system

Abstract

We provide the system. [Solution] A means for collecting project-related data from an information gathering device and inputting it into an integrated database, A means of automatically organizing and prioritizing collected data based on relevance and importance using a machine learning device, A display method that visualizes project progress in real time, Analytical tools to predict potential problems and propose countermeasures, Through multilingual translation functionality, a translation method that facilitates communication between different languages, A means of proposing the optimal allocation of resources based on past success stories, A project management 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Modern project management is required to effectively organize a large amount of data obtained from various information sources, visualize the progress of a project, and predict potential problems. Currently, project managers have the burden of individually performing these tasks by using many tools, and fragmentation of information and inefficiency of communication are contributing factors to the failure of a project. Under such circumstances, in international projects, dealing with multiple languages and managing resources across different cultures are even more difficult. To address these issues, an integrated and efficient solution is needed.

Means for Solving the Problems

[0005] This invention provides a means for collecting project-related data in real time using an information gathering device and inputting it into an integrated database. Furthermore, it utilizes a machine learning device to organize and analyze the collected data and set priorities based on its relevance and importance. This makes it possible to display project progress in real time, allowing users to immediately grasp the situation. In addition, by providing analytical means to predict potential problems and suggest appropriate countermeasures, it prevents risks that would hinder project progress. In international projects, this system enables smooth communication through multilingual translation functions and further improves the overall efficiency of the project by proposing optimal resource allocation based on past success stories.

[0006] An "information gathering device" is a technological device that automatically collects project-related data from various sources and reflects it in an integrated database.

[0007] An "integrated database" is a database system that centrally manages collected project-related data, enabling efficient searching and analysis.

[0008] A "machine learning device" is a device equipped with algorithms that identify patterns in data and automatically organize and analyze information within a project.

[0009] "Real-time display" is a means of providing users with visually generated project data that has been collected and analyzed, almost instantly.

[0010] "Analysis tool" refers to a processing device or program that predicts potential problems in a project and presents appropriate countermeasures to the user.

[0011] "Multilingual translation functionality" is a technology that supports communication between different languages, enabling project members to interact across language barriers.

[0012] "Resource optimization" is the process of allocating resources such as personnel, materials, and time in the most effective way to streamline the progress of a project. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention relates to a project management system that utilizes an information gathering device, a machine learning device, and an analysis device. The aim of this system is to efficiently manage project progress through the coordinated operation of each functional device.

[0035] First, the server connects to information gathering devices and retrieves project-related data in real time from external tools. For example, it collects task update information and team member chat history via APIs of project task management tools and communication tools. This data is then stored by the server in an integrated database.

[0036] Next, the server uses machine learning to automatically organize the information stored in the integrated database. Leveraging AI models, it analyzes the relevance and importance of the data to prioritize each task. This process is reflected in a dashboard accessible to users from their devices, allowing them to visually track project progress in real time.

[0037] The analysis system uses machine learning to predict potential problems in a project and generate countermeasures. For example, if budget overruns or schedule delays are predicted, the server notifies the user of countermeasures based on these predictions, suggesting resource reallocation or schedule revisions.

[0038] Furthermore, the server utilizes a multilingual translation function to facilitate smooth communication in international projects. When a user sends a message in a different language, it is automatically translated into the native language of other members to aid understanding.

[0039] Finally, a resource optimization feature runs in the backend, helping users make optimal resource allocations based on data from past successful projects. For example, it helps maximize project efficiency by assigning members with the appropriate skills to specific tasks.

[0040] Thus, this system seamlessly integrates all processes, from information gathering and analysis to proposing solutions, translation, and resource allocation, supporting users in smoothly managing their projects.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server periodically retrieves project-related data via APIs from external tools. For example, it collects task progress information from task management tools and chat data between team members from communication tools, and stores them in an integrated database.

[0044] Step 2:

[0045] When a user uploads new project-related information via their device, the server receives the data and integrates it into the existing project database. This ensures that all project data remains up-to-date.

[0046] Step 3:

[0047] The server uses machine learning to analyze information in the integrated database and determine its relevance and importance. Based on the analysis results, it prioritizes each task and reflects its progress on a dashboard. Users can view this dashboard from their terminals, allowing them to instantly grasp the project's progress.

[0048] Step 4:

[0049] The server uses machine learning models to predict potential problems. Based on the predicted problems, it generates optimal solutions and notifies the user as an alert. The user can then use this information to consider countermeasures and utilize them in project management.

[0050] Step 5:

[0051] The server uses a multilingual translation function to automatically translate messages between different languages. In international projects, messages exchanged between members are translated into their respective native languages ​​and sent to their devices, enabling smooth communication.

[0052] Step 6:

[0053] The server uses data from past successful projects to optimize resource allocation. This suggests appropriate resource placement within the project team, allowing users to assign tasks and improve project efficiency.

[0054] (Example 1)

[0055] 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."

[0056] In modern project management, managing information using multiple tools presents challenges such as data dispersion and inefficient communication. Furthermore, the analysis of diverse data and language barriers in international projects complicate project progress management. Additionally, a lack of appropriate guidelines for optimal resource allocation can lead to decreased overall efficiency.

[0057] 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.

[0058] In this invention, the server includes means for collecting data from multiple tools through information acquisition means and inputting it into a centralized data storage means; means for automatically organizing the acquired information based on relevance and importance and setting task priorities using data analysis means; and representation means for visualizing the project progress in real time. This makes it possible to efficiently integrate dispersed data and easily grasp the progress of the entire project. Furthermore, task prioritization through analysis and multilingual support enable smooth communication and efficient resource allocation even in international projects.

[0059] "Information acquisition means" refers to methods for collecting data from multiple external tools and inputting it into a centralized data storage system.

[0060] A "data analysis method" is a method for automatically organizing acquired information based on its relevance and importance, and for setting task priorities.

[0061] "Means of expression" refers to methods for visually displaying the progress of a project in real time.

[0062] "Analysis methods" refer to methods for evaluating the progress of a project, predicting future challenges, and proposing solutions.

[0063] "Translation methods" refer to methods that use multilingual support functions to facilitate communication between different languages.

[0064] "Methods for proposing optimal resource allocation" refer to methods for indicating the most effective allocation of resources in a project, based on past successes.

[0065] This invention is a system specifically designed for project management, integrating functions such as information acquisition, data analysis, problem prediction, translation, and resource allocation. This system operates efficiently through the coordination of servers, terminals, and users.

[0066] First, the server collects data in real time from various external tools through information acquisition devices. The software used includes task management tools and communication software APIs. This ensures that up-to-date information related to the project is always centralized.

[0067] Next, the server stores the collected data in an integrated database. This data is then analyzed using a generated AI model via data analysis tools. As a result of the analysis, the relevance and importance of the data are evaluated, and task priorities are automatically set. This process allows users to clearly understand which tasks they should prioritize.

[0068] Real-time progress visualization is provided via the device. Users can access the dashboard from their device and instantly check the status of ongoing tasks and resources. This reduces decision delays and improves project efficiency.

[0069] Furthermore, the server uses analysis tools to predict future project challenges. For example, if the analysis indicates that "the current progress will not meet the deadline," it immediately generates countermeasures and notifies the user. Additional resource allocation or schedule revisions may be proposed.

[0070] In multilingual projects, server-based translation capabilities play a crucial role. They automatically translate messages sent in different languages, facilitating shared understanding. This reduces misunderstandings among international teams and facilitates smoother communication.

[0071] Regarding optimal resource allocation, the server suggests the best placement based on past successful project data. Users can then adjust resources based on this suggestion to maximize overall project efficiency.

[0072] As a concrete example, a prompt message could say, "Please display the dashboard to see the current task status of the project," and the server would be ready to respond immediately.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects data in real time from multiple external tools via information acquisition devices. Input data includes task update information and chat history from task management and communication tools via APIs. The server retrieves this data, converts it to an appropriate format for centralization, and then stores it in an integrated database.

[0076] Step 2:

[0077] The server begins data analysis based on the data stored in the integrated database. It uses a generative AI model to analyze the relationships and importance of the data. The input is the contents of the integrated database, and the output is the set priority order for each task. Specifically, data is fed into the AI ​​model, and tasks are listed in order of priority.

[0078] Step 3:

[0079] Users access the dashboard using their devices to view real-time project progress provided by the server. Input is the latest task priority information sent from the server, and output is a visually easy-to-understand progress report for the user. This information allows users to determine their next course of action.

[0080] Step 4:

[0081] Based on the data analysis results, the server evaluates potential project problems. This step includes analyzed task priorities and resource usage as input, and generates potential problems and proposed solutions as output. The server notifies the user, who then considers the proposed solutions.

[0082] Step 5:

[0083] The server utilizes multilingual translation capabilities in international projects. When a user sends a message in a different language to the server, the translation system converts it into the native language of other members. The input is the user's message, and the output is the translated message. This facilitates smoother communication.

[0084] Step 6:

[0085] The user receives suggestions for optimal resource allocation. Past project data is used as input, as the server generates the optimal resource placement based on past successes. The output proposes suitable personnel and resource allocations for each task. The user then uses these suggestions to implement the optimal resource allocation.

[0086] (Application Example 1)

[0087] 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."

[0088] Modern production management demands efficient resource allocation and task prioritization, but achieving these requires the analysis of vast amounts of data and immediate response. Furthermore, the involvement of multinational staff poses a challenge in terms of smooth communication across multiple languages.

[0089] 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.

[0090] In this invention, the server includes means for collecting production management-related information from an information recording device and inputting it into an integrated recording device; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for monitoring operational data from automated equipment and production lines in real time to support efficient management. This enables planned and flexible production management.

[0091] An "information recording device" is a device used to collect production management-related information and input it into an integrated recording device.

[0092] An "integrated recording device" is a device that centrally manages and stores information collected from information recording devices.

[0093] A "machine learning device" is a device that uses collected information to perform automated analysis based on relevance and importance, and then sets priorities.

[0094] "Automated equipment" refers to machinery and robots set up to perform specific tasks automatically in factories and production lines.

[0095] A "production line" refers to a series of work processes within a factory for manufacturing products, and is used to support efficient production.

[0096] "Multilingual translation functionality" refers to translation technology used to facilitate communication between different languages, possessing the ability to convert text and speech into other languages.

[0097] An "analytical device" is a device used to evaluate the progress of production or a project and generate notifications by predicting potential problems.

[0098] "Optimal resource allocation" refers to the planned distribution of available resources in order to utilize them efficiently and achieve maximum results.

[0099] A description of the embodiment for carrying out the invention will be provided.

[0100] The program for implementing this system is built on general-purpose programming languages ​​such as Python and Java (registered trademark), and consists of information recording devices, machine learning devices, and analysis devices. The server first collects production management-related information using the information recording devices and inputs it into the integrated recording device. Information collection is performed in real time via APIs of external platforms. The program utilizes data processing libraries such as pandas and SQLAlchemy to organize and store this collected data.

[0101] Next, the server automatically organizes and prioritizes the collected information based on relevance and importance through a machine learning system. Machine learning libraries such as scikit-learn are used to train and apply the AI ​​model. Through this process, users can make data-driven decisions to maximize the efficiency of the production line.

[0102] Furthermore, the analysis device evaluates production progress based on algorithms generated by the server and predicts potential problems. As a result, the user is presented with countermeasures, enabling efficient production management. For example, if signs of mechanical trouble are detected, repairs can be arranged immediately to prevent unnecessary downtime.

[0103] Furthermore, the server facilitates communication between different languages ​​and reduces misunderstandings within international production teams through its multilingual translation function. This utilizes the Google® Translate API. Messages sent by users are automatically translated into the recipient's native language, ensuring smooth communication.

[0104] The optimal allocation of resources is proposed based on past success stories, and workers with the most suitable skills and equipment are automatically assigned to specific production tasks. Generative AI models are effectively utilized in this process.

[0105] As a concrete example, consider a scenario in a factory where the system detects that robot A is overloaded and quickly suggests reassigning the task to another robot. An example of a prompt to the generative AI model used in this case is: "Based on the following dataset, generate the task priorities necessary to maximize the factory's production efficiency." This dataset may include robot operating time, production volume, failure history, and the skill sets of the team members.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server collects production management-related information in real time from an external platform's API via an information recording device. It takes data from the API as input, organizes it as a dataframe using the pandas library, and inputs it into the integrated recording device. The output is the organized dataset.

[0109] Step 2:

[0110] The server uses a machine learning system to automatically organize information within a data frame based on relevance and importance, leveraging the scikit-learn library. The input is the dataset obtained in Step 1, and the AI ​​model performs data calculations, outputting task priorities. These priorities serve as important decision-making information for the user.

