System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and registering new participant profile information into a database; means for collecting and providing internal, general, and up-to-date information to a AI engine; means for learning information collected by the AI engine and building and updating an internal knowledgebase; means for receiving questions from new participants and generating and sending answers from the knowledgebase; and means for receiving, analyzing, and reflecting feedback from new participants to the AI engine.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] This invention relates to a system that enables new employees to quickly launch their work during the onboarding process. Specifically, it solves the problem of insufficient information sharing and training due to insufficient time for new employees to complete their existing work. Another objective is to support new employees so that they can begin their work with a sense of psychological safety. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system including the following means.

[0006] A means of receiving profile information of new participants and registering them in the database

[0007] A means of collecting internal, general, and up-to-date information and feeding it into the AI ​​engine

[0008] A means of learning from the information collected by the AI ​​engine to build and update its internal knowledge base

[0009] A means of receiving questions from newcomers, generating answers from a knowledge base, and sending them out

[0010] A means of receiving feedback from new participants, analyzing it, and reflecting it in the AI ​​engine

[0011] Furthermore, the present invention can include the following means.

[0012] A means of directing learning of relevant information based on newcomers' profile information

[0013] A way to regularly check for updates and update your knowledge base

[0014] This helps newcomers quickly become self-sufficient and learn the job, reducing the burden on the host team members.

[0015] "New participants" refer to people who are joining an organization or project for the first time.

[0016] "Profile information" refers to data including basic information such as the new participant's name, position, and responsibilities.

[0017] A "database" refers to an electronic storage device that systematically organizes and stores information so that it can be efficiently searched and used later.

[0018] "Internal information" refers to internal information such as documents, policies, and procedures within an organization.

[0019] "General information" refers to information that is widely available, such as industry standards and guidelines.

[0020] "Latest news" refers to the latest information on recent company news and industry trends.

[0021] An "AI engine" refers to software or a system that uses artificial intelligence technologies such as machine learning and natural language processing to learn from data, analyze it, and generate responses.

[0022] A "knowledge base" refers to a collection of information constructed based on data learned by an AI engine.

[0023] "Questions" refer to doubts or inquiries that new participants have about the work.

[0024] "Answer" refers to any pertinent information or explanation provided in response to a question.

[0025] "Feedback" refers to the evaluation and opinions that new participants give about the answers and support provided.

[0026] "Learning" refers to the process by which an AI engine analyzes collected information and reflects it in its internal knowledge base.

[0027] "Updating" refers to adding new data to existing information to keep it up to date. [Brief explanation of the drawings]

[0028] [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

[0029] 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.

[0030] First, the terms used in the following description will be explained.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] [First embodiment]

[0037] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0038] 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.

[0039] 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).

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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."

[0049] The present invention is an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, a terminal, and a user (newcomer) as follows.

[0050] Program processing

[0051] 1. New member registration

[0052] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0053] Terminal: Sends the entered information to the server.

[0054] Server: Registers the received profile information in a database and instructs the AI ​​engine to learn appropriate information depending on the new participant's role.

[0055] 2. Learning and updating information

[0056] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0057] Server: Collects general information such as industry standards and guidelines from online resources.

[0058] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0059] AI engine: Analyzes collected information and builds an internal knowledge base.

[0060] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0061] 3. Providing support

[0062] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0063] Terminal: Sends the entered question to the server.

[0064] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0065] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0066] 4. Feedback and Improvement

[0067] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0068] Device: Sends feedback information to the server.

[0069] Server: Stores the received feedback information in a database.

[0070] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0071] Specific examples

[0072] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[0073] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[0074] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[0075] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[0076] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[0077] In this way, the present invention provides an environment in which newcomers can efficiently learn the business, and significantly reduces the burden on the accepting members.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0081] Step 2:

[0082] Device: Sends the entered profile information to the server.

[0083] Step 3:

[0084] Server: Registers the received profile information in a database.

[0085] Step 4:

[0086] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[0087] Step 5:

[0088] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0089] Step 6:

[0090] Server: Collects general information such as industry standards and guidelines from online resources.

[0091] Step 7:

[0092] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0093] Step 8:

[0094] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[0095] Step 9:

[0096] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0097] Step 10:

[0098] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0099] Step 11:

[0100] Terminal: Sends the entered question to the server.

[0101] Step 12:

[0102] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0103] Step 13:

[0104] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0105] Step 14:

[0106] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0107] Step 15:

[0108] Device: Sends feedback information to the server.

[0109] Step 16:

[0110] Server: Stores the received feedback information in a database.

