System

Abstract

A system is provided.SOLUTION: The system includes a natural language processing means for analyzing a natural language message input by a user, a retrieval means for retrieving a person having an appropriate skill and career from an internal database on the basis of the analyzed message, a selection means for selecting an optimum person from among the retrieved candidates, and a notification means for providing a contact means for performing push notification to the selected person and connecting with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern companies, it is difficult for employees to find colleagues with the right skills and career backgrounds in a timely manner. This often hinders efficient task execution and project progress. Individual employees also have difficulty accessing the right people when seeking advice or mentoring for self-development. This situation reduces productivity across the organization and limits employees' career growth opportunities. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means.

[0006] The system includes a natural language processing means for analyzing a natural language message entered by a user, a search means for searching an internal database for people with appropriate skills and careers based on the analyzed message, a selection means for selecting the most suitable person from the searched candidates, and a notification means for sending a push notification to the selected person and providing a means of contact for connecting with the user.

[0007] Furthermore, it includes an update means for updating the skill and career information in the internal database in real time, making the latest data available for reference, and is equipped with a scoring means for using generative artificial intelligence to score and rank the person who is most suitable for the user's requirements, thereby supporting the efficient and effective execution of tasks and the progress of projects.

[0008] This allows employees to quickly and accurately find colleagues with the skills and careers they need, enabling efficient communication and task completion, thereby improving productivity across the organization and promoting employee career growth.

[0009] A "user" is an individual or member of an organization who uses the system to enter a natural language message with the intent of finding people with the appropriate skills and careers.

[0010] "Natural language processing means" refers to the technology and algorithms used to analyze natural language messages entered by users and extract key keywords and intent.

[0011] An "internal database" is a database that stores the skills and career information of employees within an organization.

[0012] "Search Means" refers to the technology and algorithms used to search for suitable people from the internal database based on the keywords and intent extracted by the Natural Language Processing Means.

[0013] "Selection Method" refers to the technology and algorithms used to select the most suitable candidates from the search results.

[0014] "Notification Method" refers to the technology and process that provides push notifications to selected individuals to facilitate user engagement.

[0015] "Update means" refers to the technology and process for updating the skills and career information in the internal database in real time and making the latest data available for reference.

[0016] "Scoring Means" refers to the techniques and algorithms used to assess and rank candidate suitability using generative artificial intelligence.

[0017] "Generative AI" refers to machine learning and AI technologies that select and evaluate the best candidates based on user requirements. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career. This system is composed of natural language processing means, search means, selection means, notification means, update means, and scoring means. The program processing of this system is explained in detail below.

[0040] Enter your inquiry details via chat

[0041] Users enter their questions and information about the skills and careers they need in natural language into SkillConnect's chat interface.

[0042] Chat content analysis

[0043] The server uses the generative AI's NLP (Natural Language Processing) module to analyze the user's input text, extracting key keywords (e.g., "JavaScript," "expert," "marketing," "project leader," etc.) and classifying the message's intent.

[0044] Skills and career matching search

[0045] Based on the extracted keywords and intent, the server queries an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[0046] Selection of the best talent

[0047] The server scores multiple candidates from the search results, using AI algorithms to assess each candidate's suitability and select the person who best fits the request.

[0048] Search result display and notifications

[0049] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0050] Review and feedback

[0051] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be selected and notified again. In addition, the server will collect feedback from the user and use it to improve the system's matching accuracy.

[0052] View real-time skills and career data

[0053] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available for reference. This means that new skills and career changes are immediately reflected in the database.

[0054] Specific examples

[0055] Example 1: Searching for a technician

[0056] 1. User types "I need a JavaScript expert" in chat.

[0057] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0058] 3. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0059] 4. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0060] 5. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[0061] Example 2: Searching for project members

[0062] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0063] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0064] 3. The server searches the database for relevant people and identifies the best candidates through scoring.

[0065] 4. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0066] 5. After receiving the approval, Marketer B will provide the user with a connection link.

[0067] Based on these specific examples, the present invention enables a user to quickly and accurately find people with the skills and careers they need, enabling efficient communication and task execution.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[0071] Step 2:

[0072] The server uses a natural language processing (NLP) module to parse the user's input, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[0073] Step 3:

[0074] Based on the extracted keywords and intent, the server runs a search query against an internal database to find candidates with the relevant skills and careers.

[0075] Step 4:

[0076] The server uses an AI algorithm to evaluate the list of candidates obtained from the search results and score each candidate's skills and career suitability.

[0077] Step 5:

[0078] The server selects the best candidate from the scored candidates. If there are multiple candidates, the best candidate is ranked higher.

[0079] Step 6:

[0080] The device will display the selected person's information (e.g., name, job title, contact details) to the user and notify the candidate of the inquiry via push notification.

[0081] Step 7:

[0082] The device displays a prompt to confirm the candidate's consent, and if the candidate accepts, the user is provided with a connection link. If not, the next best candidate is notified again.

[0083] Step 8:

[0084] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0085] Step 9:

[0086] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available. This update is performed regularly, and new skills and career changes are immediately reflected.

[0087] These steps enable users to quickly and accurately find people with the skills and experience they need, enabling efficient communication and task completion.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] Until now, it has been difficult to quickly and accurately find people with specific skills and careers within an organization and recommend appropriate personnel based on the user's needs. In particular, it takes time to collect information and evaluate suitability, which ultimately hinders efficient communication and task execution. A system that solves these problems is needed.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes a language processing means for analyzing a natural language message input by a user, a search means for searching an internal data store for people with appropriate qualifications and experience based on the analyzed message, and a selection means for selecting the most suitable person from the searched candidates, thereby enabling the user to quickly and accurately find people with the skills and careers they need.

[0093] A "user" is a person or organization that uses the system to provide input to search for people with specific skills or careers.

[0094] A "natural language message" refers to a query or instruction entered by a user in natural language or sentences.

[0095] "Language processing means" refers to technology or devices that analyze natural language messages entered by users and extract key keywords and intentions.

[0096] An "internal data store" is a database or data management system that stores the qualifications and experience information of all employees within an organization.

[0097] A "search tool" is a technology or device that searches an internal data store for people with the relevant skills and careers based on analyzed keywords and intent.

[0098] "Selection means" refers to the technology or device that performs evaluation and scoring to select the most suitable person from the search results.

[0099] "Notification means" refers to a technology or device that sends push notifications or contacts to selected individuals and provides a means of contact for connecting with the user.

[0100] "Update means" refers to technology or devices that update information in the internal data store in real time, making the latest qualifications and career information always available for reference.

[0101] The "evaluation means" refers to a technology or device that uses generative artificial intelligence to evaluate and rank the people who are most suitable for the user's requirements.

[0102] The present invention is a system that analyzes natural language messages entered by users and quickly finds people with appropriate skills and careers within an organization. This system is composed of a language processing means, a search means, a selection means, a notification means, an update means, and an evaluation means.

[0103] System Configuration

[0104] The system includes a language processing means (e.g., Google® NLP API) for analyzing natural language messages entered by users, a search means (e.g., MySQL® database) for searching an internal data store for people with appropriate qualifications and experience based on the analyzed messages, a selection means (e.g., an AI algorithm using TENSORFLOW®) for selecting the most suitable person from the searched candidates, and a notification means (e.g., Firebase Cloud Messaging) for notifying the selected person.

[0105] The system also includes an update mechanism that updates the information in the internal data store in real time, making the most up-to-date qualifications and career information available, and an evaluation mechanism that uses generative artificial intelligence to evaluate and rank candidates who best fit the user's requirements.

[0106] How to use

[0107] Using SkillConnect's chat interface, users enter questions and information about the skills and careers they need in natural language, such as "Looking for a JavaScript expert." The server parses this message and extracts key keywords (e.g., "JavaScript" and "expert").

[0108] Based on the analyzed and extracted keywords, the server queries an internal data store to find candidates with the relevant qualifications and experience. The resulting candidates are then scored using an AI algorithm to evaluate their suitability. The best-matched candidate is selected, and their information (e.g., name, job title, contact details) is displayed to the user. At the same time, a push notification is sent to inform the person of the inquiry.

[0109] If the person denies the request, the user will be provided with a link to connect with them. If the person declines the request, the next best candidate will be selected and the user will be notified again.

[0110] Specific examples

[0111] Example 1: Searching for a technician

[0112] A user types "I need a JavaScript expert" in chat. The server uses an NLP module to extract the keywords "JavaScript" and "expert" and searches for matching people in its internal data store. It scores the suitability to identify the best candidate and displays that person's information (e.g., Technician A) to the user. The server sends a push notification and, upon Technician A's approval, provides the user with a connection link to Technician A.

[0113] Example 2: Searching for project members

[0114] A user types in chat, "I'd like to find a new marketing project leader." The server extracts the keywords "marketing" and "project leader" and searches for matching people in the data store. It identifies the best candidate through scoring and displays that person's information (e.g., Marketer B) to the user. The server sends a push notification, and after Marketer B accepts, provides the user with a connection link.

[0115] In this way, the system of the present invention can quickly and accurately find people with the skills and careers that the user needs, enabling efficient communication and task execution.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] A user types a natural language message into SkillConnect's chat interface, for example, "I need a JavaScript expert." The input is sent as text data to the server.

[0119] Step 2:

[0120] The server analyzes the natural language message received. Specifically, it processes the text data using the generative AI's NLP (Natural Language Processing) module (e.g., Google NLP API). It analyzes the message received as input, "JavaScript expert needed," and extracts the main keywords "JavaScript" and "expert." This generates keyword data as the analysis result.

[0121] Step 3:

[0122] The server queries the internal data store based on the analysis results. It uses the analyzed keyword data "JavaScript" and "expert" as input. It generates an SQL statement (e.g. "SELECT FROM employees WHERE skill='JavaScript' AND position='expert'") and searches against the MySQL database. The output is a list of people with the corresponding qualifications and experience.

[0123] Step 4:

[0124] The server selects the best person from the search results. It uses the list of people it has obtained as input. It uses an AI algorithm (e.g., TensorFlow) to score the suitability of each candidate. Based on the scoring, it selects the person with the highest suitability and outputs that person's data (e.g., name, job title, contact details).

[0125] Step 5:

[0126] The device displays the information of the selected person to the user. The selected person's data is used as input. For example, the displayed content might be "Engineer A: JavaScript expert, contact: example@example.com." The server also sends a push notification to the selected person. Using a notification method (e.g., Firebase Cloud Messaging), the selected person is notified of the inquiry.

[0127] Step 6:

[0128] The terminal confirms with the person in question, and if approval is obtained, provides a connection link to the user. Approval information from the person in question is received as input. If approval is obtained, a connection link (e.g., "https: / / example.com / chat") is provided to the user. If the person in question declines, the next best candidate is selected again and notified again via the notification means.

[0129] Step 7:

[0130] The server collects feedback from users. For example, a user may input feedback such as "Engineer A was very helpful." This feedback information is collected and used to improve the matching accuracy of the system. The feedback data is received as input and applied to an improvement algorithm. The expected output is an improvement in the accuracy of the system.

[0131] Step 8:

[0132] The server updates the skills and career information in the internal data store in real time. It receives new skills and career change information as input. It updates the database using a cloud environment (e.g., AWS (registered trademark) API Gateway). This ensures that the latest qualifications and career information is always maintained, enabling up-to-date people searches based on user requests.

[0133] (Application example 1)

[0134] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0135] With conventional systems, it was difficult to quickly find an engineer with the appropriate skills when troubleshooting problems that occurred on the manufacturing floor or when setting up equipment. Furthermore, because engineer information was not updated in real time, it was not possible to refer to the latest skill information, which could result in delayed responses. Furthermore, if the selected engineer was not notified promptly, there was also the problem of further delays in resolving the problem. There is a need for a system that can solve these issues and achieve problem-solving and efficiency improvements on the manufacturing floor.

[0136] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0137] In this invention, the server includes natural language processing means for analyzing a natural language message input by a user, search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, selection means for selecting the most suitable person from the searched candidates, notification means for notifying the selected person in real time and providing a contact means for connecting with the user, and search means for searching for an appropriate engineer based on the content of a request to support the rapid resolution of problems that occur in the factory. This makes it possible to quickly find an appropriate engineer at the manufacturing site and respond in real time.

[0138] "User" refers to a person who uses the system to search for people with specific skills or careers.

[0139] A "natural language message" refers to a text message entered by a user in a language that the user normally uses.

[0140] "Natural language processing means" refers to technology that analyzes input natural language messages and extracts key keywords and intent.

[0141] "Search means" refers to a function that searches an internal database for talent with appropriate skills and careers based on the analyzed message.

[0142] "Selection method" refers to the technology used to evaluate and select the most suitable person from among the candidates searched.

[0143] "Notification means" refers to a communication means for sending a push notification to the selected person and connecting the user with the selected person.

[0144] "Problems occurring within the factory" refers to problems that require the attention of engineers, such as malfunctions that occur on the manufacturing floor or equipment setup.

[0145] An "engineer" is someone who has specific skills and a career and the ability to solve problems that arise on the manufacturing floor.

[0146] "Real-time" refers to information that is updated immediately and is immediately available.

[0147] "Generative AI" refers to technology that uses high-performance algorithms to make predictions and judgments in data analysis, natural language processing, and other areas.

[0148] MODE FOR CARRYING OUT THE INVENTION

[0149] This invention is a system for quickly resolving problems that occur in factories and supporting efficient operations. This system can analyze natural language messages entered by users and quickly find engineers with the appropriate skills and experience.

[0150] System configuration

[0151] The system uses the following hardware and software:

[0152] Hardware: Smartphones, tablets, and internet connection in the factory

[0153] Software: Google Cloud Natural Language API, IBM Watson(R) Natural Language Understanding, MySQL, Firebase Cloud Messaging

[0154] Data processing and calculation

[0155] The system processes and calculates data in the following steps:

[0156] Enter your inquiry details via chat

[0157] Users: Enter shop floor issues and technician requirements in natural language into a smartphone or tablet application.

[0158] Chat content analysis

[0159] Server: Analyzes the message entered by the user using the generative AI's NLP (Natural Language Processing) module, extracting key keywords and the intent of the message.

[0160] Skills and career matching search

[0161] Server: Based on the extracted keywords and intent, the server searches an internal database for personnel with the relevant skills and careers. This database stores the skill profiles and career histories of the factory's engineers.

[0162] Selection of the most suitable engineer

[0163] Server: Scores multiple candidates from the search results and uses AI algorithms to evaluate each candidate's suitability. Selects the engineer best suited to the requirements.

[0164] Search result display and notifications

[0165] Server: Displays the selected technician's information to the user and notifies the technician of the inquiry via push notification.

[0166] Review and feedback

[0167] Terminal: The system checks with the engineer and, if approved, provides the user with a connection link. If the engineer declines the request, the system selects the next best candidate and notifies the user again. Additionally, the system collects user feedback and uses it to improve the system's matching accuracy.

[0168] Specific examples

[0169] Example 1: Troubleshooting request

[0170] User input: "I'm looking for a technician who knows how to tune this press."

[0171] Prompt: "Find a technician who can identify the cause of the press malfunction and make the necessary adjustments."

[0172] Example 2: Setting up a new machine

[0173] User Input: "Can you recommend a technician who can install a new CNC machine?"

[0174] Prompt: "Find a technician who is familiar with CNC machine installation procedures and can quickly complete the setup process."

[0175] This allows the system to quickly find the right technician on the manufacturing floor and respond in real time.

[0176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0177] Step 1:

[0178] Enter your inquiry details via chat

[0179] Users input problems at the manufacturing site and requirements for engineers in natural language into an application on a smartphone or tablet.

[0180] Input: Free text (e.g. "We are looking for a technician who is knowledgeable about adjusting this press")

[0181] Output: A query message from the user is sent to the application.

[0182] Step 2:

[0183] Chat content analysis

[0184] The server analyzes the text entered by the user using an NLP (natural language processing) module with a generative AI model.

[0185] Input: The query message sent by the user

[0186] Data processing: Extract key keywords (e.g., "press machine," "adjustment," "engineer") and intent of the message.

[0187] Output: Extracted keywords and message intent

[0188] Step 3:

[0189] Skills and career matching search

[0190] The server queries an internal database based on the extracted keywords and intent to search for engineers with the relevant skills and experience.