[0111] Step 3:

[0112] The analysis device starts up and evaluates the progress of the entire production line based on the output data from Step 2. This includes metadata such as operating time, failure history, and skill sets. The output is a prediction of potential problems and proposed countermeasures based on those predictions. The server immediately notifies the user of this information.

[0113] Step 4:

[0114] The server uses a multilingual translation function to facilitate communication in different languages. It translates input messages into the recipient's native language using the Google Translate API. The output is the translated message, supporting smooth communication.

[0115] Step 5:

[0116] The server uses a generative AI model to learn from past successes and suggest optimal resource allocations. Inputs include historical project data and real-time task priorities. The server then executes an algorithm based on this data and provides the user with an optimal resource allocation proposal as output.

[0117] Step 6:

[0118] Based on the optimal allocation and countermeasures proposed by the user, they access the management screen from their terminal and make the necessary adjustments. This operation is performed through a web interface, and the user's decisions are reflected throughout the entire system.

[0119] 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.

[0120] This invention provides a system for more advanced project management that utilizes an information gathering device, a machine learning device, an analysis device, and an emotion engine. The system aims to recognize user emotions while managing project progress and enable dynamic project adjustments.

[0121] First, the server acquires project-related information from various data sources through information gathering devices. This data includes task details, progress, and communication content among members, and is stored in an integrated database. When a user updates project information using a terminal, the server integrates the latest information, keeping the database constantly up-to-date.

[0122] Next, the server uses machine learning to organize the information in the integrated database and prioritize tasks according to their relevance and importance. Users can view this information in real time on a dashboard displayed through their terminal. This dashboard visualizes the progress and analysis results for potential problems, enabling immediate action.

[0123] Furthermore, the server utilizes an emotion engine to analyze data obtained from users and evaluate their emotional state. For example, if a user provides feedback on a project in text, the emotion engine identifies the user's emotions (joy, stress, excitement, etc.) through text analysis. Based on this emotional state, the project's communication strategy and task allocation are automatically adjusted. For instance, if a user indicates high stress levels, the server can suggest reallocating some tasks to other members to reduce the user's burden.

[0124] Furthermore, the multilingual translation function facilitates smooth communication even in international project environments. Messages sent via the device in different languages ​​are translated and delivered to each user, reducing misunderstandings and promoting collaboration throughout the team.

[0125] Based on past success stories, the resource optimization feature suggests the most suitable roles for project members, supporting effective project progress. This allows users to utilize project resources most efficiently.

[0126] Thus, the present invention is a system that aims to increase the probability of project success by integrating various elements of project management and combining them with an emotional engine.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server collects project-related information, such as task management data and chat logs, through APIs of external tools. This information is stored in an integrated database, enabling centralized management of data across the entire project.

[0130] Step 2:

[0131] When a user enters or updates project information through their terminal, the server receives it and integrates it into the existing database. This ensures that the data is up-to-date in real time.

[0132] Step 3:

[0133] The server uses machine learning equipment to analyze the collected data. The analysis results are prioritized based on the importance and relevance of the tasks and displayed on a dashboard that users can view via their devices.

[0134] Step 4:

[0135] The server utilizes an emotion engine to analyze user feedback and comments and identify the user's emotional state. For example, if a user's text input is something like, "The deadline is approaching and I'm feeling anxious," the emotion engine recognizes this as a stressful state.

[0136] Step 5:

[0137] The server dynamically adjusts the project's progress based on the analysis results of the emotion engine. Specifically, if a user is experiencing high stress levels, it will suggest reducing their burden by reassigning some tasks to other team members.

[0138] Step 6:

[0139] The server supports communication in different languages ​​through its multilingual translation function. Messages between project members are automatically translated as needed and displayed on the device, ensuring consistent communication.

[0140] Step 7:

[0141] The server references past project data and proposes the optimal resource allocation based on the skills and roles of the team members. This resource allocation proposal is presented to the user via a terminal, allowing the user to adjust team members' tasks accordingly.

[0142] (Example 2)

[0143] 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".

[0144] In today's complex project management environment, where smooth communication among stakeholders and real-time situational awareness are essential, there is a need for systems that efficiently manage project progress and prevent potential problems before they occur. Furthermore, maximizing work efficiency requires considering the emotional states of team members and optimally distributing workloads. However, current systems have limitations in terms of multilingual support and sentiment analysis, making it difficult to provide dynamic adjustments based on international projects or individual emotional states.

[0145] 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.

[0146] In this invention, the server includes means for collecting business-related information from an information acquisition device and storing it in an integrated data storage unit; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for evaluating the emotional state of users using an emotion analysis function and automatically adjusting work allocation and communication strategies based on the results. This makes it possible to improve the success rate of projects by facilitating smooth communication among multinational teams and making adjustments based on emotions.

[0147] An "information acquisition device" is a device that collects business-related information from various sources and provides it to a server.

[0148] An "integrated data storage unit" is a database or storage function that stores collected information in a consolidated manner, making it easier to use for later analysis and processing.

[0149] A "machine learning device" is a device that analyzes patterns and relationships in data and uses the results to set priorities and predict future trends.

[0150] The "emotion analysis function" is a feature that analyzes information and feedback provided by users to identify their emotional state.

[0151] A "translation function" is a feature that automatically converts messages and texts into other languages ​​to facilitate smooth communication between different languages.

[0152] "Optimal resource utilization" refers to the effective and efficient allocation of resources within a project based on past data and performance.

[0153] A "display function" is a feature that visually displays data and analysis results, allowing users to grasp the project status and progress at a glance.

[0154] This invention provides a method for efficiently managing projects using a system that combines multiple devices and functions. In particular, it integrates functions such as information acquisition, data analysis, sentiment analysis, and multilingual translation to enable project progress management and smooth communication.

[0155] First, the server uses information acquisition devices to collect project-related data from various sources. This information includes task details, progress, and communication among team members, and is stored in an integrated data storage unit. To efficiently manage this data, the server also integrates with many business systems and cloud services.

[0156] Next, the server runs a machine learning system to automatically set priorities based on the collected information. This allows users to check progress in real time on a dashboard on their device. For example, AI technology can be used to prioritize tasks with approaching deadlines or to suggest optimal schedules based on past performance data.

[0157] Furthermore, the server uses sentiment analysis capabilities to analyze the user's emotional state from their feedback and communication. This allows it to detect signs of stress or dissatisfaction related to a project, for example, and then suggest task redistributions based on these findings. When a user enters feedback on their device, the content is automatically analyzed, and appropriate measures are taken based on their emotional state.

[0158] Furthermore, this system features multilingual translation capabilities, enabling smooth communication even in international projects. Messages in different languages ​​are translated via the device, ensuring that each user receives accurate information. This allows project members to collaborate efficiently, overcoming language barriers.

[0159] As an example of how the generative AI model can be used, by inputting the prompt "Identify priority tasks within the project and adjust communication methods based on the sentiment analysis results," the AI ​​will determine the importance of the tasks and generate suggestions for appropriately adjusting the team's communication plan.

[0160] Thus, the present invention provides a system that significantly improves the efficiency and accuracy of project management by clearly separating the roles of server, terminal, and user, and enabling them to cooperate with each other.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server uses information acquisition devices to collect project-related data, including task details, progress, and communication content, from various sources. It receives data feeds from external business systems and cloud services as input and stores them in an integrated data storage unit. The output is a real-time updated integrated database. Specifically, it connects to various sources via APIs and periodically retrieves data.

[0164] Step 2:

[0165] The server uses machine learning to analyze information in the integrated database. It receives collected data as input and executes algorithms to analyze the relationships and importance of the data. The output is a list of prioritized tasks. Specifically, it applies machine learning algorithms to evaluate correlations between data points and identify important tasks.

[0166] Step 3:

[0167] Users access a dashboard provided by the server using their device. Here, they can view project progress and potential issues visualized in real time. The input is analysis results from the server, and the output is a visualized progress report. Specifically, information is displayed using interactive graphs and charts, allowing users to make immediate decisions based on this information.

[0168] Step 4:

[0169] The server utilizes sentiment analysis capabilities to analyze user feedback and communication content. It receives text-based feedback and comments as input and analyzes the emotional state using natural language processing techniques. The output is adjustment suggestions based on the user's emotional state. Specifically, it executes a text analysis algorithm, calculates sentiment indicators, and uses these to reallocate tasks and adjust communication strategies.

[0170] Step 5:

[0171] The server translates messages in different languages ​​via its multilingual translation function and delivers them to each user. The input is messages written in various languages, and the output is the translated message. Specifically, it uses a machine translation engine to perform language conversion, sends the translated message to the terminal, and presents it to the user.

[0172] Step 6:

[0173] The server proposes optimal resource utilization based on past project successes. It receives historical project data as input and generates an optimal resource allocation model by comparing it with baseline data. The output is a proposal for the optimal roles within the project team. Specifically, it executes an optimization algorithm to match each member's skill set with task requirements.

[0174] (Application Example 2)

[0175] 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".

[0176] Current operational equipment management systems make it difficult to grasp progress and potential issues in real time and to implement appropriate countermeasures quickly. Furthermore, they do not adequately address communication barriers between languages ​​or automate task allocation that takes user emotions into consideration. This leads to problems such as inefficient resource allocation and excessive burden on users.

[0177] 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.

[0178] In this invention, the server includes means for collecting data from operating devices using information gathering means and storing the information in an integrated database; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for identifying the user's emotional state and automatically adjusting task allocation using an emotion analysis device. This enables efficient management of operating devices and reduces the burden on users.

[0179] An "information gathering means" is a device that has the function of acquiring data from operating equipment and storing that information in an integrated database.

[0180] A "machine learning device" is a device that automatically organizes collected information based on its relevance and importance, and sets priorities accordingly.

[0181] A "display device" is a device that visualizes progress in real time and provides information to users in an easy-to-understand manner.

[0182] An "analytical device" is a device that evaluates the status of the operational equipment, predicts potential problems, and proposes countermeasures.

[0183] A "multilingual translation device" is a device that translates languages ​​to facilitate communication between different languages.

[0184] An "emotion analysis device" is a device that identifies the emotional state based on information obtained from the user and automatically adjusts task allocation accordingly.

[0185] An "integrated database" is a database system that centrally stores various types of information and allows for the effective use of that information as needed.

[0186] A "server" is a central computer system that works in conjunction with various devices to collect and analyze data and provide a user interface.

[0187] The system for implementing this invention is a server-centered, advanced data processing and decision support system. A specific embodiment of this system is described below.

[0188] The server acquires data in real time from the operating equipment using information gathering methods. This data is stored in an integrated database and updated as needed. IoT sensors are used for information gathering, and the data is analyzed through Python scripts.

[0189] Machine learning systems analyze data and automatically organize it based on relevance and importance. This process utilizes machine learning frameworks such as TENSORFLOW®. Prioritization ensures that important tasks are executed quickly.

[0190] Users' devices (e.g., smartphones and tablets) are equipped with display devices that allow them to visually check progress in real time. Using web application frameworks such as Flask, users can access intuitively operable dashboards.

[0191] The emotion analysis device analyzes emotions and identifies emotional states based on text and voice data collected from users. This analysis uses natural language processing technology to evaluate the user's stress level and satisfaction level.

[0192] The multilingual translation device uses the Google Translate API to translate messages between different languages, enabling smooth communication among international project teams.

[0193] As a concrete example, if a robot operator in a factory reports a robot malfunction via tablet, and the system determines that the operator's emotional state at that time is high-stress, the system will immediately redistribute other tasks to reduce the operator's workload. An example of a prompt message to support this process is: "Design a system that analyzes the emotional state of a factory robot operator when they report a task and adjusts the task as needed."

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

[0195] Step 1:

[0196] The server acquires data from operating equipment in real time using information gathering methods. It receives various data from IoT sensors (e.g., temperature, operating status, error codes) as input, converts this data into a database format, and stores it in an integrated database. The output is an integrated database reflecting the latest operating status.

[0197] Step 2:

[0198] The server passes data from an integrated database to a machine learning machine learning system for relevance and importance analysis. The input is operational data extracted from the database, and the output is a prioritized task list. This process uses TensorFlow to learn from the collected data and estimate the optimal task order.

[0199] Step 3:

[0200] The user's terminal visualizes the task list from the server via a display device. It receives a prioritized task list generated by the server as input and displays it visually on the dashboard. The output is an interface display that is easy for the user to understand. Here, a web-based dashboard is built using Flask.

[0201] Step 4:

[0202] Users input feedback and comments via their terminals and report them to the server. Input consists of the user's comments and feedback. Output is the specific feedback information received by the server.

[0203] Step 5:

[0204] The server processes user comments using an emotion analyzer. It receives user feedback text as input and analyzes and identifies emotional states (joy, dissatisfaction, stress, etc.) using natural language processing. The output is the result of the evaluation of the user's emotional state.

