system

Abstract

We provide the system. [Solution] A device for entering project information, Information processing device means for analyzing the input case information, A means for extracting necessary processing tasks by referring to a collection of past case data based on the analyzed information, A means for automatically generating a work breakdown structure based on the extracted processing work, A means for automatically generating necessary instruction documents based on the work breakdown structure, A means for notifying relevant parties of the automatically generated work disassembly structure and instruction documents via a data communication device, 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional project management, it is difficult to manage standardized tasks and documents for cases that require special handling, and there is a high possibility of omissions and entry errors for each case. In addition, since the number of experienced personnel involved in the project is limited, there is a problem that knowledge becomes personalized and the burden on new staff increases. For this reason, means for effectively and efficiently generating the tasks and related documents required for a case and improving the quality and efficiency in project operation have been demanded.

Means for Solving the Problems

[0005] This invention comprises a terminal means for inputting information on each case, and a server means for analyzing the input information, referring to a database of past cases, and extracting necessary tasks. Furthermore, it automatically generates a work breakdown structure based on the extracted tasks, and automatically generates the necessary documents based on that structure, thereby preventing missed tasks and input errors. The automatically generated information can also be reviewed and fine-tuned by the user, and the final data is saved in the project folder, and stakeholders are notified. In this way, it is possible to improve the efficiency and quality of project management and reduce the burden on personnel involved in handling the cases.

[0006] "Project information" refers to information that includes basic data and descriptions related to a specific project or task.

[0007] "Terminal means" refers to a device or software used by a user to input information and interface with a system.

[0008] "Server means" refers to a server that has the functionality to receive input data and perform analysis and database access.

[0009] A "case study database" refers to a database that stores information about past projects and cases.

[0010] A "task" refers to a specific job or activity required to complete a project.

[0011] A "task breakdown structure" refers to a plan that details the tasks necessary to accomplish a project in a hierarchical structure.

[0012] "Document" refers to formal documents such as application forms and reports created based on procedures and specifications.

[0013] "Automatic generation" refers to the process by which a system generates necessary data and documents without human intervention.

[0014] "Related parties" refer to people and departments directly involved in a project or business.

[0015] "Storage" refers to the process of retaining generated data and documents in internal or external storage devices of the system.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. <9000070>It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] ]>It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] ]>It shows an emotion map to which multiple emotions are mapped. <000?085> [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined.​ [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] An embodiment of the present invention is a system for streamlining case management, which mainly consists of terminals, servers, and databases.

[0038] First, the user uses a terminal to input specific project information. This project information includes basic project details, any required special actions, and deadlines. The terminal sends the entered information to the server, and the server verifies the data's integrity at that time.

[0039] The server analyzes the received case information and refers to a database of past cases. Here, the server interprets the case information using natural language processing techniques and identifies similar past cases. During this process, necessary tasks are extracted. These tasks include specific steps and activities required to complete the project.

[0040] The server generates a Work Breakdown Structure (WBS) based on the extracted tasks. This WBS is automatically created, taking into account the order, dependencies, and priorities of the tasks. Furthermore, the server uses this information to automatically generate the necessary documents. These documents include application forms, procedures, reports, etc., covering all documents essential for the progress of the project.

[0041] Users can review the WBS and documents generated via the terminal and make adjustments as needed. Once adjustments are complete, the terminal saves the final data to the project folder and notifies stakeholders. This notification feature facilitates smooth information sharing within the project team.

[0042] For example, in a new product launch project, when a user inputs new product specifications and market information, the server references past launch examples of similar products and generates tasks based on necessary marketing strategies and technical requirements. This allows the project team to plan efficiently and proceed with their work smoothly.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user enters project information using a terminal. They enter detailed information such as the project name, overview, any necessary special actions, and the deadline into a form.

[0046] Step 2:

[0047] The terminal sends the entered case information to the server. The transmitted data is verified for integrity and sent in a standard format (e.g., JSON or XML).

[0048] Step 3:

[0049] The server analyzes the case information it receives. It interprets the input information using natural language processing techniques to identify standard tasks and specific requirements.

[0050] Step 4:

[0051] The server references a database of past cases to search for similar cases. It retrieves the information necessary to extract required tasks from successful and unsuccessful case studies.

[0052] Step 5:

[0053] The server lists all the tasks required for the project. The generating AI proposes the optimal set of tasks, adding detailed information about the person responsible and the resources required for each task.

[0054] Step 6:

[0055] The server automatically generates a Work Breakdown Structure (WBS) based on the tasks. It evaluates task dependencies, adjusts the schedule, and sets start and end dates and priorities.

[0056] Step 7:

[0057] The server automatically generates the necessary documents for each project. Using templates, it automatically fills in the required fields for application forms, specifications, etc., and outputs them in PDF or Word format.

[0058] Step 8:

[0059] The user reviews the WBS and documents generated via their terminal. After reviewing, they can make minor adjustments to the task order and document content as needed.

[0060] Step 9:

[0061] The terminal saves the final WBS and documents to the project folder. The saved data is notified to project stakeholders, and information necessary for project progress is shared.

[0062] (Example 1)

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

[0064] In project management, it was difficult to plan projects and create documentation quickly and efficiently. In particular, comparing with past cases and identifying necessary tasks required considerable effort and time, and a system was needed to ensure smooth progress while maintaining information consistency. Furthermore, the lack of effective means to share generated documents with stakeholders was a factor causing communication delays.

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

[0066] In this invention, the server includes means for analyzing input case information, means for referencing past data records and extracting necessary tasks, and means for automatically generating a work structure based on the extracted tasks. This enables efficient project planning and automatic creation of documents.

[0067] An "information processing device" refers to a terminal or electronic device used by users to input information related to a case.

[0068] A "data processing device" refers to a device that analyzes input information and performs appropriate processing based on that information.

[0069] "Data records" refer to a database containing information about past cases and examples.

[0070] "Work structure" refers to a structure that organizes the sequence and dependencies of tasks necessary for the execution of a project.

[0071] "Documents" refers to automatically generated documents, reports, and other materials necessary for carrying out the project.

[0072] "Natural language processing technology" refers to the technology that analyzes text data in a way that humans can understand and extracts meaning from it.

[0073] "Electronic documents" refer to documents or papers generated in digital format.

[0074] A "data repository" refers to a digital folder or storage device where the final data and information are stored.

[0075] "Operator" refers to a user or person in charge who manages cases through the system.

[0076] "Stakeholders" refers to all individuals and teams involved in the project, and those who will receive information from it.

[0077] This invention aims to streamline project management using a case management system that leverages information processing technology. It primarily consists of terminals, servers, and a database.

[0078] First, the user uses a terminal to enter basic project information. This information includes the project name, required tasks, and deadline. The terminal verifies the integrity of the input data and sends it to the server. This terminal can be a standard computer system or a tablet device.

[0079] The server is responsible for analyzing the received case information. This analysis utilizes natural language processing techniques, specifically libraries such as Python's NLTK and SpaCy. The server searches past data records in the database to identify similar past cases. This extracts the necessary tasks and clarifies the next steps to take.

[0080] The server generates a work structure based on the extracted tasks. This process utilizes project management software (e.g., Microsoft® Project) to automatically organize task order and dependencies. It also automatically generates necessary project documents, such as application forms, procedures, and reports. Electronic document templates are used for this document generation.

[0081] Users can review the work structure and documents generated from their devices and make adjustments as needed. Once the adjustments are complete, the data is saved to a data repository and then notified to relevant parties. Notifications are automatically sent via email and chat applications, facilitating smooth information sharing.

[0082] As a concrete example, in a new product launch project, when a user inputs specifications and market information into a terminal, the server can generate tasks such as conducting market research and preparing advertising campaigns based on past examples of similar products.

[0083] To maximize the system's potential, you can input a prompt to the generating AI model such as, "Refer to past success stories in new product launch projects and create a Work Breakdown Structure (WBS) that includes the necessary marketing tasks and technical requirements."

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user uses a terminal to enter basic project information (e.g., project name, requirements, deadline). The entered information is collected in form format, and a preliminary data integrity check is performed on the terminal. This check verifies that all required fields are filled in and that the date format is correct. Once the integrity is confirmed, the information is sent directly to the server.

[0087] Step 2:

[0088] The server analyzes the case information received from the terminal. This analysis process takes the input information as string data and uses natural language processing techniques to understand its content. Specifically, it tokenizes the information using Python's NLTK library and extracts keywords and important context. The analysis results output potential requirements related to the project.

[0089] Step 3:

[0090] Based on the analysis results, the server searches the database for past data records. It executes database queries to retrieve similar past case data. The data extracted from past cases is then analyzed to generate a task list necessary for the current project. This analysis result is output as a guideline for the specific tasks to be performed next.

[0091] Step 4:

[0092] The server automatically generates a work structure based on the retrieved task list. Specifically, it uses project management software (e.g., a project management tool) to define the order and dependencies of tasks and outputs them in Gantt chart format. This automatically generated work structure is exported as Excel or PDF and provided to the user in an intuitive format.

[0093] Step 5:

[0094] The server automatically generates the necessary documents based on the work structure. In this step, items are embedded in electronic document templates to create application forms and reports required for project progress. The documents, output in Word format, are sent to the user immediately in a usable state.

[0095] Step 6:

[0096] Users can review the work structure and documents generated via their terminal and make adjustments as needed. Through an intuitive GUI, users can add, reorder, and supplement document content. Once adjustments are complete, they can use the save function to proceed to the next step.

[0097] Step 7:

[0098] The terminal saves the final adjusted data to a data repository and automatically notifies stakeholders once the process is complete. Notification methods include email and collaboration tools, with links provided for immediate access by stakeholders. This enables rapid information sharing within the project team.

[0099] (Application Example 1)

[0100] 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."

[0101] Project management in the construction industry involves a complex interplay of multiple processes, making it difficult to track progress in real time. Furthermore, manually extracting optimal tasks by referencing similar past project examples is time-consuming, hindering efficient project management. Additionally, delays in on-site decision-making and work can occur if work orders are not created and stakeholders are notified promptly. These issues can potentially impact the overall progress of the project.

[0102] 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.

[0103] In this invention, the server includes an information processing device for analyzing case information, a means for referencing a collection of past case data and extracting necessary processing tasks, and a means for automatically generating a work breakdown structure and instruction documents and reporting the status in real time. This enables more efficient project management, faster decision-making, and timely information sharing among stakeholders.

[0104] "Project information" refers to data such as basic information, requirements, and deadlines necessary for the progress of a project.

[0105] "Equipment means" refers to physical or software devices used by users to input case information.

[0106] "Information processing device" refers to a computer system that analyzes input case information and performs necessary data manipulation.

[0107] A "case study data collection" refers to a database that stores data from projects that have been carried out in the past.

[0108] "Processing tasks" refer to the individual tasks and actions necessary for the successful completion of a project.

[0109] A "work breakdown structure" refers to a systematic plan that visualizes the sequence and dependencies of tasks necessary to complete a project.

[0110] "Instruction documents" refer to documents that include procedures and reports necessary for the execution of a project.

[0111] A "data communication device" refers to a network-connected device used to send and receive information between users.

[0112] "Data storage area" refers to the memory area used to store automatically generated information.

[0113] This invention describes embodiments for carrying out this application. The system for realizing this application provides an integrated solution for streamlining project management. The system inputs case information through the user's equipment and transmits it to a server. The server analyzes the input case information using information processing equipment, searches for similar past projects from a case data set, and extracts the necessary processing tasks. Based on this, it automatically generates a work breakdown structure and instruction documents.

[0114] In terms of program structure, React Native is used for the frontend to provide an intuitive user interface. Node.js and the Express framework are used for the backend to achieve efficient data processing and server communication. MongoDB is utilized for the database to build reliable data storage. Furthermore, Python's NLTK and spaCy are used for natural language processing to effectively analyze past cases and extract tasks.

