system

Abstract

We provide the system. [Solution] Information processing means for receiving and storing project information, An analytical means for analyzing the progress based on the aforementioned project information, A generation means that detects risks from the analysis results of the aforementioned analysis means and proposes countermeasures, A notification means for notifying the user of risk notifications and proposed countermeasures from the generation means, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In project management, a large amount of time is spent on routine tasks, making it impossible to focus on essential project management. In addition, there are problems in that the progress and risks cannot be grasped in real time and there are challenges in the efficient management of resources. Therefore, there is a need to provide an effective management system for improving the success rate of projects. To solve these problems, there is a need for a system that monitors the progress of a project in real time and automates the early detection of risks and the proposal of countermeasures.

Means for Solving the Problems

[0005] This invention provides information processing means for receiving and storing project information, and operates analysis means for analyzing the progress of a project. Furthermore, it improves efficiency in project management by employing generation means that detect risks from the analysis results and propose necessary countermeasures. In addition, the invention discloses a system that includes notification means for notifying the user of risk notifications and countermeasure proposals from the generation means. This system, in particular, uses a generation AI model to analyze delays in progress and resource shortages in real time, supporting project managers in responding quickly.

[0006] "Project information" refers to all data related to the progress of a project, including task names, start dates, planned end dates, resource information, and so on.

[0007] "Information processing means" refers to means for appropriately storing received project information and performing necessary data processing.

[0008] "Analysis tools" are means for analyzing the progress of a project based on stored project information and for evaluating potential risks.

[0009] A "generation method" is a means of detecting risks from the analysis results obtained by an analysis method and proposing countermeasures against them.

[0010] A "notification means" is a means of communicating risk notifications and countermeasure proposals created from a generation means to the user.

[0011] A "generative AI model" is an artificial intelligence model used to predict delays in progress and predict resource shortages.

[0012] "Progress status" refers to data or information that shows the current progress or achievement status of a project.

[0013] "Risk" refers to potential problems or obstacles that may arise during the progress of a project, and is a concern for successfully completing the plan.

[0014] "Proposed countermeasures" refer to information that proposes effective countermeasures for the detected risks. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

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

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

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

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

[0020] In the following embodiments, the 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.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that utilizes generative AI to monitor project progress in real time, enabling early detection of risks and proposal of countermeasures, in order to improve efficiency in project management.

[0037] First, the user enters task and resource information related to the project via a terminal. The entered information is structured as data necessary for project progress management and sent to the server.

[0038] Next, the server saves the received project information to the database. This information includes task progress and resource allocation, and serves as the basis for analysis.

[0039] The server analyzes its progress using AI generated from stored data. In particular, it performs analyses to predict delays and detect the risk of resource shortages, and periodically evaluates whether or not risks exist.

[0040] If a risk is detected, the server automatically generates a proposed solution. This solution includes specific countermeasures, such as reallocating resources to resolve delays or resetting the schedule.

[0041] The generated risk notification and proposed countermeasures are communicated to the user via the terminal. For example, if a task exceeds its scheduled time, the server generates a notification stating, "Risk detected: Task A is behind schedule. Proposed countermeasure: Allocate additional resources within the budget."

[0042] Users can review these notifications and choose from the provided solutions or take alternative actions at their own discretion. This system frees project managers from managing cumbersome tasks, allowing them to focus on the essential management of the project.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users input new project tasks, progress updates, and resource information via their terminals. This includes the task name, start date, planned end date, and details of the resources required for that task.

[0046] Step 2:

[0047] The terminal formats the input information and sends the data to the server using a communication protocol. At this stage, the data is converted to the requested format.

[0048] Step 3:

[0049] The server receives project information sent from the terminal and stores it in the database. The data is organized and stored in the relevant tables and registered as an ongoing project.

[0050] Step 4:

[0051] The server periodically analyzes the stored project data. Here, a generated AI model is used to monitor progress in real time and predict delays and resource shortages.

[0052] Step 5:

[0053] If the server detects a risk based on the analysis results, it will generate a proposed solution to address that risk. This proposal may include specific resource reallocations, task prioritization, and additional measures.

[0054] Step 6:

[0055] The server sends the generated risk notification and suggested countermeasures to the terminal. This is issued as an alert or notification to the user.

[0056] Step 7:

[0057] The device displays the received risks and suggestions in the user interface. The user reviews this and selects the necessary actions to take.

[0058] Step 8:

[0059] The countermeasures selected by the user are fed back to the server via the terminal and processed as instructions for implementation.

[0060] Step 9:

[0061] The server updates the project data, including the selected countermeasures, and resumes real-time monitoring in the next cycle. This ensures a continuous risk assessment of the project's progress.

[0062] (Example 1)

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

[0064] In project management, delays in detecting changes in progress and risks often make appropriate responses difficult. This situation can lead to overall project delays and wasted resources, thus requiring efficient management. Furthermore, it is crucial to identify risks associated with project progress early and take swift, appropriate countermeasures. Current systems face challenges in effectively and efficiently implementing these processes.

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

[0066] In this invention, the server includes information processing means for inputting, receiving, structuring, and storing project-related data; analysis means using a generative AI model to analyze the project-related data and evaluate its progress; and generation means for detecting risks from the evaluation results by the analysis means and automatically generating optimal countermeasures. This makes it possible to detect project risks early and take appropriate countermeasures quickly.

[0067] "Project-related data" refers to digital information necessary for project progress management, including task names, deadlines, assigned personnel, and resource status.

[0068] "Information processing means" refers to a means of organizing and structuring data entered by a user and transmitting that data to a server in an appropriate format.

[0069] A "generative AI model" is a model that uses artificial intelligence technology to analyze the progress of a project based on saved project-related data, and automatically predict risks and propose optimizations.

[0070] "Analysis methods" refer to the process of analyzing project-related data using a generative AI model, evaluating progress, and detecting risks.

[0071] The "generation means" refers to a function that automatically detects risks and generates optimal countermeasures using evaluation results from the analysis means.

[0072] "Notification means" refers to a process or technology for communicating generated risk notifications and proposed countermeasures to the user, and includes notifications via a terminal.

[0073] A "terminal" refers to electronic devices such as computer devices and mobile devices that users use to input project-related data or receive risk notifications.

[0074] This invention is a system for improving efficiency in project management. It utilizes a generative AI model to analyze project progress, enabling early risk detection and automated countermeasures. It is primarily implemented via servers, terminals, and users.

[0075] First, users input project-related data using a dedicated terminal or project management tool. This includes task names, deadlines, and resource status. The terminal structures this data and sends it to the server. The data is encrypted and transmitted securely over the network.

[0076] The server saves received project-related data to a database in real time. Based on the saved data, the server uses a generative AI model to analyze the progress. The generative AI model evaluates the progress from the data, predicts delays, and optimizes resource allocation. This analysis makes it possible to precisely identify risks inherent in the project.

[0077] When a risk is detected, the server automatically generates a proposed solution. This solution draws on best practices and case study data from past projects and includes concrete solutions such as efficient resource reallocation and schedule adjustments.

[0078] The generated risk notifications and proposed countermeasures are immediately sent to the user via their device, and the user can view them on the device. The device utilizes push notifications and email notifications to deliver information accurately to the user.

[0079] For example, if a task exceeds its scheduled timeframe, the server uses a generative AI model to generate a notification such as, "Risk detection: Task A is behind schedule. Proposed solution: Allocate additional resources within budget." This system frees project managers from tedious tasks, allowing them to focus on the core aspects of the project.

[0080] An example of a prompt for a generating AI model is: "Based on the resource usage and task progress of Project Y, identify potential risks that may arise in the next week. Also, suggest measures to mitigate the impact of delays and resource shortages." This prompt is used to instruct the AI ​​on specific risks and their solutions, requesting accurate analysis and suggestions.

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

[0082] Step 1:

[0083] The user enters project-related task information into the terminal, and data is generated. Specific inputs include the task name, deadline, responsible person, and required resource information. The terminal structures this data and prepares it in a standard data format such as JSON. This structured data is then sent to the server.

[0084] Step 2:

[0085] The terminal encrypts structured project data for security purposes and sends it to the server over the network. The server decodes the received data, verifies its format, and then stores it in a database. The stored data serves as the foundation for analyzing project progress and resource status.

[0086] Step 3:

[0087] The server uses stored data to analyze progress based on a generative AI model. Project data is supplied to the AI ​​as input, and the AI ​​calculates progress rates and resource utilization. The generative AI model compares historical data with current information and performs data calculations to predict potential delays and resource shortages. As output, an overview of the analyzed risk information and necessary countermeasures is generated.

[0088] Step 4:

[0089] Based on the analysis results, the server generates proposed countermeasures if risks are identified. The generating AI model utilizes past project data and case studies to construct specific proposals such as resource reallocation and schedule adjustments to solve the problem. The completed proposals are prepared as data for notification to the user.

[0090] Step 5:

[0091] The server sends the generated risk notification and proposed countermeasures to the terminal, notifying the user. The terminal visualizes the received information and informs the user via push notification or email. The user can review the notification and either implement the provided countermeasures or consider their own countermeasures. This process improves the efficiency of project management.

[0092] (Application Example 1)

[0093] 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."

[0094] In factory production lines, there is a need to efficiently manage diverse tasks and resources to improve productivity. However, current systems make it difficult to monitor progress in real time and detect risks early, resulting in problems such as being unable to respond quickly to delays and resource shortages. The objective of this invention is to solve this problem and optimize the production line.

[0095] 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.

[0096] In this invention, the server includes an information processing means for receiving and storing project information, an analysis means for analyzing the progress based on the project information, and a generation means for detecting risks from the analysis results by the analysis means and proposing countermeasures. This makes it possible to monitor the progress of each process in factory equipment in real time, propose quick countermeasures when risks are detected, and improve overall productivity.

[0097] "Information processing means" refers to a device or system that has the function of receiving and storing project information.

[0098] "Analysis means" refers to a mechanism or process for analyzing the progress of a project based on the received project information.

[0099] A "generation means" is a system or device that has the ability to detect risks from analysis results and automatically propose appropriate countermeasures.

[0100] "Notification means" refers to a method or device for communicating generated risk notifications and proposed countermeasures to the user.

[0101] A "measuring device" is a device that has the function of acquiring data for monitoring the operation of factory equipment.

[0102] "Analysis means" refers to a mechanism that uses data obtained from measurement means to analyze the progress of the production line in real time.

[0103] An "operation guidance means" is a device or system that, based on the results obtained by the analysis means, detects delays or resource shortages, notifies the operator, and proposes resource reallocation or schedule adjustments.

[0104] The following system configuration is specifically considered as a form for carrying out the invention.

[0105] First, the server receives project information as a means of information processing and stores it in a database. This information is necessary for managing the progress of each project and task within the factory. The software used includes MySQL® for the database and Flask for data communication.

[0106] Next, the server uses the received project information and data measured by various sensors within the factory to analyze the progress. It uses a generative AI model to predict potential delays and resource shortages in real time. In this process, a machine learning model is constructed and analyzed using TENSORFLOW®.

[0107] As a generation method, the server automatically generates countermeasures based on the analysis results if risks are detected. These countermeasures include resource reallocation plans and schedule adjustment plans, helping users to quickly implement countermeasures. For example, a prompt message such as "Check the process progress of part A and propose countermeasures if there are delays" can be used.

