based on data from similar past projects, if risks are predicted during the testing phase, the server warns the user in advance and recommends securing testing resources.

[0459] In this way, the system can provide efficient and proactive support at each stage of project management, thereby increasing the likelihood of project success.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The user enters project requirements into a terminal. The terminal sends the entered requirements information to the server.

[0463] Step 2:

[0464] The server analyzes the received requirements information using a natural language processing engine and automatically detects ambiguous parts and missing information. The detected information may include vague expressions and insufficient specifications.

[0465] Step 3:

[0466] The server identifies ambiguities and missing information and sends the analysis results as feedback to the terminal. The terminal then displays specific instructions to the user, such as "A more detailed explanation of item X is needed."

[0467] Step 4:

[0468] Users complete or modify the necessary requirement information based on the feedback presented on their device. This resolves any ambiguity in the requirements.

[0469] Step 5:

[0470] The server monitors the project's progress in real time, collecting progress data from each phase. This data includes the number of completed tasks and the schedule completion rate.

[0471] Step 6:

[0472] The server analyzes the collected progress data and determines whether progress is behind schedule compared to the benchmark. If a delay is detected, it sends a warning to the user via the terminal.

[0473] Step 7:

[0474] Users can receive delay alerts on their devices and plan meetings and resource reallocations to adjust project progress.

[0475] Step 8:

[0476] Using past project data, the server predicts risk. Data patterns from similar projects are used in the analysis.

[0477] Step 9:

[0478] The server generates and sends a warning about the predicted risk to the terminal. The terminal then presents the user with risk information such as, "There may be an increased rate of bugs during the testing phase."

[0479] Step 10:

[0480] Users will check risk warnings on their devices and take appropriate measures in advance. These measures include improving the testing process and increasing resources.

[0481] (Example 1)

[0482] 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."

[0483] In project management, it is difficult to identify ambiguities or deficiencies in requirements early on, and it is necessary to monitor project progress in real time and respond quickly when delays or problems occur. Furthermore, it is necessary to leverage insights gained from past projects to predict potential risks and address them proactively.

[0484] 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.

[0485] In this invention, the server includes means for automatically analyzing information and detecting information ambiguity, means for continuously monitoring the progress of work and detecting delays or problems, and means for predicting potential risks based on past data sets and issuing warnings. This enables efficient project management and risk reduction.

[0486] "Methods for automatically analyzing information and detecting information ambiguity" refers to technologies that automatically analyze input information and identify ambiguous expressions or missing elements.

[0487] "Means for continuously monitoring project progress and detecting delays or problems" refers to a system that tracks the progress of each stage of a project in real time and detects delays from the schedule or unexpected problems.

[0488] "Methods for predicting potential risks and issuing warnings based on past data" refers to technologies that analyze data from similar past projects to predict potential risks associated with ongoing projects and warn users in advance.

[0489] This invention is a system for efficiently managing projects by automatically analyzing user requirements information and assisting in the detection and correction of ambiguities. This system mainly consists of three components: a server, a terminal, and a user.

[0490] The server utilizes a natural language processing engine based on Python (e.g., SpaCy or NLTK) to analyze the requirements information entered by the user from their terminal. This allows for the automatic identification of ambiguous parts and missing elements in the information. The user can then receive these analysis results themselves through their terminal, which is equipped with a web browser and project management tools.

[0491] Furthermore, the server retrieves data through APIs of various project management tools (e.g., JIRA and Trello) to monitor the project's progress. Based on this, it detects delays and problems in real time and provides appropriate countermeasures to the user via their terminal.

[0492] The server employs a technology that collects past project data and uses a generative AI model (e.g., TensorFlow) to predict potential risks. This predictive information is sent to the terminal, allowing users to take risk mitigation measures in advance.

[0493] For example, when a user enters a general request for a new feature, the server can generate a response indicating that "the UI design is ambiguous" and prompt the user via the device to provide more specific design requirements. Furthermore, if progress is behind schedule, the server can send a notification via the device instructing the user to make necessary adjustments.

[0494] A concrete example of a prompt statement would be, "Identify any ambiguities in the requirements for the new feature and propose ways to clarify them."

[0495] In this way, this system provides efficient support at each stage of project management, thereby improving the success rate of projects.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The user enters initial project requirements via a terminal. This input includes a project overview and specific functional requirements. The entered data is retrieved through the terminal's user interface and sent to a server via the internet. The output is the requirements data stored on the server based on the user's input.

[0499] Step 2:

[0500] The server analyzes the received requirements data using a natural language processing engine (e.g., SpaCy or NLTK). The server analyzes the sentence structure of the input data and identifies ambiguous expressions and unclear elements. This process involves data calculations such as syntactic analysis and keyword extraction. The output consists of identified ambiguous requirements and recommended corrective actions.

[0501] Step 3:

[0502] The terminal receives analysis results from the server and displays them to the user as feedback. This feedback includes details of the identified ambiguities and specific correction suggestions. The terminal visualizes this information so that the user can easily identify the areas that need correction. The output is the analysis results displayed on the user interface.

[0503] Step 4:

[0504] The server continuously monitors progress using APIs from project management tools (e.g., JIRA or Trello). The server checks the progress status of each task in real time and detects schedule delays. The data processing performed here involves processing progress data obtained from the API. The output consists of warnings and alerts regarding progress.

[0505] Step 5:

[0506] The device notifies the user of progress alerts. These alerts are presented in a visual format, highlighting tasks that are behind schedule or issues that need resolving. The device displays this information on a dashboard to help the user take quick action. The output is a visualized project progress and recommended actions.

[0507] Step 6:

[0508] The server predicts risk using a generative AI model (e.g., TensorFlow) based on past project data. The server analyzes data from similar projects and deploys a model to assess potential risk factors. The input is past project data, and the output is the risk prediction result.

[0509] Step 7:

[0510] Users receive warnings based on risk predictions via their devices. These warnings include identified risks and proposed countermeasures. Users can use this information to adjust project plans and resource allocations. The output consists of risk notifications and their corresponding countermeasures.

[0511] (Application Example 1)

[0512] 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."

[0513] In factories and similar production environments, the inability to monitor the progress of each process in real time and make necessary adjustments quickly leads to a decrease in overall project efficiency and productivity. This problem needs to be solved to increase the success rate of projects.

[0514] 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.