[0115] The system uses a generative AI model to extract optimal tasks from project information and past case studies, and then creates a work plan based on those tasks. Users receive real-time work instructions and report progress via smartphones or PCs. For example, in a high-rise building construction project, the following prompt message is input to the AI ​​model during the planning phase: "Refer to past project examples similar to this building construction plan and automatically extract the necessary tasks." This improves the overall efficiency of the project and enables smoother collaboration among stakeholders.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user enters project information into the terminal. This includes basic project information, requirements, and deadlines. The terminal collects this data and verifies its integrity. Once verification is complete, it sends this data to the server.

[0119] Step 2:

[0120] The server analyzes the received case information using an information processing device. The analysis uses natural language processing techniques to understand the text data and extract its meaning. The input data consists of case information, and the output includes keywords and requirements related to the target project.

[0121] Step 3:

[0122] The server uses the analysis results to search the case data set and identify similar past projects. A generative AI model is used in this process. It executes database queries, extracts highly similar cases, and identifies the necessary processing tasks. The output is a list of the relevant processing tasks.

[0123] Step 4:

[0124] The server automatically generates a work breakdown structure based on the extracted processing tasks. This process organizes the order and dependencies of the processing tasks and develops a structured plan. The input is a list of processing tasks, and the output is a work breakdown structure, which is a visualized form of the plan.

[0125] Step 5:

[0126] The server automatically generates a work breakdown structure and related documents. These documents include procedures and reports. Based on the plan, it documents specific details and uses templates to create further details as needed. The output is a set of instructional documents necessary for project execution.

[0127] Step 6:

[0128] The server stores the generated data in a data storage area and uses the generated AI model to provide real-time status reports. The stored data is then communicated to stakeholders via data communication devices. The purpose of this step is to quickly share the project's progress. Users receive the notifications and take action as needed.

[0129] 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.

[0130] One embodiment of the present invention is to combine an emotion engine with a project management system to realize project management that takes into account the emotional state of the user at the time of input. The main components are a terminal, a server, a database, and an emotion engine.

[0131] Users input case information using a terminal. During this process, an emotion engine built into the terminal recognizes and analyzes the user's emotional state in real time to determine the emotional state under which the user is inputting the information. The emotion engine collects emotional data using facial recognition technology and voice tone analysis.

[0132] The server receives emotional data along with case information sent from the terminal and performs analysis. Based on the analysis results, it extracts necessary tasks by referring to a database of past cases and automatically generates a Work Breakdown Structure (WBS). Here, emotional data can influence task priorities, and the user's emotional state is also used in project risk assessment.

[0133] Furthermore, while the server creates the necessary documents based on the automatically generated WBS, it can also reflect the appropriate tone and expression in the document content according to the user's emotional state. For example, if the user is feeling stressed, the document will adopt more friendly language.

[0134] The generated WBS and documents are returned to the terminal for user review. Users can make adjustments as needed. Furthermore, the final data is saved to the project folder, and stakeholders are notified. At this time, the sentiment information identified by the sentiment engine is used as an important decision-making criterion in project management.

[0135] For example, in an emergency release project for a new product, if a user enters information while feeling anxious or stressed, the system can detect this and automatically review the priority of the corresponding tasks to support stable project progress.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user enters project information using a terminal. The input form includes the project name, summary, special notes, and deadline.

[0139] Step 2:

[0140] The device analyzes the user's facial expressions and voice while they are typing, and generates emotional data using an emotion engine. Specifically, it utilizes the camera and microphone.

[0141] Step 3:

[0142] The terminal sends the entered case information and sentiment data to the server. After the necessary integrity checks are performed, the data is sent in a standard format.

[0143] Step 4:

[0144] The server receives the incoming data and analyzes the case information. This analysis includes a process that uses natural language processing to understand the input content and identify the requirements for special handling.

[0145] Step 5:

[0146] The server uses sentiment data to reference a database of past cases and search for similar cases. It extracts necessary tasks and dynamically adjusts task priorities based on sentiment data.

[0147] Step 6:

[0148] The server automatically generates a Work Breakdown Structure (WBS) based on the extracted tasks. This structure takes into account the user's emotional state and sets schedules and priorities accordingly.

[0149] Step 7:

[0150] The server automatically generates the necessary documents. These documents contain the required information based on the generated WBS, and the tone and expression are adjusted according to the user's emotional state.

[0151] Step 8:

[0152] Users can review the generated WBS and documents via their terminal. After reviewing, they can fine-tune the order and content of tasks as needed.

[0153] Step 9:

[0154] The terminal saves the final WBS and documents to the project folder and notifies stakeholders. The notification includes the results of sentiment data analysis, which may be used to set agendas for meetings and conferences.

[0155] (Example 2)

[0156] 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".

[0157] In project management, there is a challenge in flexibly prioritizing tasks and creating documentation that takes into account the emotional state of users. Furthermore, there is a need to improve the current situation where the impact of user emotions on the overall progress of the project is not being considered.

[0158] 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.

[0159] In this invention, the server includes emotion analysis means for collecting and analyzing case information and user emotion data, means for referencing past case memory storage and extracting necessary activities based on the analyzed information, and means for adjusting the tone and expression of automatically generated documents according to the user's emotional state. This enables project management that reflects the user's emotional state.

[0160] "Information input means" refers to a device or interface for a user to input case information.

[0161] "Emotion analysis tools" are technologies that collect and analyze emotional data from a user's facial recognition and voice tone.

[0162] A "case memory device" is a database or storage system that stores past cases and related information.

[0163] An "activity extraction method" is a mechanism that identifies and extracts necessary project tasks based on the analysis results.

[0164] A "task decomposition structure" is a structure that hierarchically breaks down and organizes the tasks of a project.

[0165] "Automatic document generation means" refers to a function that automatically creates necessary documents based on a work breakdown structure.

[0166] "Tone adjustment techniques" refer to technologies that change the way a document is expressed and its style according to the user's emotional state.

[0167] A "project memory device" is a storage system used to save final data and documents related to a project.

[0168] This invention provides a system for managing cases while taking into account the user's emotional state. This system consists of an information input means, an emotion analysis means, an activity extraction means, a work breakdown structure creation means, an automatic document generation means, and a tone adjustment means.

[0169] When a user enters project information using a terminal, an information input method is used. During this process, the terminal collects and analyzes the user's emotional data in real time using emotion analysis technology. Specifically, it utilizes facial recognition technology using a camera and voice tone analysis using a microphone.

[0170] The server passes case information and sentiment data sent from the terminal to the sentiment analysis device and uses a case memory device to refer to similar past cases. Based on the analyzed data, the server extracts the necessary tasks using the activity extraction device and automatically generates a work breakdown structure.

[0171] Furthermore, the server generates documents based on the generated work breakdown structure and adjusts the tone and expression of the documents using tone adjustment means according to the user's emotional state. For users who are feeling stressed, the document can adopt more approachable language.

[0172] The generated work breakdown structure and documentation are stored in the project's memory and sent to relevant stakeholders as needed. This entire process effectively utilizes user sentiment in setting project risks and priorities.

[0173] To give a specific example, in a new product release project, if a user enters information while feeling anxious, the system can sense that emotion and support the smooth progress of the project by readjusting the task priorities.

[0174] An example of a prompt to be input into a generating AI model is: "Explain a method that uses an emotion engine to analyze the emotional state of a user when they input case information into a project management system, and optimizes task priorities."

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user enters project information using a terminal. This information includes the project name, deadline, and summary. The terminal then prepares to send the entered text information directly to the next step.

[0178] Step 2:

[0179] The device collects user emotional data using emotion analysis techniques. This involves recording and analyzing facial expressions and voice tone using a camera and microphone. Inputs include image and audio data, which are then analyzed to output numerical data representing the emotional state.

[0180] Step 3:

[0181] Case information and sentiment data are sent from the terminal to the server. The input here consists of the text information and sentiment numerical data collected in the previous step, which the server receives and prepares for the next process.

[0182] Step 4:

[0183] Based on the received data, the server references the case memory database to extract past case information. The server compares past and current case information, identifies highly similar cases, and obtains the corresponding task information as output.

[0184] Step 5:

[0185] The server automatically generates a Work Breakdown Structure (WBS) based on the task information obtained in the previous step. Here, the input is task information, and the output is a hierarchical list of work breakdown structures.

[0186] Step 6:

[0187] Based on the generated work breakdown structure, the server automatically generates the necessary documents. Inputs include WBS information and sentiment data. The tone and expression of the documents are adjusted according to the sentiment data, resulting in output documents that are more contextually appropriate.

[0188] Step 7:

[0189] Finally, the server sends the generated document and WBS to the terminal. The terminal receives this and displays it to the user. The output here consists of the document and WBS for review, which the user can review and adjust as needed.

[0190] (Application Example 2)

[0191] 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".

[0192] In modern life, users are burdened with a diverse range of tasks, including work, household chores, and other responsibilities, making efficiency improvements and stress reduction crucial. Furthermore, there is a need for systems that respond more flexibly to users' emotional states. However, current systems fail to adequately sense changes in user emotions in real time and adjust priorities or suggest tasks accordingly. Therefore, there is a need for automated systems that adapt to emotions.

[0193] 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.

[0194] In this invention, the server includes means for analyzing case information and emotional state information, means for extracting necessary work items by referring to past case storage means, and automated emotion sensing means for determining the priority of household support tasks using emotional data. This enables automatic suggestion of high-priority tasks according to the user's emotional state and stress reduction through emotionally adaptive document generation.

[0195] A "terminal device" is a device used by users to input case information and has the function of sensing emotional states.

[0196] A "server device" is a device that analyzes case information and emotional state information transmitted from a terminal and performs data processing based on the extracted information.

[0197] A "means for accumulating past case data" refers to a storage device that stores case data collected in the past and allows for reference as needed.

[0198] The "work element decomposition structure" is a representation of the work details and hierarchical structure, automatically generated based on the extracted work items.

[0199] "Means of automatically generating documents in an emotionally adaptive manner" refers to a device that automatically generates documents considering emotional data and reflects the tone and expression that matches the user's emotional state.

[0200] "Emotion sensing automation means" refers to a device and system that uses user emotion data to determine which household support tasks should be prioritized.

[0201] The "project storage area" is a data storage area for saving the generated work element breakdown structure and documents, making them accessible to stakeholders.

[0202] The system for realizing this invention consists mainly of a terminal, a server, a means for storing past cases, and an automated emotion sensing means. First, the terminal receives user input and senses the user's emotional state in real time using facial recognition technology and voice tone analysis. This emotion data is transmitted to the server along with other input data.

[0203] The server analyzes the received data and extracts necessary work items while referring to past case storage systems. This process generates a newly proposed work element decomposition structure. Furthermore, a generative AI model is used to adjust the tone and expression of documents in order to generate user-adapted documents based on emotional data.

[0204] For example, if the system detects that a user is feeling stressed during a busy period, it will automatically determine which household chore assistance tasks should be prioritized and notify the user as a reminder. This notification may include suggestions for playing music to help reduce stress.

[0205] An example of a prompt might be: "A family member appears to be stressed. Please suggest tasks that should be prioritized and provide assistance to help them relax." Based on this prompt, the system automatically organizes and provides the most appropriate support.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The terminal receives user input. The terminal uses a camera to scan the user's face and a microphone to capture audio. This allows for facial recognition and voice tone analysis, collecting data on the user's emotional state. Input data includes case information and emotional state. This data is then sent to the server.

[0209] Step 2:

[0210] The server analyzes the received case information and emotional state data. This analysis includes quantifying facial recognition data and extracting specific patterns from audio waveforms. This allows for the quantification of the type and intensity of emotions. The analysis results are then sent to a system for storing past cases.

[0211] Step 3:

[0212] The server queries a database of past cases based on the analysis results to search for similar past cases. An information retrieval algorithm is used to extract countermeasures for similar situations. The relevant data is used as a reference when setting the priority of the proposed work items.

[0213] Step 4:

[0214] The server automatically generates a work element decomposition structure based on comparisons with past cases. Here, it generates a list of related tasks and determines their priority. This process uses data obtained from sentiment analysis to arrange the tasks in an order that is feasible for the user.

