system

Abstract

We provide the system. [Solution] A means for receiving project information and registering data related to the project in a data storage unit, Analytical tools to collect schedule information from stakeholders and propose the optimal meeting date and time, A method for converting audio from a meeting into text data and creating meeting minutes, A means of collecting data to report on the progress of a project and automatically generating progress reports, The means of determining the appropriate person to be in charge of each task in the project and assigning those tasks, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In project management, project managers spend a lot of time on a wide range of tasks such as progress management, coordination with related parties, meeting setting, and minutes creation, and it is difficult to concentrate on essential tasks. As a result, many projects do not proceed as scheduled, leading to delays and cost overruns.

Means for Solving the Problems

[0005] This invention provides means for receiving project information and registering project-related data in a data storage unit, and means for collecting the schedules of stakeholders and analyzing them to propose the optimal meeting dates and times. It also includes means for converting meeting audio into text data and creating meeting minutes, and further provides means for collecting data reporting on project progress and automatically generating progress reports. Finally, by providing a system that includes means for determining the appropriate person in charge for each task in the project and assigning tasks, the invention reduces the workload in project management and enables efficient operation.

[0006] "Means for receiving project information" refers to the means of importing information such as project name, start date, planned end date, and list of stakeholders entered by the user into the system.

[0007] The term "data storage unit" refers to a database or recording device that stores information related to received projects and makes it accessible as needed.

[0008] "Means for collecting the schedules of stakeholders" refers to a function for obtaining calendar information and schedules of members involved in the project.

[0009] "Analysis tools for proposing the optimal meeting date and time" refers to a function where AI analyzes the collected schedule information of stakeholders to calculate the optimal meeting date and time for everyone.

[0010] "Methods for converting audio to text data" refers to methods using speech recognition technology to convert audio during a meeting into text information in real time.

[0011] "Methods for creating meeting minutes" refers to a function that automatically generates meeting minutes based on the transcribed content of a meeting.

[0012] "Means for collecting data to report project progress" refers to a function that automatically collects the completion status of tasks related to the project and other related information.

[0013] "Methods for automatically generating progress reports" refers to a function that analyzes collected progress data and automatically creates a report that visualizes the current status of the project.

[0014] "Means for determining the appropriate person in charge" refers to a function that allows AI to judge and determine the most suitable person in charge according to the characteristics of each task.

[0015] "Task distribution methods" refer to functions that automatically distribute tasks to designated personnel and manage their progress. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is an AI-powered project management system that helps users efficiently manage various types of project-related information. The system provides various functions, including project information registration, meeting scheduling, progress management, meeting minute creation, and automated task distribution.

[0038] First, the user enters project details using a terminal. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal sends this information to the server, which stores the information in its data storage unit.

[0039] The server collects the calendar information of stakeholders via their devices, and AI analyzes and suggests the optimal meeting date and time. Users can review the suggestion and make adjustments if necessary. This ensures that meetings are scheduled at the optimal time that all project stakeholders can attend.

[0040] During the meeting, the device converts the meeting audio into text data in real time. This text data is sent to a server, where AI analyzes the text and automatically creates meeting minutes. The generated minutes are distributed to all participants, and feedback is requested.

[0041] Furthermore, the server collects data on all project tasks for progress management and requests progress reports from stakeholders as needed. The AI ​​analyzes this data and automatically generates visualized progress reports. These reports are sent to users periodically so that they can stay informed about the project's current status.

[0042] For task allocation, the server uses AI to analyze the characteristics of each task and determine the most suitable person to handle it. Tasks are then automatically distributed via terminals. This ensures that the right members are assigned the necessary tasks, improving overall efficiency.

[0043] For example, if a user wants to start a new product development project, this system will support the scheduling of the initial meeting about the product and help the project succeed by quickly sharing the necessary information as it progresses.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user uses a terminal to enter basic information such as the project name, start date, planned end date, and list of stakeholders. The terminal then sends this data to the server.

[0047] Step 2:

[0048] The server registers the received project information in its data storage unit and generates a basic project schedule. This schedule is then sent to the user's terminal, allowing the user to review and edit it.

[0049] Step 3:

[0050] The server collects the calendar information of the participants via their devices. AI analyzes these schedules and determines the optimal meeting date and time. The determined date and time are displayed as a suggestion on the user's device.

[0051] Step 4:

[0052] Once the meeting begins, the device converts the meeting audio into text data in real time. The text data is sent to a server, where AI automatically analyzes it to create meeting minutes.

[0053] Step 5:

[0054] The server distributes the created meeting minutes to the terminals of the participants and collects feedback. Based on this feedback, the meeting minutes are revised as needed.

[0055] Step 6:

[0056] The server collects project task progress data from the devices of stakeholders. AI analyzes this data and automatically generates a report that visualizes the progress. The generated report is sent to the user, allowing them to check the project's progress.

[0057] Step 7:

[0058] The server uses AI to analyze the characteristics of the task and determine the most suitable person to handle it. The determined task distribution plan is sent to the relevant parties' terminals, and tasks are automatically assigned.

[0059] (Example 1)

[0060] 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."

[0061] In project management, information sharing among stakeholders, scheduling, and progress tracking are becoming increasingly complex, making efficient management difficult. Furthermore, optimal assignment of responsibilities for each task and the rapid creation of meeting minutes are required, but manual processes are time-consuming and labor-intensive. Thus, an efficient system is needed to solve these various problems in project management.

[0062] 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.

[0063] In this invention, the server includes means for an information processing device to receive project-related information and register it in a data storage device, means for the information processing device to analyze the schedules of stakeholders using a generative model and propose the optimal meeting date and time, and means for the information processing device to convert audio during a meeting into text format via a voice input device and create meeting minutes using natural language processing functions. This enables more efficient project management and more accurate information sharing.

[0064] An "information processing device" is a device that receives project-related information, registers it in a data storage device, analyzes the schedules of stakeholders, and makes suggestions.

[0065] A "data storage device" is a storage medium that holds project-related information received by an information processing device and provides it as needed.

[0066] A "generative model" refers to an algorithm or machine learning model that calculates and proposes the optimal meeting date and time based on the schedules of the stakeholders.

[0067] A "voice input device" is a device that captures audio generated during a meeting and transmits it to an information processing device.

[0068] "Natural language processing functionality" refers to software technology that analyzes text data provided by a speech input device and uses it to create meeting minutes.

[0069] A "progress report" refers to information that summarizes the progress of each task in a project and provides it to the relevant parties.

[0070] This invention is an AI-powered project management system that provides a concrete means for streamlining complex project management tasks. The following describes how the invention is implemented.

[0071] To begin processing information, the user first inputs project-related information through a terminal. The terminal converts this information into JSON format and sends it to the server. The terminal used here can be a personal computer or a mobile device.

[0072] The server functions as an information processing device, validating the received information before storing it in a data storage device. A common database system (e.g., PostgreSQL) is used as the data storage device.

[0073] The server collects the schedules of stakeholders via an API, uses a generative model to calculate the optimal meeting date and time, and proposes it to the user. This generative model is built as a machine learning algorithm.

[0074] Audio data is captured during the meeting via the terminal's voice input device and converted into text data in real time using Google® Speech-to-Text. The resulting text data is sent to a server, where meeting minutes are automatically created using natural language processing capabilities.

[0075] The server also collects and analyzes project-wide progress data, then provides users with a visualized progress report. Data analysis tools (e.g., Power BI) are used to generate this report.

[0076] For example, when starting a new product development project, users can register detailed information on their devices, and the system can automate schedule adjustments and task allocation, allowing the project to proceed efficiently.

[0077] An example of a prompt message is: "We would like to use AI to provide the information necessary to start a new product development project and to schedule meetings and manage progress. Please provide specific instructions on how to use the optimal meeting date and time suggestion and the automatic meeting minutes creation function." Based on this prompt message, users can smoothly utilize each function of the system.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user uses their device to enter project details (project name, start date, planned end date, list of stakeholders, etc.). The entered information is converted to JSON format within the device. The device then sends this JSON data to the server via an HTTP POST request.

[0081] Step 2:

[0082] The server validates the JSON data received from the terminal. After checking for invalid data, it registers the project information in the database using SQL. The database ensures the secure storage of the information.

[0083] Step 3:

[0084] The server uses the API of the scheduling service to collect schedule information from stakeholders based on the stakeholders list. This collected data is passed to an AI model to calculate the optimal meeting date and time. The suggested dates and times are then presented to the user in a later step.

[0085] Step 4:

[0086] The server sends the proposed meeting date and time to the user's device and registers it as a tentative schedule in the calendar application. The user uses the device's interface to confirm the proposed date and time and make adjustments if necessary.

[0087] Step 5:

[0088] During the meeting, the terminal captures the meeting audio in real time using its voice input device. This audio data is converted into text data using an API such as Google Speech-to-Text. The converted text data is then sent to the server.

[0089] Step 6:

[0090] The server analyzes the received text data using natural language processing capabilities. It then automatically generates meeting minutes based on the results of this analysis. These minutes are then distributed via email to the relevant parties.

[0091] Step 7:

[0092] The server collects project progress information via APIs and performs data analysis. To facilitate visualization of progress, it uses data visualization tools to generate visual progress reports. These reports are sent to users periodically.

[0093] Step 8:

[0094] The server uses an AI model to analyze information for each task and determine the appropriate person to assign it. The server sends this information to a terminal, which automatically notifies each person of their task assignment. This enables efficient project progress.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] There is a need to reduce the risk of equipment failure and lessen the burden on workers by centrally managing the operating efficiency and maintenance plans of equipment in factories. Furthermore, a system is needed to facilitate smooth information sharing among stakeholders in order to perform accurate maintenance at the appropriate time. These challenges have not been adequately addressed by conventional methods.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for receiving project information and registering project-related data in an information storage unit; means for collecting the schedules of stakeholders and calculating for proposing the optimal meeting date and time; means for converting meeting audio into text data and creating a record; means for collecting data reporting the project's progress and automatically generating a progress report; means for determining the appropriate person in charge for each task in the project and assigning the work; means for inputting and saving the operating status and maintenance plan of the equipment using a function processing device, automatically identifying equipment that requires maintenance and assigning the work to the most suitable technician; and means for handling meeting speeches, generating records, and outputting them as a report. This enables optimization of equipment maintenance and operating efficiency, as well as smooth information sharing among stakeholders.

[0100] The "information storage unit" is a storage system for saving project-related data and the operating status of equipment.

[0101] A "calculation tool" is a system that has the function of suggesting the optimal date and time for a meeting based on the schedule information of the parties involved.

[0102] "Text data" refers to data obtained by converting audio from a meeting into written text.

[0103] A "progress report" is a report that automatically generates information about the progress of a project.

[0104] A "function processing device" is hardware or software used to manage the operating status and maintenance plan of a device.

[0105] The term "optimal technician" refers to the technician best suited to perform maintenance or repair on a particular piece of equipment.

[0106] "Processing speech and generating records" refers to the process of processing the content of speeches during a meeting and creating records that can be referenced later.

[0107] In this invention, first, the terminal receives detailed information about the project, such as the project name, start date, and list of stakeholders, and registers it in the information storage unit. Next, the server uses the stakeholders' calendar information to calculate and propose the optimal meeting date and time. For example, it selects a date and time that allows the most participants to gather, taking into account the stakeholders' availability.

[0108] During the meeting, the device converts audio into text data in real time, automatically generates a meeting record using this data, and distributes it to all participants. The generated record makes it easy to review the key points of the discussion afterward.

[0109] The server also collects various data to track project progress and automatically generates progress reports by analyzing them using AI. These reports are sent regularly to stakeholders, ensuring that everyone is aware of the latest project status.

[0110] Furthermore, the operating status of the equipment can be managed via a terminal, necessary maintenance work can be identified in real time using a function processing device, and the work can be assigned to the most suitable technician.

[0111] A concrete example is a project to introduce a new product manufacturing line. In this case, it is necessary to verify the operation of each piece of equipment and to formulate a maintenance schedule. By using this system, the appropriate technicians can be automatically assigned, and a list of tasks can be provided to the terminal.

[0112] An example of a prompt is, "Please prepare the meeting minutes for this week's production line project and let me know the key decisions."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user enters project details on their terminal, such as the project name, start date, and list of stakeholders. This input data is sent to the server in digital format and registered in the information storage unit. The server receives the data and saves it in the appropriate format.

[0116] Step 2:

[0117] The server collects schedule information from stakeholders and uses calculation tools to analyze the availability of each stakeholder's calendar. Based on the data obtained through this analysis, it calculates the optimal meeting date and time and proposes it to the user. The user reviews the proposal and makes adjustments if necessary.