[0515] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks based on past project data and issuing warnings, means for grasping the progress of each process in real time and instructing necessary adjustments, and means for analyzing progress data and controlling the operation of robots. This enables efficient and rapid project management while managing the progress of each process within the factory.

[0516] "Automatic requirements analysis" is a technology in which a computer automatically analyzes project information entered by a user and detects any ambiguity or omissions in that information.

[0517] "Real-time monitoring of progress" is a technology that tracks the progress of a project sequentially and detects delays and problems immediately if they occur.

[0518] "Risk prediction" is a technique that analyzes past project data to predict and warn about risks in ongoing projects in advance.

[0519] "Understanding the progress of the process" refers to a technology that acquires the progress of each work process in a production environment such as a factory in real time and visualizes the situation.

[0520] "Adjustment instructions" is a technology that adjusts a project to ensure its efficient progress by automatically suggesting appropriate countermeasures in response to detected delays and problems.

[0521] "Progress data analysis and motion control" refers to the technology of analyzing acquired progress data and appropriately controlling the movements of production machinery, especially robots, based on that data.

[0522] The system realizing this invention is composed of three components: a server, a terminal, and a user. The server receives project requirement information entered by the user via the terminal and performs analysis using natural language processing technology. Through this analysis, the server detects ambiguity in the requirements and provides feedback to the user on the terminal. In this way, the user can clarify the project requirements and make modifications as needed.

[0523] In monitoring progress, the server acquires progress data from each stage of the project and analyzes it in real time. The server can automatically detect delays and problems, issue warnings via terminals, and instruct necessary adjustments. This process can also be applied to controlling the operation of factory robots using control systems such as ROS (Robot Operating System).

[0524] Regarding risk management, the server predicts risks related to the current project based on past project data. The server performs this risk assessment and notifies the user of any warnings. This allows the user to take preventative measures and ensure productivity.

[0525] One concrete example is the use of this system to manage production processes on automated lines within a factory. For instance, while a robotic arm is working on the factory floor, a server can monitor the progress of parts assembly in real time and immediately issue corrective instructions if any problems occur.

[0526] Example prompt: "Propose an application that allows factory robots to monitor current process progress in real time and detect unexpected delays or problems early."

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The user enters project requirements information via a terminal. The entered data is sent to the server in text format. This input data includes the user's design intent and requirements.

[0530] Step 2:

[0531] The server analyzes the received requirements information using natural language processing technology. This process automatically detects ambiguity and missing information in the text data and extracts it. The analysis results in a list of ambiguous expressions and requirements that need further clarification.

[0532] Step 3:

[0533] The server provides the analysis results as feedback to the terminal. The terminal then displays information about ambiguity to the user and prompts them to input supplementary information or revise the requirements, thereby helping to clarify the requirements.

[0534] Step 4:

[0535] The server monitors project progress data in real time. Data is collected from key locations in the factory, and progress is visualized. This data is based on production status collected from, for example, sensors and logs.

[0536] Step 5:

[0537] The server analyzes the collected progress data to detect delays and problems. The aggregated data is then compared to a baseline time, and calculations are performed to determine abnormal delays or stagnation. If an anomaly is detected, a warning is generated.

[0538] Step 6:

[0539] When the server detects an anomaly or problem, it instructs the user via the terminal on what needs to be corrected. Specific solutions and readjustment suggestions are also provided.

[0540] Step 7:

[0541] The server learns from past project data and predicts risks associated with the current project. Based on the existing database, a generative AI model performs a risk assessment and calculates the likelihood of risk. This output is used to suggest preventative measures.

[0542] Step 8:

[0543] Based on the risk assessment, the server sends warnings to the user about predicted risks. This allows the user to take preventative measures early.

[0544] Step 9:

[0545] In the actual process, the server controls the robot's movements based on the acquired progress data. Control signals are transmitted using a control system such as ROS, optimizing specific actions in real time. This series of processes improves the efficiency and accuracy of production work.

[0546] 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.

[0547] This invention provides a system for achieving efficient and effective operation in project management, and in particular, employs a form that combines user emotion recognition. The basic system configuration centers around three components: a server, a terminal, and a user. By incorporating an emotion engine, more comprehensive project support is achieved.

[0548] While the system is running, users input project requirements from a terminal. These requirements are sent to a server and automatically analyzed using natural language processing technology. Based on this information, the server detects any ambiguity or missing information in the requirements and provides necessary feedback. Users who receive feedback through their terminal can then supplement their requirements information.

[0549] During project execution, the server monitors progress in real time and detects delays and problems. If delays or problems are found, an alert is sent to the terminal, notifying the user. The user then takes appropriate action based on this information.

[0550] By incorporating an emotion engine, the system can recognize the user's emotions and assess their stress and motivation levels. For example, it can determine the emotional state through facial recognition and voice analysis during user input. The server analyzes this emotion-related data, assesses potential risks to project progress, and provides the user with suggested solutions via the terminal as needed.

[0551] As a concrete example, suppose a project meeting is frequently running late. In this case, the server not only monitors progress data but also assesses the user's emotional state. If the user is experiencing significant stress, the system generates suggestions such as "shorten the meeting" or "increase break time" and notifies the user via their terminal. This approach helps maintain user motivation while supporting the efficient progress of the project.

[0552] In this way, this system, equipped with an emotion engine, integrates technical progress management with human-centered emotional management, providing comprehensive support for project success.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The user enters project requirements information into a terminal. The terminal then sends this information to the server.

[0556] Step 2:

[0557] The server receives the input requirements information and analyzes its content using a natural language processing engine. It automatically detects ambiguous expressions and missing data.

[0558] Step 3:

[0559] The server sends the analysis results regarding the ambiguity of the requirements to the terminal. The terminal displays feedback to the user, prompting them to add specific explanations or supplementary information.

[0560] Step 4:

[0561] Based on feedback provided via the device, the user modifies or adds to the requirements information. The information is then sent back to the server for verification.

[0562] Step 5:

[0563] The server monitors the project's progress in real time and collects progress data. If it detects any delays or unexpected problems, it records them.

[0564] Step 6:

[0565] The server performs analysis based on the progress and generates appropriate warnings if delays occur. These warnings are then communicated to the user via the terminal.

[0566] Step 7:

[0567] Users will check the alerts on their devices and make adjustments to project management as needed. Specifically, this may involve considering rescheduling meetings or reallocating resources.