[0215] Step 5:

[0216] The server uses a generative AI model to create user-adapted documents based on the generated work element decomposition structure. During document creation, it leverages emotional data to adjust tone and expression, providing the document in the format most readily accepted by the user.

[0217] Step 6:

[0218] The server sends the final generated document and work structure back to the terminal. The user can review this and make adjustments as needed. The output data is stored in the project storage area and notified to the relevant parties.

[0219] Step 7:

[0220] After the user has reviewed and adjusted their settings, an automated emotion-sensing system is used to determine which household assistance tasks should be prioritized, especially if the user is experiencing stress. Tasks are then sent to the user in the form of automated suggestions.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] An embodiment of the present invention is a system for streamlining case management, which mainly consists of terminals, servers, and databases.

[0238] First, the user uses a terminal to input specific project information. This project information includes basic project details, any required special actions, and deadlines. The terminal sends the entered information to the server, and the server verifies the data's integrity at that time.

[0239] The server analyzes the received case information and refers to a database of past cases. Here, the server interprets the case information using natural language processing techniques and identifies similar past cases. During this process, necessary tasks are extracted. These tasks include specific steps and activities required to complete the project.

[0240] The server generates a Work Breakdown Structure (WBS) based on the extracted tasks. This WBS is automatically created, taking into account the order, dependencies, and priorities of the tasks. Furthermore, the server uses this information to automatically generate the necessary documents. These documents include application forms, procedures, reports, etc., covering all documents essential for the progress of the project.

[0241] Users can review the WBS and documents generated via the terminal and make adjustments as needed. Once adjustments are complete, the terminal saves the final data to the project folder and notifies stakeholders. This notification feature facilitates smooth information sharing within the project team.

[0242] For example, in a new product launch project, when a user inputs new product specifications and market information, the server references past launch examples of similar products and generates tasks based on necessary marketing strategies and technical requirements. This allows the project team to plan efficiently and proceed with their work smoothly.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user enters project information using a terminal. They enter detailed information such as the project name, overview, any necessary special actions, and the deadline into a form.

[0246] Step 2:

[0247] The terminal sends the entered case information to the server. The transmitted data is verified for integrity and sent in a standard format (e.g., JSON or XML).

[0248] Step 3:

[0249] The server analyzes the case information it receives. It interprets the input information using natural language processing techniques to identify standard tasks and specific requirements.

[0250] Step 4:

[0251] The server references a database of past cases to search for similar cases. It retrieves the information necessary to extract required tasks from successful and unsuccessful case studies.

[0252] Step 5:

[0253] The server lists all the tasks required for the project. The generating AI proposes the optimal set of tasks, adding detailed information about the person responsible and the resources required for each task.

[0254] Step 6:

[0255] The server automatically generates a Work Breakdown Structure (WBS) based on the tasks. It evaluates task dependencies, adjusts the schedule, and sets start and end dates and priorities.

[0256] Step 7:

[0257] The server automatically generates the necessary documents for each project. Using templates, it automatically fills in the required fields for application forms, specifications, etc., and outputs them in PDF or Word format.

[0258] Step 8:

[0259] The user reviews the WBS and documents generated via their terminal. After reviewing, they can make minor adjustments to the task order and document content as needed.

[0260] Step 9:

[0261] The terminal saves the final WBS and documents to the project folder. The saved data is notified to project stakeholders, and information necessary for project progress is shared.

[0262] (Example 1)

[0263] 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."

[0264] In project management, it was difficult to plan projects and create documentation quickly and efficiently. In particular, comparing with past cases and identifying necessary tasks required considerable effort and time, and a system was needed to ensure smooth progress while maintaining information consistency. Furthermore, the lack of effective means to share generated documents with stakeholders was a factor causing communication delays.

[0265] 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.

[0266] In this invention, the server includes means for analyzing input case information, means for referencing past data records and extracting necessary tasks, and means for automatically generating a work structure based on the extracted tasks. This enables efficient project planning and automatic creation of documents.

[0267] An "information processing device" refers to a terminal or electronic device used by users to input information related to a case.

[0268] A "data processing device" refers to a device that analyzes input information and performs appropriate processing based on that information.

[0269] "Data records" refer to a database containing information about past cases and examples.

[0270] "Work structure" refers to a structure that organizes the sequence and dependencies of tasks necessary for the execution of a project.

[0271] "Documents" refers to automatically generated documents, reports, and other materials necessary for carrying out the project.

[0272] "Natural language processing technology" refers to the technology that analyzes text data in a way that humans can understand and extracts meaning from it.

[0273] "Electronic documents" refer to documents or papers generated in digital format.

[0274] A "data repository" refers to a digital folder or storage device where the final data and information are stored.

[0275] "Operator" refers to a user or person in charge who manages cases through the system.

[0276] "Stakeholders" refers to all individuals and teams involved in the project, and those who will receive information from it.

[0277] This invention aims to streamline project management using a case management system that leverages information processing technology. It primarily consists of terminals, servers, and a database.

[0278] First, the user uses a terminal to enter basic project information. This information includes the project name, required tasks, and deadline. The terminal verifies the integrity of the input data and sends it to the server. This terminal can be a standard computer system or a tablet device.

[0279] The server is responsible for analyzing the received case information. For this analysis, natural language processing technology is used, specifically libraries such as NLTK and SpaCy in Python. The server searches the database for past data records to identify similar past cases. This extracts the necessary tasks and clarifies the steps to proceed.

[0280] The server generates a work structure based on the extracted tasks. In this process, project management software (e.g., Microsoft Project) is used, and the order and dependencies of tasks are automatically organized. Also, documents necessary for project execution, such as application forms, procedure manuals, and reports, are automatically generated. Electronic document templates are used for this document generation.

[0281] The user can view the generated work structure and documents from the terminal and make fine-tuning as needed. The adjusted data is saved in the data aggregate and then notified to the relevant parties. The notification is automatically sent via email or chat application, enabling smooth sharing of information.

[0282] As a specific example, in a new product launch project, when the user inputs specifications and market information into the terminal, the server can generate tasks such as conducting market research and preparing an advertising campaign based on past cases of similar products.

[0283] As an example of the prompt text for the generation AI model, by inputting "Please create a WBS including necessary marketing tasks and technical requirements by referring to past successful cases in the new product launch project.", the power of this system can be maximally utilized.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user inputs the basic information of the case (e.g., project name, requirements, delivery date) using the terminal. The input information is collected in form format, and preliminary data integrity checks are performed within the terminal. This check is to confirm whether all required fields are filled in and whether the date format is correct. The information with confirmed integrity is directly sent to the server.

[0287] Step 2:

[0288] The server analyzes the case information received from the terminal. In this analysis process, the input information is received as string data, and natural language processing technology is used to understand the content. Specifically, the NLTK library in Python is used to tokenize the information and extract keywords and important contexts. As the analysis result, potential requirements related to the project are output.

[0289] Step 3:

[0290] Based on the analysis result, the server searches the past data records from the database. Here, a database query is executed to obtain similar past case data. The data extracted from past cases is analyzed to generate a task list required for the current project. This analysis result is output as a guideline for the specific work to proceed next.

[0291] Step 4:

[0292] The server automatically generates a work structure based on the obtained task list. Specifically, project management software (e.g., project management tools) is used to define the order and dependencies of tasks and output them in Gantt chart format. This automatically generated work structure is exported as Excel or PDF and provided to the user in an intuitive format.

[0293] Step 5:

[0294] The server automatically generates the necessary documents based on the work structure. In this step, items are embedded in electronic document templates to create application forms and reports required for project progress. The documents, output in Word format, are sent to the user immediately in a usable state.

[0295] Step 6:

[0296] Users can review the work structure and documents generated via their terminal and make adjustments as needed. Through an intuitive GUI, users can add, reorder, and supplement document content. Once adjustments are complete, they can use the save function to proceed to the next step.

[0297] Step 7:

[0298] The terminal saves the final adjusted data to a data repository and automatically notifies stakeholders once the process is complete. Notification methods include email and collaboration tools, with links provided for immediate access by stakeholders. This enables rapid information sharing within the project team.

[0299] (Application Example 1)

[0300] 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."

[0301] Project management in the construction industry involves a complex interplay of multiple processes, making it difficult to track progress in real time. Furthermore, manually extracting optimal tasks by referencing similar past project examples is time-consuming, hindering efficient project management. Additionally, delays in on-site decision-making and work can occur if work orders are not created and stakeholders are notified promptly. These issues can potentially impact the overall progress of the project.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes means for analyzing project information as an information processing device, means for referring to a past case data aggregate to extract necessary processing operations, and means for automatically generating a work breakdown structure and instruction documents and reporting the situation in real time. This enables the improvement of project management efficiency, rapid decision-making, and timely information sharing among relevant parties.

[0304] "Project information" refers to data such as basic information, requirements, and delivery dates necessary for the progress of a project.

[0305] "Device means" refers to a physical or software device for a user to input project information.

[0306] "Information processing device means" refers to a computer system that analyzes the input project information and performs necessary data operations.

[0307] "Case data aggregate" refers to a database that accumulates data of past projects.

[0308] "Processing operation" refers to individual tasks and actions necessary for the execution of a project.

[0309] "Work breakdown structure" refers to a systematic plan that visualizes the order and dependencies of tasks necessary to complete a project.

[0310] "Instruction documents" refer to documents including procedure manuals and reports necessary for the execution of a project.

[0311] "Data communication device" refers to a network-connected device for transmitting and receiving information between users.

[0312] "Data storage area" refers to a storage area for storing automatically generated information.

[0313] This invention describes embodiments for carrying out this application. The system for realizing this application provides an integrated solution for streamlining project management. The system inputs case information through the user's equipment and transmits it to a server. The server analyzes the input case information using information processing equipment, searches for similar past projects from a case data set, and extracts the necessary processing tasks. Based on this, it automatically generates a work breakdown structure and instruction documents.

[0314] In terms of program structure, React Native is used for the frontend to provide an intuitive user interface. Node.js and the Express framework are used for the backend to achieve efficient data processing and server communication. MongoDB is utilized for the database to build reliable data storage. Furthermore, Python's NLTK and spaCy are used for natural language processing to effectively analyze past cases and extract tasks.

[0315] The system uses a generative AI model to extract optimal tasks from project information and past case studies, and then creates a work plan based on those tasks. Users receive real-time work instructions and report progress via smartphones or PCs. For example, in a high-rise building construction project, the following prompt message is input to the AI ​​model during the planning phase: "Refer to past project examples similar to this building construction plan and automatically extract the necessary tasks." This improves the overall efficiency of the project and enables smoother collaboration among stakeholders.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The user enters project information into the terminal. This includes basic project information, requirements, and deadlines. The terminal collects this data and verifies its integrity. Once verification is complete, it sends this data to the server.

[0319] Step 2:

[0320] The server analyzes the received case information using an information processing device. The analysis uses natural language processing techniques to understand the text data and extract its meaning. The input data consists of case information, and the output includes keywords and requirements related to the target project.

[0321] Step 3:

[0322] The server uses the analysis results to search the case data set and identify similar past projects. A generative AI model is used in this process. It executes database queries, extracts highly similar cases, and identifies the necessary processing tasks. The output is a list of the relevant processing tasks.

[0323] Step 4:

[0324] The server automatically generates a work breakdown structure based on the extracted processing tasks. This process organizes the order and dependencies of the processing tasks and develops a structured plan. The input is a list of processing tasks, and the output is a work breakdown structure, which is a visualized form of the plan.

[0325] Step 5:

[0326] The server automatically generates a work breakdown structure and related documents. These documents include procedures and reports. Based on the plan, it documents specific details and uses templates to create further details as needed. The output is a set of instructional documents necessary for project execution.

[0327] Step 6:

[0328] The server stores the generated data in a data storage area and uses the generated AI model to provide real-time status reports. The stored data is then communicated to stakeholders via data communication devices. The purpose of this step is to quickly share the project's progress. Users receive the notifications and take action as needed.

[0329] 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.

