system

Abstract

We provide the system. [Solution] A means of receiving initial project information and generating project kickoff materials based on that information, A means to automatically generate a draft of the work breakdown structure by referring to the project's past data, A means of generating project folders based on default naming rules and folder structure, and saving related documents, A means of providing recommended templates by utilizing a knowledge database of past projects, A means of presenting potential risks when setting up a project, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including 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 the planning phase from the project startup, problems faced by newly appointed or mid-career project managers include that the creation of project basic materials, the formulation of work breakdown structures, the management of project-related documents, and the method of utilizing past project knowledge are complicated and unclear. Also, in the risk management at the initial stage of the project, there is a problem that the conventional method cannot fully utilize past failures. Especially for those with little project experience, these problems accumulate and amplify the sense of uneasiness, which may hinder the success of the project.

Means for Solving the Problems

[0005] This invention provides a means for automatically generating project kickoff materials based on initial project information. It further includes a means for automatically generating a draft work breakdown structure using past project data. In addition, it provides a means for generating project folders based on predefined naming rules and folder structures, and for organizing and saving related documents. By leveraging a knowledge database of past projects, enabling the provision of recommended templates and the presentation of potential risks, it reduces the burden on newly appointed and mid-career project managers and establishes a foundation for efficient project progress from the initial stages.

[0006] A "project kickoff document" is a document that summarizes basic information such as the intentions, objectives, schedule, and stakeholders at the start of a project.

[0007] A Work Breakdown Structure (WBS) is a list that breaks down the overall tasks of a project into a hierarchical structure, and it serves as the foundation for planning and managing a project.

[0008] A "project folder" is a digital folder used to organize documents and materials related to a project and store them according to specified naming rules and folder structure.

[0009] A "knowledge database" is a collection of information that centrally stores and manages information, experience, templates, and risk information obtained from past projects, making it available for use as needed.

[0010] "Naming rules" are established to standardize the names of documents and folders within a project, promoting consistency and efficiency in document management.

[0011] Risk management is a set of processes that identify, appropriately evaluate, and control uncertainties that could hinder the success or achievement of project goals. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention relates to a system for improving the efficiency of the initial stages of project management. When a user inputs basic project information via a terminal, a server automatically generates various documents based on that information, thereby enabling efficient project execution support. Specific embodiments of this invention are described below.

[0034] The user uses a terminal to input basic project information, such as project name, objectives, client details, budget, and estimated duration. The terminal sends this information to a server, which uses an AI agent to create a draft of the project kickoff document and work breakdown structure (WBS). At this stage, the server automatically suggests tasks and milestones based on information from similar projects, referencing past project data.

[0035] Furthermore, the server automatically generates project folders according to specified naming rules and folder structures. These folders serve as a system for managing and consistently organizing generated materials and documents.

[0036] Furthermore, the server provides users with relevant templates and information from a past project knowledge database, offering them project-related knowledge. By planning projects based on the provided materials and information, users can more effectively manage risks in the initial stages of a project. The server also provides a mechanism to notify users in advance of points requiring attention based on potential risk information.

[0037] Specific example:

[0038] When a newly appointed project manager starts a new web development project, the user inputs project information using a terminal. Based on this information, the server automatically generates kickoff documents, creating materials that clarify the project's background, goals, and key stakeholders. Simultaneously, a draft WBS is generated, and a project task list is suggested by AI. Folders are then created according to naming conventions, and all relevant documents are properly saved and managed. In this way, the user can quickly launch a project and efficiently proceed with the planning.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses a terminal to enter basic project information. This information includes project name, purpose, client information, budget, and schedule.

[0042] Step 2:

[0043] The terminal sends the entered project information to the server. The transmitted information is used as the basic data for generating the document.

[0044] Step 3:

[0045] Based on the received project information, the server uses an AI agent to automatically generate a draft of the project kickoff document. This document includes the project overview, objectives, stakeholder information, and schedule.

[0046] Step 4:

[0047] The server retrieves past project data from the database and generates a draft Work Breakdown Structure (WBS) based on information from similar projects. Task and milestone proposals are made at this stage.

[0048] Step 5:

[0049] The server automatically creates new project folders based on predefined naming rules and folder structures. Furthermore, generated materials and documents related to the current project information are organized and saved within these folders.

[0050] Step 6:

[0051] The server utilizes a database of past project knowledge to present users with relevant templates and risk information. This allows users to familiarize themselves with project reference materials and important points in advance.

[0052] Step 7:

[0053] Users review the provided kickoff materials and WBS, and modify them using their terminals as needed. The modified information is then sent back to the server and stored in the database.

[0054] Step 8:

[0055] The server incorporates user feedback and scrutinizes and saves the final project documents. This ensures that the project is ready for its initial stages.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern project management, a significant amount of time and effort is often required for planning and preparation in the initial stages. Furthermore, efficient project execution necessitates the organization of information using historical data and knowledge, as well as risk assessment. However, these processes are highly complex, making it challenging to maintain effective and consistent operation.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for receiving initial project information via an information processing device, means for generating storage areas based on a predetermined naming convention and folder structure, means for providing recommended templates utilizing a knowledge base, means for presenting potential risks when setting up activities, means for transmitting information using information transmission means for managing electronic documents, and means for receiving and displaying information using information transmission means. This enables users to efficiently manage information and risks in the initial stages of a project.

[0061] An "information processing device" is an integrated system of hardware and software that receives input from a user, processes the data, and transmits it to a server.

[0062] "Activity start materials" are documents that summarize the information needed in the initial stages of a project, including the project's objectives, stakeholders, and milestones.

[0063] A "knowledge base" is a database that aggregates information and knowledge gained from past projects, and is used when carrying out new projects.

[0064] A "storage area" is a collection of directories or folders created by a server to organize and store electronic documents and files related to a project.

[0065] A "template" refers to a standardized format or document that serves as a reference when creating project materials; it is a template designed to support efficient document creation.

[0066] "Potential risks" refer to problems or obstacles that may be foreseeable during the planning stage of a project, and are factors for which countermeasures should be taken in advance.

[0067] A "naming convention" is a set of rules established to standardize and organize the names of folders and files, and is a means of maintaining consistency in project documents.

[0068] An "electronic document" is a document file created, stored, and displayed by a computer, and refers to information maintained in digital format.

[0069] This invention aims to support efficient initial planning in a project management system using an information processing device. Users input basic project information via a terminal and send it to a server, which automatically generates project documentation and structure.

[0070] The user fills in information such as project name, goals, client details, budget, and estimated duration into an input form on their device and submits it. The device securely transmits this information to the server using the HTTPS protocol. The server processes the received information using a generative AI model to generate a draft of the project kickoff document and work breakdown structure (WBS). In generating the project document, the server refers to past information resources and knowledge bases and suggests documents and milestones based on relevant examples.

[0071] Furthermore, the server generates a project storage area based on default naming conventions and folder structures, and stores the electronic documents associated with this area. During storage, the system enforces folder and file naming conventions to maintain consistency. The server also assists with planning by extracting templates from a knowledge base and presenting them to the user. This allows the user to identify risks in the early stages of the project and execute the plan effectively.

[0072] Specific example:

[0073] When launching a new website development project, the user sends the following information from their device to the server: "Please generate kickoff materials and a WBS for a new website development project. The project name is 'Online Shop Development,' the goal is 'Build a user-friendly e-commerce site,' the client is 'XYZ Corporation,' the budget is '$50,000,' and the estimated duration is '6 months.'" This allows the server to automatically generate the necessary materials and efficiently proceed with the project planning.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user enters basic project information into the terminal. Specifically, the user enters the project name, objectives, client details, budget, and estimated duration into the input form and presses the submit button. The entered information is temporarily stored in the terminal's memory.

[0077] Step 2:

[0078] The terminal sends the entered information to the server. The terminal uses the HTTPS protocol to convert the user-entered information into JSON format and send it to the server. At this point, the data is encrypted and securely transferred to the server.

[0079] Step 3:

[0080] The server receives project information and generates project kickoff materials using a generative AI model. The server analyzes the input information and provides appropriate prompts to the AI ​​to generate the materials. The generated materials are then converted into an electronic document format.

[0081] Step 4:

[0082] The server references historical information resources and generates a draft Work Breakdown Structure (WBS). The server retrieves data from similar projects in the database, and based on this, an AI model automatically suggests appropriate tasks and milestones. This suggestion is then documented as a WBS.

[0083] Step 5:

[0084] The server generates project folders based on the specified naming convention and folder structure. The server applies the system-determined naming convention and organizes the folders. Documents and WBS are appropriately stored in the generated folders.

[0085] Step 6:

[0086] The server leverages a knowledge base to provide users with relevant templates. The server searches the template database, selects the template best suited to the project type, and delivers it to the user's terminal.

[0087] Step 7:

[0088] The server analyzes the project's potential risks and notifies the user. The server extracts risk factors from past cases and sends a risk notification message to the user based on these factors. This notification allows the user to strengthen their risk management plan.

[0089] (Application Example 1)

[0090] 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."

[0091] In the initial stages of project management, it is essential to efficiently manage the entire process, from inputting project information and generating documents to creating work breakdown structures and ensuring rapid and consistent document management. However, traditional systems require significant time and effort for these processes, and in complex projects in particular, inadequate planning in the initial stages often leads to insufficient risk management. Furthermore, there has been a lack of systems that allow for easy access using smart devices and manage projects through the integration of front-end and back-end systems.

[0092] 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.

[0093] In this invention, the server includes means for receiving initial project information and generating project kickoff materials based on that information; means for referencing historical project data and automatically generating a draft of the work breakdown structure; and means for inputting information through a front-end device, which allows a back-end artificial intelligence agent to analyze the data and propose the optimal work structure and milestones. This enables efficient and consistent project planning and rapid access via smart devices.

[0094] "Initial project information" refers to basic information such as the project name, objectives, client information, and budget, which are necessary at the start of the project.

[0095] A "project kickoff document" is an introductory document used at the start of a project, clearly outlining its background, objectives, key stakeholders, and other relevant information.

[0096] A "Work Breakdown Structure (WBS)" refers to a structure that systematically organizes the overall work of a project by dividing it into smaller tasks.

[0097] "Past data" refers to the collective term for information and recorded results from projects that have been carried out in the past.

[0098] "Front-end device" refers to the part that the user directly interacts with, including hardware and software used for inputting information and displaying different data.

[0099] A "backend artificial intelligence agent" is a software program used to analyze data entered by the user and provide optimal suggestions and solutions.

[0100] "Naming rules and folder structure" are guidelines that systematically define how to name and store documents and data in order to maintain consistency when organizing them.

[0101] A "knowledge database" refers to an information resource that aggregates project-related information and templates accumulated in the past.

[0102] A "smart device" is a device that can connect to the internet and has various functions, and is used as a tool for users to directly manage projects.

[0103] A "milestone" is an important point or achievement criterion set to represent the progress of a project.

[0104] The system for implementing this invention begins with the user inputting initial project information using a smart device. The user inputs the project name, goals, client information, and budget information via a front-end device such as a smartphone or tablet. The terminal is responsible for transmitting this information to the back-end server.

[0105] The server generates project kickoff materials using a machine learning model based on the received information. Leveraging the generative AI model, it proposes the optimal work breakdown structure and milestones, referencing past project data and knowledge databases. The backend system is built using Python and the Django framework, and uses the TENSORFLOW® library to propose project plans to an artificial intelligence agent. This process enables efficient project progress.

[0106] Generated materials and documents are automatically organized and saved in the project folder based on default naming rules and folder structure. This ensures information consistency and allows for quick access to necessary materials via smart devices.