[0568] Step 8:

[0569] The emotion engine evaluates the user's emotional state. It monitors the user's facial expressions and voice during input to determine stress levels and motivation.

[0570] Step 9:

[0571] The server receives the results from the emotion engine and analyzes the user's emotional data. Based on the analysis results, it considers ways to improve project management.

[0572] Step 10:

[0573] The server generates suggestions based on the user's emotional state and provides them to the user through the terminal. These suggestions include setting break times to reduce stress and reassigning tasks.

[0574] Step 11:

[0575] Users refer to the presented suggestions and make decisions that will help guide the project's progress. This ensures the smooth progress of the project and maintains the motivation of team members.

[0576] (Example 2)

[0577] 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."

[0578] While conventional project management systems are capable of detecting ambiguous requirements and missing information, monitoring progress, and predicting risks, they lack sufficient project support that takes into account the user's emotions and stress levels. As a result, it was difficult to prevent the decline in user motivation and stress that can cause project delays, and they were unable to comprehensively support the success of projects.

[0579] 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.

[0580] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, and means for sensing the user's emotions and evaluating their stress and motivational states. This enables efficient and effective project support by understanding the user's emotional state and providing appropriate feedback and suggestions.

[0581] "Methods for automatically analyzing requirements and detecting ambiguity" refers to techniques for mechanically analyzing project requirements and identifying ambiguous or unclear parts.

[0582] "A means of monitoring project progress in real time and detecting delays and problems" refers to technologies for continuously observing the current progress of a project and immediately detecting delays and anomalies.

[0583] "A means of predicting risks and issuing warnings based on past project data" refers to a method of using previously collected project information to anticipate potential hazards and issue appropriate warnings.

[0584] "Means of sensing a user's emotions and evaluating their stress and motivational state" refers to technology that senses a user's emotional state and uses that to determine their stress and motivation levels.

[0585] "A means of generating suggestions based on emotional data and notifying users" refers to a method of creating suggestions for project improvement based on emotion-related data and informing users of these suggestions.

[0586] "Means of visualizing and presenting the current situation through a device that users interact with" refers to technology that visually displays the progress of a project and shows its current status through a user-operable device.

[0587] This invention is a system for efficiently managing projects, and in particular, it employs a form that supports projects by combining user emotions. This system is mainly composed of three components: a server, a terminal, and a user. The server automatically analyzes project requirements and detects ambiguity using natural language processing technology. One example of a natural language processing library used here is spaCy for Python.

[0588] Users input information about the project's progress using a terminal. The server integrates with the project management tool and monitors the progress in real time. This ensures that if delays or problems occur, an alert is immediately sent to the terminal. The server also has technology to predict risks and issue warnings based on past project data, allowing potential problems to be detected in advance.

[0589] The emotion engine integrated into this system has the function of sensing the user's emotional state and evaluating their stress and motivation levels. During this process, facial and voice data is collected through the camera and microphone connected to the terminal and analyzed using emotion recognition technology. Specifically, the OpenCV image processing library is used for facial recognition, and a general speech recognition API is used for voice analysis.

[0590] Furthermore, it has a function that generates optimal suggestions based on the user's emotional data and notifies the user via the device. For example, if the system evaluates that the user is experiencing high levels of stress, it will generate specific suggestions such as, "Please consider taking a break."

[0591] As a concrete example, consider a situation where project meetings are frequently delayed. In this case, the server evaluates progress data and user emotions, generates suggestions such as "shorten the meeting" or "increase break time," and notifies the user via their terminal. This makes it possible to maintain user motivation while ensuring the efficient progress of the project.

[0592] Possible prompts to input into the generative AI model include the following:

[0593] "Please tell me the best way to support users in project management while considering their emotional state."

[0594] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0595] Step 1:

[0596] The user enters the project requirements.

[0597] Through the terminal's user interface, the user enters project requirements as text. This input text is sent to the server as the system's initial data. The server prepares to receive this data and stores it for subsequent processing.

[0598] Step 2:

[0599] The server automatically analyzes the requirements.

[0600] The server analyzes the input requirements using natural language processing techniques. Specifically, it uses the Python spaCy library to analyze words and context within the text, identifying ambiguities and missing information in the requirements. Based on this analysis, the server generates data to clarify the requirements.

[0601] Step 3:

[0602] The server generates feedback.

[0603] Based on the analysis results, the server generates feedback regarding ambiguous elements and missing information. This feedback is compiled into supplementary information and specific questions, and sent to the user via the terminal.

[0604] Step 4:

[0605] Users complete and modify the requirements.

[0606] The user reviews the feedback received from the server on their device. Based on this feedback, the user completes or corrects any unclear parts of the requirements. This allows the user to re-enter more accurate requirements.

[0607] Step 5:

[0608] The server monitors the progress.

[0609] During project execution, the server retrieves and monitors progress data in real time from the project management tool. For example, it retrieves progress data via an API and stores it in a database. This stored data is then analyzed to detect delays or anomalies in the project's progress.

[0610] Step 6:

[0611] The server senses and analyzes the user's emotions.

[0612] The server uses the camera and microphone connected to the device to collect facial and audio data in order to understand the user's emotions. Facial recognition is performed using OpenCV, and the emotional state is inferred from the audio data using a speech analysis API. The results are stored on the server as quantitative data.

[0613] Step 7:

[0614] The server generates the proposal.

[0615] The system analyzes user emotional data and generates suggestions to optimize project progress. For example, it might suggest taking a break if the user is experiencing high stress levels. These suggestions are compiled in text format and communicated to the user via their device.

[0616] Step 8:

[0617] The user will respond based on the suggestion.

[0618] Based on the suggestions received on the device, users adjust the project schedule and resource allocation. For example, by shortening meeting times based on the suggestions, users can manage the project more efficiently.

[0619] (Application Example 2)

[0620] 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."

[0621] Modern project management requires not only understanding progress and risks, but also comprehensively managing the work environment by considering the emotional factors of the people involved. However, traditional systems are limited to project progress and risk assessment, lacking the ability to evaluate and intervene in the emotions and stress levels of workers. This leads to challenges such as decreased work efficiency and reduced motivation due to accumulated stress.

[0622] 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.