[0205] Step 6:

[0206] The server generates task redistribution proposals as needed, based on the sentiment analysis results. The input is the sentiment analysis results and the current task list. A machine learning model is used to design a new task allocation to reduce the user's burden. The output is the updated task list.

[0207] Step 7:

[0208] The server translates the generated task list into the required languages ​​through a multilingual translation device. The input is the updated task list, and the output is task instructions that can be viewed in multiple languages. Information is provided in a language optimized for each user using the Google Translate API.

[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 relates to a project management system that utilizes an information gathering device, a machine learning device, and an analysis device. The aim of this system is to efficiently manage project progress through the coordinated operation of each functional device.

[0226] First, the server connects to information gathering devices and retrieves project-related data in real time from external tools. For example, it collects task update information and team member chat history via APIs of project task management tools and communication tools. This data is then stored by the server in an integrated database.

[0227] Next, the server uses machine learning to automatically organize the information stored in the integrated database. Leveraging AI models, it analyzes the relevance and importance of the data to prioritize each task. This process is reflected in a dashboard accessible to users from their devices, allowing them to visually track project progress in real time.

[0228] The analysis system uses machine learning to predict potential problems in a project and generate countermeasures. For example, if budget overruns or schedule delays are predicted, the server notifies the user of countermeasures based on these predictions, suggesting resource reallocation or schedule revisions.

[0229] Furthermore, the server utilizes a multilingual translation function to facilitate smooth communication in international projects. When a user sends a message in a different language, it is automatically translated into the native language of other members to aid understanding.

[0230] Finally, a resource optimization feature runs in the backend, helping users make optimal resource allocations based on data from past successful projects. For example, it helps maximize project efficiency by assigning members with the appropriate skills to specific tasks.

[0231] Thus, this system seamlessly integrates all processes, from information gathering and analysis to proposing solutions, translation, and resource allocation, supporting users in smoothly managing their projects.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The server periodically retrieves project-related data via APIs from external tools. For example, it collects task progress information from task management tools and chat data between team members from communication tools, and stores them in an integrated database.

[0235] Step 2:

[0236] When a user uploads new project-related information via their device, the server receives the data and integrates it into the existing project database. This ensures that all project data remains up-to-date.

[0237] Step 3:

[0238] The server uses machine learning to analyze information in the integrated database and determine its relevance and importance. Based on the analysis results, it prioritizes each task and reflects its progress on a dashboard. Users can view this dashboard from their terminals, allowing them to instantly grasp the project's progress.

[0239] Step 4:

[0240] The server uses machine learning models to predict potential problems. Based on the predicted problems, it generates optimal solutions and notifies the user as an alert. The user can then use this information to consider countermeasures and utilize them in project management.

[0241] Step 5:

[0242] The server uses a multilingual translation function to automatically translate messages between different languages. In international projects, messages exchanged between members are translated into their respective native languages ​​and sent to their devices, enabling smooth communication.

[0243] Step 6:

[0244] The server uses data from past successful projects to optimize resource allocation. This suggests appropriate resource placement within the project team, allowing users to assign tasks and improve project efficiency.

[0245] (Example 1)

[0246] 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."

[0247] In modern project management, managing information using multiple tools presents challenges such as data dispersion and inefficient communication. Furthermore, the analysis of diverse data and language barriers in international projects complicate project progress management. Additionally, a lack of appropriate guidelines for optimal resource allocation can lead to decreased overall efficiency.

[0248] 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.

[0249] In this invention, the server includes means for collecting data from multiple tools through information acquisition means and inputting it into a centralized data storage means; means for automatically organizing the acquired information based on relevance and importance and setting task priorities using data analysis means; and representation means for visualizing the project progress in real time. This makes it possible to efficiently integrate dispersed data and easily grasp the progress of the entire project. Furthermore, task prioritization through analysis and multilingual support enable smooth communication and efficient resource allocation even in international projects.

[0250] "Information acquisition means" refers to methods for collecting data from multiple external tools and inputting it into a centralized data storage system.

[0251] A "data analysis method" is a method for automatically organizing acquired information based on its relevance and importance, and for setting task priorities.

[0252] "Means of expression" refers to methods for visually displaying the progress of a project in real time.

[0253] "Analysis methods" refer to methods for evaluating the progress of a project, predicting future challenges, and proposing solutions.

[0254] "Translation methods" refer to methods that use multilingual support functions to facilitate communication between different languages.

[0255] "Methods for proposing optimal resource allocation" refer to methods for indicating the most effective allocation of resources in a project, based on past successes.

[0256] This invention is a system specifically designed for project management, integrating functions such as information acquisition, data analysis, problem prediction, translation, and resource allocation. This system operates efficiently through the coordination of servers, terminals, and users.

[0257] First, the server collects data in real time from various external tools through information acquisition devices. The software used includes task management tools and communication software APIs. This ensures that up-to-date information related to the project is always centralized.

[0258] Next, the server stores the collected data in an integrated database. This data is then analyzed using a generated AI model via data analysis tools. As a result of the analysis, the relevance and importance of the data are evaluated, and task priorities are automatically set. This process allows users to clearly understand which tasks they should prioritize.

[0259] Real-time progress visualization is provided via the device. Users can access the dashboard from their device and instantly check the status of ongoing tasks and resources. This reduces decision delays and improves project efficiency.

[0260] Furthermore, the server uses analysis tools to predict future project challenges. For example, if the analysis indicates that "the current progress will not meet the deadline," it immediately generates countermeasures and notifies the user. Additional resource allocation or schedule revisions may be proposed.

[0261] In multilingual projects, server-based translation capabilities play a crucial role. They automatically translate messages sent in different languages, facilitating shared understanding. This reduces misunderstandings among international teams and facilitates smoother communication.

[0262] Regarding optimal resource allocation, the server suggests the best placement based on past successful project data. Users can then adjust resources based on this suggestion to maximize overall project efficiency.

[0263] As a concrete example, a prompt message could say, "Please display the dashboard to see the current task status of the project," and the server would be ready to respond immediately.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The server collects data in real time from multiple external tools via information acquisition devices. Input data includes task update information and chat history from task management and communication tools via APIs. The server retrieves this data, converts it to an appropriate format for centralization, and then stores it in an integrated database.

[0267] Step 2:

[0268] The server begins data analysis based on the data stored in the integrated database. It uses a generative AI model to analyze the relationships and importance of the data. The input is the contents of the integrated database, and the output is the set priority order for each task. Specifically, data is fed into the AI ​​model, and tasks are listed in order of priority.

[0269] Step 3:

[0270] Users access the dashboard using their devices to view real-time project progress provided by the server. Input is the latest task priority information sent from the server, and output is a visually easy-to-understand progress report for the user. This information allows users to determine their next course of action.

[0271] Step 4:

[0272] Based on the data analysis results, the server evaluates potential project problems. This step includes analyzed task priorities and resource usage as input, and generates potential problems and proposed solutions as output. The server notifies the user, who then considers the proposed solutions.

[0273] Step 5:

[0274] The server utilizes multilingual translation capabilities in international projects. When a user sends a message in a different language to the server, the translation system converts it into the native language of other members. The input is the user's message, and the output is the translated message. This facilitates smoother communication.

[0275] Step 6:

[0276] The user receives suggestions for optimal resource allocation. Past project data is used as input, as the server generates the optimal resource placement based on past successes. The output proposes suitable personnel and resource allocations for each task. The user then uses these suggestions to implement the optimal resource allocation.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] Modern production management demands efficient resource allocation and task prioritization, but achieving these requires the analysis of vast amounts of data and immediate response. Furthermore, the involvement of multinational staff poses a challenge in ensuring smooth communication across multiple languages.

[0280] 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.

[0281] In this invention, the server includes means for collecting production management-related information from an information recording device and inputting it into an integrated recording device; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for monitoring operational data from automated equipment and production lines in real time to support efficient management. This enables planned and flexible production management.

[0282] The "information recording device" is a device for collecting information related to production management and inputting it into the integrated recording device.

[0283] The "integrated recording device" is a device for centrally managing and storing the information collected from the information recording device.

[0284] The "machine learning device" is a device for performing automatic analysis based on relevance and importance using the collected information and setting priorities.

[0285] The "automation equipment" refers to mechanical devices and robots set to automatically perform certain operations in factories and production lines.

[0286] The "production line" refers to a continuous working process for manufacturing products within a factory and is used to support efficient production.

[0287] The "multilingual translation function" is a translation technology used to facilitate communication between different languages and has the ability to convert text and speech into other languages.

[0288] The "analysis device" is a device for generating notifications by evaluating the progress of production or projects and predicting potential problems.

[0289] "Optimal allocation of resources" refers to a planned allocation to efficiently utilize available resources and achieve maximum results.

[0290] The mode for implementing the invention will be described.

[0291] The program to implement this system is built on general-purpose programming languages ​​such as Python and Java, and consists of information recording devices, machine learning devices, and analytical devices. The server first collects production management-related information using the information recording devices and inputs it into the integrated recording device. Information collection is performed in real time via APIs of external platforms. The program utilizes data processing libraries such as pandas and SQLAlchemy to organize and store this collected data.

[0292] Next, the server automatically organizes and prioritizes the collected information based on relevance and importance through a machine learning system. Machine learning libraries such as scikit-learn are used to train and apply the AI ​​model. Through this process, users can make data-driven decisions to maximize the efficiency of the production line.

[0293] Furthermore, the analysis device evaluates production progress based on algorithms generated by the server and predicts potential problems. As a result, the user is presented with countermeasures, enabling efficient production management. For example, if signs of mechanical trouble are detected, repairs can be arranged immediately to prevent unnecessary downtime.

[0294] Furthermore, the server facilitates communication between different languages ​​and reduces misunderstandings within international production teams through its multilingual translation function. This utilizes the Google Translate API. Messages sent by users are automatically translated into the recipient's native language, ensuring smooth communication.

[0295] The optimal allocation of resources is proposed based on past success stories, and workers with the most suitable skills and equipment are automatically assigned to specific production tasks. Generative AI models are effectively utilized in this process.

[0296] As a concrete example, consider a scenario in a factory where the system detects that robot A is overloaded and quickly suggests reassigning the task to another robot. An example of a prompt to the generative AI model used in this case is: "Based on the following dataset, generate the task priorities necessary to maximize the factory's production efficiency." This dataset may include robot operating time, production volume, failure history, and the skill sets of the team members.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server collects production management-related information in real time from an external platform's API via an information recording device. It takes data from the API as input, organizes it as a dataframe using the pandas library, and inputs it into the integrated recording device. The output is the organized dataset.

[0300] Step 2:

[0301] The server uses a machine learning system to automatically organize information within a data frame based on relevance and importance, leveraging the scikit-learn library. The input is the dataset obtained in Step 1, and the AI ​​model performs data calculations, outputting task priorities. These priorities serve as important decision-making information for the user.

[0302] Step 3:

[0303] The analysis device starts up and evaluates the progress of the entire production line based on the output data from Step 2. This includes metadata such as operating time, failure history, and skill sets. The output is a prediction of potential problems and proposed countermeasures based on those predictions. The server immediately notifies the user of this information.

[0304] Step 4:

[0305] The server uses a multilingual translation function to assist communication in different languages. It translates the message entered using the Google Translate API into the native language of the recipient. The output is the translated message, which supports smooth communication.

[0306] Step 5:

[0307] The server uses a generative AI model to learn from past successful cases and present an optimal allocation of resources. The input is past project data and real-time task priorities. Based on this, the server executes an algorithm and provides the user with an optimal resource allocation plan as the output.

[0308] Step 6:

[0309] Based on the proposed optimal allocation plan and countermeasure plan, the user accesses the management screen from the terminal and makes necessary adjustments. This operation is carried out through a web interface, and the user's decision results in a reflection throughout the system.

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

[0311] The present invention is a system that uses an information collection device, a machine learning device, an analysis device, and an emotion engine to perform more advanced project management. This system aims to recognize the user's emotions during project progress management and enable dynamic adjustment of the project.

[0312] First, the server acquires project-related information from various data sources through information gathering devices. This data includes task details, progress, and communication content among members, and is stored in an integrated database. When a user updates project information using a terminal, the server integrates the latest information, keeping the database constantly up-to-date.

[0313] Next, the server uses machine learning to organize the information in the integrated database and prioritize tasks according to their relevance and importance. Users can view this information in real time on a dashboard displayed through their terminal. This dashboard visualizes the progress and analysis results for potential problems, enabling immediate action.