[0107] As a concrete example, when a user starts a new sensing sensor development project, they enter information via a smart device following prompts such as, "Please enter details of the sensing sensor development project. Please include information such as goals, timeline, key stakeholders, and budget." The system responds quickly to this input, automatically generating a detailed project plan and kickoff materials.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users input initial project information using their smart devices. Specifically, they enter the project name, goals, client information, and budget information into an input form. The entered data undergoes basic validation and is then sent to the server via a REST API. The input consists of basic project information, and the output is this information passed to the server.

[0111] Step 2:

[0112] The server stores the received project information in a database. Next, it uses a generative AI model to search for similar past project data and generates initial suggestions for corresponding work decomposition structures and milestones. Project information is given as input, and a list of optimal work decomposition structures and milestones is obtained as output. Specifically, the TensorFlow library is used to analyze past data.

[0113] Step 3:

[0114] The server automatically creates project kickoff documents based on the generated proposals. It retrieves templates from a knowledge database and incorporates project-specific information to create customized documents. Inputs include proposed milestones and project information, and the output is the completed kickoff document. The document is saved in the appropriate folder.

[0115] Step 4:

[0116] The terminal displays completed kickoff materials and proposed work structures to the user. Users can review and modify the materials via their smart devices. Project materials serve as input, and the displayed content is reflected on the smart device as output. The terminal accepts user input through an interactive UI.

[0117] Step 5:

[0118] When a user makes corrections or provides feedback on a document, the terminal resends that information to the server, which then updates the document and database based on the received corrections. This iterative process ensures that the project information is always up-to-date. The input is the user's changes, and the output is the updated project document and database status.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of project materials and feedback based on those emotions. This system provides support that takes user emotions into account during the project management process, enabling smooth project progress.

[0121] The user inputs basic project information via a terminal. During information input, the emotion engine analyzes the user's keystrokes, input speed, and voice tone (if necessary) to estimate the user's current emotional state. Based on the project information and emotion data transmitted from the terminal, the server generates optimized project kickoff materials and a Work Breakdown Structure (WBS).

[0122] If the server detects a high stress level in a user, as identified by the emotion engine, it adjusts the content of recommended templates and materials to provide support and alleviate the user's anxiety. In doing so, the server also references past project knowledge and risk information to suggest specific points of caution and solutions.

[0123] Furthermore, the emotion engine records the user's emotional progression over time, visualizing the changes in the user's emotions at each phase of the project. This data helps users understand the project's progress and their own emotional state, enabling them to take necessary actions.

[0124] For example, when a newly appointed project manager enters project information, if the emotion engine detects the user's stress level, the server automatically generates a document accompanied by words that encourage relaxation. Furthermore, recommended templates are adjusted to include concise and easy-to-understand guidelines. In this way, the system provides an environment where users receive appropriate support and can smoothly manage their projects.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user enters basic project information using their device. This includes the project name, purpose, budget, and timeline.

[0128] Step 2:

[0129] The device sends keystroke patterns and input speed to the emotion engine to measure the user's emotions during input. If voice input is available, the emotion engine also analyzes the voice tone.

[0130] Step 3:

[0131] The emotion engine analyzes the user's emotions (e.g., stress, anxiety, calmness) based on the received data and sends the results to the server.

[0132] Step 4:

[0133] The server integrates the received project's basic information and sentiment data to generate project kickoff materials. The sentiment data adds guidelines and support information tailored to the user's situation to the materials.

[0134] Step 5:

[0135] The server references past project data to create a draft Work Breakdown Structure (WBS) for similar projects. Based on the user's sentiment, the priority of suggested tasks and risk management information are also adjusted.

[0136] Step 6:

[0137] The generated documents and WBS are organized and saved in project folders by the server according to specified naming rules. This also makes it easier for users to manage documents within the folders.

[0138] Step 7:

[0139] The server provides the user with generated materials, a Work Breakdown Structure (WBS), and feedback based on sentiment analysis. If the user's emotions are unstable, the server offers additional support and templates to help alleviate tension.

[0140] Step 8:

[0141] Users review the provided documents and WBS, and make changes as needed. The changes are sent to the server via their terminal and saved to the database as the final document.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In project management, users' emotional states often significantly impact project progress. However, traditional systems lack adequate support that considers user emotions, resulting in a lack of effective means to alleviate situations that cause user stress. Therefore, there is a need to build a system that can provide dynamic information and support tailored to user emotions, thereby creating an environment conducive to smooth project progress.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for acquiring initial project information and generating project materials based on that information; means for analyzing user input data and utilizing an emotion engine to estimate the user's emotional state; and means for dynamically adjusting the content of project materials and templates according to the user's emotional state. This enables support for project management that takes user emotions into account and provides an environment in which projects can proceed smoothly.

[0147] "Initial project information" refers to basic information provided by the user at the start of a project, including data such as the project name, purpose, schedule, and participants.

[0148] An "emotion engine" is an analytical device that estimates the user's emotional state based on their input data, and it has the function of analyzing keystrokes, input speed, voice tone, and other factors.

[0149] "Project documentation" refers to documents that contain information and materials required at each phase of a project, from its start to its completion, and includes plans, progress reports, and evaluation results.

[0150] A "template" is a standardized format or guideline provided for creating project documents and reports.

[0151] "Knowledge information" refers to a database that compiles experiences and lessons learned from past projects, including information that highlights project success factors and lessons learned.

[0152] "Risk information" refers to data on potential problems and failures that may occur in a project, as well as information on precautions and solutions to prevent them.

[0153] "Recording and visualizing time-series changes" refers to a technology that continuously tracks user emotional data over time and displays it visually using graphs, charts, and other methods.

[0154] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of materials and feedback based on those emotions.

[0155] The system utilizes hardware such as personal computers, servers, and terminals for receiving user input (e.g., keyboards, mice, microphones). Software includes a suite of applications, including an emotion engine, a project management platform, and a database management system.

[0156] Users input basic project information through their devices. During this process, the emotion engine estimates the user's emotional state using keystroke speed, input patterns, and voice analysis technology. This emotion data is then used to generate project documents and select templates.

[0157] The server receives project data and sentiment data sent from the terminal and, by referencing past project knowledge from the database, provides optimal advice and materials tailored to the situation. It utilizes a generative AI model to adjust the content of the materials to match the user's state.

[0158] As a concrete example, if a newly appointed project manager is detected to be under high stress while entering project information, the server will automatically generate a document that includes text encouraging relaxation. The document will also contain concise and easy-to-understand guidelines. This example can be used as a prompt to communicate the intention to the system, such as, "When generating project kickoff materials, please add relaxing content if the user's stress level is high."

[0159] This system aims to increase the probability of project success by providing emotionally sensitive support to help users move projects forward smoothly.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user uses a terminal to enter basic project information. This information includes the project name, purpose, schedule, and assigned personnel. The entered data is sent to the server as initial data for analysis by the emotion engine.

[0163] Step 2:

[0164] The terminal transmits the speed and pattern of keystrokes to the emotion engine while the user is typing. It also uses audio data obtained through the microphone for analysis as needed. Based on this data, the emotion engine uses machine learning algorithms to estimate the user's emotional state and determine their stress level and degree of relaxation. This analysis result is then sent to the server.

[0165] Step 3:

[0166] The server receives project information and emotional state data sent from the terminal. The server analyzes this data using a generative AI model and creates project kickoff materials using a document generation algorithm. Depending on the emotional data, the tone and content of the materials are adjusted, and considerate language is added.

[0167] Step 4:

[0168] The server references a database of past projects and incorporates recommended templates and considerations into the documentation based on similar project information and their results. This allows users to receive useful advice based on past knowledge.

[0169] Step 5:

[0170] The emotion engine records changes in the user's emotional state over time. This allows for the visualization of emotional progression as a graph during or after a project. Users can use this data to improve their stress management and project management.

[0171] This series of processes enables a support system that provides project materials in a way that takes into account the user's emotional state from the start to the end of the project.

[0172] (Application Example 2)

[0173] 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".

[0174] Traditional project management systems often fail to consider user emotions when managing project documents and tasks, leading to increased user stress and potentially hindering project progress. This can result in increased anxiety and confusion, particularly for new project managers, in the early stages of a project, ultimately reducing overall productivity and efficiency.

[0175] 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.

[0176] In this invention, the server includes means for recognizing the user's emotional state and generating project kickoff materials based on emotional data; means for referencing past project data and automatically generating a draft of a work breakdown structure that takes the emotional state into account; and means for utilizing a knowledge database of past projects and providing an emotionally adaptive recommendation template. This enables appropriate support and task management in accordance with the user's emotions.

[0177] "Means for recognizing a user's emotional state" refers to technology that analyzes information and voice input from a user via a device to infer their current emotions.

[0178] "A method for generating project kickoff materials based on emotional data" refers to a technology that automatically creates materials to facilitate the smooth start of a project based on the emotional data obtained.

[0179] "A means of automatically generating a draft of a work decomposition structure that takes emotional states into consideration" refers to a technology that efficiently decomposes project tasks while taking the user's emotions into account, and automatically generates a draft of that structure.

[0180] "Methods for generating folder structures that respond to emotions" refers to a technology that creates an optimal folder structure for organizing and storing relevant data based on the user's emotions.

[0181] "Methods for providing emotionally adaptive recommendation templates" refers to technologies that provide templates useful for project progress, taking into account past project data and user emotions.

[0182] "Methods for presenting potential risks based on emotions" refer to technologies that predict project risks inferred from the user's emotional state and present them in advance.

[0183] This system has an engine that analyzes the user's emotional state and adjusts project materials accordingly. The server receives data entered by the user via a terminal and voice data, and calculates the user's emotions by analyzing this data. The analysis uses emotion recognition algorithms, for example, to estimate stress levels and emotional states by analyzing keystroke speed and voice tone.

[0184] Based on this emotional data, the server generates project kickoff materials and work breakdown structures (WBS). If the user is experiencing stress, the generated materials are adjusted to include relaxing language and concise guidelines. The server also leverages historical project data and a knowledge base to provide recommended templates and potential risks tailored to the user's emotional state.

[0185] Furthermore, it has a function to record the user's emotional changes over time and visualize the changes in the user's emotions as the project progresses. This visualized data allows users to understand their own emotional state and take countermeasures as needed.

[0186] A concrete example would be a scenario where, as a user plans a weekend family event, the robot detects their stress level, suggests activities that the whole family can enjoy, and provides relaxing music.

[0187] An example of a prompt message generated using an AI model would be: "I want to set the user's perceived stress level. Based on this data, suggest a relaxing task."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The terminal collects user keystroke data and voice data. It acquires data such as keystroke speed and voice tone as input and sends this to the server. The output is the raw data necessary for analysis.

[0191] Step 2:

[0192] The server analyzes the received keystroke and voice data to estimate the user's emotional state. Using an emotion recognition algorithm, it analyzes the input data and outputs stress levels and emotional states as numerical values. Specifically, a high value is output when stress levels are high.

[0193] Step 3:

[0194] The server combines the acquired emotional data with the project's historical data to generate project kickoff materials. Based on the emotional data and historical data as input, it generates materials optimized for the user. The materials are adjusted according to the user's emotions, such as including wording that promotes relaxation.

[0195] Step 4:

[0196] The server generates a draft Work Breakdown Structure (WBS) that reflects emotional data. It utilizes emotional data and task information as input data, adjusting the order and content of tasks to minimize user stress. Specifically, it simplifies and restructures complex tasks.

[0197] Step 5:

[0198] The server leverages the user's past emotional data to provide recommended templates that aid in project progress. This process takes a record of emotional changes as input, generates a template containing project improvement suggestions based on that data, and outputs it.