[0330] One embodiment of the present invention is to combine an emotion engine with a project management system to realize project management that takes into account the emotional state of the user at the time of input. The main components are a terminal, a server, a database, and an emotion engine.

[0331] Users input case information using a terminal. During this process, an emotion engine built into the terminal recognizes and analyzes the user's emotional state in real time to determine the emotional state under which the user is inputting the information. The emotion engine collects emotional data using facial recognition technology and voice tone analysis.

[0332] The server receives emotional data along with case information sent from the terminal and performs analysis. Based on the analysis results, it extracts necessary tasks by referring to a database of past cases and automatically generates a Work Breakdown Structure (WBS). Here, emotional data can influence task priorities, and the user's emotional state is also used in project risk assessment.

[0333] Furthermore, while the server creates the necessary documents based on the automatically generated WBS, it can also reflect the appropriate tone and expression in the document content according to the user's emotional state. For example, if the user is feeling stressed, the document will adopt more friendly language.

[0334] The generated WBS and documents are returned to the terminal for user review. Users can make adjustments as needed. Furthermore, the final data is saved to the project folder, and stakeholders are notified. At this time, the sentiment information identified by the sentiment engine is used as an important decision-making criterion in project management.

[0335] For example, in an emergency release project for a new product, if a user enters information while feeling anxious or stressed, the system can detect this and automatically review the priority of the corresponding tasks to support stable project progress.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user enters project information using a terminal. The input form includes the project name, summary, special notes, and deadline.

[0339] Step 2:

[0340] The device analyzes the user's facial expressions and voice while they are typing, and generates emotional data using an emotion engine. Specifically, it utilizes the camera and microphone.

[0341] Step 3:

[0342] The terminal sends the entered case information and sentiment data to the server. After the necessary integrity checks are performed, the data is sent in a standard format.

[0343] Step 4:

[0344] The server receives the incoming data and analyzes the case information. This analysis includes a process that uses natural language processing to understand the input content and identify the requirements for special handling.

[0345] Step 5:

[0346] The server uses sentiment data to reference a database of past cases and search for similar cases. It extracts necessary tasks and dynamically adjusts task priorities based on sentiment data.

[0347] Step 6:

[0348] The server automatically generates a Work Breakdown Structure (WBS) based on the extracted tasks. This structure takes into account the user's emotional state and sets schedules and priorities accordingly.

[0349] Step 7:

[0350] The server automatically generates the necessary documents. These documents contain the required information based on the generated WBS, and the tone and expression are adjusted according to the user's emotional state.

[0351] Step 8:

[0352] Users can review the generated WBS and documents via their terminal. After reviewing, they can fine-tune the order and content of tasks as needed.

[0353] Step 9:

[0354] The terminal saves the final WBS and documents to the project folder and notifies stakeholders. The notification includes the results of sentiment data analysis, which may be used to set agendas for meetings and conferences.

[0355] (Example 2)

[0356] 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".

[0357] In project management, there is a challenge in flexibly prioritizing tasks and creating documentation that takes into account the emotional state of users. Furthermore, there is a need to improve the current situation where the impact of user emotions on the overall progress of the project is not being considered.

[0358] 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.

[0359] In this invention, the server includes emotion analysis means for collecting and analyzing case information and user emotion data, means for referencing past case memory storage and extracting necessary activities based on the analyzed information, and means for adjusting the tone and expression of automatically generated documents according to the user's emotional state. This enables project management that reflects the user's emotional state.

[0360] "Information input means" refers to a device or interface for a user to input case information.

[0361] "Emotion analysis tools" are technologies that collect and analyze emotional data from a user's facial recognition and voice tone.

[0362] A "case memory device" is a database or storage system that stores past cases and related information.

[0363] An "activity extraction method" is a mechanism that identifies and extracts necessary project tasks based on the analysis results.

[0364] A "task decomposition structure" is a structure that hierarchically breaks down and organizes the tasks of a project.

[0365] "Automatic document generation means" refers to a function that automatically creates necessary documents based on a work breakdown structure.

[0366] "Tone adjustment techniques" refer to technologies that change the way a document is expressed and its style according to the user's emotional state.

[0367] A "project memory device" is a storage system used to save final data and documents related to a project.

[0368] This invention provides a system for managing cases while taking into account the user's emotional state. This system consists of an information input means, an emotion analysis means, an activity extraction means, a work breakdown structure creation means, an automatic document generation means, and a tone adjustment means.

[0369] When a user enters project information using a terminal, an information input method is used. During this process, the terminal collects and analyzes the user's emotional data in real time using emotion analysis technology. Specifically, it utilizes facial recognition technology using a camera and voice tone analysis using a microphone.

[0370] The server passes case information and sentiment data sent from the terminal to the sentiment analysis device and uses a case memory device to refer to similar past cases. Based on the analyzed data, the server extracts the necessary tasks using the activity extraction device and automatically generates a work breakdown structure.

[0371] Furthermore, the server generates documents based on the generated work breakdown structure and adjusts the tone and expression of the documents using tone adjustment means according to the user's emotional state. For users who are feeling stressed, the document can adopt more approachable language.

[0372] The generated work breakdown structure and documentation are stored in the project's memory and sent to relevant stakeholders as needed. This entire process effectively utilizes user sentiment in setting project risks and priorities.

[0373] To give a specific example, in a new product release project, if a user enters information while feeling anxious, the system can sense that emotion and support the smooth progress of the project by readjusting the task priorities.

[0374] An example of a prompt to be input into a generating AI model is: "Explain a method that uses an emotion engine to analyze the emotional state of a user when they input case information into a project management system, and optimizes task priorities."

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The user enters project information using a terminal. This information includes the project name, deadline, and summary. The terminal then prepares to send the entered text information directly to the next step.

[0378] Step 2:

[0379] The device collects user emotional data using emotion analysis techniques. This involves recording and analyzing facial expressions and voice tone using a camera and microphone. Inputs include image and audio data, which are then analyzed to output numerical data representing the emotional state.

[0380] Step 3:

[0381] Case information and sentiment data are sent from the terminal to the server. The input here consists of the text information and sentiment numerical data collected in the previous step, which the server receives and prepares for the next process.

[0382] Step 4:

[0383] Based on the received data, the server references the case memory database to extract past case information. The server compares past and current case information, identifies highly similar cases, and obtains the corresponding task information as output.

[0384] Step 5:

[0385] The server automatically generates a Work Breakdown Structure (WBS) based on the task information obtained in the previous step. Here, the input is task information, and the output is a hierarchical list of work breakdown structures.

[0386] Step 6:

[0387] Based on the generated work breakdown structure, the server automatically generates the necessary documents. Inputs include WBS information and sentiment data. The tone and expression of the documents are adjusted according to the sentiment data, resulting in output documents that are more contextually appropriate.

[0388] Step 7:

[0389] Finally, the server sends the generated document and WBS to the terminal. The terminal receives this and displays it to the user. The output here consists of the document and WBS for review, which the user can review and adjust as needed.

[0390] (Application Example 2)

[0391] 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."

[0392] In modern life, users are burdened with a diverse range of tasks, including work, household chores, and other responsibilities, making efficiency improvements and stress reduction crucial. Furthermore, there is a need for systems that respond more flexibly to users' emotional states. However, current systems fail to adequately sense changes in user emotions in real time and adjust priorities or suggest tasks accordingly. Therefore, there is a need for automated systems that adapt to emotions.

[0393] 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.

[0394] In this invention, the server includes means for analyzing case information and emotional state information, means for extracting necessary work items by referring to past case storage means, and automated emotion sensing means for determining the priority of household support tasks using emotional data. This enables automatic suggestion of high-priority tasks according to the user's emotional state and stress reduction through emotionally adaptive document generation.

[0395] A "terminal device" is a device used by users to input case information and has the function of sensing emotional states.

[0396] A "server device" is a device that analyzes case information and emotional state information transmitted from a terminal and performs data processing based on the extracted information.

[0397] A "means for accumulating past case data" refers to a storage device that stores case data collected in the past and allows for reference as needed.

[0398] The "work element decomposition structure" is a representation of the work details and hierarchical structure, automatically generated based on the extracted work items.

[0399] "Means of automatically generating documents in an emotionally adaptive manner" refers to a device that automatically generates documents considering emotional data and reflects the tone and expression that matches the user's emotional state.

[0400] "Emotion sensing automation means" refers to a device and system that uses user emotion data to determine which household support tasks should be prioritized.

[0401] The "project storage area" is a data storage area for saving the generated work element breakdown structure and documents, making them accessible to stakeholders.

[0402] The system for realizing this invention consists mainly of a terminal, a server, a means for storing past cases, and an automated emotion sensing means. First, the terminal receives user input and senses the user's emotional state in real time using facial recognition technology and voice tone analysis. This emotion data is transmitted to the server along with other input data.

[0403] The server analyzes the received data and extracts necessary work items while referring to past case storage systems. This process generates a newly proposed work element decomposition structure. Furthermore, a generative AI model is used to adjust the tone and expression of documents in order to generate user-adapted documents based on emotional data.

[0404] For example, if the system detects that a user is feeling stressed during a busy period, it will automatically determine which household chore assistance tasks should be prioritized and notify the user as a reminder. This notification may include suggestions for playing music to help reduce stress.

[0405] An example of a prompt might be: "A family member appears to be stressed. Please suggest tasks that should be prioritized and provide assistance to help them relax." Based on this prompt, the system automatically organizes and provides the most appropriate support.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The terminal receives user input. The terminal uses a camera to scan the user's face and a microphone to capture audio. This allows for facial recognition and voice tone analysis, collecting data on the user's emotional state. Input data includes case information and emotional state. This data is then sent to the server.

[0409] Step 2:

[0410] The server analyzes the received case information and emotional state data. This analysis includes quantifying facial recognition data and extracting specific patterns from audio waveforms. This allows for the quantification of the type and intensity of emotions. The analysis results are then sent to a system for storing past cases.

[0411] Step 3:

[0412] The server queries a database of past cases based on the analysis results to search for similar past cases. An information retrieval algorithm is used to extract countermeasures for similar situations. The relevant data is used as a reference when setting the priority of the proposed work items.

[0413] Step 4:

[0414] The server automatically generates a work element decomposition structure based on comparisons with past cases. Here, it generates a list of related tasks and determines their priority. This process uses data obtained from sentiment analysis to arrange the tasks in an order that is feasible for the user.

[0415] Step 5:

[0416] The server uses a generative AI model to create user-adapted documents based on the generated work element decomposition structure. During document creation, it leverages emotional data to adjust tone and expression, providing the document in the format most readily accepted by the user.

[0417] Step 6:

[0418] The server sends the final generated document and work structure back to the terminal. The user can review this and make adjustments as needed. The output data is stored in the project storage area and notified to the relevant parties.

[0419] Step 7:

[0420] After the user has reviewed and adjusted their settings, an automated emotion-sensing system is used to determine which household assistance tasks should be prioritized, especially if the user is experiencing stress. Tasks are then sent to the user in the form of automated suggestions.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] An embodiment of the present invention is a system for streamlining case management, which mainly consists of terminals, servers, and databases.

[0438] First, the user uses a terminal to input specific project information. This project information includes basic project details, any required special actions, and deadlines. The terminal sends the entered information to the server, and the server verifies the data's integrity at that time.

[0439] The server analyzes the received case information and refers to a database of past cases. Here, the server interprets the case information using natural language processing techniques and identifies similar past cases. During this process, necessary tasks are extracted. These tasks include specific steps and activities required to complete the project.

[0440] The server generates a Work Breakdown Structure (WBS) based on the extracted tasks. This WBS is automatically created, taking into account the order, dependencies, and priorities of the tasks. Furthermore, the server uses this information to automatically generate the necessary documents. These documents include application forms, procedures, reports, etc., covering all documents essential for the progress of the project.

[0441] Users can review the WBS and documents generated via the terminal and make adjustments as needed. Once adjustments are complete, the terminal saves the final data to the project folder and notifies stakeholders. This notification feature facilitates smooth information sharing within the project team.

