system

Abstract

We provide the system. [Solution] A means for the user to input project information, A means for the server to receive and analyze the input project information, The server searches the internal resource database based on the analysis results to identify the personnel and departments with the necessary technologies and skill sets. A means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets, A means of generating a support request message based on information that identifies the server, A means by which the server sends a request for assistance message, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including 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 recent years, in project management and operation, a rapid system construction after proposal and order reception has been required. However, it is difficult to quickly secure persons in charge who are proficient in the technical fields that are crucial for projects and appropriate resources. In particular, in the case of large-scale enterprises and projects covering a wide range of fields, the difficulty further increases. For this reason, a system for efficiently and quickly assembling a project system is required, but the current existing systems do not sufficiently solve this problem.

Means for Solving the Problems

[0005] The present invention provides a system for quickly assembling a project system after proposal and order reception. The system of the present invention includes the following means:

[0006] A means for users to input project information,

[0007] A means for the server to receive and analyze the input project information,

[0008] The server searches the internal resource database based on the analysis results to identify the personnel and departments with the necessary technologies and skill sets.

[0009] A means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets,

[0010] A means of generating a support request message based on information that identifies the server,

[0011] A means by which the server sends a request for assistance message,

[0012] Including this will enable us to respond quickly and efficiently to the core technical areas of the project.

[0013] In particular, including a project overview, required technical skills, and contact information in the support request message enables immediate response from the relevant personnel and departments. Furthermore, including a means for users to enter login information and log into the system enhances security and access control. This allows for the rapid and efficient establishment of a project structure.

[0014] A "user" is an individual or legal entity that logs into the system and enters project information.

[0015] "Project information" refers to data that includes project details such as the client name, project content, required technologies, and required skill sets.

[0016] A "server" is a computer system that receives data entered by users, analyzes it, searches a resource database, and generates and sends support request messages.

[0017] "Analysis" is a process of identifying the necessary technologies and skill sets by using technologies such as natural language processing on the received project information.

[0018] The "intra-company resource database" is a database containing information on the persons in charge and departments within the company who have technologies and skill sets.

[0019] The "external vendor database" is a database containing information on external vendors who have the necessary technologies and skill sets.

[0020] The "support request message" is a message for project handling sent to the identified in-house persons in charge, departments, or external vendors.

[0021] The "project overview" is information explaining the basic content and purpose of the project.

[0022] The "necessary technical skills" refer to the technologies and skills required for project execution.

[0023] The "contact information" is information for contacting the persons in charge or vendors who receive the support request, including, for example, email addresses and phone numbers.

[0024] The "login information" is authentication information required for a user to access the system, including, for example, a username and password.

Brief Description of the Drawings

[0025] [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. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0026] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0027] First, let's explain the terminology used in the following explanation.

[0028] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0029] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0030] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0031] 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).

[0032] 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."

[0033] [First Embodiment]

[0034] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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".

[0046] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Specific embodiments for carrying out this invention are described below.

[0047] System Overview

[0048] 1. User requirements input

[0049] The user uses a dedicated terminal to enter project details. This information includes the client name, project description, required technologies, and required skill set. After entering this information and pressing the submit button, the data is sent from the terminal to the server.

[0050] 2. Receiving and analyzing data on the server

[0051] The server receives project information sent from the terminal. After receiving the information, the server uses natural language processing to analyze it and identify the necessary technologies and required skill sets. This analysis clarifies the core technical requirements of the project.

[0052] 3. Searching for internal resources

[0053] Based on the analysis results, the server searches the company's internal resource database. This database contains information about the skill sets and work experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[0054] 4. Selection of potential external vendors

[0055] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[0056] 5. Generating a support request message

[0057] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information.

[0058] 6. Sending a request for assistance

[0059] The server sends the generated support request message to the appropriate person / department and external vendor. Messages are sent to internal personnel and departments via the internal system, and to external vendors via email or API.

[0060] Specific example

[0061] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0062] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[0063] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the necessary technologies "React, Node.js, AWS®" and required skill sets "Frontend development, backend development, cloud infrastructure".

[0064] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0065] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[0066] As described above, the present invention makes it possible to quickly assemble a project structure after proposal and order acceptance, thereby contributing to the success of the project.

[0067] The following describes the processing flow.

[0068] Step 1:

[0069] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[0070] Step 2:

[0071] The user accesses the project information input screen and enters the necessary project information. This information includes "customer name," "project details," "required technologies," and "required skill set." Once the user has entered all the information and pressed the submit button, the data is sent from the terminal to the server.

[0072] Step 3:

[0073] The server receives project information sent from the terminal. The received data is stored as detailed project information.

[0074] Step 4:

[0075] The server analyzes the stored project information using natural language processing. Specifically, it performs text analysis to extract necessary technologies and required skill sets from the project content.

[0076] Step 5:

[0077] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server identifies and creates a list of persons and departments that match the required technologies and required skill sets.

[0078] Step 6:

[0079] The server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server identifies and lists external vendors that match the required technologies and skill sets.

[0080] Step 7:

[0081] The server generates a support request message based on the identified internal contact person, department, and external vendor information. This message includes a project overview, required technical skills, and contact information.

[0082] Step 8:

[0083] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, it also sends the support request message to external vendors via email or API.

[0084] Step 9:

[0085] The server records the transmission results and monitors replies and response status from the person or department to whom the support request was sent, as well as from external vendors. This allows for real-time monitoring of project progress.

[0086] (Example 1)

[0087] 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."

[0088] Traditional project management systems made it difficult to quickly and efficiently assemble a project structure after a proposal or order was received. Specifically, the process of quickly identifying appropriate internal resources and external vendors based on detailed project information, and generating and sending support request messages, was done manually, which was time-consuming and laborious. Furthermore, the lack of centralized management for information analysis and message sending resulted in delays in project initiation.

[0089] 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.

[0090] In this invention, the server includes means for a user to input project information, means for receiving the input project information and storing it in a database, means for analyzing the stored project information using natural language processing, means for searching an internal resource database to identify personnel or departments with the necessary technologies and skill sets, means for searching an external vendor database to identify vendors with the necessary technologies and skill sets, means for generating support request messages based on the identified information, means for sending support request messages, and means for sending messages to internal personnel via an internal messaging system and to external vendors via an email service. This enables centralized management from project information input to analysis, resource identification, message generation, and transmission, making it possible to quickly and efficiently build a project structure.

[0091] "Project information" refers to detailed project information, specifically including the client name, project description, required technologies, and required skill sets.

[0092] "User" refers to a person or entity that enters project information.

[0093] A "server" refers to a computer system that receives, stores, analyzes, searches for resources, generates messages, and sends project information entered by users.

[0094] A "database" is a system for managing information used to store project information, and specifically includes relational databases such as MySQL (registered trademark) and PostgreSQL.

[0095] "Natural language processing" refers to the technology of analyzing and processing human language using computers, and specifically includes technologies that utilize libraries such as Python's NLTK and spaCy.

[0096] An "internal resource database" is a database that stores the skill sets and work experience of each individual employee and department.

[0097] An "external vendor database" is a database that stores the technical skills and services that registered external vendors can provide.

[0098] A "request for support message" refers to a message sent to a designated internal contact person or external vendor, including an overview of the project, required technical skills, and contact information.

[0099] An "internal messaging system" is a communication system used to send messages to internal personnel, and specifically refers to systems such as Slack.

[0100] A "mail service" refers to an email sending service used to send messages to external vendors, specifically services such as SendGrid.

[0101] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends a support request message.

[0102] Hardware and software

[0103] hardware

[0104] Servers: Use on-premises or cloud servers (e.g., Amazon Web Services, Microsoft Azure).

[0105] Terminal: A PC or mobile device used by a user to input information.

[0106] software

[0107] Natural language processing libraries: Use Python's NLTK and spaCy to analyze project information.

[0108] Database: We use MySQL or PostgreSQL to store information on internal resources and external vendors.

[0109] Messaging system: We use the Slack API for internal messaging and the SendGrid API for external vendors.

[0110] Detailed explanation

[0111] 1. User input

[0112] The user uses a dedicated terminal to enter project details. The input form includes fields for "Client Name," "Project Description," "Required Technologies," and "Required Skill Set." Once the user has finished entering the information, they press the "Submit" button.

[0113] 2. Receiving and storing data

[0114] The server receives project information submitted by the user. The received data is immediately stored in the database. The data is sent in JSON format and stored in a database such as MySQL.

[0115] 3. Data Analysis

[0116] The server uses natural language processing libraries (e.g., Python's NLTK or spaCy) to analyze the stored project information. The purpose of the analysis is to identify the necessary technologies and required skill sets from the project content. For example, from a project description such as "Web application development," it identifies technologies such as "React," "Node.js," and "AWS," and confirms that the required skill set includes "frontend development," "backend development," and "cloud infrastructure."

[0117] 4. Searching for internal resources

[0118] The server searches the internal resource database based on the analyzed technologies and skill sets. It executes SQL queries to list the individuals and departments with the identified technologies. For example, it might list employees who can handle "front-end development" and identify departments that can handle "back-end development."

[0119] 5. Selection of potential external vendors

[0120] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. SQL queries are used to search for external vendors that can provide "React" or "Node.js" and list the relevant vendors.

[0121] 6. Generating and sending a support request message

[0122] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[0123] Specific example

[0124] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0125] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[0126] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the required technologies "React, Node.js, AWS" and the required skill sets "frontend development, backend development, cloud infrastructure".

[0127] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0128] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[0129] Example of a prompt

[0130] Examples of prompts input to a generative AI model:

[0131] "For a new web application development project, please generate a request for assistance message to find internal resources and external vendors with the following technologies and skill sets. Required technologies: React, Node.js, AWS. Required skill sets: Frontend development, backend development, cloud infrastructure."

[0132] The above describes the embodiments for carrying out the present invention. This system makes it possible to quickly establish a project structure with minimal effort, thereby contributing to the success of the project.

[0133] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0134] Step 1:

[0135] The user uses a dedicated terminal to enter project details. Specifically, they enter the "Client Name," "Project Details," "Required Technologies," and "Required Skill Set" into a form on the terminal and press the "Submit" button. This entered data is then sent to the system.

[0136] Input: Project information (customer name, project details, required technologies, required skill set)

[0137] Output: Sending data to the server

[0138] Step 2:

[0139] The server receives project information sent from the terminal. The received data is immediately stored in the database. The data is received in JSON format and saved to a database such as MySQL. After storage, a log is generated to confirm that the data was saved correctly.

[0140] Input: Project information submitted by the user (in JSON format)

[0141] Output: Saved to database, save confirmation log

[0142] Specific actions:

[0143] Data reception: The server receives data via HTTP requests.

[0144] Data storage: Received data is parsed and stored in the database.

[0145] Log generation: Generates a log indicating successful data storage and records it in the logging system.

[0146] Step 3:

[0147] The server retrieves the stored project information and analyzes it using a natural language processing library (e.g., Python's NLTK or spaCy). This analysis identifies the necessary technologies and required skill sets from the project content. For example, from a project description like "Web application development," it identifies technologies such as "React," "Node.js," and "AWS."

[0148] Input: Project information stored in the database

[0149] Output: Analysis results (required technologies and required skill set)

[0150] Specific actions:

[0151] Data retrieval: Execute an SQL query to retrieve project information from the database.

[0152] Data Analysis: Analyze project content using natural language processing libraries.

[0153] Technical Identification: Identify the necessary technologies and required skill sets from the analysis results.

[0154] Step 4:

[0155] Based on the analysis results, the server searches the company's internal resource database. It executes SQL queries to list the individuals or departments with the identified skills. For example, it might identify employees who can handle "front-end development" or departments that can handle "back-end development."

[0156] Input: Analysis results (required technologies and skill sets)

[0157] Output: List of internal resources

[0158] Specific actions:

[0159] Database search: Use SQL queries to search internal resource databases.

[0160] Resource Identification: List the individuals or departments that match the identified technologies and skill sets.

[0161] Step 5:

[0162] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. Using SQL queries, it searches for external vendors that can provide "React" or "Node.js" and lists the relevant vendors.

[0163] Input: Analysis results (required technologies and skill sets)

[0164] Output: List of external vendors

[0165] Specific actions:

[0166] Database search: Search external vendor databases using SQL queries.

[0167] Vendor Identification: List external vendors that match the identified technologies and skill sets.

[0168] Step 6:

[0169] The server generates a support request message based on the listed information for internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[0170] Input: Information on listed internal contacts, departments, and external vendors.

[0171] Output: Assistance request message

[0172] Specific actions:

[0173] Message generation: Generate a support request message based on the listed information.

[0174] Message sending: Messages are sent to internal contacts via the Slack API and to external vendors via the SendGrid API.

[0175] The above is a detailed explanation of the program's processing flow. This makes it possible to quickly and efficiently establish a project structure.

[0176] (Application Example 1)

[0177] 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."

[0178] In modern manufacturing, launching new product lines and modifying existing production lines are frequent occurrences, but identifying and deploying the right resources with the necessary skills and skill sets in a short period of time is difficult. In particular, the appropriate selection of factory robots and operators is crucial for efficient production, but current methods are time-consuming, labor-intensive, and inefficient. To solve this problem, a system is needed that can quickly identify and deploy the necessary resources.

[0179] 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.

[0180] In this invention, the server includes means for the user to input project information, means for the server to receive and analyze the input project information, means for the server to search an internal resource database based on the analysis results and identify personnel or departments with the necessary technologies and skill sets, means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets, means for the server to generate a support request message based on the identified information, means for the server to send the support request message, and means for analyzing the necessary skills and skill sets when a manufacturing line is changed or a new project arises and identifying appropriate factory robots and operators. This enables efficient changes to manufacturing lines and rapid startup of new lines.

[0181] "Project information" refers to detailed data about changes to the manufacturing line or new projects, specifically including the project overview, required technologies, and required skill sets.

[0182] A "server" is a computer system that receives information entered by a user and performs analysis and processing on it.

[0183] An "internal resource database" is a database that contains information about the technologies, skill sets, and work experience of each individual and department within the company.

[0184] An "external vendor database" is a database that lists the technical skills and services that registered external technology providers and service vendors can offer.

[0185] A "request for support message" is a message sent by the server to a designated person, department, or external vendor to request support, and includes a project overview, required technical skills, and contact information.

[0186] A "factory robot" is an automated device used to perform specific tasks or operations on a manufacturing line or in a factory.

[0187] An "operator" refers to a person responsible for operating and managing equipment and systems in a factory or manufacturing line.

[0188] "Analysis" refers to data processing that identifies the necessary technologies and skill sets based on the input project information.

[0189] System Overview

[0190] This invention is a system for quickly identifying the necessary skills and skill sets and selecting appropriate factory robots and operators when changing manufacturing lines or launching new product lines. The system includes a process of receiving project information entered by the user, analyzing it, and automatically allocating appropriate resources.

[0191] 1. User requirements input

[0192] Users use a dedicated device (smartphone, tablet, PC, etc.) to input detailed project information. This information includes a project overview, required skills, and required skill sets. For example, it might include details such as "manufacturing line modification" or "launching a new product line."

[0193] 2. Receiving and analyzing data on the server

[0194] The server receives project information sent from the terminal. After receiving the information, the server analyzes it using natural language processing (NLP) to identify the necessary skills and skill sets. This process utilizes NLP libraries such as spaCy and NLTK.

[0195] 3. Searching for internal resources

[0196] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals and departments that possess the identified skills and skill sets. This database contains information about the skills and work experience of internal engineers and departments.

[0197] 4. Selection of potential external vendors

[0198] If necessary, the server also searches an external vendor database. This database contains information about the skills and services that registered technology providers and service vendors can offer. The server searches the database and lists external vendors that possess the identified skills and skill sets.

[0199] 5. Generating a support request message

[0200] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required skills, and contact information. Template engines such as EJS or Handlebars are used to generate the message.

[0201] 6. Sending a request for assistance

[0202] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[0203] Specific example

[0204] For example, when a manufacturer sets up an assembly line for a new smartphone, the user will enter the following information:

[0205] "Customer name: A certain customer"

[0206] "Project details: Assembling a new smartphone"

[0207] Required skills: Precision operation of robotic arms, quality inspection.

[0208] Required skill set: precision operation skills, quality inspection using image analysis.

[0209] Example of a prompt:

[0210] A new smartphone assembly project has emerged.

[0211] 1. Customer name: A certain customer

[0212] 2. Project details: Assembling a new smartphone

[0213] 3. Required skills: Precision operation of robotic arms, quality inspection

[0214] 4. Required skill set: Precision operation techniques, quality inspection using image analysis.

[0215] Identify the most suitable internal resources and external vendors.

[0216] By using this system, efficient changes to manufacturing lines and rapid launch of new product lines can be achieved, leading to expected improvements in productivity.

[0217] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0218] Step 1:

[0219] The user enters project information using a terminal. This information includes a project overview, required skills, and required skill sets. This information is then sent from the terminal to the server.