[0199] Step 6:

[0200] The user utilizes a generative AI model to provide prompts such as, "I want to set my perceived stress level. Based on this data, suggest tasks that will help me relax." This enables automated task management suggestions.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention relates to a system for improving the efficiency of the initial stages of project management. When a user inputs basic project information via a terminal, a server automatically generates various documents based on that information, thereby enabling efficient project execution support. Specific embodiments of this invention are described below.

[0218] The user uses a terminal to input basic project information, such as project name, objectives, client details, budget, and estimated duration. The terminal sends this information to a server, which uses an AI agent to create a draft of the project kickoff document and work breakdown structure (WBS). At this stage, the server automatically suggests tasks and milestones based on information from similar projects, referencing past project data.

[0219] Furthermore, the server automatically generates project folders according to specified naming rules and folder structures. These folders serve as a system for managing and consistently organizing generated materials and documents.

[0220] Furthermore, the server provides users with relevant templates and information from a past project knowledge database, offering them project-related knowledge. By planning projects based on the provided materials and information, users can more effectively manage risks in the initial stages of a project. The server also provides a mechanism to notify users in advance of points requiring attention based on potential risk information.

[0221] Specific example:

[0222] When a newly appointed project manager starts a new web development project, the user inputs project information using a terminal. Based on this information, the server automatically generates kickoff documents, creating materials that clarify the project's background, goals, and key stakeholders. Simultaneously, a draft WBS is generated, and a project task list is suggested by AI. Folders are then created according to naming conventions, and all relevant documents are properly saved and managed. In this way, the user can quickly launch a project and efficiently proceed with the planning.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The user uses a terminal to enter basic project information. This information includes project name, purpose, client information, budget, and schedule.

[0226] Step 2:

[0227] The terminal sends the entered project information to the server. The transmitted information is used as the basic data for generating the document.

[0228] Step 3:

[0229] Based on the received project information, the server uses an AI agent to automatically generate a draft of the project kickoff document. This document includes the project overview, objectives, stakeholder information, and schedule.

[0230] Step 4:

[0231] The server retrieves past project data from the database and generates a draft Work Breakdown Structure (WBS) based on information from similar projects. Task and milestone proposals are made at this stage.

[0232] Step 5:

[0233] The server automatically creates new project folders based on predefined naming rules and folder structures. Furthermore, generated materials and documents related to the current project information are organized and saved within these folders.

[0234] Step 6:

[0235] The server utilizes a database of past project knowledge to present users with relevant templates and risk information. This allows users to familiarize themselves with project reference materials and important points in advance.

[0236] Step 7:

[0237] Users review the provided kickoff materials and WBS, and modify them using their terminals as needed. The modified information is then sent back to the server and stored in the database.

[0238] Step 8:

[0239] The server incorporates user feedback and scrutinizes and saves the final project documents. This ensures that the project is ready for its initial stages.

[0240] (Example 1)

[0241] 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."

[0242] In modern project management, a significant amount of time and effort is often required for planning and preparation in the initial stages. Furthermore, efficient project execution necessitates the organization of information using historical data and knowledge, as well as risk assessment. However, these processes are highly complex, making it challenging to maintain effective and consistent operation.

[0243] 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.

[0244] In this invention, the server includes means for receiving initial project information via an information processing device, means for generating storage areas based on a predetermined naming convention and folder structure, means for providing recommended templates utilizing a knowledge base, means for presenting potential risks when setting up activities, means for transmitting information using information transmission means for managing electronic documents, and means for receiving and displaying information using information transmission means. This enables users to efficiently manage information and risks in the initial stages of a project.

[0245] An "information processing device" is an integrated system of hardware and software that receives input from a user, processes the data, and transmits it to a server.

[0246] "Activity start materials" are documents that summarize the information needed in the initial stages of a project, including the project's objectives, stakeholders, and milestones.

[0247] A "knowledge base" is a database that aggregates information and knowledge gained from past projects, and is used when carrying out new projects.

[0248] A "storage area" is a collection of directories or folders created by a server to organize and store electronic documents and files related to a project.

[0249] A "template" refers to a standardized format or document that serves as a reference when creating project materials; it is a template designed to support efficient document creation.

[0250] "Potential risks" refer to problems or obstacles that may be foreseeable during the planning stage of a project, and are factors for which countermeasures should be taken in advance.

[0251] A "naming convention" is a set of rules established to standardize and organize the names of folders and files, and is a means of maintaining consistency in project documents.

[0252] An "electronic document" is a document file created, stored, and displayed by a computer, and refers to information maintained in digital format.

[0253] This invention aims to support efficient initial planning in a project management system using an information processing device. Users input basic project information via a terminal and send it to a server, which automatically generates project documentation and structure.

[0254] The user fills in information such as project name, goals, client details, budget, and estimated duration into an input form on their device and submits it. The device securely transmits this information to the server using the HTTPS protocol. The server processes the received information using a generative AI model to generate a draft of the project kickoff document and work breakdown structure (WBS). In generating the project document, the server refers to past information resources and knowledge bases and suggests documents and milestones based on relevant examples.

[0255] Furthermore, the server generates a project storage area based on default naming conventions and folder structures, and stores the electronic documents associated with this area. During storage, the system enforces folder and file naming conventions to maintain consistency. The server also assists with planning by extracting templates from a knowledge base and presenting them to the user. This allows the user to identify risks in the early stages of the project and execute the plan effectively.

[0256] Specific example:

[0257] When launching a new website development project, the user sends the following information from their device to the server: "Please generate kickoff materials and a WBS for a new website development project. The project name is 'Online Shop Development,' the goal is 'Build a user-friendly e-commerce site,' the client is 'XYZ Corporation,' the budget is '$50,000,' and the estimated duration is '6 months.'" This allows the server to automatically generate the necessary materials and efficiently proceed with the project planning.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The user enters basic project information into the terminal. Specifically, the user enters the project name, objectives, client details, budget, and estimated duration into the input form and presses the submit button. The entered information is temporarily stored in the terminal's memory.

[0261] Step 2:

[0262] The terminal sends the entered information to the server. The terminal uses the HTTPS protocol to convert the user-entered information into JSON format and send it to the server. At this point, the data is encrypted and securely transferred to the server.

[0263] Step 3:

[0264] The server receives project information and generates project kickoff materials using a generative AI model. The server analyzes the input information and provides appropriate prompts to the AI ​​to generate the materials. The generated materials are then converted into an electronic document format.

[0265] Step 4:

[0266] The server references historical information resources and generates a draft Work Breakdown Structure (WBS). The server retrieves data from similar projects in the database, and based on this, an AI model automatically suggests appropriate tasks and milestones. This suggestion is then documented as a WBS.

[0267] Step 5:

[0268] The server generates project folders based on the specified naming convention and folder structure. The server applies the system-determined naming convention and organizes the folders. Documents and WBS are appropriately stored in the generated folders.

[0269] Step 6:

[0270] The server leverages a knowledge base to provide users with relevant templates. The server searches the template database, selects the template best suited to the project type, and delivers it to the user's terminal.

[0271] Step 7:

[0272] The server analyzes the project's potential risks and notifies the user. The server extracts risk factors from past cases and sends a risk notification message to the user based on these factors. This notification allows the user to strengthen their risk management plan.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] In the initial stages of project management, it is essential to efficiently manage the entire process, from inputting project information and generating documents to creating work breakdown structures and ensuring rapid and consistent document management. However, traditional systems require significant time and effort for these processes, and in complex projects in particular, inadequate planning in the initial stages often leads to insufficient risk management. Furthermore, there has been a lack of systems that allow for easy access using smart devices and manage projects through the integration of front-end and back-end systems.

[0276] 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.

[0277] In this invention, the server includes means for receiving initial project information and generating project kickoff materials based on that information; means for referencing historical project data and automatically generating a draft of the work breakdown structure; and means for inputting information through a front-end device, which allows a back-end artificial intelligence agent to analyze the data and propose the optimal work structure and milestones. This enables efficient and consistent project planning and rapid access via smart devices.

[0278] "Initial project information" refers to basic information such as the project name, objectives, client information, and budget, which are necessary at the start of the project.

[0279] A "project kickoff document" is an introductory document used at the start of a project, clearly outlining its background, objectives, key stakeholders, and other relevant information.

[0280] A "Work Breakdown Structure (WBS)" refers to a structure that systematically organizes the overall work of a project by dividing it into smaller tasks.

[0281] "Past data" refers to the collective term for information and recorded results from projects that have been carried out in the past.

[0282] "Front-end device" refers to the part that the user directly interacts with, including hardware and software used for inputting information and displaying different data.

[0283] A "backend artificial intelligence agent" is a software program used to analyze data entered by the user and provide optimal suggestions and solutions.

[0284] "Naming Rules and Folder Structure" refers to guidelines that systematically define how to name and where to store documents and data to maintain consistency when organizing them.

[0285] "Knowledge Database" refers to an information resource that aggregates project-related information and templates accumulated in the past.

[0286] "Smart Device" refers to a device that can be connected to the Internet and has various functions, and is used as a tool for users to directly manage projects.

[0287] "Milestone" refers to important points or achievement criteria set to represent the progress of a project.

[0288] The system for implementing this invention starts when the user inputs initial project information using a smart device. The user inputs the project name, goals, client information, and budget information via a front-end device such as a smartphone or tablet. The terminal plays a role in sending this information to the back-end server.

[0289] Based on the received information, the server uses a machine learning model to generate project kick-off materials. Utilizing a generation AI model, it proposes an optimal work breakdown structure and milestones while referring to past project data and the knowledge database. The back-end system is built using Python and the Django framework, and an artificial intelligence agent uses the TensorFlow library to propose a project plan. This process enables the efficient progress of the project.

[0290] The generated materials and documents are automatically organized based on the predefined naming rules and folder structure and saved in the project folder. This ensures the consistency of information and enables quick access to the necessary materials through the smart device.

[0291] As a concrete example, when a user starts a new sensing sensor development project, they enter information via a smart device following prompts such as, "Please enter details of the sensing sensor development project. Please include information such as goals, timeline, key stakeholders, and budget." The system responds quickly to this input, automatically generating a detailed project plan and kickoff materials.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] Users input initial project information using their smart devices. Specifically, they enter the project name, goals, client information, and budget information into an input form. The entered data undergoes basic validation and is then sent to the server via a REST API. The input consists of basic project information, and the output is this information passed to the server.

[0295] Step 2:

[0296] The server stores the received project information in a database. Next, it uses a generative AI model to search for similar past project data and generates initial suggestions for corresponding work decomposition structures and milestones. Project information is given as input, and a list of optimal work decomposition structures and milestones is obtained as output. Specifically, the TensorFlow library is used to analyze past data.

[0297] Step 3:

[0298] The server automatically creates project kickoff documents based on the generated proposals. It retrieves templates from a knowledge database and incorporates project-specific information to create customized documents. Inputs include proposed milestones and project information, and the output is the completed kickoff document. The document is saved in the appropriate folder.

[0299] Step 4:

[0300] The terminal displays completed kickoff materials and proposed work structures to the user. Users can review and modify the materials via their smart devices. Project materials serve as input, and the displayed content is reflected on the smart device as output. The terminal accepts user input through an interactive UI.

[0301] Step 5:

[0302] When a user makes corrections or provides feedback on a document, the terminal resends that information to the server, which then updates the document and database based on the received corrections. This iterative process ensures that the project information is always up-to-date. The input is the user's changes, and the output is the updated project document and database status.

[0303] 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.

[0304] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of project materials and feedback based on those emotions. This system provides support that takes user emotions into account during the project management process, enabling smooth project progress.