[0118] Step 3:

[0119] Once the meeting begins, the terminal uses speech recognition software to convert the audio during the meeting into text data in real time. The converted text data is sent to a server, which automatically generates a record and distributes it to the relevant parties.

[0120] Step 4:

[0121] The server collects project progress data and analyzes it using AI. This analysis automatically generates progress reports. The generated reports are distributed digitally to stakeholders and serve as reference materials for understanding the current status of the project.

[0122] Step 5:

[0123] The terminal collects equipment operating status and maintenance information via an input device and transmits it to the server. The functional processing unit analyzes this data, identifies equipment requiring maintenance, and automatically requests work from the most suitable technician. The output of this request is notified to the technician via email or a dedicated application.

[0124] 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.

[0125] This invention is a system that combines AI and an emotion engine to streamline project management. In addition to managing basic project information, it provides a decision-making process that takes into account the emotions of stakeholders, in order to support users in efficiently and results-oriented project management.

[0126] The user first enters basic project information using a terminal. This information is then sent from the terminal to the server and simultaneously stored in the data storage unit. The server organizes the data by project, enabling efficient information access.

[0127] During meetings, the terminal uses voice input, and the AI ​​transcribes the voice data into text in real time. This text can then be immediately used as meeting minutes. Furthermore, an emotion engine analyzes the tone of voice and facial expressions of meeting participants, collecting emotional data. This emotional data is added to the meeting minutes and used later when reviewing the meeting.

[0128] Furthermore, emotional feedback from each stakeholder is collected on the server along with project progress data. AI analyzes this information and automatically generates progress reports that reflect emotional trends. These reports help understand the project's health and stakeholder motivation, providing valuable guidance for users to fine-tune the plan.

[0129] In task assignment, the server uses AI to automatically select the most suitable person for each task based on the project content. The emotion engine considers past emotional data and takes care to avoid tasks that would be particularly stressful for specific stakeholders. By selecting personnel while considering emotional aspects in this way, the efficiency of task execution is improved.

[0130] As a concrete example, in a new product development project, the system monitors the emotions felt by stakeholders during the development process, and uses this data to help the project manager revise their strategy. If many positive emotions are detected during a meeting, the process is likely to be effective; conversely, if many negative emotions are detected, the approach needs to be re-evaluated. This enables more efficient and satisfying project management.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses a terminal to enter basic project information. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal then sends this information to the server.

[0134] Step 2:

[0135] The server stores the received project information in its data storage unit. The server automatically generates a basic schedule for each project and sends it to the user's terminal. The user can then review the generated schedule and make modifications as needed.

[0136] Step 3:

[0137] The server collects the schedules of all participants via their devices. AI analyzes this information and suggests the optimal meeting date and time for all participants. The suggested date and time are then notified to the user via their device.

[0138] Step 4:

[0139] At the start of the meeting, the device records audio in real time and converts it into text data using AI. This text data is sent to a server. An emotion engine analyzes the tone of participants' voices and activity to understand their emotional state.

[0140] Step 5:

[0141] The server adds the analyzed sentiment data to the meeting minutes. The minutes are distributed to all users and stakeholders, and feedback is accepted.

[0142] Step 6:

[0143] The server automatically collects the data necessary to create project progress reports from the terminals of stakeholders. Based on this data, the AI ​​generates a report that reflects both project progress and emotional states. This report is sent to the user and used to understand the project status.

[0144] Step 7:

[0145] The server analyzes the characteristics of project tasks and uses AI to determine the most suitable person to handle them. During this process, the emotion engine considers the past emotional data of each stakeholder and assigns tasks that are expected to reduce stress. The server then distributes task details to each person via terminals, improving overall execution efficiency.

[0146] (Example 2)

[0147] 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".

[0148] In project management, there is a challenge in adequately considering communication and emotional aspects among stakeholders. Traditional project management systems can handle progress and task management, but they struggle to consider non-quantitative aspects such as emotional changes in meetings and understanding motivation. As a result, project efficiency may decrease and the stress levels of stakeholders may increase.

[0149] 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.

[0150] In this invention, the server includes means for receiving project information and registering it in a data storage unit, means for analyzing the facial expressions and voice characteristics of stakeholders and determining their emotional state, and means for converting meeting audio into text data and creating meeting minutes that include emotional information. This improves the quality of communication in project management and enables efficient project operation that also takes emotional aspects into consideration.

[0151] "Means for receiving project information" refers to an interface for receiving project-related data entered by users, importing it into a server via the network, and recording it in a database.

[0152] The "data storage unit" is a database system designed to securely and efficiently store various project-related information and to allow for quick access as needed.

[0153] "Means for analyzing facial expressions and voice characteristics" refers to a function that receives facial expression and voice data of meeting participants as input and uses an emotion engine to determine their emotional state.

[0154] "Methods for converting audio to text data" refers to the process of converting audio captured in real time during a meeting into digital text, making the transcribed content usable as meeting minutes.

[0155] "Methods for creating meeting minutes that include emotional information" refers to a system that creates meeting minutes based on the transcription of audio data and integrates emotional data of meeting participants to support information review.

[0156] "Methods for automatically generating progress reports" refers to a system that analyzes project progress and sentiment data, automatically creates reports based on that analysis, and provides them to project managers.

[0157] "A means of determining and assigning the appropriate person to each task" refers to a method of selecting the right person for each task using AI, and assigning tasks in a way that minimizes stress for the person in charge by taking into account past emotional data.

[0158] This invention is a system for streamlining project management and is primarily composed of servers, terminals, and generative AI models. The following describes in detail how this system works.

[0159] First, the user uses a terminal to enter basic project information. This information includes the project name, participants, schedule, and tasks. This information is sent from the terminal to the server, where it is organized and securely stored in the server's data storage.

[0160] Next, when project-related meetings are held, the terminal captures the meeting audio. The audio data is sent to a server and converted into text data in real time by a generative AI model. Simultaneously, an emotion engine analyzes the facial expressions and tone of voice of the participants and collects this emotion data to understand the emotional trends of the meeting.

[0161] The server integrates collected sentiment data with project progress data and automatically generates progress reports using a generative AI model. This allows users to understand project progress and participants' emotional responses, enabling efficient fine-tuning of project management plans.

[0162] Furthermore, when assigning tasks, the server selects the appropriate person based on project data. Based on the results of sentiment analysis, tasks are assigned in a way that minimizes the stress that a particular task causes to participants. In this way, project management that takes emotions into consideration can improve overall operational efficiency.

[0163] As a concrete example, in a new product development project, the system monitors the emotions felt by team members during the development process and helps the project manager revise the strategy based on that data. During the project, users can input prompts into the generated AI model, such as "Please analyze the positive emotional trends in meetings and provide advice on progress," to gain valuable insights.

[0164] This system will make project management more efficient and effective, and will increase stakeholder satisfaction.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user uses a terminal to enter basic project information. This information includes the project name, participant list, schedule, and objectives. The terminal formats this information as electronic data and prepares it for transmission to the server. It also verifies the input data to ensure there are no errors.

[0168] Step 2:

[0169] The terminal transmits project information to the server using a secure communication protocol. Simultaneously, it saves a backup of the aforementioned project information in temporary memory. The server organizes the received data by project and securely stores it in the data storage unit. The input is the user's project information, and the output is the consistent project data stored in the data storage unit.

[0170] Step 3:

[0171] At the start of the meeting, the device uses its built-in microphone to capture audio in real time. The audio data is immediately sent to the server. The input is audio data, and the output is digital data of the meeting content transcribed into text by a generative AI model.

[0172] Step 4:

[0173] The server converts received audio data into text in real time using a generation AI model. Simultaneously, an emotion engine analyzes voice tone, rhythm, and participants' facial expressions to quantify their emotional states. The input is meeting audio and facial expression data, and the output is a textualized meeting transcript and emotional data for each participant.

[0174] Step 5:

[0175] The server integrates emotional data and project progress status, and then automatically generates progress reports using a generative AI model. These reports indicate the project's health and the emotional trends of stakeholders, and are provided to project managers. The input consists of emotional and progress information, while the output is a progress report that also takes into account stakeholders' motivations.

[0176] Step 6:

[0177] When a user requests task assignments for a project, the server automatically selects the most suitable person for each project, referencing the results of sentiment analysis. By utilizing a generative AI model to analyze the impact of specific tasks on participants, the system achieves optimal team composition. Inputs are task information and sentiment data, while output is a list of assigned personnel.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] Project management requires not only information management and task allocation, but also adjustments that take into account the emotions and stress levels of stakeholders. However, traditional systems have made it difficult to grasp the emotional changes of stakeholders in real time and adjust the workflow accordingly. This can negatively impact project progress and stakeholder motivation.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for receiving project information and registering project-related data in a storage device; means for collecting schedule information of stakeholders and analyzing it to suggest the optimal date and time for meetings; means for converting audio during meetings into text data and creating meeting minutes; means for collecting data reporting the progress of the project and automatically generating progress reports; means for determining the appropriate person in charge for each stage of the project and assigning the stages; and means for analyzing the emotional data of stakeholders and adjusting the workflow in real time. This makes it possible to improve efficiency in project management and reduce stress on stakeholders.

[0183] "Project information" is a general term for everything related to a specific task or activity, including plans, objectives, results, schedules, and resource allocation.

[0184] A "data storage device" is a device or technology for storing information and data, and is used for the storage and management of digital data.

[0185] "Schedule information of stakeholders" refers to data regarding the schedules and timetables of individuals or organizations involved in the project.

[0186] "Analytical methods" refer to the processes and techniques used to analyze data and derive conclusions or insights.

[0187] "Text data" refers to information obtained by converting audio, handwritten text, etc., into text format.

[0188] A "progress report" is a document that shows the progress of a project and is created to clarify the degree of achievement and any problems encountered.

[0189] "Emotional data" refers to information about a person's emotions and moods, and indicates an individual's emotional state.

[0190] "Real-time adjustment" refers to immediately modifying plans and processes based on ongoing information and circumstances.

[0191] The embodiment of the invention specifically relates to the implementation of a system designed to streamline project management. This system functions by combining various digital devices and technologies.

[0192] The server receives project information and registers it in a data storage device. This allows for centralized management of project-related data, including plans, objectives, and resource allocation. It collects schedule information from stakeholders and uses analytical tools to suggest optimal meeting dates and times. This enables scheduling adjustments that take into account the availability of all stakeholders. Furthermore, it uses speech recognition technology to convert meeting audio into text data and automatically creates meeting minutes. This allows for easy sharing of meeting content in text format.

[0193] Meanwhile, user terminals collect data to easily report project progress. The collected data is sent to a server, where AI analyzes it and automatically generates progress reports. In this process, emotional data of stakeholders is also taken into consideration. Emotional data is extracted from the user's voice and facial expressions and analyzed as text data. As a result, the motivation and emotional state of stakeholders regarding the project are also reflected in the report.

[0194] Within the system, the person responsible for each stage of the project is determined and tasks are assigned, and emotional data is taken into consideration during this process. This allows for the selection of personnel who will not experience excessive stress during the process. In particular, the workflow, which is adjusted in real time, is dynamically operated based on AI analysis and incorporates emotional data.

[0195] As a concrete example, when conducting a new product development project, the AI ​​analyzes the tone of voice and facial expressions of those involved during meetings. If positive emotions are dominant, the process is likely to be effective; if negative emotions are prevalent, it is determined that improvements are needed. In this way, the system can monitor the health of a project from an emotional perspective as well.

[0196] An example of a prompt for a generative AI model is: "We would like to ask for suggestions on how to use AI to collect sentiment data from factory workers and optimize the production process."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The server receives project information from the user's terminal. This information includes the project name, purpose, schedule, and resource details. The server then registers this information in a data storage device. This enables centralized project management.

[0200] Step 2:

[0201] The server retrieves the schedule information of the relevant parties from an external calendar API. It then analyzes this schedule information using an analytical tool to propose the optimal meeting date and time. The input information is the calendar information of the relevant parties, and the output is the proposed meeting date and time.

[0202] Step 3:

[0203] During the meeting, the terminal uses its microphone to collect audio in real time. This audio data is immediately sent to a server, where character recognition software converts the audio into text. The resulting text data is immediately formatted as meeting minutes and can be distributed to relevant parties.

[0204] Step 4:

[0205] Users input project progress data, which is then sent to a server. The server analyzes the progress data using AI and automatically generates a progress report. The AI ​​utilizes an emotion analysis module to incorporate the emotional data of stakeholders as input. The output is a progress report.