[0220] (Input: Project overview, required skills, required skill set / Output: Project information sent to the server)

[0221] Step 2:

[0222] The server receives project information sent from the terminal. The received information is analyzed using a natural language processing (NLP) library (e.g., spaCy or NLTK) to identify the necessary skills and skill sets.

[0223] (Input: Project information sent from the terminal / Output: Analyzed skills and skill sets)

[0224] Step 3:

[0225] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals or departments who possess the identified skills or skill sets. This search is performed using SQL queries or similar methods.

[0226] (Input: Analyzed skills and skill sets / Output: Listed personnel and departments)

[0227] Step 4:

[0228] If necessary, the server searches the external vendor database. It searches the database of registered technology providers and service vendors and lists external vendors that possess the identified skills and skill sets.

[0229] (Input: Analyzed skills and skill sets / Output: Listed external vendors)

[0230] Step 5:

[0231] The server generates a support request message based on internal resources and information from external vendors. This message includes a project overview, required skills, and contact information. A template engine (such as EJS or Handlebars) is used to generate the message.

[0232] (Input: Information on listed personnel, departments, and external vendors / Output: Support request message)

[0233] Step 6:

[0234] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[0235] (Input: Assistance request message / Output: Sent message)

[0236] This series of processing steps enables efficient and rapid launch of changes to manufacturing lines and new product lines.

[0237] 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.

[0238] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Furthermore, this invention incorporates an emotion engine to recognize and appropriately respond to the user's emotions.

[0239] System Overview

[0240] 1. User requirements input

[0241] Users input project details using a dedicated terminal. This information includes client name, project description, required technologies, and required skill sets. The emotion engine analyzes the user's facial expressions, input speed, and voice as they input information to recognize their emotions. This information is collected in real time.

[0242] 2. Receiving and analyzing data on the server

[0243] The server receives project information sent from the terminal and emotional data from the emotion engine. After receiving the data, the server analyzes the project information to identify the necessary technologies and required skill sets. It also analyzes the user's psychological state based on the emotional data and evaluates its impact on project progress.

[0244] 3. Searching for internal resources

[0245] The server searches the company's internal resource database based on the analysis results. This database contains information about the skill sets and experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[0246] 4. Selection of potential external vendors

[0247] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[0248] 5. Generating a support request message

[0249] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[0250] 6. Sending a request for assistance

[0251] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, the support request message is sent to external vendors via email or API. The emotional state of the user, as detected by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[0252] Specific example

[0253] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0254] "Customer name"

[0255] "Project Details"

[0256] "Required skills"

[0257] "Required Skill Set"

[0258] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[0259] The server then identifies the technologies required for "Web application development" ("React, Node.js, AWS") and the required skill sets ("frontend development, backend development, cloud infrastructure") based on the project description.

[0260] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0261] Finally, the server uses this information to generate a support request message, reflecting any stress the user may be experiencing and emphasizing the urgency. This allows the support request message to be sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[0265] Step 2:

[0266] The user accesses the project information input screen and enters the necessary project information. This information includes "client name," "project details," "required technologies," and "required skill set." The emotion engine monitors the user's facial expressions, voice, and input speed in real time to recognize their emotions. After the user has entered all the information and the emotion data has been collected, they press the submit button, and the data is sent from the terminal to the server.

[0267] Step 3:

[0268] The server receives project information sent from the terminal and sentiment data from the sentiment engine. The received data is stored as detailed project information.

[0269] Step 4:

[0270] The server analyzes stored project information using natural language processing. Specifically, it extracts necessary technologies and required skill sets from the project content. Meanwhile, it analyzes emotional data to evaluate the user's psychological state.

[0271] Step 5:

[0272] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server lists the persons and departments that possess the identified technologies and skill sets.

[0273] Step 6:

[0274] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services of registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[0275] Step 7:

[0276] The server generates a support request message based on the information of specific in-house personnel, departments, and external vendors. This message includes the project overview, required technical skills, and contact information. Also, based on the user's sentiment information analyzed by the sentiment engine, the wording of the message is adjusted. Appropriate expressions are added or changed according to whether the user is feeling stressed or other sentiment states.

[0277] Step 8:

[0278] The server sends the generated support request message to the in-house personnel or departments. This is notified through the in-house system. Furthermore, the support request message is sent to external vendors via email or API. The user's sentiment state detected by the sentiment engine is also notified to the project management team, and appropriate countermeasures can be taken promptly.

[0279] Step 9:

[0280] The server records the sending results and monitors the responses and handling situations from the personnel or departments to whom the support request was sent and external vendors. This enables real-time understanding of the project progress and allows for adjustment of responses as needed. [[ID=...]] [[ID=...]]

[0281] [[ID=...]] Specific example:

[0282] For example, when a user wins a new web application development project, the following information is entered into the terminal:

[0283] "Customer name"

[0284] "Project content"

[0285] "Required technologies"

[0286] "Required skill set"

[0287] During this time, the emotion engine analyzes the user's input speed, voice, and facial expressions to determine whether the user is experiencing stress. If the server determines that the user is stressed, it uses this emotional information to add urgency-indicating wording to the support request message. This message is then sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[0288] (Example 2)

[0289] 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".

[0290] Traditional project management systems make it difficult to quickly establish a project structure, requiring significant time and effort to identify the appropriate personnel and external vendors. Furthermore, they generate support request messages without considering user sentiment, resulting in problems in responding appropriately to urgency or specific situations.

[0291] 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.

[0292] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching an internal resource database and an external vendor database to identify personnel and departments with the necessary technologies and skill sets, means for generating and sending support request messages based on the identified information, means for analyzing the user's emotions in real time and adjusting the wording of the support request message based on the analysis results, and means for notifying the project management team of the analyzed emotion information. This enables the rapid and efficient construction of a project structure and the generation of appropriate support request messages that take the user's emotions into consideration.

[0293] A "user" is a person or organization that has the role of inputting project information into the system and providing the necessary data.

[0294] "Project information" refers to detailed information related to a specific project, including the client name, project description, required technologies, and required skill sets.

[0295] A "terminal" refers to a device used by a user to input project information, and includes personal computers, tablets, and smartphones.

[0296] An "emotion engine" is a combination of software and hardware that analyzes a user's facial expressions, voice, and input speed in real time to collect and determine emotional data.

[0297] A "server" is a computer system that receives, analyzes, and stores project information and sentiment data entered by users.

[0298] "Analyzing" is the process of identifying the necessary technologies and skill sets based on the received data, and evaluating the user's emotional state.

[0299] An "internal resource database" is a database that collects information about the skill sets and experience held by various individuals and departments within a company.

[0300] An "external vendor database" is a database that compiles information on the technical skills and services that external service providers can offer.

[0301] A "request for support message" is a message that includes a project overview, required technical skills, and contact information, and is a document generated to request support from internal personnel or external vendors.

[0302] A "project management team" is a team responsible for overseeing the progress of a project and managing its overall operation.

[0303] "To notify" refers to the process of transmitting analysis results and important information to the relevant responsible persons or teams.

[0304] The present invention relates to a system for quickly and efficiently constructing a project system after proposal and order acceptance. This system includes a series of processes in which a user inputs project information, a server analyzes the information, identifies appropriate in-house resources and external vendors, and generates and transmits a support request message. In addition, the present invention combines an emotion engine for recognizing the user's emotions and responding appropriately.

[0305] First, the user uses a dedicated terminal to input detailed information about the project. This information includes "customer name", "project content", "required technology", and "required skill set". The emotion engine analyzes the user's expression, input speed, voice, etc. when the user inputs, and recognizes the user's emotions. This information is collected in real time.

[0306] Next, the server receives the project information and emotion data transmitted from the terminal. The server analyzes these data, identifies the required technology and required skill set. In addition, based on the emotion data, the server analyzes the user's psychological state and evaluates the impact on the project progress.

[0307] Based on the analysis results, the server searches the in-house resource database. This database describes the skill sets and experiences of each in-house responsible person and department. The server lists up the responsible persons and departments having the identified technology and skill set. If necessary, the server also searches the external vendor database, which describes the technical skills and available services of the registered external vendors. The server lists up the external vendors having the identified technology and skill set.

[0308] The server then generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[0309] Finally, the server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. In addition, the support request message is sent to external vendors via email or API. The emotional state of the user, as perceived by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[0310] Specific example

[0311] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0312] Customer name: "X Corporation"

[0313] Project Description: "Development of a new web application for businesses"

[0314] Required technologies: React, Node.js, AWS

[0315] Required skill set: "Frontend development, backend development, cloud infrastructure"

[0316] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[0317] Example of a prompt

[0318] Please enter details for your new web application development project. Include the client name, project description, required technologies, and required skill set. Please ensure accurate input, as emotional information will also be analyzed during the input process.

[0319] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0320] Step 1: User enters project information

[0321] Specific operation: The user enters "customer name," "project details," "required technologies," and "required skill set" into a dedicated form on the terminal. The input information is analyzed in real time by an emotion engine, and emotion data is generated from facial expressions and voice.

[0322] Input: Project information entered by the user, along with the user's facial expressions and voice data.

[0323] Output: Project information and sentiment data saved on the device.

[0324] Step 2: Send

[0325] Specific operation: The terminal combines the input project information and generated emotion data into a single data packet. This data packet is then sent to the server via the network.

[0326] Input: Project information and sentiment data saved on the device.

[0327] Output: Data packets sent to the server.

[0328] Step 3: Receiving and saving data

[0329] Specific operation: The server decompresses the received data packets and saves project information and sentiment data separately.

[0330] Input: Data packets sent to the server.

[0331] Output: Project information and sentiment data stored on the server.

[0332] Step 4: Analyze project information

[0333] Specific operation: The server uses a text analysis engine to analyze the received project information. From the analysis results, it identifies the necessary technologies and required skill sets.

[0334] Input: Project information stored on the server.

[0335] Output: Identified required technologies and required skill sets.

[0336] Step 5: Analyzing emotional data

[0337] Specific operation: The server uses an emotion engine to analyze stored emotion data. It assesses whether the user is experiencing stress and to what extent.

[0338] Input: Emotional data stored on the server.

[0339] Output: Evaluation results regarding the user's psychological state.

[0340] Step 6: Search for internal resources

[0341] Specific operation: The server searches the company's internal resource database using SQL queries to identify the personnel or departments with the necessary technologies and skill sets.

[0342] Input: Identified required technologies and required skill sets.

[0343] Output: Information on listed internal contacts and departments.

[0344] Step 7: Selecting potential external vendors

[0345] Specific operation: The server uses an API to search an external vendor database and identify external vendors that possess the necessary technologies and skill sets.

[0346] Input: Identified required technologies and required skill sets.

[0347] Output: Information on the listed external vendors.

[0348] Step 8: Generating a support request message

[0349] Specific operation: The server uses a template engine to generate a request for assistance message. It takes sentiment data into consideration and adjusts the message wording as needed.

[0350] Input: Listed internal contact information, external vendor information, and sentiment data evaluation results.

[0351] Output: The generated support request message.

[0352] Step 9: Send a support request message

[0353] Specific operation: The server sends support request messages to the relevant personnel and departments through the internal system. Messages are also sent to external vendors via email or API.

[0354] Input: The generated support request message.

[0355] Output: Support request messages sent to internal staff and external vendors.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0358] Traditional systems required significant time and effort to assemble teams appropriately and quickly after a project was awarded. Furthermore, proceeding with projects without considering the mental state of on-site managers could negatively impact project progress. Additionally, the inability to dynamically generate messages based on user input and emotional states made it difficult to effectively convey the urgency and importance of support requests.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching internal and external resource databases to identify resources with the necessary skill sets, means for generating and sending support request messages, means for analyzing the user's emotions, and means for adjusting the wording of the message based on the emotion analysis results. This makes it possible to quickly and efficiently assemble an appropriate team after receiving a project order and to generate support request messages while taking the user's mental state into consideration.

[0361] A "user" is an individual or organization that inputs and manages project information.

[0362] "Project information" refers to data that includes project details, required technologies, and required skill sets.

[0363] A "server" is a central processing unit that receives and analyzes input information, searches for resources, generates messages, and sends them.

[0364] An "internal resource database" is a database that records the skill sets and experience of each individual and department within a company.

[0365] An "external resource database" is a database that records the skills and services that external vendors and service providers can offer.

[0366] An "emotion analysis system" is a system that analyzes a user's facial expressions, voice, and actions to determine their emotional state.

[0367] A "request for support message" is a notification of support requested by the person or department with the necessary skill set for the project.

[0368] "Urgency" is a concept that describes a situation requiring immediate action during project execution.

[0369] This invention is a system for quickly and efficiently establishing a project structure after a project has been awarded. This system includes a process for analyzing project information and sentiment entered by the user, identifying internal and external resources, and generating and sending support request messages.

[0370] Users input project information using smart glasses or a dedicated terminal. This information includes project details, required technologies, and required skill sets. While the user is inputting, the emotion engine analyzes the user's facial expressions and voice to determine their emotional state in real time, particularly assessing whether the user is experiencing stress.

[0371] The server receives project information and emotional data sent from the user's terminal. First, the server analyzes the project information to identify the technologies and skill sets necessary for project execution. Next, it analyzes the emotional data to evaluate the user's emotional state. Based on this information, it determines the impact on project progress.

[0372] The server then searches the internal resource database and lists the individuals or departments with the identified technologies and skill sets. Furthermore, it searches external resource databases as needed to identify relevant external resources.

[0373] Next, the server generates a request for assistance message based on this resource information. The generated message includes a project overview, required technical skills, and contact information, and the wording is adjusted according to the user's emotional state. For example, if the user is experiencing high levels of stress, language emphasizing urgency will be added.

[0374] Finally, the server sends a request for assistance message to internal personnel and external resources. The message is notified through the internal system and sent to external resources via API or email. The sentiment engine's analysis results are also notified to the project management team, ensuring prompt support.

[0375] To give a concrete example, consider a scenario where a user enters project information for the introduction of a new automated manufacturing line into smart glasses. As the user enters the project details, "Introduction of an automated manufacturing line," and the required skill sets, "Robotics, machine maintenance, programming," the emotion engine analyzes the user's stress level. If the user is experiencing high stress, the server generates a request for assistance message, including this information, clearly indicating that urgent action is required.

[0376] Example of a prompt:

[0377] For the implementation project of a new automated manufacturing line, we need resources with the following skill sets:

[0378] Robotics

[0379] Machine maintenance

[0380] programming

[0381] The supervisor is currently experiencing a high level of stress, and an immediate response is required.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The user enters project information.

[0385] In terms of specific operations, users use smart glasses or a dedicated terminal to input project details, required technologies, and required skill sets via voice input or touch gestures. The entered information is stored on the terminal and sent to the server. The input data includes a project overview, technical skills, and required skill sets.

[0386] Step 2:

[0387] The emotion engine analyzes the user's emotions.

[0388] In terms of operation, the camera and microphone built into the smart glasses capture the user's facial expressions and voice, and emotion analysis is performed in real time. The emotion engine (e.g., Affectiva SDK) uses facial recognition algorithms to evaluate the user's stress level and emotional state. The analysis results are stored as emotion data on the device and sent to the server. The input data is the user's facial expressions and voice data, and the output is the analysis result of the emotional state.

[0389] Step 3:

[0390] The server receives project information and sentiment data.

[0391] In terms of specific operation, the server receives project information and sentiment data sent from the terminal and stores each data independently. The input data consists of project information and sentiment data, and the output consists of these data entries.

[0392] Step 4:

[0393] The server analyzes the project information.

[0394] In terms of specific operations, the server analyzes the received project information to identify the technologies and skill sets required for project execution. This analysis utilizes natural language processing models and data mining techniques. The input data is project information, and the output is a list of required technical skill sets.

[0395] Step 5:

[0396] The server analyzes the emotional data.

[0397] In terms of its specific operation, the server processes the received emotional data and evaluates the user's emotional state. In particular, it determines the user's stress level and analyzes its impact on project progress. The input data is emotional data, and the output is the user's emotional evaluation.

[0398] Step 6:

[0399] The server searches the company's internal resource database.

[0400] In practice, the server searches the internal resource database based on the analysis results to identify individuals and departments with the necessary technologies and skill sets. SQL queries and similarity search algorithms are used for the search. Input data includes the identified technical skills, and the output is a list of corresponding internal resources.

[0401] Step 7:

[0402] The server searches the external resource database.

[0403] In practice, the server searches an external resource database to identify external resources possessing the necessary technologies and skill sets. The search method is similar to internal resource searches, but differs in that it uses an external database. The input data includes identified technical skills, and the output is a list of corresponding external resources.

[0404] Step 8:

[0405] The server generates a support request message.

[0406] Specifically, the server generates a request for assistance message based on identified resource information and the user's emotional state. The message includes a project overview, required technical skills, and contact information. If the user is experiencing high stress levels, urgency is emphasized. Input data consists of the corresponding resource list and emotional analysis results, while output is the request for assistance message.