[0314] Furthermore, the server utilizes an emotion engine to analyze data obtained from users and evaluate their emotional state. For example, if a user provides feedback on a project in text, the emotion engine identifies the user's emotions (joy, stress, excitement, etc.) through text analysis. Based on this emotional state, the project's communication strategy and task allocation are automatically adjusted. For instance, if a user indicates high stress levels, the server can suggest reallocating some tasks to other members to reduce the user's burden.

[0315] Furthermore, the multilingual translation function facilitates smooth communication even in international project environments. Messages sent via the device in different languages ​​are translated and delivered to each user, reducing misunderstandings and promoting collaboration throughout the team.

[0316] Based on past success stories, the resource optimization feature suggests the most suitable roles for project members, supporting effective project progress. This allows users to utilize project resources most efficiently.

[0317] Thus, the present invention is a system that aims to increase the probability of project success by integrating various elements of project management and combining them with an emotional engine.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The server collects project-related information, such as task management data and chat logs, through APIs of external tools. This information is stored in an integrated database, enabling centralized management of data across the entire project.

[0321] Step 2:

[0322] When a user enters or updates project information through their terminal, the server receives it and integrates it into the existing database. This ensures that the data is up-to-date in real time.

[0323] Step 3:

[0324] The server uses machine learning equipment to analyze the collected data. The analysis results are prioritized based on the importance and relevance of the tasks and displayed on a dashboard that users can view via their devices.

[0325] Step 4:

[0326] The server utilizes an emotion engine to analyze user feedback and comments and identify the user's emotional state. For example, if a user's text input is something like, "The deadline is approaching and I'm feeling anxious," the emotion engine recognizes this as a stressful state.

[0327] Step 5:

[0328] The server dynamically adjusts the project's progress based on the analysis results of the emotion engine. Specifically, if a user is experiencing high stress levels, it will suggest reducing their burden by reassigning some tasks to other team members.

[0329] Step 6:

[0330] The server supports communication in different languages ​​through its multilingual translation function. Messages between project members are automatically translated as needed and displayed on the device, ensuring consistent communication.

[0331] Step 7:

[0332] The server references past project data and proposes the optimal resource allocation based on the skills and roles of the team members. This resource allocation proposal is presented to the user via a terminal, allowing the user to adjust team members' tasks accordingly.

[0333] (Example 2)

[0334] 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".

[0335] In today's complex project management environment, where smooth communication among stakeholders and real-time situational awareness are essential, there is a need for systems that efficiently manage project progress and prevent potential problems before they occur. Furthermore, maximizing work efficiency requires considering the emotional states of team members and optimally distributing workloads. However, current systems have limitations in terms of multilingual support and sentiment analysis, making it difficult to provide dynamic adjustments based on international projects or individual emotional states.

[0336] 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.

[0337] In this invention, the server includes means for collecting business-related information from an information acquisition device and storing it in an integrated data storage unit; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for evaluating the emotional state of users using an emotion analysis function and automatically adjusting work allocation and communication strategies based on the results. This makes it possible to improve the success rate of projects by facilitating smooth communication among multinational teams and making adjustments based on emotions.

[0338] An "information acquisition device" is a device that collects business-related information from various sources and provides it to a server.

[0339] An "integrated data storage unit" is a database or storage function that stores collected information in a consolidated manner, making it easier to use for later analysis and processing.

[0340] A "machine learning device" is a device that analyzes patterns and relationships in data and uses the results to set priorities and predict future trends.

[0341] The "emotion analysis function" is a feature that analyzes information and feedback provided by users to identify their emotional state.

[0342] A "translation function" is a feature that automatically converts messages and texts into other languages ​​to facilitate smooth communication between different languages.

[0343] "Optimal resource utilization" refers to the effective and efficient allocation of resources within a project based on past data and performance.

[0344] A "display function" is a feature that visually displays data and analysis results, allowing users to grasp the project status and progress at a glance.

[0345] This invention provides a method for efficiently managing projects using a system that combines multiple devices and functions. In particular, it integrates functions such as information acquisition, data analysis, sentiment analysis, and multilingual translation to enable project progress management and smooth communication.

[0346] First, the server uses information acquisition devices to collect project-related data from various sources. This information includes task details, progress, and communication among team members, and is stored in an integrated data storage unit. To efficiently manage this data, the server also integrates with many business systems and cloud services.

[0347] Next, the server runs a machine learning system to automatically set priorities based on the collected information. This allows users to check progress in real time on a dashboard on their device. For example, AI technology can be used to prioritize tasks with approaching deadlines or to suggest optimal schedules based on past performance data.

[0348] Furthermore, the server uses sentiment analysis capabilities to analyze the user's emotional state from their feedback and communication. This allows it to detect signs of stress or dissatisfaction related to a project, for example, and then suggest task redistributions based on these findings. When a user enters feedback on their device, the content is automatically analyzed, and appropriate measures are taken based on their emotional state.

[0349] Furthermore, this system features multilingual translation capabilities, enabling smooth communication even in international projects. Messages in different languages ​​are translated via the device, ensuring that each user receives accurate information. This allows project members to collaborate efficiently, overcoming language barriers.

[0350] As an example of how the generative AI model can be used, by inputting the prompt "Identify priority tasks within the project and adjust communication methods based on the sentiment analysis results," the AI ​​will determine the importance of the tasks and generate suggestions for appropriately adjusting the team's communication plan.

[0351] Thus, the present invention provides a system that significantly improves the efficiency and accuracy of project management by clearly separating the roles of server, terminal, and user, and enabling them to cooperate with each other.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The server uses information acquisition devices to collect project-related data, including task details, progress, and communication content, from various sources. It receives data feeds from external business systems and cloud services as input and stores them in an integrated data storage unit. The output is a real-time updated integrated database. Specifically, it connects to various sources via APIs and periodically retrieves data.

[0355] Step 2:

[0356] The server uses machine learning to analyze information in the integrated database. It receives collected data as input and executes algorithms to analyze the relationships and importance of the data. The output is a list of prioritized tasks. Specifically, it applies machine learning algorithms to evaluate correlations between data points and identify important tasks.

[0357] Step 3:

[0358] Users access a dashboard provided by the server using their device. Here, they can view project progress and potential issues visualized in real time. The input is analysis results from the server, and the output is a visualized progress report. Specifically, information is displayed using interactive graphs and charts, allowing users to make immediate decisions based on this information.

[0359] Step 4:

[0360] The server utilizes sentiment analysis capabilities to analyze user feedback and communication content. It receives text-based feedback and comments as input and analyzes the emotional state using natural language processing techniques. The output is adjustment suggestions based on the user's emotional state. Specifically, it executes a text analysis algorithm, calculates sentiment indicators, and uses these to reallocate tasks and adjust communication strategies.

[0361] Step 5:

[0362] The server translates messages in different languages ​​via its multilingual translation function and delivers them to each user. The input is messages written in various languages, and the output is the translated message. Specifically, it uses a machine translation engine to perform language conversion, sends the translated message to the terminal, and presents it to the user.

[0363] Step 6:

[0364] The server proposes optimal resource utilization based on past project successes. It receives historical project data as input and generates an optimal resource allocation model by comparing it with baseline data. The output is a proposal for the optimal roles within the project team. Specifically, it executes an optimization algorithm to match each member's skill set with task requirements.

[0365] (Application Example 2)

[0366] 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."

[0367] Current operational equipment management systems make it difficult to grasp progress and potential issues in real time and to implement appropriate countermeasures quickly. Furthermore, they do not adequately address communication barriers between languages ​​or automate task allocation that takes user emotions into consideration. This leads to problems such as inefficient resource allocation and excessive burden on users.

[0368] 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.

[0369] In this invention, the server includes means for collecting data from operating devices using information gathering means and storing the information in an integrated database; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for identifying the user's emotional state and automatically adjusting task allocation using an emotion analysis device. This enables efficient management of operating devices and reduces the burden on users.

[0370] An "information gathering means" is a device that has the function of acquiring data from operating equipment and storing that information in an integrated database.

[0371] A "machine learning device" is a device that automatically organizes collected information based on its relevance and importance, and sets priorities accordingly.

[0372] A "display device" is a device that visualizes progress in real time and provides information to users in an easy-to-understand manner.

[0373] An "analytical device" is a device that evaluates the status of the operational equipment, predicts potential problems, and proposes countermeasures.

[0374] A "multilingual translation device" is a device that translates languages ​​to facilitate communication between different languages.

[0375] An "emotion analysis device" is a device that identifies the emotional state based on information obtained from the user and automatically adjusts task allocation accordingly.

[0376] An "integrated database" is a database system that centrally stores various types of information and allows for the effective use of that information as needed.

[0377] A "server" is a central computer system that works in conjunction with various devices to collect and analyze data and provide a user interface.

[0378] The system for implementing this invention is a server-centered, advanced data processing and decision support system. A specific embodiment of this system is described below.

[0379] The server acquires data in real time from the operating equipment using information gathering methods. This data is stored in an integrated database and updated as needed. IoT sensors are used for information gathering, and the data is analyzed through Python scripts.

[0380] Machine learning systems analyze data and automatically organize it based on relevance and importance. This process utilizes machine learning frameworks such as TensorFlow. Prioritization ensures that important tasks are executed quickly.

[0381] Users' devices (e.g., smartphones and tablets) are equipped with display devices that allow them to visually check progress in real time. Using web application frameworks such as Flask, users can access intuitively operable dashboards.

[0382] The emotion analysis device analyzes emotions and identifies emotional states based on text and voice data collected from users. This analysis uses natural language processing technology to evaluate the user's stress level and satisfaction level.

[0383] The multilingual translation device uses the Google Translate API to translate messages between different languages, enabling smooth communication among international project teams.

[0384] As a concrete example, if a robot operator in a factory reports a robot malfunction via tablet, and the system determines that the operator's emotional state at that time is high-stress, the system will immediately redistribute other tasks to reduce the operator's workload. An example of a prompt message to support this process is: "Design a system that analyzes the emotional state of a factory robot operator when they report a task and adjusts the task as needed."

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The server acquires data from operating equipment in real time using information gathering methods. It receives various data from IoT sensors (e.g., temperature, operating status, error codes) as input, converts this data into a database format, and stores it in an integrated database. The output is an integrated database reflecting the latest operating status.

[0388] Step 2:

[0389] The server passes data from an integrated database to a machine learning machine learning system for relevance and importance analysis. The input is operational data extracted from the database, and the output is a prioritized task list. This process uses TensorFlow to learn from the collected data and estimate the optimal task order.

[0390] Step 3:

[0391] The user's terminal visualizes the task list from the server via a display device. It receives a prioritized task list generated by the server as input and displays it visually on the dashboard. The output is an interface display that is easy for the user to understand. Here, a web-based dashboard is built using Flask.

[0392] Step 4:

[0393] Users input feedback and comments via their terminals and report them to the server. Input consists of the user's comments and feedback. Output is the specific feedback information received by the server.

[0394] Step 5:

[0395] The server processes user comments using an emotion analyzer. It receives user feedback text as input and analyzes and identifies emotional states (joy, dissatisfaction, stress, etc.) using natural language processing. The output is the result of the evaluation of the user's emotional state.

[0396] Step 6:

[0397] The server generates task redistribution proposals as needed, based on the sentiment analysis results. The input is the sentiment analysis results and the current task list. A machine learning model is used to design a new task allocation to reduce the user's burden. The output is an updated task list.

[0398] Step 7:

[0399] The server translates the generated task list into the required languages ​​through a multilingual translation device. The input is the updated task list, and the output is task instructions that can be viewed in multiple languages. Using the Google Translate API, information is provided in a language optimized for each user.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] This invention relates to a project management system that utilizes an information gathering device, a machine learning device, and an analysis device. The aim of this system is to efficiently manage project progress through the coordinated operation of each functional device.

[0417] First, the server connects to information gathering devices and retrieves project-related data in real time from external tools. For example, it collects task update information and team member chat history via APIs of project task management tools and communication tools. This data is then stored by the server in an integrated database.

[0418] Next, the server uses machine learning to automatically organize the information stored in the integrated database. Leveraging AI models, it analyzes the relevance and importance of the data to prioritize each task. This process is reflected in a dashboard accessible to users from their devices, allowing them to visually track project progress in real time.

[0419] The analysis system uses machine learning to predict potential problems in a project and generate countermeasures. For example, if budget overruns or schedule delays are predicted, the server notifies the user of countermeasures based on these predictions, suggesting resource reallocation or schedule revisions.

[0420] Furthermore, the server utilizes a multilingual translation function to facilitate smooth communication in international projects. When a user sends a message in a different language, it is automatically translated into the native language of other members to aid understanding.

[0421] Finally, a resource optimization feature runs in the backend, helping users make optimal resource allocations based on data from past successful projects. For example, it helps maximize project efficiency by assigning members with the appropriate skills to specific tasks.