[0111] Step 17:

[0112] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0113] Example 1

[0114] 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."

[0115] It is important for new entrants to companies and organizations to quickly and efficiently learn their work. However, the onboarding process for new entrants is often time-consuming and laborious, placing a burden on existing members as well. Furthermore, if the information required for onboarding is scattered, new entrants have difficulty obtaining the information they need. There is a need for a system that can solve these issues and enable new entrants to begin work smoothly.

[0116] 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.

[0117] In this invention, the server includes means for receiving profile information of new entrants and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest industry trend information and supplying them to an AI engine, means for analyzing the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from new entrants and generating and sending answers from the knowledge base, and means for receiving feedback from new entrants, analyzing it, and reflecting it in the AI ​​engine. This enables new entrants to quickly obtain the information they need and achieves effective onboarding.

[0118] "Newcomers" refer to new members who join a company or organization.

[0119] "Profile Information" refers to personal information about a Newcomer, such as their name, job title, and responsibilities.

[0120] "Database" refers to a system for efficiently storing, managing, and accessing information.

[0121] "Internal documents" refers to information such as documents, policies, procedures, etc. used within a company.

[0122] "Industry standard knowledge" refers to knowledge of generally recognized standards and guidelines in a particular industry.

[0123] "Industry trend information" refers to information about the latest trends and news within an industry.

[0124] "AI engine" refers to a system that uses artificial intelligence to analyze information and build and update a knowledge base.

[0125] A "knowledge base" refers to a collection of information collected and organized by an AI engine.

[0126] "Questions" refer to any doubts or clarifications that newcomers have during the onboarding process.

[0127] "Answer" refers to the answer to a newcomer's question that the AI ​​engine generates based on its knowledge base.

[0128] "Feedback" refers to the evaluation and opinions that newcomers give of the answers and support provided.

[0129] "Server" refers to a central computer for processing information and communicating with multiple terminals.

[0130] "Terminals" refer to devices such as PCs and smartphones used by new entrants and administrators.

[0131] MODE FOR CARRYING OUT THE INVENTION

[0132] The present invention provides an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, terminals, and users.

[0133] The system consists of the following main components:

[0134] 1. Server: A central computer that processes information and accesses databases and AI engines.

[0135] 2. Terminals: Devices used by newcomers and administrators, including PCs and smartphones.

[0136] 3. Database: A system for storing profile and feedback information.

[0137] 4. AI Engine: Artificial intelligence for analyzing information, building and updating knowledge bases, and generating answers to questions.

[0138] Specific hardware and software

[0139] Server: Uses a Linux-based operating system, a database management system such as MySQL, and a programming language such as Python or Java.

[0140] Device: PC or smartphone with operating system such as Windows, macOS, Android, iOS.

[0141] AI engine: Machine learning frameworks such as TensorFlow and PyTorch are used to implement NLP models, and GPT-3 and other models are used as generative AI models.

[0142] System processing overview

[0143] The device (user) enters the profile information of new entrants (such as name, job title, and responsibilities) using a dedicated input form. The entered information is sent from the device to the server, which stores the received information in a database. The server then collects internal company documents, industry standard knowledge, and the latest industry trend information, and supplies this information to the AI ​​engine.

[0144] The AI ​​engine analyzes the collected information and builds and updates its internal knowledge base. When a newcomer enters a question using a dedicated app or web chat tool, the device sends the question to the server. The server passes the question to the AI ​​engine, which generates an answer from the knowledge base. The answer is then sent to the user's device via the server.

[0145] Furthermore, when newcomers provide feedback, that feedback is sent from the device to the server, which stores the information in a database. The saved feedback is analyzed and reflected in the AI ​​engine's learning data, allowing it to generate even more accurate answers.

[0146] Specific examples

[0147] Example 1: New employee "A" is assigned to the sales department

[0148] 1. Profile registration: A human resources officer uses a PC to enter A's name, job title, and job duties (sales) into a dedicated input form and submits it. The server saves this information in a new employee information table and instructs the AI ​​engine on learning tasks appropriate to A's role.

[0149] 2. Information learning: The server retrieves sales process and product information from the company's network drive and collects industry-standard guidelines from online resources via API. The AI ​​engine analyzes this information and builds a knowledge base.

[0150] 3. Providing support: Person A enters a question into a dedicated app on their smartphone, such as "What is the schedule for the next sales meeting?", and sends it. The server passes the question to the AI ​​engine, which generates an answer, "The next sales meeting will be held at 10:00 next Monday," and sends it to Person A's smartphone.