[0407] Step 9:

[0408] The server sends a request for assistance message.

[0409] In terms of specific operation, the server sends the generated support request message to internal systems and external resources. For internal systems, it uses a notification system; for external resources, it sends the message via API or email. The input data is the support request message, and the output is the transmission status.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] [Second Embodiment]

[0414] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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".

[0426] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Specific embodiments for carrying out this invention are described below.

[0427] System Overview

[0428] 1. User requirements input

[0429] The user uses a dedicated terminal to enter project details. This information includes the client name, project description, required technologies, and required skill set. After entering this information and pressing the submit button, the data is sent from the terminal to the server.

[0430] 2. Receiving and analyzing data on the server

[0431] The server receives project information sent from the terminal. After receiving the information, the server uses natural language processing to analyze it and identify the necessary technologies and required skill sets. This analysis clarifies the core technical requirements of the project.

[0432] 3. Searching for internal resources

[0433] Based on the analysis results, the server searches the company's internal resource database. This database contains information about the skill sets and work experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[0434] 4. Selection of potential external vendors

[0435] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[0436] 5. Generating a support request message

[0437] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information.

[0438] 6. Sending a request for assistance

[0439] The server sends the generated support request message to the appropriate person / department and external vendor. Messages are sent to internal personnel and departments via the internal system, and to external vendors via email or API.

[0440] Specific example

[0441] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0442] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[0443] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the required technologies "React, Node.js, AWS" and the required skill sets "frontend development, backend development, cloud infrastructure".

[0444] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0445] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[0446] As described above, the present invention makes it possible to quickly assemble a project structure after proposal and order acceptance, thereby contributing to the success of the project.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[0450] Step 2:

[0451] The user accesses the project information input screen and enters the necessary project information. This information includes "customer name," "project details," "required technologies," and "required skill set." Once the user has entered all the information and pressed the submit button, the data is sent from the terminal to the server.

[0452] Step 3:

[0453] The server receives project information sent from the terminal. The received data is stored as detailed project information.

[0454] Step 4:

[0455] The server analyzes the stored project information using natural language processing. Specifically, it performs text analysis to extract necessary technologies and required skill sets from the project content.

[0456] Step 5:

[0457] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server identifies and creates a list of persons and departments that match the required technologies and required skill sets.

[0458] Step 6:

[0459] The server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server identifies and lists external vendors that match the required technologies and skill sets.

[0460] Step 7:

[0461] The server generates a support request message based on the identified internal contact person, department, and external vendor information. This message includes a project overview, required technical skills, and contact information.

[0462] Step 8:

[0463] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, it also sends the support request message to external vendors via email or API.

[0464] Step 9:

[0465] The server records the transmission results and monitors replies and response status from the person or department to whom the support request was sent, as well as from external vendors. This allows for real-time monitoring of project progress.

[0466] (Example 1)

[0467] 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".

[0468] Traditional project management systems made it difficult to quickly and efficiently assemble a project structure after a proposal or order was received. Specifically, the process of quickly identifying appropriate internal resources and external vendors based on detailed project information, and generating and sending support request messages, was done manually, which was time-consuming and laborious. Furthermore, the lack of centralized management for information analysis and message sending resulted in delays in project initiation.

[0469] 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.

[0470] In this invention, the server includes means for a user to input project information, means for receiving the input project information and storing it in a database, means for analyzing the stored project information using natural language processing, means for searching an internal resource database to identify personnel or departments with the necessary technologies and skill sets, means for searching an external vendor database to identify vendors with the necessary technologies and skill sets, means for generating support request messages based on the identified information, means for sending support request messages, and means for sending messages to internal personnel via an internal messaging system and to external vendors via an email service. This enables centralized management from project information input to analysis, resource identification, message generation, and transmission, making it possible to quickly and efficiently build a project structure.

[0471] "Project information" refers to detailed project information, specifically including the client name, project description, required technologies, and required skill sets.

[0472] "User" refers to a person or entity that enters project information.

[0473] A "server" refers to a computer system that receives, stores, analyzes, searches for resources, generates messages, and sends project information entered by users.

[0474] A "database" is a system for managing information used to store project information, and specifically includes relational databases such as MySQL and PostgreSQL.

[0475] "Natural language processing" refers to the technology of analyzing and processing human language using computers, and specifically includes technologies that utilize libraries such as Python's NLTK and spaCy.

[0476] An "internal resource database" is a database that stores the skill sets and work experience of each individual employee and department.

[0477] An "external vendor database" is a database that stores the technical skills and services that registered external vendors can provide.

[0478] A "request for support message" refers to a message sent to a designated internal contact person or external vendor, including an overview of the project, required technical skills, and contact information.

[0479] An "internal messaging system" is a communication system used to send messages to internal personnel, and specifically refers to systems such as Slack.

[0480] A "mail service" refers to an email sending service used to send messages to external vendors, specifically services such as SendGrid.

[0481] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends a support request message.

[0482] Hardware and software

[0483] hardware

[0484] Servers: Use on-premises or cloud servers (e.g., Amazon Web Services, Microsoft Azure).

[0485] Terminal: A PC or mobile device used by a user to input information.

[0486] software

[0487] Natural language processing libraries: Use Python's NLTK and spaCy to analyze project information.

[0488] Database: We use MySQL or PostgreSQL to store information on internal resources and external vendors.

[0489] Messaging system: We use the Slack API for internal messaging and the SendGrid API for external vendors.

[0490] Detailed explanation

[0491] 1. User input

[0492] The user uses a dedicated terminal to enter project details. The input form includes fields for "Client Name," "Project Description," "Required Technologies," and "Required Skill Set." Once the user has finished entering the information, they press the "Submit" button.

[0493] 2. Receiving and storing data

[0494] The server receives project information submitted by the user. The received data is immediately stored in the database. The data is sent in JSON format and stored in a database such as MySQL.

[0495] 3. Data Analysis

[0496] The server uses natural language processing libraries (e.g., Python's NLTK or spaCy) to analyze the stored project information. The purpose of the analysis is to identify the necessary technologies and required skill sets from the project content. For example, from a project description such as "Web application development," it identifies technologies such as "React," "Node.js," and "AWS," and confirms that the required skill set includes "frontend development," "backend development," and "cloud infrastructure."

[0497] 4. Searching for internal resources

[0498] The server searches the internal resource database based on the analyzed technologies and skill sets. It executes SQL queries to list the individuals and departments with the identified technologies. For example, it might list employees who can handle "front-end development" and identify departments that can handle "back-end development."

[0499] 5. Selection of potential external vendors

[0500] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. SQL queries are used to search for external vendors that can provide "React" or "Node.js" and list the relevant vendors.

[0501] 6. Generating and sending a support request message

[0502] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[0503] Specific example

[0504] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0505] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[0506] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the required technologies "React, Node.js, AWS" and the required skill sets "frontend development, backend development, cloud infrastructure".

[0507] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0508] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[0509] Example of a prompt

[0510] Examples of prompts input to a generative AI model:

[0511] "For a new web application development project, please generate a request for assistance message to find internal resources and external vendors with the following technologies and skill sets. Required technologies: React, Node.js, AWS. Required skill sets: Frontend development, backend development, cloud infrastructure."

[0512] The above describes the embodiments for carrying out the present invention. This system makes it possible to quickly establish a project structure with minimal effort, thereby contributing to the success of the project.

[0513] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0514] Step 1:

[0515] The user uses a dedicated terminal to enter project details. Specifically, they enter the "Client Name," "Project Details," "Required Technologies," and "Required Skill Set" into a form on the terminal and press the "Submit" button. This entered data is then sent to the system.

[0516] Input: Project information (customer name, project details, required technologies, required skill set)

[0517] Output: Sending data to the server

[0518] Step 2:

[0519] The server receives project information sent from the terminal. The received data is immediately stored in the database. The data is received in JSON format and saved to a database such as MySQL. After storage, a log is generated to confirm that the data was saved correctly.

[0520] Input: Project information submitted by the user (in JSON format)

[0521] Output: Saved to database, save confirmation log

[0522] Specific actions:

[0523] Data reception: The server receives data via HTTP requests.

[0524] Data storage: Received data is parsed and stored in the database.

[0525] Log generation: Generates a log indicating successful data storage and records it in the logging system.

[0526] Step 3:

[0527] The server retrieves the stored project information and analyzes it using a natural language processing library (e.g., Python's NLTK or spaCy). This analysis identifies the necessary technologies and required skill sets from the project content. For example, from a project description like "Web application development," it identifies technologies such as "React," "Node.js," and "AWS."

[0528] Input: Project information stored in the database

[0529] Output: Analysis results (required technologies and required skill set)

[0530] Specific actions:

[0531] Data retrieval: Execute an SQL query to retrieve project information from the database.

[0532] Data Analysis: Analyze project content using natural language processing libraries.

[0533] Technical Identification: Identify the necessary technologies and required skill sets from the analysis results.

[0534] Step 4:

[0535] Based on the analysis results, the server searches the company's internal resource database. It executes SQL queries to list the individuals or departments with the identified skills. For example, it might identify employees who can handle "front-end development" or departments that can handle "back-end development."

[0536] Input: Analysis results (required technologies and skill sets)

[0537] Output: List of internal resources

[0538] Specific actions:

[0539] Database search: Use SQL queries to search internal resource databases.

[0540] Resource Identification: List the individuals or departments that match the identified technologies and skill sets.

[0541] Step 5:

[0542] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. Using SQL queries, it searches for external vendors that can provide "React" or "Node.js" and lists the relevant vendors.

[0543] Input: Analysis results (required technologies and skill sets)

[0544] Output: List of external vendors

[0545] Specific actions:

[0546] Database search: Search external vendor databases using SQL queries.

[0547] Vendor Identification: List external vendors that match the identified technologies and skill sets.

[0548] Step 6:

[0549] The server generates a support request message based on the listed information for internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[0550] Input: Information on listed internal contacts, departments, and external vendors.

[0551] Output: Assistance request message

[0552] Specific actions:

[0553] Message generation: Generate a support request message based on the listed information.

[0554] Message sending: Messages are sent to internal contacts via the Slack API and to external vendors via the SendGrid API.

[0555] The above is a detailed explanation of the program's processing flow. This makes it possible to quickly and efficiently establish a project structure.

[0556] (Application Example 1)

[0557] 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."

[0558] In modern manufacturing, launching new product lines and modifying existing production lines are frequent occurrences, but identifying and deploying the right resources with the necessary skills and skill sets in a short period of time is difficult. In particular, the appropriate selection of factory robots and operators is crucial for efficient production, but current methods are time-consuming, labor-intensive, and inefficient. To solve this problem, a system is needed that can quickly identify and deploy the necessary resources.

[0559] 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.

[0560] In this invention, the server includes means for the user to input project information, means for the server to receive and analyze the input project information, means for the server to search an internal resource database based on the analysis results and identify personnel or departments with the necessary technologies and skill sets, means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets, means for the server to generate a support request message based on the identified information, means for the server to send the support request message, and means for analyzing the necessary skills and skill sets when a manufacturing line is changed or a new project arises and identifying appropriate factory robots and operators. This enables efficient changes to manufacturing lines and rapid startup of new lines.

[0561] "Project information" refers to detailed data about changes to the manufacturing line or new projects, specifically including the project overview, required technologies, and required skill sets.

[0562] A "server" is a computer system that receives information entered by a user and performs analysis and processing on it.

[0563] An "internal resource database" is a database that contains information about the technologies, skill sets, and work experience of each individual and department within the company.

[0564] An "external vendor database" is a database that lists the technical skills and services that registered external technology providers and service vendors can offer.

[0565] A "request for support message" is a message sent by the server to a designated person, department, or external vendor to request support, and includes a project overview, required technical skills, and contact information.

[0566] A "factory robot" is an automated device used to perform specific tasks or operations on a manufacturing line or in a factory.

[0567] An "operator" refers to a person responsible for operating and managing equipment and systems in a factory or manufacturing line.

[0568] "Analysis" refers to data processing that identifies the necessary technologies and skill sets based on the input project information.

[0569] System Overview

[0570] This invention is a system for quickly identifying the necessary skills and skill sets and selecting appropriate factory robots and operators when changing manufacturing lines or launching new product lines. The system includes a process of receiving project information entered by the user, analyzing it, and automatically allocating appropriate resources.

[0571] 1. User requirements input

[0572] Users use a dedicated device (smartphone, tablet, PC, etc.) to input detailed project information. This information includes a project overview, required skills, and required skill sets. For example, it might include details such as "manufacturing line modification" or "launching a new product line."

[0573] 2. Receiving and analyzing data on the server

[0574] The server receives project information sent from the terminal. After receiving the information, the server analyzes it using natural language processing (NLP) to identify the necessary skills and skill sets. This process utilizes NLP libraries such as spaCy and NLTK.

[0575] 3. Searching for internal resources

[0576] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals and departments that possess the identified skills and skill sets. This database contains information about the skills and work experience of internal engineers and departments.

[0577] 4. Selection of potential external vendors

[0578] If necessary, the server also searches an external vendor database. This database contains information about the skills and services that registered technology providers and service vendors can offer. The server searches the database and lists external vendors that possess the identified skills and skill sets.

[0579] 5. Generating a support request message

[0580] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required skills, and contact information. Template engines such as EJS or Handlebars are used to generate the message.

[0581] 6. Sending a request for assistance

[0582] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[0583] Specific example

[0584] For example, when a manufacturer sets up an assembly line for a new smartphone, the user will enter the following information:

[0585] "Customer name: A certain customer"

[0586] "Project details: Assembling a new smartphone"

[0587] Required skills: Precision operation of robotic arms, quality inspection.

[0588] Required skill set: precision operation skills, quality inspection using image analysis.

[0589] Example of a prompt:

[0590] A new smartphone assembly project has emerged.

[0591] 1. Customer name: A certain customer

[0592] 2. Project details: Assembling a new smartphone

[0593] 3. Required skills: Precision operation of robotic arms, quality inspection

[0594] 4. Required skill set: Precision operation techniques, quality inspection using image analysis.

[0595] Identify the most suitable internal resources and external vendors.

[0596] By using this system, efficient changes to manufacturing lines and rapid launch of new product lines can be achieved, leading to expected improvements in productivity.

[0597] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0598] Step 1:

[0599] The user enters project information using a terminal. This information includes a project overview, required skills, and required skill sets. This information is then sent from the terminal to the server.

[0600] (Input: Project overview, required skills, required skill set / Output: Project information sent to the server)

[0601] Step 2:

[0602] The server receives project information sent from the terminal. The received information is analyzed using a natural language processing (NLP) library (e.g., spaCy or NLTK) to identify the necessary skills and skill sets.

[0603] (Input: Project information sent from the terminal / Output: Analyzed skills and skill sets)

[0604] Step 3:

[0605] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals or departments who possess the identified skills or skill sets. This search is performed using SQL queries or similar methods.

[0606] (Input: Analyzed skills and skill sets / Output: Listed personnel and departments)

[0607] Step 4:

[0608] If necessary, the server searches the external vendor database. It searches the database of registered technology providers and service vendors and lists external vendors that possess the identified skills and skill sets.

[0609] (Input: Analyzed skills and skill sets / Output: Listed external vendors)

[0610] Step 5:

[0611] The server generates a support request message based on internal resources and information from external vendors. This message includes a project overview, required skills, and contact information. A template engine (such as EJS or Handlebars) is used to generate the message.

[0612] (Input: Information on listed personnel, departments, and external vendors / Output: Support request message)

[0613] Step 6:

[0614] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[0615] (Input: Assistance request message / Output: Sent message)

[0616] This series of processing steps enables efficient and rapid launch of changes to manufacturing lines and new product lines.

[0617] 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.

[0618] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Furthermore, this invention incorporates an emotion engine to recognize and appropriately respond to the user's emotions.

[0619] System Overview

[0620] 1. User requirements input

[0621] Users input project details using a dedicated terminal. This information includes client name, project description, required technologies, and required skill sets. The emotion engine analyzes the user's facial expressions, input speed, and voice as they input information to recognize their emotions. This information is collected in real time.

[0622] 2. Receiving and analyzing data on the server

[0623] The server receives project information sent from the terminal and emotional data from the emotion engine. After receiving the data, the server analyzes the project information to identify the necessary technologies and required skill sets. It also analyzes the user's psychological state based on the emotional data and evaluates its impact on project progress.

[0624] 3. Searching for internal resources

[0625] The server searches the company's internal resource database based on the analysis results. This database contains information about the skill sets and experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[0626] 4. Selection of potential external vendors

[0627] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[0628] 5. Generating a support request message

[0629] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[0630] 6. Sending a request for assistance

[0631] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, the support request message is sent to external vendors via email or API. The emotional state of the user, as detected by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[0632] Specific example

[0633] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0634] "Customer name"

[0635] "Project Details"

[0636] "Required skills"

[0637] "Required Skill Set"

[0638] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[0639] The server then identifies the technologies required for "Web application development" ("React, Node.js, AWS") and the required skill sets ("frontend development, backend development, cloud infrastructure") based on the project description.