[0442] For example, in a new product launch project, when a user inputs new product specifications and market information, the server references past launch examples of similar products and generates tasks based on necessary marketing strategies and technical requirements. This allows the project team to plan efficiently and proceed with their work smoothly.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The user enters project information using a terminal. They enter detailed information such as the project name, overview, any necessary special actions, and the deadline into a form.

[0446] Step 2:

[0447] The terminal sends the entered case information to the server. The transmitted data is verified for integrity and sent in a standard format (e.g., JSON or XML).

[0448] Step 3:

[0449] The server analyzes the case information it receives. It interprets the input information using natural language processing techniques to identify standard tasks and specific requirements.

[0450] Step 4:

[0451] The server references a database of past cases to search for similar cases. It retrieves the information necessary to extract required tasks from successful and unsuccessful case studies.

[0452] Step 5:

[0453] The server lists all the tasks required for the project. The generating AI proposes the optimal set of tasks, adding detailed information about the person responsible and the resources required for each task.

[0454] Step 6:

[0455] The server automatically generates a Work Breakdown Structure (WBS) based on the tasks. It evaluates task dependencies, adjusts the schedule, and sets start and end dates and priorities.

[0456] Step 7:

[0457] The server automatically generates the necessary documents for each project. Using templates, it automatically fills in the required fields for application forms, specifications, etc., and outputs them in PDF or Word format.

[0458] Step 8:

[0459] The user reviews the WBS and documents generated via their terminal. After reviewing, they can make minor adjustments to the task order and document content as needed.

[0460] Step 9:

[0461] The terminal saves the final WBS and documents to the project folder. The saved data is notified to project stakeholders, and information necessary for project progress is shared.

[0462] (Example 1)

[0463] 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."

[0464] In project management, it was difficult to plan projects and create documentation quickly and efficiently. In particular, comparing with past cases and identifying necessary tasks required considerable effort and time, and a system was needed to ensure smooth progress while maintaining information consistency. Furthermore, the lack of effective means to share generated documents with stakeholders was a factor causing communication delays.

[0465] 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.

[0466] In this invention, the server includes means for analyzing input case information, means for referencing past data records and extracting necessary tasks, and means for automatically generating a work structure based on the extracted tasks. This enables efficient project planning and automatic creation of documents.

[0467] An "information processing device" refers to a terminal or electronic device used by users to input information related to a case.

[0468] A "data processing device" refers to a device that analyzes input information and performs appropriate processing based on that information.

[0469] "Data records" refer to a database containing information about past cases and examples.

[0470] "Work structure" refers to a structure that organizes the sequence and dependencies of tasks necessary for the execution of a project.

[0471] "Documents" refers to automatically generated documents, reports, and other materials necessary for carrying out the project.

[0472] "Natural language processing technology" refers to the technology that analyzes text data in a way that humans can understand and extracts meaning from it.

[0473] "Electronic documents" refer to documents or papers generated in digital format.

[0474] A "data repository" refers to a digital folder or storage device where the final data and information are stored.

[0475] "Operator" refers to a user or person in charge who manages cases through the system.

[0476] "Stakeholders" refers to all individuals and teams involved in the project, and those who will receive information from it.

[0477] This invention aims to streamline project management using a case management system that leverages information processing technology. It primarily consists of terminals, servers, and a database.

[0478] First, the user uses a terminal to enter basic project information. This information includes the project name, required tasks, and deadline. The terminal verifies the integrity of the input data and sends it to the server. This terminal can be a standard computer system or a tablet device.

[0479] The server is responsible for analyzing the received case information. This analysis utilizes natural language processing techniques, specifically libraries such as Python's NLTK and SpaCy. The server searches past data records in the database to identify similar past cases. This extracts the necessary tasks and clarifies the next steps to take.

[0480] The server generates a work structure based on the extracted tasks. This process uses project management software (e.g., Microsoft Project) to automatically organize task order and dependencies. It also automatically generates necessary documents for project execution, such as application forms, procedures, and reports. Electronic document templates are used for this document generation.

[0481] Users can review the work structure and documents generated from their devices and make adjustments as needed. Once the adjustments are complete, the data is saved to a data repository and then notified to relevant parties. Notifications are automatically sent via email and chat applications, facilitating smooth information sharing.

[0482] As a concrete example, in a new product launch project, when a user inputs specifications and market information into a terminal, the server can generate tasks such as conducting market research and preparing advertising campaigns based on past examples of similar products.

[0483] To maximize the system's potential, you can input a prompt to the generating AI model such as, "Refer to past success stories in new product launch projects and create a Work Breakdown Structure (WBS) that includes the necessary marketing tasks and technical requirements."

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] The user uses a terminal to enter basic project information (e.g., project name, requirements, deadline). The entered information is collected in form format, and a preliminary data integrity check is performed on the terminal. This check verifies that all required fields are filled in and that the date format is correct. Once the integrity is confirmed, the information is sent directly to the server.

[0487] Step 2:

[0488] The server analyzes the case information received from the terminal. This analysis process takes the input information as string data and uses natural language processing techniques to understand its content. Specifically, it tokenizes the information using Python's NLTK library and extracts keywords and important context. The analysis results output potential requirements related to the project.

[0489] Step 3:

[0490] Based on the analysis results, the server searches the database for past data records. It executes database queries to retrieve similar past case data. The data extracted from past cases is then analyzed to generate a task list necessary for the current project. This analysis result is output as a guideline for the specific tasks to be performed next.

[0491] Step 4:

[0492] The server automatically generates a work structure based on the retrieved task list. Specifically, it uses project management software (e.g., a project management tool) to define the order and dependencies of tasks and outputs them in Gantt chart format. This automatically generated work structure is exported as Excel or PDF and provided to the user in an intuitive format.

[0493] Step 5:

[0494] The server automatically generates the necessary documents based on the work structure. In this step, items are embedded in electronic document templates to create application forms and reports required for project progress. The documents, output in Word format, are sent to the user immediately in a usable state.

[0495] Step 6:

[0496] Users can review the work structure and documents generated via their terminal and make adjustments as needed. Through an intuitive GUI, users can add, reorder, and supplement document content. Once adjustments are complete, they can use the save function to proceed to the next step.

[0497] Step 7:

[0498] The terminal saves the final adjusted data to a data repository and automatically notifies stakeholders once the process is complete. Notification methods include email and collaboration tools, with links provided for immediate access by stakeholders. This enables rapid information sharing within the project team.

[0499] (Application Example 1)

[0500] 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."

[0501] Project management in the construction industry involves a complex interplay of multiple processes, making it difficult to track progress in real time. Furthermore, manually extracting optimal tasks by referencing similar past project examples is time-consuming, hindering efficient project management. Additionally, delays in on-site decision-making and work can occur if work orders are not created and stakeholders are notified promptly. These issues can potentially impact the overall progress of the project.

[0502] 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.

[0503] In this invention, the server includes an information processing device for analyzing case information, a means for referencing a collection of past case data and extracting necessary processing tasks, and a means for automatically generating a work breakdown structure and instruction documents and reporting the status in real time. This enables more efficient project management, faster decision-making, and timely information sharing among stakeholders.

[0504] "Project information" refers to data such as basic information, requirements, and deadlines necessary for the progress of a project.

[0505] "Equipment means" refers to physical or software devices used by users to input case information.

[0506] "Information processing device" refers to a computer system that analyzes input case information and performs necessary data manipulation.

[0507] A "case study data collection" refers to a database that stores data from projects that have been carried out in the past.

[0508] "Processing tasks" refer to the individual tasks and actions necessary for the successful completion of a project.

[0509] A "work breakdown structure" refers to a systematic plan that visualizes the sequence and dependencies of tasks necessary to complete a project.

[0510] "Instruction documents" refer to documents that include procedures and reports necessary for the execution of a project.

[0511] A "data communication device" refers to a network-connected device used to send and receive information between users.

[0512] "Data storage area" refers to the memory area used to store automatically generated information.

[0513] This invention describes embodiments for carrying out this application. The system for realizing this application provides an integrated solution for streamlining project management. The system inputs case information through the user's equipment and transmits it to a server. The server analyzes the input case information using information processing equipment, searches for similar past projects from a case data set, and extracts the necessary processing tasks. Based on this, it automatically generates a work breakdown structure and instruction documents.

[0514] In terms of program structure, React Native is used for the frontend to provide an intuitive user interface. Node.js and the Express framework are used for the backend to achieve efficient data processing and server communication. MongoDB is utilized for the database to build reliable data storage. Furthermore, Python's NLTK and spaCy are used for natural language processing to effectively analyze past cases and extract tasks.

[0515] The system uses a generative AI model to extract optimal tasks from project information and past case studies, and then creates a work plan based on those tasks. Users receive real-time work instructions and report progress via smartphones or PCs. For example, in a high-rise building construction project, the following prompt message is input to the AI ​​model during the planning phase: "Refer to past project examples similar to this building construction plan and automatically extract the necessary tasks." This improves the overall efficiency of the project and enables smoother collaboration among stakeholders.

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The user enters project information into the terminal. This includes basic project information, requirements, and deadlines. The terminal collects this data and verifies its integrity. Once verification is complete, it sends this data to the server.

[0519] Step 2:

[0520] The server analyzes the received case information using an information processing device. The analysis uses natural language processing techniques to understand the text data and extract its meaning. The input data consists of case information, and the output includes keywords and requirements related to the target project.

[0521] Step 3:

[0522] The server uses the analysis results to search the case data set and identify similar past projects. A generative AI model is used in this process. It executes database queries, extracts highly similar cases, and identifies the necessary processing tasks. The output is a list of the relevant processing tasks.

[0523] Step 4:

[0524] The server automatically generates a work breakdown structure based on the extracted processing tasks. This process organizes the order and dependencies of the processing tasks and develops a structured plan. The input is a list of processing tasks, and the output is a work breakdown structure, which is a visualized form of the plan.

[0525] Step 5:

[0526] The server automatically generates a work breakdown structure and related documents. These documents include procedures and reports. Based on the plan, it documents specific details and uses templates to create further details as needed. The output is a set of instructional documents necessary for project execution.

[0527] Step 6:

[0528] The server stores the generated data in a data storage area and uses the generated AI model to provide real-time status reports. The stored data is then communicated to stakeholders via data communication devices. The purpose of this step is to quickly share the project's progress. Users receive the notifications and take action as needed.

[0529] 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.

[0530] One embodiment of the present invention is to combine an emotion engine with a project management system to realize project management that takes into account the emotional state of the user at the time of input. The main components are a terminal, a server, a database, and an emotion engine.

[0531] Users input case information using a terminal. During this process, an emotion engine built into the terminal recognizes and analyzes the user's emotional state in real time to determine the emotional state under which the user is inputting the information. The emotion engine collects emotional data using facial recognition technology and voice tone analysis.

[0532] The server receives emotional data along with case information sent from the terminal and performs analysis. Based on the analysis results, it extracts necessary tasks by referring to a database of past cases and automatically generates a Work Breakdown Structure (WBS). Here, emotional data can influence task priorities, and the user's emotional state is also used in project risk assessment.

[0533] Furthermore, while the server creates the necessary documents based on the automatically generated WBS, it can also reflect the appropriate tone and expression in the document content according to the user's emotional state. For example, if the user is feeling stressed, the document will adopt more friendly language.

[0534] The generated WBS and documents are returned to the terminal for user review. Users can make adjustments as needed. Furthermore, the final data is saved to the project folder, and stakeholders are notified. At this time, the sentiment information identified by the sentiment engine is used as an important decision-making criterion in project management.

[0535] For example, in an emergency release project for a new product, if a user enters information while feeling anxious or stressed, the system can detect this and automatically review the priority of the corresponding tasks to support stable project progress.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user enters project information using a terminal. The input form includes the project name, summary, special notes, and deadline.

[0539] Step 2:

[0540] The device analyzes the user's facial expressions and voice while they are typing, and generates emotional data using an emotion engine. Specifically, it utilizes the camera and microphone.

[0541] Step 3:

[0542] The terminal sends the entered case information and sentiment data to the server. After the necessary integrity checks are performed, the data is sent in a standard format.