[0305] The user inputs the basic information of the project via the terminal. When inputting the information, the emotion engine analyzes the user's keystrokes, input speed, and voice tone (if necessary) to estimate the user's current emotional state. For the project information and emotion data transmitted from the terminal, the server generates optimized project kick-off materials and a work breakdown structure (WBS).

[0306] When the stress level of the user recognized by the emotion engine is high, the server adjusts the content of the recommended templates and materials and provides support to reduce the user's anxiety. At this time, the server also has the function of referring to past project knowledge and risk information and presenting specific points for attention and solutions.

[0307] Furthermore, the emotion engine records the user's emotional transitions over time and visualizes the user's emotional changes in each phase of the project. This data helps the user understand the progress of the project and their own emotional state and take appropriate countermeasures.

[0308] As a specific example, when a new project manager inputs project information and the emotion engine detects the user's stress level, the server automatically generates materials with words that encourage relaxation. Also, the recommended templates are adjusted to include concise and easy-to-understand guidelines. In this way, an environment is provided where the user can receive appropriate support and the project can proceed smoothly.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The user uses the terminal to input the basic information of the project. Here, it includes the project name, purpose, budget, timeline, etc.

[0312] Step 2:

[0313] The device sends keystroke patterns and input speed to the emotion engine to measure the user's emotions during input. If voice input is available, the emotion engine also analyzes the voice tone.

[0314] Step 3:

[0315] The emotion engine analyzes the user's emotions (e.g., stress, anxiety, calmness) based on the received data and sends the results to the server.

[0316] Step 4:

[0317] The server integrates the received project's basic information and sentiment data to generate project kickoff materials. The sentiment data adds guidelines and support information tailored to the user's situation to the materials.

[0318] Step 5:

[0319] The server references past project data to create a draft Work Breakdown Structure (WBS) for similar projects. Based on the user's sentiment, the priority of suggested tasks and risk management information are also adjusted.

[0320] Step 6:

[0321] The generated documents and WBS are organized and saved in project folders by the server according to specified naming rules. This also makes it easier for users to manage documents within the folders.

[0322] Step 7:

[0323] The server provides the user with generated materials, a Work Breakdown Structure (WBS), and feedback based on sentiment analysis. If the user's emotions are unstable, the server offers additional support and templates to help alleviate tension.

[0324] Step 8:

[0325] Users review the provided documents and WBS, and make changes as needed. The changes are sent to the server via their terminal and saved to the database as the final document.

[0326] (Example 2)

[0327] 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".

[0328] In project management, users' emotional states often significantly impact project progress. However, traditional systems lack adequate support that considers user emotions, resulting in a lack of effective means to alleviate situations that cause user stress. Therefore, there is a need to build a system that can provide dynamic information and support tailored to user emotions, thereby creating an environment conducive to smooth project progress.

[0329] 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.

[0330] In this invention, the server includes means for acquiring initial project information and generating project materials based on that information; means for analyzing user input data and utilizing an emotion engine to estimate the user's emotional state; and means for dynamically adjusting the content of project materials and templates according to the user's emotional state. This enables support for project management that takes user emotions into account and provides an environment in which projects can proceed smoothly.

[0331] "Initial project information" refers to basic information provided by the user at the start of a project, including data such as the project name, purpose, schedule, and participants.

[0332] An "emotion engine" is an analytical device that estimates the user's emotional state based on their input data, and it has the function of analyzing keystrokes, input speed, voice tone, and other factors.

[0333] "Project documentation" refers to documents that contain information and materials required at each phase of a project, from its start to its completion, and includes plans, progress reports, and evaluation results.

[0334] A "template" is a standardized format or guideline provided for creating project documents and reports.

[0335] "Knowledge information" refers to a database that compiles experiences and lessons learned from past projects, including information that highlights project success factors and lessons learned.

[0336] "Risk information" refers to data on potential problems and failures that may occur in a project, as well as information on precautions and solutions to prevent them.

[0337] "Recording and visualizing time-series changes" refers to a technology that continuously tracks user emotional data over time and displays it visually using graphs, charts, and other methods.

[0338] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of materials and feedback based on those emotions.

[0339] The system utilizes hardware such as personal computers, servers, and terminals for receiving user input (e.g., keyboards, mice, microphones). Software includes a suite of applications, including an emotion engine, a project management platform, and a database management system.

[0340] Users input basic project information through their devices. During this process, the emotion engine estimates the user's emotional state using keystroke speed, input patterns, and voice analysis technology. This emotion data is then used to generate project documents and select templates.

[0341] The server receives project data and sentiment data sent from the terminal and, by referencing past project knowledge from the database, provides optimal advice and materials tailored to the situation. It utilizes a generative AI model to adjust the content of the materials to match the user's state.

[0342] As a concrete example, if a newly appointed project manager is detected to be under high stress while entering project information, the server will automatically generate a document that includes text encouraging relaxation. The document will also contain concise and easy-to-understand guidelines. This example can be used as a prompt to communicate the intention to the system, such as, "When generating project kickoff materials, please add relaxing content if the user's stress level is high."

[0343] This system aims to increase the probability of project success by providing emotionally sensitive support to help users move projects forward smoothly.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The user uses a terminal to enter basic project information. This information includes the project name, purpose, schedule, and assigned personnel. The entered data is sent to the server as initial data for analysis by the emotion engine.

[0347] Step 2:

[0348] The terminal transmits the speed and pattern of keystrokes to the emotion engine while the user is typing. It also uses audio data obtained through the microphone for analysis as needed. Based on this data, the emotion engine uses machine learning algorithms to estimate the user's emotional state and determine their stress level and degree of relaxation. This analysis result is then sent to the server.

[0349] Step 3:

[0350] The server receives project information and emotional state data sent from the terminal. The server analyzes this data using a generative AI model and creates project kickoff materials using a document generation algorithm. Depending on the emotional data, the tone and content of the materials are adjusted, and considerate language is added.

[0351] Step 4:

[0352] The server references a database of past projects and incorporates recommended templates and considerations into the documentation based on similar project information and their results. This allows users to receive useful advice based on past knowledge.

[0353] Step 5:

[0354] The emotion engine records changes in the user's emotional state over time. This allows for the visualization of emotional progression as a graph during or after a project. Users can use this data to improve their stress management and project management.

[0355] This series of processes enables a support system that provides project materials in a way that takes into account the user's emotional state from the start to the end of the project.

[0356] (Application Example 2)

[0357] 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."

[0358] Traditional project management systems often fail to consider user emotions when managing project documents and tasks, leading to increased user stress and potentially hindering project progress. This can result in increased anxiety and confusion, particularly for new project managers, in the early stages of a project, ultimately reducing overall productivity and efficiency.

[0359] 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.

[0360] In this invention, the server includes means for recognizing the user's emotional state and generating project kickoff materials based on emotional data; means for referencing past project data and automatically generating a draft of a work breakdown structure that takes the emotional state into account; and means for utilizing a knowledge database of past projects and providing an emotionally adaptive recommendation template. This enables appropriate support and task management in accordance with the user's emotions.

[0361] "Means for recognizing a user's emotional state" refers to technology that analyzes information and voice input from a user via a device to infer their current emotions.

[0362] "A method for generating project kickoff materials based on emotional data" refers to a technology that automatically creates materials to facilitate the smooth start of a project based on the emotional data obtained.

[0363] "A means of automatically generating a draft of a work decomposition structure that takes emotional states into consideration" refers to a technology that efficiently decomposes project tasks while taking the user's emotions into account, and automatically generates a draft of that structure.

[0364] "Methods for generating folder structures that respond to emotions" refers to a technology that creates an optimal folder structure for organizing and storing relevant data based on the user's emotions.

[0365] "Methods for providing emotionally adaptive recommendation templates" refers to technologies that provide templates useful for project progress, taking into account past project data and user emotions.

[0366] "Methods for presenting potential risks based on emotions" refer to technologies that predict project risks inferred from the user's emotional state and present them in advance.

[0367] This system has an engine that analyzes the user's emotional state and adjusts project materials accordingly. The server receives data entered by the user via a terminal and voice data, and calculates the user's emotions by analyzing this data. The analysis uses emotion recognition algorithms, for example, to estimate stress levels and emotional states by analyzing keystroke speed and voice tone.

[0368] Based on this emotional data, the server generates project kickoff materials and work breakdown structures (WBS). If the user is experiencing stress, the generated materials are adjusted to include relaxing language and concise guidelines. The server also leverages historical project data and a knowledge base to provide recommended templates and potential risks tailored to the user's emotional state.

[0369] Furthermore, it has a function to record the user's emotional changes over time and visualize the changes in the user's emotions as the project progresses. This visualized data allows users to understand their own emotional state and take countermeasures as needed.

[0370] A concrete example would be a scenario where, as a user plans a weekend family event, the robot detects their stress level, suggests activities that the whole family can enjoy, and provides relaxing music.

[0371] An example of a prompt message generated using an AI model would be: "I want to set the user's perceived stress level. Based on this data, suggest a relaxing task."

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The terminal collects user keystroke data and voice data. It acquires data such as keystroke speed and voice tone as input and sends this to the server. The output is the raw data necessary for analysis.

[0375] Step 2:

[0376] The server analyzes the received keystroke and voice data to estimate the user's emotional state. Using an emotion recognition algorithm, it analyzes the input data and outputs stress levels and emotional states as numerical values. Specifically, a high value is output when stress levels are high.

[0377] Step 3:

[0378] The server combines the acquired emotional data with the project's historical data to generate project kickoff materials. Based on the emotional data and historical data as input, it generates materials optimized for the user. The materials are adjusted according to the user's emotions, such as including wording that promotes relaxation.

[0379] Step 4:

[0380] The server generates a draft Work Breakdown Structure (WBS) that reflects emotional data. It utilizes emotional data and task information as input data, adjusting the order and content of tasks to minimize user stress. Specifically, it simplifies and restructures complex tasks.

[0381] Step 5:

[0382] The server leverages the user's past emotional data to provide recommended templates that aid in project progress. This process takes a record of emotional changes as input, generates a template containing project improvement suggestions based on that data, and outputs it.

[0383] Step 6:

[0384] The user utilizes a generative AI model to provide prompts such as, "I want to set my perceived stress level. Based on this data, suggest tasks that will help me relax." This enables automated task management suggestions.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention relates to a system for improving the efficiency of the initial stages of project management. When a user inputs basic project information via a terminal, a server automatically generates various documents based on that information, thereby enabling efficient project execution support. Specific embodiments of this invention are described below.

[0402] The user uses a terminal to input basic project information, such as project name, objectives, client details, budget, and estimated duration. The terminal sends this information to a server, which uses an AI agent to create a draft of the project kickoff document and work breakdown structure (WBS). At this stage, the server automatically suggests tasks and milestones based on information from similar projects, referencing past project data.

[0403] Furthermore, the server automatically generates project folders according to specified naming rules and folder structures. These folders serve as a system for managing and consistently organizing generated materials and documents.

[0404] Furthermore, the server provides users with relevant templates and information from a past project knowledge database, offering them project-related knowledge. By planning projects based on the provided materials and information, users can more effectively manage risks in the initial stages of a project. The server also provides a mechanism to notify users in advance of points requiring attention based on potential risk information.

[0405] Specific example:

[0406] When a newly appointed project manager starts a new web development project, the user inputs project information using a terminal. Based on this information, the server automatically generates kickoff documents, creating materials that clarify the project's background, goals, and key stakeholders. Simultaneously, a draft WBS is generated, and a project task list is suggested by AI. Folders are then created according to naming conventions, and all relevant documents are properly saved and managed. In this way, the user can quickly launch a project and efficiently proceed with the planning.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The user uses a terminal to enter basic project information. This information includes project name, purpose, client information, budget, and schedule.