[0108] The notification method involves transmitting risk notifications and proposed countermeasures generated via terminal devices to users. An on-screen interface is provided to allow users to easily review notifications. Terminal devices include smartphones and tablets.

[0109] As a concrete example of its use, if the assembly of a certain part is behind schedule on a factory production line, the server can detect the delay in real time and quickly notify the assembly staff with a message such as, "Assembly of part A is behind schedule. Please move resources from process B." In this way, it is possible to improve production efficiency and achieve a smooth manufacturing process.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The server receives project information from terminals and data measured from sensors within the factory. Project task information and sensor data are provided as input, and this data is stored in a database. This data serves as the basis for subsequent analysis.

[0113] Step 2:

[0114] The server is called to analyze stored project information and sensor data, and uses a generative AI model to analyze progress. It processes the input data to predict delays and resource shortages, and generates the results. Here, TensorFlow is used to perform predictions using a machine learning model.

[0115] Step 3:

[0116] Based on the analysis results, the server automatically generates countermeasures if risks are detected. These generated countermeasures include specific resource reallocations and schedule adjustments, which are then output to the user. Prompt messages may be used in the generation process.

[0117] Step 4:

[0118] The server transmits the generated risk notification and proposed countermeasures to the terminal via a notification system. It receives the proposed countermeasures as input and outputs them to the user for visualization, such as on a screen display. The terminal is assumed to be a smartphone or tablet.

[0119] Step 5:

[0120] The user reviews the risk notification and suggested countermeasures displayed on their device, selects one of the proposed solutions, or independently decides on a new solution. The user's chosen countermeasure is then entered back into the server and re-evaluated and implemented in the next cycle.

[0121] 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.

[0122] This invention is a project management system that combines an emotion engine that recognizes user emotions, and is designed not only to efficiently manage the progress of projects but also to optimize notification content by taking into account the user's emotional state.

[0123] First, the user enters project-related information via a terminal. This includes information about new tasks, progress, and resources. The terminal formats this information and sends it to the server.

[0124] The server stores received project information in a database and uses AI to monitor project progress in real time. It predicts delays and resource shortages, detects risks as needed, and generates countermeasures.

[0125] The emotion engine is built into the user's device and analyzes the user's emotions through voice recognition and facial recognition using the camera. For example, if the server determines that the user is feeling stressed, it will adjust the content and tone of risk notifications to be gentler, taking care not to put excessive pressure on the user.

[0126] The device displays risk notifications and suggested countermeasures sent from the server in its user interface, but because adjustments are made by the emotion engine, the user receives optimized information.

[0127] For example, if a user is facing a resource shortage risk in a "new product development project," the server would normally notify them of this risk immediately. However, if the emotion engine determines that it is necessary to calm the user's emotions, a gentler notification such as, "The current problem is a resource shortage. Let's review the next steps together and think of a solution," will be displayed on the device.

[0128] Thus, this invention combines emotion recognition with project management to provide project management support tailored to the user's emotional state. This enables users to manage projects efficiently while reducing their psychological burden.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] Users input new project tasks, progress updates, and resource information via their terminals. This includes task details, start date, estimated end date, and the type and amount of resources required.

[0132] Step 2:

[0133] The terminal formats the entered information appropriately and sends the data to the server via a communication protocol. The data is then converted to the format required for project management.

[0134] Step 3:

[0135] The server stores the received project information in a database. It organizes the data into the appropriate tables and prepares to record it as an ongoing project.

[0136] Step 4:

[0137] The server uses AI-generated data based on stored data to periodically analyze the project's progress. Here, it assesses potential delays and resource shortages, and detects any risks.

[0138] Step 5:

[0139] When the server detects a risk, it generates a proposed solution. Specifically, this solution may include suggestions such as securing additional resources or rescheduling tasks.

[0140] Step 6:

[0141] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, it can measure stress levels based on the user's tone of voice and facial expressions.

[0142] Step 7:

[0143] The server, based on the emotion engine's assessment, provides the user with risk notifications and suggested countermeasures through notification channels, using appropriate tone and timing. It employs an approach tailored to the user's emotional state.

[0144] Step 8:

[0145] The device displays risk notifications and suggested actions, tailored by an emotion engine, in its user interface. Based on this information, the user devises appropriate countermeasures and selects actionable steps.

[0146] Step 9:

[0147] The user's selected action is fed back to the server via the device. The server receives the feedback information, updates the project data, and prepares to incorporate it into the next cycle of project progress.

[0148] (Example 2)

[0149] 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".

[0150] Traditional project management systems provide information for managing progress and risks, but they lack the flexibility to consider the emotional state of users. This creates a problem where users experience stress or excessive pressure, hindering the efficient progress of projects.

[0151] 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.

[0152] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing progress and evaluating emotional state, and generation means for detecting risks and adjusting and proposing countermeasures based on the user's emotional state. This enables flexible project management that responds to the user's emotional state.

[0153] "Information processing means" refers to a device or system that has the function of receiving project information and storing that information.

[0154] "Analysis means" refers to a device or system for analyzing the progress of a project based on project information and for evaluating the emotional state of the user.

[0155] A "generation means" is a device or system that detects risks from the results of an analysis means and adjusts and proposes countermeasures based on the user's emotional state.

[0156] A "notification means" is a device or system for informing a user of risk notifications and proposed countermeasures from a generation means in a manner that takes into account the user's emotional state.

[0157] A "generative AI model" is an artificial intelligence model used to analyze the progress and risks of a project and to make predictions and suggestions as needed.

[0158] An "emotion engine" is a device or software that analyzes a user's voice and facial expressions and evaluates their emotional state.

[0159] A "terminal device" is an electronic device used by a user to operate an interface and has the function of inputting and outputting information.

[0160] An "interface" is a screen or control panel on a terminal device that facilitates the exchange of information between the user and the system.

[0161] This invention is a system that analyzes user emotions and optimizes project management based on those emotions. The following describes an embodiment of the system in detail.

[0162] First, the user inputs project-related information using a terminal. The terminal converts this information into a predetermined format and sends it to the server using a secure communication method. The terminal has a built-in emotion engine that analyzes the user's voice and facial expressions in real time and evaluates their emotional state. The emotion engine uses a voice recognition device and a camera to detect the user's stress level and changes in emotions.

[0163] The server stores the received project information in a database. A generative AI model is used here to analyze the project's progress and resources. This model predicts delays and resource shortages, and identifies risks as needed. The server also receives feedback from an emotion engine and generates risk notifications and mitigation suggestions that take the user's psychological state into consideration.

[0164] For example, if a resource shortage is detected in a "new product development project," the server would normally notify the user of the risk immediately. However, if the emotion engine determines that the user is experiencing stress, the server will notify the user in a gentler tone, saying, "The current problem is a resource shortage. Let's review the next steps together and think of a solution."

[0165] The terminal displays optimized risk notifications and mitigation suggestions sent from the server in its user interface. The displayed information is intuitive and emotionally resonant, and presented in a way that is easy for the user to understand.

[0166] For the generative AI model, it is possible to use prompts such as, "Please tell me how to customize notification content based on the user's emotional state." This allows for flexible, human-centered project management and reduces the psychological burden on the user.

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

[0168] Step 1:

[0169] The user enters project-related data into the terminal. Specifically, they enter information about new tasks, progress, and resources. This input data is converted to a predetermined format within the terminal. The converted data is then ready to be sent to the server.

[0170] Step 2:

[0171] The terminal sends formatted data to the server using a secure communication method. The transmitted data includes the overall status and details of the project. Through this process, the server receives complete project information.

[0172] Step 3:

[0173] The server stores the received data in a database. Next, a generative AI model is used to analyze the project's progress and resource usage. The generative AI model executes mathematical algorithms to predict delays and resource shortages. As a result of this analysis, project risks are identified, and a list of possible countermeasures is output.

[0174] Step 4:

[0175] An emotion engine built into the device analyzes the user's voice and facial expressions in real time. A voice recognition device and camera are used to evaluate the user's emotional state and determine stress levels, etc. These evaluation results are sent to a server.

[0176] Step 5:

[0177] The server uses the results of the emotion engine's evaluation to adjust the generated risk notifications and suggested actions. During this process, if the user is experiencing stress, consideration is given to softening the wording of the notification. The adjusted notifications and suggested actions are then sent to the device.

[0178] Step 6:

[0179] The terminal displays adjusted risk notifications and suggested countermeasures sent from the server in its user interface. The displayed content is designed to be intuitively understandable to the user and considerate of their emotional state. Based on this, the user can take appropriate action.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] In project management, users are required to properly understand the progress and risks and take countermeasures. However, conventional notification systems cannot respond to users' emotional states, which can lead to stress and confusion. Therefore, a flexible notification method that takes users' emotions into consideration is necessary.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing the progress based on the project information, and emotion analysis means for recognizing the user's emotions. This enables project management that is easy for the user to understand and reduces stress by providing appropriate risk notifications and countermeasure suggestions in accordance with the user's emotions.

[0185] "Information processing means" refers to the function of a computer or electronic device for receiving and storing project information.

[0186] "Analysis means" refers to a computer program or device capable of analyzing the progress of a project based on the received project information.

[0187] "Generating means" refers to a computer or system that has the function of detecting risks based on analysis results and proposing appropriate countermeasures.

[0188] "Emotional analysis means" refers to a device or system consisting of sensors and software that can recognize and analyze a user's emotional state.

[0189] A "notification means" is a device or interface that has the ability to provide the user with generated risk notifications and suggested countermeasures.

[0190] This invention begins with the user inputting project-related information. The terminal receives this information, formats it, and sends it to the server. The server stores the project information using information processing means and analyzes the project's progress in detail using analysis means. Here, a generative AI model is utilized to predict delays and resource shortages.

[0191] The server uses the results obtained by the analysis means to detect risks using the generation means and devise countermeasures. Furthermore, the emotion analysis means analyzes the user's voice and facial expressions in real time on the terminal. This emotion information is sent to the server and considered together with the risk countermeasures acquired by the generation means.

[0192] Using notification methods, the server sends risk alerts and suggested countermeasures to the user, but this information is subtly adjusted according to the user's emotional state. For example, if the user is showing signs of stress, the notification will be delivered in a gentle tone. The device displays this optimized information intuitively to the user.

[0193] For example, if the emotion analysis system determines that a customer at a retail store is having trouble choosing a product, a prompt message such as "At this stage, would you like us to help you find the product you're looking for?" will be displayed on the terminal. In this way, the invention reduces the psychological burden on the user and enables smoother project management and customer service.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The user uses a terminal to input project-related information (e.g., tasks, progress, resource information). The terminal formats this data and prepares it for transmission to the server. The input here is the user's project data, and the output is formatted data.

[0197] Step 2:

[0198] The server receives project information sent from the terminal and stores it as structured data using information processing tools. At this time, preparations are made for using a generative AI model. The input is formatted project information, and the output is the stored data.

[0199] Step 3:

[0200] The server analyzes stored project information using analytical tools to predict the risk of delays and resource shortages in the project's progress. A generative AI model is used in this process. The input is stored project data, and the output is the risk analysis results.

[0201] Step 4:

[0202] The server uses a generation mechanism to detect risks from the obtained risk analysis results and proposes corresponding countermeasures. In this case, the input is the risk analysis results, and the output is the proposed countermeasures.

[0203] Step 5:

[0204] The emotion analysis system built into the device analyzes the user's voice and facial expressions in real time. This collects the user's emotional state as data and sends it to a server. The input is the user's facial expressions and voice data, and the output is the user's emotional state.