[0422] Thus, this system seamlessly integrates all processes, from information gathering and analysis to proposing solutions, translation, and resource allocation, supporting users in smoothly managing their projects.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The server periodically retrieves project-related data via APIs from external tools. For example, it collects task progress information from task management tools and chat data between team members from communication tools, and stores them in an integrated database.

[0426] Step 2:

[0427] When a user uploads new project-related information via their device, the server receives the data and integrates it into the existing project database. This ensures that all project data remains up-to-date.

[0428] Step 3:

[0429] The server uses machine learning to analyze information in the integrated database and determine its relevance and importance. Based on the analysis results, it prioritizes each task and reflects its progress on a dashboard. Users can view this dashboard from their terminals, allowing them to instantly grasp the project's progress.

[0430] Step 4:

[0431] The server uses machine learning models to predict potential problems. Based on the predicted problems, it generates optimal solutions and notifies the user as an alert. The user can then use this information to consider countermeasures and utilize them in project management.

[0432] Step 5:

[0433] The server uses a multilingual translation function to automatically translate messages between different languages. In international projects, messages exchanged between members are translated into their respective native languages ​​and sent to their devices, enabling smooth communication.

[0434] Step 6:

[0435] The server uses data from past successful projects to optimize resource allocation. This suggests appropriate resource placement within the project team, allowing users to assign tasks and improve project efficiency.

[0436] (Example 1)

[0437] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0438] In modern project management, managing information using multiple tools presents challenges such as data dispersion and inefficient communication. Furthermore, the analysis of diverse data and language barriers in international projects complicate project progress management. Additionally, a lack of appropriate guidelines for optimal resource allocation can lead to decreased overall efficiency.

[0439] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0440] In this invention, the server includes means for collecting data from multiple tools through information acquisition means and inputting it into a centralized data storage means; means for automatically organizing the acquired information based on relevance and importance and setting task priorities using data analysis means; and representation means for visualizing the project progress in real time. This makes it possible to efficiently integrate dispersed data and easily grasp the progress of the entire project. Furthermore, task prioritization through analysis and multilingual support enable smooth communication and efficient resource allocation even in international projects.

[0441] "Information acquisition means" refers to methods for collecting data from multiple external tools and inputting it into a centralized data storage system.

[0442] A "data analysis method" is a method for automatically organizing acquired information based on its relevance and importance, and for setting task priorities.

[0443] "Means of expression" refers to methods for visually displaying the progress of a project in real time.

[0444] "Analysis methods" refer to methods for evaluating the progress of a project, predicting future challenges, and proposing solutions.

[0445] "Translation methods" refer to methods that use multilingual support functions to facilitate communication between different languages.

[0446] "Methods for proposing optimal resource allocation" refer to methods for indicating the most effective allocation of resources in a project, based on past successes.

[0447] This invention is a system specifically designed for project management, integrating functions such as information acquisition, data analysis, problem prediction, translation, and resource allocation. This system operates efficiently through the coordinated interaction of servers, terminals, and users.

[0448] First, the server collects data in real time from various external tools through information acquisition devices. The software used includes task management tools and communication software APIs. This ensures that up-to-date information related to the project is always centralized.

[0449] Next, the server stores the collected data in an integrated database. This data is then analyzed using a generated AI model via data analysis tools. As a result of the analysis, the relevance and importance of the data are evaluated, and task priorities are automatically set. This process allows users to clearly understand which tasks they should prioritize.

[0450] Real-time progress visualization is provided via the device. Users can access the dashboard from their device and instantly check the status of ongoing tasks and resources. This reduces decision delays and improves project efficiency.

[0451] Furthermore, the server uses analysis tools to predict future project challenges. For example, if the analysis indicates that "the current progress will not meet the deadline," it immediately generates countermeasures and notifies the user. Additional resource allocation or schedule revisions may be proposed.

[0452] In multilingual projects, server-based translation capabilities play a crucial role. They automatically translate messages sent in different languages, facilitating shared understanding. This reduces misunderstandings among international teams and facilitates smoother communication.

[0453] Regarding optimal resource allocation, the server suggests the best placement based on past successful project data. Users can then adjust resources based on this suggestion to maximize overall project efficiency.

[0454] As a concrete example, a prompt message could say, "Please display the dashboard to see the current task status of the project," and the server would be ready to respond immediately.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] The server collects data in real time from multiple external tools via information acquisition devices. Input data includes task update information and chat history from task management and communication tools via APIs. The server retrieves this data, converts it to an appropriate format for centralization, and then stores it in an integrated database.

[0458] Step 2:

[0459] The server begins data analysis based on the data stored in the integrated database. It uses a generative AI model to analyze the relationships and importance of the data. The input is the contents of the integrated database, and the output is the set priority order for each task. Specifically, data is fed into the AI ​​model, and tasks are listed in order of priority.

[0460] Step 3:

[0461] Users access the dashboard using their devices to view real-time project progress provided by the server. Input is the latest task priority information sent from the server, and output is a visually easy-to-understand progress report for the user. This information allows users to determine their next course of action.

[0462] Step 4:

[0463] Based on the data analysis results, the server evaluates potential project problems. This step includes analyzed task priorities and resource usage as input, and generates potential problems and proposed solutions as output. The server notifies the user, who then considers the proposed solutions.

[0464] Step 5:

[0465] The server utilizes multilingual translation capabilities in international projects. When a user sends a message in a different language to the server, the translation system converts it into the native language of other members. The input is the user's message, and the output is the translated message. This facilitates smoother communication.

[0466] Step 6:

[0467] The user receives suggestions for optimal resource allocation. Past project data is used as input, as the server generates the optimal resource placement based on past successes. The output proposes suitable personnel and resource allocations for each task. The user then uses these suggestions to implement the optimal resource allocation.

[0468] (Application Example 1)

[0469] 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."

[0470] Modern production management demands efficient resource allocation and task prioritization, but achieving these requires the analysis of vast amounts of data and immediate response. Furthermore, the involvement of multinational staff poses a challenge in ensuring smooth communication across multiple languages.

[0471] 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.

[0472] In this invention, the server includes means for collecting production management-related information from an information recording device and inputting it into an integrated recording device; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for monitoring operational data from automated equipment and production lines in real time to support efficient management. This enables planned and flexible production management.

[0473] An "information recording device" is a device used to collect production management-related information and input it into an integrated recording device.

[0474] An "integrated recording device" is a device that centrally manages and stores information collected from information recording devices.

[0475] A "machine learning device" is a device that uses collected information to perform automated analysis based on relevance and importance, and then sets priorities.

[0476] "Automated equipment" refers to machinery and robots set up to perform specific tasks automatically in factories and production lines.

[0477] A "production line" refers to a series of work processes within a factory for manufacturing products, and is used to support efficient production.

[0478] "Multilingual translation functionality" refers to translation technology used to facilitate communication between different languages, possessing the ability to convert text and speech into other languages.

[0479] An "analytical device" is a device used to evaluate the progress of production or a project and generate notifications by predicting potential problems.

[0480] "Optimal resource allocation" refers to the planned distribution of available resources in order to utilize them efficiently and achieve maximum results.

[0481] A description of the embodiment for carrying out the invention will be provided.

[0482] The program to implement this system is built on general-purpose programming languages ​​such as Python and Java, and consists of information recording devices, machine learning devices, and analytical devices. The server first collects production management-related information using the information recording devices and inputs it into the integrated recording device. Information collection is performed in real time via APIs of external platforms. The program utilizes data processing libraries such as pandas and SQLAlchemy to organize and store this collected data.

[0483] Next, the server automatically organizes and prioritizes the collected information based on relevance and importance through a machine learning system. Machine learning libraries such as scikit-learn are used to train and apply the AI ​​model. Through this process, users can make data-driven decisions to maximize the efficiency of the production line.

[0484] Furthermore, the analysis device evaluates production progress based on algorithms generated by the server and predicts potential problems. As a result, the user is presented with countermeasures, enabling efficient production management. For example, if signs of mechanical trouble are detected, repairs can be arranged immediately to prevent unnecessary downtime.

[0485] Furthermore, the server facilitates communication between different languages ​​and reduces misunderstandings within international production teams through its multilingual translation function. This utilizes the Google Translate API. Messages sent by users are automatically translated into the recipient's native language, ensuring smooth communication.

[0486] The optimal allocation of resources is proposed based on past success stories, and workers with the most suitable skills and equipment are automatically assigned to specific production tasks. Generative AI models are effectively utilized in this process.

[0487] As a concrete example, consider a scenario in a factory where the system detects that robot A is overloaded and quickly suggests reassigning the task to another robot. An example of a prompt to the generative AI model used in this case is: "Based on the following dataset, generate the task priorities necessary to maximize the factory's production efficiency." This dataset may include robot operating time, production volume, failure history, and the skill sets of the team members.

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

[0489] Step 1:

[0490] The server collects production management-related information in real time from an external platform's API via an information recording device. It takes data from the API as input, organizes it as a dataframe using the pandas library, and inputs it into the integrated recording device. The output is the organized dataset.

[0491] Step 2:

[0492] The server uses a machine learning system to automatically organize information within a data frame based on relevance and importance, leveraging the scikit-learn library. The input is the dataset obtained in Step 1, and the AI ​​model performs data calculations, outputting task priorities. These priorities serve as important decision-making information for the user.

[0493] Step 3:

[0494] The analysis device starts up and evaluates the progress of the entire production line based on the output data from Step 2. This includes metadata such as operating time, failure history, and skill sets. The output is a prediction of potential problems and proposed countermeasures based on those predictions. The server immediately notifies the user of this information.

[0495] Step 4:

[0496] The server uses a multilingual translation function to facilitate communication in different languages. It translates input messages into the recipient's native language using the Google Translate API. The output is the translated message, supporting smooth communication.

[0497] Step 5:

[0498] The server uses a generative AI model to learn from past successes and suggest optimal resource allocations. Inputs include historical project data and real-time task priorities. The server then executes an algorithm based on this data and provides the user with an optimal resource allocation proposal as output.

[0499] Step 6:

[0500] Based on the optimal allocation and countermeasures proposed by the user, they access the management screen from their terminal and make the necessary adjustments. This operation is performed through a web interface, and the user's decisions are reflected throughout the entire system.

[0501] 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.

[0502] This invention provides a system for more advanced project management that utilizes an information gathering device, a machine learning device, an analysis device, and an emotion engine. The system aims to recognize user emotions while managing project progress and enable dynamic project adjustments.

[0503] First, the server acquires project-related information from various data sources through information gathering devices. This data includes task details, progress, and communication content among members, and is stored in an integrated database. When a user updates project information using a terminal, the server integrates the latest information, keeping the database constantly up-to-date.

[0504] Next, the server uses machine learning to organize the information in the integrated database and prioritize tasks according to their relevance and importance. Users can view this information in real time on a dashboard displayed through their terminal. This dashboard visualizes the progress and analysis results for potential problems, enabling immediate action.

[0505] Furthermore, the server utilizes an emotion engine to analyze data obtained from users and evaluate their emotional state. For example, if a user provides feedback on a project in text, the emotion engine identifies the user's emotions (joy, stress, excitement, etc.) through text analysis. Based on this emotional state, the project's communication strategy and task allocation are automatically adjusted. For instance, if a user indicates high stress levels, the server can suggest reallocating some tasks to other members to reduce the user's burden.

[0506] Furthermore, the multilingual translation function facilitates smooth communication even in international project environments. Messages sent via the device in different languages ​​are translated and delivered to each user, reducing misunderstandings and promoting collaboration throughout the team.

[0507] Based on past success stories, the resource optimization feature suggests the most suitable roles for project members, supporting effective project progress. This allows users to utilize project resources most efficiently.

[0508] Thus, the present invention is a system that aims to increase the probability of project success by integrating various elements of project management and combining them with an emotional engine.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The server collects project-related information, such as task management data and chat logs, through APIs of external tools. This information is stored in an integrated database, enabling centralized management of data across the entire project.

[0512] Step 2:

[0513] When a user enters or updates project information through their terminal, the server receives it and integrates it into the existing database. This ensures that the data is up-to-date in real time.

[0514] Step 3:

[0515] The server uses machine learning equipment to analyze the collected data. The analysis results are prioritized based on the importance and relevance of the tasks and displayed on a dashboard that users can view via their devices.

[0516] Step 4:

[0517] The server utilizes an emotion engine to analyze user feedback and comments and identify the user's emotional state. For example, if a user's text input is something like, "The deadline is approaching and I'm feeling anxious," the emotion engine recognizes this as a stressful state.