[0640] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0641] Finally, the server uses this information to generate a support request message, reflecting any stress the user may be experiencing and emphasizing the urgency. This allows the support request message to be sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[0645] Step 2:

[0646] The user accesses the project information input screen and enters the necessary project information. This information includes "client name," "project details," "required technologies," and "required skill set." The emotion engine monitors the user's facial expressions, voice, and input speed in real time to recognize their emotions. After the user has entered all the information and the emotion data has been collected, they press the submit button, and the data is sent from the terminal to the server.

[0647] Step 3:

[0648] The server receives project information sent from the terminal and sentiment data from the sentiment engine. The received data is stored as detailed project information.

[0649] Step 4:

[0650] The server analyzes stored project information using natural language processing. Specifically, it extracts necessary technologies and required skill sets from the project content. Meanwhile, it analyzes emotional data to evaluate the user's psychological state.

[0651] Step 5:

[0652] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server lists the persons and departments that possess the identified technologies and skill sets.

[0653] Step 6:

[0654] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services of registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[0655] Step 7:

[0656] The server generates a support request message based on identified internal contacts, departments, and external vendor information. This message includes a project overview, required technical skills, and contact information. The message's wording is also adjusted based on the user's emotional state, as analyzed by the emotion engine. Appropriate phrasing is added or modified depending on whether the user is stressed or experiencing other emotional states.

[0657] Step 8:

[0658] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, support request messages are sent to external vendors via email or API. The emotional state of the user detected by the emotion engine is also notified to the project management team, allowing appropriate measures to be taken quickly.

[0659] Step 9:

[0660] The server records the transmission results and monitors the responses and response status from the person or department to whom the support request was sent, as well as from external vendors. This allows for real-time monitoring of project progress and adjustments to responses as needed.

[0661] Specific example:

[0662] For example, if a user receives a new web application development project, they would enter the following information into their terminal:

[0663] "Customer name"

[0664] "Project Details"

[0665] "Required skills"

[0666] "Required Skill Set"

[0667] During this time, the emotion engine analyzes the user's input speed, voice, and facial expressions to determine whether the user is experiencing stress. If the server determines that the user is stressed, it uses this emotional information to add urgency-indicating wording to the support request message. This message is then sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[0668] (Example 2)

[0669] 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".

[0670] Traditional project management systems make it difficult to quickly establish a project structure, requiring significant time and effort to identify the appropriate personnel and external vendors. Furthermore, they generate support request messages without considering user sentiment, resulting in problems in responding appropriately to urgency or specific situations.

[0671] 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.

[0672] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching an internal resource database and an external vendor database to identify personnel and departments with the necessary technologies and skill sets, means for generating and sending support request messages based on the identified information, means for analyzing the user's emotions in real time and adjusting the wording of the support request message based on the analysis results, and means for notifying the project management team of the analyzed emotion information. This enables the rapid and efficient construction of a project structure and the generation of appropriate support request messages that take the user's emotions into consideration.

[0673] A "user" is a person or organization that has the role of inputting project information into the system and providing the necessary data.

[0674] "Project information" refers to detailed information related to a specific project, including the client name, project description, required technologies, and required skill sets.

[0675] A "terminal" refers to a device used by a user to input project information, and includes personal computers, tablets, and smartphones.

[0676] An "emotion engine" is a combination of software and hardware that analyzes a user's facial expressions, voice, and input speed in real time to collect and determine emotional data.

[0677] A "server" is a computer system that receives, analyzes, and stores project information and sentiment data entered by users.

[0678] "Analyzing" is the process of identifying the necessary technologies and skill sets based on the received data, and evaluating the user's emotional state.

[0679] An "internal resource database" is a database that collects information about the skill sets and experience held by various individuals and departments within a company.

[0680] An "external vendor database" is a database that compiles information on the technical skills and services that external service providers can offer.

[0681] A "request for support message" is a message that includes a project overview, required technical skills, and contact information, and is a document generated to request support from internal personnel or external vendors.

[0682] A "project management team" is a team responsible for overseeing the progress of a project and managing its overall operation.

[0683] "Notifying" refers to the process of communicating analysis results and important information to the relevant personnel or teams.

[0684] This invention relates to a system for quickly and efficiently establishing a project structure after a proposal and order have been received. This system includes a series of processes in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Furthermore, the invention incorporates an emotion engine to recognize and appropriately respond to the user's emotions.

[0685] First, the user enters project details using a dedicated terminal. This information includes "client name," "project description," "required technologies," and "required skill set." The emotion engine analyzes the user's facial expressions, input speed, and voice as they input the information to recognize their emotions. This information is collected in real time.

[0686] Next, the server receives project information and sentiment data sent from the terminal. The server analyzes this data to identify the necessary technologies and required skill sets. It also analyzes the user's psychological state based on the sentiment data and evaluates its impact on project progress.

[0687] Based on the analysis results, the server searches the internal resource database. This database contains information on the skill sets and experience of each person and department within the company. The server lists the persons and departments that possess the identified technologies and skill sets. If necessary, the server also searches the external vendor database, which contains information on the technical skills and services that registered external vendors can provide. The server lists the external vendors that possess the identified technologies and skill sets.

[0688] The server then generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[0689] Finally, the server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. In addition, the support request message is sent to external vendors via email or API. The emotional state of the user, as perceived by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[0690] Specific example

[0691] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0692] Customer name: "X Corporation"

[0693] Project Description: "Development of a new web application for businesses"

[0694] Required technologies: React, Node.js, AWS

[0695] Required skill set: "Frontend development, backend development, cloud infrastructure"

[0696] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[0697] Example of a prompt

[0698] Please enter details for your new web application development project. Include the client name, project description, required technologies, and required skill set. Please ensure accurate input, as emotional information will also be analyzed during the input process.

[0699] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0700] Step 1: User enters project information

[0701] Specific operation: The user enters "customer name," "project details," "required technologies," and "required skill set" into a dedicated form on the terminal. The input information is analyzed in real time by an emotion engine, and emotion data is generated from facial expressions and voice.

[0702] Input: Project information entered by the user, along with the user's facial expressions and voice data.

[0703] Output: Project information and sentiment data saved on the device.

[0704] Step 2: Send

[0705] Specific operation: The terminal combines the input project information and generated emotion data into a single data packet. This data packet is then sent to the server via the network.

[0706] Input: Project information and sentiment data saved on the device.

[0707] Output: Data packets sent to the server.

[0708] Step 3: Receiving and saving data

[0709] Specific operation: The server decompresses the received data packets and saves project information and sentiment data separately.

[0710] Input: Data packets sent to the server.

[0711] Output: Project information and sentiment data stored on the server.

[0712] Step 4: Analyze project information

[0713] Specific operation: The server uses a text analysis engine to analyze the received project information. From the analysis results, it identifies the necessary technologies and required skill sets.

[0714] Input: Project information stored on the server.

[0715] Output: Identified required technologies and required skill sets.

[0716] Step 5: Analyzing emotional data

[0717] Specific operation: The server uses an emotion engine to analyze stored emotion data. It assesses whether the user is experiencing stress and to what extent.

[0718] Input: Emotional data stored on the server.

[0719] Output: Evaluation results regarding the user's psychological state.

[0720] Step 6: Search for internal resources

[0721] Specific operation: The server searches the company's internal resource database using SQL queries to identify the personnel or departments with the necessary technologies and skill sets.

[0722] Input: Identified required technologies and required skill sets.

[0723] Output: Information on listed internal contacts and departments.

[0724] Step 7: Selecting potential external vendors

[0725] Specific operation: The server uses an API to search an external vendor database and identify external vendors that possess the necessary technologies and skill sets.

[0726] Input: Identified required technologies and required skill sets.

[0727] Output: Information on the listed external vendors.

[0728] Step 8: Generating a support request message

[0729] Specific operation: The server uses a template engine to generate a request for assistance message. It takes sentiment data into consideration and adjusts the message wording as needed.

[0730] Input: Listed internal contact information, external vendor information, and sentiment data evaluation results.

[0731] Output: The generated support request message.

[0732] Step 9: Send a support request message

[0733] Specific operation: The server sends support request messages to the relevant personnel and departments through the internal system. Messages are also sent to external vendors via email or API.

[0734] Input: The generated support request message.

[0735] Output: Support request messages sent to internal staff and external vendors.

[0736] (Application Example 2)

[0737] 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."

[0738] Traditional systems required significant time and effort to assemble teams appropriately and quickly after a project was awarded. Furthermore, proceeding with projects without considering the mental state of on-site managers could negatively impact project progress. Additionally, the inability to dynamically generate messages based on user input and emotional states made it difficult to effectively convey the urgency and importance of support requests.

[0739] 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.

[0740] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching internal and external resource databases to identify resources with the necessary skill sets, means for generating and sending support request messages, means for analyzing the user's emotions, and means for adjusting the wording of the message based on the emotion analysis results. This makes it possible to quickly and efficiently assemble an appropriate team after receiving a project order and to generate support request messages while taking the user's mental state into consideration.

[0741] A "user" is an individual or organization that inputs and manages project information.

[0742] "Project information" refers to data that includes project details, required technologies, and required skill sets.

[0743] A "server" is a central processing unit that receives and analyzes input information, searches for resources, generates messages, and sends them.

[0744] An "internal resource database" is a database that records the skill sets and experience of each individual and department within a company.

[0745] An "external resource database" is a database that records the skills and services that external vendors and service providers can offer.

[0746] An "emotion analysis system" is a system that analyzes a user's facial expressions, voice, and actions to determine their emotional state.

[0747] A "request for support message" is a notification of support requested by the person or department with the necessary skill set for the project.

[0748] "Urgency" is a concept that describes a situation requiring immediate action during project execution.

[0749] This invention is a system for quickly and efficiently establishing a project structure after a project has been awarded. This system includes a process for analyzing project information and sentiment entered by the user, identifying internal and external resources, and generating and sending support request messages.

[0750] Users input project information using smart glasses or a dedicated terminal. This information includes project details, required technologies, and required skill sets. While the user is inputting, the emotion engine analyzes the user's facial expressions and voice to determine their emotional state in real time, particularly assessing whether the user is experiencing stress.

[0751] The server receives project information and emotional data sent from the user's terminal. First, the server analyzes the project information to identify the technologies and skill sets necessary for project execution. Next, it analyzes the emotional data to evaluate the user's emotional state. Based on this information, it determines the impact on project progress.

[0752] The server then searches the internal resource database and lists the individuals or departments with the identified technologies and skill sets. Furthermore, it searches external resource databases as needed to identify relevant external resources.

[0753] Next, the server generates a request for assistance message based on this resource information. The generated message includes a project overview, required technical skills, and contact information, and the wording is adjusted according to the user's emotional state. For example, if the user is experiencing high levels of stress, language emphasizing urgency will be added.

[0754] Finally, the server sends a request for assistance message to internal personnel and external resources. The message is notified through the internal system and sent to external resources via API or email. The sentiment engine's analysis results are also notified to the project management team, ensuring prompt support.

[0755] To give a concrete example, consider a scenario where a user enters project information for the introduction of a new automated manufacturing line into smart glasses. As the user enters the project details, "Introduction of an automated manufacturing line," and the required skill sets, "Robotics, machine maintenance, programming," the emotion engine analyzes the user's stress level. If the user is experiencing high stress, the server generates a request for assistance message, including this information, clearly indicating that urgent action is required.

[0756] Example of a prompt:

[0757] For the implementation project of a new automated manufacturing line, we need resources with the following skill sets:

[0758] Robotics

[0759] Machine maintenance

[0760] programming

[0761] The supervisor is currently experiencing a high level of stress, and an immediate response is required.

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

[0763] Step 1:

[0764] The user enters project information.

[0765] In terms of specific operations, users use smart glasses or a dedicated terminal to input project details, required technologies, and required skill sets via voice input or touch gestures. The entered information is stored on the terminal and sent to the server. The input data includes a project overview, technical skills, and required skill sets.

[0766] Step 2:

[0767] The emotion engine analyzes the user's emotions.

[0768] In terms of operation, the camera and microphone built into the smart glasses capture the user's facial expressions and voice, and emotion analysis is performed in real time. The emotion engine (e.g., Affectiva SDK) uses facial recognition algorithms to evaluate the user's stress level and emotional state. The analysis results are stored as emotion data on the device and sent to the server. The input data is the user's facial expressions and voice data, and the output is the analysis result of the emotional state.

[0769] Step 3:

[0770] The server receives project information and sentiment data.

[0771] In terms of specific operation, the server receives project information and sentiment data sent from the terminal and stores each data independently. The input data consists of project information and sentiment data, and the output consists of these data entries.

[0772] Step 4:

[0773] The server analyzes the project information.

[0774] In terms of specific operations, the server analyzes the received project information to identify the technologies and skill sets required for project execution. This analysis utilizes natural language processing models and data mining techniques. The input data is project information, and the output is a list of required technical skill sets.

[0775] Step 5:

[0776] The server analyzes the emotional data.

[0777] In terms of its specific operation, the server processes the received emotional data and evaluates the user's emotional state. In particular, it determines the user's stress level and analyzes its impact on project progress. The input data is emotional data, and the output is the user's emotional evaluation.

[0778] Step 6:

[0779] The server searches the company's internal resource database.

[0780] In practice, the server searches the internal resource database based on the analysis results to identify individuals and departments with the necessary technologies and skill sets. SQL queries and similarity search algorithms are used for the search. Input data includes the identified technical skills, and the output is a list of corresponding internal resources.

[0781] Step 7:

[0782] The server searches the external resource database.

[0783] In practice, the server searches an external resource database to identify external resources possessing the necessary technologies and skill sets. The search method is similar to internal resource searches, but differs in that it uses an external database. The input data includes identified technical skills, and the output is a list of corresponding external resources.

[0784] Step 8:

[0785] The server generates a support request message.

[0786] Specifically, the server generates a request for assistance message based on identified resource information and the user's emotional state. The message includes a project overview, required technical skills, and contact information. If the user is experiencing high stress levels, urgency is emphasized. Input data consists of the corresponding resource list and emotional analysis results, while output is the request for assistance message.

[0787] Step 9:

[0788] The server sends a request for assistance message.

[0789] In terms of specific operation, the server sends the generated support request message to internal systems and external resources. For internal systems, it uses a notification system; for external resources, it sends the message via API or email. The input data is the support request message, and the output is the transmission status.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] [Third Embodiment]

[0794] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0795] 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.

[0796] 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).

[0797] 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.

[0798] 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.

[0799] 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).

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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".

[0806] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Specific embodiments for carrying out this invention are described below.

[0807] System Overview

[0808] 1. User requirements input

[0809] The user uses a dedicated terminal to enter project details. This information includes the client name, project description, required technologies, and required skill set. After entering this information and pressing the submit button, the data is sent from the terminal to the server.

[0810] 2. Receiving and analyzing data on the server

[0811] The server receives project information sent from the terminal. After receiving the information, the server uses natural language processing to analyze it and identify the necessary technologies and required skill sets. This analysis clarifies the core technical requirements of the project.

[0812] 3. Searching for internal resources

[0813] Based on the analysis results, the server searches the company's internal resource database. This database contains information about the skill sets and work experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[0814] 4. Selection of potential external vendors

[0815] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[0816] 5. Generating a support request message

[0817] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information.

[0818] 6. Sending a request for assistance

[0819] The server sends the generated support request message to the appropriate person / department and external vendor. Messages are sent to internal personnel and departments via the internal system, and to external vendors via email or API.

[0820] Specific example

[0821] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0822] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[0823] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the required technologies "React, Node.js, AWS" and the required skill sets "frontend development, backend development, cloud infrastructure".

[0824] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0825] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[0826] As described above, the present invention makes it possible to quickly assemble a project structure after proposal and order acceptance, thereby contributing to the success of the project.

[0827] The following describes the processing flow.

[0828] Step 1:

[0829] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[0830] Step 2:

[0831] The user accesses the project information input screen and enters the necessary project information. This information includes "customer name," "project details," "required technologies," and "required skill set." Once the user has entered all the information and pressed the submit button, the data is sent from the terminal to the server.

[0832] Step 3:

[0833] The server receives project information sent from the terminal. The received data is stored as detailed project information.

[0834] Step 4:

[0835] The server analyzes the stored project information using natural language processing. Specifically, it performs text analysis to extract necessary technologies and required skill sets from the project content.

[0836] Step 5:

[0837] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server identifies and creates a list of persons and departments that match the required technologies and required skill sets.

[0838] Step 6:

[0839] The server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server identifies and lists external vendors that match the required technologies and skill sets.

[0840] Step 7:

[0841] The server generates a support request message based on the identified internal contact person, department, and external vendor information. This message includes a project overview, required technical skills, and contact information.

[0842] Step 8:

[0843] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, it also sends the support request message to external vendors via email or API.