[0623] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks and issuing warnings based on past project data, means for recognizing the emotional state of workers in the work environment and evaluating stress, and means for providing suggestions for improving work efficiency based on emotional state. This enables emotional intervention in project management, making it possible to create an efficient and sustainable work environment.

[0624] "Means for automatically analyzing requirements and detecting ambiguity" refers to a function that mechanically analyzes the requirements of a project and identifies unclear information.

[0625] "A means of monitoring project progress in real time and detecting delays and problems" refers to a function that instantly checks whether a project is progressing according to schedule and identifies delays or obstacles.

[0626] "A means of predicting risks and issuing warnings based on past project data" refers to a function that uses information from previous projects to foresee potential risks and notify users before problems occur.

[0627] "Means for recognizing the emotional state of workers in the work environment and assessing stress" refers to a function that identifies the emotions of employees in the workplace and uses that to determine their mental burden.

[0628] "A means of providing suggestions for improving work efficiency based on emotional state" refers to a function that presents suggestions for more effective work methods based on the mental state of the worker.

[0629] To implement this invention, a system is needed in which three parties—a server, a terminal, and a user—work together to perform their functions. The server acts as the central processing unit, analyzing various data for project management and providing real-time monitoring capabilities. The server utilizes the following hardware and software. The hardware requires a computer with a high-performance processor and sufficient storage capacity, and natural language processing technology is used for data processing. Specifically, Google Cloud Natural Language API and Azure Cognitive Services Face API are used to analyze requirements data and the emotional state of workers.

[0630] The device acts as an interface with the user, displaying various notifications and suggestions. Applications on the device are integrated into smart glasses and mobile devices, allowing users to check project progress and recommended actions based on their own emotional state. For example, if a worker is detected as experiencing high stress levels, the device might suggest, "Taking a 5-minute break may improve your efficiency."

[0631] Users input data about their work on the project into a terminal and receive feedback from the server. The user's emotional state is captured in real time through the camera and microphone of smart glasses, and an emotion engine evaluates stress and motivation levels.

[0632] As a concrete example, suppose a worker at a manufacturing facility notices that many delivery delays are occurring during the project's progress. This system can use emotion recognition to detect that the worker is stressed and suggest measures such as "shortening meetings" or "increasing break times."

[0633] An example of a prompt message is: "Explain the function that uses the camera and microphone of smart glasses to recognize emotions in a factory work environment, evaluates the worker's stress level in real time, and suggests efficient break times and work methods."

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The user enters project requirements using a terminal. The entered requirement data is sent to the server using natural language processing technology. During this process, ambiguity and missing information in the data are detected. The input is the requirement text entered by the user on the terminal, and the output is the ambiguity detection results and requests for supplementary information as needed.

[0637] Step 2:

[0638] The server monitors the project's progress in real time. It retrieves progress data and verifies whether it is proceeding according to schedule. Its input is the project data in progress, and its output is the detection of delays and problems based on the progress. If there are any abnormalities in the progress, it prepares to notify the terminal.

[0639] Step 3:

[0640] Past project data is analyzed on the server to predict potential risks. Using past data as input, a risk model is used to calculate future risks, which are then output as warnings. If a specific project element contains potential risks, the server issues a warning to the user via the terminal.

[0641] Step 4:

[0642] The smart glasses recognize the user's emotional state in real time. This is achieved by combining facial expression analysis using a camera and voice analysis using a microphone. Real-time voice and video data of the user are used as input, and stress and motivation levels are output through emotion recognition software.

[0643] Step 5:

[0644] The server generates suggestions to improve work efficiency based on the user's emotional state. Using emotional state data and current work data as input, it outputs improvement suggestions using a generative AI model. The generated suggestions are displayed to the user via the terminal, such as "Take a 5-minute break."

[0645] 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.

[0646] 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.

[0647] 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.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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".

[0662] This invention provides a system for efficiently managing projects, and a specific embodiment thereof is described below.

[0663] This system is primarily composed of three entities: servers, terminals, and users.

[0664] First, the user inputs project requirements information via a terminal. The server receives this information and automatically analyzes the requirements using natural language processing technology. This analysis identifies ambiguous information and missing elements, which are then provided to the user as feedback via the terminal. The user then uses this feedback to make revisions to clarify the requirements.

[0665] Next, regarding progress management, the server monitors the overall project progress in real time. To this end, the server has the ability to retrieve progress data from each phase of the project and detect delays and unexpected problems. The terminal displays this information visually and provides the user with warnings and suggestions for necessary actions.

[0666] Furthermore, for risk management, the server predicts risks based on past project data. In this process, it analyzes data from similar past projects to identify risks relevant to the current project. Identified risks are notified to the user as warnings via the terminal, and the user can take preventative measures based on the information provided.

[0667] As a concrete example, in a software development project, suppose the user enters general requirements for a new feature into the system during the requirements definition phase. The server analyzes the entered data and generates feedback indicating that "the UI design is ambiguous," prompting the terminal to provide more specific design requirements. After the corrections are made, the server tracks the progress and, if progress is behind schedule, notifies the user via the terminal and instructs them to make necessary adjustments. Based on data from similar past projects, if risks are predicted during the testing phase, the server warns the user in advance and recommends securing testing resources.

[0668] In this way, the system can provide efficient and proactive support at each stage of project management, thereby increasing the likelihood of project success.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The user enters project requirements into a terminal. The terminal sends the entered requirements information to the server.

[0672] Step 2:

[0673] The server analyzes the received requirements information using a natural language processing engine and automatically detects ambiguous parts and missing information. The detected information may include vague expressions and insufficient specifications.

[0674] Step 3:

[0675] The server identifies ambiguities and missing information and sends the analysis results as feedback to the terminal. The terminal then displays specific instructions to the user, such as "A more detailed explanation of item X is needed."

[0676] Step 4:

[0677] Users complete or modify the necessary requirement information based on the feedback presented on their device. This resolves any ambiguity in the requirements.

[0678] Step 5:

[0679] The server monitors the project's progress in real time, collecting progress data from each phase. This data includes the number of completed tasks and the schedule completion rate.

[0680] Step 6:

[0681] The server analyzes the collected progress data and determines whether progress is behind schedule compared to the benchmark. If a delay is detected, it sends a warning to the user via the terminal.

[0682] Step 7:

[0683] Users can receive delay alerts on their devices and plan meetings and resource reallocations to adjust project progress.