[0518] Step 5:

[0519] The server dynamically adjusts the project's progress based on the analysis results of the emotion engine. Specifically, if a user is experiencing high stress levels, it will suggest reducing their burden by reassigning some tasks to other team members.

[0520] Step 6:

[0521] The server supports communication in different languages ​​through its multilingual translation function. Messages between project members are automatically translated as needed and displayed on the device, ensuring consistent communication.

[0522] Step 7:

[0523] The server references past project data and proposes the optimal resource allocation based on the skills and roles of the team members. This resource allocation proposal is presented to the user via a terminal, allowing the user to adjust team members' tasks accordingly.

[0524] (Example 2)

[0525] 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."

[0526] In today's complex project management environment, where smooth communication among stakeholders and real-time situational awareness are essential, there is a need for systems that efficiently manage project progress and prevent potential problems before they occur. Furthermore, maximizing work efficiency requires considering the emotional states of team members and optimally distributing workloads. However, current systems have limitations in terms of multilingual support and sentiment analysis, making it difficult to provide dynamic adjustments based on international projects or individual emotional states.

[0527] 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.

[0528] In this invention, the server includes means for collecting business-related information from an information acquisition device and storing it in an integrated data storage unit; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for evaluating the emotional state of users using an emotion analysis function and automatically adjusting work allocation and communication strategies based on the results. This makes it possible to improve the success rate of projects by facilitating smooth communication among multinational teams and making adjustments based on emotions.

[0529] An "information acquisition device" is a device that collects business-related information from various sources and provides it to a server.

[0530] An "integrated data storage unit" is a database or storage function that stores collected information in a consolidated manner, making it easier to use for later analysis and processing.

[0531] A "machine learning device" is a device that analyzes patterns and relationships in data and uses the results to set priorities and predict future trends.

[0532] The "emotion analysis function" is a feature that analyzes information and feedback provided by users to identify their emotional state.

[0533] A "translation function" is a feature that automatically converts messages and texts into other languages ​​to facilitate smooth communication between different languages.

[0534] "Optimal resource utilization" refers to the effective and efficient allocation of resources within a project based on past data and performance.

[0535] A "display function" is a feature that visually displays data and analysis results, allowing users to grasp the project status and progress at a glance.

[0536] This invention provides a method for efficiently managing projects using a system that combines multiple devices and functions. In particular, it integrates functions such as information acquisition, data analysis, sentiment analysis, and multilingual translation to enable project progress management and smooth communication.

[0537] First, the server uses information acquisition devices to collect project-related data from various sources. This information includes task details, progress, and communication among team members, and is stored in an integrated data storage unit. To efficiently manage this data, the server also integrates with many business systems and cloud services.

[0538] Next, the server runs a machine learning system to automatically set priorities based on the collected information. This allows users to check progress in real time on a dashboard on their device. For example, AI technology can be used to prioritize tasks with approaching deadlines or to suggest optimal schedules based on past performance data.

[0539] Furthermore, the server uses sentiment analysis capabilities to analyze the user's emotional state from their feedback and communication. This allows it to detect signs of stress or dissatisfaction related to a project, for example, and then suggest task redistributions based on these findings. When a user enters feedback on their device, the content is automatically analyzed, and appropriate measures are taken based on their emotional state.

[0540] Furthermore, this system features multilingual translation capabilities, enabling smooth communication even in international projects. Messages in different languages ​​are translated via the device, ensuring that each user receives accurate information. This allows project members to collaborate efficiently, overcoming language barriers.

[0541] As an example of how the generative AI model can be used, by inputting the prompt "Identify priority tasks within the project and adjust communication methods based on the sentiment analysis results," the AI ​​will determine the importance of the tasks and generate suggestions for appropriately adjusting the team's communication plan.

[0542] Thus, the present invention provides a system that significantly improves the efficiency and accuracy of project management by clearly separating the roles of server, terminal, and user, and enabling them to cooperate with each other.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The server uses information acquisition devices to collect project-related data, including task details, progress, and communication content, from various sources. It receives data feeds from external business systems and cloud services as input and stores them in an integrated data storage unit. The output is a real-time updated integrated database. Specifically, it connects to various sources via APIs and periodically retrieves data.

[0546] Step 2:

[0547] The server uses machine learning to analyze information in the integrated database. It receives collected data as input and executes algorithms to analyze the relationships and importance of the data. The output is a list of prioritized tasks. Specifically, it applies machine learning algorithms to evaluate correlations between data points and identify important tasks.

[0548] Step 3:

[0549] Users access a dashboard provided by the server using their device. Here, they can view project progress and potential issues visualized in real time. The input is analysis results from the server, and the output is a visualized progress report. Specifically, information is displayed using interactive graphs and charts, allowing users to make immediate decisions based on this information.

[0550] Step 4:

[0551] The server utilizes sentiment analysis capabilities to analyze user feedback and communication content. It receives text-based feedback and comments as input and analyzes the emotional state using natural language processing techniques. The output is adjustment suggestions based on the user's emotional state. Specifically, it executes a text analysis algorithm, calculates sentiment indicators, and uses these to reallocate tasks and adjust communication strategies.

[0552] Step 5:

[0553] The server translates messages in different languages ​​via its multilingual translation function and delivers them to each user. The input is messages written in various languages, and the output is the translated message. Specifically, it uses a machine translation engine to perform language conversion, sends the translated message to the terminal, and presents it to the user.

[0554] Step 6:

[0555] The server proposes optimal resource utilization based on past project successes. It receives historical project data as input and generates an optimal resource allocation model by comparing it with baseline data. The output is a proposal for the optimal roles within the project team. Specifically, it executes an optimization algorithm to match each member's skill set with task requirements.

[0556] (Application Example 2)

[0557] 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."

[0558] Current operational equipment management systems make it difficult to grasp progress and potential issues in real time and to implement appropriate countermeasures quickly. Furthermore, they do not adequately address communication barriers between languages ​​or automate task allocation that takes user emotions into consideration. This leads to problems such as inefficient resource allocation and excessive burden on users.

[0559] 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.

[0560] In this invention, the server includes means for collecting data from operating devices using information gathering means and storing the information in an integrated database; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for identifying the user's emotional state and automatically adjusting task allocation using an emotion analysis device. This enables efficient management of operating devices and reduces the burden on users.

[0561] An "information gathering means" is a device that has the function of acquiring data from operating equipment and storing that information in an integrated database.

[0562] A "machine learning device" is a device that automatically organizes collected information based on its relevance and importance, and sets priorities accordingly.

[0563] A "display device" is a device that visualizes progress in real time and provides information to users in an easy-to-understand manner.

[0564] An "analytical device" is a device that evaluates the status of the operational equipment, predicts potential problems, and proposes countermeasures.

[0565] A "multilingual translation device" is a device that translates languages ​​to facilitate communication between different languages.

[0566] An "emotion analysis device" is a device that identifies the emotional state based on information obtained from the user and automatically adjusts task allocation accordingly.

[0567] An "integrated database" is a database system that centrally stores various types of information and allows for the effective use of that information as needed.

[0568] A "server" is a central computer system that works in conjunction with various devices to collect and analyze data and provide a user interface.

[0569] The system for implementing this invention is a server-centered, advanced data processing and decision support system. A specific embodiment of this system is described below.

[0570] The server acquires data in real time from the operating equipment using information gathering methods. This data is stored in an integrated database and updated as needed. IoT sensors are used for information gathering, and the data is analyzed through Python scripts.

[0571] Machine learning systems analyze data and automatically organize it based on relevance and importance. This process utilizes machine learning frameworks such as TensorFlow. Prioritization ensures that important tasks are executed quickly.

[0572] Users' devices (e.g., smartphones and tablets) are equipped with display devices that allow them to visually check progress in real time. Using web application frameworks such as Flask, users can access intuitively operable dashboards.

[0573] The emotion analysis device analyzes emotions and identifies emotional states based on text and voice data collected from users. This analysis uses natural language processing technology to evaluate the user's stress level and satisfaction level.

[0574] The multilingual translation device uses the Google Translate API to translate messages between different languages, enabling smooth communication among international project teams.

[0575] As a concrete example, if a robot operator in a factory reports a robot malfunction via tablet, and the system determines that the operator's emotional state at that time is high-stress, the system will immediately redistribute other tasks to reduce the operator's workload. An example of a prompt message to support this process is: "Design a system that analyzes the emotional state of a factory robot operator when they report a task and adjusts the task as needed."

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

[0577] Step 1:

[0578] The server acquires data from operating equipment in real time using information gathering methods. It receives various data from IoT sensors (e.g., temperature, operating status, error codes) as input, converts this data into a database format, and stores it in an integrated database. The output is an integrated database reflecting the latest operating status.

[0579] Step 2:

[0580] The server passes data from an integrated database to a machine learning machine learning system for relevance and importance analysis. The input is operational data extracted from the database, and the output is a prioritized task list. This process uses TensorFlow to learn from the collected data and estimate the optimal task order.

[0581] Step 3:

[0582] The user's terminal visualizes the task list from the server via a display device. It receives a prioritized task list generated by the server as input and displays it visually on the dashboard. The output is an interface display that is easy for the user to understand. Here, a web-based dashboard is built using Flask.

[0583] Step 4:

[0584] Users input feedback and comments via their terminals and report them to the server. Input consists of the user's comments and feedback. Output is the specific feedback information received by the server.

[0585] Step 5:

[0586] The server processes user comments using an emotion analyzer. It receives user feedback text as input and analyzes and identifies emotional states (joy, dissatisfaction, stress, etc.) using natural language processing. The output is the result of the evaluation of the user's emotional state.

[0587] Step 6:

[0588] The server generates task redistribution proposals as needed, based on the sentiment analysis results. The input is the sentiment analysis results and the current task list. A machine learning model is used to design a new task allocation to reduce the user's burden. The output is the updated task list.

[0589] Step 7:

[0590] The server translates the generated task list into the required languages ​​through a multilingual translation device. The input is the updated task list, and the output is task instructions that can be viewed in multiple languages. Information is provided in a language optimized for each user using the Google Translate API.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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".

[0608] This invention relates to a project management system that utilizes an information gathering device, a machine learning device, and an analysis device. The aim of this system is to efficiently manage project progress through the coordinated operation of each functional device.

[0609] First, the server connects to information gathering devices and retrieves project-related data in real time from external tools. For example, it collects task update information and team member chat history via APIs of project task management tools and communication tools. This data is then stored by the server in an integrated database.

[0610] Next, the server uses machine learning to automatically organize the information stored in the integrated database. Leveraging AI models, it analyzes the relevance and importance of the data to prioritize each task. This process is reflected in a dashboard accessible to users from their devices, allowing them to visually track project progress in real time.

[0611] The analysis system uses machine learning to predict potential problems in a project and generate countermeasures. For example, if budget overruns or schedule delays are predicted, the server notifies the user of countermeasures based on these predictions, suggesting resource reallocation or schedule revisions.

[0612] Furthermore, the server utilizes a multilingual translation function to facilitate smooth communication in international projects. When a user sends a message in a different language, it is automatically translated into the native language of other members to aid understanding.

[0613] Finally, a resource optimization feature runs in the backend, helping users make optimal resource allocations based on data from past successful projects. For example, it helps maximize project efficiency by assigning members with the appropriate skills to specific tasks.

[0614] Thus, this system seamlessly integrates all processes, from information gathering and analysis to proposing solutions, translation, and resource allocation, supporting users in smoothly managing their projects.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] The server periodically retrieves project-related data via APIs from external tools. For example, it collects task progress information from task management tools and chat data between team members from communication tools, and stores them in an integrated database.

[0618] Step 2:

[0619] When a user uploads new project-related information via their device, the server receives the data and integrates it into the existing project database. This ensures that all project data remains up-to-date.

[0620] Step 3:

[0621] The server uses machine learning to analyze information in the integrated database and determine its relevance and importance. Based on the analysis results, it prioritizes each task and reflects its progress on a dashboard. Users can view this dashboard from their terminals, allowing them to instantly grasp the project's progress.

[0622] Step 4:

[0623] The server uses machine learning models to predict potential problems. Based on the predicted problems, it generates optimal solutions and notifies the user as an alert. The user can then use this information to consider countermeasures and utilize them in project management.

[0624] Step 5:

[0625] The server uses a multilingual translation function to automatically translate messages between different languages. In international projects, messages exchanged between members are translated into their respective native languages ​​and sent to their devices, enabling smooth communication.

[0626] Step 6:

[0627] The server uses data from past successful projects to optimize resource allocation. This suggests appropriate resource placement within the project team, allowing users to assign tasks and improve project efficiency.

[0628] (Example 1)

[0629] 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".