[0191] Input: Extracted keywords and message intent

[0192] Data crunching: Run a database query to look at technicians' skill profiles and career histories to come up with a list of matches.

[0193] Output: A list of matching technicians

[0194] Step 4:

[0195] Selection of the most suitable engineer

[0196] The server scores multiple candidates obtained from the search results and evaluates each candidate's suitability using a generative AI model.

[0197] Input: List of matched technicians

[0198] Data calculation: Score and rank suitability based on work history, skill set, past evaluations, etc.

[0199] Output: Selection of the best technician

[0200] Step 5:

[0201] Search result display and notifications

[0202] The server displays the information of the selected technician to the user and notifies the technician of the inquiry via a push notification.

[0203] Input: Selected engineer's information

[0204] Specific behavior:

[0205] 1. The technician's information (e.g., name, title, contact information) is displayed on the user's device.

[0206] 2. The server sends a push notification to the technician, sharing the details of the issue.

[0207] Output: Display to user and notify technician

[0208] Step 6:

[0209] Review and feedback

[0210] The terminal checks with the relevant engineer, and if approval is obtained, provides the user with a connection link.

[0211] Input: Technician's response

[0212] Specific behavior:

[0213] 1. The technician terminal responds with either approval or denial.

[0214] 2. If the technician approves, he / she will provide the connection link to the user's device.

[0215] 3. If the technician declines, the server will select the next best candidate and notify them again.

[0216] 4. The server collects feedback from users and uses it to improve the system's matching accuracy.

[0217] Output: Provides user with a connection link or suggests next best candidate

[0218] In this way, the system can quickly find the right technician on the manufacturing floor and respond in real time.

[0219] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0220] The present invention combines a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career, with an emotion engine that recognizes the user's emotions. This system is composed of natural language processing means, search means, selection means, notification means, update means, scoring means, and the emotion engine. The program processing of this system is explained in detail below.

[0221] Enter your inquiry details via chat

[0222] Users enter questions and information about desired skills and careers in natural language into SkillConnect's chat interface, and messages entered by users may contain emotions.

[0223] Chat content analysis

[0224] The server uses the generative AI's NLP (Natural Language Processing) module to parse the user's input text, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[0225] Emotion Analysis

[0226] The server uses an emotion engine to recognize and analyze emotions from the user's input message, for example, to determine whether the user is feeling stressed or expressing urgency.

[0227] Skills and career matching search

[0228] The server then uses the extracted keywords, intent, and sentiment analysis results to query an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[0229] Selection of the best talent

[0230] The server uses an AI algorithm to evaluate multiple candidates from the search results, scoring each candidate's skill and career compatibility, and selecting the most suitable candidate, taking into account the user's emotional state.

[0231] Search result display and notifications

[0232] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0233] Review and feedback

[0234] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be notified. Furthermore, the server collects and analyzes feedback from users, which is used as data to improve the system's matching accuracy.

[0235] View real-time skills and career data

[0236] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available. This update is performed periodically, and new skills and career changes are immediately reflected in the database.

[0237] Specific examples

[0238] Example 1: Searching for a technician

[0239] 1. User types "I need a JavaScript expert" in chat.

[0240] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0241] 3. The server uses an emotion engine to analyze the user's emotions and recognize the high urgency of the message.

[0242] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0243] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0244] 6. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[0245] Example 2: Searching for project members

[0246] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0247] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0248] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[0249] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[0250] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0251] 6. After Marketer B receives the approval, the device provides the user with a connection link.

[0252] Based on these specific examples, the present invention enables users to quickly and accurately find people with the skills and careers they need, and provides optimal feedback and mentoring based on emotional changes, thereby enabling efficient communication and task completion.

[0253] The processing flow will be explained below.

[0254] Step 1:

[0255] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[0256] Step 2:

[0257] The server parses the user's input using a natural language processing (NLP) module, which extracts key keywords and intent from the entered message.

[0258] Step 3:

[0259] The server uses the extracted keywords and intent to run search queries against an internal database that contains the skill profiles and career histories of all employees.

[0260] Step 4:

[0261] The server uses the emotion engine to recognize and analyze emotions from the user's input message, identifying the user's emotional state (e.g., stress, urgency, etc.).

[0262] Step 5:

[0263] The server integrates the candidate list obtained from the skill and career search with the results of sentiment analysis, and uses an AI algorithm to score the person who best suits the user's requirements and emotional state.

[0264] Step 6:

[0265] The server selects the best candidate from the scored candidates. If there are multiple candidates, it takes into account emotional information and ranks the most suitable candidate at the top.

[0266] Step 7:

[0267] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0268] Step 8:

[0269] The device will prompt the person for confirmation, and if approved, provide the user with a connection link. If not, the next best candidate will be notified again.

[0270] Step 9:

[0271] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0272] Step 10:

[0273] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available and that new skills and career changes are immediately reflected in the database.

[0274] Example 2

[0275] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0276] Within a company, users are faced with the challenge of quickly and accurately finding people with the skills and experience they need. In particular, there is a demand for systems that can respond optimally while taking into account the user's feelings. It is also important that the skills and experience information is always up to date. Conventional systems have difficulty meeting these requirements, so an effective solution is needed.

[0277] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a natural language processing means for analyzing a natural language message input by a user, a search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, an emotion recognition means for recognizing and analyzing emotions from the user's message, a selection means for selecting the most suitable person from the searched candidates, and a notification means for sending a push notification to the selected person and providing a contact means for connecting with the user. This makes it possible to quickly and accurately find people with the necessary skills and experience while taking the user's emotions into consideration.

[0278] "Natural language processing means" is a means for analyzing a natural language message entered by a user and extracting key keywords and intentions.

[0279] "Search means" refers to means for searching an internal database for a person with appropriate skills and background based on the analyzed message.

[0280] The "emotion recognition means" is a means for recognizing and analyzing emotions from a message input by a user.

[0281] The "selection means" is a means for selecting the most suitable person from among the searched candidates.

[0282] The "notification means" is a means for sending a push notification to the selected person and providing a means of contact for connecting with the user.

[0283] The "update means" is a means for updating the skills and career information in the internal database in real time, so that the latest data can always be referenced.

[0284] "Scoring means" is a means for using generative artificial intelligence to score and rank the person who best suits the user's requirements.

[0285] The present invention is a system that analyzes natural language messages entered by users and quickly finds people in a company who have the appropriate skills and background. This system is composed of natural language processing means, search means, emotion recognition means, selection means, notification means, update means, and scoring means.

[0286] Configuration and Operation

[0287] First, a user logs into SkillConnect's chat interface and types in natural language information about their question and desired skill or career, such as "I need a JavaScript expert." This message may contain the user's sentiment.

[0288] The server then uses the generative AI's NLP (natural language processing) module to parse the user's input text, using Microsoft® Azure® Cognitive Services to extract key keywords (e.g., "JavaScript" or "expert") and intent (e.g., skill request).

[0289] The server uses an emotion engine, such as IBM Watson Tone Analyzer, to recognize and analyze emotions from the input message, determining whether the user is feeling stressed or expressing urgency.

[0290] Based on the extracted keywords, intent, and sentiment analysis results, the server queries an internal database (e.g., SharePoint, an internal portal software) to search for candidates with the relevant skills and experience.

[0291] From the multiple candidates found, the server evaluates them using an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background, and also takes into account the user's emotional state to select the most suitable candidate.

[0292] The device then displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via push notification (e.g., Firebase Cloud Messaging). The selected person confirms, and if they agree, the server provides the user with a connection link. If the selected person declines the inquiry, the server notifies the next best candidate. Additionally, the server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0293] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. This update is performed periodically, and new skills and experience changes are immediately reflected in the database.

[0294] Specific examples

[0295] Example 1: Searching for engineers

[0296] 1. User types "I need a JavaScript expert" in chat.

[0297] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0298] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[0299] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0300] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0301] 6. Once Technician A replies with approval, the terminal provides the user with a connection link to Technician A.

[0302] Example 2: Searching for project members

[0303] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0304] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0305] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[0306] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[0307] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0308] 6. After receiving the approval from Marketer B, the device provides the user with a connection link.

[0309] As described above, the system based on the present invention not only quickly and accurately finds people with the skills and experience required by the user, but also provides optimal feedback and mentoring based on emotional changes, thereby realizing efficient communication and task completion.

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Step 1:

[0312] A user logs into SkillConnect's chat interface and types in natural language information about their question or desired skill or career. For example, they can send a natural language message like, "I need a JavaScript expert." This message may contain the user's sentiment.

[0313] Step 2:

[0314] The server uses the generative AI's NLP (natural language processing) module to analyze the user's input text. Specifically, it uses Microsoft's Azure Cognitive Services to perform the analysis. It receives the input text, tokenizes it, extracts key keywords (such as "JavaScript" or "expert") and intent (such as a skill request), and outputs these.

[0315] Step 3:

[0316] The server uses an emotion engine to recognize and analyze emotions from the user's input message. For example, it uses IBM Watson Tone Analyzer. It uses the analyzed text as input to determine the user's emotional state (e.g., "urgent" or "relaxed"). The extracted emotional information is output.

[0317] Step 4:

[0318] The server queries an internal database (for example, SharePoint, an internal portal software) based on the extracted keywords, intent, and sentiment analysis results to search for candidates with the relevant skills and experience. Keywords and sentiment information are used as input to retrieve relevant entries from the internal database, and the search results are the output.

[0319] Step 5:

[0320] The server uses an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background from the multiple candidates obtained from the search results and selects the most suitable person. The search results are used as input to calculate suitability, and information about the most suitable person is output.

[0321] Step 6:

[0322] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification (e.g., Firebase Cloud Messaging). Using the information of the best match as input, a push notification is generated and sent. The notification sent result is the output.

[0323] Step 7:

[0324] The device confirms with the person in question and, if consent is obtained, provides the user with a connection link. If the person in question declines the inquiry, the next best candidate is notified again. In addition, the server collects and analyzes feedback from users and uses it as data to improve the system's matching accuracy. Using the confirmation results and feedback information as input, the final result is a connection link or the next notification.

[0325] Step 8:

[0326] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. Updates are made periodically, and new skills and experience changes are immediately reflected in the database. The latest skills and experience information is used as input, and the updated database state is the output.

[0327] (Application example 2)

[0328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0329] Conventional technologies provide systems that analyze natural language messages entered by users to find people in a company with the appropriate skills and career experience. However, because selection is based solely on skills and career experience without considering the user's emotional state, it is difficult to respond promptly to urgent issues or the user's emotions. Furthermore, because data is not updated in real time, matching accuracy declines, making it difficult to quickly find the right person. Therefore, the present invention aims to provide a system that analyzes a user's emotional state and quickly and accurately finds the right person.

[0330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0331] In this invention, the server includes a natural language processing unit that analyzes a natural language message entered by a user, a search unit that searches an internal database for people with appropriate skills and careers based on the analyzed message, a selection unit that selects the most suitable person from the searched candidates, a notification unit that sends a push notification to the selected person and provides a contact means for connecting with the user, an emotion analysis unit that analyzes the user's emotional state using an emotion engine that recognizes emotions, and an urgency evaluation unit that determines the urgency and optimizes candidates based on the analyzed emotional state and the user's message. This makes it possible to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. Furthermore, real-time data updates enable accurate matching based on the latest information.

[0332] "Natural language processing means" is a processing technology for analyzing natural language messages entered by users and extracting key keywords and intentions.

[0333] The "search means" is a function for searching an internal database for people with appropriate skills and careers based on the analyzed message.

[0334] The "selection method" is the algorithm or method used to select the most suitable person from the searched candidates.

[0335] "Notification means" is a function that sends push notifications to selected people and provides a means of contact for connecting with the user.

[0336] "Emotion analysis means" is a technology that uses an emotion engine to recognize and analyze emotions from the user's input message.

[0337] The "urgency evaluation means" is an evaluation method for determining urgency and optimizing candidates based on the analyzed emotional state and the user's message.

[0338] The "update means" is a mechanism for updating the skill and career information in the internal database in real time, making the latest data always available for reference.

[0339] The "scoring means" is a function that uses generative artificial intelligence to score and rank the person who is most suitable for the user's requirements.

[0340] The present invention relates to a system that analyzes natural language messages entered by users and combines them with an emotion recognition engine to quickly find people with suitable skills and careers.

[0341] System Configuration

[0342] Natural language processing tools

[0343] The server uses a natural language processing module that analyzes the natural language messages entered by the user into the device, leveraging a generative AI model to extract key keywords and user intent.

[0344] Search methods

[0345] Based on the analyzed keywords and intent, the server searches for candidates with the appropriate skills and careers from an internal database that contains the skill profiles and career histories of all employees within the organization.

[0346] Emotion analysis means

[0347] The server uses an emotion engine to recognize and analyze emotions from the user's input message, determining whether the user is feeling stressed or expressing urgency.

[0348] Urgency assessment tools

[0349] The server determines the urgency of the message based on the results of sentiment analysis and the user's message. If the urgency is high, candidates who require a quick response are notified first.

[0350] Selection method

[0351] The server selects the best candidate from the candidates obtained by the search tool. This selection process includes a scoring tool using a generative AI model to evaluate each candidate's skill and career fit.

[0352] Notification means

[0353] As a notification method, the server will send a push notification to the selected candidate, and the user will be provided with the appropriate contact method and a connection link if necessary.

[0354] Update method

[0355] The server updates the skills and career information in the internal database in real time, ensuring that the latest data is always available. This update is performed periodically.

[0356] Specific processing examples

[0357] Example 1: Searching for a technician

[0358] 1. The user types "The motor temperature is rising abnormally" into the terminal.

[0359] 2. The server analyzes this message and extracts the keywords "motor," "temperature," and "abnormal."

[0360] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[0361] 4. The server searches its internal database for technicians with the right skills and scores them to identify the best candidates.

[0362] 5. The server notifies the selected technician and provides the user with the technician's contact information.

[0363] Prompt Sentence Examples

[0364] "The user inputs 'The motor temperature is abnormally high.' Please generate a program that analyzes this message and executes the process of finding the most suitable technician. Start by using NLP and an emotion engine to extract keywords and emotions, find the most suitable technician from the database, and notify the system."

[0365] This allows the system of the present invention to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. In addition, real-time data updates enable accurate matching based on the latest information.

[0366] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0367] Step 1:

[0368] The user inputs the details of the abnormality into the terminal in natural language. For example, if the user inputs "The motor temperature is rising abnormally," this text becomes the input data. The input data is sent to the server.

[0369] Step 2:

[0370] The server receives the natural language message and analyzes it using a natural language processing method that uses a generative AI model. Here, key keywords such as "motor," "temperature," and "abnormality" are extracted from the text, along with the intent of "reporting an abnormality." The analysis results are output as keywords to be queried in the database.

[0371] Step 3:

[0372] The server uses an emotion engine to recognize and analyze emotions from the user's text. The analysis evaluates the stress and urgency the user feels. The input for this step is the user's text, and the output is a score indicating the urgency.

[0373] Step 4:

[0374] The server searches its internal database for candidates with the appropriate skills and experience based on the analyzed keywords and urgency score, issues a query to the database, and outputs a list of relevant engineers.

[0375] Step 5:

[0376] The server uses a generative AI model to score the multiple candidates found in the search results. This takes into account not only skill and career compatibility but also urgency scores. As a result of the scoring, a profile of the most suitable candidate is output.

[0377] Step 6:

[0378] The server sends a push notification to the selected best candidate. Specifically, a notification containing details of the anomaly report is sent to the selected candidate's contact information. The notification sending result is recorded.

[0379] Step 7:

[0380] The server notifies the user of the selected candidate. The technician's name, title, contact information, etc. are displayed on the terminal, and a connection link is provided if necessary. The input of this step is the selected candidate's information, and the output is the notification to the user.

[0381] Step 8:

[0382] The server waits for a response from the candidate, and if they agree, it provides a connection link to the user. If the candidate refuses to report the anomaly, it notifies the next best candidate again. Until it receives a response, the server monitors the communication and decides the next action.

[0383] In this way, the user can simply enter details of the abnormality in natural language, and the server will take appropriate action, enabling them to receive a prompt and appropriate response.

[0384] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0385] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0387] [Second embodiment]

[0388] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0389] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0390] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0392] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0394] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0395] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0396] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0397] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0399] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0400] The present invention is a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career. This system is composed of natural language processing means, search means, selection means, notification means, update means, and scoring means. The program processing of this system is explained in detail below.