[0205] Step 6:

[0206] The server receives information about the user's emotional state and adjusts risk notifications and countermeasures according to the user's emotional state. This adjustment is performed through a notification mechanism and incorporates prompt messages. The input is the user's emotional state and the proposed countermeasures, and the output is the adjusted notification content.

[0207] Step 7:

[0208] The device displays tailored risk notifications and suggested countermeasures to the user. These notifications are presented in a gentle tone to aid user understanding. The input is the tailored notification content, and the output is what is displayed to the user.

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

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

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

[0212] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0225] This invention is a system that utilizes generative AI to monitor project progress in real time, enabling early detection of risks and proposal of countermeasures, in order to improve efficiency in project management.

[0226] First, the user enters task and resource information related to the project via a terminal. The entered information is structured as data necessary for project progress management and sent to the server.

[0227] Next, the server saves the received project information to the database. This information includes task progress and resource allocation, and serves as the basis for analysis.

[0228] The server analyzes its progress using AI generated from stored data. In particular, it performs analyses to predict delays and detect the risk of resource shortages, and periodically evaluates whether or not risks exist.

[0229] If a risk is detected, the server automatically generates a proposed solution. This solution includes specific countermeasures, such as reallocating resources to resolve delays or resetting the schedule.

[0230] The generated risk notification and proposed countermeasures are communicated to the user via the terminal. For example, if a task exceeds its scheduled time, the server generates a notification stating, "Risk detected: Task A is behind schedule. Proposed countermeasure: Allocate additional resources within the budget."

[0231] Users can review these notifications and choose from the provided solutions or take alternative actions at their own discretion. This system frees project managers from managing cumbersome tasks, allowing them to focus on the essential management of the project.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] Users input new project tasks, progress updates, and resource information via their terminals. This includes the task name, start date, planned end date, and details of the resources required for that task.

[0235] Step 2:

[0236] The terminal formats the input information and sends the data to the server using a communication protocol. At this stage, the data is converted to the requested format.

[0237] Step 3:

[0238] The server receives project information sent from the terminal and stores it in the database. The data is organized and stored in the relevant tables and registered as an ongoing project.

[0239] Step 4:

[0240] The server periodically analyzes the stored project data. Here, a generated AI model is used to monitor progress in real time and predict delays and resource shortages.

[0241] Step 5:

[0242] If the server detects a risk based on the analysis results, it will generate a proposed solution to address that risk. This proposal may include specific resource reallocations, task prioritization, and additional measures.

[0243] Step 6:

[0244] The server sends the generated risk notification and suggested countermeasures to the terminal. This is issued as an alert or notification to the user.

[0245] Step 7:

[0246] The device displays the received risks and suggestions in the user interface. The user reviews this and selects the necessary actions to take.

[0247] Step 8:

[0248] The countermeasures selected by the user are fed back to the server via the terminal and processed as instructions for implementation.

[0249] Step 9:

[0250] The server updates the project data, including the selected countermeasures, and resumes real-time monitoring in the next cycle. This ensures a continuous risk assessment of the project's progress.

[0251] (Example 1)

[0252] 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."

[0253] In project management, delays in detecting changes in progress and risks often make appropriate responses difficult. This situation can lead to overall project delays and wasted resources, thus requiring efficient management. Furthermore, it is crucial to identify risks associated with project progress early and take swift, appropriate countermeasures. Current systems face challenges in effectively and efficiently implementing these processes.

[0254] 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.

[0255] In this invention, the server includes information processing means for inputting, receiving, structuring, and storing project-related data; analysis means using a generative AI model to analyze the project-related data and evaluate its progress; and generation means for detecting risks from the evaluation results by the analysis means and automatically generating optimal countermeasures. This makes it possible to detect project risks early and take appropriate countermeasures quickly.

[0256] "Project-related data" refers to digital information necessary for project progress management, including task names, deadlines, assigned personnel, and resource status.

[0257] "Information processing means" refers to a means of organizing and structuring data entered by a user and transmitting that data to a server in an appropriate format.

[0258] A "generative AI model" is a model that uses artificial intelligence technology to analyze the progress of a project based on saved project-related data, and automatically predict risks and propose optimizations.

[0259] "Analysis methods" refer to the process of analyzing project-related data using a generative AI model, evaluating progress, and detecting risks.

[0260] The "generation means" refers to a function that automatically detects risks and generates optimal countermeasures using evaluation results from the analysis means.

[0261] "Notification means" refers to a process or technology for communicating generated risk notifications and proposed countermeasures to the user, and includes notifications via a terminal.

[0262] A "terminal" refers to electronic devices such as computer devices and mobile devices that users use to input project-related data or receive risk notifications.

[0263] This invention is a system for improving efficiency in project management. It utilizes a generative AI model to analyze project progress, enabling early risk detection and automated countermeasures. It is primarily implemented via servers, terminals, and users.

[0264] First, users input project-related data using a dedicated terminal or project management tool. This includes task names, deadlines, and resource status. The terminal structures this data and sends it to the server. The data is encrypted and transmitted securely over the network.

[0265] The server saves received project-related data to a database in real time. Based on the saved data, the server uses a generative AI model to analyze the progress. The generative AI model evaluates the progress from the data, predicts delays, and optimizes resource allocation. This analysis makes it possible to precisely identify risks inherent in the project.

[0266] When a risk is detected, the server automatically generates a proposed solution. This solution draws on best practices and case study data from past projects and includes concrete solutions such as efficient resource reallocation and schedule adjustments.

[0267] The generated risk notifications and proposed countermeasures are immediately sent to the user via their device, and the user can view them on the device. The device utilizes push notifications and email notifications to deliver information accurately to the user.

[0268] For example, if a task exceeds its scheduled timeframe, the server uses a generative AI model to generate a notification such as, "Risk detection: Task A is behind schedule. Proposed solution: Allocate additional resources within budget." This system frees project managers from tedious tasks, allowing them to focus on the core aspects of the project.

[0269] An example of a prompt for a generating AI model is: "Based on the resource usage and task progress of Project Y, identify potential risks that may arise in the next week. Also, suggest measures to mitigate the impact of delays and resource shortages." This prompt is used to instruct the AI ​​on specific risks and their solutions, requesting accurate analysis and suggestions.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The user enters project-related task information into the terminal, and data is generated. Specific inputs include the task name, deadline, responsible person, and required resource information. The terminal structures this data and prepares it in a standard data format such as JSON. This structured data is then sent to the server.

[0273] Step 2:

[0274] The terminal encrypts structured project data for security purposes and sends it to the server over the network. The server decodes the received data, verifies its format, and then stores it in a database. The stored data serves as the foundation for analyzing project progress and resource status.

[0275] Step 3:

[0276] The server uses stored data to analyze progress based on a generative AI model. Project data is supplied to the AI ​​as input, and the AI ​​calculates progress rates and resource utilization. The generative AI model compares historical data with current information and performs data calculations to predict potential delays and resource shortages. As output, an overview of the analyzed risk information and necessary countermeasures is generated.

[0277] Step 4:

[0278] Based on the analysis results, the server generates proposed countermeasures if risks are identified. The generating AI model utilizes past project data and case studies to construct specific proposals such as resource reallocation and schedule adjustments to solve the problem. The completed proposals are prepared as data for notification to the user.

[0279] Step 5:

[0280] The server sends the generated risk notification and countermeasure proposal to the terminal and notifies the user. The terminal visualizes the received information and notifies the user via push notification or email. The user can check the notification and execute the provided countermeasures or consider their own countermeasures. This process improves the management efficiency of the project.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] In a factory production line, it is required to efficiently manage various tasks and resources and improve productivity. However, in the current system, it is difficult to monitor progress in real time and detect risks at an early stage, resulting in a problem that delays and resource shortages cannot be quickly addressed. Solving this problem and optimizing the production line is the object of this invention.

[0284] The specific processing by the specific processing unit 290 of the data processing device in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing the progress based on the project information, and generation means for detecting risks from the analysis results of the analysis means and proposing countermeasures. Thereby, it becomes possible to monitor the progress of each process in factory facilities in real time, propose prompt countermeasures when risks are detected, and improve overall productivity.

[0286] The "information processing means" is a device or system having a function of receiving and storing project information.

[0287] The "analysis means" refers to a mechanism or process for analyzing the progress based on the received project information.

[0288] A "generation means" is a system or device that has the ability to detect risks from analysis results and automatically propose appropriate countermeasures.

[0289] "Notification means" refers to a method or device for communicating generated risk notifications and proposed countermeasures to the user.

[0290] A "measuring device" is a device that has the function of acquiring data for monitoring the operation of factory equipment.

[0291] "Analysis means" refers to a mechanism that uses data obtained from measurement means to analyze the progress of the production line in real time.

[0292] An "operation guidance means" is a device or system that, based on the results obtained by the analysis means, detects delays or resource shortages, notifies the operator, and proposes resource reallocation or schedule adjustments.

[0293] The following system configuration is specifically considered as a form for carrying out the invention.

[0294] First, the server receives project information as a means of information processing and stores it in a database. This information is necessary for managing the progress of each project and task within the factory. The software used includes MySQL for the database and Flask for data communication.

[0295] Next, the server uses the received project information and data measured by various sensors within the factory to analyze the progress. It uses a generative AI model to predict potential delays and resource shortages in real time. In this process, a machine learning model is built using TensorFlow, and the analysis is performed.

[0296] As a generation method, the server automatically generates countermeasures based on the analysis results if risks are detected. These countermeasures include resource reallocation plans and schedule adjustment plans, helping users to quickly implement countermeasures. For example, a prompt message such as "Check the process progress of part A and propose countermeasures if there are delays" can be used.

[0297] The notification method involves transmitting risk notifications and proposed countermeasures generated via terminal devices to users. An on-screen interface is provided to allow users to easily review notifications. Terminal devices include smartphones and tablets.

[0298] As a concrete example of its use, if the assembly of a certain part is behind schedule on a factory production line, the server can detect the delay in real time and quickly notify the assembly staff with a message such as, "Assembly of part A is behind schedule. Please move resources from process B." In this way, it is possible to improve production efficiency and achieve a smooth manufacturing process.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] The server receives project information from terminals and data measured from sensors within the factory. Project task information and sensor data are provided as input, and this data is stored in a database. This data serves as the basis for subsequent analysis.

[0302] Step 2:

[0303] The server is called to analyze stored project information and sensor data, and uses a generative AI model to analyze progress. It processes the input data to predict delays and resource shortages, and generates the results. Here, TensorFlow is used to perform predictions using a machine learning model.

[0304] Step 3:

[0305] Based on the analysis results, when the server detects a risk, it automatically generates a countermeasure plan. The generated countermeasure plan includes specific resource reallocation and schedule adjustment, which serve as the output for communicating to the user. A prompt sentence may be used for generation.

[0306] Step 4:

[0307] The server relays the generated risk notification and countermeasure plan to the terminal through the notification means. It receives the countermeasure plan as input and outputs it for visualizing it for the user, such as on a screen. The terminal is assumed to be a smartphone or a tablet.

[0308] Step 5:

[0309] The user checks the risk notification and countermeasure plan displayed on the terminal and selects the presented solution or determines a new solution independently. The countermeasure selected by the user is input to the server again and is re-evaluated and implemented in the next cycle.

[0310] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0311] This invention is a project management system combined with an emotion engine that recognizes the user's emotion, and is for not only efficiently managing the progress of a project but also optimizing the notification content in consideration of the user's emotional state.