[0684] Step 8:

[0685] Using past project data, the server predicts risk. Data patterns from similar projects are used in the analysis.

[0686] Step 9:

[0687] The server generates and sends a warning about the predicted risk to the terminal. The terminal then presents the user with risk information such as, "There may be an increased rate of bugs during the testing phase."

[0688] Step 10:

[0689] Users will check risk warnings on their devices and take appropriate measures in advance. These measures include improving the testing process and increasing resources.

[0690] (Example 1)

[0691] 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".

[0692] In project management, it is difficult to identify ambiguities or deficiencies in requirements early on, and it is necessary to monitor project progress in real time and respond quickly when delays or problems occur. Furthermore, it is necessary to leverage insights gained from past projects to predict potential risks and address them proactively.

[0693] 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.

[0694] In this invention, the server includes means for automatically analyzing information and detecting information ambiguity, means for continuously monitoring the progress of work and detecting delays or problems, and means for predicting potential risks based on past data sets and issuing warnings. This enables efficient project management and risk reduction.

[0695] "Methods for automatically analyzing information and detecting information ambiguity" refers to technologies that automatically analyze input information and identify ambiguous expressions or missing elements.

[0696] "Means for continuously monitoring project progress and detecting delays or problems" refers to a system that tracks the progress of each stage of a project in real time and detects delays from the schedule or unexpected problems.

[0697] "Methods for predicting potential risks and issuing warnings based on past data" refers to technologies that analyze data from similar past projects to predict potential risks associated with ongoing projects and warn users in advance.

[0698] This invention is a system for efficiently managing projects by automatically analyzing user requirements information and assisting in the detection and correction of ambiguities. This system mainly consists of three components: a server, a terminal, and a user.

[0699] The server utilizes a natural language processing engine based on Python (e.g., SpaCy or NLTK) to analyze the requirements information entered by the user from their terminal. This allows for the automatic identification of ambiguous parts and missing elements in the information. The user can then receive these analysis results themselves through their terminal, which is equipped with a web browser and project management tools.

[0700] Furthermore, the server retrieves data through APIs of various project management tools (e.g., JIRA and Trello) to monitor the project's progress. Based on this, it detects delays and problems in real time and provides appropriate countermeasures to the user via their terminal.

[0701] The server employs a technology that collects past project data and uses a generative AI model (e.g., TensorFlow) to predict potential risks. This predictive information is sent to the terminal, allowing users to take risk mitigation measures in advance.

[0702] For example, when a user enters a general request for a new feature, the server can generate a response indicating that "the UI design is ambiguous" and prompt the user via the device to provide more specific design requirements. Furthermore, if progress is behind schedule, the server can send a notification via the device instructing the user to make necessary adjustments.

[0703] A concrete example of a prompt statement would be, "Identify any ambiguities in the requirements for the new feature and propose ways to clarify them."

[0704] In this way, this system provides efficient support at each stage of project management, thereby improving the success rate of projects.

[0705] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0706] Step 1:

[0707] The user enters initial project requirements via a terminal. This input includes a project overview and specific functional requirements. The entered data is retrieved through the terminal's user interface and sent to a server via the internet. The output is the requirements data stored on the server based on the user's input.

[0708] Step 2:

[0709] The server analyzes the received requirements data using a natural language processing engine (e.g., SpaCy or NLTK). The server analyzes the sentence structure of the input data and identifies ambiguous expressions and unclear elements. This process involves data calculations such as syntactic analysis and keyword extraction. The output consists of identified ambiguous requirements and recommended corrective actions.

[0710] Step 3:

[0711] The terminal receives analysis results from the server and displays them to the user as feedback. This feedback includes details of the identified ambiguities and specific correction suggestions. The terminal visualizes this information so that the user can easily identify the areas that need correction. The output is the analysis results displayed on the user interface.

[0712] Step 4:

[0713] The server continuously monitors progress using APIs from project management tools (e.g., JIRA or Trello). The server checks the progress status of each task in real time and detects schedule delays. The data processing performed here involves processing progress data obtained from the API. The output consists of warnings and alerts regarding progress.

[0714] Step 5:

[0715] The device notifies the user of progress alerts. These alerts are presented in a visual format, highlighting tasks that are behind schedule or issues that need resolving. The device displays this information on a dashboard to help the user take quick action. The output is a visualized project progress and recommended actions.

[0716] Step 6:

[0717] The server predicts risk using a generative AI model (e.g., TensorFlow) based on past project data. The server analyzes data from similar projects and deploys a model to assess potential risk factors. The input is past project data, and the output is the risk prediction result.

[0718] Step 7:

[0719] Users receive warnings based on risk predictions via their devices. These warnings include identified risks and proposed countermeasures. Users can use this information to adjust project plans and resource allocations. The output consists of risk notifications and their corresponding countermeasures.

[0720] (Application Example 1)

[0721] 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".

[0722] In factories and similar production environments, the inability to monitor the progress of each process in real time and make necessary adjustments quickly leads to a decrease in overall project efficiency and productivity. This problem needs to be solved to increase the success rate of projects.

[0723] 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.

[0724] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks based on past project data and issuing warnings, means for grasping the progress of each process in real time and instructing necessary adjustments, and means for analyzing progress data and controlling the operation of robots. This enables efficient and rapid project management while managing the progress of each process within the factory.

[0725] "Automatic requirements analysis" is a technology in which a computer automatically analyzes project information entered by a user and detects any ambiguity or omissions in that information.

[0726] "Real-time monitoring of progress" is a technology that tracks the progress of a project sequentially and detects delays and problems immediately if they occur.

[0727] "Risk prediction" is a technique that analyzes past project data to predict and warn about risks in ongoing projects in advance.

[0728] "Understanding the progress of the process" refers to a technology that acquires the progress of each work process in a production environment such as a factory in real time and visualizes the situation.

[0729] "Adjustment instructions" is a technology that adjusts a project to ensure its efficient progress by automatically suggesting appropriate countermeasures in response to detected delays and problems.

[0730] "Progress data analysis and motion control" refers to the technology of analyzing acquired progress data and appropriately controlling the movements of production machinery, especially robots, based on that data.

[0731] The system realizing this invention is composed of three components: a server, a terminal, and a user. The server receives project requirement information entered by the user via the terminal and performs analysis using natural language processing technology. Through this analysis, the server detects ambiguity in the requirements and provides feedback to the user on the terminal. In this way, the user can clarify the project requirements and make modifications as needed.