[0401] Enter your inquiry details via chat

[0402] Users enter their questions and information about the skills and careers they need in natural language into SkillConnect's chat interface.

[0403] Chat content analysis

[0404] The server uses the generative AI's NLP (Natural Language Processing) module to analyze the user's input text, extracting key keywords (e.g., "JavaScript," "expert," "marketing," "project leader," etc.) and classifying the message's intent.

[0405] Skills and career matching search

[0406] Based on the extracted keywords and intent, the server queries an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[0407] Selection of the best talent

[0408] The server scores multiple candidates from the search results, using AI algorithms to assess each candidate's suitability and select the person who best fits the request.

[0409] Search result display and notifications

[0410] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0411] Review and feedback

[0412] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be selected and notified again. In addition, the server will collect feedback from the user and use it to improve the system's matching accuracy.

[0413] View real-time skills and career data

[0414] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available for reference. This means that new skills and career changes are immediately reflected in the database.

[0415] Specific examples

[0416] Example 1: Searching for a technician

[0417] 1. User types "I need a JavaScript expert" in chat.

[0418] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0419] 3. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0420] 4. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0421] 5. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[0422] Example 2: Searching for project members

[0423] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0424] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0425] 3. The server searches the database for relevant people and identifies the best candidates through scoring.

[0426] 4. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0427] 5. After receiving the approval, Marketer B will provide the user with a connection link.

[0428] Based on these specific examples, the present invention enables a user to quickly and accurately find people with the skills and careers they need, enabling efficient communication and task execution.

[0429] The processing flow will be explained below.

[0430] Step 1:

[0431] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[0432] Step 2:

[0433] The server uses a natural language processing (NLP) module to parse the user's input, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[0434] Step 3:

[0435] Based on the extracted keywords and intent, the server runs a search query against an internal database to find candidates with the relevant skills and careers.

[0436] Step 4:

[0437] The server uses an AI algorithm to evaluate the list of candidates obtained from the search results and score each candidate's skills and career suitability.

[0438] Step 5:

[0439] The server selects the best candidate from the scored candidates. If there are multiple candidates, the best candidate is ranked higher.

[0440] Step 6:

[0441] The device will display the selected person's information (e.g., name, job title, contact details) to the user and notify the candidate of the inquiry via push notification.

[0442] Step 7:

[0443] The device displays a prompt to confirm the candidate's consent, and if the candidate accepts, the user is provided with a connection link. If not, the next best candidate is notified again.

[0444] Step 8:

[0445] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0446] Step 9:

[0447] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available. This update is performed regularly, and new skills and career changes are immediately reflected.

[0448] These steps enable users to quickly and accurately find people with the skills and experience they need, enabling efficient communication and task completion.

[0449] Example 1

[0450] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0451] Until now, it has been difficult to quickly and accurately find people with specific skills and careers within an organization and recommend appropriate personnel based on the user's needs. In particular, it takes time to collect information and evaluate suitability, which ultimately hinders efficient communication and task execution. A system that solves these problems is needed.

[0452] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0453] In this invention, the server includes a language processing means for analyzing a natural language message input by a user, a search means for searching an internal data store for people with appropriate qualifications and experience based on the analyzed message, and a selection means for selecting the most suitable person from the searched candidates, thereby enabling the user to quickly and accurately find people with the skills and careers they need.

[0454] A "user" is a person or organization that uses the system to provide input to search for people with specific skills or careers.

[0455] A "natural language message" refers to a query or instruction entered by a user in natural language or sentences.

[0456] "Language processing means" refers to technology or devices that analyze natural language messages entered by users and extract key keywords and intentions.

[0457] An "internal data store" is a database or data management system that stores the qualifications and experience information of all employees within an organization.

[0458] A "search tool" is a technology or device that searches an internal data store for people with the relevant skills and careers based on analyzed keywords and intent.

[0459] "Selection means" refers to the technology or device that performs evaluation and scoring to select the most suitable person from the search results.

[0460] "Notification means" refers to a technology or device that sends push notifications or contacts to selected individuals and provides a means of contact for connecting with the user.

[0461] "Update means" refers to technology or devices that update information in the internal data store in real time, making the latest qualifications and career information always available for reference.

[0462] The "evaluation means" refers to a technology or device that uses generative artificial intelligence to evaluate and rank the people who are most suitable for the user's requirements.

[0463] The present invention is a system that analyzes natural language messages entered by users and quickly finds people with appropriate skills and careers within an organization. This system is composed of a language processing means, a search means, a selection means, a notification means, an update means, and an evaluation means.

[0464] System Configuration

[0465] The system includes a language processing means (e.g., Google NLP API) for analyzing natural language messages entered by users, a search means (e.g., MySQL database) for searching an internal data store for people with appropriate qualifications and experience based on the analyzed messages, a selection means (e.g., an AI algorithm using TensorFlow) for selecting the most suitable person from the searched candidates, and a notification means (e.g., Firebase Cloud Messaging) for notifying the selected person.

[0466] The system also includes an update mechanism that updates the information in the internal data store in real time, making the most up-to-date qualifications and career information available, and an evaluation mechanism that uses generative artificial intelligence to evaluate and rank candidates who best fit the user's requirements.

[0467] How to use

[0468] Using SkillConnect's chat interface, users enter questions and information about the skills and careers they need in natural language, such as "Looking for a JavaScript expert." The server parses this message and extracts key keywords (e.g., "JavaScript" and "expert").

[0469] Based on the analyzed and extracted keywords, the server queries an internal data store to find candidates with the relevant qualifications and experience. The resulting candidates are then scored using an AI algorithm to evaluate their suitability. The best-matched candidate is selected, and their information (e.g., name, job title, contact details) is displayed to the user. At the same time, a push notification is sent to inform the person of the inquiry.

[0470] If the person denies the request, the user will be provided with a link to connect with them. If the person declines the request, the next best candidate will be selected and the user will be notified again.

[0471] Specific examples

[0472] Example 1: Searching for a technician

[0473] A user types "I need a JavaScript expert" in chat. The server uses an NLP module to extract the keywords "JavaScript" and "expert" and searches for matching people in its internal data store. It scores the suitability to identify the best candidate and displays that person's information (e.g., Technician A) to the user. The server sends a push notification and, upon Technician A's approval, provides the user with a connection link to Technician A.

[0474] Example 2: Searching for project members

[0475] A user types in chat, "I'd like to find a new marketing project leader." The server extracts the keywords "marketing" and "project leader" and searches for matching people in the data store. It identifies the best candidate through scoring and displays that person's information (e.g., Marketer B) to the user. The server sends a push notification, and after Marketer B accepts, provides the user with a connection link.

[0476] In this way, the system of the present invention can quickly and accurately find people with the skills and careers that the user needs, enabling efficient communication and task execution.

[0477] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0478] Step 1:

[0479] A user types a natural language message into SkillConnect's chat interface, for example, "I need a JavaScript expert." The input is sent as text data to the server.

[0480] Step 2:

[0481] The server analyzes the natural language message received. Specifically, it processes the text data using the generative AI's NLP (Natural Language Processing) module (e.g., Google NLP API). It analyzes the message received as input, "JavaScript expert needed," and extracts the main keywords "JavaScript" and "expert." This generates keyword data as the analysis result.

[0482] Step 3:

[0483] The server queries the internal data store based on the analysis results. It uses the analyzed keyword data "JavaScript" and "expert" as input. It generates an SQL statement (e.g. "SELECT FROM employees WHERE skill='JavaScript' AND position='expert'") and searches against the MySQL database. The output is a list of people with the corresponding qualifications and experience.

[0484] Step 4:

[0485] The server selects the best person from the search results. It uses the list of people it has obtained as input. It uses an AI algorithm (e.g., TensorFlow) to score the suitability of each candidate. Based on the scoring, it selects the person with the highest suitability and outputs that person's data (e.g., name, job title, contact details).

[0486] Step 5:

[0487] The device displays the information of the selected person to the user. The selected person's data is used as input. For example, the displayed content might be "Engineer A: JavaScript expert, contact: example@example.com." The server also sends a push notification to the selected person. Using a notification method (e.g., Firebase Cloud Messaging), the selected person is notified of the inquiry.

[0488] Step 6:

[0489] The terminal confirms with the person in question, and if approval is obtained, provides a connection link to the user. Approval information from the person in question is received as input. If approval is obtained, a connection link (e.g., "https: / / example.com / chat") is provided to the user. If the person in question declines, the next best candidate is selected again and notified again via the notification means.

[0490] Step 7:

[0491] The server collects feedback from users. For example, a user may input feedback such as "Engineer A was very helpful." This feedback information is collected and used to improve the matching accuracy of the system. The feedback data is received as input and applied to an improvement algorithm. The expected output is an improvement in the accuracy of the system.

[0492] Step 8:

[0493] The server updates the skills and career information in the internal data store in real time. It receives new skills and career change information as input. It updates the database using a cloud environment (e.g., AWS API Gateway). This ensures that the latest qualifications and career information is always maintained, enabling up-to-date people searches based on user requests.

[0494] (Application example 1)

[0495] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0496] With conventional systems, it was difficult to quickly find an engineer with the appropriate skills when troubleshooting problems that occurred on the manufacturing floor or when setting up equipment. Furthermore, because engineer information was not updated in real time, it was not possible to refer to the latest skill information, which could result in delayed responses. Furthermore, if the selected engineer was not notified promptly, there was also the problem of further delays in resolving the problem. There is a need for a system that can solve these issues and achieve problem-solving and efficiency improvements on the manufacturing floor.

[0497] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0498] In this invention, the server includes natural language processing means for analyzing a natural language message input by a user, search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, selection means for selecting the most suitable person from the searched candidates, notification means for notifying the selected person in real time and providing a contact means for connecting with the user, and search means for searching for an appropriate engineer based on the content of a request to support the rapid resolution of problems that occur in the factory. This makes it possible to quickly find an appropriate engineer at the manufacturing site and respond in real time.

[0499] "User" refers to a person who uses the system to search for people with specific skills or careers.

[0500] A "natural language message" refers to a text message entered by a user in a language that the user normally uses.

[0501] "Natural language processing means" refers to technology that analyzes input natural language messages and extracts key keywords and intent.

[0502] "Search means" refers to a function that searches an internal database for talent with appropriate skills and careers based on the analyzed message.

[0503] "Selection method" refers to the technology used to evaluate and select the most suitable person from among the candidates searched.

[0504] "Notification means" refers to a communication means for sending a push notification to the selected person and connecting the user with the selected person.

[0505] "Problems occurring within the factory" refers to problems that require the attention of engineers, such as malfunctions that occur on the manufacturing floor or equipment setup.

[0506] An "engineer" is someone who has specific skills and a career and the ability to solve problems that arise on the manufacturing floor.

[0507] "Real-time" refers to information that is updated immediately and is immediately available.

[0508] "Generative AI" refers to technology that uses high-performance algorithms to make predictions and judgments in data analysis, natural language processing, and other areas.

[0509] MODE FOR CARRYING OUT THE INVENTION

[0510] This invention is a system for quickly resolving problems that occur in factories and supporting efficient operations. This system can analyze natural language messages entered by users and quickly find engineers with the appropriate skills and experience.

[0511] System configuration

[0512] The system uses the following hardware and software:

[0513] Hardware: Smartphones, tablets, and internet connection in the factory

[0514] Software: Google Cloud Natural Language API, IBM Watson Natural Language Understanding, MySQL, Firebase Cloud Messaging

[0515] Data processing and calculation

[0516] The system processes and calculates data in the following steps:

[0517] Enter your inquiry details via chat

[0518] Users: Enter shop floor issues and technician requirements in natural language into a smartphone or tablet application.

[0519] Chat content analysis

[0520] Server: Analyzes the message entered by the user using the generative AI's NLP (Natural Language Processing) module, extracting key keywords and the intent of the message.

[0521] Skills and career matching search

[0522] Server: Based on the extracted keywords and intent, the server searches an internal database for personnel with the relevant skills and careers. This database stores the skill profiles and career histories of the factory's engineers.

[0523] Selection of the most suitable engineer

[0524] Server: Scores multiple candidates from the search results and uses AI algorithms to evaluate each candidate's suitability. Selects the engineer best suited to the requirements.

[0525] Search result display and notifications

[0526] Server: Displays the selected technician's information to the user and notifies the technician of the inquiry via push notification.

[0527] Review and feedback

[0528] Terminal: The system checks with the engineer and, if approved, provides the user with a connection link. If the engineer declines the request, the system selects the next best candidate and notifies the user again. Additionally, the system collects user feedback and uses it to improve the system's matching accuracy.

[0529] Specific examples

[0530] Example 1: Troubleshooting request

[0531] User input: "I'm looking for a technician who knows how to tune this press."

[0532] Prompt: "Find a technician who can identify the cause of the press malfunction and make the necessary adjustments."

[0533] Example 2: Setting up a new machine

[0534] User Input: "Can you recommend a technician who can install a new CNC machine?"

[0535] Prompt: "Find a technician who is familiar with CNC machine installation procedures and can quickly complete the setup process."

[0536] This allows the system to quickly find the right technician on the manufacturing floor and respond in real time.

[0537] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0538] Step 1:

[0539] Enter your inquiry details via chat

[0540] Users input problems at the manufacturing site and requirements for engineers in natural language into an application on a smartphone or tablet.

[0541] Input: Free text (e.g. "We are looking for a technician who is knowledgeable about adjusting this press")

[0542] Output: A query message from the user is sent to the application.

[0543] Step 2:

[0544] Chat content analysis

[0545] The server analyzes the text entered by the user using an NLP (natural language processing) module with a generative AI model.

[0546] Input: The query message sent by the user

[0547] Data processing: Extract key keywords (e.g., "press machine," "adjustment," "engineer") and intent of the message.

[0548] Output: Extracted keywords and message intent

[0549] Step 3:

[0550] Skills and career matching search

[0551] The server queries an internal database based on the extracted keywords and intent to search for engineers with the relevant skills and experience.

[0552] Input: Extracted keywords and message intent

[0553] Data crunching: Run a database query to look at technicians' skill profiles and career histories to come up with a list of matches.

[0554] Output: A list of matching technicians

[0555] Step 4:

[0556] Selection of the most suitable engineer

[0557] The server scores multiple candidates obtained from the search results and evaluates each candidate's suitability using a generative AI model.

[0558] Input: List of matched technicians

[0559] Data calculation: Score and rank suitability based on work history, skill set, past evaluations, etc.

[0560] Output: Selection of the best technician

[0561] Step 5:

[0562] Search result display and notifications

[0563] The server displays the information of the selected technician to the user and notifies the technician of the inquiry via a push notification.

[0564] Input: Selected engineer's information

[0565] Specific behavior:

[0566] 1. The technician's information (e.g., name, title, contact information) is displayed on the user's device.

[0567] 2. The server sends a push notification to the technician, sharing the details of the issue.

[0568] Output: Display to user and notify technician

[0569] Step 6:

[0570] Review and feedback

[0571] The terminal checks with the relevant engineer, and if approval is obtained, provides the user with a connection link.

[0572] Input: Technician's response

[0573] Specific behavior:

[0574] 1. The technician terminal responds with either approval or denial.

[0575] 2. If the technician approves, he / she will provide the connection link to the user's device.

[0576] 3. If the technician declines, the server will select the next best candidate and notify them again.

[0577] 4. The server collects feedback from users and uses it to improve the system's matching accuracy.

[0578] Output: Provides user with a connection link or suggests next best candidate

[0579] In this way, the system can quickly find the right technician on the manufacturing floor and respond in real time.

[0580] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0581] The present invention combines a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career, with an emotion engine that recognizes the user's emotions. This system is composed of natural language processing means, search means, selection means, notification means, update means, scoring means, and the emotion engine. The program processing of this system is explained in detail below.

[0582] Enter your inquiry details via chat

[0583] Users enter questions and information about desired skills and careers in natural language into SkillConnect's chat interface, and messages entered by users may contain emotions.

[0584] Chat content analysis

[0585] The server uses the generative AI's NLP (Natural Language Processing) module to parse the user's input text, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[0586] Emotion Analysis

[0587] The server uses an emotion engine to recognize and analyze emotions from the user's input message, for example, to determine whether the user is feeling stressed or expressing urgency.