[0312] First, the user inputs project-related information through the terminal. This includes information on new tasks, progress, and resources. The terminal formats this information and transmits it to the server.

[0313] The server stores received project information in a database and uses AI to monitor project progress in real time. It predicts delays and resource shortages, detects risks as needed, and generates countermeasures.

[0314] The emotion engine is built into the user's device and analyzes the user's emotions through voice recognition and facial recognition using the camera. For example, if the server determines that the user is feeling stressed, it will adjust the content and tone of risk notifications to be gentler, taking care not to put excessive pressure on the user.

[0315] The device displays risk notifications and suggested countermeasures sent from the server in its user interface, but because adjustments are made by the emotion engine, the user receives optimized information.

[0316] For example, if a user is facing a resource shortage risk in a "new product development project," the server would normally notify them of this risk immediately. However, if the emotion engine determines that it is necessary to calm the user's emotions, a gentler notification such as, "The current problem is a resource shortage. Let's review the next steps together and think of a solution," will be displayed on the device.

[0317] Thus, this invention combines emotion recognition with project management to provide project management support tailored to the user's emotional state. This enables users to manage projects efficiently while reducing their psychological burden.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] Users input new project tasks, progress updates, and resource information via their terminals. This includes task details, start date, estimated end date, and the type and amount of resources required.

[0321] Step 2:

[0322] The terminal formats the entered information appropriately and sends the data to the server via a communication protocol. The data is then converted to the format required for project management.

[0323] Step 3:

[0324] The server stores the received project information in a database. It organizes the data into the appropriate tables and prepares to record it as an ongoing project.

[0325] Step 4:

[0326] The server uses AI-generated data based on stored data to periodically analyze the project's progress. Here, it assesses potential delays and resource shortages, and detects any risks.

[0327] Step 5:

[0328] When the server detects a risk, it generates a proposed solution. Specifically, this solution may include suggestions such as securing additional resources or rescheduling tasks.

[0329] Step 6:

[0330] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, it can measure stress levels based on the user's tone of voice and facial expressions.

[0331] Step 7:

[0332] The server, based on the emotion engine's assessment, provides the user with risk notifications and suggested countermeasures through notification channels, using appropriate tone and timing. It employs an approach tailored to the user's emotional state.

[0333] Step 8:

[0334] The device displays risk notifications and suggested actions, tailored by an emotion engine, in its user interface. Based on this information, the user devises appropriate countermeasures and selects actionable steps.

[0335] Step 9:

[0336] The user's selected action is fed back to the server via the device. The server receives the feedback information, updates the project data, and prepares to incorporate it into the next cycle of project progress.

[0337] (Example 2)

[0338] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0339] Traditional project management systems provide information for managing progress and risks, but they lack the flexibility to consider the emotional state of users. This creates a problem where users experience stress or excessive pressure, hindering the efficient progress of projects.

[0340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0341] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing progress and evaluating emotional state, and generation means for detecting risks and adjusting and proposing countermeasures based on the user's emotional state. This enables flexible project management that responds to the user's emotional state.

[0342] "Information processing means" refers to a device or system that has the function of receiving project information and storing that information.

[0343] "Analysis means" refers to a device or system for analyzing the progress of a project based on project information and for evaluating the emotional state of the user.

[0344] A "generation means" is a device or system that detects risks from the results of an analysis means and adjusts and proposes countermeasures based on the user's emotional state.

[0345] A "notification means" is a device or system for informing a user of risk notifications and proposed countermeasures from a generation means in a manner that takes into account the user's emotional state.

[0346] A "generative AI model" is an artificial intelligence model used to analyze the progress and risks of a project and to make predictions and suggestions as needed.

[0347] An "emotion engine" is a device or software that analyzes a user's voice and facial expressions and evaluates their emotional state.

[0348] A "terminal device" is an electronic device used by a user to operate an interface and has the function of inputting and outputting information.

[0349] An "interface" is a screen or control panel on a terminal device that facilitates the exchange of information between the user and the system.

[0350] This invention is a system that analyzes user emotions and optimizes project management based on those emotions. The following describes an embodiment of the system in detail.

[0351] First, the user inputs project-related information using a terminal. The terminal converts this information into a predetermined format and sends it to the server using a secure communication method. The terminal has a built-in emotion engine that analyzes the user's voice and facial expressions in real time and evaluates their emotional state. The emotion engine uses a voice recognition device and a camera to detect the user's stress level and changes in emotions.

[0352] The server stores the received project information in a database. A generative AI model is used here to analyze the project's progress and resources. This model predicts delays and resource shortages, and identifies risks as needed. The server also receives feedback from an emotion engine and generates risk notifications and mitigation suggestions that take the user's psychological state into consideration.

[0353] For example, if a resource shortage is detected in a "new product development project," the server would normally notify the user of the risk immediately. However, if the emotion engine determines that the user is experiencing stress, the server will notify the user in a gentler tone, saying, "The current problem is a resource shortage. Let's review the next steps together and think of a solution."

[0354] The terminal displays optimized risk notifications and mitigation suggestions sent from the server in its user interface. The displayed information is intuitive and emotionally resonant, and presented in a way that is easy for the user to understand.

[0355] For the generative AI model, it is possible to use prompts such as, "Please tell me how to customize notification content based on the user's emotional state." This allows for flexible, human-centered project management and reduces the psychological burden on the user.

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

[0357] Step 1:

[0358] The user enters project-related data into the terminal. Specifically, they enter information about new tasks, progress, and resources. This input data is converted to a predetermined format within the terminal. The converted data is then ready to be sent to the server.

[0359] Step 2:

[0360] The terminal sends formatted data to the server using a secure communication method. The transmitted data includes the overall status and details of the project. Through this process, the server receives complete project information.

[0361] Step 3:

[0362] The server stores the received data in a database. Next, a generative AI model is used to analyze the project's progress and resource usage. The generative AI model executes mathematical algorithms to predict delays and resource shortages. As a result of this analysis, project risks are identified, and a list of possible countermeasures is output.

[0363] Step 4:

[0364] An emotion engine built into the device analyzes the user's voice and facial expressions in real time. A voice recognition device and camera are used to evaluate the user's emotional state and determine stress levels, etc. These evaluation results are sent to a server.

[0365] Step 5:

[0366] The server uses the results of the emotion engine's evaluation to adjust the generated risk notifications and suggested actions. During this process, if the user is experiencing stress, consideration is given to softening the wording of the notification. The adjusted notifications and suggested actions are then sent to the device.

[0367] Step 6:

[0368] The terminal displays adjusted risk notifications and suggested countermeasures sent from the server in its user interface. The displayed content is designed to be intuitively understandable to the user and considerate of their emotional state. Based on this, the user can take appropriate action.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] In project management, users are required to properly understand the progress and risks and take countermeasures. However, conventional notification systems cannot respond to users' emotional states, which can lead to stress and confusion. Therefore, a flexible notification method that takes users' emotions into consideration is necessary.

[0372] 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.

[0373] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing the progress based on the project information, and emotion analysis means for recognizing the user's emotions. This enables project management that is easy for the user to understand and reduces stress by providing appropriate risk notifications and countermeasure suggestions in accordance with the user's emotions.

[0374] "Information processing means" refers to the function of a computer or electronic device for receiving and storing project information.

[0375] "Analysis means" refers to a computer program or device capable of analyzing the progress of a project based on the received project information.

[0376] "Generating means" refers to a computer or system that has the function of detecting risks based on analysis results and proposing appropriate countermeasures.

[0377] "Emotional analysis means" refers to a device or system consisting of sensors and software that can recognize and analyze a user's emotional state.

[0378] A "notification means" is a device or interface that has the ability to provide the user with generated risk notifications and suggested countermeasures.

[0379] This invention begins with the user inputting project-related information. The terminal receives this information, formats it, and sends it to the server. The server stores the project information using information processing means and analyzes the project's progress in detail using analysis means. Here, a generative AI model is utilized to predict delays and resource shortages.

[0380] The server uses the results obtained by the analysis means to detect risks using the generation means and devise countermeasures. Furthermore, the emotion analysis means analyzes the user's voice and facial expressions in real time on the terminal. This emotion information is sent to the server and considered together with the risk countermeasures acquired by the generation means.

[0381] Using notification methods, the server sends risk alerts and suggested countermeasures to the user, but this information is subtly adjusted according to the user's emotional state. For example, if the user is showing signs of stress, the notification will be delivered in a gentle tone. The device displays this optimized information intuitively to the user.

[0382] For example, if the emotion analysis system determines that a customer at a retail store is having trouble choosing a product, a prompt message such as "At this stage, would you like us to help you find the product you're looking for?" will be displayed on the terminal. In this way, the invention reduces the psychological burden on the user and enables smoother project management and customer service.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The user uses a terminal to input project-related information (e.g., tasks, progress, resource information). The terminal formats this data and prepares it for transmission to the server. The input here is the user's project data, and the output is formatted data.

[0386] Step 2:

[0387] The server receives project information sent from the terminal and stores it as structured data using information processing tools. At this time, preparations are made for using a generative AI model. The input is formatted project information, and the output is the stored data.

[0388] Step 3:

[0389] The server analyzes stored project information using analytical tools to predict the risk of delays and resource shortages in the project's progress. A generative AI model is used in this process. The input is stored project data, and the output is the risk analysis results.

[0390] Step 4:

[0391] The server uses a generation mechanism to detect risks from the obtained risk analysis results and proposes corresponding countermeasures. In this case, the input is the risk analysis results, and the output is the proposed countermeasures.

[0392] Step 5:

[0393] The emotion analysis system built into the device analyzes the user's voice and facial expressions in real time. This collects the user's emotional state as data and sends it to a server. The input is the user's facial expressions and voice data, and the output is the user's emotional state.

[0394] Step 6:

[0395] The server receives information about the user's emotional state and adjusts risk notifications and countermeasures according to the user's emotional state. This adjustment is performed through a notification mechanism and incorporates prompt messages. The input is the user's emotional state and the proposed countermeasures, and the output is the adjusted notification content.

[0396] Step 7:

[0397] The device displays tailored risk notifications and suggested countermeasures to the user. These notifications are presented in a gentle tone to aid user understanding. The input is the tailored notification content, and the output is what is displayed to the user.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] This invention is a system that utilizes generative AI to monitor project progress in real time, enabling early detection of risks and proposal of countermeasures, in order to improve efficiency in project management.

[0415] First, the user enters task and resource information related to the project via a terminal. The entered information is structured as data necessary for project progress management and sent to the server.

[0416] Next, the server saves the received project information to the database. This information includes task progress and resource allocation, and serves as the basis for analysis.

[0417] The server analyzes its progress using AI generated from stored data. In particular, it performs analyses to predict delays and detect the risk of resource shortages, and periodically evaluates whether or not risks exist.

[0418] If a risk is detected, the server automatically generates a proposed solution. This solution includes specific countermeasures, such as reallocating resources to resolve delays or resetting the schedule.

[0419] The generated risk notification and proposed countermeasures are communicated to the user via the terminal. For example, if a task exceeds its scheduled time, the server generates a notification stating, "Risk detected: Task A is behind schedule. Proposed countermeasure: Allocate additional resources within the budget."

[0420] Users can review these notifications and choose from the provided solutions or take alternative actions at their own discretion. This system frees project managers from managing cumbersome tasks, allowing them to focus on the essential management of the project.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] Users input new project tasks, progress updates, and resource information via their terminals. This includes the task name, start date, planned end date, and details of the resources required for that task.