[0543] Step 4:

[0544] The server receives the incoming data and analyzes the case information. This analysis includes a process that uses natural language processing to understand the input content and identify the requirements for special handling.

[0545] Step 5:

[0546] The server uses sentiment data to reference a database of past cases and search for similar cases. It extracts necessary tasks and dynamically adjusts task priorities based on sentiment data.

[0547] Step 6:

[0548] The server automatically generates a Work Breakdown Structure (WBS) based on the extracted tasks. This structure takes into account the user's emotional state and sets schedules and priorities accordingly.

[0549] Step 7:

[0550] The server automatically generates the necessary documents. These documents contain the required information based on the generated WBS, and the tone and expression are adjusted according to the user's emotional state.

[0551] Step 8:

[0552] Users can review the generated WBS and documents via their terminal. After reviewing, they can fine-tune the order and content of tasks as needed.

[0553] Step 9:

[0554] The terminal saves the final WBS and documents to the project folder and notifies stakeholders. The notification includes the results of sentiment data analysis, which may be used to set agendas for meetings and conferences.

[0555] (Example 2)

[0556] 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."

[0557] In project management, there is a challenge in flexibly prioritizing tasks and creating documentation that takes into account the emotional state of users. Furthermore, there is a need to improve the current situation where the impact of user emotions on the overall progress of the project is not being considered.

[0558] 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.

[0559] In this invention, the server includes emotion analysis means for collecting and analyzing case information and user emotion data, means for referencing past case memory storage and extracting necessary activities based on the analyzed information, and means for adjusting the tone and expression of automatically generated documents according to the user's emotional state. This enables project management that reflects the user's emotional state.

[0560] "Information input means" refers to a device or interface for a user to input case information.

[0561] "Emotion analysis tools" are technologies that collect and analyze emotional data from a user's facial recognition and voice tone.

[0562] A "case memory device" is a database or storage system that stores past cases and related information.

[0563] An "activity extraction method" is a mechanism that identifies and extracts necessary project tasks based on the analysis results.

[0564] A "task decomposition structure" is a structure that hierarchically breaks down and organizes the tasks of a project.

[0565] "Automatic document generation means" refers to a function that automatically creates necessary documents based on a work breakdown structure.

[0566] "Tone adjustment techniques" refer to technologies that change the way a document is expressed and its style according to the user's emotional state.

[0567] A "project memory device" is a storage system used to save final data and documents related to a project.

[0568] This invention provides a system for managing cases while taking into account the user's emotional state. This system consists of an information input means, an emotion analysis means, an activity extraction means, a work breakdown structure creation means, an automatic document generation means, and a tone adjustment means.

[0569] When a user enters project information using a terminal, an information input method is used. During this process, the terminal collects and analyzes the user's emotional data in real time using emotion analysis technology. Specifically, it utilizes facial recognition technology using a camera and voice tone analysis using a microphone.

[0570] The server passes case information and sentiment data sent from the terminal to the sentiment analysis device and uses a case memory device to refer to similar past cases. Based on the analyzed data, the server extracts the necessary tasks using the activity extraction device and automatically generates a work breakdown structure.

[0571] Furthermore, the server generates documents based on the generated work breakdown structure and adjusts the tone and expression of the documents using tone adjustment means according to the user's emotional state. For users who are feeling stressed, the document can adopt more approachable language.

[0572] The generated work breakdown structure and documentation are stored in the project's memory and sent to relevant stakeholders as needed. This entire process effectively utilizes user sentiment in setting project risks and priorities.

[0573] To give a specific example, in a new product release project, if a user enters information while feeling anxious, the system can sense that emotion and support the smooth progress of the project by readjusting the task priorities.

[0574] An example of a prompt to be input into a generating AI model is: "Explain a method that uses an emotion engine to analyze the emotional state of a user when they input case information into a project management system, and optimizes task priorities."

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1:

[0577] The user enters project information using a terminal. This information includes the project name, deadline, and summary. The terminal then prepares to send the entered text information directly to the next step.

[0578] Step 2:

[0579] The device collects user emotional data using emotion analysis techniques. This involves recording and analyzing facial expressions and voice tone using a camera and microphone. Inputs include image and audio data, which are then analyzed to output numerical data representing the emotional state.

[0580] Step 3:

[0581] Case information and sentiment data are sent from the terminal to the server. The input here consists of the text information and sentiment numerical data collected in the previous step, which the server receives and prepares for the next process.

[0582] Step 4:

[0583] Based on the received data, the server references the case memory database to extract past case information. The server compares past and current case information, identifies highly similar cases, and obtains the corresponding task information as output.

[0584] Step 5:

[0585] The server automatically generates a Work Breakdown Structure (WBS) based on the task information obtained in the previous step. Here, the input is task information, and the output is a hierarchical list of work breakdown structures.

[0586] Step 6:

[0587] Based on the generated work breakdown structure, the server automatically generates the necessary documents. Inputs include WBS information and sentiment data. The tone and expression of the documents are adjusted according to the sentiment data, resulting in output documents that are more contextually appropriate.

[0588] Step 7:

[0589] Finally, the server sends the generated document and WBS to the terminal. The terminal receives this and displays it to the user. The output here consists of the document and WBS for review, which the user can review and adjust as needed.

[0590] (Application Example 2)

[0591] 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."

[0592] In modern life, users are burdened with a diverse range of tasks, including work, household chores, and other responsibilities, making efficiency improvements and stress reduction crucial. Furthermore, there is a need for systems that respond more flexibly to users' emotional states. However, current systems fail to adequately sense changes in user emotions in real time and adjust priorities or suggest tasks accordingly. Therefore, there is a need for automated systems that adapt to emotions.

[0593] 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.

[0594] In this invention, the server includes means for analyzing case information and emotional state information, means for extracting necessary work items by referring to past case storage means, and automated emotion sensing means for determining the priority of household support tasks using emotional data. This enables automatic suggestion of high-priority tasks according to the user's emotional state and stress reduction through emotionally adaptive document generation.

[0595] A "terminal device" is a device used by users to input case information and has the function of sensing emotional states.

[0596] A "server device" is a device that analyzes case information and emotional state information transmitted from a terminal and performs data processing based on the extracted information.

[0597] A "means for accumulating past case data" refers to a storage device that stores case data collected in the past and allows for reference as needed.

[0598] The "work element decomposition structure" is a representation of the work details and hierarchical structure, automatically generated based on the extracted work items.

[0599] "Means of automatically generating documents in an emotionally adaptive manner" refers to a device that automatically generates documents considering emotional data and reflects the tone and expression that matches the user's emotional state.

[0600] "Emotion sensing automation means" refers to a device and system that uses user emotion data to determine which household support tasks should be prioritized.

[0601] The "project storage area" is a data storage area for saving the generated work element breakdown structure and documents, making them accessible to stakeholders.

[0602] The system for realizing this invention consists mainly of a terminal, a server, a means for storing past cases, and an automated emotion sensing means. First, the terminal receives user input and senses the user's emotional state in real time using facial recognition technology and voice tone analysis. This emotion data is transmitted to the server along with other input data.

[0603] The server analyzes the received data and extracts necessary work items while referring to past case storage systems. This process generates a newly proposed work element decomposition structure. Furthermore, a generative AI model is used to adjust the tone and expression of documents in order to generate user-adapted documents based on emotional data.

[0604] For example, if the system detects that a user is feeling stressed during a busy period, it will automatically determine which household chore assistance tasks should be prioritized and notify the user as a reminder. This notification may include suggestions for playing music to help reduce stress.

[0605] An example of a prompt might be: "A family member appears to be stressed. Please suggest tasks that should be prioritized and provide assistance to help them relax." Based on this prompt, the system automatically organizes and provides the most appropriate support.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The terminal receives user input. The terminal uses a camera to scan the user's face and a microphone to capture audio. This allows for facial recognition and voice tone analysis, collecting data on the user's emotional state. Input data includes case information and emotional state. This data is then sent to the server.

[0609] Step 2:

[0610] The server analyzes the received case information and emotional state data. This analysis includes quantifying facial recognition data and extracting specific patterns from audio waveforms. This allows for the quantification of the type and intensity of emotions. The analysis results are then sent to a system for storing past cases.

[0611] Step 3:

[0612] The server queries a database of past cases based on the analysis results to search for similar past cases. An information retrieval algorithm is used to extract countermeasures for similar situations. The relevant data is used as a reference when setting the priority of the proposed work items.

[0613] Step 4:

[0614] The server automatically generates a work element decomposition structure based on comparisons with past cases. Here, it generates a list of related tasks and determines their priority. This process uses data obtained from sentiment analysis to arrange the tasks in an order that is feasible for the user.

[0615] Step 5:

[0616] The server uses a generative AI model to create user-adapted documents based on the generated work element decomposition structure. During document creation, it leverages emotional data to adjust tone and expression, providing the document in the format most readily accepted by the user.

[0617] Step 6:

[0618] The server sends the final generated document and work structure back to the terminal. The user can review this and make adjustments as needed. The output data is stored in the project storage area and notified to the relevant parties.

[0619] Step 7:

[0620] After the user has reviewed and adjusted their settings, an automated emotion-sensing system is used to determine which household assistance tasks should be prioritized, especially if the user is experiencing stress. Tasks are then sent to the user in the form of automated suggestions.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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".

[0638] An embodiment of the present invention is a system for streamlining case management, which mainly consists of terminals, servers, and databases.

[0639] First, the user uses a terminal to input specific project information. This project information includes basic project details, any required special actions, and deadlines. The terminal sends the entered information to the server, and the server verifies the data's integrity at that time.

[0640] The server analyzes the received case information and refers to a database of past cases. Here, the server interprets the case information using natural language processing techniques and identifies similar past cases. During this process, necessary tasks are extracted. These tasks include specific steps and activities required to complete the project.

[0641] The server generates a Work Breakdown Structure (WBS) based on the extracted tasks. This WBS is automatically created, taking into account the order, dependencies, and priorities of the tasks. Furthermore, the server uses this information to automatically generate the necessary documents. These documents include application forms, procedures, reports, etc., covering all documents essential for the progress of the project.

[0642] Users can review the WBS and documents generated via the terminal and make adjustments as needed. Once adjustments are complete, the terminal saves the final data to the project folder and notifies stakeholders. This notification feature facilitates smooth information sharing within the project team.

[0643] For example, in a new product launch project, when a user inputs new product specifications and market information, the server references past launch examples of similar products and generates tasks based on necessary marketing strategies and technical requirements. This allows the project team to plan efficiently and proceed with their work smoothly.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The user enters project information using a terminal. They enter detailed information such as the project name, overview, any necessary special actions, and the deadline into a form.

[0647] Step 2:

[0648] The terminal sends the entered case information to the server. The transmitted data is verified for integrity and sent in a standard format (e.g., JSON or XML).

[0649] Step 3:

[0650] The server analyzes the case information it receives. It interprets the input information using natural language processing techniques to identify standard tasks and specific requirements.

[0651] Step 4:

[0652] The server references a database of past cases to search for similar cases. It retrieves the information necessary to extract required tasks from successful and unsuccessful case studies.

[0653] Step 5:

[0654] The server lists all the tasks required for the project. The generating AI proposes the optimal set of tasks, adding detailed information about the person responsible and the resources required for each task.

[0655] Step 6:

[0656] The server automatically generates a Work Breakdown Structure (WBS) based on the tasks. It evaluates task dependencies, adjusts the schedule, and sets start and end dates and priorities.

[0657] Step 7:

[0658] The server automatically generates the necessary documents for each project. Using templates, it automatically fills in the required fields for application forms, specifications, etc., and outputs them in PDF or Word format.

[0659] Step 8:

[0660] The user reviews the WBS and documents generated via their terminal. After reviewing, they can make minor adjustments to the task order and document content as needed.

[0661] Step 9:

[0662] The terminal saves the final WBS and documents to the project folder. The saved data is notified to project stakeholders, and information necessary for project progress is shared.