[0588] Skills and career matching search

[0589] The server then uses the extracted keywords, intent, and sentiment analysis results to query an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[0590] Selection of the best talent

[0591] The server uses an AI algorithm to evaluate multiple candidates from the search results, scoring each candidate's skill and career compatibility, and selecting the most suitable candidate, taking into account the user's emotional state.

[0592] Search result display and notifications

[0593] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0594] Review and feedback

[0595] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be notified. Furthermore, the server collects and analyzes feedback from users, which is used as data to improve the system's matching accuracy.

[0596] View real-time skills and career data

[0597] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available. This update is performed periodically, and new skills and career changes are immediately reflected in the database.

[0598] Specific examples

[0599] Example 1: Searching for a technician

[0600] 1. User types "I need a JavaScript expert" in chat.

[0601] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0602] 3. The server uses an emotion engine to analyze the user's emotions and recognize the high urgency of the message.

[0603] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0604] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0605] 6. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[0606] Example 2: Searching for project members

[0607] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0608] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0609] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[0610] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[0611] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0612] 6. After Marketer B receives the approval, the device provides the user with a connection link.

[0613] Based on these specific examples, the present invention enables users to quickly and accurately find people with the skills and careers they need, and provides optimal feedback and mentoring based on emotional changes, thereby enabling efficient communication and task completion.

[0614] The processing flow will be explained below.

[0615] Step 1:

[0616] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[0617] Step 2:

[0618] The server parses the user's input using a natural language processing (NLP) module, which extracts key keywords and intent from the entered message.

[0619] Step 3:

[0620] The server uses the extracted keywords and intent to run search queries against an internal database that contains the skill profiles and career histories of all employees.

[0621] Step 4:

[0622] The server uses the emotion engine to recognize and analyze emotions from the user's input message, identifying the user's emotional state (e.g., stress, urgency, etc.).

[0623] Step 5:

[0624] The server integrates the candidate list obtained from the skill and career search with the results of sentiment analysis, and uses an AI algorithm to score the person who best suits the user's requirements and emotional state.

[0625] Step 6:

[0626] The server selects the best candidate from the scored candidates. If there are multiple candidates, it takes into account emotional information and ranks the most suitable candidate at the top.

[0627] Step 7:

[0628] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0629] Step 8:

[0630] The device will prompt the person for confirmation, and if approved, provide the user with a connection link. If not, the next best candidate will be notified again.

[0631] Step 9:

[0632] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0633] Step 10:

[0634] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available and that new skills and career changes are immediately reflected in the database.

[0635] Example 2

[0636] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0637] Within a company, users are faced with the challenge of quickly and accurately finding people with the skills and experience they need. In particular, there is a demand for systems that can respond optimally while taking into account the user's feelings. It is also important that the skills and experience information is always up to date. Conventional systems have difficulty meeting these requirements, so an effective solution is needed.

[0638] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a natural language processing means for analyzing a natural language message input by a user, a search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, an emotion recognition means for recognizing and analyzing emotions from the user's message, a selection means for selecting the most suitable person from the searched candidates, and a notification means for sending a push notification to the selected person and providing a contact means for connecting with the user. This makes it possible to quickly and accurately find people with the necessary skills and experience while taking the user's emotions into consideration.

[0639] "Natural language processing means" is a means for analyzing a natural language message entered by a user and extracting key keywords and intentions.

[0640] "Search means" refers to means for searching an internal database for a person with appropriate skills and background based on the analyzed message.

[0641] The "emotion recognition means" is a means for recognizing and analyzing emotions from a message input by a user.

[0642] The "selection means" is a means for selecting the most suitable person from among the searched candidates.

[0643] The "notification means" is a means for sending a push notification to the selected person and providing a means of contact for connecting with the user.

[0644] The "update means" is a means for updating the skills and career information in the internal database in real time, so that the latest data can always be referenced.

[0645] "Scoring means" is a means for using generative artificial intelligence to score and rank the person who best suits the user's requirements.

[0646] The present invention is a system that analyzes natural language messages entered by users and quickly finds people in a company who have the appropriate skills and background. This system is composed of natural language processing means, search means, emotion recognition means, selection means, notification means, update means, and scoring means.

[0647] Configuration and Operation

[0648] First, a user logs into SkillConnect's chat interface and types in natural language information about their question and desired skill or career, such as "I need a JavaScript expert." This message may contain the user's sentiment.

[0649] The server then uses the generative AI's NLP (natural language processing) module to parse the user's input text, which uses Microsoft's Azure Cognitive Services to extract key keywords (e.g., "JavaScript" or "expert") and intent (e.g., skill request).

[0650] The server uses an emotion engine, such as IBM Watson Tone Analyzer, to recognize and analyze emotions from the input message, determining whether the user is feeling stressed or expressing urgency.

[0651] Based on the extracted keywords, intent, and sentiment analysis results, the server queries an internal database (e.g., SharePoint, an internal portal software) to search for candidates with the relevant skills and experience.

[0652] From the multiple candidates found, the server evaluates them using an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background, and also takes into account the user's emotional state to select the most suitable candidate.

[0653] The device then displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via push notification (e.g., Firebase Cloud Messaging). The selected person confirms, and if they agree, the server provides the user with a connection link. If the selected person declines the inquiry, the server notifies the next best candidate. Additionally, the server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0654] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. This update is performed periodically, and new skills and experience changes are immediately reflected in the database.

[0655] Specific examples

[0656] Example 1: Searching for engineers

[0657] 1. User types "I need a JavaScript expert" in chat.

[0658] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0659] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[0660] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0661] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0662] 6. Once Technician A replies with approval, the terminal provides the user with a connection link to Technician A.

[0663] Example 2: Searching for project members

[0664] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0665] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0666] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[0667] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[0668] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0669] 6. After receiving the approval from Marketer B, the device provides the user with a connection link.

[0670] As described above, the system based on the present invention not only quickly and accurately finds people with the skills and experience required by the user, but also provides optimal feedback and mentoring based on emotional changes, thereby realizing efficient communication and task completion.

[0671] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0672] Step 1:

[0673] A user logs into SkillConnect's chat interface and types in natural language information about their question or desired skill or career. For example, they can send a natural language message like, "I need a JavaScript expert." This message may contain the user's sentiment.

[0674] Step 2:

[0675] The server uses the generative AI's NLP (natural language processing) module to analyze the user's input text. Specifically, it uses Microsoft's Azure Cognitive Services to perform the analysis. It receives the input text, tokenizes it, extracts key keywords (such as "JavaScript" or "expert") and intent (such as a skill request), and outputs these.

[0676] Step 3:

[0677] The server uses an emotion engine to recognize and analyze emotions from the user's input message. For example, it uses IBM Watson Tone Analyzer. It uses the analyzed text as input to determine the user's emotional state (e.g., "urgent" or "relaxed"). The extracted emotional information is output.

[0678] Step 4:

[0679] The server queries an internal database (for example, SharePoint, an internal portal software) based on the extracted keywords, intent, and sentiment analysis results to search for candidates with the relevant skills and experience. Keywords and sentiment information are used as input to retrieve relevant entries from the internal database, and the search results are the output.

[0680] Step 5:

[0681] The server uses an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background from the multiple candidates obtained from the search results and selects the most suitable person. The search results are used as input to calculate suitability, and information about the most suitable person is output.

[0682] Step 6:

[0683] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification (e.g., Firebase Cloud Messaging). Using the information of the best match as input, a push notification is generated and sent. The notification sent result is the output.

[0684] Step 7:

[0685] The device confirms with the person in question and, if consent is obtained, provides the user with a connection link. If the person in question declines the inquiry, the next best candidate is notified again. In addition, the server collects and analyzes feedback from users and uses it as data to improve the system's matching accuracy. Using the confirmation results and feedback information as input, the final result is a connection link or the next notification.

[0686] Step 8:

[0687] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. Updates are made periodically, and new skills and experience changes are immediately reflected in the database. The latest skills and experience information is used as input, and the updated database state is the output.

[0688] (Application example 2)

[0689] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0690] Conventional technologies provide systems that analyze natural language messages entered by users to find people in a company with the appropriate skills and career experience. However, because selection is based solely on skills and career experience without considering the user's emotional state, it is difficult to respond promptly to urgent issues or the user's emotions. Furthermore, because data is not updated in real time, matching accuracy declines, making it difficult to quickly find the right person. Therefore, the present invention aims to provide a system that analyzes a user's emotional state and quickly and accurately finds the right person.

[0691] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0692] In this invention, the server includes a natural language processing unit that analyzes a natural language message entered by a user, a search unit that searches an internal database for people with appropriate skills and careers based on the analyzed message, a selection unit that selects the most suitable person from the searched candidates, a notification unit that sends a push notification to the selected person and provides a contact means for connecting with the user, an emotion analysis unit that analyzes the user's emotional state using an emotion engine that recognizes emotions, and an urgency evaluation unit that determines the urgency and optimizes candidates based on the analyzed emotional state and the user's message. This makes it possible to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. Furthermore, real-time data updates enable accurate matching based on the latest information.

[0693] "Natural language processing means" is a processing technology for analyzing natural language messages entered by users and extracting key keywords and intentions.

[0694] The "search means" is a function for searching an internal database for people with appropriate skills and careers based on the analyzed message.

[0695] The "selection method" is the algorithm or method used to select the most suitable person from the searched candidates.

[0696] "Notification means" is a function that sends push notifications to selected people and provides a means of contact for connecting with the user.

[0697] "Emotion analysis means" is a technology that uses an emotion engine to recognize and analyze emotions from the user's input message.

[0698] The "urgency evaluation means" is an evaluation method for determining urgency and optimizing candidates based on the analyzed emotional state and the user's message.

[0699] The "update means" is a mechanism for updating the skill and career information in the internal database in real time, making the latest data always available for reference.

[0700] The "scoring means" is a function that uses generative artificial intelligence to score and rank the person who is most suitable for the user's requirements.

[0701] The present invention relates to a system that analyzes natural language messages entered by users and combines them with an emotion recognition engine to quickly find people with suitable skills and careers.

[0702] System Configuration

[0703] Natural language processing tools

[0704] The server uses a natural language processing module that analyzes the natural language messages entered by the user into the device, leveraging a generative AI model to extract key keywords and user intent.

[0705] Search methods

[0706] Based on the analyzed keywords and intent, the server searches for candidates with the appropriate skills and careers from an internal database that contains the skill profiles and career histories of all employees within the organization.

[0707] Emotion analysis means

[0708] The server uses an emotion engine to recognize and analyze emotions from the user's input message, determining whether the user is feeling stressed or expressing urgency.

[0709] Urgency assessment tools

[0710] The server determines the urgency of the message based on the results of sentiment analysis and the user's message. If the urgency is high, candidates who require a quick response are notified first.

[0711] Selection method

[0712] The server selects the best candidate from the candidates obtained by the search tool. This selection process includes a scoring tool using a generative AI model to evaluate each candidate's skill and career fit.

[0713] Notification means

[0714] As a notification method, the server will send a push notification to the selected candidate, and the user will be provided with the appropriate contact method and a connection link if necessary.

[0715] Update method

[0716] The server updates the skills and career information in the internal database in real time, ensuring that the latest data is always available. This update is performed periodically.

[0717] Specific processing examples

[0718] Example 1: Searching for a technician

[0719] 1. The user types "The motor temperature is rising abnormally" into the terminal.

[0720] 2. The server analyzes this message and extracts the keywords "motor," "temperature," and "abnormal."

[0721] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[0722] 4. The server searches its internal database for technicians with the right skills and scores them to identify the best candidates.

[0723] 5. The server notifies the selected technician and provides the user with the technician's contact information.

[0724] Prompt Sentence Examples

[0725] "The user inputs 'The motor temperature is abnormally high.' Please generate a program that analyzes this message and executes the process of finding the most suitable technician. Start by using NLP and an emotion engine to extract keywords and emotions, find the most suitable technician from the database, and notify the system."

[0726] This allows the system of the present invention to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. In addition, real-time data updates enable accurate matching based on the latest information.

[0727] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0728] Step 1:

[0729] The user inputs the details of the abnormality into the terminal in natural language. For example, if the user inputs "The motor temperature is rising abnormally," this text becomes the input data. The input data is sent to the server.

[0730] Step 2:

[0731] The server receives the natural language message and analyzes it using a natural language processing method that uses a generative AI model. Here, key keywords such as "motor," "temperature," and "abnormality" are extracted from the text, along with the intent of "reporting an abnormality." The analysis results are output as keywords to be queried in the database.

[0732] Step 3:

[0733] The server uses an emotion engine to recognize and analyze emotions from the user's text. The analysis evaluates the stress and urgency the user feels. The input for this step is the user's text, and the output is a score indicating the urgency.

[0734] Step 4:

[0735] The server searches its internal database for candidates with the appropriate skills and experience based on the analyzed keywords and urgency score, issues a query to the database, and outputs a list of relevant engineers.

[0736] Step 5:

[0737] The server uses a generative AI model to score the multiple candidates found in the search results. This takes into account not only skill and career compatibility but also urgency scores. As a result of the scoring, a profile of the most suitable candidate is output.

[0738] Step 6:

[0739] The server sends a push notification to the selected best candidate. Specifically, a notification containing details of the anomaly report is sent to the selected candidate's contact information. The notification sending result is recorded.

[0740] Step 7:

[0741] The server notifies the user of the selected candidate. The technician's name, title, contact information, etc. are displayed on the terminal, and a connection link is provided if necessary. The input of this step is the selected candidate's information, and the output is the notification to the user.

[0742] Step 8:

[0743] The server waits for a response from the candidate, and if they agree, it provides a connection link to the user. If the candidate refuses to report the anomaly, it notifies the next best candidate again. Until it receives a response, the server monitors the communication and decides the next action.

[0744] In this way, the user can simply enter details of the abnormality in natural language, and the server will take appropriate action, enabling them to receive a prompt and appropriate response.

[0745] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0746] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0747] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0748] [Third embodiment]

[0749] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0750] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0751] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0752] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0753] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0754] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0755] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0756] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0757] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0758] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0759] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0760] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0761] The present invention is a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career. This system is composed of natural language processing means, search means, selection means, notification means, update means, and scoring means. The program processing of this system is explained in detail below.

[0762] Enter your inquiry details via chat

[0763] Users enter their questions and information about the skills and careers they need in natural language into SkillConnect's chat interface.

[0764] Chat content analysis

[0765] The server uses the generative AI's NLP (Natural Language Processing) module to analyze the user's input text, extracting key keywords (e.g., "JavaScript," "expert," "marketing," "project leader," etc.) and classifying the message's intent.

[0766] Skills and career matching search

[0767] Based on the extracted keywords and intent, the server queries an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[0768] Selection of the best talent

[0769] The server scores multiple candidates from the search results, using AI algorithms to assess each candidate's suitability and select the person who best fits the request.

[0770] Search result display and notifications

[0771] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0772] Review and feedback

[0773] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be selected and notified again. In addition, the server will collect feedback from the user and use it to improve the system's matching accuracy.

[0774] View real-time skills and career data

[0775] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available for reference. This means that new skills and career changes are immediately reflected in the database.

[0776] Specific examples

[0777] Example 1: Searching for a technician

[0778] 1. User types "I need a JavaScript expert" in chat.

[0779] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0780] 3. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0781] 4. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0782] 5. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[0783] Example 2: Searching for project members

[0784] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0785] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0786] 3. The server searches the database for relevant people and identifies the best candidates through scoring.

[0787] 4. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0788] 5. After receiving the approval, Marketer B will provide the user with a connection link.

[0789] Based on these specific examples, the present invention enables a user to quickly and accurately find people with the skills and careers they need, enabling efficient communication and task execution.

[0790] The processing flow will be explained below.

[0791] Step 1:

[0792] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[0793] Step 2:

[0794] The server uses a natural language processing (NLP) module to parse the user's input, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[0795] Step 3:

[0796] Based on the extracted keywords and intent, the server runs a search query against an internal database to find candidates with the relevant skills and careers.

[0797] Step 4:

[0798] The server uses an AI algorithm to evaluate the list of candidates obtained from the search results and score each candidate's skills and career suitability.

[0799] Step 5:

[0800] The server selects the best candidate from the scored candidates. If there are multiple candidates, the best candidate is ranked higher.