[0151] 4. Feedback and Improvement: User A enters and sends feedback on their smartphone, saying, "The explanation was easy to understand." The server saves the feedback in a feedback information table and reflects it in the AI ​​engine's learning data.

[0152] Prompt Sentence Examples

[0153] Prompt for newcomers to register their profile: "Please register your profile as a newcomer. Your name is A, your job title is sales, and your job is product sales."

[0154] Information learning prompt: "Gather internal sales information and industry-standard sales guidelines."

[0155] Prompt for support question: "When is the next sales meeting?"

[0156] Feedback prompt: "The support instructions were easy to understand."

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1: Complete and submit your newcomer profile information

[0159] Terminal (user): The user uses a dedicated input form to enter profile information such as the new entrant's name, job title, and responsibilities (input: profile information).

[0160] Terminal: Send the entered profile information to the server as an HTTP POST request (Output: Send profile information).

[0161] Step 2: Receive profile information and register in database

[0162] Server: Receives the transmitted profile information (input: profile information).

[0163] Server: Save the profile information to the database using the INSERT statement (data processing: save information, output: register to database).

[0164] Step 3: Information Collection Instructions

[0165] Server: Based on the profile information, instructs the AI ​​engine on information gathering tasks that correspond to the new entrant's role (input: profile information, output: information gathering task).

[0166] Step 4: Gathering internal information

[0167] Server: Retrieves internal documents, policies, procedures, etc. through the company's internal network drive or internal system API (input: information gathering tasks, data processing: information gathering, output: internal information).

[0168] Server: Analyzes the acquired internal information using a text analysis engine and organizes it by category (data processing: text analysis, output: organized information).

[0169] Step 5: Gathering external resources

[0170] Server: Collects industry standard knowledge and guidelines from online resources via API (input: information gathering task, data processing: information gathering, output: industry standard knowledge).

[0171] Server: Obtains the latest industry trend information from various RSS feeds and news feeds and extracts the necessary information (data processing: information extraction, output: industry trend information).

[0172] Step 6: Build and update your knowledge base

[0173] AI engine: The NLP engine analyzes collected internal information, industry standard knowledge, and industry trend information and converts it into meaningful structured data (input: collected information, data processing: NLP analysis, output: structured data).

[0174] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base (data processing: periodic updates, output: updated knowledge base).

[0175] Step 7: Enter and submit newcomer questions

[0176] Terminal (user): Newcomers use a dedicated app or web chat tool to input questions (input: question).

[0177] Terminal: Sends the entered question to the server via an HTTP POST request (Output: Send question).

[0178] Step 8: Parsing the question and generating an answer

[0179] Server: Receives questions from users and requests the AI ​​engine to analyze them (input: question).

[0180] AI engine: Analyzes the question using a natural language processing engine, searches for relevant information from a knowledge base, and generates an appropriate answer (data calculation: question analysis, data processing: answer generation, output: generated answer).

[0181] Server: Sends the generated answer to the user's terminal (output: Send answer).

[0182] Step 9: Enter and submit your feedback

[0183] Terminal (user): The newcomer inputs feedback on the answers and support provided (input:feedback).

[0184] Terminal: Send the entered feedback to the server via an HTTP POST request (Output: Send Feedback).

[0185] Step 10: Store and analyze feedback

[0186] Server: Stores the received feedback information in a database (input: feedback, data processing: information storage, output: storage in database).

[0187] Server: Passes the saved feedback to the AI ​​engine and analyzes it using a machine learning algorithm (data calculation: feedback analysis, output: analysis results).

[0188] AI engine: Reflects the analysis results in the learning data and performs model tuning to improve answer accuracy (data processing: model tuning, output: improved answer accuracy).

[0189] (Application example 1)

[0190] 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."

[0191] New employees need an onboarding support system to quickly and efficiently learn their jobs. In particular, advanced support is needed to help them adapt to the work environment and work procedures at factories and other work sites. There is also a demand for devices that display work procedures and safety measures in real time, along with related AI-based support.

[0192] 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.

[0193] In this invention, the server includes a means for receiving new employee profile information and registering it in a database; a means for collecting internal company information, general information, and the latest information and providing it to an AI engine; and a means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base. This allows new employees to learn work procedures and safety measures in real time and efficiently master their work. Furthermore, the use of a head-mounted display allows for immediate support on-site. Furthermore, prompt sentence processing utilizing a generative AI model enables real-time question resolution.