[0206] Step 5:

[0207] The server analyzes project process data and stakeholders' emotional data to select the most suitable person for each stage. This selection includes an analysis of emotional states based on historical data. The output is an updated task assignment list.

[0208] Step 6:

[0209] The server dynamically readjusts the workflow based on emotional data collected in real time. This involves AI using a generated AI model to analyze prompt sentences and optimize them to reduce worker stress. The input for this step is real-time emotional data, and the output is the adjusted workflow.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

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

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention is an AI-powered project management system that helps users efficiently manage various types of project-related information. The system provides various functions, including project information registration, meeting scheduling, progress management, meeting minute creation, and automated task distribution.

[0227] First, the user enters project details using a terminal. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal sends this information to the server, which stores the information in its data storage unit.

[0228] The server collects the calendar information of stakeholders via their devices, and AI analyzes and suggests the optimal meeting date and time. Users can review the suggestion and make adjustments if necessary. This ensures that meetings are scheduled at the optimal time that all project stakeholders can attend.

[0229] During the meeting, the device converts the meeting audio into text data in real time. This text data is sent to a server, where AI analyzes the text and automatically creates meeting minutes. The generated minutes are distributed to all participants, and feedback is requested.

[0230] Furthermore, the server collects data on all project tasks for progress management and requests progress reports from stakeholders as needed. The AI ​​analyzes this data and automatically generates visualized progress reports. These reports are sent to users periodically so that they can stay informed about the project's current status.

[0231] For task allocation, the server uses AI to analyze the characteristics of each task and determine the most suitable person to handle it. Tasks are then automatically distributed via terminals. This ensures that the right members are assigned the necessary tasks, improving overall efficiency.

[0232] For example, if a user wants to start a new product development project, this system will support the scheduling of the initial meeting about the product and help the project succeed by quickly sharing the necessary information as it progresses.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The user uses a terminal to enter basic information such as the project name, start date, planned end date, and list of stakeholders. The terminal then sends this data to the server.

[0236] Step 2:

[0237] The server registers the received project information in its data storage unit and generates a basic project schedule. This schedule is then sent to the user's terminal, allowing the user to review and edit it.

[0238] Step 3:

[0239] The server collects the calendar information of the participants via their devices. AI analyzes these schedules and determines the optimal meeting date and time. The determined date and time are displayed as a suggestion on the user's device.

[0240] Step 4:

[0241] Once the meeting begins, the device converts the meeting audio into text data in real time. The text data is sent to a server, where AI automatically analyzes it to create meeting minutes.

[0242] Step 5:

[0243] The server distributes the created meeting minutes to the terminals of the participants and collects feedback. Based on this feedback, the meeting minutes are revised as needed.

[0244] Step 6:

[0245] The server collects project task progress data from the devices of stakeholders. AI analyzes this data and automatically generates a report that visualizes the progress. The generated report is sent to the user, allowing them to check the project's progress.

[0246] Step 7:

[0247] The server uses AI to analyze the characteristics of the task and determine the most suitable person to handle it. The determined task distribution plan is sent to the relevant parties' terminals, and tasks are automatically assigned.

[0248] (Example 1)

[0249] 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."

[0250] In project management, information sharing among stakeholders, scheduling, and progress tracking are becoming increasingly complex, making efficient management difficult. Furthermore, optimal assignment of responsibilities for each task and the rapid creation of meeting minutes are required, but manual processes are time-consuming and labor-intensive. Thus, an efficient system is needed to solve these various problems in project management.

[0251] 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.

[0252] In this invention, the server includes means for an information processing device to receive project-related information and register it in a data storage device, means for the information processing device to analyze the schedules of stakeholders using a generative model and propose the optimal meeting date and time, and means for the information processing device to convert audio during a meeting into text format via a voice input device and create meeting minutes using natural language processing functions. This enables more efficient project management and more accurate information sharing.

[0253] An "information processing device" is a device that receives project-related information, registers it in a data storage device, analyzes the schedules of stakeholders, and makes suggestions.

[0254] A "data storage device" is a storage medium that holds project-related information received by an information processing device and provides it as needed.

[0255] A "generative model" refers to an algorithm or machine learning model that calculates and proposes the optimal meeting date and time based on the schedules of the stakeholders.

[0256] A "voice input device" is a device that captures audio generated during a meeting and transmits it to an information processing device.

[0257] "Natural language processing functionality" refers to software technology that analyzes text data provided by a speech input device and uses it to create meeting minutes.

[0258] A "progress report" refers to information that summarizes the progress of each task in a project and provides it to the relevant parties.

[0259] This invention is an AI-powered project management system that provides a concrete means for streamlining complex project management tasks. The following describes how the invention is implemented.

[0260] To begin processing information, the user first inputs project-related information through a terminal. The terminal converts this information into JSON format and sends it to the server. The terminal used here can be a personal computer or a mobile device.

[0261] The server functions as an information processing device, validating the received information before storing it in a data storage device. A common database system (e.g., PostgreSQL) is used as the data storage device.

[0262] The server collects the schedules of stakeholders via an API, uses a generative model to calculate the optimal meeting date and time, and proposes it to the user. This generative model is built as a machine learning algorithm.

[0263] Audio data is captured during the meeting via the terminal's voice input device and converted into text data in real time using Google Speech-to-Text. The resulting text data is sent to a server, where meeting minutes are automatically created using natural language processing capabilities.

[0264] The server also collects and analyzes project-wide progress data, then provides users with a visualized progress report. Data analysis tools (e.g., Power BI) are used to generate this report.

[0265] For example, when starting a new product development project, users can register detailed information on their devices, and the system can automate schedule adjustments and task allocation, allowing the project to proceed efficiently.

[0266] An example of a prompt message is: "We would like to use AI to provide the information necessary to start a new product development project and to schedule meetings and manage progress. Please provide specific instructions on how to use the optimal meeting date and time suggestion and the automatic meeting minutes creation function." Based on this prompt message, users can smoothly utilize each function of the system.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The user uses their device to enter project details (project name, start date, planned end date, list of stakeholders, etc.). The entered information is converted to JSON format within the device. The device then sends this JSON data to the server via an HTTP POST request.

[0270] Step 2:

[0271] The server validates the JSON data received from the terminal. After checking for invalid data, it registers the project information in the database using SQL. The database ensures the secure storage of the information.

[0272] Step 3:

[0273] The server uses the API of the scheduling service to collect schedule information from stakeholders based on the stakeholders list. This collected data is passed to an AI model to calculate the optimal meeting date and time. The suggested dates and times are then presented to the user in a later step.

[0274] Step 4:

[0275] The server sends the proposed meeting date and time to the user's device and registers it as a tentative schedule in the calendar application. The user uses the device's interface to confirm the proposed date and time and make adjustments if necessary.

[0276] Step 5:

[0277] During the meeting, the terminal captures the meeting audio in real time using its voice input device. This audio data is converted into text data using an API such as Google Speech-to-Text. The converted text data is then sent to the server.

[0278] Step 6:

[0279] The server analyzes the received text data using natural language processing capabilities. It then automatically generates meeting minutes based on the results of this analysis. These minutes are then distributed via email to the relevant parties.

[0280] Step 7:

[0281] The server collects project progress information via APIs and performs data analysis. To facilitate visualization of progress, it uses data visualization tools to generate visual progress reports. These reports are sent to users periodically.

[0282] Step 8:

[0283] The server analyzes the information of each task using an AI model to determine the appropriate person in charge. The server sends this information to the terminal, and the terminal automatically notifies each person in charge of the task assignment. This enables the efficient progress of the project.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] There is a need to centrally manage the operation efficiency and maintenance plan of devices in a factory to reduce the risk of device failures and the burden on workers. Also, in order to perform accurate maintenance at the appropriate time, a mechanism for smooth information sharing among relevant parties is required. These issues have not been fully resolved by conventional methods.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for receiving project information and registering data related to the project in the information storage unit, means for collecting the schedule information of relevant parties and calculating means for proposing the optimal meeting date and time, means for converting the voice during the meeting into text data and creating a record, means for collecting data reporting the progress status of the project and automatically generating a progress report, means for determining the appropriate person in charge for each task of the project and allocating the work, means for inputting and storing the operation status and maintenance plan of the device using a functional processing device, automatically identifying the devices that require maintenance and allocating the work to the optimal technicians, and means for handling the remarks of the meeting, generating a record, and outputting it as a report. This enables the optimization of device maintenance and operation efficiency, as well as smooth information sharing among relevant parties.

[0289] The "information storage unit" is a storage system for saving project-related data and the operating status of equipment.

[0290] A "calculation tool" is a system that has the function of suggesting the optimal date and time for a meeting based on the schedule information of the parties involved.

[0291] "Text data" refers to data obtained by converting audio from a meeting into written text.

[0292] A "progress report" is a report that automatically generates information about the progress of a project.

[0293] A "function processing device" is hardware or software used to manage the operating status and maintenance plan of a device.

[0294] The term "optimal technician" refers to the technician best suited to perform maintenance or repair on a particular piece of equipment.

[0295] "Processing speech and generating records" refers to the process of processing the content of speeches during a meeting and creating records that can be referenced later.

[0296] In this invention, first, the terminal receives detailed information about the project, such as the project name, start date, and list of stakeholders, and registers it in the information storage unit. Next, the server uses the stakeholders' calendar information to calculate and propose the optimal meeting date and time. For example, it selects a date and time that allows the most participants to gather, taking into account the stakeholders' availability.

[0297] During the meeting, the device converts audio into text data in real time, automatically generates a meeting record using this data, and distributes it to all participants. The generated record makes it easy to review the key points of the discussion afterward.

[0298] The server also collects various data to track project progress and automatically generates progress reports by analyzing them using AI. These reports are sent regularly to stakeholders, ensuring that everyone is aware of the latest project status.

[0299] Furthermore, the operating status of the equipment can be managed via a terminal, necessary maintenance work can be identified in real time using a function processing device, and the work can be assigned to the most suitable technician.

[0300] A concrete example is a project to introduce a new product manufacturing line. In this case, it is necessary to verify the operation of each piece of equipment and to formulate a maintenance schedule. By using this system, the appropriate technicians can be automatically assigned, and a list of tasks can be provided to the terminal.

[0301] An example of a prompt is, "Please prepare the meeting minutes for this week's production line project and let me know the key decisions."

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

[0303] Step 1:

[0304] The user enters project details on their terminal, such as the project name, start date, and list of stakeholders. This input data is sent to the server in digital format and registered in the information storage unit. The server receives the data and saves it in the appropriate format.

[0305] Step 2:

[0306] The server collects schedule information from stakeholders and uses calculation tools to analyze the availability of each stakeholder's calendar. Based on the data obtained through this analysis, it calculates the optimal meeting date and time and proposes it to the user. The user reviews the proposal and makes adjustments if necessary.

[0307] Step 3:

[0308] When the meeting starts, the terminal uses voice recognition software to convert the voice during the meeting into text data in real time. The converted text data is sent to the server, and the server automatically generates a record and distributes this record to the relevant personnel.

[0309] Step 4:

[0310] The server collects the progress data of the project and analyzes it by utilizing AI. Based on this analysis, a progress report is automatically generated. The generated report is distributed to the relevant personnel in digital form and serves as materials for understanding the current status of the project.

[0311] Step 5:

[0312] The terminal collects the operating status and maintenance information of the device through the input device and sends it to the server. The functional processing device analyzes these data, identifies the devices that require maintenance, and automatically requests work from the most suitable technicians. The output of this request is notified to the technicians through email or a dedicated application.

[0313] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0314] The present invention is a system that combines AI and an emotion engine to improve project management efficiency. In order to support the user in performing project management efficiently and focusing on results, in addition to managing basic project information, it provides a decision-making process that takes into account the emotions of the relevant personnel.

[0315] The user first inputs the basic information of the project using the terminal. The input information is sent from the terminal to the server and simultaneously stored in the data storage unit. The server organizes the data for each project to enable efficient information access.

[0316] During meetings, the terminal uses voice input, and the AI ​​transcribes the voice data into text in real time. This text can then be immediately used as meeting minutes. Furthermore, an emotion engine analyzes the tone of voice and facial expressions of meeting participants, collecting emotional data. This emotional data is added to the meeting minutes and used later when reviewing the meeting.

[0317] Furthermore, emotional feedback from each stakeholder is collected on the server along with project progress data. AI analyzes this information and automatically generates progress reports that reflect emotional trends. These reports help understand the project's health and stakeholder motivation, providing valuable guidance for users to fine-tune the plan.