[0801] Step 6:

[0802] The device will display the selected person's information (e.g., name, job title, contact details) to the user and notify the candidate of the inquiry via push notification.

[0803] Step 7:

[0804] The device displays a prompt to confirm the candidate's consent, and if the candidate accepts, the user is provided with a connection link. If not, the next best candidate is notified again.

[0805] Step 8:

[0806] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0807] Step 9:

[0808] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available. This update is performed regularly, and new skills and career changes are immediately reflected.

[0809] These steps enable users to quickly and accurately find people with the skills and experience they need, enabling efficient communication and task completion.

[0810] Example 1

[0811] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0812] Until now, it has been difficult to quickly and accurately find people with specific skills and careers within an organization and recommend appropriate personnel based on the user's needs. In particular, it takes time to collect information and evaluate suitability, which ultimately hinders efficient communication and task execution. A system that solves these problems is needed.

[0813] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0814] In this invention, the server includes a language processing means for analyzing a natural language message input by a user, a search means for searching an internal data store for people with appropriate qualifications and experience based on the analyzed message, and a selection means for selecting the most suitable person from the searched candidates, thereby enabling the user to quickly and accurately find people with the skills and careers they need.

[0815] A "user" is a person or organization that uses the system to provide input to search for people with specific skills or careers.

[0816] A "natural language message" refers to a query or instruction entered by a user in natural language or sentences.

[0817] "Language processing means" refers to technology or devices that analyze natural language messages entered by users and extract key keywords and intentions.

[0818] An "internal data store" is a database or data management system that stores the qualifications and experience information of all employees within an organization.

[0819] A "search tool" is a technology or device that searches an internal data store for people with the relevant skills and careers based on analyzed keywords and intent.

[0820] "Selection means" refers to the technology or device that performs evaluation and scoring to select the most suitable person from the search results.

[0821] "Notification means" refers to a technology or device that sends push notifications or contacts to selected individuals and provides a means of contact for connecting with the user.

[0822] "Update means" refers to technology or devices that update information in the internal data store in real time, making the latest qualifications and career information always available for reference.

[0823] The "evaluation means" refers to a technology or device that uses generative artificial intelligence to evaluate and rank the people who are most suitable for the user's requirements.

[0824] The present invention is a system that analyzes natural language messages entered by users and quickly finds people with appropriate skills and careers within an organization. This system is composed of a language processing means, a search means, a selection means, a notification means, an update means, and an evaluation means.

[0825] System Configuration

[0826] The system includes a language processing means (e.g., Google NLP API) for analyzing natural language messages entered by users, a search means (e.g., MySQL database) for searching an internal data store for people with appropriate qualifications and experience based on the analyzed messages, a selection means (e.g., an AI algorithm using TensorFlow) for selecting the most suitable person from the searched candidates, and a notification means (e.g., Firebase Cloud Messaging) for notifying the selected person.

[0827] The system also includes an update mechanism that updates the information in the internal data store in real time, making the most up-to-date qualifications and career information available, and an evaluation mechanism that uses generative artificial intelligence to evaluate and rank candidates who best fit the user's requirements.

[0828] How to use

[0829] Using SkillConnect's chat interface, users enter questions and information about the skills and careers they need in natural language, such as "Looking for a JavaScript expert." The server parses this message and extracts key keywords (e.g., "JavaScript" and "expert").

[0830] Based on the analyzed and extracted keywords, the server queries an internal data store to find candidates with the relevant qualifications and experience. The resulting candidates are then scored using an AI algorithm to evaluate their suitability. The best-matched candidate is selected, and their information (e.g., name, job title, contact details) is displayed to the user. At the same time, a push notification is sent to inform the person of the inquiry.

[0831] If the person denies the request, the user will be provided with a link to connect with them. If the person declines the request, the next best candidate will be selected and the user will be notified again.

[0832] Specific examples

[0833] Example 1: Searching for a technician

[0834] A user types "I need a JavaScript expert" in chat. The server uses an NLP module to extract the keywords "JavaScript" and "expert" and searches for matching people in its internal data store. It scores the suitability to identify the best candidate and displays that person's information (e.g., Technician A) to the user. The server sends a push notification and, upon Technician A's approval, provides the user with a connection link to Technician A.

[0835] Example 2: Searching for project members

[0836] A user types in chat, "I'd like to find a new marketing project leader." The server extracts the keywords "marketing" and "project leader" and searches for matching people in the data store. It identifies the best candidate through scoring and displays that person's information (e.g., Marketer B) to the user. The server sends a push notification, and after Marketer B accepts, provides the user with a connection link.

[0837] In this way, the system of the present invention can quickly and accurately find people with the skills and careers that the user needs, enabling efficient communication and task execution.

[0838] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0839] Step 1:

[0840] A user types a natural language message into SkillConnect's chat interface, for example, "I need a JavaScript expert." The input is sent as text data to the server.

[0841] Step 2:

[0842] The server analyzes the natural language message received. Specifically, it processes the text data using the generative AI's NLP (Natural Language Processing) module (e.g., Google NLP API). It analyzes the message received as input, "JavaScript expert needed," and extracts the main keywords "JavaScript" and "expert." This generates keyword data as the analysis result.

[0843] Step 3:

[0844] The server queries the internal data store based on the analysis results. It uses the analyzed keyword data "JavaScript" and "expert" as input. It generates an SQL statement (e.g. "SELECT FROM employees WHERE skill='JavaScript' AND position='expert'") and searches against the MySQL database. The output is a list of people with the corresponding qualifications and experience.

[0845] Step 4:

[0846] The server selects the best person from the search results. It uses the list of people it has obtained as input. It uses an AI algorithm (e.g., TensorFlow) to score the suitability of each candidate. Based on the scoring, it selects the person with the highest suitability and outputs that person's data (e.g., name, job title, contact details).

[0847] Step 5:

[0848] The device displays the information of the selected person to the user. The selected person's data is used as input. For example, the displayed content might be "Engineer A: JavaScript expert, contact: example@example.com." The server also sends a push notification to the selected person. Using a notification method (e.g., Firebase Cloud Messaging), the selected person is notified of the inquiry.

[0849] Step 6:

[0850] The terminal confirms with the person in question, and if approval is obtained, provides a connection link to the user. Approval information from the person in question is received as input. If approval is obtained, a connection link (e.g., "https: / / example.com / chat") is provided to the user. If the person in question declines, the next best candidate is selected again and notified again via the notification means.

[0851] Step 7:

[0852] The server collects feedback from users. For example, a user may input feedback such as "Engineer A was very helpful." This feedback information is collected and used to improve the matching accuracy of the system. The feedback data is received as input and applied to an improvement algorithm. The expected output is an improvement in the accuracy of the system.

[0853] Step 8:

[0854] The server updates the skills and career information in the internal data store in real time. It receives new skills and career change information as input. It updates the database using a cloud environment (e.g., AWS API Gateway). This ensures that the latest qualifications and career information is always maintained, enabling up-to-date people searches based on user requests.

[0855] (Application example 1)

[0856] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0857] With conventional systems, it was difficult to quickly find an engineer with the appropriate skills when troubleshooting problems that occurred on the manufacturing floor or when setting up equipment. Furthermore, because engineer information was not updated in real time, it was not possible to refer to the latest skill information, which could result in delayed responses. Furthermore, if the selected engineer was not notified promptly, there was also the problem of further delays in resolving the problem. There is a need for a system that can solve these issues and achieve problem-solving and efficiency improvements on the manufacturing floor.

[0858] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0859] In this invention, the server includes natural language processing means for analyzing a natural language message input by a user, search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, selection means for selecting the most suitable person from the searched candidates, notification means for notifying the selected person in real time and providing a contact means for connecting with the user, and search means for searching for an appropriate engineer based on the content of a request to support the rapid resolution of problems that occur in the factory. This makes it possible to quickly find an appropriate engineer at the manufacturing site and respond in real time.

[0860] "User" refers to a person who uses the system to search for people with specific skills or careers.

[0861] A "natural language message" refers to a text message entered by a user in a language that the user normally uses.

[0862] "Natural language processing means" refers to technology that analyzes input natural language messages and extracts key keywords and intent.

[0863] "Search means" refers to a function that searches an internal database for talent with appropriate skills and careers based on the analyzed message.

[0864] "Selection method" refers to the technology used to evaluate and select the most suitable person from among the candidates searched.

[0865] "Notification means" refers to a communication means for sending a push notification to the selected person and connecting the user with the selected person.

[0866] "Problems occurring within the factory" refers to problems that require the attention of engineers, such as malfunctions that occur on the manufacturing floor or equipment setup.

[0867] An "engineer" is someone who has specific skills and a career and the ability to solve problems that arise on the manufacturing floor.

[0868] "Real-time" refers to information that is updated immediately and is immediately available.

[0869] "Generative AI" refers to technology that uses high-performance algorithms to make predictions and judgments in data analysis, natural language processing, and other areas.

[0870] MODE FOR CARRYING OUT THE INVENTION

[0871] This invention is a system for quickly resolving problems that occur in factories and supporting efficient operations. This system can analyze natural language messages entered by users and quickly find engineers with the appropriate skills and experience.

[0872] System configuration

[0873] The system uses the following hardware and software:

[0874] Hardware: Smartphones, tablets, and internet connection in the factory

[0875] Software: Google Cloud Natural Language API, IBM Watson Natural Language Understanding, MySQL, Firebase Cloud Messaging

[0876] Data processing and calculation

[0877] The system processes and calculates data in the following steps:

[0878] Enter your inquiry details via chat

[0879] Users: Enter shop floor issues and technician requirements in natural language into a smartphone or tablet application.

[0880] Chat content analysis

[0881] Server: Analyzes the message entered by the user using the generative AI's NLP (Natural Language Processing) module, extracting key keywords and the intent of the message.

[0882] Skills and career matching search

[0883] Server: Based on the extracted keywords and intent, the server searches an internal database for personnel with the relevant skills and careers. This database stores the skill profiles and career histories of the factory's engineers.

[0884] Selection of the most suitable engineer

[0885] Server: Scores multiple candidates from the search results and uses AI algorithms to evaluate each candidate's suitability. Selects the engineer best suited to the requirements.

[0886] Search result display and notifications

[0887] Server: Displays the selected technician's information to the user and notifies the technician of the inquiry via push notification.

[0888] Review and feedback

[0889] Terminal: The system checks with the engineer and, if approved, provides the user with a connection link. If the engineer declines the request, the system selects the next best candidate and notifies the user again. Additionally, the system collects user feedback and uses it to improve the system's matching accuracy.

[0890] Specific examples

[0891] Example 1: Troubleshooting request

[0892] User input: "I'm looking for a technician who knows how to tune this press."

[0893] Prompt: "Find a technician who can identify the cause of the press malfunction and make the necessary adjustments."

[0894] Example 2: Setting up a new machine

[0895] User Input: "Can you recommend a technician who can install a new CNC machine?"

[0896] Prompt: "Find a technician who is familiar with CNC machine installation procedures and can quickly complete the setup process."

[0897] This allows the system to quickly find the right technician on the manufacturing floor and respond in real time.

[0898] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0899] Step 1:

[0900] Enter your inquiry details via chat

[0901] Users input problems at the manufacturing site and requirements for engineers in natural language into an application on a smartphone or tablet.

[0902] Input: Free text (e.g. "We are looking for a technician who is knowledgeable about adjusting this press")

[0903] Output: A query message from the user is sent to the application.

[0904] Step 2:

[0905] Chat content analysis

[0906] The server analyzes the text entered by the user using an NLP (natural language processing) module with a generative AI model.

[0907] Input: The query message sent by the user

[0908] Data processing: Extract key keywords (e.g., "press machine," "adjustment," "engineer") and intent of the message.

[0909] Output: Extracted keywords and message intent

[0910] Step 3:

[0911] Skills and career matching search

[0912] The server queries an internal database based on the extracted keywords and intent to search for engineers with the relevant skills and experience.

[0913] Input: Extracted keywords and message intent

[0914] Data crunching: Run a database query to look at technicians' skill profiles and career histories to come up with a list of matches.

[0915] Output: A list of matching technicians

[0916] Step 4:

[0917] Selection of the most suitable engineer

[0918] The server scores multiple candidates obtained from the search results and evaluates each candidate's suitability using a generative AI model.

[0919] Input: List of matched technicians

[0920] Data calculation: Score and rank suitability based on work history, skill set, past evaluations, etc.

[0921] Output: Selection of the best technician

[0922] Step 5:

[0923] Search result display and notifications

[0924] The server displays the information of the selected technician to the user and notifies the technician of the inquiry via a push notification.

[0925] Input: Selected engineer's information

[0926] Specific behavior:

[0927] 1. The technician's information (e.g., name, title, contact information) is displayed on the user's device.

[0928] 2. The server sends a push notification to the technician, sharing the details of the issue.

[0929] Output: Display to user and notify technician

[0930] Step 6:

[0931] Review and feedback

[0932] The terminal checks with the relevant engineer, and if approval is obtained, provides the user with a connection link.

[0933] Input: Technician's response

[0934] Specific behavior:

[0935] 1. The technician terminal responds with either approval or denial.

[0936] 2. If the technician approves, he / she will provide the connection link to the user's device.

[0937] 3. If the technician declines, the server will select the next best candidate and notify them again.

[0938] 4. The server collects feedback from users and uses it to improve the system's matching accuracy.

[0939] Output: Provides user with a connection link or suggests next best candidate

[0940] In this way, the system can quickly find the right technician on the manufacturing floor and respond in real time.

[0941] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0942] The present invention combines a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career, with an emotion engine that recognizes the user's emotions. This system is composed of natural language processing means, search means, selection means, notification means, update means, scoring means, and the emotion engine. The program processing of this system is explained in detail below.

[0943] Enter your inquiry details via chat

[0944] Users enter questions and information about desired skills and careers in natural language into SkillConnect's chat interface, and messages entered by users may contain emotions.

[0945] Chat content analysis

[0946] The server uses the generative AI's NLP (Natural Language Processing) module to parse the user's input text, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[0947] Emotion Analysis

[0948] The server uses an emotion engine to recognize and analyze emotions from the user's input message, for example, to determine whether the user is feeling stressed or expressing urgency.

[0949] Skills and career matching search

[0950] The server then uses the extracted keywords, intent, and sentiment analysis results to query an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[0951] Selection of the best talent

[0952] The server uses an AI algorithm to evaluate multiple candidates from the search results, scoring each candidate's skill and career compatibility, and selecting the most suitable candidate, taking into account the user's emotional state.

[0953] Search result display and notifications

[0954] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0955] Review and feedback

[0956] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be notified. Furthermore, the server collects and analyzes feedback from users, which is used as data to improve the system's matching accuracy.

[0957] View real-time skills and career data

[0958] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available. This update is performed periodically, and new skills and career changes are immediately reflected in the database.

[0959] Specific examples

[0960] Example 1: Searching for a technician

[0961] 1. User types "I need a JavaScript expert" in chat.

[0962] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[0963] 3. The server uses an emotion engine to analyze the user's emotions and recognize the high urgency of the message.

[0964] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[0965] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[0966] 6. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[0967] Example 2: Searching for project members

[0968] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[0969] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[0970] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[0971] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[0972] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[0973] 6. After Marketer B receives the approval, the device provides the user with a connection link.

[0974] Based on these specific examples, the present invention enables users to quickly and accurately find people with the skills and careers they need, and provides optimal feedback and mentoring based on emotional changes, thereby enabling efficient communication and task completion.

[0975] The processing flow will be explained below.

[0976] Step 1:

[0977] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[0978] Step 2:

[0979] The server parses the user's input using a natural language processing (NLP) module, which extracts key keywords and intent from the entered message.

[0980] Step 3:

[0981] The server uses the extracted keywords and intent to run search queries against an internal database that contains the skill profiles and career histories of all employees.

[0982] Step 4:

[0983] The server uses the emotion engine to recognize and analyze emotions from the user's input message, identifying the user's emotional state (e.g., stress, urgency, etc.).

[0984] Step 5:

[0985] The server integrates the candidate list obtained from the skill and career search with the results of sentiment analysis, and uses an AI algorithm to score the person who best suits the user's requirements and emotional state.

[0986] Step 6:

[0987] The server selects the best candidate from the scored candidates. If there are multiple candidates, it takes into account emotional information and ranks the most suitable candidate at the top.