[0732] In monitoring progress, the server acquires progress data from each stage of the project and analyzes it in real time. The server can automatically detect delays and problems, issue warnings via terminals, and instruct necessary adjustments. This process can also be applied to controlling the operation of factory robots using control systems such as ROS (Robot Operating System).

[0733] Regarding risk management, the server predicts risks related to the current project based on past project data. The server performs this risk assessment and notifies the user of any warnings. This allows the user to take preventative measures and ensure productivity.

[0734] One concrete example is the use of this system to manage production processes on automated lines within a factory. For instance, while a robotic arm is working on the factory floor, a server can monitor the progress of parts assembly in real time and immediately issue corrective instructions if any problems occur.

[0735] Example prompt: "Propose an application that allows factory robots to monitor current process progress in real time and detect unexpected delays or problems early."

[0736] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0737] Step 1:

[0738] The user enters project requirements information via a terminal. The entered data is sent to the server in text format. This input data includes the user's design intent and requirements.

[0739] Step 2:

[0740] The server analyzes the received requirements information using natural language processing technology. This process automatically detects ambiguity and missing information in the text data and extracts it. The analysis results in a list of ambiguous expressions and requirements that need further clarification.

[0741] Step 3:

[0742] The server provides the analysis results as feedback to the terminal. The terminal then displays information about ambiguity to the user and prompts them to input supplementary information or revise the requirements, thereby helping to clarify the requirements.

[0743] Step 4:

[0744] The server monitors project progress data in real time. Data is collected from key locations in the factory, and progress is visualized. This data is based on production status collected from, for example, sensors and logs.

[0745] Step 5:

[0746] The server analyzes the collected progress data to detect delays and problems. The aggregated data is then compared to a baseline time, and calculations are performed to determine abnormal delays or stagnation. If an anomaly is detected, a warning is generated.

[0747] Step 6:

[0748] When the server detects an anomaly or problem, it instructs the user via the terminal on what needs to be corrected. Specific solutions and readjustment suggestions are also provided.

[0749] Step 7:

[0750] The server learns from past project data and predicts risks associated with the current project. Based on the existing database, a generative AI model performs a risk assessment and calculates the likelihood of risk. This output is used to suggest preventative measures.

[0751] Step 8:

[0752] Based on the risk assessment, the server sends warnings to the user about predicted risks. This allows the user to take preventative measures early.

[0753] Step 9:

[0754] In the actual process, the server controls the robot's movements based on the acquired progress data. Control signals are transmitted using a control system such as ROS, optimizing specific actions in real time. This series of processes improves the efficiency and accuracy of production work.

[0755] 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.

[0756] This invention provides a system for achieving efficient and effective operation in project management, and in particular, employs a form that combines user emotion recognition. The basic system configuration centers around three components: a server, a terminal, and a user. By incorporating an emotion engine, more comprehensive project support is achieved.

[0757] While the system is running, users input project requirements from a terminal. These requirements are sent to a server and automatically analyzed using natural language processing technology. Based on this information, the server detects any ambiguity or missing information in the requirements and provides necessary feedback. Users who receive feedback through their terminal can then supplement their requirements information.

[0758] During project execution, the server monitors progress in real time and detects delays and problems. If delays or problems are found, an alert is sent to the terminal, notifying the user. The user then takes appropriate action based on this information.

[0759] By incorporating an emotion engine, the system can recognize the user's emotions and assess their stress and motivation levels. For example, it can determine the emotional state through facial recognition and voice analysis during user input. The server analyzes this emotion-related data, assesses potential risks to project progress, and provides the user with suggested solutions via the terminal as needed.

[0760] As a concrete example, suppose a project meeting is frequently running late. In this case, the server not only monitors progress data but also assesses the user's emotional state. If the user is experiencing significant stress, the system generates suggestions such as "shorten the meeting" or "increase break time" and notifies the user via their terminal. This approach helps maintain user motivation while supporting the efficient progress of the project.

[0761] In this way, this system, equipped with an emotion engine, integrates technical progress management with human-centered emotional management, providing comprehensive support for project success.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The user enters project requirements information into a terminal. The terminal then sends this information to the server.

[0765] Step 2:

[0766] The server receives the input requirements information and analyzes its content using a natural language processing engine. It automatically detects ambiguous expressions and missing data.

[0767] Step 3:

[0768] The server sends the analysis results regarding the ambiguity of the requirements to the terminal. The terminal displays feedback to the user, prompting them to add specific explanations or supplementary information.

[0769] Step 4:

[0770] Based on feedback provided via the device, the user modifies or adds to the requirements information. The information is then sent back to the server for verification.

[0771] Step 5:

[0772] The server monitors the project's progress in real time and collects progress data. If it detects any delays or unexpected problems, it records them.

[0773] Step 6:

[0774] The server performs analysis based on the progress and generates appropriate warnings if delays occur. These warnings are then communicated to the user via the terminal.

[0775] Step 7:

[0776] Users will check the alerts on their devices and make adjustments to project management as needed. Specifically, this may involve considering rescheduling meetings or reallocating resources.

[0777] Step 8:

[0778] The emotion engine evaluates the user's emotional state. It monitors the user's facial expressions and voice during input to determine stress levels and motivation.

[0779] Step 9:

[0780] The server receives the results from the emotion engine and analyzes the user's emotional data. Based on the analysis results, it considers ways to improve project management.

[0781] Step 10:

[0782] The server generates suggestions based on the user's emotional state and provides them to the user through the terminal. These suggestions include setting break times to reduce stress and reassigning tasks.

[0783] Step 11:

[0784] Users refer to the presented suggestions and make decisions that will help guide the project's progress. This ensures the smooth progress of the project and maintains the motivation of team members.

[0785] (Example 2)

[0786] 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".

[0787] While conventional project management systems are capable of detecting ambiguous requirements and missing information, monitoring progress, and predicting risks, they lack sufficient project support that takes into account the user's emotions and stress levels. As a result, it was difficult to prevent the decline in user motivation and stress that can cause project delays, and they were unable to comprehensively support the success of projects.

[0788] 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.

[0789] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, and means for sensing the user's emotions and evaluating their stress and motivational states. This enables efficient and effective project support by understanding the user's emotional state and providing appropriate feedback and suggestions.