[0663] (Example 1)

[0664] 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".

[0665] In project management, it was difficult to plan projects and create documentation quickly and efficiently. In particular, comparing with past cases and identifying necessary tasks required considerable effort and time, and a system was needed to ensure smooth progress while maintaining information consistency. Furthermore, the lack of effective means to share generated documents with stakeholders was a factor causing communication delays.

[0666] 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.

[0667] In this invention, the server includes means for analyzing input case information, means for referencing past data records and extracting necessary tasks, and means for automatically generating a work structure based on the extracted tasks. This enables efficient project planning and automatic creation of documents.

[0668] An "information processing device" refers to a terminal or electronic device used by users to input information related to a case.

[0669] A "data processing device" refers to a device that analyzes input information and performs appropriate processing based on that information.

[0670] "Data records" refer to a database containing information about past cases and examples.

[0671] "Work structure" refers to a structure that organizes the sequence and dependencies of tasks necessary for the execution of a project.

[0672] "Documents" refers to automatically generated documents, reports, and other materials necessary for carrying out the project.

[0673] "Natural language processing technology" refers to the technology that analyzes text data in a way that humans can understand and extracts meaning from it.

[0674] "Electronic documents" refer to documents or papers generated in digital format.

[0675] A "data repository" refers to a digital folder or storage device where the final data and information are stored.

[0676] "Operator" refers to a user or person in charge who manages cases through the system.

[0677] "Stakeholders" refers to all individuals and teams involved in the project, and those who will receive information from it.

[0678] This invention aims to streamline project management using a case management system that leverages information processing technology. It primarily consists of terminals, servers, and a database.

[0679] First, the user uses a terminal to enter basic project information. This information includes the project name, required tasks, and deadline. The terminal verifies the integrity of the input data and sends it to the server. This terminal can be a standard computer system or a tablet device.

[0680] The server is responsible for analyzing the received case information. This analysis utilizes natural language processing techniques, specifically libraries such as Python's NLTK and SpaCy. The server searches past data records in the database to identify similar past cases. This extracts the necessary tasks and clarifies the next steps to take.

[0681] The server generates a work structure based on the extracted tasks. This process uses project management software (e.g., Microsoft Project) to automatically organize task order and dependencies. It also automatically generates necessary documents for project execution, such as application forms, procedures, and reports. Electronic document templates are used for this document generation.

[0682] Users can review the work structure and documents generated from their devices and make adjustments as needed. Once the adjustments are complete, the data is saved to a data repository and then notified to relevant parties. Notifications are automatically sent via email and chat applications, facilitating smooth information sharing.

[0683] As a concrete example, in a new product launch project, when a user inputs specifications and market information into a terminal, the server can generate tasks such as conducting market research and preparing advertising campaigns based on past examples of similar products.

[0684] To maximize the system's potential, you can input a prompt to the generating AI model such as, "Refer to past success stories in new product launch projects and create a Work Breakdown Structure (WBS) that includes the necessary marketing tasks and technical requirements."

[0685] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0686] Step 1:

[0687] The user uses a terminal to enter basic project information (e.g., project name, requirements, deadline). The entered information is collected in form format, and a preliminary data integrity check is performed on the terminal. This check verifies that all required fields are filled in and that the date format is correct. Once the integrity is confirmed, the information is sent directly to the server.

[0688] Step 2:

[0689] The server analyzes the case information received from the terminal. This analysis process takes the input information as string data and uses natural language processing techniques to understand its content. Specifically, it tokenizes the information using Python's NLTK library and extracts keywords and important context. The analysis results output potential requirements related to the project.

[0690] Step 3:

[0691] Based on the analysis results, the server searches the database for past data records. It executes database queries to retrieve similar past case data. The data extracted from past cases is then analyzed to generate a task list necessary for the current project. This analysis result is output as a guideline for the specific tasks to be performed next.

[0692] Step 4:

[0693] The server automatically generates a work structure based on the retrieved task list. Specifically, it uses project management software (e.g., a project management tool) to define the order and dependencies of tasks and outputs them in Gantt chart format. This automatically generated work structure is exported as Excel or PDF and provided to the user in an intuitive format.

[0694] Step 5:

[0695] The server automatically generates the necessary documents based on the work structure. In this step, items are embedded in electronic document templates to create application forms and reports required for project progress. The documents, output in Word format, are sent to the user immediately in a usable state.

[0696] Step 6:

[0697] Users can review the work structure and documents generated via their terminal and make adjustments as needed. Through an intuitive GUI, users can add, reorder, and supplement document content. Once adjustments are complete, they can use the save function to proceed to the next step.

[0698] Step 7:

[0699] The terminal saves the final adjusted data to a data repository and automatically notifies stakeholders once the process is complete. Notification methods include email and collaboration tools, with links provided for immediate access by stakeholders. This enables rapid information sharing within the project team.

[0700] (Application Example 1)

[0701] 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".

[0702] Project management in the construction industry involves a complex interplay of multiple processes, making it difficult to track progress in real time. Furthermore, manually extracting optimal tasks by referencing similar past project examples is time-consuming, hindering efficient project management. Additionally, delays in on-site decision-making and work can occur if work orders are not created and stakeholders are notified promptly. These issues can potentially impact the overall progress of the project.

[0703] 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.

[0704] In this invention, the server includes an information processing device for analyzing case information, a means for referencing a collection of past case data and extracting necessary processing tasks, and a means for automatically generating a work breakdown structure and instruction documents and reporting the status in real time. This enables more efficient project management, faster decision-making, and timely information sharing among stakeholders.

[0705] "Project information" refers to data such as basic information, requirements, and deadlines necessary for the progress of a project.

[0706] "Equipment means" refers to physical or software devices used by users to input case information.

[0707] "Information processing device" refers to a computer system that analyzes input case information and performs necessary data manipulation.

[0708] A "case study data collection" refers to a database that stores data from projects that have been carried out in the past.

[0709] "Processing tasks" refer to the individual tasks and actions necessary for the successful completion of a project.

[0710] A "work breakdown structure" refers to a systematic plan that visualizes the sequence and dependencies of tasks necessary to complete a project.

[0711] "Instruction documents" refer to documents that include procedures and reports necessary for the execution of a project.

[0712] A "data communication device" refers to a network-connected device used to send and receive information between users.

[0713] "Data storage area" refers to the memory area used to store automatically generated information.

[0714] This invention describes embodiments for carrying out this application. The system for realizing this application provides an integrated solution for streamlining project management. The system inputs case information through the user's equipment and transmits it to a server. The server analyzes the input case information using information processing equipment, searches for similar past projects from a case data set, and extracts the necessary processing tasks. Based on this, it automatically generates a work breakdown structure and instruction documents.

[0715] In terms of program structure, React Native is used for the frontend to provide an intuitive user interface. Node.js and the Express framework are used for the backend to achieve efficient data processing and server communication. MongoDB is utilized for the database to build reliable data storage. Furthermore, Python's NLTK and spaCy are used for natural language processing to effectively analyze past cases and extract tasks.

[0716] The system uses a generative AI model to extract optimal tasks from project information and past case studies, and then creates a work plan based on those tasks. Users receive real-time work instructions and report progress via smartphones or PCs. For example, in a high-rise building construction project, the following prompt message is input to the AI ​​model during the planning phase: "Refer to past project examples similar to this building construction plan and automatically extract the necessary tasks." This improves the overall efficiency of the project and enables smoother collaboration among stakeholders.

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] The user enters project information into the terminal. This includes basic project information, requirements, and deadlines. The terminal collects this data and verifies its integrity. Once verification is complete, it sends this data to the server.

[0720] Step 2:

[0721] The server analyzes the received case information using an information processing device. The analysis uses natural language processing techniques to understand the text data and extract its meaning. The input data consists of case information, and the output includes keywords and requirements related to the target project.

[0722] Step 3:

[0723] The server uses the analysis results to search the case data set and identify similar past projects. A generative AI model is used in this process. It executes database queries, extracts highly similar cases, and identifies the necessary processing tasks. The output is a list of the relevant processing tasks.

[0724] Step 4:

[0725] The server automatically generates a work breakdown structure based on the extracted processing tasks. This process organizes the order and dependencies of the processing tasks and develops a structured plan. The input is a list of processing tasks, and the output is a work breakdown structure, which is a visualized form of the plan.

[0726] Step 5:

[0727] The server automatically generates a work breakdown structure and related documents. These documents include procedures and reports. Based on the plan, it documents specific details and uses templates to create further details as needed. The output is a set of instructional documents necessary for project execution.

[0728] Step 6:

[0729] The server stores the generated data in a data storage area and uses the generated AI model to provide real-time status reports. The stored data is then communicated to stakeholders via data communication devices. The purpose of this step is to quickly share the project's progress. Users receive the notifications and take action as needed.

[0730] 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.

[0731] One embodiment of the present invention is to combine an emotion engine with a project management system to realize project management that takes into account the emotional state of the user at the time of input. The main components are a terminal, a server, a database, and an emotion engine.

[0732] Users input case information using a terminal. During this process, an emotion engine built into the terminal recognizes and analyzes the user's emotional state in real time to determine the emotional state under which the user is inputting the information. The emotion engine collects emotional data using facial recognition technology and voice tone analysis.

[0733] The server receives emotional data along with case information sent from the terminal and performs analysis. Based on the analysis results, it extracts necessary tasks by referring to a database of past cases and automatically generates a Work Breakdown Structure (WBS). Here, emotional data can influence task priorities, and the user's emotional state is also used in project risk assessment.

[0734] Furthermore, while the server creates the necessary documents based on the automatically generated WBS, it can also reflect the appropriate tone and expression in the document content according to the user's emotional state. For example, if the user is feeling stressed, the document will adopt more friendly language.

[0735] The generated WBS and documents are returned to the terminal for user review. Users can make adjustments as needed. Furthermore, the final data is saved to the project folder, and stakeholders are notified. At this time, the sentiment information identified by the sentiment engine is used as an important decision-making criterion in project management.

[0736] For example, in an emergency release project for a new product, if a user enters information while feeling anxious or stressed, the system can detect this and automatically review the priority of the corresponding tasks to support stable project progress.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] The user enters project information using a terminal. The input form includes the project name, summary, special notes, and deadline.

[0740] Step 2:

[0741] The device analyzes the user's facial expressions and voice while they are typing, and generates emotional data using an emotion engine. Specifically, it utilizes the camera and microphone.

[0742] Step 3:

[0743] The terminal sends the entered case information and sentiment data to the server. After the necessary integrity checks are performed, the data is sent in a standard format.

[0744] Step 4:

[0745] The server receives the incoming data and analyzes the case information. This analysis includes a process that uses natural language processing to understand the input content and identify the requirements for special handling.

[0746] Step 5:

[0747] The server uses sentiment data to reference a database of past cases and search for similar cases. It extracts necessary tasks and dynamically adjusts task priorities based on sentiment data.

[0748] Step 6:

[0749] The server automatically generates a Work Breakdown Structure (WBS) based on the extracted tasks. This structure takes into account the user's emotional state and sets schedules and priorities accordingly.

[0750] Step 7:

[0751] The server automatically generates the necessary documents. These documents contain the required information based on the generated WBS, and the tone and expression are adjusted according to the user's emotional state.

[0752] Step 8:

[0753] Users can review the generated WBS and documents via their terminal. After reviewing, they can fine-tune the order and content of tasks as needed.

[0754] Step 9:

[0755] The terminal saves the final WBS and documents to the project folder and notifies stakeholders. The notification includes the results of sentiment data analysis, which may be used to set agendas for meetings and conferences.

[0756] (Example 2)

[0757] 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".

[0758] In project management, there is a challenge in flexibly prioritizing tasks and creating documentation that takes into account the emotional state of users. Furthermore, there is a need to improve the current situation where the impact of user emotions on the overall progress of the project is not being considered.

[0759] 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.

[0760] In this invention, the server includes emotion analysis means for collecting and analyzing case information and user emotion data, means for referencing past case memory storage and extracting necessary activities based on the analyzed information, and means for adjusting the tone and expression of automatically generated documents according to the user's emotional state. This enables project management that reflects the user's emotional state.