[0410] Step 2:

[0411] The terminal sends the entered project information to the server. The transmitted information is used as the basic data for generating the document.

[0412] Step 3:

[0413] Based on the received project information, the server uses an AI agent to automatically generate a draft of the project kickoff document. This document includes the project overview, objectives, stakeholder information, and schedule.

[0414] Step 4:

[0415] The server retrieves past project data from the database and generates a draft Work Breakdown Structure (WBS) based on information from similar projects. Task and milestone proposals are made at this stage.

[0416] Step 5:

[0417] The server automatically creates new project folders based on predefined naming rules and folder structures. Furthermore, generated materials and documents related to the current project information are organized and saved within these folders.

[0418] Step 6:

[0419] The server utilizes a database of past project knowledge to present users with relevant templates and risk information. This allows users to familiarize themselves with project reference materials and important points in advance.

[0420] Step 7:

[0421] Users review the provided kickoff materials and WBS, and modify them using their terminals as needed. The modified information is then sent back to the server and stored in the database.

[0422] Step 8:

[0423] The server incorporates user feedback and scrutinizes and saves the final project documents. This ensures that the project is ready for its initial stages.

[0424] (Example 1)

[0425] 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."

[0426] In modern project management, a significant amount of time and effort is often required for planning and preparation in the initial stages. Furthermore, efficient project execution necessitates the organization of information using historical data and knowledge, as well as risk assessment. However, these processes are highly complex, making it challenging to maintain effective and consistent operation.

[0427] 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.

[0428] In this invention, the server includes means for receiving initial project information via an information processing device, means for generating storage areas based on a predetermined naming convention and folder structure, means for providing recommended templates utilizing a knowledge base, means for presenting potential risks when setting up activities, means for transmitting information using information transmission means for managing electronic documents, and means for receiving and displaying information using information transmission means. This enables users to efficiently manage information and risks in the initial stages of a project.

[0429] An "information processing device" is an integrated system of hardware and software that receives input from a user, processes the data, and transmits it to a server.

[0430] "Activity start materials" are documents that summarize the information needed in the initial stages of a project, including the project's objectives, stakeholders, and milestones.

[0431] A "knowledge base" is a database that aggregates information and knowledge gained from past projects, and is used when carrying out new projects.

[0432] A "storage area" is a collection of directories or folders created by a server to organize and store electronic documents and files related to a project.

[0433] A "template" refers to a standardized format or document that serves as a reference when creating project materials; it is a template designed to support efficient document creation.

[0434] "Potential risks" refer to problems or obstacles that may be foreseeable during the planning stage of a project, and are factors for which countermeasures should be taken in advance.

[0435] A "naming convention" is a set of rules established to standardize and organize the names of folders and files, and is a means of maintaining consistency in project documents.

[0436] An "electronic document" is a document file created, stored, and displayed by a computer, and refers to information maintained in digital format.

[0437] This invention aims to support efficient initial planning in a project management system using an information processing device. Users input basic project information via a terminal and send it to a server, which automatically generates project documentation and structure.

[0438] The user fills in information such as project name, goals, client details, budget, and estimated duration into an input form on their device and submits it. The device securely transmits this information to the server using the HTTPS protocol. The server processes the received information using a generative AI model to generate a draft of the project kickoff document and work breakdown structure (WBS). In generating the project document, the server refers to past information resources and knowledge bases and suggests documents and milestones based on relevant examples.

[0439] Furthermore, the server generates a project storage area based on default naming conventions and folder structures, and stores the electronic documents associated with this area. During storage, the system enforces folder and file naming conventions to maintain consistency. The server also assists with planning by extracting templates from a knowledge base and presenting them to the user. This allows the user to identify risks in the early stages of the project and execute the plan effectively.

[0440] Specific example:

[0441] When launching a new website development project, the user sends the following information from their device to the server: "Please generate kickoff materials and a WBS for a new website development project. The project name is 'Online Shop Development,' the goal is 'Build a user-friendly e-commerce site,' the client is 'XYZ Corporation,' the budget is '$50,000,' and the estimated duration is '6 months.'" This allows the server to automatically generate the necessary materials and efficiently proceed with the project planning.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The user enters basic project information into the terminal. Specifically, the user enters the project name, objectives, client details, budget, and estimated duration into the input form and presses the submit button. The entered information is temporarily stored in the terminal's memory.

[0445] Step 2:

[0446] The terminal sends the entered information to the server. The terminal uses the HTTPS protocol to convert the user-entered information into JSON format and send it to the server. At this point, the data is encrypted and securely transferred to the server.

[0447] Step 3:

[0448] The server receives project information and generates project kickoff materials using a generative AI model. The server analyzes the input information and provides appropriate prompts to the AI ​​to generate the materials. The generated materials are then converted into an electronic document format.

[0449] Step 4:

[0450] The server references historical information resources and generates a draft Work Breakdown Structure (WBS). The server retrieves data from similar projects in the database, and based on this, an AI model automatically suggests appropriate tasks and milestones. This suggestion is then documented as a WBS.

[0451] Step 5:

[0452] The server generates project folders based on the specified naming convention and folder structure. The server applies the system-determined naming convention and organizes the folders. Documents and WBS are appropriately stored in the generated folders.

[0453] Step 6:

[0454] The server leverages a knowledge base to provide users with relevant templates. The server searches the template database, selects the template best suited to the project type, and delivers it to the user's terminal.

[0455] Step 7:

[0456] The server analyzes the project's potential risks and notifies the user. The server extracts risk factors from past cases and sends a risk notification message to the user based on these factors. This notification allows the user to strengthen their risk management plan.

[0457] (Application Example 1)

[0458] 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."

[0459] In the initial stages of project management, it is essential to efficiently manage the entire process, from inputting project information and generating documents to creating work breakdown structures and ensuring rapid and consistent document management. However, traditional systems require significant time and effort for these processes, and in complex projects in particular, inadequate planning in the initial stages often leads to insufficient risk management. Furthermore, there has been a lack of systems that allow for easy access using smart devices and manage projects through the integration of front-end and back-end systems.

[0460] 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.

[0461] In this invention, the server includes means for receiving initial project information and generating project kickoff materials based on that information; means for referencing historical project data and automatically generating a draft of the work breakdown structure; and means for inputting information through a front-end device, which allows a back-end artificial intelligence agent to analyze the data and propose the optimal work structure and milestones. This enables efficient and consistent project planning and rapid access via smart devices.

[0462] "Initial project information" refers to basic information such as the project name, objectives, client information, and budget, which are necessary at the start of the project.

[0463] A "project kickoff document" is an introductory document used at the start of a project, clearly outlining its background, objectives, key stakeholders, and other relevant information.

[0464] A "Work Breakdown Structure (WBS)" refers to a structure that systematically organizes the overall work of a project by dividing it into smaller tasks.

[0465] "Past data" refers to the collective term for information and recorded results from projects that have been carried out in the past.

[0466] "Front-end device" refers to the part that the user directly interacts with, including hardware and software used for inputting information and displaying different data.

[0467] A "backend artificial intelligence agent" is a software program used to analyze data entered by the user and provide optimal suggestions and solutions.

[0468] "Naming rules and folder structure" are guidelines that systematically define how to name and store documents and data in order to maintain consistency when organizing them.

[0469] A "knowledge database" refers to an information resource that aggregates project-related information and templates accumulated in the past.

[0470] A "smart device" is a device that can connect to the internet and has various functions, and is used as a tool for users to directly manage projects.

[0471] A "milestone" is an important point or achievement criterion set to represent the progress of a project.

[0472] The system for implementing this invention begins with the user inputting initial project information using a smart device. The user inputs the project name, goals, client information, and budget information via a front-end device such as a smartphone or tablet. The terminal is responsible for transmitting this information to the back-end server.

[0473] The server generates project kickoff materials using a machine learning model based on the received information. Leveraging the generative AI model, it proposes the optimal work breakdown structure and milestones, referencing past project data and knowledge databases. The backend system is built using Python and the Django framework, and uses the TensorFlow library to provide project plan suggestions to an artificial intelligence agent. This process enables efficient project progress.

[0474] Generated materials and documents are automatically organized and saved in the project folder based on default naming rules and folder structure. This ensures information consistency and allows for quick access to necessary materials via smart devices.

[0475] As a concrete example, when a user starts a new sensing sensor development project, they enter information via a smart device following prompts such as, "Please enter details of the sensing sensor development project. Please include information such as goals, timeline, key stakeholders, and budget." The system responds quickly to this input, automatically generating a detailed project plan and kickoff materials.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] Users input initial project information using their smart devices. Specifically, they enter the project name, goals, client information, and budget information into an input form. The entered data undergoes basic validation and is then sent to the server via a REST API. The input consists of basic project information, and the output is this information passed to the server.

[0479] Step 2:

[0480] The server stores the received project information in a database. Next, it uses a generative AI model to search for similar past project data and generates initial suggestions for corresponding work decomposition structures and milestones. Project information is given as input, and a list of optimal work decomposition structures and milestones is obtained as output. Specifically, the TensorFlow library is used to analyze past data.

[0481] Step 3:

[0482] The server automatically creates project kickoff documents based on the generated proposals. It retrieves templates from a knowledge database and incorporates project-specific information to create customized documents. Inputs include proposed milestones and project information, and the output is the completed kickoff document. The document is saved in the appropriate folder.

[0483] Step 4:

[0484] The terminal displays completed kickoff materials and proposed work structures to the user. Users can review and modify the materials via their smart devices. Project materials serve as input, and the displayed content is reflected on the smart device as output. The terminal accepts user input through an interactive UI.

[0485] Step 5:

[0486] When a user makes corrections or provides feedback on a document, the terminal resends that information to the server, which then updates the document and database based on the received corrections. This iterative process ensures that the project information is always up-to-date. The input is the user's changes, and the output is the updated project document and database status.

[0487] 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.

[0488] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of project materials and feedback based on those emotions. This system provides support that takes user emotions into account during the project management process, enabling smooth project progress.

[0489] The user inputs basic project information via a terminal. During information input, the emotion engine analyzes the user's keystrokes, input speed, and voice tone (if necessary) to estimate the user's current emotional state. Based on the project information and emotion data transmitted from the terminal, the server generates optimized project kickoff materials and a Work Breakdown Structure (WBS).

[0490] If the server detects a high stress level in a user, as identified by the emotion engine, it adjusts the content of recommended templates and materials to provide support and alleviate the user's anxiety. In doing so, the server also references past project knowledge and risk information to suggest specific points of caution and solutions.

[0491] Furthermore, the emotion engine records the user's emotional progression over time, visualizing the changes in the user's emotions at each phase of the project. This data helps users understand the project's progress and their own emotional state, enabling them to take necessary actions.

[0492] For example, when a newly appointed project manager enters project information, if the emotion engine detects the user's stress level, the server automatically generates a document accompanied by words that encourage relaxation. Furthermore, recommended templates are adjusted to include concise and easy-to-understand guidelines. In this way, the system provides an environment where users receive appropriate support and can smoothly manage their projects.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The user enters basic project information using their device. This includes the project name, purpose, budget, and timeline.

[0496] Step 2:

[0497] The device sends keystroke patterns and input speed to the emotion engine to measure the user's emotions during input. If voice input is available, the emotion engine also analyzes the voice tone.

[0498] Step 3:

[0499] The emotion engine analyzes the user's emotions (e.g., stress, anxiety, calmness) based on the received data and sends the results to the server.