[0988] Step 7:

[0989] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[0990] Step 8:

[0991] The device will prompt the person for confirmation, and if approved, provide the user with a connection link. If not, the next best candidate will be notified again.

[0992] Step 9:

[0993] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[0994] Step 10:

[0995] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available and that new skills and career changes are immediately reflected in the database.

[0996] Example 2

[0997] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0998] Within a company, users are faced with the challenge of quickly and accurately finding people with the skills and experience they need. In particular, there is a demand for systems that can respond optimally while taking into account the user's feelings. It is also important that the skills and experience information is always up to date. Conventional systems have difficulty meeting these requirements, so an effective solution is needed.

[0999] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a natural language processing means for analyzing a natural language message input by a user, a search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, an emotion recognition means for recognizing and analyzing emotions from the user's message, a selection means for selecting the most suitable person from the searched candidates, and a notification means for sending a push notification to the selected person and providing a contact means for connecting with the user. This makes it possible to quickly and accurately find people with the necessary skills and experience while taking the user's emotions into consideration.

[1000] "Natural language processing means" is a means for analyzing a natural language message entered by a user and extracting key keywords and intentions.

[1001] "Search means" refers to means for searching an internal database for a person with appropriate skills and background based on the analyzed message.

[1002] The "emotion recognition means" is a means for recognizing and analyzing emotions from a message input by a user.

[1003] The "selection means" is a means for selecting the most suitable person from among the searched candidates.

[1004] The "notification means" is a means for sending a push notification to the selected person and providing a means of contact for connecting with the user.

[1005] The "update means" is a means for updating the skills and career information in the internal database in real time, so that the latest data can always be referenced.

[1006] "Scoring means" is a means for using generative artificial intelligence to score and rank the person who best suits the user's requirements.

[1007] The present invention is a system that analyzes natural language messages entered by users and quickly finds people in a company who have the appropriate skills and background. This system is composed of natural language processing means, search means, emotion recognition means, selection means, notification means, update means, and scoring means.

[1008] Configuration and Operation

[1009] First, a user logs into SkillConnect's chat interface and types in natural language information about their question and desired skill or career, such as "I need a JavaScript expert." This message may contain the user's sentiment.

[1010] The server then uses the generative AI's NLP (natural language processing) module to parse the user's input text, which uses Microsoft's Azure Cognitive Services to extract key keywords (e.g., "JavaScript" or "expert") and intent (e.g., skill request).

[1011] The server uses an emotion engine, such as IBM Watson Tone Analyzer, to recognize and analyze emotions from the input message, determining whether the user is feeling stressed or expressing urgency.

[1012] Based on the extracted keywords, intent, and sentiment analysis results, the server queries an internal database (e.g., SharePoint, an internal portal software) to search for candidates with the relevant skills and experience.

[1013] From the multiple candidates found, the server evaluates them using an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background, and also takes into account the user's emotional state to select the most suitable candidate.

[1014] The device then displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via push notification (e.g., Firebase Cloud Messaging). The selected person confirms, and if they agree, the server provides the user with a connection link. If the selected person declines the inquiry, the server notifies the next best candidate. Additionally, the server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[1015] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. This update is performed periodically, and new skills and experience changes are immediately reflected in the database.

[1016] Specific examples

[1017] Example 1: Searching for engineers

[1018] 1. User types "I need a JavaScript expert" in chat.

[1019] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[1020] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[1021] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[1022] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[1023] 6. Once Technician A replies with approval, the terminal provides the user with a connection link to Technician A.

[1024] Example 2: Searching for project members

[1025] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[1026] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[1027] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[1028] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[1029] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[1030] 6. After receiving the approval from Marketer B, the device provides the user with a connection link.

[1031] As described above, the system based on the present invention not only quickly and accurately finds people with the skills and experience required by the user, but also provides optimal feedback and mentoring based on emotional changes, thereby realizing efficient communication and task completion.

[1032] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1033] Step 1:

[1034] A user logs into SkillConnect's chat interface and types in natural language information about their question or desired skill or career. For example, they can send a natural language message like, "I need a JavaScript expert." This message may contain the user's sentiment.

[1035] Step 2:

[1036] The server uses the generative AI's NLP (natural language processing) module to analyze the user's input text. Specifically, it uses Microsoft's Azure Cognitive Services to perform the analysis. It receives the input text, tokenizes it, extracts key keywords (such as "JavaScript" or "expert") and intent (such as a skill request), and outputs these.

[1037] Step 3:

[1038] The server uses an emotion engine to recognize and analyze emotions from the user's input message. For example, it uses IBM Watson Tone Analyzer. It uses the analyzed text as input to determine the user's emotional state (e.g., "urgent" or "relaxed"). The extracted emotional information is output.

[1039] Step 4:

[1040] The server queries an internal database (for example, SharePoint, an internal portal software) based on the extracted keywords, intent, and sentiment analysis results to search for candidates with the relevant skills and experience. Keywords and sentiment information are used as input to retrieve relevant entries from the internal database, and the search results are the output.

[1041] Step 5:

[1042] The server uses an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background from the multiple candidates obtained from the search results and selects the most suitable person. The search results are used as input to calculate suitability, and information about the most suitable person is output.

[1043] Step 6:

[1044] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification (e.g., Firebase Cloud Messaging). Using the information of the best match as input, a push notification is generated and sent. The notification sent result is the output.

[1045] Step 7:

[1046] The device confirms with the person in question and, if consent is obtained, provides the user with a connection link. If the person in question declines the inquiry, the next best candidate is notified again. In addition, the server collects and analyzes feedback from users and uses it as data to improve the system's matching accuracy. Using the confirmation results and feedback information as input, the final result is a connection link or the next notification.

[1047] Step 8:

[1048] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. Updates are made periodically, and new skills and experience changes are immediately reflected in the database. The latest skills and experience information is used as input, and the updated database state is the output.

[1049] (Application example 2)

[1050] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] Conventional technologies provide systems that analyze natural language messages entered by users to find people in a company with the appropriate skills and career experience. However, because selection is based solely on skills and career experience without considering the user's emotional state, it is difficult to respond promptly to urgent issues or the user's emotions. Furthermore, because data is not updated in real time, matching accuracy declines, making it difficult to quickly find the right person. Therefore, the present invention aims to provide a system that analyzes a user's emotional state and quickly and accurately finds the right person.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1053] In this invention, the server includes a natural language processing unit that analyzes a natural language message entered by a user, a search unit that searches an internal database for people with appropriate skills and careers based on the analyzed message, a selection unit that selects the most suitable person from the searched candidates, a notification unit that sends a push notification to the selected person and provides a contact means for connecting with the user, an emotion analysis unit that analyzes the user's emotional state using an emotion engine that recognizes emotions, and an urgency evaluation unit that determines the urgency and optimizes candidates based on the analyzed emotional state and the user's message. This makes it possible to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. Furthermore, real-time data updates enable accurate matching based on the latest information.

[1054] "Natural language processing means" is a processing technology for analyzing natural language messages entered by users and extracting key keywords and intentions.

[1055] The "search means" is a function for searching an internal database for people with appropriate skills and careers based on the analyzed message.

[1056] The "selection method" is the algorithm or method used to select the most suitable person from the searched candidates.

[1057] "Notification means" is a function that sends push notifications to selected people and provides a means of contact for connecting with the user.

[1058] "Emotion analysis means" is a technology that uses an emotion engine to recognize and analyze emotions from the user's input message.

[1059] The "urgency evaluation means" is an evaluation method for determining urgency and optimizing candidates based on the analyzed emotional state and the user's message.

[1060] The "update means" is a mechanism for updating the skill and career information in the internal database in real time, making the latest data always available for reference.

[1061] The "scoring means" is a function that uses generative artificial intelligence to score and rank the person who is most suitable for the user's requirements.

[1062] The present invention relates to a system that analyzes natural language messages entered by users and combines them with an emotion recognition engine to quickly find people with suitable skills and careers.

[1063] System Configuration

[1064] Natural language processing tools

[1065] The server uses a natural language processing module that analyzes the natural language messages entered by the user into the device, leveraging a generative AI model to extract key keywords and user intent.

[1066] Search methods

[1067] Based on the analyzed keywords and intent, the server searches for candidates with the appropriate skills and careers from an internal database that contains the skill profiles and career histories of all employees within the organization.

[1068] Emotion analysis means

[1069] The server uses an emotion engine to recognize and analyze emotions from the user's input message, determining whether the user is feeling stressed or expressing urgency.

[1070] Urgency assessment tools

[1071] The server determines the urgency of the message based on the results of sentiment analysis and the user's message. If the urgency is high, candidates who require a quick response are notified first.

[1072] Selection method

[1073] The server selects the best candidate from the candidates obtained by the search tool. This selection process includes a scoring tool using a generative AI model to evaluate each candidate's skill and career fit.

[1074] Notification means

[1075] As a notification method, the server will send a push notification to the selected candidate, and the user will be provided with the appropriate contact method and a connection link if necessary.

[1076] Update method

[1077] The server updates the skills and career information in the internal database in real time, ensuring that the latest data is always available. This update is performed periodically.

[1078] Specific processing examples

[1079] Example 1: Searching for a technician

[1080] 1. The user types "The motor temperature is rising abnormally" into the terminal.

[1081] 2. The server analyzes this message and extracts the keywords "motor," "temperature," and "abnormal."

[1082] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[1083] 4. The server searches its internal database for technicians with the right skills and scores them to identify the best candidates.

[1084] 5. The server notifies the selected technician and provides the user with the technician's contact information.

[1085] Prompt Sentence Examples

[1086] "The user inputs 'The motor temperature is abnormally high.' Please generate a program that analyzes this message and executes the process of finding the most suitable technician. Start by using NLP and an emotion engine to extract keywords and emotions, find the most suitable technician from the database, and notify the system."

[1087] This allows the system of the present invention to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. In addition, real-time data updates enable accurate matching based on the latest information.

[1088] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1089] Step 1:

[1090] The user inputs the details of the abnormality into the terminal in natural language. For example, if the user inputs "The motor temperature is rising abnormally," this text becomes the input data. The input data is sent to the server.

[1091] Step 2:

[1092] The server receives the natural language message and analyzes it using a natural language processing method that uses a generative AI model. Here, key keywords such as "motor," "temperature," and "abnormality" are extracted from the text, along with the intent of "reporting an abnormality." The analysis results are output as keywords to be queried in the database.

[1093] Step 3:

[1094] The server uses an emotion engine to recognize and analyze emotions from the user's text. The analysis evaluates the stress and urgency the user feels. The input for this step is the user's text, and the output is a score indicating the urgency.

[1095] Step 4:

[1096] The server searches its internal database for candidates with the appropriate skills and experience based on the analyzed keywords and urgency score, issues a query to the database, and outputs a list of relevant engineers.

[1097] Step 5:

[1098] The server uses a generative AI model to score the multiple candidates found in the search results. This takes into account not only skill and career compatibility but also urgency scores. As a result of the scoring, a profile of the most suitable candidate is output.

[1099] Step 6:

[1100] The server sends a push notification to the selected best candidate. Specifically, a notification containing details of the anomaly report is sent to the selected candidate's contact information. The notification sending result is recorded.

[1101] Step 7:

[1102] The server notifies the user of the selected candidate. The technician's name, title, contact information, etc. are displayed on the terminal, and a connection link is provided if necessary. The input of this step is the selected candidate's information, and the output is the notification to the user.

[1103] Step 8:

[1104] The server waits for a response from the candidate, and if they agree, it provides a connection link to the user. If the candidate refuses to report the anomaly, it notifies the next best candidate again. Until it receives a response, the server monitors the communication and decides the next action.

[1105] In this way, the user can simply enter details of the abnormality in natural language, and the server will take appropriate action, enabling them to receive a prompt and appropriate response.

[1106] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1108] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1109] [Fourth embodiment]

[1110] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1111] 7, a 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.

[1112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1113] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1114] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1117] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1118] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1119] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1121] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1122] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1123] The present invention is a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career. This system is composed of natural language processing means, search means, selection means, notification means, update means, and scoring means. The program processing of this system is explained in detail below.

[1124] Enter your inquiry details via chat

[1125] Users enter their questions and information about the skills and careers they need in natural language into SkillConnect's chat interface.

[1126] Chat content analysis

[1127] The server uses the generative AI's NLP (Natural Language Processing) module to analyze the user's input text, extracting key keywords (e.g., "JavaScript," "expert," "marketing," "project leader," etc.) and classifying the message's intent.

[1128] Skills and career matching search

[1129] Based on the extracted keywords and intent, the server queries an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[1130] Selection of the best talent

[1131] The server scores multiple candidates from the search results, using AI algorithms to assess each candidate's suitability and select the person who best fits the request.

[1132] Search result display and notifications

[1133] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[1134] Review and feedback

[1135] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be selected and notified again. In addition, the server will collect feedback from the user and use it to improve the system's matching accuracy.

[1136] View real-time skills and career data

[1137] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available for reference. This means that new skills and career changes are immediately reflected in the database.

[1138] Specific examples

[1139] Example 1: Searching for a technician

[1140] 1. User types "I need a JavaScript expert" in chat.

[1141] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[1142] 3. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[1143] 4. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[1144] 5. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[1145] Example 2: Searching for project members

[1146] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[1147] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[1148] 3. The server searches the database for relevant people and identifies the best candidates through scoring.

[1149] 4. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[1150] 5. After receiving the approval, Marketer B will provide the user with a connection link.

[1151] Based on these specific examples, the present invention enables a user to quickly and accurately find people with the skills and careers they need, enabling efficient communication and task execution.

[1152] The processing flow will be explained below.

[1153] Step 1:

[1154] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[1155] Step 2:

[1156] The server uses a natural language processing (NLP) module to parse the user's input, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[1157] Step 3:

[1158] Based on the extracted keywords and intent, the server runs a search query against an internal database to find candidates with the relevant skills and careers.

[1159] Step 4:

[1160] The server uses an AI algorithm to evaluate the list of candidates obtained from the search results and score each candidate's skills and career suitability.

[1161] Step 5:

[1162] The server selects the best candidate from the scored candidates. If there are multiple candidates, the best candidate is ranked higher.

[1163] Step 6:

[1164] The device will display the selected person's information (e.g., name, job title, contact details) to the user and notify the candidate of the inquiry via push notification.

[1165] Step 7:

[1166] The device displays a prompt to confirm the candidate's consent, and if the candidate accepts, the user is provided with a connection link. If not, the next best candidate is notified again.

[1167] Step 8:

[1168] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[1169] Step 9:

[1170] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available. This update is performed regularly, and new skills and career changes are immediately reflected.

[1171] These steps enable users to quickly and accurately find people with the skills and experience they need, enabling efficient communication and task completion.

[1172] Example 1

[1173] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1174] Until now, it has been difficult to quickly and accurately find people with specific skills and careers within an organization and recommend appropriate personnel based on the user's needs. In particular, it takes time to collect information and evaluate suitability, which ultimately hinders efficient communication and task execution. A system that solves these problems is needed.

[1175] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1176] In this invention, the server includes a language processing means for analyzing a natural language message input by a user, a search means for searching an internal data store for people with appropriate qualifications and experience based on the analyzed message, and a selection means for selecting the most suitable person from the searched candidates, thereby enabling the user to quickly and accurately find people with the skills and careers they need.

[1177] A "user" is a person or organization that uses the system to provide input to search for people with specific skills or careers.

[1178] A "natural language message" refers to a query or instruction entered by a user in natural language or sentences.

[1179] "Language processing means" refers to technology or devices that analyze natural language messages entered by users and extract key keywords and intentions.

[1180] An "internal data store" is a database or data management system that stores the qualifications and experience information of all employees within an organization.

[1181] A "search tool" is a technology or device that searches an internal data store for people with the relevant skills and careers based on analyzed keywords and intent.

[1182] "Selection means" refers to the technology or device that performs evaluation and scoring to select the most suitable person from the search results.

[1183] "Notification means" refers to a technology or device that sends push notifications or contacts to selected individuals and provides a means of contact for connecting with the user.

[1184] "Update means" refers to technology or devices that update information in the internal data store in real time, making the latest qualifications and career information always available for reference.

[1185] The "evaluation means" refers to a technology or device that uses generative artificial intelligence to evaluate and rank the people who are most suitable for the user's requirements.