[0630] In modern project management, managing information using multiple tools presents challenges such as data dispersion and inefficient communication. Furthermore, the analysis of diverse data and language barriers in international projects complicate project progress management. Additionally, a lack of appropriate guidelines for optimal resource allocation can lead to decreased overall efficiency.

[0631] 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.

[0632] In this invention, the server includes means for collecting data from multiple tools through information acquisition means and inputting it into a centralized data storage means; means for automatically organizing the acquired information based on relevance and importance and setting task priorities using data analysis means; and representation means for visualizing the project progress in real time. This makes it possible to efficiently integrate dispersed data and easily grasp the progress of the entire project. Furthermore, task prioritization through analysis and multilingual support enable smooth communication and efficient resource allocation even in international projects.

[0633] "Information acquisition means" refers to methods for collecting data from multiple external tools and inputting it into a centralized data storage system.

[0634] A "data analysis method" is a method for automatically organizing acquired information based on its relevance and importance, and for setting task priorities.

[0635] "Means of expression" refers to methods for visually displaying the progress of a project in real time.

[0636] "Analysis methods" refer to methods for evaluating the progress of a project, predicting future challenges, and proposing solutions.

[0637] "Translation methods" refer to methods that use multilingual support functions to facilitate communication between different languages.

[0638] "Methods for proposing optimal resource allocation" refer to methods for indicating the most effective allocation of resources in a project, based on past successes.

[0639] This invention is a system specifically designed for project management, integrating functions such as information acquisition, data analysis, problem prediction, translation, and resource allocation. This system operates efficiently through the coordination of servers, terminals, and users.

[0640] First, the server collects data in real time from various external tools through information acquisition devices. The software used includes task management tools and communication software APIs. This ensures that up-to-date information related to the project is always centralized.

[0641] Next, the server stores the collected data in an integrated database. This data is then analyzed using a generated AI model via data analysis tools. As a result of the analysis, the relevance and importance of the data are evaluated, and task priorities are automatically set. This process allows users to clearly understand which tasks they should prioritize.

[0642] Real-time progress visualization is provided via the device. Users can access the dashboard from their device and instantly check the status of ongoing tasks and resources. This reduces decision delays and improves project efficiency.

[0643] Furthermore, the server uses analysis tools to predict future project challenges. For example, if the analysis indicates that "the current progress will not meet the deadline," it immediately generates countermeasures and notifies the user. Additional resource allocation or schedule revisions may be proposed.

[0644] In multilingual projects, server-based translation capabilities play a crucial role. They automatically translate messages sent in different languages, facilitating shared understanding. This reduces misunderstandings among international teams and facilitates smoother communication.

[0645] Regarding optimal resource allocation, the server suggests the best placement based on past successful project data. Users can then adjust resources based on this suggestion to maximize overall project efficiency.

[0646] As a concrete example, a prompt message could say, "Please display the dashboard to see the current task status of the project," and the server would be ready to respond immediately.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] The server collects data in real time from multiple external tools via information acquisition devices. Input data includes task update information and chat history from task management and communication tools via APIs. The server retrieves this data, converts it to an appropriate format for centralization, and then stores it in an integrated database.

[0650] Step 2:

[0651] The server begins data analysis based on the data stored in the integrated database. It uses a generative AI model to analyze the relationships and importance of the data. The input is the contents of the integrated database, and the output is the set priority order for each task. Specifically, data is fed into the AI ​​model, and tasks are listed in order of priority.

[0652] Step 3:

[0653] Users access the dashboard using their devices to view real-time project progress provided by the server. Input is the latest task priority information sent from the server, and output is a visually easy-to-understand progress report for the user. This information allows users to determine their next course of action.

[0654] Step 4:

[0655] Based on the data analysis results, the server evaluates potential project problems. This step includes analyzed task priorities and resource usage as input, and generates potential problems and proposed solutions as output. The server notifies the user, who then considers the proposed solutions.

[0656] Step 5:

[0657] The server utilizes multilingual translation capabilities in international projects. When a user sends a message in a different language to the server, the translation system converts it into the native language of other members. The input is the user's message, and the output is the translated message. This facilitates smoother communication.

[0658] Step 6:

[0659] The user receives suggestions for optimal resource allocation. Past project data is used as input, as the server generates the optimal resource placement based on past successes. The output proposes suitable personnel and resource allocations for each task. The user then uses these suggestions to implement the optimal resource allocation.

[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] Modern production management demands efficient resource allocation and task prioritization, but achieving these requires the analysis of vast amounts of data and immediate response. Furthermore, the involvement of multinational staff poses a challenge in terms of smooth communication across multiple languages.

[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 means for collecting production management-related information from an information recording device and inputting it into an integrated recording device; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for monitoring operational data from automated equipment and production lines in real time to support efficient management. This enables planned and flexible production management.

[0665] An "information recording device" is a device used to collect production management-related information and input it into an integrated recording device.

[0666] An "integrated recording device" is a device that centrally manages and stores information collected from information recording devices.

[0667] A "machine learning device" is a device that uses collected information to perform automated analysis based on relevance and importance, and then sets priorities.

[0668] "Automated equipment" refers to machinery and robots set up to perform specific tasks automatically in factories and production lines.

[0669] A "production line" refers to a series of work processes within a factory for manufacturing products, and is used to support efficient production.

[0670] "Multilingual translation functionality" refers to translation technology used to facilitate communication between different languages, possessing the ability to convert text and speech into other languages.

[0671] An "analytical device" is a device used to evaluate the progress of production or a project and generate notifications by predicting potential problems.

[0672] "Optimal resource allocation" refers to the planned distribution of available resources in order to utilize them efficiently and achieve maximum results.

[0673] A description of the embodiment for carrying out the invention will be provided.

[0674] The program to implement this system is built on general-purpose programming languages ​​such as Python and Java, and consists of information recording devices, machine learning devices, and analytical devices. The server first collects production management-related information using the information recording devices and inputs it into the integrated recording device. Information collection is performed in real time via APIs of external platforms. The program utilizes data processing libraries such as pandas and SQLAlchemy to organize and store this collected data.

[0675] Next, the server automatically organizes and prioritizes the collected information based on relevance and importance through a machine learning system. Machine learning libraries such as scikit-learn are used to train and apply the AI ​​model. Through this process, users can make data-driven decisions to maximize the efficiency of the production line.

[0676] Furthermore, the analysis device evaluates production progress based on algorithms generated by the server and predicts potential problems. As a result, the user is presented with countermeasures, enabling efficient production management. For example, if signs of mechanical trouble are detected, repairs can be arranged immediately to prevent unnecessary downtime.

[0677] Furthermore, the server facilitates communication between different languages ​​and reduces misunderstandings within international production teams through its multilingual translation function. This utilizes the Google Translate API. Messages sent by users are automatically translated into the recipient's native language, ensuring smooth communication.

[0678] The optimal allocation of resources is proposed based on past success stories, and workers with the most suitable skills and equipment are automatically assigned to specific production tasks. Generative AI models are effectively utilized in this process.

[0679] As a concrete example, consider a scenario in a factory where the system detects that robot A is overloaded and quickly suggests reassigning the task to another robot. An example of a prompt to the generative AI model used in this case is: "Based on the following dataset, generate the task priorities necessary to maximize the factory's production efficiency." This dataset may include robot operating time, production volume, failure history, and the skill sets of the team members.

[0680] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0681] Step 1:

[0682] The server collects production management-related information in real time from an external platform's API via an information recording device. It takes data from the API as input, organizes it as a dataframe using the pandas library, and inputs it into the integrated recording device. The output is the organized dataset.

[0683] Step 2:

[0684] The server uses a machine learning system to automatically organize information within a data frame based on relevance and importance, leveraging the scikit-learn library. The input is the dataset obtained in Step 1, and the AI ​​model performs data calculations, outputting task priorities. These priorities serve as important decision-making information for the user.

[0685] Step 3:

[0686] The analysis device starts up and evaluates the progress of the entire production line based on the output data from Step 2. This includes metadata such as operating time, failure history, and skill sets. The output is a prediction of potential problems and proposed countermeasures based on those predictions. The server immediately notifies the user of this information.

[0687] Step 4:

[0688] The server uses a multilingual translation function to facilitate communication in different languages. It translates input messages into the recipient's native language using the Google Translate API. The output is the translated message, supporting smooth communication.

[0689] Step 5:

[0690] The server uses a generative AI model to learn from past successes and suggest optimal resource allocations. Inputs include historical project data and real-time task priorities. The server then executes an algorithm based on this data and provides the user with an optimal resource allocation proposal as output.

[0691] Step 6:

[0692] Based on the optimal allocation and countermeasures proposed by the user, they access the management screen from their terminal and make the necessary adjustments. This operation is performed through a web interface, and the user's decisions are reflected throughout the entire system.

[0693] 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.

[0694] This invention provides a system for more advanced project management that utilizes an information gathering device, a machine learning device, an analysis device, and an emotion engine. The system aims to recognize user emotions while managing project progress and enable dynamic project adjustments.

[0695] First, the server acquires project-related information from various data sources through information gathering devices. This data includes task details, progress, and communication content among members, and is stored in an integrated database. When a user updates project information using a terminal, the server integrates the latest information, keeping the database constantly up-to-date.

[0696] Next, the server uses machine learning to organize the information in the integrated database and prioritize tasks according to their relevance and importance. Users can view this information in real time on a dashboard displayed through their terminal. This dashboard visualizes the progress and analysis results for potential problems, enabling immediate action.

[0697] Furthermore, the server utilizes an emotion engine to analyze data obtained from users and evaluate their emotional state. For example, if a user provides feedback on a project in text, the emotion engine identifies the user's emotions (joy, stress, excitement, etc.) through text analysis. Based on this emotional state, the project's communication strategy and task allocation are automatically adjusted. For instance, if a user indicates high stress levels, the server can suggest reallocating some tasks to other members to reduce the user's burden.

[0698] Furthermore, the multilingual translation function facilitates smooth communication even in international project environments. Messages sent via the device in different languages ​​are translated and delivered to each user, reducing misunderstandings and promoting collaboration throughout the team.

[0699] Based on past success stories, the resource optimization feature suggests the most suitable roles for project members, supporting effective project progress. This allows users to utilize project resources most efficiently.

[0700] Thus, the present invention is a system that aims to increase the probability of project success by integrating various elements of project management and combining them with an emotional engine.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The server collects project-related information, such as task management data and chat logs, through APIs of external tools. This information is stored in an integrated database, enabling centralized management of data across the entire project.

[0704] Step 2:

[0705] When a user enters or updates project information through their terminal, the server receives it and integrates it into the existing database. This ensures that the data is up-to-date in real time.

[0706] Step 3:

[0707] The server uses machine learning equipment to analyze the collected data. The analysis results are prioritized based on the importance and relevance of the tasks and displayed on a dashboard that users can view via their devices.

[0708] Step 4:

[0709] The server utilizes an emotion engine to analyze user feedback and comments and identify the user's emotional state. For example, if a user's text input is something like, "The deadline is approaching and I'm feeling anxious," the emotion engine recognizes this as a stressful state.

[0710] Step 5:

[0711] The server dynamically adjusts the project's progress based on the analysis results of the emotion engine. Specifically, if a user is experiencing high stress levels, it will suggest reducing their burden by reassigning some tasks to other team members.

[0712] Step 6:

[0713] The server supports communication in different languages ​​through its multilingual translation function. Messages between project members are automatically translated as needed and displayed on the device, ensuring consistent communication.

[0714] Step 7:

[0715] The server references past project data and proposes the optimal resource allocation based on the skills and roles of the team members. This resource allocation proposal is presented to the user via a terminal, allowing the user to adjust team members' tasks accordingly.

[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] In today's complex project management environment, where smooth communication among stakeholders and real-time situational awareness are essential, there is a need for systems that efficiently manage project progress and prevent potential problems before they occur. Furthermore, maximizing work efficiency requires considering the emotional states of team members and optimally distributing workloads. However, current systems have limitations in terms of multilingual support and sentiment analysis, making it difficult to provide dynamic adjustments based on international projects or individual emotional states.

[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 means for collecting business-related information from an information acquisition device and storing it in an integrated data storage unit; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for evaluating the emotional state of users using an emotion analysis function and automatically adjusting work allocation and communication strategies based on the results. This makes it possible to improve the success rate of projects by facilitating smooth communication among multinational teams and making adjustments based on emotions.

[0721] An "information acquisition device" is a device that collects business-related information from various sources and provides it to a server.

[0722] An "integrated data storage unit" is a database or storage function that stores collected information in a consolidated manner, making it easier to use for later analysis and processing.

[0723] A "machine learning device" is a device that analyzes patterns and relationships in data and uses the results to set priorities and predict future trends.

[0724] The "emotion analysis function" is a feature that analyzes information and feedback provided by users to identify their emotional state.