[0790] "Methods for automatically analyzing requirements and detecting ambiguity" refers to techniques for mechanically analyzing project requirements and identifying ambiguous or unclear parts.

[0791] "A means of monitoring project progress in real time and detecting delays and problems" refers to technologies for continuously observing the current progress of a project and immediately detecting delays and anomalies.

[0792] "A means of predicting risks and issuing warnings based on past project data" refers to a method of using previously collected project information to anticipate potential hazards and issue appropriate warnings.

[0793] "Means of sensing a user's emotions and evaluating their stress and motivational state" refers to technology that senses a user's emotional state and uses that to determine their stress and motivation levels.

[0794] "A means of generating suggestions based on emotional data and notifying users" refers to a method of creating suggestions for project improvement based on emotion-related data and informing users of these suggestions.

[0795] "Means of visualizing and presenting the current situation through a device that users interact with" refers to technology that visually displays the progress of a project and shows its current status through a user-operable device.

[0796] This invention is a system for efficiently managing projects, and in particular, it employs a form that supports projects by combining user emotions. This system is mainly composed of three components: a server, a terminal, and a user. The server automatically analyzes project requirements and detects ambiguity using natural language processing technology. One example of a natural language processing library used here is spaCy for Python.

[0797] Users input information about the project's progress using a terminal. The server integrates with the project management tool and monitors the progress in real time. This ensures that if delays or problems occur, an alert is immediately sent to the terminal. The server also has technology to predict risks and issue warnings based on past project data, allowing potential problems to be detected in advance.

[0798] The emotion engine integrated into this system has the function of sensing the user's emotional state and evaluating their stress and motivation levels. During this process, facial and voice data is collected through the camera and microphone connected to the terminal and analyzed using emotion recognition technology. Specifically, the OpenCV image processing library is used for facial recognition, and a general speech recognition API is used for voice analysis.

[0799] Furthermore, it has a function that generates optimal suggestions based on the user's emotional data and notifies the user via the device. For example, if the system evaluates that the user is experiencing high levels of stress, it will generate specific suggestions such as, "Please consider taking a break."

[0800] As a concrete example, consider a situation where project meetings are frequently delayed. In this case, the server evaluates progress data and user emotions, generates suggestions such as "shorten the meeting" or "increase break time," and notifies the user via their terminal. This makes it possible to maintain user motivation while ensuring the efficient progress of the project.

[0801] Possible prompts to input into the generative AI model include the following:

[0802] "Please tell me the best way to support users in project management while considering their emotional state."

[0803] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0804] Step 1:

[0805] The user enters the project requirements.

[0806] Through the terminal's user interface, the user enters project requirements as text. This input text is sent to the server as the system's initial data. The server prepares to receive this data and stores it for subsequent processing.

[0807] Step 2:

[0808] The server automatically analyzes the requirements.

[0809] The server analyzes the input requirements using natural language processing techniques. Specifically, it uses the Python spaCy library to analyze words and context within the text, identifying ambiguities and missing information in the requirements. Based on this analysis, the server generates data to clarify the requirements.

[0810] Step 3:

[0811] The server generates feedback.

[0812] Based on the analysis results, the server generates feedback regarding ambiguous elements and missing information. This feedback is compiled into supplementary information and specific questions, and sent to the user via the terminal.

[0813] Step 4:

[0814] Users complete and modify the requirements.

[0815] The user reviews the feedback received from the server on their device. Based on this feedback, the user completes or corrects any unclear parts of the requirements. This allows the user to re-enter more accurate requirements.

[0816] Step 5:

[0817] The server monitors the progress.

[0818] During project execution, the server retrieves and monitors progress data in real time from the project management tool. For example, it retrieves progress data via an API and stores it in a database. This stored data is then analyzed to detect delays or anomalies in the project's progress.

[0819] Step 6:

[0820] The server senses and analyzes the user's emotions.

[0821] The server uses the camera and microphone connected to the device to collect facial and audio data in order to understand the user's emotions. Facial recognition is performed using OpenCV, and the emotional state is inferred from the audio data using a speech analysis API. The results are stored on the server as quantitative data.

[0822] Step 7:

[0823] The server generates the proposal.

[0824] The system analyzes user emotional data and generates suggestions to optimize project progress. For example, it might suggest taking a break if the user is experiencing high stress levels. These suggestions are compiled in text format and communicated to the user via their device.

[0825] Step 8:

[0826] The user will respond based on the suggestion.

[0827] Based on the suggestions received on the device, users adjust the project schedule and resource allocation. For example, by shortening meeting times based on the suggestions, users can manage the project more efficiently.

[0828] (Application Example 2)

[0829] 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".

[0830] Modern project management requires not only understanding progress and risks, but also comprehensively managing the work environment by considering the emotional factors of the people involved. However, traditional systems are limited to project progress and risk assessment, lacking the ability to evaluate and intervene in the emotions and stress levels of workers. This leads to challenges such as decreased work efficiency and reduced motivation due to accumulated stress.

[0831] 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.

[0832] In this invention, the server includes means for automatically analyzing requirements and detecting ambiguities, means for monitoring project progress in real time and detecting delays and problems, means for predicting risks and issuing warnings based on past project data, means for recognizing the emotional state of workers in the work environment and evaluating stress, and means for providing suggestions for improving work efficiency based on emotional state. This enables emotional intervention in project management, making it possible to create an efficient and sustainable work environment.

[0833] "Means for automatically analyzing requirements and detecting ambiguity" refers to a function that mechanically analyzes the requirements of a project and identifies unclear information.

[0834] "A means of monitoring project progress in real time and detecting delays and problems" refers to a function that instantly checks whether a project is progressing according to schedule and identifies delays or obstacles.

[0835] "A means of predicting risks and issuing warnings based on past project data" refers to a function that uses information from previous projects to foresee potential risks and notify users before problems occur.

[0836] "Means for recognizing the emotional state of workers in the work environment and assessing stress" refers to a function that identifies the emotions of employees in the workplace and uses that to determine their mental burden.

[0837] "A means of providing suggestions for improving work efficiency based on emotional state" refers to a function that presents suggestions for more effective work methods based on the mental state of the worker.