[1186] The present invention is a system that analyzes natural language messages entered by users and quickly finds people with appropriate skills and careers within an organization. This system is composed of a language processing means, a search means, a selection means, a notification means, an update means, and an evaluation means.

[1187] System Configuration

[1188] The system includes a language processing means (e.g., Google NLP API) for analyzing natural language messages entered by users, a search means (e.g., MySQL database) for searching an internal data store for people with appropriate qualifications and experience based on the analyzed messages, a selection means (e.g., an AI algorithm using TensorFlow) for selecting the most suitable person from the searched candidates, and a notification means (e.g., Firebase Cloud Messaging) for notifying the selected person.

[1189] The system also includes an update mechanism that updates the information in the internal data store in real time, making the most up-to-date qualifications and career information available, and an evaluation mechanism that uses generative artificial intelligence to evaluate and rank candidates who best fit the user's requirements.

[1190] How to use

[1191] Using SkillConnect's chat interface, users enter questions and information about the skills and careers they need in natural language, such as "Looking for a JavaScript expert." The server parses this message and extracts key keywords (e.g., "JavaScript" and "expert").

[1192] Based on the analyzed and extracted keywords, the server queries an internal data store to find candidates with the relevant qualifications and experience. The resulting candidates are then scored using an AI algorithm to evaluate their suitability. The best-matched candidate is selected, and their information (e.g., name, job title, contact details) is displayed to the user. At the same time, a push notification is sent to inform the person of the inquiry.

[1193] If the person denies the request, the user will be provided with a link to connect with them. If the person declines the request, the next best candidate will be selected and the user will be notified again.

[1194] Specific examples

[1195] Example 1: Searching for a technician

[1196] A user types "I need a JavaScript expert" in chat. The server uses an NLP module to extract the keywords "JavaScript" and "expert" and searches for matching people in its internal data store. It scores the suitability to identify the best candidate and displays that person's information (e.g., Technician A) to the user. The server sends a push notification and, upon Technician A's approval, provides the user with a connection link to Technician A.

[1197] Example 2: Searching for project members

[1198] A user types in chat, "I'd like to find a new marketing project leader." The server extracts the keywords "marketing" and "project leader" and searches for matching people in the data store. It identifies the best candidate through scoring and displays that person's information (e.g., Marketer B) to the user. The server sends a push notification, and after Marketer B accepts, provides the user with a connection link.

[1199] In this way, the system of the present invention can quickly and accurately find people with the skills and careers that the user needs, enabling efficient communication and task execution.

[1200] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1201] Step 1:

[1202] A user types a natural language message into SkillConnect's chat interface, for example, "I need a JavaScript expert." The input is sent as text data to the server.

[1203] Step 2:

[1204] The server analyzes the natural language message received. Specifically, it processes the text data using the generative AI's NLP (Natural Language Processing) module (e.g., Google NLP API). It analyzes the message received as input, "JavaScript expert needed," and extracts the main keywords "JavaScript" and "expert." This generates keyword data as the analysis result.

[1205] Step 3:

[1206] The server queries the internal data store based on the analysis results. It uses the analyzed keyword data "JavaScript" and "expert" as input. It generates an SQL statement (e.g. "SELECT FROM employees WHERE skill='JavaScript' AND position='expert'") and searches against the MySQL database. The output is a list of people with the corresponding qualifications and experience.

[1207] Step 4:

[1208] The server selects the best person from the search results. It uses the list of people it has obtained as input. It uses an AI algorithm (e.g., TensorFlow) to score the suitability of each candidate. Based on the scoring, it selects the person with the highest suitability and outputs that person's data (e.g., name, job title, contact details).

[1209] Step 5:

[1210] The device displays the information of the selected person to the user. The selected person's data is used as input. For example, the displayed content might be "Engineer A: JavaScript expert, contact: example@example.com." The server also sends a push notification to the selected person. Using a notification method (e.g., Firebase Cloud Messaging), the selected person is notified of the inquiry.

[1211] Step 6:

[1212] The terminal confirms with the person in question, and if approval is obtained, provides a connection link to the user. Approval information from the person in question is received as input. If approval is obtained, a connection link (e.g., "https: / / example.com / chat") is provided to the user. If the person in question declines, the next best candidate is selected again and notified again via the notification means.

[1213] Step 7:

[1214] The server collects feedback from users. For example, a user may input feedback such as "Engineer A was very helpful." This feedback information is collected and used to improve the matching accuracy of the system. The feedback data is received as input and applied to an improvement algorithm. The expected output is an improvement in the accuracy of the system.

[1215] Step 8:

[1216] The server updates the skills and career information in the internal data store in real time. It receives new skills and career change information as input. It updates the database using a cloud environment (e.g., AWS API Gateway). This ensures that the latest qualifications and career information is always maintained, enabling up-to-date people searches based on user requests.

[1217] (Application example 1)

[1218] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1219] With conventional systems, it was difficult to quickly find an engineer with the appropriate skills when troubleshooting problems that occurred on the manufacturing floor or when setting up equipment. Furthermore, because engineer information was not updated in real time, it was not possible to refer to the latest skill information, which could result in delayed responses. Furthermore, if the selected engineer was not notified promptly, there was also the problem of further delays in resolving the problem. There is a need for a system that can solve these issues and achieve problem-solving and efficiency improvements on the manufacturing floor.

[1220] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1221] In this invention, the server includes natural language processing means for analyzing a natural language message input by a user, search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, selection means for selecting the most suitable person from the searched candidates, notification means for notifying the selected person in real time and providing a contact means for connecting with the user, and search means for searching for an appropriate engineer based on the content of a request to support the rapid resolution of problems that occur in the factory. This makes it possible to quickly find an appropriate engineer at the manufacturing site and respond in real time.

[1222] "User" refers to a person who uses the system to search for people with specific skills or careers.

[1223] A "natural language message" refers to a text message entered by a user in a language that the user normally uses.

[1224] "Natural language processing means" refers to technology that analyzes input natural language messages and extracts key keywords and intent.

[1225] "Search means" refers to a function that searches an internal database for talent with appropriate skills and careers based on the analyzed message.

[1226] "Selection method" refers to the technology used to evaluate and select the most suitable person from among the candidates searched.

[1227] "Notification means" refers to a communication means for sending a push notification to the selected person and connecting the user with the selected person.

[1228] "Problems occurring within the factory" refers to problems that require the attention of engineers, such as malfunctions that occur on the manufacturing floor or equipment setup.

[1229] An "engineer" is someone who has specific skills and a career and the ability to solve problems that arise on the manufacturing floor.

[1230] "Real-time" refers to information that is updated immediately and is immediately available.

[1231] "Generative AI" refers to technology that uses high-performance algorithms to make predictions and judgments in data analysis, natural language processing, and other areas.

[1232] MODE FOR CARRYING OUT THE INVENTION

[1233] This invention is a system for quickly resolving problems that occur in factories and supporting efficient operations. This system can analyze natural language messages entered by users and quickly find engineers with the appropriate skills and experience.

[1234] System configuration

[1235] The system uses the following hardware and software:

[1236] Hardware: Smartphones, tablets, and internet connection in the factory

[1237] Software: Google Cloud Natural Language API, IBM Watson Natural Language Understanding, MySQL, Firebase Cloud Messaging

[1238] Data processing and calculation

[1239] The system processes and calculates data in the following steps:

[1240] Enter your inquiry details via chat

[1241] Users: Enter shop floor issues and technician requirements in natural language into a smartphone or tablet application.

[1242] Chat content analysis

[1243] Server: Analyzes the message entered by the user using the generative AI's NLP (Natural Language Processing) module, extracting key keywords and the intent of the message.

[1244] Skills and career matching search

[1245] Server: Based on the extracted keywords and intent, the server searches an internal database for personnel with the relevant skills and careers. This database stores the skill profiles and career histories of the factory's engineers.

[1246] Selection of the most suitable engineer

[1247] Server: Scores multiple candidates from the search results and uses AI algorithms to evaluate each candidate's suitability. Selects the engineer best suited to the requirements.

[1248] Search result display and notifications

[1249] Server: Displays the selected technician's information to the user and notifies the technician of the inquiry via push notification.

[1250] Review and feedback

[1251] Terminal: The system checks with the engineer and, if approved, provides the user with a connection link. If the engineer declines the request, the system selects the next best candidate and notifies the user again. Additionally, the system collects user feedback and uses it to improve the system's matching accuracy.

[1252] Specific examples

[1253] Example 1: Troubleshooting request

[1254] User input: "I'm looking for a technician who knows how to tune this press."

[1255] Prompt: "Find a technician who can identify the cause of the press malfunction and make the necessary adjustments."

[1256] Example 2: Setting up a new machine

[1257] User Input: "Can you recommend a technician who can install a new CNC machine?"

[1258] Prompt: "Find a technician who is familiar with CNC machine installation procedures and can quickly complete the setup process."

[1259] This allows the system to quickly find the right technician on the manufacturing floor and respond in real time.

[1260] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1261] Step 1:

[1262] Enter your inquiry details via chat

[1263] Users input problems at the manufacturing site and requirements for engineers in natural language into an application on a smartphone or tablet.

[1264] Input: Free text (e.g. "We are looking for a technician who is knowledgeable about adjusting this press")

[1265] Output: A query message from the user is sent to the application.

[1266] Step 2:

[1267] Chat content analysis

[1268] The server analyzes the text entered by the user using an NLP (natural language processing) module with a generative AI model.

[1269] Input: The query message sent by the user

[1270] Data processing: Extract key keywords (e.g., "press machine," "adjustment," "engineer") and intent of the message.

[1271] Output: Extracted keywords and message intent

[1272] Step 3:

[1273] Skills and career matching search

[1274] The server queries an internal database based on the extracted keywords and intent to search for engineers with the relevant skills and experience.

[1275] Input: Extracted keywords and message intent

[1276] Data crunching: Run a database query to look at technicians' skill profiles and career histories to come up with a list of matches.

[1277] Output: A list of matching technicians

[1278] Step 4:

[1279] Selection of the most suitable engineer

[1280] The server scores multiple candidates obtained from the search results and evaluates each candidate's suitability using a generative AI model.

[1281] Input: List of matched technicians

[1282] Data calculation: Score and rank suitability based on work history, skill set, past evaluations, etc.

[1283] Output: Selection of the best technician

[1284] Step 5:

[1285] Search result display and notifications

[1286] The server displays the information of the selected technician to the user and notifies the technician of the inquiry via a push notification.

[1287] Input: Selected engineer's information

[1288] Specific behavior:

[1289] 1. The technician's information (e.g., name, title, contact information) is displayed on the user's device.

[1290] 2. The server sends a push notification to the technician, sharing the details of the issue.

[1291] Output: Display to user and notify technician

[1292] Step 6:

[1293] Review and feedback

[1294] The terminal checks with the relevant engineer, and if approval is obtained, provides the user with a connection link.

[1295] Input: Technician's response

[1296] Specific behavior:

[1297] 1. The technician terminal responds with either approval or denial.

[1298] 2. If the technician approves, he / she will provide the connection link to the user's device.

[1299] 3. If the technician declines, the server will select the next best candidate and notify them again.

[1300] 4. The server collects feedback from users and uses it to improve the system's matching accuracy.

[1301] Output: Provides user with a connection link or suggests next best candidate

[1302] In this way, the system can quickly find the right technician on the manufacturing floor and respond in real time.

[1303] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1304] The present invention combines a system that analyzes natural language messages entered by users and can quickly find people in a company who have the appropriate skills and career, with an emotion engine that recognizes the user's emotions. This system is composed of natural language processing means, search means, selection means, notification means, update means, scoring means, and the emotion engine. The program processing of this system is explained in detail below.

[1305] Enter your inquiry details via chat

[1306] Users enter questions and information about desired skills and careers in natural language into SkillConnect's chat interface, and messages entered by users may contain emotions.

[1307] Chat content analysis

[1308] The server uses the generative AI's NLP (Natural Language Processing) module to parse the user's input text, extracting key keywords (e.g., "JavaScript," "expert," etc.) and intent (e.g., skill request, mentoring request, etc.).

[1309] Emotion Analysis

[1310] The server uses an emotion engine to recognize and analyze emotions from the user's input message, for example, to determine whether the user is feeling stressed or expressing urgency.

[1311] Skills and career matching search

[1312] The server then uses the extracted keywords, intent, and sentiment analysis results to query an internal database that contains the skill profiles and career histories of all employees within the organization to find candidates with the relevant skills and careers.

[1313] Selection of the best talent

[1314] The server uses an AI algorithm to evaluate multiple candidates from the search results, scoring each candidate's skill and career compatibility, and selecting the most suitable candidate, taking into account the user's emotional state.

[1315] Search result display and notifications

[1316] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[1317] Review and feedback

[1318] The device will contact the person in question and, if they agree, provide a connection link to the user. If the person declines the inquiry, the next best candidate will be notified. Furthermore, the server collects and analyzes feedback from users, which is used as data to improve the system's matching accuracy.

[1319] View real-time skills and career data

[1320] The server updates employee skill and career information in real time in a cloud environment, making the latest data always available. This update is performed periodically, and new skills and career changes are immediately reflected in the database.

[1321] Specific examples

[1322] Example 1: Searching for a technician

[1323] 1. User types "I need a JavaScript expert" in chat.

[1324] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[1325] 3. The server uses an emotion engine to analyze the user's emotions and recognize the high urgency of the message.

[1326] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[1327] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[1328] 6. Once Technician A replies with approval, the device provides the user with a connection link to Technician A.

[1329] Example 2: Searching for project members

[1330] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[1331] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[1332] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[1333] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[1334] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[1335] 6. After Marketer B receives the approval, the device provides the user with a connection link.

[1336] Based on these specific examples, the present invention enables users to quickly and accurately find people with the skills and careers they need, and provides optimal feedback and mentoring based on emotional changes, thereby enabling efficient communication and task completion.

[1337] The processing flow will be explained below.

[1338] Step 1:

[1339] Users enter questions and desired skill and career information in natural language into SkillConnect's chat interface.

[1340] Step 2:

[1341] The server parses the user's input using a natural language processing (NLP) module, which extracts key keywords and intent from the entered message.

[1342] Step 3:

[1343] The server uses the extracted keywords and intent to run search queries against an internal database that contains the skill profiles and career histories of all employees.

[1344] Step 4:

[1345] The server uses the emotion engine to recognize and analyze emotions from the user's input message, identifying the user's emotional state (e.g., stress, urgency, etc.).

[1346] Step 5:

[1347] The server integrates the candidate list obtained from the skill and career search with the results of sentiment analysis, and uses an AI algorithm to score the person who best suits the user's requirements and emotional state.

[1348] Step 6:

[1349] The server selects the best candidate from the scored candidates. If there are multiple candidates, it takes into account emotional information and ranks the most suitable candidate at the top.

[1350] Step 7:

[1351] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification.

[1352] Step 8:

[1353] The device will prompt the person for confirmation, and if approved, provide the user with a connection link. If not, the next best candidate will be notified again.

[1354] Step 9:

[1355] The server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[1356] Step 10:

[1357] The server updates employee skill and career information in real time in a cloud environment, ensuring that the latest data is always available and that new skills and career changes are immediately reflected in the database.

[1358] Example 2

[1359] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1360] Within a company, users are faced with the challenge of quickly and accurately finding people with the skills and experience they need. In particular, there is a demand for systems that can respond optimally while taking into account the user's feelings. It is also important that the skills and experience information is always up to date. Conventional systems have difficulty meeting these requirements, so an effective solution is needed.

[1361] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a natural language processing means for analyzing a natural language message input by a user, a search means for searching an internal database for people with appropriate skills and experience based on the analyzed message, an emotion recognition means for recognizing and analyzing emotions from the user's message, a selection means for selecting the most suitable person from the searched candidates, and a notification means for sending a push notification to the selected person and providing a contact means for connecting with the user. This makes it possible to quickly and accurately find people with the necessary skills and experience while taking the user's emotions into consideration.

[1362] "Natural language processing means" is a means for analyzing a natural language message entered by a user and extracting key keywords and intentions.

[1363] "Search means" refers to means for searching an internal database for a person with appropriate skills and background based on the analyzed message.

[1364] The "emotion recognition means" is a means for recognizing and analyzing emotions from a message input by a user.

[1365] The "selection means" is a means for selecting the most suitable person from among the searched candidates.

[1366] The "notification means" is a means for sending a push notification to the selected person and providing a means of contact for connecting with the user.