[0424] Step 2:

[0425] The terminal formats the input information and sends the data to the server using a communication protocol. At this stage, the data is converted to the requested format.

[0426] Step 3:

[0427] The server receives project information sent from the terminal and stores it in the database. The data is organized and stored in the relevant tables and registered as an ongoing project.

[0428] Step 4:

[0429] The server periodically analyzes the stored project data. Here, a generated AI model is used to monitor progress in real time and predict delays and resource shortages.

[0430] Step 5:

[0431] If the server detects a risk based on the analysis results, it will generate a proposed solution to address that risk. This proposal may include specific resource reallocations, task prioritization, and additional measures.

[0432] Step 6:

[0433] The server sends the generated risk notification and suggested countermeasures to the terminal. This is issued as an alert or notification to the user.

[0434] Step 7:

[0435] The device displays the received risks and suggestions in the user interface. The user reviews this and selects the necessary actions to take.

[0436] Step 8:

[0437] The countermeasures selected by the user are fed back to the server via the terminal and processed as instructions for implementation.

[0438] Step 9:

[0439] The server updates the project data, including the selected countermeasures, and resumes real-time monitoring in the next cycle. This ensures a continuous risk assessment of the project's progress.

[0440] (Example 1)

[0441] 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."

[0442] In project management, delays in detecting changes in progress and risks often make appropriate responses difficult. This situation can lead to overall project delays and wasted resources, thus requiring efficient management. Furthermore, it is crucial to identify risks associated with project progress early and take swift, appropriate countermeasures. Current systems face challenges in effectively and efficiently implementing these processes.

[0443] 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.

[0444] In this invention, the server includes information processing means for inputting, receiving, structuring, and storing project-related data; analysis means using a generative AI model to analyze the project-related data and evaluate its progress; and generation means for detecting risks from the evaluation results by the analysis means and automatically generating optimal countermeasures. This makes it possible to detect project risks early and take appropriate countermeasures quickly.

[0445] "Project-related data" refers to digital information necessary for project progress management, including task names, deadlines, assigned personnel, and resource status.

[0446] "Information processing means" refers to a means of organizing and structuring data entered by a user and transmitting that data to a server in an appropriate format.

[0447] A "generative AI model" is a model that uses artificial intelligence technology to analyze the progress of a project based on saved project-related data, and automatically predict risks and propose optimizations.

[0448] "Analysis methods" refer to the process of analyzing project-related data using a generative AI model, evaluating progress, and detecting risks.

[0449] The "generation means" refers to a function that automatically detects risks and generates optimal countermeasures using evaluation results from the analysis means.

[0450] "Notification means" refers to a process or technology for communicating generated risk notifications and proposed countermeasures to the user, and includes notifications via a terminal.

[0451] A "terminal" refers to electronic devices such as computer devices and mobile devices that users use to input project-related data or receive risk notifications.

[0452] This invention is a system for improving efficiency in project management. It utilizes a generative AI model to analyze project progress, enabling early risk detection and automated countermeasures. It is primarily implemented via servers, terminals, and users.

[0453] First, users input project-related data using a dedicated terminal or project management tool. This includes task names, deadlines, and resource status. The terminal structures this data and sends it to the server. The data is encrypted and transmitted securely over the network.

[0454] The server saves received project-related data to a database in real time. Based on the saved data, the server uses a generative AI model to analyze the progress. The generative AI model evaluates the progress from the data, predicts delays, and optimizes resource allocation. This analysis makes it possible to precisely identify risks inherent in the project.

[0455] When a risk is detected, the server automatically generates a proposed solution. This solution draws on best practices and case study data from past projects and includes concrete solutions such as efficient resource reallocation and schedule adjustments.

[0456] The generated risk notifications and proposed countermeasures are immediately sent to the user via their device, and the user can view them on the device. The device utilizes push notifications and email notifications to deliver information accurately to the user.

[0457] For example, if a task exceeds its scheduled timeframe, the server uses a generative AI model to generate a notification such as, "Risk detection: Task A is behind schedule. Proposed solution: Allocate additional resources within budget." This system frees project managers from tedious tasks, allowing them to focus on the core aspects of the project.

[0458] An example of a prompt for a generating AI model is: "Based on the resource usage and task progress of Project Y, identify potential risks that may arise in the next week. Also, suggest measures to mitigate the impact of delays and resource shortages." This prompt is used to instruct the AI ​​on specific risks and their solutions, requesting accurate analysis and suggestions.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The user enters project-related task information into the terminal, and data is generated. Specific inputs include the task name, deadline, responsible person, and required resource information. The terminal structures this data and prepares it in a standard data format such as JSON. This structured data is then sent to the server.

[0462] Step 2:

[0463] The terminal encrypts structured project data for security purposes and sends it to the server over the network. The server decodes the received data, verifies its format, and then stores it in a database. The stored data serves as the foundation for analyzing project progress and resource status.

[0464] Step 3:

[0465] The server uses stored data to analyze progress based on a generative AI model. Project data is supplied to the AI ​​as input, and the AI ​​calculates progress rates and resource utilization. The generative AI model compares historical data with current information and performs data calculations to predict potential delays and resource shortages. As output, an overview of the analyzed risk information and necessary countermeasures is generated.

[0466] Step 4:

[0467] Based on the analysis results, the server generates proposed countermeasures if risks are identified. The generating AI model utilizes past project data and case studies to construct specific proposals such as resource reallocation and schedule adjustments to solve the problem. The completed proposals are prepared as data for notification to the user.

[0468] Step 5:

[0469] The server sends the generated risk notification and proposed countermeasures to the terminal, notifying the user. The terminal visualizes the received information and informs the user via push notification or email. The user can review the notification and either implement the provided countermeasures or consider their own countermeasures. This process improves the efficiency of project management.

[0470] (Application Example 1)

[0471] 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."

[0472] In factory production lines, there is a need to efficiently manage diverse tasks and resources to improve productivity. However, current systems make it difficult to monitor progress in real time and detect risks early, resulting in problems such as being unable to respond quickly to delays and resource shortages. The objective of this invention is to solve this problem and optimize the production line.

[0473] 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.

[0474] In this invention, the server includes an information processing means for receiving and storing project information, an analysis means for analyzing the progress based on the project information, and a generation means for detecting risks from the analysis results by the analysis means and proposing countermeasures. This makes it possible to monitor the progress of each process in factory equipment in real time, propose quick countermeasures when risks are detected, and improve overall productivity.

[0475] "Information processing means" refers to a device or system that has the function of receiving and storing project information.

[0476] "Analysis means" refers to a mechanism or process for analyzing the progress of a project based on the received project information.

[0477] A "generation means" is a system or device that has the ability to detect risks from analysis results and automatically propose appropriate countermeasures.

[0478] "Notification means" refers to a method or device for communicating generated risk notifications and proposed countermeasures to the user.

[0479] A "measuring device" is a device that has the function of acquiring data for monitoring the operation of factory equipment.

[0480] "Analysis means" refers to a mechanism that uses data obtained from measurement means to analyze the progress of the production line in real time.

[0481] An "operation guidance means" is a device or system that, based on the results obtained by the analysis means, detects delays or resource shortages, notifies the operator, and proposes resource reallocation or schedule adjustments.

[0482] The following system configuration is specifically considered as a form for carrying out the invention.

[0483] First, the server receives project information as a means of information processing and stores it in a database. This information is necessary for managing the progress of each project and task within the factory. The software used includes MySQL for the database and Flask for data communication.

[0484] Next, the server uses the received project information and data measured by various sensors within the factory to analyze the progress. It uses a generative AI model to predict potential delays and resource shortages in real time. In this process, a machine learning model is built using TensorFlow, and the analysis is performed.

[0485] As a generation method, the server automatically generates countermeasures based on the analysis results if risks are detected. These countermeasures include resource reallocation plans and schedule adjustment plans, helping users to quickly implement countermeasures. For example, a prompt message such as "Check the process progress of part A and propose countermeasures if there are delays" can be used.

[0486] The notification method involves transmitting risk notifications and proposed countermeasures generated via terminal devices to users. An on-screen interface is provided to allow users to easily review notifications. Terminal devices include smartphones and tablets.

[0487] As a concrete example of its use, if the assembly of a certain part is behind schedule on a factory production line, the server can detect the delay in real time and quickly notify the assembly staff with a message such as, "Assembly of part A is behind schedule. Please move resources from process B." In this way, it is possible to improve production efficiency and achieve a smooth manufacturing process.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The server receives project information from terminals and data measured from sensors within the factory. Project task information and sensor data are provided as input, and this data is stored in a database. This data serves as the basis for subsequent analysis.

[0491] Step 2:

[0492] The server is called to analyze stored project information and sensor data, and uses a generative AI model to analyze progress. It processes the input data to predict delays and resource shortages, and generates the results. Here, TensorFlow is used to perform predictions using a machine learning model.

[0493] Step 3:

[0494] Based on the analysis results, the server automatically generates countermeasures if risks are detected. These generated countermeasures include specific resource reallocations and schedule adjustments, which are then output to the user. Prompt messages may be used in the generation process.

[0495] Step 4:

[0496] The server transmits the generated risk notification and proposed countermeasures to the terminal via a notification system. It receives the proposed countermeasures as input and outputs them to the user for visualization, such as on a screen display. The terminal is assumed to be a smartphone or tablet.

[0497] Step 5:

[0498] The user reviews the risk notification and suggested countermeasures displayed on their device, selects one of the proposed solutions, or independently decides on a new solution. The user's chosen countermeasure is then entered back into the server and re-evaluated and implemented in the next cycle.

[0499] 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.

[0500] This invention is a project management system that combines an emotion engine that recognizes user emotions, and is designed not only to efficiently manage the progress of projects but also to optimize notification content by taking into account the user's emotional state.

[0501] First, the user enters project-related information via a terminal. This includes information about new tasks, progress, and resources. The terminal formats this information and sends it to the server.

[0502] The server stores received project information in a database and uses AI to monitor project progress in real time. It predicts delays and resource shortages, detects risks as needed, and generates countermeasures.

[0503] The emotion engine is built into the user's device and analyzes the user's emotions through voice recognition and facial recognition using the camera. For example, if the server determines that the user is feeling stressed, it will adjust the content and tone of risk notifications to be gentler, taking care not to put excessive pressure on the user.

[0504] The device displays risk notifications and suggested countermeasures sent from the server in its user interface, but because adjustments are made by the emotion engine, the user receives optimized information.

[0505] For example, if a user is facing a resource shortage risk in a "new product development project," the server would normally notify them of this risk immediately. However, if the emotion engine determines that it is necessary to calm the user's emotions, a gentler notification such as, "The current problem is a resource shortage. Let's review the next steps together and think of a solution," will be displayed on the device.

[0506] Thus, this invention combines emotion recognition with project management to provide project management support tailored to the user's emotional state. This enables users to manage projects efficiently while reducing their psychological burden.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] Users input new project tasks, progress updates, and resource information via their terminals. This includes task details, start date, estimated end date, and the type and amount of resources required.

[0510] Step 2:

[0511] The terminal formats the entered information appropriately and sends the data to the server via a communication protocol. The data is then converted to the format required for project management.

[0512] Step 3:

[0513] The server stores the received project information in a database. It organizes the data into the appropriate tables and prepares to record it as an ongoing project.