[0500] Step 4:

[0501] The server integrates the received project's basic information and sentiment data to generate project kickoff materials. The sentiment data adds guidelines and support information tailored to the user's situation to the materials.

[0502] Step 5:

[0503] The server references past project data to create a draft Work Breakdown Structure (WBS) for similar projects. Based on the user's sentiment, the priority of suggested tasks and risk management information are also adjusted.

[0504] Step 6:

[0505] The generated documents and WBS are organized and saved in project folders by the server according to specified naming rules. This also makes it easier for users to manage documents within the folders.

[0506] Step 7:

[0507] The server provides the user with generated materials, a Work Breakdown Structure (WBS), and feedback based on sentiment analysis. If the user's emotions are unstable, the server offers additional support and templates to help alleviate tension.

[0508] Step 8:

[0509] Users review the provided documents and WBS, and make changes as needed. The changes are sent to the server via their terminal and saved to the database as the final document.

[0510] (Example 2)

[0511] 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."

[0512] In project management, users' emotional states often significantly impact project progress. However, traditional systems lack adequate support that considers user emotions, resulting in a lack of effective means to alleviate situations that cause user stress. Therefore, there is a need to build a system that can provide dynamic information and support tailored to user emotions, thereby creating an environment conducive to smooth project progress.

[0513] 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.

[0514] In this invention, the server includes means for acquiring initial project information and generating project materials based on that information; means for analyzing user input data and utilizing an emotion engine to estimate the user's emotional state; and means for dynamically adjusting the content of project materials and templates according to the user's emotional state. This enables support for project management that takes user emotions into account and provides an environment in which projects can proceed smoothly.

[0515] "Initial project information" refers to basic information provided by the user at the start of a project, including data such as the project name, purpose, schedule, and participants.

[0516] An "emotion engine" is an analytical device that estimates the user's emotional state based on their input data, and it has the function of analyzing keystrokes, input speed, voice tone, and other factors.

[0517] "Project documentation" refers to documents that contain information and materials required at each phase of a project, from its start to its completion, and includes plans, progress reports, and evaluation results.

[0518] A "template" is a standardized format or guideline provided for creating project documents and reports.

[0519] "Knowledge information" refers to a database that compiles experiences and lessons learned from past projects, including information that highlights project success factors and lessons learned.

[0520] "Risk information" refers to data on potential problems and failures that may occur in a project, as well as information on precautions and solutions to prevent them.

[0521] "Recording and visualizing time-series changes" refers to a technology that continuously tracks user emotional data over time and displays it visually using graphs, charts, and other methods.

[0522] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of materials and feedback based on those emotions.

[0523] The system utilizes hardware such as personal computers, servers, and terminals for receiving user input (e.g., keyboards, mice, microphones). Software includes a suite of applications, including an emotion engine, a project management platform, and a database management system.

[0524] Users input basic project information through their devices. During this process, the emotion engine estimates the user's emotional state using keystroke speed, input patterns, and voice analysis technology. This emotion data is then used to generate project documents and select templates.

[0525] The server receives project data and sentiment data sent from the terminal and, by referencing past project knowledge from the database, provides optimal advice and materials tailored to the situation. It utilizes a generative AI model to adjust the content of the materials to match the user's state.

[0526] As a concrete example, if a newly appointed project manager is detected to be under high stress while entering project information, the server will automatically generate a document that includes text encouraging relaxation. The document will also contain concise and easy-to-understand guidelines. This example can be used as a prompt to communicate the intention to the system, such as, "When generating project kickoff materials, please add relaxing content if the user's stress level is high."

[0527] This system aims to increase the probability of project success by providing emotionally sensitive support to help users move projects forward smoothly.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The user uses a terminal to enter basic project information. This information includes the project name, purpose, schedule, and assigned personnel. The entered data is sent to the server as initial data for analysis by the emotion engine.

[0531] Step 2:

[0532] The terminal transmits the speed and pattern of keystrokes to the emotion engine while the user is typing. It also uses audio data obtained through the microphone for analysis as needed. Based on this data, the emotion engine uses machine learning algorithms to estimate the user's emotional state and determine their stress level and degree of relaxation. This analysis result is then sent to the server.

[0533] Step 3:

[0534] The server receives project information and emotional state data sent from the terminal. The server analyzes this data using a generative AI model and creates project kickoff materials using a document generation algorithm. Depending on the emotional data, the tone and content of the materials are adjusted, and considerate language is added.

[0535] Step 4:

[0536] The server references a database of past projects and incorporates recommended templates and considerations into the documentation based on similar project information and their results. This allows users to receive useful advice based on past knowledge.

[0537] Step 5:

[0538] The emotion engine records changes in the user's emotional state over time. This allows for the visualization of emotional progression as a graph during or after a project. Users can use this data to improve their stress management and project management.

[0539] This series of processes enables a support system that provides project materials in a way that takes into account the user's emotional state from the start to the end of the project.

[0540] (Application Example 2)

[0541] 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."

[0542] Traditional project management systems often fail to consider user emotions when managing project documents and tasks, leading to increased user stress and potentially hindering project progress. This can result in increased anxiety and confusion, particularly for new project managers, in the early stages of a project, ultimately reducing overall productivity and efficiency.

[0543] 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.

[0544] In this invention, the server includes means for recognizing the user's emotional state and generating project kickoff materials based on emotional data; means for referencing past project data and automatically generating a draft of a work breakdown structure that takes the emotional state into account; and means for utilizing a knowledge database of past projects and providing an emotionally adaptive recommendation template. This enables appropriate support and task management in accordance with the user's emotions.

[0545] "Means for recognizing a user's emotional state" refers to technology that analyzes information and voice input from a user via a device to infer their current emotions.

[0546] "A method for generating project kickoff materials based on emotional data" refers to a technology that automatically creates materials to facilitate the smooth start of a project based on the emotional data obtained.

[0547] "A means of automatically generating a draft of a work decomposition structure that takes emotional states into consideration" refers to a technology that efficiently decomposes project tasks while taking the user's emotions into account, and automatically generates a draft of that structure.

[0548] "Methods for generating folder structures that respond to emotions" refers to a technology that creates an optimal folder structure for organizing and storing relevant data based on the user's emotions.

[0549] "Methods for providing emotionally adaptive recommendation templates" refers to technologies that provide templates useful for project progress, taking into account past project data and user emotions.

[0550] "Methods for presenting potential risks based on emotions" refer to technologies that predict project risks inferred from the user's emotional state and present them in advance.

[0551] This system has an engine that analyzes the user's emotional state and adjusts project materials accordingly. The server receives data entered by the user via a terminal and voice data, and calculates the user's emotions by analyzing this data. The analysis uses emotion recognition algorithms, for example, to estimate stress levels and emotional states by analyzing keystroke speed and voice tone.

[0552] Based on this emotional data, the server generates project kickoff materials and work breakdown structures (WBS). If the user is experiencing stress, the generated materials are adjusted to include relaxing language and concise guidelines. The server also leverages historical project data and a knowledge base to provide recommended templates and potential risks tailored to the user's emotional state.

[0553] Furthermore, it has a function to record the user's emotional changes over time and visualize the changes in the user's emotions as the project progresses. This visualized data allows users to understand their own emotional state and take countermeasures as needed.

[0554] A concrete example would be a scenario where, as a user plans a weekend family event, the robot detects their stress level, suggests activities that the whole family can enjoy, and provides relaxing music.

[0555] An example of a prompt message generated using an AI model would be: "I want to set the user's perceived stress level. Based on this data, suggest a relaxing task."

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The terminal collects user keystroke data and voice data. It acquires data such as keystroke speed and voice tone as input and sends this to the server. The output is the raw data necessary for analysis.

[0559] Step 2:

[0560] The server analyzes the received keystroke and voice data to estimate the user's emotional state. Using an emotion recognition algorithm, it analyzes the input data and outputs stress levels and emotional states as numerical values. Specifically, a high value is output when stress levels are high.

[0561] Step 3:

[0562] The server combines the acquired emotional data with the project's historical data to generate project kickoff materials. Based on the emotional data and historical data as input, it generates materials optimized for the user. The materials are adjusted according to the user's emotions, such as including wording that promotes relaxation.

[0563] Step 4:

[0564] The server generates a draft Work Breakdown Structure (WBS) that reflects emotional data. It utilizes emotional data and task information as input data, adjusting the order and content of tasks to minimize user stress. Specifically, it simplifies and restructures complex tasks.

[0565] Step 5:

[0566] The server leverages the user's past emotional data to provide recommended templates that aid in project progress. This process takes a record of emotional changes as input, generates a template containing project improvement suggestions based on that data, and outputs it.

[0567] Step 6:

[0568] The user utilizes a generative AI model to provide prompts such as, "I want to set my perceived stress level. Based on this data, suggest tasks that will help me relax." This enables automated task management suggestions.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Fourth Embodiment]

[0573] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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".

[0586] This invention relates to a system for improving the efficiency of the initial stages of project management. When a user inputs basic project information via a terminal, a server automatically generates various documents based on that information, thereby enabling efficient project execution support. Specific embodiments of this invention are described below.

[0587] The user uses a terminal to input basic project information, such as project name, objectives, client details, budget, and estimated duration. The terminal sends this information to a server, which uses an AI agent to create a draft of the project kickoff document and work breakdown structure (WBS). At this stage, the server automatically suggests tasks and milestones based on information from similar projects, referencing past project data.

[0588] Furthermore, the server automatically generates project folders according to specified naming rules and folder structures. These folders serve as a system for managing and consistently organizing generated materials and documents.

[0589] Furthermore, the server provides users with relevant templates and information from a past project knowledge database, offering them project-related knowledge. By planning projects based on the provided materials and information, users can more effectively manage risks in the initial stages of a project. The server also provides a mechanism to notify users in advance of points requiring attention based on potential risk information.

[0590] Specific example:

[0591] When a newly appointed project manager starts a new web development project, the user inputs project information using a terminal. Based on this information, the server automatically generates kickoff documents, creating materials that clarify the project's background, goals, and key stakeholders. Simultaneously, a draft WBS is generated, and a project task list is suggested by AI. Folders are then created according to naming conventions, and all relevant documents are properly saved and managed. In this way, the user can quickly launch a project and efficiently proceed with the planning.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The user uses a terminal to enter basic project information. This information includes project name, purpose, client information, budget, and schedule.

[0595] Step 2:

[0596] The terminal sends the entered project information to the server. The transmitted information is used as the basic data for generating the document.

[0597] Step 3:

[0598] Based on the received project information, the server uses an AI agent to automatically generate a draft of the project kickoff document. This document includes the project overview, objectives, stakeholder information, and schedule.

[0599] Step 4:

[0600] The server retrieves past project data from the database and generates a draft Work Breakdown Structure (WBS) based on information from similar projects. Task and milestone proposals are made at this stage.

[0601] Step 5:

[0602] The server automatically creates new project folders based on predefined naming rules and folder structures. Furthermore, generated materials and documents related to the current project information are organized and saved within these folders.

[0603] Step 6:

[0604] The server utilizes a database of past project knowledge to present users with relevant templates and risk information. This allows users to familiarize themselves with project reference materials and important points in advance.

[0605] Step 7:

[0606] Users review the provided kickoff materials and WBS, and modify them using their terminals as needed. The modified information is then sent back to the server and stored in the database.

[0607] Step 8:

[0608] The server incorporates user feedback and scrutinizes and saves the final project documents. This ensures that the project is ready for its initial stages.

[0609] (Example 1)

[0610] 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".