[0318] In task assignment, the server uses AI to automatically select the most suitable person for each task based on the project content. The emotion engine considers past emotional data and takes care to avoid tasks that would be particularly stressful for specific stakeholders. By selecting personnel while considering emotional aspects in this way, the efficiency of task execution is improved.

[0319] As a concrete example, in a new product development project, the system monitors the emotions felt by stakeholders during the development process, and uses this data to help the project manager revise their strategy. If many positive emotions are detected during a meeting, the process is likely to be effective; conversely, if many negative emotions are detected, the approach needs to be re-evaluated. This enables more efficient and satisfying project management.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The user uses a terminal to enter basic project information. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal then sends this information to the server.

[0323] Step 2:

[0324] The server stores the received project information in its data storage unit. The server automatically generates a basic schedule for each project and sends it to the user's terminal. The user can then review the generated schedule and make modifications as needed.

[0325] Step 3:

[0326] The server collects the schedules of all participants via their devices. AI analyzes this information and suggests the optimal meeting date and time for all participants. The suggested date and time are then notified to the user via their device.

[0327] Step 4:

[0328] At the start of the meeting, the device records audio in real time and converts it into text data using AI. This text data is sent to a server. An emotion engine analyzes the tone of participants' voices and activity to understand their emotional state.

[0329] Step 5:

[0330] The server adds the analyzed sentiment data to the meeting minutes. The minutes are distributed to all users and stakeholders, and feedback is accepted.

[0331] Step 6:

[0332] The server automatically collects the data necessary to create project progress reports from the terminals of stakeholders. Based on this data, the AI ​​generates a report that reflects both project progress and emotional states. This report is sent to the user and used to understand the project status.

[0333] Step 7:

[0334] The server analyzes the characteristics of project tasks and uses AI to determine the most suitable person to handle them. During this process, the emotion engine considers the past emotional data of each stakeholder and assigns tasks that are expected to reduce stress. The server then distributes task details to each person via terminals, improving overall execution efficiency.

[0335] (Example 2)

[0336] 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".

[0337] In project management, there is a challenge in adequately considering communication and emotional aspects among stakeholders. Traditional project management systems can handle progress and task management, but they struggle to consider non-quantitative aspects such as emotional changes in meetings and understanding motivation. As a result, project efficiency may decrease and the stress levels of stakeholders may increase.

[0338] 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.

[0339] In this invention, the server includes means for receiving project information and registering it in a data storage unit, means for analyzing the facial expressions and voice characteristics of stakeholders and determining their emotional state, and means for converting meeting audio into text data and creating meeting minutes that include emotional information. This improves the quality of communication in project management and enables efficient project operation that also takes emotional aspects into consideration.

[0340] "Means for receiving project information" refers to an interface for receiving project-related data entered by users, importing it into a server via the network, and recording it in a database.

[0341] The "data storage unit" is a database system designed to securely and efficiently store various project-related information and to allow for quick access as needed.

[0342] "Means for analyzing facial expressions and voice characteristics" refers to a function that receives facial expression and voice data of meeting participants as input and uses an emotion engine to determine their emotional state.

[0343] "Methods for converting audio to text data" refers to the process of converting audio captured in real time during a meeting into digital text, making the transcribed content usable as meeting minutes.

[0344] "Methods for creating meeting minutes that include emotional information" refers to a system that creates meeting minutes based on the transcription of audio data and integrates emotional data of meeting participants to support information review.

[0345] "Methods for automatically generating progress reports" refers to a system that analyzes project progress and sentiment data, automatically creates reports based on that analysis, and provides them to project managers.

[0346] "A means of determining and assigning the appropriate person to each task" refers to a method of selecting the right person for each task using AI, and assigning tasks in a way that minimizes stress for the person in charge by taking into account past emotional data.

[0347] This invention is a system for streamlining project management and is primarily composed of servers, terminals, and generative AI models. The following describes in detail how this system works.

[0348] First, the user uses a terminal to enter basic project information. This information includes the project name, participants, schedule, and tasks. This information is sent from the terminal to the server, where it is organized and securely stored in the server's data storage.

[0349] Next, when project-related meetings are held, the terminal captures the meeting audio. The audio data is sent to a server and converted into text data in real time by a generative AI model. Simultaneously, an emotion engine analyzes the facial expressions and tone of voice of the participants and collects this emotion data to understand the emotional trends of the meeting.

[0350] The server integrates collected sentiment data with project progress data and automatically generates progress reports using a generative AI model. This allows users to understand project progress and participants' emotional responses, enabling efficient fine-tuning of project management plans.

[0351] Furthermore, when assigning tasks, the server selects the appropriate person based on project data. Based on the results of sentiment analysis, tasks are assigned in a way that minimizes the stress that a particular task causes to participants. In this way, project management that takes emotions into consideration can improve overall operational efficiency.

[0352] As a concrete example, in a new product development project, the system monitors the emotions felt by team members during the development process and helps the project manager revise the strategy based on that data. During the project, users can input prompts into the generated AI model, such as "Please analyze the positive emotional trends in meetings and provide advice on progress," to gain valuable insights.

[0353] This system will make project management more efficient and effective, and will increase stakeholder satisfaction.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user uses a terminal to enter basic project information. This information includes the project name, participant list, schedule, and objectives. The terminal formats this information as electronic data and prepares it for transmission to the server. It also verifies the input data to ensure there are no errors.

[0357] Step 2:

[0358] The terminal transmits project information to the server using a secure communication protocol. Simultaneously, it saves a backup of the aforementioned project information in temporary memory. The server organizes the received data by project and securely stores it in the data storage unit. The input is the user's project information, and the output is the consistent project data stored in the data storage unit.

[0359] Step 3:

[0360] At the start of the meeting, the device uses its built-in microphone to capture audio in real time. The audio data is immediately sent to the server. The input is audio data, and the output is digital data of the meeting content transcribed into text by a generative AI model.

[0361] Step 4:

[0362] The server converts received audio data into text in real time using a generation AI model. Simultaneously, an emotion engine analyzes voice tone, rhythm, and participants' facial expressions to quantify their emotional states. The input is meeting audio and facial expression data, and the output is a textualized meeting transcript and emotional data for each participant.

[0363] Step 5:

[0364] The server integrates emotional data and project progress status, and then automatically generates progress reports using a generative AI model. These reports indicate the project's health and the emotional trends of stakeholders, and are provided to project managers. The input consists of emotional and progress information, while the output is a progress report that also takes into account stakeholders' motivations.

[0365] Step 6:

[0366] When a user requests task assignments for a project, the server automatically selects the most suitable person for each project, referencing the results of sentiment analysis. By utilizing a generative AI model to analyze the impact of specific tasks on participants, the system achieves optimal team composition. Inputs are task information and sentiment data, while output is a list of assigned personnel.

[0367] (Application Example 2)

[0368] 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."

[0369] Project management requires not only information management and task allocation, but also adjustments that take into account the emotions and stress levels of stakeholders. However, traditional systems have made it difficult to grasp the emotional changes of stakeholders in real time and adjust the workflow accordingly. This can negatively impact project progress and stakeholder motivation.

[0370] 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.

[0371] In this invention, the server includes means for receiving project information and registering project-related data in a storage device; means for collecting schedule information of stakeholders and analyzing it to suggest the optimal date and time for meetings; means for converting audio during meetings into text data and creating meeting minutes; means for collecting data reporting the progress of the project and automatically generating progress reports; means for determining the appropriate person in charge for each stage of the project and assigning the stages; and means for analyzing the emotional data of stakeholders and adjusting the workflow in real time. This makes it possible to improve efficiency in project management and reduce stress on stakeholders.

[0372] "Project information" is a general term for everything related to a specific task or activity, including plans, objectives, results, schedules, and resource allocation.

[0373] A "data storage device" is a device or technology for storing information and data, and is used for the storage and management of digital data.

[0374] "Schedule information of stakeholders" refers to data regarding the schedules and timetables of individuals or organizations involved in the project.

[0375] "Analytical methods" refer to the processes and techniques used to analyze data and derive conclusions or insights.

[0376] "Text data" refers to information obtained by converting audio, handwritten text, etc., into text format.

[0377] A "progress report" is a document that shows the progress of a project and is created to clarify the degree of achievement and any problems encountered.

[0378] "Emotional data" refers to information about a person's emotions and moods, and indicates an individual's emotional state.

[0379] "Real-time adjustment" refers to immediately modifying plans and processes based on ongoing information and circumstances.

[0380] The embodiment of the invention specifically relates to the implementation of a system designed to streamline project management. This system functions by combining various digital devices and technologies.

[0381] The server receives project information and registers it in a data storage device. This allows for centralized management of project-related data, including plans, objectives, and resource allocation. It collects schedule information from stakeholders and uses analytical tools to suggest optimal meeting dates and times. This enables scheduling adjustments that take into account the availability of all stakeholders. Furthermore, it uses speech recognition technology to convert meeting audio into text data and automatically creates meeting minutes. This allows for easy sharing of meeting content in text format.

[0382] Meanwhile, user terminals collect data to easily report project progress. The collected data is sent to a server, where AI analyzes it and automatically generates progress reports. In this process, emotional data of stakeholders is also taken into consideration. Emotional data is extracted from the user's voice and facial expressions and analyzed as text data. As a result, the motivation and emotional state of stakeholders regarding the project are also reflected in the report.

[0383] Within the system, the person responsible for each stage of the project is determined and tasks are assigned, and emotional data is taken into consideration during this process. This allows for the selection of personnel who will not experience excessive stress during the process. In particular, the workflow, which is adjusted in real time, is dynamically operated based on AI analysis and incorporates emotional data.

[0384] As a concrete example, when conducting a new product development project, the AI ​​analyzes the tone of voice and facial expressions of those involved during meetings. If positive emotions are dominant, the process is likely to be effective; if negative emotions are prevalent, it is determined that improvements are needed. In this way, the system can monitor the health of a project from an emotional perspective as well.

[0385] An example of a prompt for a generative AI model is: "We would like to ask for suggestions on how to use AI to collect sentiment data from factory workers and optimize the production process."

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

[0387] Step 1:

[0388] The server receives project information from the user's terminal. This information includes the project name, purpose, schedule, and resource details. The server then registers this information in a data storage device. This enables centralized project management.

[0389] Step 2:

[0390] The server retrieves the schedule information of the relevant parties from an external calendar API. It then analyzes this schedule information using an analytical tool to propose the optimal meeting date and time. The input information is the calendar information of the relevant parties, and the output is the proposed meeting date and time.

[0391] Step 3:

[0392] During the meeting, the terminal uses its microphone to collect audio in real time. This audio data is immediately sent to a server, where character recognition software converts the audio into text. The resulting text data is immediately formatted as meeting minutes and can be distributed to relevant parties.

[0393] Step 4:

[0394] Users input project progress data, which is then sent to a server. The server analyzes the progress data using AI and automatically generates a progress report. The AI ​​utilizes an emotion analysis module to incorporate the emotional data of stakeholders as input. The output is a progress report.

[0395] Step 5:

[0396] The server analyzes project process data and stakeholders' emotional data to select the most suitable person for each stage. This selection includes an analysis of emotional states based on historical data. The output is an updated task assignment list.

[0397] Step 6:

[0398] The server dynamically readjusts the workflow based on emotional data collected in real time. This involves AI using a generated AI model to analyze prompt sentences and optimize them to reduce worker stress. The input for this step is real-time emotional data, and the output is the adjusted workflow.

[0399] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0400] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0401] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0402] [Third Embodiment]

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

[0404] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0405] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0406] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0407] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0408] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0409] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0410] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0411] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0412] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0413] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0414] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0415] This invention is an AI-powered project management system that helps users efficiently manage various types of project-related information. The system provides various functions, including project information registration, meeting scheduling, progress management, meeting minute creation, and automated task distribution.

[0416] First, the user enters project details using a terminal. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal sends this information to the server, which stores the information in its data storage unit.

[0417] The server collects the calendar information of stakeholders via their devices, and AI analyzes and suggests the optimal meeting date and time. Users can review the suggestion and make adjustments if necessary. This ensures that meetings are scheduled at the optimal time that all project stakeholders can attend.

[0418] During the meeting, the device converts the meeting audio into text data in real time. This text data is sent to a server, where AI analyzes the text and automatically creates meeting minutes. The generated minutes are distributed to all participants, and feedback is requested.

[0419] Furthermore, the server collects data on all project tasks for progress management and requests progress reports from stakeholders as needed. The AI ​​analyzes this data and automatically generates visualized progress reports. These reports are sent to users periodically so that they can stay informed about the project's current status.