[0725] A "translation function" is a feature that automatically converts messages and texts into other languages ​​to facilitate smooth communication between different languages.

[0726] "Optimal resource utilization" refers to the effective and efficient allocation of resources within a project based on past data and performance.

[0727] A "display function" is a feature that visually displays data and analysis results, allowing users to grasp the project status and progress at a glance.

[0728] This invention provides a method for efficiently managing projects using a system that combines multiple devices and functions. In particular, it integrates functions such as information acquisition, data analysis, sentiment analysis, and multilingual translation to enable project progress management and smooth communication.

[0729] First, the server uses information acquisition devices to collect project-related data from various sources. This information includes task details, progress, and communication among team members, and is stored in an integrated data storage unit. To efficiently manage this data, the server also integrates with many business systems and cloud services.

[0730] Next, the server runs a machine learning system to automatically set priorities based on the collected information. This allows users to check progress in real time on a dashboard on their device. For example, AI technology can be used to prioritize tasks with approaching deadlines or to suggest optimal schedules based on past performance data.

[0731] Furthermore, the server uses sentiment analysis capabilities to analyze the user's emotional state from their feedback and communication. This allows it to detect signs of stress or dissatisfaction related to a project, for example, and then suggest task redistributions based on these findings. When a user enters feedback on their device, the content is automatically analyzed, and appropriate measures are taken based on their emotional state.

[0732] Furthermore, this system features multilingual translation capabilities, enabling smooth communication even in international projects. Messages in different languages ​​are translated via the device, ensuring that each user receives accurate information. This allows project members to collaborate efficiently, overcoming language barriers.

[0733] As an example of how the generative AI model can be used, by inputting the prompt "Identify priority tasks within the project and adjust communication methods based on the sentiment analysis results," the AI ​​will determine the importance of the tasks and generate suggestions for appropriately adjusting the team's communication plan.

[0734] Thus, the present invention provides a system that significantly improves the efficiency and accuracy of project management by clearly separating the roles of server, terminal, and user, and enabling them to cooperate with each other.

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

[0736] Step 1:

[0737] The server uses information acquisition devices to collect project-related data, including task details, progress, and communication content, from various sources. It receives data feeds from external business systems and cloud services as input and stores them in an integrated data storage unit. The output is a real-time updated integrated database. Specifically, it connects to various sources via APIs and periodically retrieves data.

[0738] Step 2:

[0739] The server uses machine learning to analyze information in the integrated database. It receives collected data as input and executes algorithms to analyze the relationships and importance of the data. The output is a list of prioritized tasks. Specifically, it applies machine learning algorithms to evaluate correlations between data points and identify important tasks.

[0740] Step 3:

[0741] Users access a dashboard provided by the server using their device. Here, they can view project progress and potential issues visualized in real time. The input is analysis results from the server, and the output is a visualized progress report. Specifically, information is displayed using interactive graphs and charts, allowing users to make immediate decisions based on this information.

[0742] Step 4:

[0743] The server utilizes sentiment analysis capabilities to analyze user feedback and communication content. It receives text-based feedback and comments as input and analyzes the emotional state using natural language processing techniques. The output is adjustment suggestions based on the user's emotional state. Specifically, it executes a text analysis algorithm, calculates sentiment indicators, and uses these to reallocate tasks and adjust communication strategies.

[0744] Step 5:

[0745] The server translates messages in different languages ​​via its multilingual translation function and delivers them to each user. The input is messages written in various languages, and the output is the translated message. Specifically, it uses a machine translation engine to perform language conversion, sends the translated message to the terminal, and presents it to the user.

[0746] Step 6:

[0747] The server proposes optimal resource utilization based on past project successes. It receives historical project data as input and generates an optimal resource allocation model by comparing it with baseline data. The output is a proposal for the optimal roles within the project team. Specifically, it executes an optimization algorithm to match each member's skill set with task requirements.

[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] Current operational equipment management systems make it difficult to grasp progress and potential issues in real time and to implement appropriate countermeasures quickly. Furthermore, they do not adequately address communication barriers between languages ​​or automate task allocation that takes user emotions into consideration. This leads to problems such as inefficient resource allocation and excessive burden on users.

[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 means for collecting data from operating devices using information gathering means and storing the information in an integrated database; means for automatically organizing the collected information based on relevance and importance and setting priorities using a machine learning device; and means for identifying the user's emotional state and automatically adjusting task allocation using an emotion analysis device. This enables efficient management of operating devices and reduces the burden on users.

[0753] An "information gathering means" is a device that has the function of acquiring data from operating equipment and storing that information in an integrated database.

[0754] A "machine learning device" is a device that automatically organizes collected information based on its relevance and importance, and sets priorities accordingly.

[0755] A "display device" is a device that visualizes progress in real time and provides information to users in an easy-to-understand manner.

[0756] An "analytical device" is a device that evaluates the status of the operational equipment, predicts potential problems, and proposes countermeasures.

[0757] A "multilingual translation device" is a device that translates languages ​​to facilitate communication between different languages.

[0758] An "emotion analysis device" is a device that identifies the emotional state based on information obtained from the user and automatically adjusts task allocation accordingly.

[0759] An "integrated database" is a database system that centrally stores various types of information and allows for the effective use of that information as needed.

[0760] A "server" is a central computer system that works in conjunction with various devices to collect and analyze data and provide a user interface.

[0761] The system for implementing this invention is a server-centered, advanced data processing and decision support system. A specific embodiment of this system is described below.

[0762] The server acquires data in real time from the operating equipment using information gathering methods. This data is stored in an integrated database and updated as needed. IoT sensors are used for information gathering, and the data is analyzed through Python scripts.

[0763] Machine learning systems analyze data and automatically organize it based on relevance and importance. This process utilizes machine learning frameworks such as TensorFlow. Prioritization ensures that important tasks are executed quickly.

[0764] Users' devices (e.g., smartphones and tablets) are equipped with display devices that allow them to visually check progress in real time. Using web application frameworks such as Flask, users can access intuitively operable dashboards.

[0765] The emotion analysis device analyzes emotions and identifies emotional states based on text and voice data collected from users. This analysis uses natural language processing technology to evaluate the user's stress level and satisfaction level.

[0766] The multilingual translation device uses the Google Translate API to translate messages between different languages, enabling smooth communication among international project teams.

[0767] As a concrete example, if a robot operator in a factory reports a robot malfunction via tablet, and the system determines that the operator's emotional state at that time is high-stress, the system will immediately redistribute other tasks to reduce the operator's workload. An example of a prompt message to support this process is: "Design a system that analyzes the emotional state of a factory robot operator when they report a task and adjusts the task as needed."

[0768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0769] Step 1:

[0770] The server acquires data from operating equipment in real time using information gathering methods. It receives various data from IoT sensors (e.g., temperature, operating status, error codes) as input, converts this data into a database format, and stores it in an integrated database. The output is an integrated database reflecting the latest operating status.

[0771] Step 2:

[0772] The server passes data from an integrated database to a machine learning machine learning system for relevance and importance analysis. The input is operational data extracted from the database, and the output is a prioritized task list. This process uses TensorFlow to learn from the collected data and estimate the optimal task order.

[0773] Step 3:

[0774] The user's terminal visualizes the task list from the server via a display device. It receives a prioritized task list generated by the server as input and displays it visually on the dashboard. The output is an interface display that is easy for the user to understand. Here, a web-based dashboard is built using Flask.

[0775] Step 4:

[0776] Users input feedback and comments via their terminals and report them to the server. Input consists of the user's comments and feedback. Output is the specific feedback information received by the server.

[0777] Step 5:

[0778] The server processes user comments using an emotion analyzer. It receives user feedback text as input and analyzes and identifies emotional states (joy, dissatisfaction, stress, etc.) using natural language processing. The output is the result of the evaluation of the user's emotional state.

[0779] Step 6:

[0780] The server generates task redistribution proposals as needed, based on the sentiment analysis results. The input is the sentiment analysis results and the current task list. A machine learning model is used to design a new task allocation to reduce the user's burden. The output is an updated task list.

[0781] Step 7:

[0782] The server translates the generated task list into the required languages ​​through a multilingual translation device. The input is the updated task list, and the output is task instructions that can be viewed in multiple languages. Using the Google Translate API, information is provided in a language optimized for each user.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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."

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] The following is further disclosed regarding the embodiments described above.

[0805] (Claim 1)

[0806] A means for collecting project-related data from an information gathering device and inputting it into an integrated database,

[0807] A means of automatically organizing and prioritizing collected data based on relevance and importance using a machine learning device,

[0808] A display method that visualizes project progress in real time,

[0809] Analytical tools to predict potential problems and propose countermeasures,

[0810] Through multilingual translation functionality, a translation method that facilitates communication between different languages,

[0811] A means of proposing the optimal allocation of resources based on past success stories,

[0812] A project management system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the information gathering device has means for acquiring data in real time through a communication channel of an external tool.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the analytical device comprises means for evaluating the progress of a project and generating alerts.

[0817] "Example 1"

[0818] (Claim 1)

[0819] A means of collecting data from multiple tools through information acquisition means and inputting it into a centralized data storage means,

[0820] A means for automatically organizing acquired information based on relevance and importance using data analysis tools, and for setting task priorities,

[0821] A means of visualizing project progress in real time,

[0822] Analytical tools that predict future challenges and propose solutions,

[0823] A translation method that uses multilingual support to facilitate communication between different languages,

[0824] A means of proposing the optimal resource allocation based on past successes,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the information acquisition means includes means for acquiring information in real time through communication paths of multiple external tools.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the analysis means comprises means for evaluating the progress of a project and generating warnings.

[0830] "Application Example 1"

[0831] (Claim 1)

[0832] A means for collecting production management-related information from an information recording device and inputting it into an integrated recording device,

[0833] A means of automatically organizing and prioritizing collected information based on relevance and importance using a machine learning device,

[0834] A display method that visualizes production progress in real time,

[0835] Analytical tools to predict potential problems and propose countermeasures,

[0836] A translation method that facilitates communication between different languages ​​through multilingual translation functionality,

[0837] A means of proposing the optimal allocation of resources based on past success stories,

[0838] A means to monitor operational data from automated equipment and production lines in real time and support efficient management,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the information recording device has means for acquiring information in real time through a communication channel of an external platform.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the analytical device comprises means for evaluating the progress of production and generating a notification.

[0844] "Example 2 of combining an emotion engine"

[0845] (Claim 1)

[0846] A means for collecting business-related information from an information acquisition device and storing it in an integrated data storage unit,

[0847] A means for automatically organizing and prioritizing collected information based on relevance and importance using a machine learning device,

[0848] A means of providing a display function that visualizes the progress of work in real time,

[0849] A means of analysis that predicts potential problems and proposes countermeasures,

[0850] A means of using emotion analysis functionality to evaluate the emotional state of users and automatically adjusting work allocation and communication strategies based on the results,

[0851] A means equipped with a translation function that facilitates communication between different languages ​​through multilingual conversion,

[0852] A means of proposing the optimal use of resources based on past successes,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the information acquisition device has means for acquiring information in real time through a communication channel of an external resource.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the analysis function includes means for evaluating the progress of work and generating warnings.

[0858] "Application example 2 when combining with an emotional engine"

[0859] (Claim 1)

[0860] A means of collecting data from operating equipment using information gathering means and storing the information in an integrated database,

[0861] A means of automatically organizing and prioritizing collected information based on relevance and importance using a machine learning device,

[0862] A means equipped with a display device that visualizes the progress in real time,

[0863] A means including an analytical device that predicts potential problems and proposes countermeasures,

[0864] A translation means that facilitates communication between different languages ​​using a multilingual translation device,

[0865] A means of proposing the optimal allocation of resources based on past success stories,

[0866] A means of automatically adjusting task allocation by identifying the user's emotional state using an emotion analysis device,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, wherein the information gathering means has the function of acquiring information in real time through an external communication path.

[0870] (Claim 3)

[0871] The system according to claim 1, wherein the analytical device has a function to evaluate the progress and generate a warning. [Explanation of symbols]

[0872] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting project-related data from an information gathering device and inputting it into an integrated database, A means of automatically organizing and prioritizing collected data based on relevance and importance using a machine learning device, A display method that visualizes project progress in real time, Analytical tools to predict potential problems and propose countermeasures, Through multilingual translation functionality, a translation method that facilitates communication between different languages, A means of proposing the optimal allocation of resources based on past success stories, A project management system that includes this.

2. The system according to claim 1, wherein the information gathering device has means for acquiring data in real time through a communication channel of an external tool.

3. The system according to claim 1, wherein the analytical device comprises means for evaluating the progress of a project and generating alerts.

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