[0611] In modern project management, a significant amount of time and effort is often required for planning and preparation in the initial stages. Furthermore, efficient project execution necessitates the organization of information using historical data and knowledge, as well as risk assessment. However, these processes are highly complex, making it challenging to maintain effective and consistent operation.

[0612] 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.

[0613] In this invention, the server includes means for receiving initial project information via an information processing device, means for generating storage areas based on a predetermined naming convention and folder structure, means for providing recommended templates utilizing a knowledge base, means for presenting potential risks when setting up activities, means for transmitting information using information transmission means for managing electronic documents, and means for receiving and displaying information using information transmission means. This enables users to efficiently manage information and risks in the initial stages of a project.

[0614] An "information processing device" is an integrated system of hardware and software that receives input from a user, processes the data, and transmits it to a server.

[0615] "Activity start materials" are documents that summarize the information needed in the initial stages of a project, including the project's objectives, stakeholders, and milestones.

[0616] A "knowledge base" is a database that aggregates information and knowledge gained from past projects, and is used when carrying out new projects.

[0617] A "storage area" is a collection of directories or folders created by a server to organize and store electronic documents and files related to a project.

[0618] A "template" refers to a standardized format or document that serves as a reference when creating project materials; it is a template designed to support efficient document creation.

[0619] "Potential risks" refer to problems or obstacles that may be foreseeable during the planning stage of a project, and are factors for which countermeasures should be taken in advance.

[0620] A "naming convention" is a set of rules established to standardize and organize the names of folders and files, and is a means of maintaining consistency in project documents.

[0621] An "electronic document" is a document file created, stored, and displayed by a computer, and refers to information maintained in digital format.

[0622] This invention aims to support efficient initial planning in a project management system using an information processing device. Users input basic project information via a terminal and send it to a server, which automatically generates project documentation and structure.

[0623] The user fills in information such as project name, goals, client details, budget, and estimated duration into an input form on their device and submits it. The device securely transmits this information to the server using the HTTPS protocol. The server processes the received information using a generative AI model to generate a draft of the project kickoff document and work breakdown structure (WBS). In generating the project document, the server refers to past information resources and knowledge bases and suggests documents and milestones based on relevant examples.

[0624] Furthermore, the server generates a project storage area based on default naming conventions and folder structures, and stores the electronic documents associated with this area. During storage, the system enforces folder and file naming conventions to maintain consistency. The server also assists with planning by extracting templates from a knowledge base and presenting them to the user. This allows the user to identify risks in the early stages of the project and execute the plan effectively.

[0625] Specific example:

[0626] When launching a new website development project, the user sends the following information from their device to the server: "Please generate kickoff materials and a WBS for a new website development project. The project name is 'Online Shop Development,' the goal is 'Build a user-friendly e-commerce site,' the client is 'XYZ Corporation,' the budget is '$50,000,' and the estimated duration is '6 months.'" This allows the server to automatically generate the necessary materials and efficiently proceed with the project planning.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] The user enters basic project information into the terminal. Specifically, the user enters the project name, objectives, client details, budget, and estimated duration into the input form and presses the submit button. The entered information is temporarily stored in the terminal's memory.

[0630] Step 2:

[0631] The terminal sends the entered information to the server. The terminal uses the HTTPS protocol to convert the user-entered information into JSON format and send it to the server. At this point, the data is encrypted and securely transferred to the server.

[0632] Step 3:

[0633] The server receives project information and generates project kickoff materials using a generative AI model. The server analyzes the input information and provides appropriate prompts to the AI ​​to generate the materials. The generated materials are then converted into an electronic document format.

[0634] Step 4:

[0635] The server references historical information resources and generates a draft Work Breakdown Structure (WBS). The server retrieves data from similar projects in the database, and based on this, an AI model automatically suggests appropriate tasks and milestones. This suggestion is then documented as a WBS.

[0636] Step 5:

[0637] The server generates project folders based on the specified naming convention and folder structure. The server applies the system-determined naming convention and organizes the folders. Documents and WBS are appropriately stored in the generated folders.

[0638] Step 6:

[0639] The server leverages a knowledge base to provide users with relevant templates. The server searches the template database, selects the template best suited to the project type, and delivers it to the user's terminal.

[0640] Step 7:

[0641] The server analyzes the project's potential risks and notifies the user. The server extracts risk factors from past cases and sends a risk notification message to the user based on these factors. This notification allows the user to strengthen their risk management plan.

[0642] (Application Example 1)

[0643] 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".

[0644] In the initial stages of project management, it is essential to efficiently manage the entire process, from inputting project information and generating documents to creating work breakdown structures and ensuring rapid and consistent document management. However, traditional systems require significant time and effort for these processes, and in complex projects in particular, inadequate planning in the initial stages often leads to insufficient risk management. Furthermore, there has been a lack of systems that allow for easy access using smart devices and manage projects through the integration of front-end and back-end systems.

[0645] 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.

[0646] In this invention, the server includes means for receiving initial project information and generating project kickoff materials based on that information; means for referencing historical project data and automatically generating a draft of the work breakdown structure; and means for inputting information through a front-end device, which allows a back-end artificial intelligence agent to analyze the data and propose the optimal work structure and milestones. This enables efficient and consistent project planning and rapid access via smart devices.

[0647] "Initial project information" refers to basic information such as the project name, objectives, client information, and budget, which are necessary at the start of the project.

[0648] A "project kickoff document" is an introductory document used at the start of a project, clearly outlining its background, objectives, key stakeholders, and other relevant information.

[0649] A "Work Breakdown Structure (WBS)" refers to a structure that systematically organizes the overall work of a project by dividing it into smaller tasks.

[0650] "Past data" refers to the collective term for information and recorded results from projects that have been carried out in the past.

[0651] "Front-end device" refers to the part that the user directly interacts with, including hardware and software used for inputting information and displaying different data.

[0652] A "backend artificial intelligence agent" is a software program used to analyze data entered by the user and provide optimal suggestions and solutions.

[0653] "Naming rules and folder structure" are guidelines that systematically define how to name and store documents and data in order to maintain consistency when organizing them.

[0654] A "knowledge database" refers to an information resource that aggregates project-related information and templates accumulated in the past.

[0655] A "smart device" is a device that can connect to the internet and has various functions, and is used as a tool for users to directly manage projects.

[0656] A "milestone" is an important point or achievement criterion set to represent the progress of a project.

[0657] The system for implementing this invention begins with the user inputting initial project information using a smart device. The user inputs the project name, goals, client information, and budget information via a front-end device such as a smartphone or tablet. The terminal is responsible for transmitting this information to the back-end server.

[0658] The server generates project kickoff materials using a machine learning model based on the received information. Leveraging the generative AI model, it proposes the optimal work breakdown structure and milestones, referencing past project data and knowledge databases. The backend system is built using Python and the Django framework, and uses the TensorFlow library to provide project plan suggestions to an artificial intelligence agent. This process enables efficient project progress.

[0659] Generated materials and documents are automatically organized and saved in the project folder based on default naming rules and folder structure. This ensures information consistency and allows for quick access to necessary materials via smart devices.

[0660] As a concrete example, when a user starts a new sensing sensor development project, they enter information via a smart device following prompts such as, "Please enter details of the sensing sensor development project. Please include information such as goals, timeline, key stakeholders, and budget." The system responds quickly to this input, automatically generating a detailed project plan and kickoff materials.

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] Users input initial project information using their smart devices. Specifically, they enter the project name, goals, client information, and budget information into an input form. The entered data undergoes basic validation and is then sent to the server via a REST API. The input consists of basic project information, and the output is this information passed to the server.

[0664] Step 2:

[0665] The server stores the received project information in a database. Next, it uses a generative AI model to search for similar past project data and generates initial suggestions for corresponding work decomposition structures and milestones. Project information is given as input, and a list of optimal work decomposition structures and milestones is obtained as output. Specifically, the TensorFlow library is used to analyze past data.

[0666] Step 3:

[0667] The server automatically creates project kickoff documents based on the generated proposals. It retrieves templates from a knowledge database and incorporates project-specific information to create customized documents. Inputs include proposed milestones and project information, and the output is the completed kickoff document. The document is saved in the appropriate folder.

[0668] Step 4:

[0669] The terminal displays completed kickoff materials and proposed work structures to the user. Users can review and modify the materials via their smart devices. Project materials serve as input, and the displayed content is reflected on the smart device as output. The terminal accepts user input through an interactive UI.

[0670] Step 5:

[0671] When a user makes corrections or provides feedback on a document, the terminal resends that information to the server, which then updates the document and database based on the received corrections. This iterative process ensures that the project information is always up-to-date. The input is the user's changes, and the output is the updated project document and database status.

[0672] 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.

[0673] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of project materials and feedback based on those emotions. This system provides support that takes user emotions into account during the project management process, enabling smooth project progress.

[0674] The user inputs basic project information via a terminal. During information input, the emotion engine analyzes the user's keystrokes, input speed, and voice tone (if necessary) to estimate the user's current emotional state. Based on the project information and emotion data transmitted from the terminal, the server generates optimized project kickoff materials and a Work Breakdown Structure (WBS).

[0675] If the server detects a high stress level in a user, as identified by the emotion engine, it adjusts the content of recommended templates and materials to provide support and alleviate the user's anxiety. In doing so, the server also references past project knowledge and risk information to suggest specific points of caution and solutions.

[0676] Furthermore, the emotion engine records the user's emotional progression over time, visualizing the changes in the user's emotions at each phase of the project. This data helps users understand the project's progress and their own emotional state, enabling them to take necessary actions.

[0677] For example, when a newly appointed project manager enters project information, if the emotion engine detects the user's stress level, the server automatically generates a document accompanied by words that encourage relaxation. Furthermore, recommended templates are adjusted to include concise and easy-to-understand guidelines. In this way, the system provides an environment where users receive appropriate support and can smoothly manage their projects.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The user enters basic project information using their device. This includes the project name, purpose, budget, and timeline.

[0681] Step 2:

[0682] The device sends keystroke patterns and input speed to the emotion engine to measure the user's emotions during input. If voice input is available, the emotion engine also analyzes the voice tone.

[0683] Step 3:

[0684] The emotion engine analyzes the user's emotions (e.g., stress, anxiety, calmness) based on the received data and sends the results to the server.

[0685] Step 4:

[0686] The server integrates the received project's basic information and sentiment data to generate project kickoff materials. The sentiment data adds guidelines and support information tailored to the user's situation to the materials.

[0687] Step 5:

[0688] The server references past project data to create a draft Work Breakdown Structure (WBS) for similar projects. Based on the user's sentiment, the priority of suggested tasks and risk management information are also adjusted.

[0689] Step 6:

[0690] The generated documents and WBS are organized and saved in project folders by the server according to specified naming rules. This also makes it easier for users to manage documents within the folders.

[0691] Step 7:

[0692] The server provides the user with generated materials, a Work Breakdown Structure (WBS), and feedback based on sentiment analysis. If the user's emotions are unstable, the server offers additional support and templates to help alleviate tension.

[0693] Step 8:

[0694] Users review the provided documents and WBS, and make changes as needed. The changes are sent to the server via their terminal and saved to the database as the final document.

[0695] (Example 2)

[0696] 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".

[0697] In project management, users' emotional states often significantly impact project progress. However, traditional systems lack adequate support that considers user emotions, resulting in a lack of effective means to alleviate situations that cause user stress. Therefore, there is a need to build a system that can provide dynamic information and support tailored to user emotions, thereby creating an environment conducive to smooth project progress.

[0698] 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.