[0420] For task allocation, the server uses AI to analyze the characteristics of each task and determine the most suitable person to handle it. Tasks are then automatically distributed via terminals. This ensures that the right members are assigned the necessary tasks, improving overall efficiency.

[0421] For example, if a user wants to start a new product development project, this system will support the scheduling of the initial meeting about the product and help the project succeed by quickly sharing the necessary information as it progresses.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user uses a terminal to enter basic information such as the project name, start date, planned end date, and list of stakeholders. The terminal then sends this data to the server.

[0425] Step 2:

[0426] The server registers the received project information in its data storage unit and generates a basic project schedule. This schedule is then sent to the user's terminal, allowing the user to review and edit it.

[0427] Step 3:

[0428] The server collects the calendar information of the participants via their devices. AI analyzes these schedules and determines the optimal meeting date and time. The determined date and time are displayed as a suggestion on the user's device.

[0429] Step 4:

[0430] Once the meeting begins, the device converts the meeting audio into text data in real time. The text data is sent to a server, where AI automatically analyzes it to create meeting minutes.

[0431] Step 5:

[0432] The server distributes the created meeting minutes to the terminals of the participants and collects feedback. Based on this feedback, the meeting minutes are revised as needed.

[0433] Step 6:

[0434] The server collects project task progress data from the devices of stakeholders. AI analyzes this data and automatically generates a report that visualizes the progress. The generated report is sent to the user, allowing them to check the project's progress.

[0435] Step 7:

[0436] The server uses AI to analyze the characteristics of the task and determine the most suitable person to handle it. The determined task distribution plan is sent to the relevant parties' terminals, and tasks are automatically assigned.

[0437] (Example 1)

[0438] 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."

[0439] In project management, information sharing among stakeholders, scheduling, and progress tracking are becoming increasingly complex, making efficient management difficult. Furthermore, optimal assignment of responsibilities for each task and the rapid creation of meeting minutes are required, but manual processes are time-consuming and labor-intensive. Thus, an efficient system is needed to solve these various problems in project management.

[0440] 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.

[0441] In this invention, the server includes means for an information processing device to receive project-related information and register it in a data storage device, means for the information processing device to analyze the schedules of stakeholders using a generative model and propose the optimal meeting date and time, and means for the information processing device to convert audio during a meeting into text format via a voice input device and create meeting minutes using natural language processing functions. This enables more efficient project management and more accurate information sharing.

[0442] An "information processing device" is a device that receives project-related information, registers it in a data storage device, analyzes the schedules of stakeholders, and makes suggestions.

[0443] A "data storage device" is a storage medium that holds project-related information received by an information processing device and provides it as needed.

[0444] A "generative model" refers to an algorithm or machine learning model that calculates and proposes the optimal meeting date and time based on the schedules of the stakeholders.

[0445] A "voice input device" is a device that captures audio generated during a meeting and transmits it to an information processing device.

[0446] "Natural language processing functionality" refers to software technology that analyzes text data provided by a speech input device and uses it to create meeting minutes.

[0447] A "progress report" refers to information that summarizes the progress of each task in a project and provides it to the relevant parties.

[0448] This invention is an AI-powered project management system that provides a concrete means for streamlining complex project management tasks. The following describes how the invention is implemented.

[0449] To begin processing information, the user first inputs project-related information through a terminal. The terminal converts this information into JSON format and sends it to the server. The terminal used here can be a personal computer or a mobile device.

[0450] The server functions as an information processing device, validating the received information before storing it in a data storage device. A common database system (e.g., PostgreSQL) is used as the data storage device.

[0451] The server collects the schedules of stakeholders via an API, uses a generative model to calculate the optimal meeting date and time, and proposes it to the user. This generative model is built as a machine learning algorithm.

[0452] Audio data is captured during the meeting via the terminal's voice input device and converted into text data in real time using Google Speech-to-Text. The resulting text data is sent to a server, where meeting minutes are automatically created using natural language processing capabilities.

[0453] The server also collects and analyzes project-wide progress data, then provides users with a visualized progress report. Data analysis tools (e.g., Power BI) are used to generate this report.

[0454] For example, when starting a new product development project, users can register detailed information on their devices, and the system can automate schedule adjustments and task allocation, allowing the project to proceed efficiently.

[0455] An example of a prompt message is: "We would like to use AI to provide the information necessary to start a new product development project and to schedule meetings and manage progress. Please provide specific instructions on how to use the optimal meeting date and time suggestion and the automatic meeting minutes creation function." Based on this prompt message, users can smoothly utilize each function of the system.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The user uses their device to enter project details (project name, start date, planned end date, list of stakeholders, etc.). The entered information is converted to JSON format within the device. The device then sends this JSON data to the server via an HTTP POST request.

[0459] Step 2:

[0460] The server validates the JSON data received from the terminal. After checking for invalid data, it registers the project information in the database using SQL. The database ensures the secure storage of the information.

[0461] Step 3:

[0462] The server uses the API of the scheduling service to collect schedule information from stakeholders based on the stakeholders list. This collected data is passed to an AI model to calculate the optimal meeting date and time. The suggested dates and times are then presented to the user in a later step.

[0463] Step 4:

[0464] The server sends the proposed meeting date and time to the user's device and registers it as a tentative schedule in the calendar application. The user uses the device's interface to confirm the proposed date and time and make adjustments if necessary.

[0465] Step 5:

[0466] During the meeting, the terminal captures the meeting audio in real time using its voice input device. This audio data is converted into text data using an API such as Google Speech-to-Text. The converted text data is then sent to the server.

[0467] Step 6:

[0468] The server analyzes the received text data using natural language processing capabilities. It then automatically generates meeting minutes based on the results of this analysis. These minutes are then distributed via email to the relevant parties.

[0469] Step 7:

[0470] The server collects project progress information via APIs and performs data analysis. To facilitate visualization of progress, it uses data visualization tools to generate visual progress reports. These reports are sent to users periodically.

[0471] Step 8:

[0472] The server uses an AI model to analyze information for each task and determine the appropriate person to assign it. The server sends this information to a terminal, which automatically notifies each person of their task assignment. This enables efficient project progress.

[0473] (Application Example 1)

[0474] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0475] There is a need to reduce the risk of equipment failure and lessen the burden on workers by centrally managing the operating efficiency and maintenance plans of equipment in factories. Furthermore, a system is needed to facilitate smooth information sharing among stakeholders in order to perform accurate maintenance at the appropriate time. These challenges have not been adequately addressed by conventional methods.

[0476] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0477] In this invention, the server includes means for receiving project information and registering project-related data in an information storage unit; means for collecting the schedules of stakeholders and calculating for proposing the optimal meeting date and time; means for converting meeting audio into text data and creating a record; means for collecting data reporting the project's progress and automatically generating a progress report; means for determining the appropriate person in charge for each task in the project and assigning the work; means for inputting and saving the operating status and maintenance plan of the equipment using a function processing device, automatically identifying equipment that requires maintenance and assigning the work to the most suitable technician; and means for handling meeting speeches, generating records, and outputting them as a report. This enables optimization of equipment maintenance and operating efficiency, as well as smooth information sharing among stakeholders.

[0478] The "information storage unit" is a storage system for saving project-related data and the operating status of equipment.

[0479] A "calculation tool" is a system that has the function of suggesting the optimal date and time for a meeting based on the schedule information of the parties involved.

[0480] "Text data" refers to data obtained by converting audio from a meeting into written text.

[0481] A "progress report" is a report that automatically generates information about the progress of a project.

[0482] A "function processing device" is hardware or software used to manage the operating status and maintenance plan of a device.

[0483] The term "optimal technician" refers to the technician best suited to perform maintenance or repair on a particular piece of equipment.

[0484] "Processing speech and generating records" refers to the process of processing the content of speeches during a meeting and creating records that can be referenced later.

[0485] In this invention, first, the terminal receives detailed information about the project, such as the project name, start date, and list of stakeholders, and registers it in the information storage unit. Next, the server uses the stakeholders' calendar information to calculate and propose the optimal meeting date and time. For example, it selects a date and time that allows the most participants to gather, taking into account the stakeholders' availability.

[0486] During the meeting, the device converts audio into text data in real time, automatically generates a meeting record using this data, and distributes it to all participants. The generated record makes it easy to review the key points of the discussion afterward.

[0487] The server also collects various data to track project progress and automatically generates progress reports by analyzing them using AI. These reports are sent regularly to stakeholders, ensuring that everyone is aware of the latest project status.

[0488] Furthermore, the operating status of the equipment can be managed via a terminal, necessary maintenance work can be identified in real time using a function processing device, and the work can be assigned to the most suitable technician.

[0489] A concrete example is a project to introduce a new product manufacturing line. In this case, it is necessary to verify the operation of each piece of equipment and to formulate a maintenance schedule. By using this system, the appropriate technicians can be automatically assigned, and a list of tasks can be provided to the terminal.

[0490] An example of a prompt is, "Please prepare the meeting minutes for this week's production line project and let me know the key decisions."

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

[0492] Step 1:

[0493] The user enters project details on their terminal, such as the project name, start date, and list of stakeholders. This input data is sent to the server in digital format and registered in the information storage unit. The server receives the data and saves it in the appropriate format.

[0494] Step 2:

[0495] The server collects schedule information from stakeholders and uses calculation tools to analyze the availability of each stakeholder's calendar. Based on the data obtained through this analysis, it calculates the optimal meeting date and time and proposes it to the user. The user reviews the proposal and makes adjustments if necessary.

[0496] Step 3:

[0497] Once the meeting begins, the terminal uses speech recognition software to convert the audio during the meeting into text data in real time. The converted text data is sent to a server, which automatically generates a record and distributes it to the relevant parties.

[0498] Step 4:

[0499] The server collects project progress data and analyzes it using AI. This analysis automatically generates progress reports. The generated reports are distributed digitally to stakeholders and serve as reference materials for understanding the current status of the project.

[0500] Step 5:

[0501] The terminal collects equipment operating status and maintenance information via an input device and transmits it to the server. The functional processing unit analyzes this data, identifies equipment requiring maintenance, and automatically requests work from the most suitable technician. The output of this request is notified to the technician via email or a dedicated application.

[0502] 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.

[0503] This invention is a system that combines AI and an emotion engine to streamline project management. In addition to managing basic project information, it provides a decision-making process that takes into account the emotions of stakeholders, in order to support users in efficiently and results-oriented project management.

[0504] The user first enters basic project information using a terminal. This information is then sent from the terminal to the server and simultaneously stored in the data storage unit. The server organizes the data by project, enabling efficient information access.

[0505] During meetings, the terminal uses voice input, and the AI ​​transcribes the voice data into text in real time. This text can then be immediately used as meeting minutes. Furthermore, an emotion engine analyzes the tone of voice and facial expressions of meeting participants, collecting emotional data. This emotional data is added to the meeting minutes and used later when reviewing the meeting.

[0506] Furthermore, emotional feedback from each stakeholder is collected on the server along with project progress data. AI analyzes this information and automatically generates progress reports that reflect emotional trends. These reports help understand the project's health and stakeholder motivation, providing valuable guidance for users to fine-tune the plan.

[0507] In task assignment, the server uses AI to automatically select the most suitable person for each task based on the project content. The emotion engine considers past emotional data and takes care to avoid tasks that would be particularly stressful for specific stakeholders. By selecting personnel while considering emotional aspects in this way, the efficiency of task execution is improved.

[0508] As a concrete example, in a new product development project, the system monitors the emotions felt by stakeholders during the development process, and uses this data to help the project manager revise their strategy. If many positive emotions are detected during a meeting, the process is likely to be effective; conversely, if many negative emotions are detected, the approach needs to be re-evaluated. This enables more efficient and satisfying project management.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user uses a terminal to enter basic project information. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal then sends this information to the server.

[0512] Step 2:

[0513] The server stores the received project information in its data storage unit. The server automatically generates a basic schedule for each project and sends it to the user's terminal. The user can then review the generated schedule and make modifications as needed.

[0514] Step 3:

[0515] The server collects the schedules of all participants via their devices. AI analyzes this information and suggests the optimal meeting date and time for all participants. The suggested date and time are then notified to the user via their device.

[0516] Step 4:

[0517] At the start of the meeting, the device records audio in real time and converts it into text data using AI. This text data is sent to a server. An emotion engine analyzes the tone of participants' voices and activity to understand their emotional state.