[0844] Step 9:

[0845] The server records the transmission results and monitors replies and response status from the person or department to whom the support request was sent, as well as from external vendors. This allows for real-time monitoring of project progress.

[0846] (Example 1)

[0847] 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."

[0848] Traditional project management systems made it difficult to quickly and efficiently assemble a project structure after a proposal or order was received. Specifically, the process of quickly identifying appropriate internal resources and external vendors based on detailed project information, and generating and sending support request messages, was done manually, which was time-consuming and laborious. Furthermore, the lack of centralized management for information analysis and message sending resulted in delays in project initiation.

[0849] 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.

[0850] In this invention, the server includes means for a user to input project information, means for receiving the input project information and storing it in a database, means for analyzing the stored project information using natural language processing, means for searching an internal resource database to identify personnel or departments with the necessary technologies and skill sets, means for searching an external vendor database to identify vendors with the necessary technologies and skill sets, means for generating support request messages based on the identified information, means for sending support request messages, and means for sending messages to internal personnel via an internal messaging system and to external vendors via an email service. This enables centralized management from project information input to analysis, resource identification, message generation, and transmission, making it possible to quickly and efficiently build a project structure.

[0851] "Project information" refers to detailed project information, specifically including the client name, project description, required technologies, and required skill sets.

[0852] "User" refers to a person or entity that enters project information.

[0853] A "server" refers to a computer system that receives, stores, analyzes, searches for resources, generates messages, and sends project information entered by users.

[0854] A "database" is a system for managing information used to store project information, and specifically includes relational databases such as MySQL and PostgreSQL.

[0855] "Natural language processing" refers to the technology of analyzing and processing human language using computers, and specifically includes technologies that utilize libraries such as Python's NLTK and spaCy.

[0856] An "internal resource database" is a database that stores the skill sets and work experience of each individual employee and department.

[0857] An "external vendor database" is a database that stores the technical skills and services that registered external vendors can provide.

[0858] A "request for support message" refers to a message sent to a designated internal contact person or external vendor, including an overview of the project, required technical skills, and contact information.

[0859] An "internal messaging system" is a communication system used to send messages to internal personnel, and specifically refers to systems such as Slack.

[0860] A "mail service" refers to an email sending service used to send messages to external vendors, specifically services such as SendGrid.

[0861] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends a support request message.

[0862] Hardware and software

[0863] hardware

[0864] Servers: Use on-premises or cloud servers (e.g., Amazon Web Services, Microsoft Azure).

[0865] Terminal: A PC or mobile device used by a user to input information.

[0866] software

[0867] Natural language processing libraries: Use Python's NLTK and spaCy to analyze project information.

[0868] Database: We use MySQL or PostgreSQL to store information on internal resources and external vendors.

[0869] Messaging system: We use the Slack API for internal messaging and the SendGrid API for external vendors.

[0870] Detailed explanation

[0871] 1. User input

[0872] The user uses a dedicated terminal to enter project details. The input form includes fields for "Client Name," "Project Description," "Required Technologies," and "Required Skill Set." Once the user has finished entering the information, they press the "Submit" button.

[0873] 2. Receiving and storing data

[0874] The server receives project information submitted by the user. The received data is immediately stored in the database. The data is sent in JSON format and stored in a database such as MySQL.

[0875] 3. Data Analysis

[0876] The server uses natural language processing libraries (e.g., Python's NLTK or spaCy) to analyze the stored project information. The purpose of the analysis is to identify the necessary technologies and required skill sets from the project content. For example, from a project description such as "Web application development," it identifies technologies such as "React," "Node.js," and "AWS," and confirms that the required skill set includes "frontend development," "backend development," and "cloud infrastructure."

[0877] 4. Searching for internal resources

[0878] The server searches the internal resource database based on the analyzed technologies and skill sets. It executes SQL queries to list the individuals and departments with the identified technologies. For example, it might list employees who can handle "front-end development" and identify departments that can handle "back-end development."

[0879] 5. Selection of potential external vendors

[0880] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. SQL queries are used to search for external vendors that can provide "React" or "Node.js" and list the relevant vendors.

[0881] 6. Generating and sending a support request message

[0882] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[0883] Specific example

[0884] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[0885] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[0886] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the required technologies "React, Node.js, AWS" and the required skill sets "frontend development, backend development, cloud infrastructure".

[0887] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[0888] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[0889] Example of a prompt

[0890] Examples of prompts input to a generative AI model:

[0891] "For a new web application development project, please generate a request for assistance message to find internal resources and external vendors with the following technologies and skill sets. Required technologies: React, Node.js, AWS. Required skill sets: Frontend development, backend development, cloud infrastructure."

[0892] The above describes the embodiments for carrying out the present invention. This system makes it possible to quickly establish a project structure with minimal effort, thereby contributing to the success of the project.

[0893] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0894] Step 1:

[0895] The user uses a dedicated terminal to enter project details. Specifically, they enter the "Client Name," "Project Details," "Required Technologies," and "Required Skill Set" into a form on the terminal and press the "Submit" button. This entered data is then sent to the system.

[0896] Input: Project information (customer name, project details, required technologies, required skill set)

[0897] Output: Sending data to the server

[0898] Step 2:

[0899] The server receives project information sent from the terminal. The received data is immediately stored in the database. The data is received in JSON format and saved to a database such as MySQL. After storage, a log is generated to confirm that the data was saved correctly.

[0900] Input: Project information submitted by the user (in JSON format)

[0901] Output: Saved to database, save confirmation log

[0902] Specific actions:

[0903] Data reception: The server receives data via HTTP requests.

[0904] Data storage: Received data is parsed and stored in the database.

[0905] Log generation: Generates a log indicating successful data storage and records it in the logging system.

[0906] Step 3:

[0907] The server retrieves the stored project information and analyzes it using a natural language processing library (e.g., Python's NLTK or spaCy). This analysis identifies the necessary technologies and required skill sets from the project content. For example, from a project description like "Web application development," it identifies technologies such as "React," "Node.js," and "AWS."

[0908] Input: Project information stored in the database

[0909] Output: Analysis results (required technologies and required skill set)

[0910] Specific actions:

[0911] Data retrieval: Execute an SQL query to retrieve project information from the database.

[0912] Data Analysis: Analyze project content using natural language processing libraries.

[0913] Technical Identification: Identify the necessary technologies and required skill sets from the analysis results.

[0914] Step 4:

[0915] Based on the analysis results, the server searches the company's internal resource database. It executes SQL queries to list the individuals or departments with the identified skills. For example, it might identify employees who can handle "front-end development" or departments that can handle "back-end development."

[0916] Input: Analysis results (required technologies and skill sets)

[0917] Output: List of internal resources

[0918] Specific actions:

[0919] Database search: Use SQL queries to search internal resource databases.

[0920] Resource Identification: List the individuals or departments that match the identified technologies and skill sets.

[0921] Step 5:

[0922] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. Using SQL queries, it searches for external vendors that can provide "React" or "Node.js" and lists the relevant vendors.

[0923] Input: Analysis results (required technologies and skill sets)

[0924] Output: List of external vendors

[0925] Specific actions:

[0926] Database search: Search external vendor databases using SQL queries.

[0927] Vendor Identification: List external vendors that match the identified technologies and skill sets.

[0928] Step 6:

[0929] The server generates a support request message based on the listed information for internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[0930] Input: Information on listed internal contacts, departments, and external vendors.

[0931] Output: Assistance request message

[0932] Specific actions:

[0933] Message generation: Generate a support request message based on the listed information.

[0934] Message sending: Messages are sent to internal contacts via the Slack API and to external vendors via the SendGrid API.

[0935] The above is a detailed explanation of the program's processing flow. This makes it possible to quickly and efficiently establish a project structure.

[0936] (Application Example 1)

[0937] 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."

[0938] In modern manufacturing, launching new product lines and modifying existing production lines are frequent occurrences, but identifying and deploying the right resources with the necessary skills and skill sets in a short period of time is difficult. In particular, the appropriate selection of factory robots and operators is crucial for efficient production, but current methods are time-consuming, labor-intensive, and inefficient. To solve this problem, a system is needed that can quickly identify and deploy the necessary resources.

[0939] 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.

[0940] In this invention, the server includes means for the user to input project information, means for the server to receive and analyze the input project information, means for the server to search an internal resource database based on the analysis results and identify personnel or departments with the necessary technologies and skill sets, means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets, means for the server to generate a support request message based on the identified information, means for the server to send the support request message, and means for analyzing the necessary skills and skill sets when a manufacturing line is changed or a new project arises and identifying appropriate factory robots and operators. This enables efficient changes to manufacturing lines and rapid startup of new lines.

[0941] "Project information" refers to detailed data about changes to the manufacturing line or new projects, specifically including the project overview, required technologies, and required skill sets.

[0942] A "server" is a computer system that receives information entered by a user and performs analysis and processing on it.

[0943] An "internal resource database" is a database that contains information about the technologies, skill sets, and work experience of each individual and department within the company.

[0944] An "external vendor database" is a database that lists the technical skills and services that registered external technology providers and service vendors can offer.

[0945] A "request for support message" is a message sent by the server to a designated person, department, or external vendor to request support, and includes a project overview, required technical skills, and contact information.

[0946] A "factory robot" is an automated device used to perform specific tasks or operations on a manufacturing line or in a factory.

[0947] An "operator" refers to a person responsible for operating and managing equipment and systems in a factory or manufacturing line.

[0948] "Analysis" refers to data processing that identifies the necessary technologies and skill sets based on the input project information.

[0949] System Overview

[0950] This invention is a system for quickly identifying the necessary skills and skill sets and selecting appropriate factory robots and operators when changing manufacturing lines or launching new product lines. The system includes a process of receiving project information entered by the user, analyzing it, and automatically allocating appropriate resources.

[0951] 1. User requirements input

[0952] Users use a dedicated device (smartphone, tablet, PC, etc.) to input detailed project information. This information includes a project overview, required skills, and required skill sets. For example, it might include details such as "manufacturing line modification" or "launching a new product line."

[0953] 2. Receiving and analyzing data on the server

[0954] The server receives project information sent from the terminal. After receiving the information, the server analyzes it using natural language processing (NLP) to identify the necessary skills and skill sets. This process utilizes NLP libraries such as spaCy and NLTK.

[0955] 3. Searching for internal resources

[0956] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals and departments that possess the identified skills and skill sets. This database contains information about the skills and work experience of internal engineers and departments.

[0957] 4. Selection of potential external vendors

[0958] If necessary, the server also searches an external vendor database. This database contains information about the skills and services that registered technology providers and service vendors can offer. The server searches the database and lists external vendors that possess the identified skills and skill sets.

[0959] 5. Generating a support request message

[0960] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required skills, and contact information. Template engines such as EJS or Handlebars are used to generate the message.

[0961] 6. Sending a request for assistance

[0962] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[0963] Specific example

[0964] For example, when a manufacturer sets up an assembly line for a new smartphone, the user will enter the following information:

[0965] "Customer name: A certain customer"

[0966] "Project details: Assembling a new smartphone"

[0967] Required skills: Precision operation of robotic arms, quality inspection.

[0968] Required skill set: precision operation skills, quality inspection using image analysis.

[0969] Example of a prompt:

[0970] A new smartphone assembly project has emerged.

[0971] 1. Customer name: A certain customer

[0972] 2. Project details: Assembling a new smartphone

[0973] 3. Required skills: Precision operation of robotic arms, quality inspection

[0974] 4. Required skill set: Precision operation techniques, quality inspection using image analysis.

[0975] Identify the most suitable internal resources and external vendors.

[0976] By using this system, efficient changes to manufacturing lines and rapid launch of new product lines can be achieved, leading to expected improvements in productivity.

[0977] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0978] Step 1:

[0979] The user enters project information using a terminal. This information includes a project overview, required skills, and required skill sets. This information is then sent from the terminal to the server.

[0980] (Input: Project overview, required skills, required skill set / Output: Project information sent to the server)

[0981] Step 2:

[0982] The server receives project information sent from the terminal. The received information is analyzed using a natural language processing (NLP) library (e.g., spaCy or NLTK) to identify the necessary skills and skill sets.

[0983] (Input: Project information sent from the terminal / Output: Analyzed skills and skill sets)

[0984] Step 3:

[0985] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals or departments who possess the identified skills or skill sets. This search is performed using SQL queries or similar methods.

[0986] (Input: Analyzed skills and skill sets / Output: Listed personnel and departments)

[0987] Step 4:

[0988] If necessary, the server searches the external vendor database. It searches the database of registered technology providers and service vendors and lists external vendors that possess the identified skills and skill sets.

[0989] (Input: Analyzed skills and skill sets / Output: Listed external vendors)

[0990] Step 5:

[0991] The server generates a support request message based on internal resources and information from external vendors. This message includes a project overview, required skills, and contact information. A template engine (such as EJS or Handlebars) is used to generate the message.

[0992] (Input: Information on listed personnel, departments, and external vendors / Output: Support request message)

[0993] Step 6:

[0994] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[0995] (Input: Assistance request message / Output: Sent message)

[0996] This series of processing steps enables efficient and rapid launch of changes to manufacturing lines and new product lines.

[0997] 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.

[0998] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Furthermore, this invention incorporates an emotion engine to recognize and appropriately respond to the user's emotions.

[0999] System Overview

[1000] 1. User requirements input

[1001] Users input project details using a dedicated terminal. This information includes client name, project description, required technologies, and required skill sets. The emotion engine analyzes the user's facial expressions, input speed, and voice as they input information to recognize their emotions. This information is collected in real time.

[1002] 2. Receiving and analyzing data on the server

[1003] The server receives project information sent from the terminal and emotional data from the emotion engine. After receiving the data, the server analyzes the project information to identify the necessary technologies and required skill sets. It also analyzes the user's psychological state based on the emotional data and evaluates its impact on project progress.

[1004] 3. Searching for internal resources

[1005] The server searches the company's internal resource database based on the analysis results. This database contains information about the skill sets and experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[1006] 4. Selection of potential external vendors

[1007] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[1008] 5. Generating a support request message

[1009] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[1010] 6. Sending a request for assistance

[1011] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, the support request message is sent to external vendors via email or API. The emotional state of the user, as detected by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[1012] Specific example

[1013] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[1014] "Customer name"

[1015] "Project Details"

[1016] "Required skills"

[1017] "Required Skill Set"

[1018] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[1019] The server then identifies the technologies required for "Web application development" ("React, Node.js, AWS") and the required skill sets ("frontend development, backend development, cloud infrastructure") based on the project description.

[1020] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[1021] Finally, the server uses this information to generate a support request message, reflecting any stress the user may be experiencing and emphasizing the urgency. This allows the support request message to be sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[1022] The following describes the processing flow.

[1023] Step 1:

[1024] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[1025] Step 2:

[1026] The user accesses the project information input screen and enters the necessary project information. This information includes "client name," "project details," "required technologies," and "required skill set." The emotion engine monitors the user's facial expressions, voice, and input speed in real time to recognize their emotions. After the user has entered all the information and the emotion data has been collected, they press the submit button, and the data is sent from the terminal to the server.

[1027] Step 3:

[1028] The server receives project information sent from the terminal and sentiment data from the sentiment engine. The received data is stored as detailed project information.

[1029] Step 4:

[1030] The server analyzes stored project information using natural language processing. Specifically, it extracts necessary technologies and required skill sets from the project content. Meanwhile, it analyzes emotional data to evaluate the user's psychological state.

[1031] Step 5:

[1032] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server lists the persons and departments that possess the identified technologies and skill sets.

[1033] Step 6:

[1034] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services of registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[1035] Step 7:

[1036] The server generates a support request message based on identified internal contacts, departments, and external vendor information. This message includes a project overview, required technical skills, and contact information. The message's wording is also adjusted based on the user's emotional state, as analyzed by the emotion engine. Appropriate phrasing is added or modified depending on whether the user is stressed or experiencing other emotional states.

[1037] Step 8:

[1038] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, support request messages are sent to external vendors via email or API. The emotional state of the user detected by the emotion engine is also notified to the project management team, allowing appropriate measures to be taken quickly.

[1039] Step 9:

[1040] The server records the transmission results and monitors the responses and response status from the person or department to whom the support request was sent, as well as from external vendors. This allows for real-time monitoring of project progress and adjustments to responses as needed.

[1041] Specific example:

[1042] For example, if a user receives a new web application development project, they would enter the following information into their terminal:

[1043] "Customer name"

[1044] "Project Details"

[1045] "Required skills"

[1046] "Required Skill Set"

[1047] During this time, the emotion engine analyzes the user's input speed, voice, and facial expressions to determine whether the user is experiencing stress. If the server determines that the user is stressed, it uses this emotional information to add urgency-indicating wording to the support request message. This message is then sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[1048] (Example 2)

[1049] 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."

[1050] Traditional project management systems make it difficult to quickly establish a project structure, requiring significant time and effort to identify the appropriate personnel and external vendors. Furthermore, they generate support request messages without considering user sentiment, resulting in problems in responding appropriately to urgency or specific situations.