[0838] To implement this invention, a system is needed in which three parties—a server, a terminal, and a user—work together to perform their functions. The server acts as the central processing unit, analyzing various data for project management and providing real-time monitoring capabilities. The server utilizes the following hardware and software. The hardware requires a computer with a high-performance processor and sufficient storage capacity, and natural language processing technology is used for data processing. Specifically, Google Cloud Natural Language API and Azure Cognitive Services Face API are used to analyze requirements data and the emotional state of workers.

[0839] The device acts as an interface with the user, displaying various notifications and suggestions. Applications on the device are integrated into smart glasses and mobile devices, allowing users to check project progress and recommended actions based on their own emotional state. For example, if a worker is detected as experiencing high stress levels, the device might suggest, "Taking a 5-minute break may improve your efficiency."

[0840] Users input data about their work on the project into a terminal and receive feedback from the server. The user's emotional state is captured in real time through the camera and microphone of smart glasses, and an emotion engine evaluates stress and motivation levels.

[0841] As a concrete example, suppose a worker at a manufacturing facility notices that many delivery delays are occurring during the project's progress. This system can use emotion recognition to detect that the worker is stressed and suggest measures such as "shortening meetings" or "increasing break times."

[0842] An example of a prompt message is: "Explain the function that uses the camera and microphone of smart glasses to recognize emotions in a factory work environment, evaluates the worker's stress level in real time, and suggests efficient break times and work methods."

[0843] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0844] Step 1:

[0845] The user enters project requirements using a terminal. The entered requirement data is sent to the server using natural language processing technology. During this process, ambiguity and missing information in the data are detected. The input is the requirement text entered by the user on the terminal, and the output is the ambiguity detection results and requests for supplementary information as needed.

[0846] Step 2:

[0847] The server monitors the project's progress in real time. It retrieves progress data and verifies whether it is proceeding according to schedule. Its input is the project data in progress, and its output is the detection of delays and problems based on the progress. If there are any abnormalities in the progress, it prepares to notify the terminal.

[0848] Step 3:

[0849] Past project data is analyzed on the server to predict potential risks. Using past data as input, a risk model is used to calculate future risks, which are then output as warnings. If a specific project element contains potential risks, the server issues a warning to the user via the terminal.

[0850] Step 4:

[0851] The smart glasses recognize the user's emotional state in real time. This is achieved by combining facial expression analysis using a camera and voice analysis using a microphone. Real-time voice and video data of the user are used as input, and stress and motivation levels are output through emotion recognition software.

[0852] Step 5:

[0853] The server generates suggestions to improve work efficiency based on the user's emotional state. Using emotional state data and current work data as input, it outputs improvement suggestions using a generative AI model. The generated suggestions are displayed to the user via the terminal, such as "Take a 5-minute break."

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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."

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] The following is further disclosed regarding the embodiments described above.

[0876] (Claim 1)

[0877] In project management, a means to automatically analyze requirements and detect ambiguities,

[0878] A means to monitor the project's progress in real time and detect delays and problems,

[0879] A means of predicting risks and issuing warnings based on past project data,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, further comprising means for scrutinizing project requirements using natural language processing technology and requesting supplementary information.

[0883] (Claim 3)

[0884] The system according to claim 1, further comprising means for visualizing acquired progress data through a user interface and presenting the current status.

[0885] "Example 1"

[0886] (Claim 1)

[0887] A means for automatically analyzing information and detecting information ambiguity,

[0888] A means to continuously monitor the progress of work and detect delays and problems,

[0889] A means of predicting potential risks and issuing warnings based on past data sets,

[0890] A means of notifying users of the information processing results and supporting them in correcting the issues pointed out,

[0891] A means of conducting information predictive analysis using historical data,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising means for scrutinizing information requirements using language processing technology and requesting supplementary information.

[0895] (Claim 3)

[0896] The system according to claim 1, further comprising means for visualizing acquired progress data through a user interface and presenting the current status.

[0897] "Application Example 1"

[0898] (Claim 1)

[0899] In project management, a means to automatically analyze requirements and detect ambiguities,

[0900] A means to monitor the project's progress in real time and detect delays and problems,

[0901] A means of predicting risks and issuing warnings based on past project data,

[0902] A means to monitor the progress of each process in real time and to instruct necessary adjustments,

[0903] A means of analyzing progress data and controlling the robot's movements,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, further comprising means for scrutinizing project requirements using natural language processing technology and requesting supplementary information.

[0907] (Claim 3)

[0908] The system according to claim 1, further comprising means for visualizing acquired progress data through a user interface and presenting the current status.

[0909] "Example 2 of combining an emotion engine"

[0910] (Claim 1)

[0911] In project management, a means to automatically analyze requirements and detect ambiguities,

[0912] A means to monitor the project's progress in real time and detect delays and problems,

[0913] A means of predicting risks and issuing warnings based on past project data,

[0914] A means of sensing the user's emotions and evaluating their stress and motivational state,

[0915] A means of generating suggestions based on emotional data and notifying the user,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, further comprising means for scrutinizing project requirements using natural language processing technology and requesting supplementary information.

[0919] (Claim 3)

[0920] The system according to claim 1, further comprising means for visualizing acquired progress data through a device that allows the user to interact with it and for presenting the current status.

[0921] "Application example 2 when combining with an emotional engine"

[0922] (Claim 1)

[0923] In project management, a means to automatically analyze requirements and detect ambiguities,

[0924] A means to monitor the project's progress in real time and detect delays and problems,

[0925] A means of predicting risks and issuing warnings based on past project data,

[0926] A means of recognizing the emotional state of workers in the work environment and assessing stress,

[0927] A means of providing suggestions for improving work efficiency based on emotional state,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, further comprising means for scrutinizing project requirements using natural language processing technology and requesting supplementary information.

[0931] (Claim 3)

[0932] The system according to claim 1, further comprising means for visualizing acquired progress data and emotional state data through a user interface and presenting the current status. [Explanation of symbols]

[0933] 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. In project management, a means to automatically analyze requirements and detect ambiguities, A means to monitor the project's progress in real time and detect delays and problems, A means of predicting risks and issuing warnings based on past project data, A means to monitor the progress of each process in real time and to instruct necessary adjustments, A means of analyzing progress data and controlling the robot's movements, A system that includes this.

2. The system according to claim 1, further comprising means for scrutinizing project requirements using natural language processing technology and requesting supplementary information.

3. The system according to claim 1, further comprising means for visualizing acquired progress data through a user interface and presenting the current status.

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