[0518] Step 5:

[0519] The server adds the analyzed sentiment data to the meeting minutes. The minutes are distributed to all users and stakeholders, and feedback is accepted.

[0520] Step 6:

[0521] The server automatically collects the data necessary to create project progress reports from the terminals of stakeholders. Based on this data, the AI ​​generates a report that reflects both project progress and emotional states. This report is sent to the user and used to understand the project status.

[0522] Step 7:

[0523] The server analyzes the characteristics of project tasks and uses AI to determine the most suitable person to handle them. During this process, the emotion engine considers the past emotional data of each stakeholder and assigns tasks that are expected to reduce stress. The server then distributes task details to each person via terminals, improving overall execution efficiency.

[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 project management, there is a challenge in adequately considering communication and emotional aspects among stakeholders. Traditional project management systems can handle progress and task management, but they struggle to consider non-quantitative aspects such as emotional changes in meetings and understanding motivation. As a result, project efficiency may decrease and the stress levels of stakeholders may increase.

[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 receiving project information and registering it in a data storage unit, means for analyzing the facial expressions and voice characteristics of stakeholders and determining their emotional state, and means for converting meeting audio into text data and creating meeting minutes that include emotional information. This improves the quality of communication in project management and enables efficient project operation that also takes emotional aspects into consideration.

[0529] "Means for receiving project information" refers to an interface for receiving project-related data entered by users, importing it into a server via the network, and recording it in a database.

[0530] The "data storage unit" is a database system designed to securely and efficiently store various project-related information and to allow for quick access as needed.

[0531] "Means for analyzing facial expressions and voice characteristics" refers to a function that receives facial expression and voice data of meeting participants as input and uses an emotion engine to determine their emotional state.

[0532] "Methods for converting audio to text data" refers to the process of converting audio captured in real time during a meeting into digital text, making the transcribed content usable as meeting minutes.

[0533] "Methods for creating meeting minutes that include emotional information" refers to a system that creates meeting minutes based on the transcription of audio data and integrates emotional data of meeting participants to support information review.

[0534] "Methods for automatically generating progress reports" refers to a system that analyzes project progress and sentiment data, automatically creates reports based on that analysis, and provides them to project managers.

[0535] "A means of determining and assigning the appropriate person to each task" refers to a method of selecting the right person for each task using AI, and assigning tasks in a way that minimizes stress for the person in charge by taking into account past emotional data.

[0536] This invention is a system for streamlining project management and is primarily composed of servers, terminals, and generative AI models. The following describes in detail how this system works.

[0537] First, the user uses a terminal to enter basic project information. This information includes the project name, participants, schedule, and tasks. This information is sent from the terminal to the server, where it is organized and securely stored in the server's data storage.

[0538] Next, when project-related meetings are held, the terminal captures the meeting audio. The audio data is sent to a server and converted into text data in real time by a generative AI model. Simultaneously, an emotion engine analyzes the facial expressions and tone of voice of the participants and collects this emotion data to understand the emotional trends of the meeting.

[0539] The server integrates collected sentiment data with project progress data and automatically generates progress reports using a generative AI model. This allows users to understand project progress and participants' emotional responses, enabling efficient fine-tuning of project management plans.

[0540] Furthermore, when assigning tasks, the server selects the appropriate person based on project data. Based on the results of sentiment analysis, tasks are assigned in a way that minimizes the stress that a particular task causes to participants. In this way, project management that takes emotions into consideration can improve overall operational efficiency.

[0541] As a concrete example, in a new product development project, the system monitors the emotions felt by team members during the development process and helps the project manager revise the strategy based on that data. During the project, users can input prompts into the generated AI model, such as "Please analyze the positive emotional trends in meetings and provide advice on progress," to gain valuable insights.

[0542] This system will make project management more efficient and effective, and will increase stakeholder satisfaction.

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

[0544] Step 1:

[0545] The user uses a terminal to enter basic project information. This information includes the project name, participant list, schedule, and objectives. The terminal formats this information as electronic data and prepares it for transmission to the server. It also verifies the input data to ensure there are no errors.

[0546] Step 2:

[0547] The terminal transmits project information to the server using a secure communication protocol. Simultaneously, it saves a backup of the aforementioned project information in temporary memory. The server organizes the received data by project and securely stores it in the data storage unit. The input is the user's project information, and the output is the consistent project data stored in the data storage unit.

[0548] Step 3:

[0549] At the start of the meeting, the device uses its built-in microphone to capture audio in real time. The audio data is immediately sent to the server. The input is audio data, and the output is digital data of the meeting content transcribed into text by a generative AI model.

[0550] Step 4:

[0551] The server converts received audio data into text in real time using a generation AI model. Simultaneously, an emotion engine analyzes voice tone, rhythm, and participants' facial expressions to quantify their emotional states. The input is meeting audio and facial expression data, and the output is a textualized meeting transcript and emotional data for each participant.

[0552] Step 5:

[0553] The server integrates emotional data and project progress status, and then automatically generates progress reports using a generative AI model. These reports indicate the project's health and the emotional trends of stakeholders, and are provided to project managers. The input consists of emotional and progress information, while the output is a progress report that also takes into account stakeholders' motivations.

[0554] Step 6:

[0555] When a user requests task assignments for a project, the server automatically selects the most suitable person for each project, referencing the results of sentiment analysis. By utilizing a generative AI model to analyze the impact of specific tasks on participants, the system achieves optimal team composition. Inputs are task information and sentiment data, while output is a list of assigned personnel.

[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] Project management requires not only information management and task allocation, but also adjustments that take into account the emotions and stress levels of stakeholders. However, traditional systems have made it difficult to grasp the emotional changes of stakeholders in real time and adjust the workflow accordingly. This can negatively impact project progress and stakeholder motivation.

[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 receiving project information and registering project-related data in a storage device; means for collecting schedule information of stakeholders and analyzing it to suggest the optimal date and time for meetings; means for converting audio during meetings into text data and creating meeting minutes; means for collecting data reporting the progress of the project and automatically generating progress reports; means for determining the appropriate person in charge for each stage of the project and assigning the stages; and means for analyzing the emotional data of stakeholders and adjusting the workflow in real time. This makes it possible to improve efficiency in project management and reduce stress on stakeholders.

[0561] "Project information" is a general term for everything related to a specific task or activity, including plans, objectives, results, schedules, and resource allocation.

[0562] A "data storage device" is a device or technology for storing information and data, and is used for the storage and management of digital data.

[0563] "Schedule information of stakeholders" refers to data regarding the schedules and timetables of individuals or organizations involved in the project.

[0564] "Analytical methods" refer to the processes and techniques used to analyze data and derive conclusions or insights.

[0565] "Text data" refers to information obtained by converting audio, handwritten text, etc., into text format.

[0566] A "progress report" is a document that shows the progress of a project and is created to clarify the degree of achievement and any problems encountered.

[0567] "Emotional data" refers to information about a person's emotions and moods, and indicates an individual's emotional state.

[0568] "Real-time adjustment" refers to immediately modifying plans and processes based on ongoing information and circumstances.

[0569] The embodiment of the invention specifically relates to the implementation of a system designed to streamline project management. This system functions by combining various digital devices and technologies.

[0570] The server receives project information and registers it in a data storage device. This allows for centralized management of project-related data, including plans, objectives, and resource allocation. It collects schedule information from stakeholders and uses analytical tools to suggest optimal meeting dates and times. This enables scheduling adjustments that take into account the availability of all stakeholders. Furthermore, it uses speech recognition technology to convert meeting audio into text data and automatically creates meeting minutes. This allows for easy sharing of meeting content in text format.

[0571] Meanwhile, user terminals collect data to easily report project progress. The collected data is sent to a server, where AI analyzes it and automatically generates progress reports. In this process, emotional data of stakeholders is also taken into consideration. Emotional data is extracted from the user's voice and facial expressions and analyzed as text data. As a result, the motivation and emotional state of stakeholders regarding the project are also reflected in the report.

[0572] Within the system, the person responsible for each stage of the project is determined and tasks are assigned, and emotional data is taken into consideration during this process. This allows for the selection of personnel who will not experience excessive stress during the process. In particular, the workflow, which is adjusted in real time, is dynamically operated based on AI analysis and incorporates emotional data.

[0573] As a concrete example, when conducting a new product development project, the AI ​​analyzes the tone of voice and facial expressions of those involved during meetings. If positive emotions are dominant, the process is likely to be effective; if negative emotions are prevalent, it is determined that improvements are needed. In this way, the system can monitor the health of a project from an emotional perspective as well.

[0574] An example of a prompt for a generative AI model is: "We would like to ask for suggestions on how to use AI to collect sentiment data from factory workers and optimize the production process."

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The server receives project information from the user's terminal. This information includes the project name, purpose, schedule, and resource details. The server then registers this information in a data storage device. This enables centralized project management.

[0578] Step 2:

[0579] The server retrieves the schedule information of the relevant parties from an external calendar API. It then analyzes this schedule information using an analytical tool to propose the optimal meeting date and time. The input information is the calendar information of the relevant parties, and the output is the proposed meeting date and time.

[0580] Step 3:

[0581] During the meeting, the terminal uses its microphone to collect audio in real time. This audio data is immediately sent to a server, where character recognition software converts the audio into text. The resulting text data is immediately formatted as meeting minutes and can be distributed to relevant parties.

[0582] Step 4:

[0583] Users input project progress data, which is then sent to a server. The server analyzes the progress data using AI and automatically generates a progress report. The AI ​​utilizes an emotion analysis module to incorporate the emotional data of stakeholders as input. The output is a progress report.

[0584] Step 5:

[0585] The server analyzes project process data and stakeholders' emotional data to select the most suitable person for each stage. This selection includes an analysis of emotional states based on historical data. The output is an updated task assignment list.

[0586] Step 6:

[0587] The server dynamically readjusts the workflow based on emotional data collected in real time. This involves AI using a generated AI model to analyze prompt sentences and optimize them to reduce worker stress. The input for this step is real-time emotional data, and the output is the adjusted workflow.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] This invention is an AI-powered project management system that helps users efficiently manage various types of project-related information. The system provides various functions, including project information registration, meeting scheduling, progress management, meeting minute creation, and automated task distribution.

[0606] First, the user enters project details using a terminal. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal sends this information to the server, which stores the information in its data storage unit.

[0607] The server collects the calendar information of stakeholders via their devices, and AI analyzes and suggests the optimal meeting date and time. Users can review the suggestion and make adjustments if necessary. This ensures that meetings are scheduled at the optimal time that all project stakeholders can attend.

[0608] During the meeting, the device converts the meeting audio into text data in real time. This text data is sent to a server, where AI analyzes the text and automatically creates meeting minutes. The generated minutes are distributed to all participants, and feedback is requested.

[0609] Furthermore, the server collects data on all project tasks for progress management and requests progress reports from stakeholders as needed. The AI ​​analyzes this data and automatically generates visualized progress reports. These reports are sent to users periodically so that they can stay informed about the project's current status.

[0610] For task allocation, the server uses AI to analyze the characteristics of each task and determine the most suitable person to handle it. Tasks are then automatically distributed via terminals. This ensures that the right members are assigned the necessary tasks, improving overall efficiency.

[0611] For example, if a user wants to start a new product development project, this system will support the scheduling of the initial meeting about the product and help the project succeed by quickly sharing the necessary information as it progresses.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The user uses a terminal to enter basic information such as the project name, start date, planned end date, and list of stakeholders. The terminal then sends this data to the server.

[0615] Step 2:

[0616] The server registers the received project information in its data storage unit and generates a basic project schedule. This schedule is then sent to the user's terminal, allowing the user to review and edit it.

[0617] Step 3:

[0618] The server collects the calendar information of the participants via their devices. AI analyzes these schedules and determines the optimal meeting date and time. The determined date and time are displayed as a suggestion on the user's device.

[0619] Step 4:

[0620] Once the meeting begins, the device converts the meeting audio into text data in real time. The text data is sent to a server, where AI automatically analyzes it to create meeting minutes.

[0621] Step 5:

[0622] The server distributes the created meeting minutes to the terminals of the participants and collects feedback. Based on this feedback, the meeting minutes are revised as needed.

[0623] Step 6:

[0624] The server collects project task progress data from the devices of stakeholders. AI analyzes this data and automatically generates a report that visualizes the progress. The generated report is sent to the user, allowing them to check the project's progress.

[0625] Step 7:

[0626] The server uses AI to analyze the characteristics of the task and determine the most suitable person to handle it. The determined task distribution plan is sent to the relevant parties' terminals, and tasks are automatically assigned.