[1051] 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.

[1052] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching an internal resource database and an external vendor database to identify personnel and departments with the necessary technologies and skill sets, means for generating and sending support request messages based on the identified information, means for analyzing the user's emotions in real time and adjusting the wording of the support request message based on the analysis results, and means for notifying the project management team of the analyzed emotion information. This enables the rapid and efficient construction of a project structure and the generation of appropriate support request messages that take the user's emotions into consideration.

[1053] A "user" is a person or organization that has the role of inputting project information into the system and providing the necessary data.

[1054] "Project information" refers to detailed information related to a specific project, including the client name, project description, required technologies, and required skill sets.

[1055] A "terminal" refers to a device used by a user to input project information, and includes personal computers, tablets, and smartphones.

[1056] An "emotion engine" is a combination of software and hardware that analyzes a user's facial expressions, voice, and input speed in real time to collect and determine emotional data.

[1057] A "server" is a computer system that receives, analyzes, and stores project information and sentiment data entered by users.

[1058] "Analyzing" is the process of identifying the necessary technologies and skill sets based on the received data, and evaluating the user's emotional state.

[1059] An "internal resource database" is a database that collects information about the skill sets and experience held by various individuals and departments within a company.

[1060] An "external vendor database" is a database that compiles information on the technical skills and services that external service providers can offer.

[1061] A "request for support message" is a message that includes a project overview, required technical skills, and contact information, and is a document generated to request support from internal personnel or external vendors.

[1062] A "project management team" is a team responsible for overseeing the progress of a project and managing its overall operation.

[1063] "Notifying" refers to the process of communicating analysis results and important information to the relevant personnel or teams.

[1064] This invention relates to a system for quickly and efficiently establishing a project structure after a proposal and order have been received. This system includes a series of processes in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Furthermore, the invention incorporates an emotion engine to recognize and appropriately respond to the user's emotions.

[1065] First, the user enters project details using a dedicated terminal. This information includes "client name," "project description," "required technologies," and "required skill set." The emotion engine analyzes the user's facial expressions, input speed, and voice as they input the information to recognize their emotions. This information is collected in real time.

[1066] Next, the server receives project information and sentiment data sent from the terminal. The server analyzes this data to identify the necessary technologies and required skill sets. It also analyzes the user's psychological state based on the sentiment data and evaluates its impact on project progress.

[1067] Based on the analysis results, the server searches the internal resource database. This database contains information on the skill sets and experience of each person and department within the company. The server lists the persons and departments that possess the identified technologies and skill sets. If necessary, the server also searches the external vendor database, which contains information on the technical skills and services that registered external vendors can provide. The server lists the external vendors that possess the identified technologies and skill sets.

[1068] The server then generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[1069] Finally, the server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. In addition, the support request message is sent to external vendors via email or API. The emotional state of the user, as perceived by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[1070] Specific example

[1071] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[1072] Customer name: "X Corporation"

[1073] Project Description: "Development of a new web application for businesses"

[1074] Required technologies: React, Node.js, AWS

[1075] Required skill set: "Frontend development, backend development, cloud infrastructure"

[1076] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[1077] Example of a prompt

[1078] Please enter details for your new web application development project. Include the client name, project description, required technologies, and required skill set. Please ensure accurate input, as emotional information will also be analyzed during the input process.

[1079] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1080] Step 1: User enters project information

[1081] Specific operation: The user enters "customer name," "project details," "required technologies," and "required skill set" into a dedicated form on the terminal. The input information is analyzed in real time by an emotion engine, and emotion data is generated from facial expressions and voice.

[1082] Input: Project information entered by the user, along with the user's facial expressions and voice data.

[1083] Output: Project information and sentiment data saved on the device.

[1084] Step 2: Send

[1085] Specific operation: The terminal combines the input project information and generated emotion data into a single data packet. This data packet is then sent to the server via the network.

[1086] Input: Project information and sentiment data saved on the device.

[1087] Output: Data packets sent to the server.

[1088] Step 3: Receiving and saving data

[1089] Specific operation: The server decompresses the received data packets and saves project information and sentiment data separately.

[1090] Input: Data packets sent to the server.

[1091] Output: Project information and sentiment data stored on the server.

[1092] Step 4: Analyze project information

[1093] Specific operation: The server uses a text analysis engine to analyze the received project information. From the analysis results, it identifies the necessary technologies and required skill sets.

[1094] Input: Project information stored on the server.

[1095] Output: Identified required technologies and required skill sets.

[1096] Step 5: Analyzing emotional data

[1097] Specific operation: The server uses an emotion engine to analyze stored emotion data. It assesses whether the user is experiencing stress and to what extent.

[1098] Input: Emotional data stored on the server.

[1099] Output: Evaluation results regarding the user's psychological state.

[1100] Step 6: Search for internal resources

[1101] Specific operation: The server searches the company's internal resource database using SQL queries to identify the personnel or departments with the necessary technologies and skill sets.

[1102] Input: Identified required technologies and required skill sets.

[1103] Output: Information on listed internal contacts and departments.

[1104] Step 7: Selecting potential external vendors

[1105] Specific operation: The server uses an API to search an external vendor database and identify external vendors that possess the necessary technologies and skill sets.

[1106] Input: Identified required technologies and required skill sets.

[1107] Output: Information on the listed external vendors.

[1108] Step 8: Generating a support request message

[1109] Specific operation: The server uses a template engine to generate a request for assistance message. It takes sentiment data into consideration and adjusts the message wording as needed.

[1110] Input: Listed internal contact information, external vendor information, and sentiment data evaluation results.

[1111] Output: The generated support request message.

[1112] Step 9: Send a support request message

[1113] Specific operation: The server sends support request messages to the relevant personnel and departments through the internal system. Messages are also sent to external vendors via email or API.

[1114] Input: The generated support request message.

[1115] Output: Support request messages sent to internal staff and external vendors.

[1116] (Application Example 2)

[1117] 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."

[1118] Traditional systems required significant time and effort to assemble teams appropriately and quickly after a project was awarded. Furthermore, proceeding with projects without considering the mental state of on-site managers could negatively impact project progress. Additionally, the inability to dynamically generate messages based on user input and emotional states made it difficult to effectively convey the urgency and importance of support requests.

[1119] 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.

[1120] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching internal and external resource databases to identify resources with the necessary skill sets, means for generating and sending support request messages, means for analyzing the user's emotions, and means for adjusting the wording of the message based on the emotion analysis results. This makes it possible to quickly and efficiently assemble an appropriate team after receiving a project order and to generate support request messages while taking the user's mental state into consideration.

[1121] A "user" is an individual or organization that inputs and manages project information.

[1122] "Project information" refers to data that includes project details, required technologies, and required skill sets.

[1123] A "server" is a central processing unit that receives and analyzes input information, searches for resources, generates messages, and sends them.

[1124] An "internal resource database" is a database that records the skill sets and experience of each individual and department within a company.

[1125] An "external resource database" is a database that records the skills and services that external vendors and service providers can offer.

[1126] An "emotion analysis system" is a system that analyzes a user's facial expressions, voice, and actions to determine their emotional state.

[1127] A "request for support message" is a notification of support requested by the person or department with the necessary skill set for the project.

[1128] "Urgency" is a concept that describes a situation requiring immediate action during project execution.

[1129] This invention is a system for quickly and efficiently establishing a project structure after a project has been awarded. This system includes a process for analyzing project information and sentiment entered by the user, identifying internal and external resources, and generating and sending support request messages.

[1130] Users input project information using smart glasses or a dedicated terminal. This information includes project details, required technologies, and required skill sets. While the user is inputting, the emotion engine analyzes the user's facial expressions and voice to determine their emotional state in real time, particularly assessing whether the user is experiencing stress.

[1131] The server receives project information and emotional data sent from the user's terminal. First, the server analyzes the project information to identify the technologies and skill sets necessary for project execution. Next, it analyzes the emotional data to evaluate the user's emotional state. Based on this information, it determines the impact on project progress.

[1132] The server then searches the internal resource database and lists the individuals or departments with the identified technologies and skill sets. Furthermore, it searches external resource databases as needed to identify relevant external resources.

[1133] Next, the server generates a request for assistance message based on this resource information. The generated message includes a project overview, required technical skills, and contact information, and the wording is adjusted according to the user's emotional state. For example, if the user is experiencing high levels of stress, language emphasizing urgency will be added.

[1134] Finally, the server sends a request for assistance message to internal personnel and external resources. The message is notified through the internal system and sent to external resources via API or email. The sentiment engine's analysis results are also notified to the project management team, ensuring prompt support.

[1135] To give a concrete example, consider a scenario where a user enters project information for the introduction of a new automated manufacturing line into smart glasses. As the user enters the project details, "Introduction of an automated manufacturing line," and the required skill sets, "Robotics, machine maintenance, programming," the emotion engine analyzes the user's stress level. If the user is experiencing high stress, the server generates a request for assistance message, including this information, clearly indicating that urgent action is required.

[1136] Example of a prompt:

[1137] For the implementation project of a new automated manufacturing line, we need resources with the following skill sets:

[1138] Robotics

[1139] Machine maintenance

[1140] programming

[1141] The supervisor is currently experiencing a high level of stress, and an immediate response is required.

[1142] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1143] Step 1:

[1144] The user enters project information.

[1145] In terms of specific operations, users use smart glasses or a dedicated terminal to input project details, required technologies, and required skill sets via voice input or touch gestures. The entered information is stored on the terminal and sent to the server. The input data includes a project overview, technical skills, and required skill sets.

[1146] Step 2:

[1147] The emotion engine analyzes the user's emotions.

[1148] In terms of operation, the camera and microphone built into the smart glasses capture the user's facial expressions and voice, and emotion analysis is performed in real time. The emotion engine (e.g., Affectiva SDK) uses facial recognition algorithms to evaluate the user's stress level and emotional state. The analysis results are stored as emotion data on the device and sent to the server. The input data is the user's facial expressions and voice data, and the output is the analysis result of the emotional state.

[1149] Step 3:

[1150] The server receives project information and sentiment data.

[1151] In terms of specific operation, the server receives project information and sentiment data sent from the terminal and stores each data independently. The input data consists of project information and sentiment data, and the output consists of these data entries.

[1152] Step 4:

[1153] The server analyzes the project information.

[1154] In terms of specific operations, the server analyzes the received project information to identify the technologies and skill sets required for project execution. This analysis utilizes natural language processing models and data mining techniques. The input data is project information, and the output is a list of required technical skill sets.

[1155] Step 5:

[1156] The server analyzes the emotional data.

[1157] In terms of its specific operation, the server processes the received emotional data and evaluates the user's emotional state. In particular, it determines the user's stress level and analyzes its impact on project progress. The input data is emotional data, and the output is the user's emotional evaluation.

[1158] Step 6:

[1159] The server searches the company's internal resource database.

[1160] In practice, the server searches the internal resource database based on the analysis results to identify individuals and departments with the necessary technologies and skill sets. SQL queries and similarity search algorithms are used for the search. Input data includes the identified technical skills, and the output is a list of corresponding internal resources.

[1161] Step 7:

[1162] The server searches the external resource database.

[1163] In practice, the server searches an external resource database to identify external resources possessing the necessary technologies and skill sets. The search method is similar to internal resource searches, but differs in that it uses an external database. The input data includes identified technical skills, and the output is a list of corresponding external resources.

[1164] Step 8:

[1165] The server generates a support request message.

[1166] Specifically, the server generates a request for assistance message based on identified resource information and the user's emotional state. The message includes a project overview, required technical skills, and contact information. If the user is experiencing high stress levels, urgency is emphasized. Input data consists of the corresponding resource list and emotional analysis results, while output is the request for assistance message.

[1167] Step 9:

[1168] The server sends a request for assistance message.

[1169] In terms of specific operation, the server sends the generated support request message to internal systems and external resources. For internal systems, it uses a notification system; for external resources, it sends the message via API or email. The input data is the support request message, and the output is the transmission status.

[1170] 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.

[1171] 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.

[1172] 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.

[1173] [Fourth Embodiment]

[1174] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1175] 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.

[1176] 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).

[1177] 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.

[1178] 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.

[1179] 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).

[1180] 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.

[1181] 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.

[1182] 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.

[1183] 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.

[1184] 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.

[1185] 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.

[1186] 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".

[1187] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Specific embodiments for carrying out this invention are described below.

[1188] System Overview

[1189] 1. User requirements input

[1190] The user uses a dedicated terminal to enter project details. This information includes the client name, project description, required technologies, and required skill set. After entering this information and pressing the submit button, the data is sent from the terminal to the server.

[1191] 2. Receiving and analyzing data on the server

[1192] The server receives project information sent from the terminal. After receiving the information, the server uses natural language processing to analyze it and identify the necessary technologies and required skill sets. This analysis clarifies the core technical requirements of the project.

[1193] 3. Searching for internal resources

[1194] Based on the analysis results, the server searches the company's internal resource database. This database contains information about the skill sets and work experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[1195] 4. Selection of potential external vendors

[1196] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[1197] 5. Generating a support request message

[1198] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information.

[1199] 6. Sending a request for assistance

[1200] The server sends the generated support request message to the appropriate person / department and external vendor. Messages are sent to internal personnel and departments via the internal system, and to external vendors via email or API.

[1201] Specific example

[1202] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[1203] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[1204] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the required technologies "React, Node.js, AWS" and the required skill sets "frontend development, backend development, cloud infrastructure".

[1205] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[1206] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[1207] As described above, the present invention makes it possible to quickly assemble a project structure after proposal and order acceptance, thereby contributing to the success of the project.

[1208] The following describes the processing flow.

[1209] Step 1:

[1210] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[1211] Step 2:

[1212] The user accesses the project information input screen and enters the necessary project information. This information includes "customer name," "project details," "required technologies," and "required skill set." Once the user has entered all the information and pressed the submit button, the data is sent from the terminal to the server.

[1213] Step 3:

[1214] The server receives project information sent from the terminal. The received data is stored as detailed project information.

[1215] Step 4:

[1216] The server analyzes the stored project information using natural language processing. Specifically, it performs text analysis to extract necessary technologies and required skill sets from the project content.

[1217] Step 5:

[1218] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server identifies and creates a list of persons and departments that match the required technologies and required skill sets.

[1219] Step 6:

[1220] The server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server identifies and lists external vendors that match the required technologies and skill sets.

[1221] Step 7:

[1222] The server generates a support request message based on the identified internal contact person, department, and external vendor information. This message includes a project overview, required technical skills, and contact information.

[1223] Step 8:

[1224] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, it also sends the support request message to external vendors via email or API.

[1225] Step 9:

[1226] The server records the transmission results and monitors replies and response status from the person or department to whom the support request was sent, as well as from external vendors. This allows for real-time monitoring of project progress.

[1227] (Example 1)

[1228] 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".

[1229] Traditional project management systems made it difficult to quickly and efficiently assemble a project structure after a proposal or order was received. Specifically, the process of quickly identifying appropriate internal resources and external vendors based on detailed project information, and generating and sending support request messages, was done manually, which was time-consuming and laborious. Furthermore, the lack of centralized management for information analysis and message sending resulted in delays in project initiation.

[1230] 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.

[1231] In this invention, the server includes means for a user to input project information, means for receiving the input project information and storing it in a database, means for analyzing the stored project information using natural language processing, means for searching an internal resource database to identify personnel or departments with the necessary technologies and skill sets, means for searching an external vendor database to identify vendors with the necessary technologies and skill sets, means for generating support request messages based on the identified information, means for sending support request messages, and means for sending messages to internal personnel via an internal messaging system and to external vendors via an email service. This enables centralized management from project information input to analysis, resource identification, message generation, and transmission, making it possible to quickly and efficiently build a project structure.

[1232] "Project information" refers to detailed project information, specifically including the client name, project description, required technologies, and required skill sets.

[1233] "User" refers to a person or entity that enters project information.

[1234] A "server" refers to a computer system that receives, stores, analyzes, searches for resources, generates messages, and sends project information entered by users.

[1235] A "database" is a system for managing information used to store project information, and specifically includes relational databases such as MySQL and PostgreSQL.

[1236] "Natural language processing" refers to the technology of analyzing and processing human language using computers, and specifically includes technologies that utilize libraries such as Python's NLTK and spaCy.

[1237] An "internal resource database" is a database that stores the skill sets and work experience of each individual employee and department.

[1238] An "external vendor database" is a database that stores the technical skills and services that registered external vendors can provide.

[1239] A "request for support message" refers to a message sent to a designated internal contact person or external vendor, including an overview of the project, required technical skills, and contact information.

[1240] An "internal messaging system" is a communication system used to send messages to internal personnel, and specifically refers to systems such as Slack.

[1241] A "mail service" refers to an email sending service used to send messages to external vendors, specifically services such as SendGrid.

[1242] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends a support request message.

[1243] Hardware and software

[1244] hardware

[1245] Servers: Use on-premises or cloud servers (e.g., Amazon Web Services, Microsoft Azure).