[0699] In this invention, the server includes means for acquiring initial project information and generating project materials based on that information; means for analyzing user input data and utilizing an emotion engine to estimate the user's emotional state; and means for dynamically adjusting the content of project materials and templates according to the user's emotional state. This enables support for project management that takes user emotions into account and provides an environment in which projects can proceed smoothly.

[0700] "Initial project information" refers to basic information provided by the user at the start of a project, including data such as the project name, purpose, schedule, and participants.

[0701] An "emotion engine" is an analytical device that estimates the user's emotional state based on their input data, and it has the function of analyzing keystrokes, input speed, voice tone, and other factors.

[0702] "Project documentation" refers to documents that contain information and materials required at each phase of a project, from its start to its completion, and includes plans, progress reports, and evaluation results.

[0703] A "template" is a standardized format or guideline provided for creating project documents and reports.

[0704] "Knowledge information" refers to a database that compiles experiences and lessons learned from past projects, including information that highlights project success factors and lessons learned.

[0705] "Risk information" refers to data on potential problems and failures that may occur in a project, as well as information on precautions and solutions to prevent them.

[0706] "Recording and visualizing time-series changes" refers to a technology that continuously tracks user emotional data over time and displays it visually using graphs, charts, and other methods.

[0707] This invention integrates an emotion engine into a project management system that recognizes user emotions and adjusts the generation of materials and feedback based on those emotions.

[0708] The system utilizes hardware such as personal computers, servers, and terminals for receiving user input (e.g., keyboards, mice, microphones). Software includes a suite of applications, including an emotion engine, a project management platform, and a database management system.

[0709] Users input basic project information through their devices. During this process, the emotion engine estimates the user's emotional state using keystroke speed, input patterns, and voice analysis technology. This emotion data is then used to generate project documents and select templates.

[0710] The server receives project data and sentiment data sent from the terminal and, by referencing past project knowledge from the database, provides optimal advice and materials tailored to the situation. It utilizes a generative AI model to adjust the content of the materials to match the user's state.

[0711] As a concrete example, if a newly appointed project manager is detected to be under high stress while entering project information, the server will automatically generate a document that includes text encouraging relaxation. The document will also contain concise and easy-to-understand guidelines. This example can be used as a prompt to communicate the intention to the system, such as, "When generating project kickoff materials, please add relaxing content if the user's stress level is high."

[0712] This system aims to increase the probability of project success by providing emotionally sensitive support to help users move projects forward smoothly.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] The user uses a terminal to enter basic project information. This information includes the project name, purpose, schedule, and assigned personnel. The entered data is sent to the server as initial data for analysis by the emotion engine.

[0716] Step 2:

[0717] The terminal transmits the speed and pattern of keystrokes to the emotion engine while the user is typing. It also uses audio data obtained through the microphone for analysis as needed. Based on this data, the emotion engine uses machine learning algorithms to estimate the user's emotional state and determine their stress level and degree of relaxation. This analysis result is then sent to the server.

[0718] Step 3:

[0719] The server receives project information and emotional state data sent from the terminal. The server analyzes this data using a generative AI model and creates project kickoff materials using a document generation algorithm. Depending on the emotional data, the tone and content of the materials are adjusted, and considerate language is added.

[0720] Step 4:

[0721] The server references a database of past projects and incorporates recommended templates and considerations into the documentation based on similar project information and their results. This allows users to receive useful advice based on past knowledge.

[0722] Step 5:

[0723] The emotion engine records changes in the user's emotional state over time. This allows for the visualization of emotional progression as a graph during or after a project. Users can use this data to improve their stress management and project management.

[0724] This series of processes enables a support system that provides project materials in a way that takes into account the user's emotional state from the start to the end of the project.

[0725] (Application Example 2)

[0726] 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".

[0727] Traditional project management systems often fail to consider user emotions when managing project documents and tasks, leading to increased user stress and potentially hindering project progress. This can result in increased anxiety and confusion, particularly for new project managers, in the early stages of a project, ultimately reducing overall productivity and efficiency.

[0728] 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.

[0729] In this invention, the server includes means for recognizing the user's emotional state and generating project kickoff materials based on emotional data; means for referencing past project data and automatically generating a draft of a work breakdown structure that takes the emotional state into account; and means for utilizing a knowledge database of past projects and providing an emotionally adaptive recommendation template. This enables appropriate support and task management in accordance with the user's emotions.

[0730] "Means for recognizing a user's emotional state" refers to technology that analyzes information and voice input from a user via a device to infer their current emotions.

[0731] "A method for generating project kickoff materials based on emotional data" refers to a technology that automatically creates materials to facilitate the smooth start of a project based on the emotional data obtained.

[0732] "A means of automatically generating a draft of a work decomposition structure that takes emotional states into consideration" refers to a technology that efficiently decomposes project tasks while taking the user's emotions into account, and automatically generates a draft of that structure.

[0733] "Methods for generating folder structures that respond to emotions" refers to a technology that creates an optimal folder structure for organizing and storing relevant data based on the user's emotions.

[0734] "Methods for providing emotionally adaptive recommendation templates" refers to technologies that provide templates useful for project progress, taking into account past project data and user emotions.

[0735] "Methods for presenting potential risks based on emotions" refer to technologies that predict project risks inferred from the user's emotional state and present them in advance.

[0736] This system has an engine that analyzes the user's emotional state and adjusts project materials accordingly. The server receives data entered by the user via a terminal and voice data, and calculates the user's emotions by analyzing this data. The analysis uses emotion recognition algorithms, for example, to estimate stress levels and emotional states by analyzing keystroke speed and voice tone.

[0737] Based on this emotional data, the server generates project kickoff materials and work breakdown structures (WBS). If the user is experiencing stress, the generated materials are adjusted to include relaxing language and concise guidelines. The server also leverages historical project data and a knowledge base to provide recommended templates and potential risks tailored to the user's emotional state.

[0738] Furthermore, it has a function to record the user's emotional changes over time and visualize the changes in the user's emotions as the project progresses. This visualized data allows users to understand their own emotional state and take countermeasures as needed.

[0739] A concrete example would be a scenario where, as a user plans a weekend family event, the robot detects their stress level, suggests activities that the whole family can enjoy, and provides relaxing music.

[0740] An example of a prompt message generated using an AI model would be: "I want to set the user's perceived stress level. Based on this data, suggest a relaxing task."

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The terminal collects user keystroke data and voice data. It acquires data such as keystroke speed and voice tone as input and sends this to the server. The output is the raw data necessary for analysis.

[0744] Step 2:

[0745] The server analyzes the received keystroke and voice data to estimate the user's emotional state. Using an emotion recognition algorithm, it analyzes the input data and outputs stress levels and emotional states as numerical values. Specifically, a high value is output when stress levels are high.

[0746] Step 3:

[0747] The server combines the acquired emotional data with the project's historical data to generate project kickoff materials. Based on the emotional data and historical data as input, it generates materials optimized for the user. The materials are adjusted according to the user's emotions, such as including wording that promotes relaxation.

[0748] Step 4:

[0749] The server generates a draft Work Breakdown Structure (WBS) that reflects emotional data. It utilizes emotional data and task information as input data, adjusting the order and content of tasks to minimize user stress. Specifically, it simplifies and restructures complex tasks.

[0750] Step 5:

[0751] The server leverages the user's past emotional data to provide recommended templates that aid in project progress. This process takes a record of emotional changes as input, generates a template containing project improvement suggestions based on that data, and outputs it.

[0752] Step 6:

[0753] The user utilizes a generative AI model to provide prompts such as, "I want to set my perceived stress level. Based on this data, suggest tasks that will help me relax." This enables automated task management suggestions.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] The following is further disclosed regarding the embodiments described above.

[0776] (Claim 1)

[0777] A means of receiving initial project information and generating project kickoff materials based on that information,

[0778] A means to automatically generate a draft of the work breakdown structure by referring to the project's past data,

[0779] A means of generating project folders based on default naming rules and folder structure, and saving related documents,

[0780] A means of providing recommended templates by utilizing a knowledge database of past projects,

[0781] A means of presenting potential risks when setting up a project,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, which receives client information and budget information along with initial project information, and generates documents and drafts based on them.

[0785] (Claim 3)

[0786] The system according to claim 1, which applies naming rules and saving rules to documents in the generated project folder to achieve consistent document management.

[0787] "Example 1"

[0788] (Claim 1)

[0789] A means for receiving initial project information via an information processing device and generating activity commencement materials based on that information,

[0790] A means of automatically generating a draft of the decomposition structure of activities by referring to past information resources,

[0791] A means for generating a storage area based on a default naming convention and folder structure, and for storing related electronic documents,

[0792] A means of providing recommended templates by utilizing a knowledge base,

[0793] Means of presenting potential risks when setting up activities,

[0794] A means of transmitting information using an information transmission means for managing electronic documents,

[0795] A means for receiving and displaying information using an information transmission means,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which receives customer information and financial information along with initial activity information, and generates documents and drafts based on them.

[0799] (Claim 3)

[0800] The system according to claim 1, which applies naming conventions and storage rules to electronic documents in the generated storage area to achieve consistent electronic document management.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A means of receiving initial project information and generating project kickoff materials based on that information,

[0804] A means to automatically generate a draft of the work breakdown structure by referring to the project's past data,

[0805] A means of generating project folders based on default naming rules and folder structure, and saving related documents,

[0806] A means of providing recommended templates by utilizing a knowledge database of past projects,

[0807] A means of presenting potential risks when setting up a project,

[0808] A means of inputting information through a front-end device, and having a back-end artificial intelligence agent analyze the data to propose the optimal work structure and milestones,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, which receives client information and budget information along with initial project information, and generates documents and drafts based on them.

[0812] (Claim 3)

[0813] The system according to claim 1, which applies naming rules and saving rules to documents in the generated project folder to achieve consistent document management and enables quick access via smart devices.

[0814] "Example 2 of combining an emotion engine"

[0815] (Claim 1)

[0816] A means of obtaining initial project information and generating project documents based on that information,

[0817] A means of utilizing an emotion engine to analyze user input data and estimate emotional states,

[0818] A means of dynamically adjusting the content of project materials and templates according to the user's emotional state,

[0819] A means of providing advice to users by referring to a database and using historical knowledge and risk information,

[0820] A means to record and visualize the time-series changes in users' emotions,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, which receives client information and budget information along with initial project information, and generates documents and drafts based on them.

[0824] (Claim 3)

[0825] The system according to claim 1, which applies unified rules to the information in the generated project documents and achieves consistent information management.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] A means for recognizing the user's emotional state and generating project kickoff materials based on emotional data,

[0829] A means to automatically generate a draft of a work breakdown structure that takes into account emotional states by referring to past project data,

[0830] A means of generating a folder structure corresponding to emotions and saving related data,

[0831] A means of providing emotion-adaptive recommendation templates by utilizing a knowledge database of past projects,

[0832] A means of presenting potential risks based on user emotions during project setup,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which receives client information and budget information along with the user's emotional state, and generates emotionally adaptive materials and drafts based on them.

[0836] (Claim 3)

[0837] The system according to claim 1, which applies emotion-adaptive naming rules and saving rules to the data in the generated folders to achieve consistent management. [Explanation of symbols]

[0838] 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 of receiving initial project information and generating project kickoff materials based on that information, A means to automatically generate a draft of the work breakdown structure by referring to the project's past data, A means of generating project folders based on default naming rules and folder structure, and saving related documents, A means of providing recommended templates by utilizing a knowledge database of past projects, A means of presenting potential risks when setting up a project, A system that includes this.

2. The system according to claim 1, which receives client information and budget information along with initial project information, and generates documents and drafts based on them.

3. The system according to claim 1, which applies naming rules and saving rules to documents in the generated project folder to achieve consistent document management.

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