[0514] Step 4:

[0515] The server uses AI-generated data based on stored data to periodically analyze the project's progress. Here, it assesses potential delays and resource shortages, and detects any risks.

[0516] Step 5:

[0517] When the server detects a risk, it generates a proposed solution. Specifically, this solution may include suggestions such as securing additional resources or rescheduling tasks.

[0518] Step 6:

[0519] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, it can measure stress levels based on the user's tone of voice and facial expressions.

[0520] Step 7:

[0521] The server, based on the emotion engine's assessment, provides the user with risk notifications and suggested countermeasures through notification channels, using appropriate tone and timing. It employs an approach tailored to the user's emotional state.

[0522] Step 8:

[0523] The device displays risk notifications and suggested actions, tailored by an emotion engine, in its user interface. Based on this information, the user devises appropriate countermeasures and selects actionable steps.

[0524] Step 9:

[0525] The user's selected action is fed back to the server via the device. The server receives the feedback information, updates the project data, and prepares to incorporate it into the next cycle of project progress.

[0526] (Example 2)

[0527] 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."

[0528] Traditional project management systems provide information for managing progress and risks, but they lack the flexibility to consider the emotional state of users. This creates a problem where users experience stress or excessive pressure, hindering the efficient progress of projects.

[0529] 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.

[0530] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing progress and evaluating emotional state, and generation means for detecting risks and adjusting and proposing countermeasures based on the user's emotional state. This enables flexible project management that responds to the user's emotional state.

[0531] "Information processing means" refers to a device or system that has the function of receiving project information and storing that information.

[0532] "Analysis means" refers to a device or system for analyzing the progress of a project based on project information and for evaluating the emotional state of the user.

[0533] A "generation means" is a device or system that detects risks from the results of an analysis means and adjusts and proposes countermeasures based on the user's emotional state.

[0534] A "notification means" is a device or system for informing a user of risk notifications and proposed countermeasures from a generation means in a manner that takes into account the user's emotional state.

[0535] A "generative AI model" is an artificial intelligence model used to analyze the progress and risks of a project and to make predictions and suggestions as needed.

[0536] An "emotion engine" is a device or software that analyzes a user's voice and facial expressions and evaluates their emotional state.

[0537] A "terminal device" is an electronic device used by a user to operate an interface and has the function of inputting and outputting information.

[0538] An "interface" is a screen or control panel on a terminal device that facilitates the exchange of information between the user and the system.

[0539] This invention is a system that analyzes user emotions and optimizes project management based on those emotions. The following describes an embodiment of the system in detail.

[0540] First, the user inputs project-related information using a terminal. The terminal converts this information into a predetermined format and sends it to the server using a secure communication method. The terminal has a built-in emotion engine that analyzes the user's voice and facial expressions in real time and evaluates their emotional state. The emotion engine uses a voice recognition device and a camera to detect the user's stress level and changes in emotions.

[0541] The server stores the received project information in a database. A generative AI model is used here to analyze the project's progress and resources. This model predicts delays and resource shortages, and identifies risks as needed. The server also receives feedback from an emotion engine and generates risk notifications and mitigation suggestions that take the user's psychological state into consideration.

[0542] For example, if a resource shortage is detected in a "new product development project," the server would normally notify the user of the risk immediately. However, if the emotion engine determines that the user is experiencing stress, the server will notify the user in a gentler tone, saying, "The current problem is a resource shortage. Let's review the next steps together and think of a solution."

[0543] The terminal displays optimized risk notifications and mitigation suggestions sent from the server in its user interface. The displayed information is intuitive and emotionally resonant, and presented in a way that is easy for the user to understand.

[0544] For the generative AI model, it is possible to use prompts such as, "Please tell me how to customize notification content based on the user's emotional state." This allows for flexible, human-centered project management and reduces the psychological burden on the user.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] The user enters project-related data into the terminal. Specifically, they enter information about new tasks, progress, and resources. This input data is converted to a predetermined format within the terminal. The converted data is then ready to be sent to the server.

[0548] Step 2:

[0549] The terminal sends formatted data to the server using a secure communication method. The transmitted data includes the overall status and details of the project. Through this process, the server receives complete project information.

[0550] Step 3:

[0551] The server stores the received data in a database. Next, a generative AI model is used to analyze the project's progress and resource usage. The generative AI model executes mathematical algorithms to predict delays and resource shortages. As a result of this analysis, project risks are identified, and a list of possible countermeasures is output.

[0552] Step 4:

[0553] An emotion engine built into the device analyzes the user's voice and facial expressions in real time. A voice recognition device and camera are used to evaluate the user's emotional state and determine stress levels, etc. These evaluation results are sent to a server.

[0554] Step 5:

[0555] The server uses the results of the emotion engine's evaluation to adjust the generated risk notifications and suggested actions. During this process, if the user is experiencing stress, consideration is given to softening the wording of the notification. The adjusted notifications and suggested actions are then sent to the device.

[0556] Step 6:

[0557] The terminal displays adjusted risk notifications and suggested countermeasures sent from the server in its user interface. The displayed content is designed to be intuitively understandable to the user and considerate of their emotional state. Based on this, the user can take appropriate action.

[0558] (Application Example 2)

[0559] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0560] In project management, users are required to properly understand the progress and risks and take countermeasures. However, conventional notification systems cannot respond to users' emotional states, which can lead to stress and confusion. Therefore, a flexible notification method that takes users' emotions into consideration is necessary.

[0561] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0562] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing the progress based on the project information, and emotion analysis means for recognizing the user's emotions. This enables project management that is easy for the user to understand and reduces stress by providing appropriate risk notifications and countermeasure suggestions in accordance with the user's emotions.

[0563] "Information processing means" refers to the function of a computer or electronic device for receiving and storing project information.

[0564] "Analysis means" refers to a computer program or device capable of analyzing the progress of a project based on the received project information.

[0565] "Generating means" refers to a computer or system that has the function of detecting risks based on analysis results and proposing appropriate countermeasures.

[0566] "Emotional analysis means" refers to a device or system consisting of sensors and software that can recognize and analyze a user's emotional state.

[0567] A "notification means" is a device or interface that has the ability to provide the user with generated risk notifications and suggested countermeasures.

[0568] This invention begins with the user inputting project-related information. The terminal receives this information, formats it, and sends it to the server. The server stores the project information using information processing means and analyzes the project's progress in detail using analysis means. Here, a generative AI model is utilized to predict delays and resource shortages.

[0569] The server uses the results obtained by the analysis means to detect risks using the generation means and devise countermeasures. Furthermore, the emotion analysis means analyzes the user's voice and facial expressions in real time on the terminal. This emotion information is sent to the server and considered together with the risk countermeasures acquired by the generation means.

[0570] Using notification methods, the server sends risk alerts and suggested countermeasures to the user, but this information is subtly adjusted according to the user's emotional state. For example, if the user is showing signs of stress, the notification will be delivered in a gentle tone. The device displays this optimized information intuitively to the user.

[0571] For example, if the emotion analysis system determines that a customer at a retail store is having trouble choosing a product, a prompt message such as "At this stage, would you like us to help you find the product you're looking for?" will be displayed on the terminal. In this way, the invention reduces the psychological burden on the user and enables smoother project management and customer service.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The user uses a terminal to input project-related information (e.g., tasks, progress, resource information). The terminal formats this data and prepares it for transmission to the server. The input here is the user's project data, and the output is formatted data.

[0575] Step 2:

[0576] The server receives project information sent from the terminal and stores it as structured data using information processing tools. At this time, preparations are made for using a generative AI model. The input is formatted project information, and the output is the stored data.

[0577] Step 3:

[0578] The server analyzes stored project information using analytical tools to predict the risk of delays and resource shortages in the project's progress. A generative AI model is used in this process. The input is stored project data, and the output is the risk analysis results.

[0579] Step 4:

[0580] The server uses a generation mechanism to detect risks from the obtained risk analysis results and proposes corresponding countermeasures. In this case, the input is the risk analysis results, and the output is the proposed countermeasures.

[0581] Step 5:

[0582] The emotion analysis system built into the device analyzes the user's voice and facial expressions in real time. This collects the user's emotional state as data and sends it to a server. The input is the user's facial expressions and voice data, and the output is the user's emotional state.

[0583] Step 6:

[0584] The server receives information about the user's emotional state and adjusts risk notifications and countermeasures according to the user's emotional state. This adjustment is performed through a notification mechanism and incorporates prompt messages. The input is the user's emotional state and the proposed countermeasures, and the output is the adjusted notification content.

[0585] Step 7:

[0586] The device displays tailored risk notifications and suggested countermeasures to the user. These notifications are presented in a gentle tone to aid user understanding. The input is the tailored notification content, and the output is what is displayed to the user.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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".

[0604] This invention is a system that utilizes generative AI to monitor project progress in real time, enabling early detection of risks and proposal of countermeasures, in order to improve efficiency in project management.

[0605] First, the user enters task and resource information related to the project via a terminal. The entered information is structured as data necessary for project progress management and sent to the server.

[0606] Next, the server saves the received project information to the database. This information includes task progress and resource allocation, and serves as the basis for analysis.

[0607] The server analyzes its progress using AI generated from stored data. In particular, it performs analyses to predict delays and detect the risk of resource shortages, and periodically evaluates whether or not risks exist.

[0608] If a risk is detected, the server automatically generates a proposed solution. This solution includes specific countermeasures, such as reallocating resources to resolve delays or resetting the schedule.

[0609] The generated risk notification and proposed countermeasures are communicated to the user via the terminal. For example, if a task exceeds its scheduled time, the server generates a notification stating, "Risk detected: Task A is behind schedule. Proposed countermeasure: Allocate additional resources within the budget."

[0610] Users can review these notifications and choose from the provided solutions or take alternative actions at their own discretion. This system frees project managers from managing cumbersome tasks, allowing them to focus on the essential management of the project.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] Users input new project tasks, progress updates, and resource information via their terminals. This includes the task name, start date, planned end date, and details of the resources required for that task.

[0614] Step 2:

[0615] The terminal formats the input information and sends the data to the server using a communication protocol. At this stage, the data is converted to the requested format.

[0616] Step 3:

[0617] The server receives project information sent from the terminal and stores it in the database. The data is organized and stored in the relevant tables and registered as an ongoing project.

[0618] Step 4:

[0619] The server periodically analyzes the stored project data. Here, a generated AI model is used to monitor progress in real time and predict delays and resource shortages.

[0620] Step 5:

[0621] If the server detects a risk based on the analysis results, it will generate a proposed solution to address that risk. This proposal may include specific resource reallocations, task prioritization, and additional measures.

[0622] Step 6:

[0623] The server sends the generated risk notification and suggested countermeasures to the terminal. This is issued as an alert or notification to the user.

[0624] Step 7:

[0625] The device displays the received risks and suggestions in the user interface. The user reviews this and selects the necessary actions to take.

[0626] Step 8:

[0627] The countermeasures selected by the user are fed back to the server via the terminal and processed as instructions for implementation.

[0628] Step 9:

[0629] The server updates the project data, including the selected countermeasures, and resumes real-time monitoring in the next cycle. This ensures a continuous risk assessment of the project's progress.

[0630] (Example 1)

[0631] 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".

[0632] In project management, delays in detecting changes in progress and risks often make appropriate responses difficult. This situation can lead to overall project delays and wasted resources, thus requiring efficient management. Furthermore, it is crucial to identify risks associated with project progress early and take swift, appropriate countermeasures. Current systems face challenges in effectively and efficiently implementing these processes.