[1246] Terminal: A PC or mobile device used by a user to input information.

[1247] software

[1248] Natural language processing libraries: Use Python's NLTK and spaCy to analyze project information.

[1249] Database: We use MySQL or PostgreSQL to store information on internal resources and external vendors.

[1250] Messaging system: We use the Slack API for internal messaging and the SendGrid API for external vendors.

[1251] Detailed explanation

[1252] 1. User input

[1253] The user uses a dedicated terminal to enter project details. The input form includes fields for "Client Name," "Project Description," "Required Technologies," and "Required Skill Set." Once the user has finished entering the information, they press the "Submit" button.

[1254] 2. Receiving and storing data

[1255] The server receives project information submitted by the user. The received data is immediately stored in the database. The data is sent in JSON format and stored in a database such as MySQL.

[1256] 3. Data Analysis

[1257] The server uses natural language processing libraries (e.g., Python's NLTK or spaCy) to analyze the stored project information. The purpose of the analysis is to identify the necessary technologies and required skill sets from the project content. For example, from a project description such as "Web application development," it identifies technologies such as "React," "Node.js," and "AWS," and confirms that the required skill set includes "frontend development," "backend development," and "cloud infrastructure."

[1258] 4. Searching for internal resources

[1259] The server searches the internal resource database based on the analyzed technologies and skill sets. It executes SQL queries to list the individuals and departments with the identified technologies. For example, it might list employees who can handle "front-end development" and identify departments that can handle "back-end development."

[1260] 5. Selection of potential external vendors

[1261] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. SQL queries are used to search for external vendors that can provide "React" or "Node.js" and list the relevant vendors.

[1262] 6. Generating and sending a support request message

[1263] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[1264] Specific example

[1265] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[1266] "Client Name", "Project Details", "Required Technologies", "Required Skill Set"

[1267] This information is sent to the server, which analyzes the project details to determine "Web application development" and identifies the required technologies "React, Node.js, AWS" and the required skill sets "frontend development, backend development, cloud infrastructure".

[1268] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[1269] Finally, the server uses this information to generate a support request message and sends it to internal personnel and external vendors. This enables the rapid and efficient establishment of the project structure.

[1270] Example of a prompt

[1271] Examples of prompts input to a generative AI model:

[1272] "For a new web application development project, please generate a request for assistance message to find internal resources and external vendors with the following technologies and skill sets. Required technologies: React, Node.js, AWS. Required skill sets: Frontend development, backend development, cloud infrastructure."

[1273] The above describes the embodiments for carrying out the present invention. This system makes it possible to quickly establish a project structure with minimal effort, thereby contributing to the success of the project.

[1274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1275] Step 1:

[1276] The user uses a dedicated terminal to enter project details. Specifically, they enter the "Client Name," "Project Details," "Required Technologies," and "Required Skill Set" into a form on the terminal and press the "Submit" button. This entered data is then sent to the system.

[1277] Input: Project information (customer name, project details, required technologies, required skill set)

[1278] Output: Sending data to the server

[1279] Step 2:

[1280] The server receives project information sent from the terminal. The received data is immediately stored in the database. The data is received in JSON format and saved to a database such as MySQL. After storage, a log is generated to confirm that the data was saved correctly.

[1281] Input: Project information submitted by the user (in JSON format)

[1282] Output: Saved to database, save confirmation log

[1283] Specific actions:

[1284] Data reception: The server receives data via HTTP requests.

[1285] Data storage: Received data is parsed and stored in the database.

[1286] Log generation: Generates a log indicating successful data storage and records it in the logging system.

[1287] Step 3:

[1288] The server retrieves the stored project information and analyzes it using a natural language processing library (e.g., Python's NLTK or spaCy). This analysis identifies the necessary technologies and required skill sets from the project content. For example, from a project description like "Web application development," it identifies technologies such as "React," "Node.js," and "AWS."

[1289] Input: Project information stored in the database

[1290] Output: Analysis results (required technologies and required skill set)

[1291] Specific actions:

[1292] Data retrieval: Execute an SQL query to retrieve project information from the database.

[1293] Data Analysis: Analyze project content using natural language processing libraries.

[1294] Technical Identification: Identify the necessary technologies and required skill sets from the analysis results.

[1295] Step 4:

[1296] Based on the analysis results, the server searches the company's internal resource database. It executes SQL queries to list the individuals or departments with the identified skills. For example, it might identify employees who can handle "front-end development" or departments that can handle "back-end development."

[1297] Input: Analysis results (required technologies and skill sets)

[1298] Output: List of internal resources

[1299] Specific actions:

[1300] Database search: Use SQL queries to search internal resource databases.

[1301] Resource Identification: List the individuals or departments that match the identified technologies and skill sets.

[1302] Step 5:

[1303] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. Using SQL queries, it searches for external vendors that can provide "React" or "Node.js" and lists the relevant vendors.

[1304] Input: Analysis results (required technologies and skill sets)

[1305] Output: List of external vendors

[1306] Specific actions:

[1307] Database search: Search external vendor databases using SQL queries.

[1308] Vendor Identification: List external vendors that match the identified technologies and skill sets.

[1309] Step 6:

[1310] The server generates a support request message based on the listed information for internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. The generated message is sent to internal contacts via the internal messaging system (e.g., Slack API) and to external vendors via an email service (e.g., SendGrid API).

[1311] Input: Information on listed internal contacts, departments, and external vendors.

[1312] Output: Assistance request message

[1313] Specific actions:

[1314] Message generation: Generate a support request message based on the listed information.

[1315] Message sending: Messages are sent to internal contacts via the Slack API and to external vendors via the SendGrid API.

[1316] The above is a detailed explanation of the program's processing flow. This makes it possible to quickly and efficiently establish a project structure.

[1317] (Application Example 1)

[1318] 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".

[1319] In modern manufacturing, launching new product lines and modifying existing production lines are frequent occurrences, but identifying and deploying the right resources with the necessary skills and skill sets in a short period of time is difficult. In particular, the appropriate selection of factory robots and operators is crucial for efficient production, but current methods are time-consuming, labor-intensive, and inefficient. To solve this problem, a system is needed that can quickly identify and deploy the necessary resources.

[1320] 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.

[1321] In this invention, the server includes means for the user to input project information, means for the server to receive and analyze the input project information, means for the server to search an internal resource database based on the analysis results and identify personnel or departments with the necessary technologies and skill sets, means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets, means for the server to generate a support request message based on the identified information, means for the server to send the support request message, and means for analyzing the necessary skills and skill sets when a manufacturing line is changed or a new project arises and identifying appropriate factory robots and operators. This enables efficient changes to manufacturing lines and rapid startup of new lines.

[1322] "Project information" refers to detailed data about changes to the manufacturing line or new projects, specifically including the project overview, required technologies, and required skill sets.

[1323] A "server" is a computer system that receives information entered by a user and performs analysis and processing on it.

[1324] An "internal resource database" is a database that contains information about the technologies, skill sets, and work experience of each individual and department within the company.

[1325] An "external vendor database" is a database that lists the technical skills and services that registered external technology providers and service vendors can offer.

[1326] A "request for support message" is a message sent by the server to a designated person, department, or external vendor to request support, and includes a project overview, required technical skills, and contact information.

[1327] A "factory robot" is an automated device used to perform specific tasks or operations on a manufacturing line or in a factory.

[1328] An "operator" refers to a person responsible for operating and managing equipment and systems in a factory or manufacturing line.

[1329] "Analysis" refers to data processing that identifies the necessary technologies and skill sets based on the input project information.

[1330] System Overview

[1331] This invention is a system for quickly identifying the necessary skills and skill sets and selecting appropriate factory robots and operators when changing manufacturing lines or launching new product lines. The system includes a process of receiving project information entered by the user, analyzing it, and automatically allocating appropriate resources.

[1332] 1. User requirements input

[1333] Users use a dedicated device (smartphone, tablet, PC, etc.) to input detailed project information. This information includes a project overview, required skills, and required skill sets. For example, it might include details such as "manufacturing line modification" or "launching a new product line."

[1334] 2. Receiving and analyzing data on the server

[1335] The server receives project information sent from the terminal. After receiving the information, the server analyzes it using natural language processing (NLP) to identify the necessary skills and skill sets. This process utilizes NLP libraries such as spaCy and NLTK.

[1336] 3. Searching for internal resources

[1337] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals and departments that possess the identified skills and skill sets. This database contains information about the skills and work experience of internal engineers and departments.

[1338] 4. Selection of potential external vendors

[1339] If necessary, the server also searches an external vendor database. This database contains information about the skills and services that registered technology providers and service vendors can offer. The server searches the database and lists external vendors that possess the identified skills and skill sets.

[1340] 5. Generating a support request message

[1341] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required skills, and contact information. Template engines such as EJS or Handlebars are used to generate the message.

[1342] 6. Sending a request for assistance

[1343] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[1344] Specific example

[1345] For example, when a manufacturer sets up an assembly line for a new smartphone, the user will enter the following information:

[1346] "Customer name: A certain customer"

[1347] "Project details: Assembling a new smartphone"

[1348] Required skills: Precision operation of robotic arms, quality inspection.

[1349] Required skill set: precision operation skills, quality inspection using image analysis.

[1350] Example of a prompt:

[1351] A new smartphone assembly project has emerged.

[1352] 1. Customer name: A certain customer

[1353] 2. Project details: Assembling a new smartphone

[1354] 3. Required skills: Precision operation of robotic arms, quality inspection

[1355] 4. Required skill set: Precision operation techniques, quality inspection using image analysis.

[1356] Identify the most suitable internal resources and external vendors.

[1357] By using this system, efficient changes to manufacturing lines and rapid launch of new product lines can be achieved, leading to expected improvements in productivity.

[1358] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1359] Step 1:

[1360] The user enters project information using a terminal. This information includes a project overview, required skills, and required skill sets. This information is then sent from the terminal to the server.

[1361] (Input: Project overview, required skills, required skill set / Output: Project information sent to the server)

[1362] Step 2:

[1363] The server receives project information sent from the terminal. The received information is analyzed using a natural language processing (NLP) library (e.g., spaCy or NLTK) to identify the necessary skills and skill sets.

[1364] (Input: Project information sent from the terminal / Output: Analyzed skills and skill sets)

[1365] Step 3:

[1366] Based on the analysis results, the server searches the company's internal resource database (MySQL or MongoDB) and lists the individuals or departments who possess the identified skills or skill sets. This search is performed using SQL queries or similar methods.

[1367] (Input: Analyzed skills and skill sets / Output: Listed personnel and departments)

[1368] Step 4:

[1369] If necessary, the server searches the external vendor database. It searches the database of registered technology providers and service vendors and lists external vendors that possess the identified skills and skill sets.

[1370] (Input: Analyzed skills and skill sets / Output: Listed external vendors)

[1371] Step 5:

[1372] The server generates a support request message based on internal resources and information from external vendors. This message includes a project overview, required skills, and contact information. A template engine (such as EJS or Handlebars) is used to generate the message.

[1373] (Input: Information on listed personnel, departments, and external vendors / Output: Support request message)

[1374] Step 6:

[1375] The server sends the generated support request message to the appropriate person / department and external vendor. Internal contacts and departments receive the message via the internal email system or WebSocket, while external vendors receive it via email using the SMTP protocol.

[1376] (Input: Assistance request message / Output: Sent message)

[1377] This series of processing steps enables efficient and rapid launch of changes to manufacturing lines and new product lines.

[1378] 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.

[1379] This invention relates to a system for quickly assembling a project structure after a proposal and order have been received. This system includes a process in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Furthermore, this invention incorporates an emotion engine to recognize and appropriately respond to the user's emotions.

[1380] System Overview

[1381] 1. User requirements input

[1382] Users input project details using a dedicated terminal. This information includes client name, project description, required technologies, and required skill sets. The emotion engine analyzes the user's facial expressions, input speed, and voice as they input information to recognize their emotions. This information is collected in real time.

[1383] 2. Receiving and analyzing data on the server

[1384] The server receives project information sent from the terminal and emotional data from the emotion engine. After receiving the data, the server analyzes the project information to identify the necessary technologies and required skill sets. It also analyzes the user's psychological state based on the emotional data and evaluates its impact on project progress.

[1385] 3. Searching for internal resources

[1386] The server searches the company's internal resource database based on the analysis results. This database contains information about the skill sets and experience of each individual and department within the company. The server lists the individuals and departments that possess the identified technologies and skill sets.

[1387] 4. Selection of potential external vendors

[1388] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services offered by registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[1389] 5. Generating a support request message

[1390] The server generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[1391] 6. Sending a request for assistance

[1392] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, the support request message is sent to external vendors via email or API. The emotional state of the user, as detected by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[1393] Specific example

[1394] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[1395] "Customer name"

[1396] "Project Details"

[1397] "Required skills"

[1398] "Required Skill Set"

[1399] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[1400] The server then identifies the technologies required for "Web application development" ("React, Node.js, AWS") and the required skill sets ("frontend development, backend development, cloud infrastructure") based on the project description.

[1401] Next, the server searches the internal resource database and lists individuals or departments with the identified technical skills (e.g., "Frontend Developer," "Backend Development Department," "Cloud Infrastructure Developer"). It also searches the external vendor database to identify relevant external vendors (e.g., "Technical Service Provider").

[1402] Finally, the server uses this information to generate a support request message, reflecting any stress the user may be experiencing and emphasizing the urgency. This allows the support request message to be sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[1403] The following describes the processing flow.

[1404] Step 1:

[1405] Users access a dedicated terminal application or web interface and enter their login information, which includes their username and password. If the login information is correct, the user is authenticated and can proceed to the next step.

[1406] Step 2:

[1407] The user accesses the project information input screen and enters the necessary project information. This information includes "client name," "project details," "required technologies," and "required skill set." The emotion engine monitors the user's facial expressions, voice, and input speed in real time to recognize their emotions. After the user has entered all the information and the emotion data has been collected, they press the submit button, and the data is sent from the terminal to the server.

[1408] Step 3:

[1409] The server receives project information sent from the terminal and sentiment data from the sentiment engine. The received data is stored as detailed project information.

[1410] Step 4:

[1411] The server analyzes stored project information using natural language processing. Specifically, it extracts necessary technologies and required skill sets from the project content. Meanwhile, it analyzes emotional data to evaluate the user's psychological state.

[1412] Step 5:

[1413] The server searches the internal resource database based on the analysis results. This database contains the skill sets and experience of each person and department within the company. The server lists the persons and departments that possess the identified technologies and skill sets.

[1414] Step 6:

[1415] If necessary, the server also searches an external vendor database. This database contains information about the technical skills and services of registered external vendors. The server lists external vendors that possess the identified technologies and skill sets.

[1416] Step 7:

[1417] The server generates a support request message based on identified internal contacts, departments, and external vendor information. This message includes a project overview, required technical skills, and contact information. The message's wording is also adjusted based on the user's emotional state, as analyzed by the emotion engine. Appropriate phrasing is added or modified depending on whether the user is stressed or experiencing other emotional states.

[1418] Step 8:

[1419] The server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. Furthermore, support request messages are sent to external vendors via email or API. The emotional state of the user detected by the emotion engine is also notified to the project management team, allowing appropriate measures to be taken quickly.

[1420] Step 9:

[1421] The server records the transmission results and monitors the responses and response status from the person or department to whom the support request was sent, as well as from external vendors. This allows for real-time monitoring of project progress and adjustments to responses as needed.

[1422] Specific example:

[1423] For example, if a user receives a new web application development project, they would enter the following information into their terminal:

[1424] "Customer name"

[1425] "Project Details"

[1426] "Required skills"

[1427] "Required Skill Set"

[1428] During this time, the emotion engine analyzes the user's input speed, voice, and facial expressions to determine whether the user is experiencing stress. If the server determines that the user is stressed, it uses this emotional information to add urgency-indicating wording to the support request message. This message is then sent to internal personnel and external vendors, supporting the rapid and efficient establishment of the project structure.

[1429] (Example 2)

[1430] 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".

[1431] Traditional project management systems make it difficult to quickly establish a project structure, requiring significant time and effort to identify the appropriate personnel and external vendors. Furthermore, they generate support request messages without considering user sentiment, resulting in problems in responding appropriately to urgency or specific situations.

[1432] 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.

[1433] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching an internal resource database and an external vendor database to identify personnel and departments with the necessary technologies and skill sets, means for generating and sending support request messages based on the identified information, means for analyzing the user's emotions in real time and adjusting the wording of the support request message based on the analysis results, and means for notifying the project management team of the analyzed emotion information. This enables the rapid and efficient construction of a project structure and the generation of appropriate support request messages that take the user's emotions into consideration.

[1434] A "user" is a person or organization that has the role of inputting project information into the system and providing the necessary data.

[1435] "Project information" refers to detailed information related to a specific project, including the client name, project description, required technologies, and required skill sets.