[0627] (Example 1)

[0628] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] In project management, information sharing among stakeholders, scheduling, and progress tracking are becoming increasingly complex, making efficient management difficult. Furthermore, optimal assignment of responsibilities for each task and the rapid creation of meeting minutes are required, but manual processes are time-consuming and labor-intensive. Thus, an efficient system is needed to solve these various problems in project management.

[0630] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0631] In this invention, the server includes means for an information processing device to receive project-related information and register it in a data storage device, means for the information processing device to analyze the schedules of stakeholders using a generative model and propose the optimal meeting date and time, and means for the information processing device to convert audio during a meeting into text format via a voice input device and create meeting minutes using natural language processing functions. This enables more efficient project management and more accurate information sharing.

[0632] An "information processing device" is a device that receives project-related information, registers it in a data storage device, analyzes the schedules of stakeholders, and makes suggestions.

[0633] A "data storage device" is a storage medium that holds project-related information received by an information processing device and provides it as needed.

[0634] A "generative model" refers to an algorithm or machine learning model that calculates and proposes the optimal meeting date and time based on the schedules of the stakeholders.

[0635] A "voice input device" is a device that captures audio generated during a meeting and transmits it to an information processing device.

[0636] "Natural language processing functionality" refers to software technology that analyzes text data provided by a speech input device and uses it to create meeting minutes.

[0637] A "progress report" refers to information that summarizes the progress of each task in a project and provides it to the relevant parties.

[0638] This invention is an AI-powered project management system that provides a concrete means for streamlining complex project management tasks. The following describes how the invention is implemented.

[0639] To begin processing information, the user first inputs project-related information through a terminal. The terminal converts this information into JSON format and sends it to the server. The terminal used here can be a personal computer or a mobile device.

[0640] The server functions as an information processing device, validating the received information before storing it in a data storage device. A common database system (e.g., PostgreSQL) is used as the data storage device.

[0641] The server collects the schedules of stakeholders via an API, uses a generative model to calculate the optimal meeting date and time, and proposes it to the user. This generative model is built as a machine learning algorithm.

[0642] Audio data is captured during the meeting via the terminal's voice input device and converted into text data in real time using Google Speech-to-Text. The resulting text data is sent to a server, where meeting minutes are automatically created using natural language processing capabilities.

[0643] The server also collects and analyzes project-wide progress data, then provides users with a visualized progress report. Data analysis tools (e.g., Power BI) are used to generate this report.

[0644] For example, when starting a new product development project, users can register detailed information on their devices, and the system can automate schedule adjustments and task allocation, allowing the project to proceed efficiently.

[0645] An example of a prompt message is: "We would like to use AI to provide the information necessary to start a new product development project and to schedule meetings and manage progress. Please provide specific instructions on how to use the optimal meeting date and time suggestion and the automatic meeting minutes creation function." Based on this prompt message, users can smoothly utilize each function of the system.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The user uses their device to enter project details (project name, start date, planned end date, list of stakeholders, etc.). The entered information is converted to JSON format within the device. The device then sends this JSON data to the server via an HTTP POST request.

[0649] Step 2:

[0650] The server validates the JSON data received from the terminal. After checking for invalid data, it registers the project information in the database using SQL. The database ensures the secure storage of the information.

[0651] Step 3:

[0652] The server uses the API of the scheduling service to collect schedule information from stakeholders based on the stakeholders list. This collected data is passed to an AI model to calculate the optimal meeting date and time. The suggested dates and times are then presented to the user in a later step.

[0653] Step 4:

[0654] The server sends the proposed meeting date and time to the user's device and registers it as a tentative schedule in the calendar application. The user uses the device's interface to confirm the proposed date and time and make adjustments if necessary.

[0655] Step 5:

[0656] During the meeting, the terminal captures the meeting audio in real time using its voice input device. This audio data is converted into text data using an API such as Google Speech-to-Text. The converted text data is then sent to the server.

[0657] Step 6:

[0658] The server analyzes the received text data using natural language processing capabilities. It then automatically generates meeting minutes based on the results of this analysis. These minutes are then distributed via email to the relevant parties.

[0659] Step 7:

[0660] The server collects project progress information via APIs and performs data analysis. To facilitate visualization of progress, it uses data visualization tools to generate visual progress reports. These reports are sent to users periodically.

[0661] Step 8:

[0662] The server uses an AI model to analyze information for each task and determine the appropriate person to assign it. The server sends this information to a terminal, which automatically notifies each person of their task assignment. This enables efficient project progress.

[0663] (Application Example 1)

[0664] 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".

[0665] There is a need to reduce the risk of equipment failure and lessen the burden on workers by centrally managing the operating efficiency and maintenance plans of equipment in factories. Furthermore, a system is needed to facilitate smooth information sharing among stakeholders in order to perform accurate maintenance at the appropriate time. These challenges have not been adequately addressed by conventional methods.

[0666] 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.

[0667] In this invention, the server includes means for receiving project information and registering project-related data in an information storage unit; means for collecting the schedules of stakeholders and calculating for proposing the optimal meeting date and time; means for converting meeting audio into text data and creating a record; means for collecting data reporting the project's progress and automatically generating a progress report; means for determining the appropriate person in charge for each task in the project and assigning the work; means for inputting and saving the operating status and maintenance plan of the equipment using a function processing device, automatically identifying equipment that requires maintenance and assigning the work to the most suitable technician; and means for handling meeting speeches, generating records, and outputting them as a report. This enables optimization of equipment maintenance and operating efficiency, as well as smooth information sharing among stakeholders.

[0668] The "information storage unit" is a storage system for saving project-related data and the operating status of equipment.

[0669] A "calculation tool" is a system that has the function of suggesting the optimal date and time for a meeting based on the schedule information of the parties involved.

[0670] "Text data" refers to data obtained by converting audio from a meeting into written text.

[0671] A "progress report" is a report that automatically generates information about the progress of a project.

[0672] A "function processing device" is hardware or software used to manage the operating status and maintenance plan of a device.

[0673] The term "optimal technician" refers to the technician best suited to perform maintenance or repair on a particular piece of equipment.

[0674] "Processing speech and generating records" refers to the process of processing the content of speeches during a meeting and creating records that can be referenced later.

[0675] In this invention, first, the terminal receives detailed information about the project, such as the project name, start date, and list of stakeholders, and registers it in the information storage unit. Next, the server uses the stakeholders' calendar information to calculate and propose the optimal meeting date and time. For example, it selects a date and time that allows the most participants to gather, taking into account the stakeholders' availability.

[0676] During the meeting, the device converts audio into text data in real time, automatically generates a meeting record using this data, and distributes it to all participants. The generated record makes it easy to review the key points of the discussion afterward.

[0677] The server also collects various data to track project progress and automatically generates progress reports by analyzing them using AI. These reports are sent regularly to stakeholders, ensuring that everyone is aware of the latest project status.

[0678] Furthermore, the operating status of the equipment can be managed via a terminal, necessary maintenance work can be identified in real time using a function processing device, and the work can be assigned to the most suitable technician.

[0679] A concrete example is a project to introduce a new product manufacturing line. In this case, it is necessary to verify the operation of each piece of equipment and to formulate a maintenance schedule. By using this system, the appropriate technicians can be automatically assigned, and a list of tasks can be provided to the terminal.

[0680] An example of a prompt is, "Please prepare the meeting minutes for this week's production line project and let me know the key decisions."

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The user enters project details on their terminal, such as the project name, start date, and list of stakeholders. This input data is sent to the server in digital format and registered in the information storage unit. The server receives the data and saves it in the appropriate format.

[0684] Step 2:

[0685] The server collects schedule information from stakeholders and uses calculation tools to analyze the availability of each stakeholder's calendar. Based on the data obtained through this analysis, it calculates the optimal meeting date and time and proposes it to the user. The user reviews the proposal and makes adjustments if necessary.

[0686] Step 3:

[0687] Once the meeting begins, the terminal uses speech recognition software to convert the audio during the meeting into text data in real time. The converted text data is sent to a server, which automatically generates a record and distributes it to the relevant parties.

[0688] Step 4:

[0689] The server collects project progress data and analyzes it using AI. This analysis automatically generates progress reports. The generated reports are distributed digitally to stakeholders and serve as reference materials for understanding the current status of the project.

[0690] Step 5:

[0691] The terminal collects equipment operating status and maintenance information via an input device and transmits it to the server. The functional processing unit analyzes this data, identifies equipment requiring maintenance, and automatically requests work from the most suitable technician. The output of this request is notified to the technician via email or a dedicated application.

[0692] 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.

[0693] This invention is a system that combines AI and an emotion engine to streamline project management. In addition to managing basic project information, it provides a decision-making process that takes into account the emotions of stakeholders, in order to support users in efficiently and results-oriented project management.

[0694] The user first enters basic project information using a terminal. This information is then sent from the terminal to the server and simultaneously stored in the data storage unit. The server organizes the data by project, enabling efficient information access.

[0695] During meetings, the terminal uses voice input, and the AI ​​transcribes the voice data into text in real time. This text can then be immediately used as meeting minutes. Furthermore, an emotion engine analyzes the tone of voice and facial expressions of meeting participants, collecting emotional data. This emotional data is added to the meeting minutes and used later when reviewing the meeting.

[0696] Furthermore, emotional feedback from each stakeholder is collected on the server along with project progress data. AI analyzes this information and automatically generates progress reports that reflect emotional trends. These reports help understand the project's health and stakeholder motivation, providing valuable guidance for users to fine-tune the plan.

[0697] In task assignment, the server uses AI to automatically select the most suitable person for each task based on the project content. The emotion engine considers past emotional data and takes care to avoid tasks that would be particularly stressful for specific stakeholders. By selecting personnel while considering emotional aspects in this way, the efficiency of task execution is improved.

[0698] As a concrete example, in a new product development project, the system monitors the emotions felt by stakeholders during the development process, and uses this data to help the project manager revise their strategy. If many positive emotions are detected during a meeting, the process is likely to be effective; conversely, if many negative emotions are detected, the approach needs to be re-evaluated. This enables more efficient and satisfying project management.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The user uses a terminal to enter basic project information. This information includes the project name, start date, planned end date, and a list of stakeholders. The terminal then sends this information to the server.

[0702] Step 2:

[0703] The server stores the received project information in its data storage unit. The server automatically generates a basic schedule for each project and sends it to the user's terminal. The user can then review the generated schedule and make modifications as needed.

[0704] Step 3:

[0705] The server collects the schedules of all participants via their devices. AI analyzes this information and suggests the optimal meeting date and time for all participants. The suggested date and time are then notified to the user via their device.

[0706] Step 4:

[0707] At the start of the meeting, the device records audio in real time and converts it into text data using AI. This text data is sent to a server. An emotion engine analyzes the tone of participants' voices and activity to understand their emotional state.

[0708] Step 5:

[0709] The server adds the analyzed sentiment data to the meeting minutes. The minutes are distributed to all users and stakeholders, and feedback is accepted.

[0710] Step 6:

[0711] The server automatically collects the data necessary to create project progress reports from the terminals of stakeholders. Based on this data, the AI ​​generates a report that reflects both project progress and emotional states. This report is sent to the user and used to understand the project status.

[0712] Step 7:

[0713] The server analyzes the characteristics of project tasks and uses AI to determine the most suitable person to handle them. During this process, the emotion engine considers the past emotional data of each stakeholder and assigns tasks that are expected to reduce stress. The server then distributes task details to each person via terminals, improving overall execution efficiency.

[0714] (Example 2)

[0715] 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".

[0716] In project management, there is a challenge in adequately considering communication and emotional aspects among stakeholders. Traditional project management systems can handle progress and task management, but they struggle to consider non-quantitative aspects such as emotional changes in meetings and understanding motivation. As a result, project efficiency may decrease and the stress levels of stakeholders may increase.

[0717] 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.

[0718] In this invention, the server includes means for receiving project information and registering it in a data storage unit, means for analyzing the facial expressions and voice characteristics of stakeholders and determining their emotional state, and means for converting meeting audio into text data and creating meeting minutes that include emotional information. This improves the quality of communication in project management and enables efficient project operation that also takes emotional aspects into consideration.

[0719] "Means for receiving project information" refers to an interface for receiving project-related data entered by users, importing it into a server via the network, and recording it in a database.

[0720] The "data storage unit" is a database system designed to securely and efficiently store various project-related information and to allow for quick access as needed.

[0721] "Means for analyzing facial expressions and voice characteristics" refers to a function that receives facial expression and voice data of meeting participants as input and uses an emotion engine to determine their emotional state.