[0633] 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.

[0634] In this invention, the server includes information processing means for inputting, receiving, structuring, and storing project-related data; analysis means using a generative AI model to analyze the project-related data and evaluate its progress; and generation means for detecting risks from the evaluation results by the analysis means and automatically generating optimal countermeasures. This makes it possible to detect project risks early and take appropriate countermeasures quickly.

[0635] "Project-related data" refers to digital information necessary for project progress management, including task names, deadlines, assigned personnel, and resource status.

[0636] "Information processing means" refers to a means of organizing and structuring data entered by a user and transmitting that data to a server in an appropriate format.

[0637] A "generative AI model" is a model that uses artificial intelligence technology to analyze the progress of a project based on saved project-related data, and automatically predict risks and propose optimizations.

[0638] "Analysis methods" refer to the process of analyzing project-related data using a generative AI model, evaluating progress, and detecting risks.

[0639] The "generation means" refers to a function that automatically detects risks and generates optimal countermeasures using evaluation results from the analysis means.

[0640] "Notification means" refers to a process or technology for communicating generated risk notifications and proposed countermeasures to the user, and includes notifications via a terminal.

[0641] A "terminal" refers to electronic devices such as computer devices and mobile devices that users use to input project-related data or receive risk notifications.

[0642] This invention is a system for improving efficiency in project management. It utilizes a generative AI model to analyze project progress, enabling early risk detection and automated countermeasures. It is primarily implemented via servers, terminals, and users.

[0643] First, users input project-related data using a dedicated terminal or project management tool. This includes task names, deadlines, and resource status. The terminal structures this data and sends it to the server. The data is encrypted and transmitted securely over the network.

[0644] The server saves received project-related data to a database in real time. Based on the saved data, the server uses a generative AI model to analyze the progress. The generative AI model evaluates the progress from the data, predicts delays, and optimizes resource allocation. This analysis makes it possible to precisely identify risks inherent in the project.

[0645] When a risk is detected, the server automatically generates a proposed solution. This solution draws on best practices and case study data from past projects and includes concrete solutions such as efficient resource reallocation and schedule adjustments.

[0646] The generated risk notifications and proposed countermeasures are immediately sent to the user via their device, and the user can view them on the device. The device utilizes push notifications and email notifications to deliver information accurately to the user.

[0647] For example, if a task exceeds its scheduled timeframe, the server uses a generative AI model to generate a notification such as, "Risk detection: Task A is behind schedule. Proposed solution: Allocate additional resources within budget." This system frees project managers from tedious tasks, allowing them to focus on the core aspects of the project.

[0648] An example of a prompt for a generating AI model is: "Based on the resource usage and task progress of Project Y, identify potential risks that may arise in the next week. Also, suggest measures to mitigate the impact of delays and resource shortages." This prompt is used to instruct the AI ​​on specific risks and their solutions, requesting accurate analysis and suggestions.

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The user enters project-related task information into the terminal, and data is generated. Specific inputs include the task name, deadline, responsible person, and required resource information. The terminal structures this data and prepares it in a standard data format such as JSON. This structured data is then sent to the server.

[0652] Step 2:

[0653] The terminal encrypts structured project data for security purposes and sends it to the server over the network. The server decodes the received data, verifies its format, and then stores it in a database. The stored data serves as the foundation for analyzing project progress and resource status.

[0654] Step 3:

[0655] The server uses stored data to analyze progress based on a generative AI model. Project data is supplied to the AI ​​as input, and the AI ​​calculates progress rates and resource utilization. The generative AI model compares historical data with current information and performs data calculations to predict potential delays and resource shortages. As output, an overview of the analyzed risk information and necessary countermeasures is generated.

[0656] Step 4:

[0657] Based on the analysis results, the server generates proposed countermeasures if risks are identified. The generating AI model utilizes past project data and case studies to construct specific proposals such as resource reallocation and schedule adjustments to solve the problem. The completed proposals are prepared as data for notification to the user.

[0658] Step 5:

[0659] The server sends the generated risk notification and proposed countermeasures to the terminal, notifying the user. The terminal visualizes the received information and informs the user via push notification or email. The user can review the notification and either implement the provided countermeasures or consider their own countermeasures. This process improves the efficiency of project management.

[0660] (Application Example 1)

[0661] 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".

[0662] In factory production lines, there is a need to efficiently manage diverse tasks and resources to improve productivity. However, current systems make it difficult to monitor progress in real time and detect risks early, resulting in problems such as being unable to respond quickly to delays and resource shortages. The objective of this invention is to solve this problem and optimize the production line.

[0663] 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.

[0664] In this invention, the server includes an information processing means for receiving and storing project information, an analysis means for analyzing the progress based on the project information, and a generation means for detecting risks from the analysis results by the analysis means and proposing countermeasures. This makes it possible to monitor the progress of each process in factory equipment in real time, propose quick countermeasures when risks are detected, and improve overall productivity.

[0665] "Information processing means" refers to a device or system that has the function of receiving and storing project information.

[0666] "Analysis means" refers to a mechanism or process for analyzing the progress of a project based on the received project information.

[0667] A "generation means" is a system or device that has the ability to detect risks from analysis results and automatically propose appropriate countermeasures.

[0668] "Notification means" refers to a method or device for communicating generated risk notifications and proposed countermeasures to the user.

[0669] A "measuring device" is a device that has the function of acquiring data for monitoring the operation of factory equipment.

[0670] "Analysis means" refers to a mechanism that uses data obtained from measurement means to analyze the progress of the production line in real time.

[0671] An "operation guidance means" is a device or system that, based on the results obtained by the analysis means, detects delays or resource shortages, notifies the operator, and proposes resource reallocation or schedule adjustments.

[0672] The following system configuration is specifically considered as a form for carrying out the invention.

[0673] First, the server receives project information as a means of information processing and stores it in a database. This information is necessary for managing the progress of each project and task within the factory. The software used includes MySQL for the database and Flask for data communication.

[0674] Next, the server uses the received project information and data measured by various sensors within the factory to analyze the progress. It uses a generative AI model to predict potential delays and resource shortages in real time. In this process, a machine learning model is built using TensorFlow, and the analysis is performed.

[0675] As a generation method, the server automatically generates countermeasures based on the analysis results if risks are detected. These countermeasures include resource reallocation plans and schedule adjustment plans, helping users to quickly implement countermeasures. For example, a prompt message such as "Check the process progress of part A and propose countermeasures if there are delays" can be used.

[0676] The notification method involves transmitting risk notifications and proposed countermeasures generated via terminal devices to users. An on-screen interface is provided to allow users to easily review notifications. Terminal devices include smartphones and tablets.

[0677] As a concrete example of its use, if the assembly of a certain part is behind schedule on a factory production line, the server can detect the delay in real time and quickly notify the assembly staff with a message such as, "Assembly of part A is behind schedule. Please move resources from process B." In this way, it is possible to improve production efficiency and achieve a smooth manufacturing process.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The server receives project information from terminals and data measured from sensors within the factory. Project task information and sensor data are provided as input, and this data is stored in a database. This data serves as the basis for subsequent analysis.

[0681] Step 2:

[0682] The server is called to analyze stored project information and sensor data, and uses a generative AI model to analyze progress. It processes the input data to predict delays and resource shortages, and generates the results. Here, TensorFlow is used to perform predictions using a machine learning model.

[0683] Step 3:

[0684] Based on the analysis results, the server automatically generates countermeasures if risks are detected. These generated countermeasures include specific resource reallocations and schedule adjustments, which are then output to the user. Prompt messages may be used in the generation process.

[0685] Step 4:

[0686] The server transmits the generated risk notification and proposed countermeasures to the terminal via a notification system. It receives the proposed countermeasures as input and outputs them to the user for visualization, such as on a screen display. The terminal is assumed to be a smartphone or tablet.

[0687] Step 5:

[0688] The user reviews the risk notification and suggested countermeasures displayed on their device, selects one of the proposed solutions, or independently decides on a new solution. The user's chosen countermeasure is then entered back into the server and re-evaluated and implemented in the next cycle.

[0689] 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.

[0690] This invention is a project management system that combines an emotion engine that recognizes user emotions, and is designed not only to efficiently manage the progress of projects but also to optimize notification content by taking into account the user's emotional state.

[0691] First, the user enters project-related information via a terminal. This includes information about new tasks, progress, and resources. The terminal formats this information and sends it to the server.

[0692] The server stores received project information in a database and uses AI to monitor project progress in real time. It predicts delays and resource shortages, detects risks as needed, and generates countermeasures.

[0693] The emotion engine is built into the user's device and analyzes the user's emotions through voice recognition and facial recognition using the camera. For example, if the server determines that the user is feeling stressed, it will adjust the content and tone of risk notifications to be gentler, taking care not to put excessive pressure on the user.

[0694] The device displays risk notifications and suggested countermeasures sent from the server in its user interface, but because adjustments are made by the emotion engine, the user receives optimized information.

[0695] For example, if a user is facing a resource shortage risk in a "new product development project," the server would normally notify them of this risk immediately. However, if the emotion engine determines that it is necessary to calm the user's emotions, a gentler notification such as, "The current problem is a resource shortage. Let's review the next steps together and think of a solution," will be displayed on the device.

[0696] Thus, this invention combines emotion recognition with project management to provide project management support tailored to the user's emotional state. This enables users to manage projects efficiently while reducing their psychological burden.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] Users input new project tasks, progress updates, and resource information via their terminals. This includes task details, start date, estimated end date, and the type and amount of resources required.

[0700] Step 2:

[0701] The terminal formats the entered information appropriately and sends the data to the server via a communication protocol. The data is then converted to the format required for project management.

[0702] Step 3:

[0703] The server stores the received project information in a database. It organizes the data into the appropriate tables and prepares to record it as an ongoing project.

[0704] Step 4:

[0705] The server uses AI-generated data based on stored data to periodically analyze the project's progress. Here, it assesses potential delays and resource shortages, and detects any risks.

[0706] Step 5:

[0707] When the server detects a risk, it generates a proposed solution. Specifically, this solution may include suggestions such as securing additional resources or rescheduling tasks.

[0708] Step 6:

[0709] The device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotional state. For example, it can measure stress levels based on the user's tone of voice and facial expressions.

[0710] Step 7:

[0711] The server, based on the emotion engine's assessment, provides the user with risk notifications and suggested countermeasures through notification channels, using appropriate tone and timing. It employs an approach tailored to the user's emotional state.

[0712] Step 8:

[0713] The device displays risk notifications and suggested actions, tailored by an emotion engine, in its user interface. Based on this information, the user devises appropriate countermeasures and selects actionable steps.

[0714] Step 9:

[0715] The user's selected action is fed back to the server via the device. The server receives the feedback information, updates the project data, and prepares to incorporate it into the next cycle of project progress.

[0716] (Example 2)

[0717] 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".

[0718] Traditional project management systems provide information for managing progress and risks, but they lack the flexibility to consider the emotional state of users. This creates a problem where users experience stress or excessive pressure, hindering the efficient progress of projects.

[0719] 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.

[0720] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing progress and evaluating emotional state, and generation means for detecting risks and adjusting and proposing countermeasures based on the user's emotional state. This enables flexible project management that responds to the user's emotional state.

[0721] "Information processing means" refers to a device or system that has the function of receiving project information and storing that information.