[1436] A "terminal" refers to a device used by a user to input project information, and includes personal computers, tablets, and smartphones.

[1437] An "emotion engine" is a combination of software and hardware that analyzes a user's facial expressions, voice, and input speed in real time to collect and determine emotional data.

[1438] A "server" is a computer system that receives, analyzes, and stores project information and sentiment data entered by users.

[1439] "Analyzing" is the process of identifying the necessary technologies and skill sets based on the received data, and evaluating the user's emotional state.

[1440] An "internal resource database" is a database that collects information about the skill sets and experience held by various individuals and departments within a company.

[1441] An "external vendor database" is a database that compiles information on the technical skills and services that external service providers can offer.

[1442] A "request for support message" is a message that includes a project overview, required technical skills, and contact information, and is a document generated to request support from internal personnel or external vendors.

[1443] A "project management team" is a team responsible for overseeing the progress of a project and managing its overall operation.

[1444] "Notifying" refers to the process of communicating analysis results and important information to the relevant personnel or teams.

[1445] This invention relates to a system for quickly and efficiently establishing a project structure after a proposal and order have been received. This system includes a series of processes in which the user inputs project information, a server analyzes that information to identify appropriate internal resources and external vendors, and generates and sends support request messages. Furthermore, the invention incorporates an emotion engine to recognize and appropriately respond to the user's emotions.

[1446] First, the user enters project details using a dedicated terminal. This information includes "client name," "project description," "required technologies," and "required skill set." The emotion engine analyzes the user's facial expressions, input speed, and voice as they input the information to recognize their emotions. This information is collected in real time.

[1447] Next, the server receives project information and sentiment data sent from the terminal. The server analyzes this data to identify the necessary technologies and required skill sets. It also analyzes the user's psychological state based on the sentiment data and evaluates its impact on project progress.

[1448] Based on the analysis results, the server searches the internal resource database. This database contains information on the skill sets and experience of each person and department within the company. The server lists the persons and departments that possess the identified technologies and skill sets. If necessary, the server also searches the external vendor database, which contains information on the technical skills and services that registered external vendors can provide. The server lists the external vendors that possess the identified technologies and skill sets.

[1449] The server then generates a support request message based on the listed information about internal contacts, departments, and external vendors. This message includes a project overview, required technical skills, and contact information. Furthermore, the message's wording is adjusted based on the user's emotional information analyzed by the emotion engine. For example, if the user is feeling stressed, the support request message will include wording that emphasizes urgency.

[1450] Finally, the server sends the generated support request message to the relevant personnel and departments within the company. This is notified through the internal system. In addition, the support request message is sent to external vendors via email or API. The emotional state of the user, as perceived by the emotion engine, is also notified to the project management team, ensuring that necessary support is provided promptly.

[1451] Specific example

[1452] For example, if a new web application development project is awarded, the user will enter the following information into their terminal:

[1453] Customer name: "X Corporation"

[1454] Project Description: "Development of a new web application for businesses"

[1455] Required technologies: React, Node.js, AWS

[1456] Required skill set: "Frontend development, backend development, cloud infrastructure"

[1457] While this information is being transmitted, the emotion engine analyzes the user's facial expressions and voice to determine if the user is experiencing stress. If the user is stressed, the server includes this information in the analysis results and alerts the project management team.

[1458] Example of a prompt

[1459] Please enter details for your new web application development project. Include the client name, project description, required technologies, and required skill set. Please ensure accurate input, as emotional information will also be analyzed during the input process.

[1460] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1461] Step 1: User enters project information

[1462] Specific operation: The user enters "customer name," "project details," "required technologies," and "required skill set" into a dedicated form on the terminal. The input information is analyzed in real time by an emotion engine, and emotion data is generated from facial expressions and voice.

[1463] Input: Project information entered by the user, along with the user's facial expressions and voice data.

[1464] Output: Project information and sentiment data saved on the device.

[1465] Step 2: Send

[1466] Specific operation: The terminal combines the input project information and generated emotion data into a single data packet. This data packet is then sent to the server via the network.

[1467] Input: Project information and sentiment data saved on the device.

[1468] Output: Data packets sent to the server.

[1469] Step 3: Receiving and saving data

[1470] Specific operation: The server decompresses the received data packets and saves project information and sentiment data separately.

[1471] Input: Data packets sent to the server.

[1472] Output: Project information and sentiment data stored on the server.

[1473] Step 4: Analyze project information

[1474] Specific operation: The server uses a text analysis engine to analyze the received project information. From the analysis results, it identifies the necessary technologies and required skill sets.

[1475] Input: Project information stored on the server.

[1476] Output: Identified required technologies and required skill sets.

[1477] Step 5: Analyzing emotional data

[1478] Specific operation: The server uses an emotion engine to analyze stored emotion data. It assesses whether the user is experiencing stress and to what extent.

[1479] Input: Emotional data stored on the server.

[1480] Output: Evaluation results regarding the user's psychological state.

[1481] Step 6: Search for internal resources

[1482] Specific operation: The server searches the company's internal resource database using SQL queries to identify the personnel or departments with the necessary technologies and skill sets.

[1483] Input: Identified required technologies and required skill sets.

[1484] Output: Information on listed internal contacts and departments.

[1485] Step 7: Selecting potential external vendors

[1486] Specific operation: The server uses an API to search an external vendor database and identify external vendors that possess the necessary technologies and skill sets.

[1487] Input: Identified required technologies and required skill sets.

[1488] Output: Information on the listed external vendors.

[1489] Step 8: Generating a support request message

[1490] Specific operation: The server uses a template engine to generate a request for assistance message. It takes sentiment data into consideration and adjusts the message wording as needed.

[1491] Input: Listed internal contact information, external vendor information, and sentiment data evaluation results.

[1492] Output: The generated support request message.

[1493] Step 9: Send a support request message

[1494] Specific operation: The server sends support request messages to the relevant personnel and departments through the internal system. Messages are also sent to external vendors via email or API.

[1495] Input: The generated support request message.

[1496] Output: Support request messages sent to internal staff and external vendors.

[1497] (Application Example 2)

[1498] 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".

[1499] Traditional systems required significant time and effort to assemble teams appropriately and quickly after a project was awarded. Furthermore, proceeding with projects without considering the mental state of on-site managers could negatively impact project progress. Additionally, the inability to dynamically generate messages based on user input and emotional states made it difficult to effectively convey the urgency and importance of support requests.

[1500] 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.

[1501] In this invention, the server includes means for the user to input project information, means for receiving and analyzing project information, means for searching internal and external resource databases to identify resources with the necessary skill sets, means for generating and sending support request messages, means for analyzing the user's emotions, and means for adjusting the wording of the message based on the emotion analysis results. This makes it possible to quickly and efficiently assemble an appropriate team after receiving a project order and to generate support request messages while taking the user's mental state into consideration.

[1502] A "user" is an individual or organization that inputs and manages project information.

[1503] "Project information" refers to data that includes project details, required technologies, and required skill sets.

[1504] A "server" is a central processing unit that receives and analyzes input information, searches for resources, generates messages, and sends them.

[1505] An "internal resource database" is a database that records the skill sets and experience of each individual and department within a company.

[1506] An "external resource database" is a database that records the skills and services that external vendors and service providers can offer.

[1507] An "emotion analysis system" is a system that analyzes a user's facial expressions, voice, and actions to determine their emotional state.

[1508] A "request for support message" is a notification of support requested by the person or department with the necessary skill set for the project.

[1509] "Urgency" is a concept that describes a situation requiring immediate action during project execution.

[1510] This invention is a system for quickly and efficiently establishing a project structure after a project has been awarded. This system includes a process for analyzing project information and sentiment entered by the user, identifying internal and external resources, and generating and sending support request messages.

[1511] Users input project information using smart glasses or a dedicated terminal. This information includes project details, required technologies, and required skill sets. While the user is inputting, the emotion engine analyzes the user's facial expressions and voice to determine their emotional state in real time, particularly assessing whether the user is experiencing stress.

[1512] The server receives project information and emotional data sent from the user's terminal. First, the server analyzes the project information to identify the technologies and skill sets necessary for project execution. Next, it analyzes the emotional data to evaluate the user's emotional state. Based on this information, it determines the impact on project progress.

[1513] The server then searches the internal resource database and lists the individuals or departments with the identified technologies and skill sets. Furthermore, it searches external resource databases as needed to identify relevant external resources.

[1514] Next, the server generates a request for assistance message based on this resource information. The generated message includes a project overview, required technical skills, and contact information, and the wording is adjusted according to the user's emotional state. For example, if the user is experiencing high levels of stress, language emphasizing urgency will be added.

[1515] Finally, the server sends a request for assistance message to internal personnel and external resources. The message is notified through the internal system and sent to external resources via API or email. The sentiment engine's analysis results are also notified to the project management team, ensuring prompt support.

[1516] To give a concrete example, consider a scenario where a user enters project information for the introduction of a new automated manufacturing line into smart glasses. As the user enters the project details, "Introduction of an automated manufacturing line," and the required skill sets, "Robotics, machine maintenance, programming," the emotion engine analyzes the user's stress level. If the user is experiencing high stress, the server generates a request for assistance message, including this information, clearly indicating that urgent action is required.

[1517] Example of a prompt:

[1518] For the implementation project of a new automated manufacturing line, we need resources with the following skill sets:

[1519] Robotics

[1520] Machine maintenance

[1521] programming

[1522] The supervisor is currently experiencing a high level of stress, and an immediate response is required.

[1523] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1524] Step 1:

[1525] The user enters project information.

[1526] In terms of specific operations, users use smart glasses or a dedicated terminal to input project details, required technologies, and required skill sets via voice input or touch gestures. The entered information is stored on the terminal and sent to the server. The input data includes a project overview, technical skills, and required skill sets.

[1527] Step 2:

[1528] The emotion engine analyzes the user's emotions.

[1529] In terms of operation, the camera and microphone built into the smart glasses capture the user's facial expressions and voice, and emotion analysis is performed in real time. The emotion engine (e.g., Affectiva SDK) uses facial recognition algorithms to evaluate the user's stress level and emotional state. The analysis results are stored as emotion data on the device and sent to the server. The input data is the user's facial expressions and voice data, and the output is the analysis result of the emotional state.

[1530] Step 3:

[1531] The server receives project information and sentiment data.

[1532] In terms of specific operation, the server receives project information and sentiment data sent from the terminal and stores each data independently. The input data consists of project information and sentiment data, and the output consists of these data entries.

[1533] Step 4:

[1534] The server analyzes the project information.

[1535] In terms of specific operations, the server analyzes the received project information to identify the technologies and skill sets required for project execution. This analysis utilizes natural language processing models and data mining techniques. The input data is project information, and the output is a list of required technical skill sets.

[1536] Step 5:

[1537] The server analyzes the emotional data.

[1538] In terms of its specific operation, the server processes the received emotional data and evaluates the user's emotional state. In particular, it determines the user's stress level and analyzes its impact on project progress. The input data is emotional data, and the output is the user's emotional evaluation.

[1539] Step 6:

[1540] The server searches the company's internal resource database.

[1541] In practice, the server searches the internal resource database based on the analysis results to identify individuals and departments with the necessary technologies and skill sets. SQL queries and similarity search algorithms are used for the search. Input data includes the identified technical skills, and the output is a list of corresponding internal resources.

[1542] Step 7:

[1543] The server searches the external resource database.

[1544] In practice, the server searches an external resource database to identify external resources possessing the necessary technologies and skill sets. The search method is similar to internal resource searches, but differs in that it uses an external database. The input data includes identified technical skills, and the output is a list of corresponding external resources.

[1545] Step 8:

[1546] The server generates a support request message.

[1547] Specifically, the server generates a request for assistance message based on identified resource information and the user's emotional state. The message includes a project overview, required technical skills, and contact information. If the user is experiencing high stress levels, urgency is emphasized. Input data consists of the corresponding resource list and emotional analysis results, while output is the request for assistance message.

[1548] Step 9:

[1549] The server sends a request for assistance message.

[1550] In terms of specific operation, the server sends the generated support request message to internal systems and external resources. For internal systems, it uses a notification system; for external resources, it sends the message via API or email. The input data is the support request message, and the output is the transmission status.

[1551] 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.

[1552] 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.

[1553] 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 robot 414.

[1554] 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.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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."

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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.

[1572] The following is further disclosed regarding the embodiments described above.

[1573] (Claim 1)

[1574] A means for users to input project information,

[1575] A means for the server to receive and analyze the input project information,

[1576] The server searches the internal resource database based on the analysis results to identify the personnel and departments with the necessary technologies and skill sets.

[1577] A means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets,

[1578] A means of generating a support request message based on information that identifies the server,

[1579] A means by which the server sends a request for assistance message,

[1580] A system that includes this.

[1581] (Claim 2)

[1582] The system according to claim 1, having means of including a project overview, required technical skills, and contact information in a support request message.

[1583] (Claim 3)

[1584] The system according to claim 1, which has means for a user to enter login information and log in to the system.

[1585] "Example 1"

[1586] (Claim 1)

[1587] A means for users to input project information,

[1588] A means for the server to receive the entered project information and store it in the database,

[1589] A means for analyzing project information stored on a server using natural language processing,

[1590] The server searches the internal resource database based on the analysis results to identify the personnel and departments with the necessary technologies and skill sets.

[1591] A means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets,

[1592] A means of generating a support request message based on information that identifies the server,

[1593] A means by which the server sends a request for assistance message,

[1594] The means of sending messages to internal staff via the internal messaging system and to external vendors via email services,

[1595] A system that includes this.

[1596] (Claim 2)

[1597] The system according to claim 1, having means of including a project overview, required technical skills, and contact information in a support request message.

[1598] (Claim 3)

[1599] The system according to claim 1, which has means for a user to enter login information and log in to the system.

[1600] "Application Example 1"

[1601] (Claim 1)

[1602] A means for users to input project information,

[1603] A means for the server to receive and analyze the input project information,

[1604] The server searches the internal resource database based on the analysis results to identify the personnel and departments with the necessary technologies and skill sets.

[1605] A means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets,

[1606] A means of generating a support request message based on information that identifies the server,

[1607] A means by which the server sends a request for assistance message,

[1608] A means of analyzing the necessary skills and skill sets when manufacturing lines are changed or new projects arise, and identifying appropriate factory robots and operators.

[1609] A system that includes this.

[1610] (Claim 2)

[1611] The system according to claim 1, which has a means of including a project overview, required technical skills, and contact information in a support request message, thereby streamlining the project launch process.

[1612] (Claim 3)

[1613] The system according to claim 1, which has means for a user to enter login information and log in to the system.

[1614] "Example 2 of combining an emotion engine"

[1615] (Claim 1)

[1616] A means for users to input project information,

[1617] A means for the server to receive and analyze the input project information,

[1618] The server searches its internal resource database based on the analysis results to identify the personnel or departments with the necessary technologies and skill sets.

[1619] A means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets,

[1620] A means of generating a support request message based on information that identifies the server,

[1621] A means by which the server sends a request for assistance message,

[1622] A means of analyzing user emotions in real time and adjusting the wording of support request messages based on the analysis results,

[1623] A means of notifying the project management team of the analyzed emotional information,

[1624] A system that includes this.

[1625] (Claim 2)

[1626] The system according to claim 1, having means of including a project overview, required technical skills, and contact information in a support request message.

[1627] (Claim 3)

[1628] The system according to claim 1, which has means for a user to enter login information and log in to the system.

[1629] "Application example 2 when combining with an emotional engine"

[1630] (Claim 1)

[1631] A means for users to input project information,

[1632] A means for the server to receive and analyze the input project information,

[1633] The server searches the internal resource database based on the analysis results to identify the personnel and departments with the necessary technologies and skill sets.

[1634] A means for the server to search an external resource database and identify external resources that possess the necessary technologies and skill sets,

[1635] A means of generating a support request message based on information that identifies the server,

[1636] A means by which the server sends a request for assistance message,

[1637] A means of analyzing user emotions,

[1638] A means of adjusting the wording of a support request message based on the results of emotion analysis,

[1639] A system that includes this.

[1640] (Claim 2)

[1641] The system according to claim 1, which includes a project overview, required technical skills, and contact information in the support request message, and has means of emphasizing urgency based on the user's emotional state.

[1642] (Claim 3)

[1643] The system according to claim 1, which has means for a user to enter login information and log in to the system, and means for providing appropriate guidance to the user based on the sentiment analysis results after login. [Explanation of Symbols]

[1644] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input project information, A means for the server to receive and analyze the input project information, The server searches the internal resource database based on the analysis results to identify the personnel and departments with the necessary technologies and skill sets. A means for the server to search an external vendor database and identify vendors with the necessary technologies and skill sets, A means of generating a support request message based on information that identifies the server, A means by which the server sends a request for assistance message, A system that includes this.

2. The system according to claim 1, having means of including a project overview, required technical skills, and contact information in a support request message.

3. The system according to claim 1, which has means for a user to enter login information and log in to the system.

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