[1367] The "update means" is a means for updating the skills and career information in the internal database in real time, so that the latest data can always be referenced.

[1368] "Scoring means" is a means for using generative artificial intelligence to score and rank the person who best suits the user's requirements.

[1369] The present invention is a system that analyzes natural language messages entered by users and quickly finds people in a company who have the appropriate skills and background. This system is composed of natural language processing means, search means, emotion recognition means, selection means, notification means, update means, and scoring means.

[1370] Configuration and Operation

[1371] First, a user logs into SkillConnect's chat interface and types in natural language information about their question and desired skill or career, such as "I need a JavaScript expert." This message may contain the user's sentiment.

[1372] The server then uses the generative AI's NLP (natural language processing) module to parse the user's input text, which uses Microsoft's Azure Cognitive Services to extract key keywords (e.g., "JavaScript" or "expert") and intent (e.g., skill request).

[1373] The server uses an emotion engine, such as IBM Watson Tone Analyzer, to recognize and analyze emotions from the input message, determining whether the user is feeling stressed or expressing urgency.

[1374] Based on the extracted keywords, intent, and sentiment analysis results, the server queries an internal database (e.g., SharePoint, an internal portal software) to search for candidates with the relevant skills and experience.

[1375] From the multiple candidates found, the server evaluates them using an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background, and also takes into account the user's emotional state to select the most suitable candidate.

[1376] The device then displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via push notification (e.g., Firebase Cloud Messaging). The selected person confirms, and if they agree, the server provides the user with a connection link. If the selected person declines the inquiry, the server notifies the next best candidate. Additionally, the server collects and analyzes user feedback and uses it as data to improve the system's matching accuracy.

[1377] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. This update is performed periodically, and new skills and experience changes are immediately reflected in the database.

[1378] Specific examples

[1379] Example 1: Searching for engineers

[1380] 1. User types "I need a JavaScript expert" in chat.

[1381] 2. The server parses this message and extracts the keywords "JavaScript" and "expert."

[1382] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[1383] 4. The server searches its internal database for employees with JavaScript skills, scores them based on suitability, and identifies the best candidates.

[1384] 5. The device displays the information of the selected person (e.g., Engineer A) to the user and sends a push notification.

[1385] 6. Once Technician A replies with approval, the terminal provides the user with a connection link to Technician A.

[1386] Example 2: Searching for project members

[1387] 1. A user types in chat, "I'm looking to find a new marketing project leader."

[1388] 2. The server analyzes this message and extracts the keywords "marketing" and "project leader."

[1389] 3. The server uses an emotion engine to analyze the user's emotions and recognizes that the user is in a relaxed state.

[1390] 4. The server searches the database for relevant people and identifies the best candidates through scoring.

[1391] 5. The device displays the information of the selected person (e.g., Marketing Manager B) to the user and sends a push notification.

[1392] 6. After receiving the approval from Marketer B, the device provides the user with a connection link.

[1393] As described above, the system based on the present invention not only quickly and accurately finds people with the skills and experience required by the user, but also provides optimal feedback and mentoring based on emotional changes, thereby realizing efficient communication and task completion.

[1394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1395] Step 1:

[1396] A user logs into SkillConnect's chat interface and types in natural language information about their question or desired skill or career. For example, they can send a natural language message like, "I need a JavaScript expert." This message may contain the user's sentiment.

[1397] Step 2:

[1398] The server uses the generative AI's NLP (natural language processing) module to analyze the user's input text. Specifically, it uses Microsoft's Azure Cognitive Services to perform the analysis. It receives the input text, tokenizes it, extracts key keywords (such as "JavaScript" or "expert") and intent (such as a skill request), and outputs these.

[1399] Step 3:

[1400] The server uses an emotion engine to recognize and analyze emotions from the user's input message. For example, it uses IBM Watson Tone Analyzer. It uses the analyzed text as input to determine the user's emotional state (e.g., "urgent" or "relaxed"). The extracted emotional information is output.

[1401] Step 4:

[1402] The server queries an internal database (for example, SharePoint, an internal portal software) based on the extracted keywords, intent, and sentiment analysis results to search for candidates with the relevant skills and experience. Keywords and sentiment information are used as input to retrieve relevant entries from the internal database, and the search results are the output.

[1403] Step 5:

[1404] The server uses an AI algorithm (e.g., Scikit-learn) to score the suitability of each candidate's skills and background from the multiple candidates obtained from the search results and selects the most suitable person. The search results are used as input to calculate suitability, and information about the most suitable person is output.

[1405] Step 6:

[1406] The device displays the selected person's information (e.g., name, job title, contact information) to the user, and the server notifies the selected person of the inquiry via a push notification (e.g., Firebase Cloud Messaging). Using the information of the best match as input, a push notification is generated and sent. The notification sent result is the output.

[1407] Step 7:

[1408] The device confirms with the person in question and, if consent is obtained, provides the user with a connection link. If the person in question declines the inquiry, the next best candidate is notified again. In addition, the server collects and analyzes feedback from users and uses it as data to improve the system's matching accuracy. Using the confirmation results and feedback information as input, the final result is a connection link or the next notification.

[1409] Step 8:

[1410] The server updates employee skills and experience information in real time in a cloud environment (e.g., AWS or Google Cloud), ensuring that the latest data is always available. Updates are made periodically, and new skills and experience changes are immediately reflected in the database. The latest skills and experience information is used as input, and the updated database state is the output.

[1411] (Application example 2)

[1412] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1413] Conventional technologies provide systems that analyze natural language messages entered by users to find people in a company with the appropriate skills and career experience. However, because selection is based solely on skills and career experience without considering the user's emotional state, it is difficult to respond promptly to urgent issues or the user's emotions. Furthermore, because data is not updated in real time, matching accuracy declines, making it difficult to quickly find the right person. Therefore, the present invention aims to provide a system that analyzes a user's emotional state and quickly and accurately finds the right person.

[1414] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1415] In this invention, the server includes a natural language processing unit that analyzes a natural language message entered by a user, a search unit that searches an internal database for people with appropriate skills and careers based on the analyzed message, a selection unit that selects the most suitable person from the searched candidates, a notification unit that sends a push notification to the selected person and provides a contact means for connecting with the user, an emotion analysis unit that analyzes the user's emotional state using an emotion engine that recognizes emotions, and an urgency evaluation unit that determines the urgency and optimizes candidates based on the analyzed emotional state and the user's message. This makes it possible to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. Furthermore, real-time data updates enable accurate matching based on the latest information.

[1416] "Natural language processing means" is a processing technology for analyzing natural language messages entered by users and extracting key keywords and intentions.

[1417] The "search means" is a function for searching an internal database for people with appropriate skills and careers based on the analyzed message.

[1418] The "selection method" is the algorithm or method used to select the most suitable person from the searched candidates.

[1419] "Notification means" is a function that sends push notifications to selected people and provides a means of contact for connecting with the user.

[1420] "Emotion analysis means" is a technology that uses an emotion engine to recognize and analyze emotions from the user's input message.

[1421] The "urgency evaluation means" is an evaluation method for determining urgency and optimizing candidates based on the analyzed emotional state and the user's message.

[1422] The "update means" is a mechanism for updating the skill and career information in the internal database in real time, making the latest data always available for reference.

[1423] The "scoring means" is a function that uses generative artificial intelligence to score and rank the person who is most suitable for the user's requirements.

[1424] The present invention relates to a system that analyzes natural language messages entered by users and combines them with an emotion recognition engine to quickly find people with suitable skills and careers.

[1425] System Configuration

[1426] Natural language processing tools

[1427] The server uses a natural language processing module that analyzes the natural language messages entered by the user into the device, leveraging a generative AI model to extract key keywords and user intent.

[1428] Search methods

[1429] Based on the analyzed keywords and intent, the server searches for candidates with the appropriate skills and careers from an internal database that contains the skill profiles and career histories of all employees within the organization.

[1430] Emotion analysis means

[1431] The server uses an emotion engine to recognize and analyze emotions from the user's input message, determining whether the user is feeling stressed or expressing urgency.

[1432] Urgency assessment tools

[1433] The server determines the urgency of the message based on the results of sentiment analysis and the user's message. If the urgency is high, candidates who require a quick response are notified first.

[1434] Selection method

[1435] The server selects the best candidate from the candidates obtained by the search tool. This selection process includes a scoring tool using a generative AI model to evaluate each candidate's skill and career fit.

[1436] Notification means

[1437] As a notification method, the server will send a push notification to the selected candidate, and the user will be provided with the appropriate contact method and a connection link if necessary.

[1438] Update method

[1439] The server updates the skills and career information in the internal database in real time, ensuring that the latest data is always available. This update is performed periodically.

[1440] Specific processing examples

[1441] Example 1: Searching for a technician

[1442] 1. The user types "The motor temperature is rising abnormally" into the terminal.

[1443] 2. The server analyzes this message and extracts the keywords "motor," "temperature," and "abnormal."

[1444] 3. The server uses an emotion engine to analyze the user's emotions and recognizes the high urgency of the message.

[1445] 4. The server searches its internal database for technicians with the right skills and scores them to identify the best candidates.

[1446] 5. The server notifies the selected technician and provides the user with the technician's contact information.

[1447] Prompt Sentence Examples

[1448] "The user inputs 'The motor temperature is abnormally high.' Please generate a program that analyzes this message and executes the process of finding the most suitable technician. Start by using NLP and an emotion engine to extract keywords and emotions, find the most suitable technician from the database, and notify the system."

[1449] This allows the system of the present invention to quickly find the most suitable person taking into account the user's emotions and urgency, improving the accuracy and efficiency of the system. In addition, real-time data updates enable accurate matching based on the latest information.

[1450] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1451] Step 1:

[1452] The user inputs the details of the abnormality into the terminal in natural language. For example, if the user inputs "The motor temperature is rising abnormally," this text becomes the input data. The input data is sent to the server.

[1453] Step 2:

[1454] The server receives the natural language message and analyzes it using a natural language processing method that uses a generative AI model. Here, key keywords such as "motor," "temperature," and "abnormality" are extracted from the text, along with the intent of "reporting an abnormality." The analysis results are output as keywords to be queried in the database.

[1455] Step 3:

[1456] The server uses an emotion engine to recognize and analyze emotions from the user's text. The analysis evaluates the stress and urgency the user feels. The input for this step is the user's text, and the output is a score indicating the urgency.

[1457] Step 4:

[1458] The server searches its internal database for candidates with the appropriate skills and experience based on the analyzed keywords and urgency score, issues a query to the database, and outputs a list of relevant engineers.

[1459] Step 5:

[1460] The server uses a generative AI model to score the multiple candidates found in the search results. This takes into account not only skill and career compatibility but also urgency scores. As a result of the scoring, a profile of the most suitable candidate is output.

[1461] Step 6:

[1462] The server sends a push notification to the selected best candidate. Specifically, a notification containing details of the anomaly report is sent to the selected candidate's contact information. The notification sending result is recorded.

[1463] Step 7:

[1464] The server notifies the user of the selected candidate. The technician's name, title, contact information, etc. are displayed on the terminal, and a connection link is provided if necessary. The input of this step is the selected candidate's information, and the output is the notification to the user.

[1465] Step 8:

[1466] The server waits for a response from the candidate, and if they agree, it provides a connection link to the user. If the candidate refuses to report the anomaly, it notifies the next best candidate again. Until it receives a response, the server monitors the communication and decides the next action.

[1467] In this way, the user can simply enter details of the abnormality in natural language, and the server will take appropriate action, enabling them to receive a prompt and appropriate response.

[1468] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1469] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1470] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1471] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1472] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1473] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1474] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1475] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1476] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1477] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1478] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1479] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1480] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1481] 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.

[1482] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1483] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1484] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1485] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1486] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1487] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1488] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1489] The following is further disclosed regarding the above embodiment.

[1490] (Claim 1)

[1491] natural language processing means for analyzing a natural language message input by a user;

[1492] a search means for searching an internal database for people with appropriate skills and careers based on the analyzed message;

[1493] a selection means for selecting the most suitable person from the searched candidates;

[1494] A notification means for sending a push notification to the selected person and providing a contact means for connecting with the user;

[1495] A system including:

[1496] (Claim 2)

[1497] 10. The system of claim 1, further comprising an update means for updating the skill and career information in the internal database in real time, thereby making the latest data available for reference.

[1498] (Claim 3)

[1499] 10. The system of claim 1, further comprising a scoring means for using generative artificial intelligence to score and rank the people who best fit the user's requirements.

[1500] "Example 1"

[1501] (Claim 1)

[1502] a language processing means for analyzing a natural language message input by a user;

[1503] a search means for searching an internal data store for a person with appropriate qualifications and experience based on the parsed message;

[1504] a selection means for selecting the most suitable person from the searched candidates;

[1505] a notification means for notifying the selected person and providing a contact means for connecting with the user;

[1506] A system including:

[1507] (Claim 2)

[1508] 10. The system of claim 1, further comprising: an updating means for updating the qualifications and experience information in the internal data store in real time to make the most current data available for reference.

[1509] (Claim 3)

[1510] 10. The system of claim 1, further comprising an evaluation means for evaluating and ranking the individuals who best fit the user's requirements using generative artificial intelligence.

[1511] "Application Example 1"

[1512] (Claim 1)

[1513] natural language processing means for analyzing a natural language message input by a user;

[1514] a search means for searching an internal database for people with appropriate skills and careers based on the analyzed message;

[1515] a selection means for selecting the most suitable person from the searched candidates;

[1516] a notification means for notifying the selected person in real time and providing a contact means for connecting with the user;

[1517] A search tool to find the appropriate engineer based on the request to help quickly solve problems that occur in the factory;

[1518] A system including:

[1519] (Claim 2)

[1520] 10. The system of claim 1, further comprising an update means for updating the skill and career information in the internal database in real time, thereby making the latest data available for reference.

[1521] (Claim 3)

[1522] 10. The system of claim 1, further comprising a scoring means for using generative artificial intelligence to score and rank the people who best fit the user's requirements.

[1523] "Example 2: Combining Emotion Engines"

[1524] (Claim 1)

[1525] natural language processing means for analyzing a natural language message input by a user;

[1526] a search means for searching an internal database for people with appropriate skills and experience based on the analyzed message;

[1527] An emotion recognition means for recognizing and analyzing emotions from a user's message;

[1528] a selection means for selecting the most suitable person from the searched candidates;

[1529] A notification means for sending a push notification to the selected person and providing a contact means for connecting with the user;

[1530] A system including:

[1531] (Claim 2)

[1532] 10. The system of claim 1, further comprising an update means for updating the skills and career information in the internal database in real time to enable reference to the latest data.

[1533] (Claim 3)

[1534] 10. The system of claim 1, further comprising a scoring means for using generative artificial intelligence to score and rank the people who best fit the user's requirements.

[1535] "Application example 2 when combining emotion engines"

[1536] (Claim 1)

[1537] natural language processing means for analyzing a natural language message input by a user;

[1538] a search means for searching an internal database for people with appropriate skills and careers based on the analyzed message;

[1539] a selection means for selecting the most suitable person from the searched candidates;

[1540] A notification means for sending a push notification to the selected person and providing a contact means for connecting with the user;

[1541] emotion analysis means for analyzing the user's emotional state using an emotion engine that recognizes emotions;

[1542] an urgency assessment means for determining urgency and optimizing candidates based on the analyzed emotional state and the user's message;

[1543] A system including:

[1544] (Claim 2)

[1545] 10. The system of claim 1, further comprising an update means for updating the skill and career information in the internal database in real time, thereby making the latest data available for reference.

[1546] (Claim 3)

[1547] 10. The system of claim 1, further comprising a scoring means for using generative artificial intelligence to score and rank the people who best fit the user's requirements. [Explanation of symbols]

[1548] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. natural language processing means for analyzing a natural language message input by a user; a search means for searching an internal database for people with appropriate skills and careers based on the analyzed message; a selection means for selecting the most suitable person from the searched candidates; A notification means for sending a push notification to the selected person and providing a contact means for connecting with the user; A system including:

2. 2. The system according to claim 1, further comprising an update unit that updates the skill and career information in the internal database in real time, making the latest data available for reference.

3. 10. The system of claim 1, further comprising a scoring means for using generative artificial intelligence to score and rank the people who best fit the user's requirements.

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