[0722] "Analysis means" refers to a device or system for analyzing the progress of a project based on project information and for evaluating the emotional state of the user.

[0723] A "generation means" is a device or system that detects risks from the results of an analysis means and adjusts and proposes countermeasures based on the user's emotional state.

[0724] A "notification means" is a device or system for informing a user of risk notifications and proposed countermeasures from a generation means in a manner that takes into account the user's emotional state.

[0725] A "generative AI model" is an artificial intelligence model used to analyze the progress and risks of a project and to make predictions and suggestions as needed.

[0726] An "emotion engine" is a device or software that analyzes a user's voice and facial expressions and evaluates their emotional state.

[0727] A "terminal device" is an electronic device used by a user to operate an interface and has the function of inputting and outputting information.

[0728] An "interface" is a screen or control panel on a terminal device that facilitates the exchange of information between the user and the system.

[0729] This invention is a system that analyzes user emotions and optimizes project management based on those emotions. The following describes an embodiment of the system in detail.

[0730] First, the user inputs project-related information using a terminal. The terminal converts this information into a predetermined format and sends it to the server using a secure communication method. The terminal has a built-in emotion engine that analyzes the user's voice and facial expressions in real time and evaluates their emotional state. The emotion engine uses a voice recognition device and a camera to detect the user's stress level and changes in emotions.

[0731] The server stores the received project information in a database. A generative AI model is used here to analyze the project's progress and resources. This model predicts delays and resource shortages, and identifies risks as needed. The server also receives feedback from an emotion engine and generates risk notifications and mitigation suggestions that take the user's psychological state into consideration.

[0732] For example, if a resource shortage is detected in a "new product development project," the server would normally notify the user of the risk immediately. However, if the emotion engine determines that the user is experiencing stress, the server will notify the user in a gentler tone, saying, "The current problem is a resource shortage. Let's review the next steps together and think of a solution."

[0733] The terminal displays optimized risk notifications and mitigation suggestions sent from the server in its user interface. The displayed information is intuitive and emotionally resonant, and presented in a way that is easy for the user to understand.

[0734] For the generative AI model, it is possible to use prompts such as, "Please tell me how to customize notification content based on the user's emotional state." This allows for flexible, human-centered project management and reduces the psychological burden on the user.

[0735] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0736] Step 1:

[0737] The user enters project-related data into the terminal. Specifically, they enter information about new tasks, progress, and resources. This input data is converted to a predetermined format within the terminal. The converted data is then ready to be sent to the server.

[0738] Step 2:

[0739] The terminal sends formatted data to the server using a secure communication method. The transmitted data includes the overall status and details of the project. Through this process, the server receives complete project information.

[0740] Step 3:

[0741] The server stores the received data in a database. Next, a generative AI model is used to analyze the project's progress and resource usage. The generative AI model executes mathematical algorithms to predict delays and resource shortages. As a result of this analysis, project risks are identified, and a list of possible countermeasures is output.

[0742] Step 4:

[0743] An emotion engine built into the device analyzes the user's voice and facial expressions in real time. A voice recognition device and camera are used to evaluate the user's emotional state and determine stress levels, etc. These evaluation results are sent to a server.

[0744] Step 5:

[0745] The server uses the results of the emotion engine's evaluation to adjust the generated risk notifications and suggested actions. During this process, if the user is experiencing stress, consideration is given to softening the wording of the notification. The adjusted notifications and suggested actions are then sent to the device.

[0746] Step 6:

[0747] The terminal displays adjusted risk notifications and suggested countermeasures sent from the server in its user interface. The displayed content is designed to be intuitively understandable to the user and considerate of their emotional state. Based on this, the user can take appropriate action.

[0748] (Application Example 2)

[0749] 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".

[0750] In project management, users are required to properly understand the progress and risks and take countermeasures. However, conventional notification systems cannot respond to users' emotional states, which can lead to stress and confusion. Therefore, a flexible notification method that takes users' emotions into consideration is necessary.

[0751] 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.

[0752] In this invention, the server includes information processing means for receiving and storing project information, analysis means for analyzing the progress based on the project information, and emotion analysis means for recognizing the user's emotions. This enables project management that is easy for the user to understand and reduces stress by providing appropriate risk notifications and countermeasure suggestions in accordance with the user's emotions.

[0753] "Information processing means" refers to the function of a computer or electronic device for receiving and storing project information.

[0754] "Analysis means" refers to a computer program or device capable of analyzing the progress of a project based on the received project information.

[0755] "Generating means" refers to a computer or system that has the function of detecting risks based on analysis results and proposing appropriate countermeasures.

[0756] "Emotional analysis means" refers to a device or system consisting of sensors and software that can recognize and analyze a user's emotional state.

[0757] A "notification means" is a device or interface that has the ability to provide the user with generated risk notifications and suggested countermeasures.

[0758] This invention begins with the user inputting project-related information. The terminal receives this information, formats it, and sends it to the server. The server stores the project information using information processing means and analyzes the project's progress in detail using analysis means. Here, a generative AI model is utilized to predict delays and resource shortages.

[0759] The server uses the results obtained by the analysis means to detect risks using the generation means and devise countermeasures. Furthermore, the emotion analysis means analyzes the user's voice and facial expressions in real time on the terminal. This emotion information is sent to the server and considered together with the risk countermeasures acquired by the generation means.

[0760] Using notification methods, the server sends risk alerts and suggested countermeasures to the user, but this information is subtly adjusted according to the user's emotional state. For example, if the user is showing signs of stress, the notification will be delivered in a gentle tone. The device displays this optimized information intuitively to the user.

[0761] For example, if the emotion analysis system determines that a customer at a retail store is having trouble choosing a product, a prompt message such as "At this stage, would you like us to help you find the product you're looking for?" will be displayed on the terminal. In this way, the invention reduces the psychological burden on the user and enables smoother project management and customer service.

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The user uses a terminal to input project-related information (e.g., tasks, progress, resource information). The terminal formats this data and prepares it for transmission to the server. The input here is the user's project data, and the output is formatted data.

[0765] Step 2:

[0766] The server receives project information sent from the terminal and stores it as structured data using information processing tools. At this time, preparations are made for using a generative AI model. The input is formatted project information, and the output is the stored data.

[0767] Step 3:

[0768] The server analyzes stored project information using analytical tools to predict the risk of delays and resource shortages in the project's progress. A generative AI model is used in this process. The input is stored project data, and the output is the risk analysis results.

[0769] Step 4:

[0770] The server uses a generation mechanism to detect risks from the obtained risk analysis results and proposes corresponding countermeasures. In this case, the input is the risk analysis results, and the output is the proposed countermeasures.

[0771] Step 5:

[0772] The emotion analysis system built into the device analyzes the user's voice and facial expressions in real time. This collects the user's emotional state as data and sends it to a server. The input is the user's facial expressions and voice data, and the output is the user's emotional state.

[0773] Step 6:

[0774] The server receives information about the user's emotional state and adjusts risk notifications and countermeasures according to the user's emotional state. This adjustment is performed through a notification mechanism and incorporates prompt messages. The input is the user's emotional state and the proposed countermeasures, and the output is the adjusted notification content.

[0775] Step 7:

[0776] The device displays tailored risk notifications and suggested countermeasures to the user. These notifications are presented in a gentle tone to aid user understanding. The input is the tailored notification content, and the output is what is displayed to the user.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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."

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] The following is further disclosed regarding the embodiments described above.

[0799] (Claim 1)

[0800] Information processing means for receiving and storing project information,

[0801] An analytical means for analyzing the progress based on the aforementioned project information,

[0802] A generation means that detects risks from the analysis results of the aforementioned analysis means and proposes countermeasures,

[0803] A notification means for notifying the user of risk notifications and proposed countermeasures from the generation means,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, characterized in that the analysis means uses a generative AI model that predicts delays in progress and resource shortages.

[0807] (Claim 3)

[0808] The system according to claim 1, characterized in that the notification means includes an interface that displays risk notifications and suggested countermeasures to the user via a terminal device.

[0809] "Example 1"

[0810] (Claim 1)

[0811] An information processing system that inputs, receives, structures, and stores project-related data,

[0812] An analysis means using a generative AI model to analyze the aforementioned project-related data and evaluate its progress,

[0813] A generation means that detects risks from the evaluation results of the aforementioned analysis means and automatically generates optimal countermeasures,

[0814] A notification means that notifies the user of risk notifications and proposed countermeasures from the generation means via a terminal,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, characterized in that the analysis means uses a generative AI model that predicts delays in progress and optimizes resource allocation.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the notification means includes an interface that provides users with risk notifications and countermeasures suggestions using push notifications and email.

[0820] "Application Example 1"

[0821] (Claim 1)

[0822] Information processing means for receiving and storing project information,

[0823] An analytical means for analyzing the progress based on the aforementioned project information,

[0824] A generation means that detects risks from the analysis results of the aforementioned analysis means and proposes countermeasures,

[0825] A notification means for notifying the user of risk notifications and proposed countermeasures from the generation means,

[0826] Measurement means for monitoring the operation of factory equipment,

[0827] An analysis means for analyzing the progress of the production line in real time using data obtained by the measurement means,

[0828] When the aforementioned analysis means detects delays or resource shortages, the operation guidance means notifies the operator and proposes resource reallocation or schedule adjustments.

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, characterized in that the analysis means uses a generative AI model that predicts delays in progress and resource shortages.

[0832] (Claim 3)

[0833] The system according to claim 1, characterized in that the operation guidance means includes a screen display that shows risk notifications and countermeasure suggestions to the operator via a terminal device.

[0834] "Example 2 of combining an emotion engine"

[0835] (Claim 1)

[0836] Information processing means for receiving and storing project information,

[0837] An analytical means for analyzing the progress based on the aforementioned project information and evaluating the emotional state,

[0838] A generation means that detects risks from the analysis results of the aforementioned analysis means and adjusts and proposes countermeasures based on the user's emotional state,

[0839] A notification means that notifies the user of risk notifications and proposed countermeasures from the generation means in a manner that takes into account the user's emotional state,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, characterized in that the analysis means uses a generative AI model that predicts delays in progress and resource shortages, and an emotion engine for analyzing user emotions.

[0843] (Claim 3)

[0844] The system according to claim 1, characterized in that the notification means includes an interface that displays risk notifications and suggested countermeasures to the user in a gentle tone via a terminal device.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] Information processing means for receiving and storing project information,

[0848] An analytical means for analyzing the progress based on the aforementioned project information,

[0849] A generation means that detects risks from the analysis results of the aforementioned analysis means and proposes countermeasures,

[0850] A means of analyzing user emotions,

[0851] A notification means that adjusts and notifies the user of risk notifications and countermeasure suggestions from the generation means according to the user's emotional state,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, characterized in that the analysis means uses a generative AI model that predicts delays in progress and resource shortages.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that the notification means includes an interface that displays risk notifications and suggested countermeasures to the user via a terminal device. [Explanation of Symbols]

[0857] 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. Information processing means for receiving and storing project information, An analytical means for analyzing the progress based on the aforementioned project information, A generation means that detects risks from the analysis results of the aforementioned analysis means and proposes countermeasures, A notification means for notifying the user of risk notifications and proposed countermeasures from the generation means, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means uses a generative AI model that predicts delays in progress and resource shortages.

3. The system according to claim 1, characterized in that the notification means includes an interface for displaying risk notifications and suggested countermeasures to the user via a terminal device.

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