[0761] "Information input means" refers to a device or interface for a user to input case information.

[0762] "Emotion analysis tools" are technologies that collect and analyze emotional data from a user's facial recognition and voice tone.

[0763] A "case memory device" is a database or storage system that stores past cases and related information.

[0764] An "activity extraction method" is a mechanism that identifies and extracts necessary project tasks based on the analysis results.

[0765] A "task decomposition structure" is a structure that hierarchically breaks down and organizes the tasks of a project.

[0766] "Automatic document generation means" refers to a function that automatically creates necessary documents based on a work breakdown structure.

[0767] "Tone adjustment techniques" refer to technologies that change the way a document is expressed and its style according to the user's emotional state.

[0768] A "project memory device" is a storage system used to save final data and documents related to a project.

[0769] This invention provides a system for managing cases while taking into account the user's emotional state. This system consists of an information input means, an emotion analysis means, an activity extraction means, a work breakdown structure creation means, an automatic document generation means, and a tone adjustment means.

[0770] When a user enters project information using a terminal, an information input method is used. During this process, the terminal collects and analyzes the user's emotional data in real time using emotion analysis technology. Specifically, it utilizes facial recognition technology using a camera and voice tone analysis using a microphone.

[0771] The server passes case information and sentiment data sent from the terminal to the sentiment analysis device and uses a case memory device to refer to similar past cases. Based on the analyzed data, the server extracts the necessary tasks using the activity extraction device and automatically generates a work breakdown structure.

[0772] Furthermore, the server generates documents based on the generated work breakdown structure and adjusts the tone and expression of the documents using tone adjustment means according to the user's emotional state. For users who are feeling stressed, the document can adopt more approachable language.

[0773] The generated work breakdown structure and documentation are stored in the project's memory and sent to relevant stakeholders as needed. This entire process effectively utilizes user sentiment in setting project risks and priorities.

[0774] To give a specific example, in a new product release project, if a user enters information while feeling anxious, the system can sense that emotion and support the smooth progress of the project by readjusting the task priorities.

[0775] An example of a prompt to be input into a generating AI model is: "Explain a method that uses an emotion engine to analyze the emotional state of a user when they input case information into a project management system, and optimizes task priorities."

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The user enters project information using a terminal. This information includes the project name, deadline, and summary. The terminal then prepares to send the entered text information directly to the next step.

[0779] Step 2:

[0780] The device collects user emotional data using emotion analysis techniques. This involves recording and analyzing facial expressions and voice tone using a camera and microphone. Inputs include image and audio data, which are then analyzed to output numerical data representing the emotional state.

[0781] Step 3:

[0782] Case information and sentiment data are sent from the terminal to the server. The input here consists of the text information and sentiment numerical data collected in the previous step, which the server receives and prepares for the next process.

[0783] Step 4:

[0784] Based on the received data, the server references the case memory database to extract past case information. The server compares past and current case information, identifies highly similar cases, and obtains the corresponding task information as output.

[0785] Step 5:

[0786] The server automatically generates a Work Breakdown Structure (WBS) based on the task information obtained in the previous step. Here, the input is task information, and the output is a hierarchical list of work breakdown structures.

[0787] Step 6:

[0788] Based on the generated work breakdown structure, the server automatically generates the necessary documents. Inputs include WBS information and sentiment data. The tone and expression of the documents are adjusted according to the sentiment data, resulting in output documents that are more contextually appropriate.

[0789] Step 7:

[0790] Finally, the server sends the generated document and WBS to the terminal. The terminal receives this and displays it to the user. The output here consists of the document and WBS for review, which the user can review and adjust as needed.

[0791] (Application Example 2)

[0792] 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".

[0793] In modern life, users are burdened with a diverse range of tasks, including work, household chores, and other responsibilities, making efficiency improvements and stress reduction crucial. Furthermore, there is a need for systems that respond more flexibly to users' emotional states. However, current systems fail to adequately sense changes in user emotions in real time and adjust priorities or suggest tasks accordingly. Therefore, there is a need for automated systems that adapt to emotions.

[0794] 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.

[0795] In this invention, the server includes means for analyzing case information and emotional state information, means for extracting necessary work items by referring to past case storage means, and automated emotion sensing means for determining the priority of household support tasks using emotional data. This enables automatic suggestion of high-priority tasks according to the user's emotional state and stress reduction through emotionally adaptive document generation.

[0796] A "terminal device" is a device used by users to input case information and has the function of sensing emotional states.

[0797] A "server device" is a device that analyzes case information and emotional state information transmitted from a terminal and performs data processing based on the extracted information.

[0798] A "means for accumulating past case data" refers to a storage device that stores case data collected in the past and allows for reference as needed.

[0799] The "work element decomposition structure" is a representation of the work details and hierarchical structure, automatically generated based on the extracted work items.

[0800] "Means of automatically generating documents in an emotionally adaptive manner" refers to a device that automatically generates documents considering emotional data and reflects the tone and expression that matches the user's emotional state.

[0801] "Emotion sensing automation means" refers to a device and system that uses user emotion data to determine which household support tasks should be prioritized.

[0802] The "project storage area" is a data storage area for saving the generated work element breakdown structure and documents, making them accessible to stakeholders.

[0803] The system for realizing this invention consists mainly of a terminal, a server, a means for storing past cases, and an automated emotion sensing means. First, the terminal receives user input and senses the user's emotional state in real time using facial recognition technology and voice tone analysis. This emotion data is transmitted to the server along with other input data.

[0804] The server analyzes the received data and extracts necessary work items while referring to past case storage systems. This process generates a newly proposed work element decomposition structure. Furthermore, a generative AI model is used to adjust the tone and expression of documents in order to generate user-adapted documents based on emotional data.

[0805] For example, if the system detects that a user is feeling stressed during a busy period, it will automatically determine which household chore assistance tasks should be prioritized and notify the user as a reminder. This notification may include suggestions for playing music to help reduce stress.

[0806] An example of a prompt might be: "A family member appears to be stressed. Please suggest tasks that should be prioritized and provide assistance to help them relax." Based on this prompt, the system automatically organizes and provides the most appropriate support.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The terminal receives user input. The terminal uses a camera to scan the user's face and a microphone to capture audio. This allows for facial recognition and voice tone analysis, collecting data on the user's emotional state. Input data includes case information and emotional state. This data is then sent to the server.

[0810] Step 2:

[0811] The server analyzes the received case information and emotional state data. This analysis includes quantifying facial recognition data and extracting specific patterns from audio waveforms. This allows for the quantification of the type and intensity of emotions. The analysis results are then sent to a system for storing past cases.

[0812] Step 3:

[0813] The server queries a database of past cases based on the analysis results to search for similar past cases. An information retrieval algorithm is used to extract countermeasures for similar situations. The relevant data is used as a reference when setting the priority of the proposed work items.

[0814] Step 4:

[0815] The server automatically generates a work element decomposition structure based on comparisons with past cases. Here, it generates a list of related tasks and determines their priority. This process uses data obtained from sentiment analysis to arrange the tasks in an order that is feasible for the user.

[0816] Step 5:

[0817] The server uses a generative AI model to create user-adapted documents based on the generated work element decomposition structure. During document creation, it leverages emotional data to adjust tone and expression, providing the document in the format most readily accepted by the user.

[0818] Step 6:

[0819] The server sends the final generated document and work structure back to the terminal. The user can review this and make adjustments as needed. The output data is stored in the project storage area and notified to the relevant parties.

[0820] Step 7:

[0821] After the user has reviewed and adjusted their settings, an automated emotion-sensing system is used to determine which household assistance tasks should be prioritized, especially if the user is experiencing stress. Tasks are then sent to the user in the form of automated suggestions.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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 as being incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A terminal for entering project information,

[0846] A server means for analyzing the input case information,

[0847] Based on the analyzed information, a means for extracting necessary tasks by referring to a database of past cases,

[0848] A means for automatically generating a work breakdown structure based on the extracted tasks,

[0849] A means for automatically generating necessary documents based on the work breakdown structure,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which allows the user to review the automatically generated work breakdown structure and document and make adjustments as necessary.

[0853] (Claim 3)

[0854] The system according to claim 1, which includes a function to save the automatically generated work breakdown structure and document to a project folder and notify relevant parties.

[0855] "Example 1"

[0856] (Claim 1)

[0857] Information processing device for inputting case information,

[0858] A data processing device for analyzing the input case information,

[0859] A means for extracting necessary tasks by referring to past data records based on the analyzed information,

[0860] A means for automatically generating a work structure based on the extracted work,

[0861] A means for automatically generating necessary documents based on the work structure,

[0862] A means of verifying the integrity of the information and preparing the data,

[0863] A method of using natural language processing technology to search past cases,

[0864] A means for generating electronic documents based on templates,

[0865] Information processing methods including

[0866] (Claim 2)

[0867] The information processing method according to claim 1, which enables an operator to review the automatically generated work structure and materials and make fine adjustments as necessary.

[0868] (Claim 3)

[0869] The information processing method according to claim 1, which includes a function to store the automatically generated work structure and materials in a data repository and notify relevant parties.

[0870] "Application Example 1"

[0871] (Claim 1)

[0872] A device for entering project information,

[0873] Information processing device means for analyzing the input case information,

[0874] A means for extracting necessary processing tasks by referring to a collection of past case data based on the analyzed information,

[0875] A means for automatically generating a work breakdown structure based on the extracted processing work,

[0876] A means for automatically generating necessary instruction documents based on the work breakdown structure,

[0877] A means for notifying relevant parties of the automatically generated work disassembly structure and instruction documents via a data communication device,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the user can review the automatically generated work disassembly structure and instruction documents and make fine adjustments as necessary.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for storing the automatically generated work breakdown structure and instruction documents in a data storage area and reporting the status in real time.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] Information input method for entering project information,

[0886] A sentiment analysis means for collecting and analyzing the input case information and user sentiment data,

[0887] Based on the analyzed information, a means for referencing past case memory devices and extracting necessary activities,

[0888] A means for automatically generating a work breakdown structure based on the extracted activities,

[0889] A means for automatically generating necessary documents based on the work breakdown structure,

[0890] A means for adjusting the tone and expression of automatically generated documents according to the user's emotional state,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, which allows the user to review the automatically generated work breakdown structure and document and make adjustments as necessary.

[0894] (Claim 3)

[0895] The system according to claim 1, which includes a function to save the automatically generated work breakdown structure and document to a project storage device and notify relevant parties.

[0896] "Application example 2 when combining with an emotional engine"

[0897] (Claim 1)

[0898] A terminal for entering project information,

[0899] A server means for analyzing the input case information and emotional state information,

[0900] Based on the analyzed information, a means for extracting necessary work items by referring to a means for accumulating past cases,

[0901] A means for automatically generating a work element decomposition structure based on the extracted work items,

[0902] A means for automatically generating necessary documents in an emotionally adaptive manner based on the work element decomposition structure,

[0903] An automated emotion sensing method that uses emotion data to determine the priority of household chore support tasks,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, which allows the user to review the automatically generated work element breakdown structure and document and make adjustments as necessary.

[0907] (Claim 3)

[0908] The system according to claim 1, which includes a function to save the automatically generated work element breakdown structure and document in a project storage area and notify relevant parties. [Explanation of Symbols]

[0909] 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 device for entering project information, Information processing device means for analyzing the input case information, A means for extracting necessary processing tasks by referring to a collection of past case data based on the analyzed information, A means for automatically generating a work breakdown structure based on the extracted processing work, A means for automatically generating necessary instruction documents based on the work breakdown structure, A means for notifying relevant parties of the automatically generated work disassembly structure and instruction documents via a data communication device, A system that includes this.

2. The system according to claim 1, wherein the user can review the automatically generated work disassembly structure and instruction documents and make fine adjustments as necessary.

3. The system according to claim 1, further comprising means for storing the automatically generated work breakdown structure and instruction documents in a data storage area and reporting the status in real time.

Images