[0722] "Methods for converting audio to text data" refers to the process of converting audio captured in real time during a meeting into digital text, making the transcribed content usable as meeting minutes.

[0723] "Methods for creating meeting minutes that include emotional information" refers to a system that creates meeting minutes based on the transcription of audio data and integrates emotional data of meeting participants to support information review.

[0724] "Methods for automatically generating progress reports" refers to a system that analyzes project progress and sentiment data, automatically creates reports based on that analysis, and provides them to project managers.

[0725] "A means of determining and assigning the appropriate person to each task" refers to a method of selecting the right person for each task using AI, and assigning tasks in a way that minimizes stress for the person in charge by taking into account past emotional data.

[0726] This invention is a system for streamlining project management and is primarily composed of servers, terminals, and generative AI models. The following describes in detail how this system works.

[0727] First, the user uses a terminal to enter basic project information. This information includes the project name, participants, schedule, and tasks. This information is sent from the terminal to the server, where it is organized and securely stored in the server's data storage.

[0728] Next, when project-related meetings are held, the terminal captures the meeting audio. The audio data is sent to a server and converted into text data in real time by a generative AI model. Simultaneously, an emotion engine analyzes the facial expressions and tone of voice of the participants and collects this emotion data to understand the emotional trends of the meeting.

[0729] The server integrates collected sentiment data with project progress data and automatically generates progress reports using a generative AI model. This allows users to understand project progress and participants' emotional responses, enabling efficient fine-tuning of project management plans.

[0730] Furthermore, when assigning tasks, the server selects the appropriate person based on project data. Based on the results of sentiment analysis, tasks are assigned in a way that minimizes the stress that a particular task causes to participants. In this way, project management that takes emotions into consideration can improve overall operational efficiency.

[0731] As a concrete example, in a new product development project, the system monitors the emotions felt by team members during the development process and helps the project manager revise the strategy based on that data. During the project, users can input prompts into the generated AI model, such as "Please analyze the positive emotional trends in meetings and provide advice on progress," to gain valuable insights.

[0732] This system will make project management more efficient and effective, and will increase stakeholder satisfaction.

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The user uses a terminal to enter basic project information. This information includes the project name, participant list, schedule, and objectives. The terminal formats this information as electronic data and prepares it for transmission to the server. It also verifies the input data to ensure there are no errors.

[0736] Step 2:

[0737] The terminal transmits project information to the server using a secure communication protocol. Simultaneously, it saves a backup of the aforementioned project information in temporary memory. The server organizes the received data by project and securely stores it in the data storage unit. The input is the user's project information, and the output is the consistent project data stored in the data storage unit.

[0738] Step 3:

[0739] At the start of the meeting, the device uses its built-in microphone to capture audio in real time. The audio data is immediately sent to the server. The input is audio data, and the output is digital data of the meeting content transcribed into text by a generative AI model.

[0740] Step 4:

[0741] The server converts received audio data into text in real time using a generation AI model. Simultaneously, an emotion engine analyzes voice tone, rhythm, and participants' facial expressions to quantify their emotional states. The input is meeting audio and facial expression data, and the output is a textualized meeting transcript and emotional data for each participant.

[0742] Step 5:

[0743] The server integrates emotional data and project progress status, and then automatically generates progress reports using a generative AI model. These reports indicate the project's health and the emotional trends of stakeholders, and are provided to project managers. The input consists of emotional and progress information, while the output is a progress report that also takes into account stakeholders' motivations.

[0744] Step 6:

[0745] When a user requests task assignments for a project, the server automatically selects the most suitable person for each project, referencing the results of sentiment analysis. By utilizing a generative AI model to analyze the impact of specific tasks on participants, the system achieves optimal team composition. Inputs are task information and sentiment data, while output is a list of assigned personnel.

[0746] (Application Example 2)

[0747] 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".

[0748] Project management requires not only information management and task allocation, but also adjustments that take into account the emotions and stress levels of stakeholders. However, traditional systems have made it difficult to grasp the emotional changes of stakeholders in real time and adjust the workflow accordingly. This can negatively impact project progress and stakeholder motivation.

[0749] 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.

[0750] In this invention, the server includes means for receiving project information and registering project-related data in a storage device; means for collecting schedule information of stakeholders and analyzing it to suggest the optimal date and time for meetings; means for converting audio during meetings into text data and creating meeting minutes; means for collecting data reporting the progress of the project and automatically generating progress reports; means for determining the appropriate person in charge for each stage of the project and assigning the stages; and means for analyzing the emotional data of stakeholders and adjusting the workflow in real time. This makes it possible to improve efficiency in project management and reduce stress on stakeholders.

[0751] "Project information" is a general term for everything related to a specific task or activity, including plans, objectives, results, schedules, and resource allocation.

[0752] A "data storage device" is a device or technology for storing information and data, and is used for the storage and management of digital data.

[0753] "Schedule information of stakeholders" refers to data regarding the schedules and timetables of individuals or organizations involved in the project.

[0754] "Analytical methods" refer to the processes and techniques used to analyze data and derive conclusions or insights.

[0755] "Text data" refers to information obtained by converting audio, handwritten text, etc., into text format.

[0756] A "progress report" is a document that shows the progress of a project and is created to clarify the degree of achievement and any problems encountered.

[0757] "Emotional data" refers to information about a person's emotions and moods, and indicates an individual's emotional state.

[0758] "Real-time adjustment" refers to immediately modifying plans and processes based on ongoing information and circumstances.

[0759] The embodiment of the invention specifically relates to the implementation of a system designed to streamline project management. This system functions by combining various digital devices and technologies.

[0760] The server receives project information and registers it in a data storage device. This allows for centralized management of project-related data, including plans, objectives, and resource allocation. It collects schedule information from stakeholders and uses analytical tools to suggest optimal meeting dates and times. This enables scheduling adjustments that take into account the availability of all stakeholders. Furthermore, it uses speech recognition technology to convert meeting audio into text data and automatically creates meeting minutes. This allows for easy sharing of meeting content in text format.

[0761] Meanwhile, user terminals collect data to easily report project progress. The collected data is sent to a server, where AI analyzes it and automatically generates progress reports. In this process, emotional data of stakeholders is also taken into consideration. Emotional data is extracted from the user's voice and facial expressions and analyzed as text data. As a result, the motivation and emotional state of stakeholders regarding the project are also reflected in the report.

[0762] Within the system, the person responsible for each stage of the project is determined and tasks are assigned, and emotional data is taken into consideration during this process. This allows for the selection of personnel who will not experience excessive stress during the process. In particular, the workflow, which is adjusted in real time, is dynamically operated based on AI analysis and incorporates emotional data.

[0763] As a concrete example, when conducting a new product development project, the AI ​​analyzes the tone of voice and facial expressions of those involved during meetings. If positive emotions are dominant, the process is likely to be effective; if negative emotions are prevalent, it is determined that improvements are needed. In this way, the system can monitor the health of a project from an emotional perspective as well.

[0764] An example of a prompt for a generative AI model is: "We would like to ask for suggestions on how to use AI to collect sentiment data from factory workers and optimize the production process."

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The server receives project information from the user's terminal. This information includes the project name, purpose, schedule, and resource details. The server then registers this information in a data storage device. This enables centralized project management.

[0768] Step 2:

[0769] The server retrieves the schedule information of the relevant parties from an external calendar API. It then analyzes this schedule information using an analytical tool to propose the optimal meeting date and time. The input information is the calendar information of the relevant parties, and the output is the proposed meeting date and time.

[0770] Step 3:

[0771] During the meeting, the terminal uses its microphone to collect audio in real time. This audio data is immediately sent to a server, where character recognition software converts the audio into text. The resulting text data is immediately formatted as meeting minutes and can be distributed to relevant parties.

[0772] Step 4:

[0773] Users input project progress data, which is then sent to a server. The server analyzes the progress data using AI and automatically generates a progress report. The AI ​​utilizes an emotion analysis module to incorporate the emotional data of stakeholders as input. The output is a progress report.

[0774] Step 5:

[0775] The server analyzes project process data and stakeholders' emotional data to select the most suitable person for each stage. This selection includes an analysis of emotional states based on historical data. The output is an updated task assignment list.

[0776] Step 6:

[0777] The server dynamically readjusts the workflow based on emotional data collected in real time. This involves AI using a generated AI model to analyze prompt sentences and optimize them to reduce worker stress. The input for this step is real-time emotional data, and the output is the adjusted workflow.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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."

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A means for receiving project information and registering data related to the project in a data storage unit,

[0802] Analytical tools to collect schedule information from stakeholders and propose the optimal meeting date and time,

[0803] A method for converting audio from a meeting into text data and creating meeting minutes,

[0804] A means of collecting data to report on the progress of a project and automatically generating progress reports,

[0805] The means of determining the appropriate person to be in charge of each task in the project and assigning those tasks,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which proposes meeting dates and times based on the calendar information of the relevant parties.

[0809] (Claim 3)

[0810] The system according to claim 1, which generates a project progress report based on information analyzed by AI.

[0811] "Example 1"

[0812] (Claim 1)

[0813] The information processing device includes means for receiving project-related information and registering said information in a data storage device,

[0814] The information processing device collects the schedule information of stakeholders and uses a generative model to analyze it in order to propose the optimal date and time for the meeting.

[0815] An information processing device converts audio from a meeting into text format via a voice input device and creates meeting minutes using natural language processing capabilities.

[0816] An information processing device collects project progress information, performs analysis, and automatically generates progress reports.

[0817] The information processing device determines the person responsible for each task based on characteristic analysis and distributes the tasks through a management function,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which proposes meeting dates and times based on the schedules of the relevant parties.

[0821] (Claim 3)

[0822] The system according to claim 1, which generates a project progress report based on analyzed and processed information.

[0823] "Application Example 1"

[0824] (Claim 1)

[0825] A means for receiving project information and registering data related to the project in an information storage unit,

[0826] A calculation method for collecting the schedules of stakeholders and proposing the optimal date and time for the meeting,

[0827] A means of converting audio from a meeting into text data and creating a record,

[0828] A means of collecting data to report on project progress and automatically generating progress reports,

[0829] The means of determining the appropriate person to be in charge of each task in the project and assigning the work,

[0830] A means of inputting and saving the operating status and maintenance plan of equipment using a functional processing device, automatically identifying equipment that requires maintenance, and assigning the work to the most suitable technician,

[0831] A means of handling meeting discussions, generating records, and outputting them as reports,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, which proposes a schedule based on the planning information of the relevant parties.

[0835] (Claim 3)

[0836] The system according to claim 1, which generates a project progress report based on information analyzed by artificial intelligence.

[0837] "Example 2 of combining an emotion engine"

[0838] (Claim 1)

[0839] A means for receiving project information and registering data related to the project in a data storage unit,

[0840] A means for analyzing the facial expressions and vocal characteristics of those involved to determine their emotional state,

[0841] A method for converting audio from a meeting into text data and creating meeting minutes that include emotional information,

[0842] A method for integrating emotional data and project progress data to automatically generate progress reports that reflect emotional trends,

[0843] A method for determining and assigning the appropriate person to each task in a project, taking into account the results of sentiment analysis,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which proposes meeting dates and times based on the calendar information of the relevant parties.

[0847] (Claim 3)

[0848] The system according to claim 1, which generates a project progress report based on emotional information analyzed by a generative AI model.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] A means for receiving project information and registering data related to the project in a storage device,

[0852] A means of analysis to collect schedule information from stakeholders and propose the optimal date and time for the meeting,

[0853] A method for converting audio from a meeting into text data and creating meeting minutes,

[0854] A means of collecting data to report on project progress and automatically generating progress reports,

[0855] A means of determining the appropriate person responsible for each stage of the project and assigning those stages,

[0856] A means to analyze the emotional data of stakeholders and adjust the workflow in real time,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which proposes meeting dates and times based on the calendar information and sentiment data of the parties involved.

[0860] (Claim 3)

[0861] The system according to claim 1, which generates a project progress report based on information and emotional data analyzed by AI. [Explanation of symbols]

[0862] 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 receiving project information and registering data related to the project in a data storage unit, Analytical tools to collect schedule information from stakeholders and propose the optimal meeting date and time, A method for converting audio from a meeting into text data and creating meeting minutes, A means of collecting data to report on the progress of a project and automatically generating progress reports, Determining the appropriate person to handle each task in the project and the means to assign those tasks, A system that includes this.

2. The system according to claim 1, which proposes meeting dates and times based on the calendar information of the relevant parties.

3. The system according to claim 1, which generates a project progress report based on information analyzed by AI.

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