[0194] "Newcomers" refers to employees or staff who are joining an organization or project for the first time.

[0195] "Profile Information" refers to data about an individual, such as name, job title, and job responsibilities.

[0196] "Database" refers to a system for systematically storing user profile information and other related data.

[0197] "Internal information" refers to documents and data created and held within a company or organization.

[0198] "General information" refers to information that is commonly used outside the company, such as industry standards and guidelines.

[0199] "Latest information" refers to news and trend information that is updated in real time.

[0200] "AI engine" refers to artificial intelligence technology that analyzes collected data and builds and updates a knowledge base.

[0201] A "knowledge base" refers to a collection of data that systematically organizes and provides information learned by an AI engine.

[0202] "Means for receiving questions and generating answers" refers to the process of receiving questions from new participants and generating appropriate answers from the knowledge base.

[0203] "Feedback" refers to the opinions and evaluations that new participants enter regarding the support provided.

[0204] "Analyzing" refers to processing the feedback information and using it to improve the AI ​​engine.

[0205] A "head-mounted display" refers to a device that can display visual information when worn.

[0206] "Real-time" refers to a time frame in which processing and response occur instantaneously.

[0207] A "generative AI model" refers to a model that uses artificial intelligence to generate text and data.

[0208] A "prompt" refers to an instruction or question input to a generative AI model.

[0209] To implement this invention, it is necessary to build a system that is mainly comprised of a server, terminals, and users (new participants). A specific embodiment of this system is shown below.

[0210] System configuration

[0211] Server: Mainly responsible for the database, AI engine, and information collection and update functions.

[0212] Terminal: Mainly used to input information for new participants, receive questions, display answers, and collect feedback. A head-mounted display (HMD) is worn to display real-time work instructions.

[0213] User: The new participant operates the device (HMD) to acquire and learn the necessary information.

[0214] Hardware and software used

[0215] Hardware:

[0216] Head-mounted displays (HMDs): Examples include Oculus Rift and HTC Vive.

[0217] Server: A powerful cloud server (e.g., an AWS EC2 instance).

[0218] software:

[0219] Database management system (e.g. MySQL)

[0220] AI engines (e.g., Google Cloud AI, IBM Watson)

[0221] API communication libraries (e.g., Python's Requests library)

[0222] Explanation of each process

[0223] 1. New participant registration:

[0224] New participants' profile information (such as name, position, and responsibilities) is entered on a terminal and sent to the server, which then registers it in the database.

[0225] 2. Information Collection and Update:

[0226] The server collects internal documents, industry standard knowledge, the latest news, etc. and supplies them to the AI ​​engine, which then learns from this information and builds and updates its knowledge base.

[0227] 3. Real-time support:

[0228] When a new participant wears an HMD and sends a question from their device while working, the server passes the question to the AI ​​engine, which generates an appropriate answer from the knowledge base. For example, if the question is, "When is the next maintenance work scheduled?", the AI ​​engine generates the answer, "The next maintenance is scheduled for tomorrow at 2 p.m." The generative AI model processes the prompt sentence and resolves the question in real time.

[0229] 4. Feedback and Improvement:

[0230] New participants enter feedback on the support provided through their devices and send it to the server, which then stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[0231] Specific examples

[0232] Example: New factory worker

[0233] When a new employee is assigned to a factory production line, he or she puts on the HMD and asks, "When is the next maintenance work scheduled?" The server uses an AI engine to instantly respond. For example, it might generate a response such as, "The next maintenance work is scheduled for 2:00 PM tomorrow." Actual work procedures and safety measures are also displayed in real time through the HMD.

[0234] Example prompt for a generative AI model:

[0235] "A new employee has been assigned to work as a production line operator in a factory. He puts on the HMD and asks, 'When is the next maintenance task scheduled?' Please generate an appropriate answer."

[0236] In this way, the system provides an environment in which newcomers can quickly and efficiently learn their jobs, significantly reducing the burden on the accepting party.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] A new participant enters their profile information (name, job title, and responsibilities) through a terminal. The terminal sends this profile information to the server, which then registers the received profile information in a database. The input is the new participant's profile information, and the output is the registered profile information in the database. Specifically, the terminal receives the form input and sends it to the server in JSON format as a POST request, which the server receives and saves in the database.

[0240] Step 2:

[0241] The server collects internal information, general information, and the latest information. The server uses APIs to obtain information from internal databases, industry-standard knowledge bases, and news feeds. The obtained information is supplied to the AI ​​engine, which learns from this information and builds and updates its internal knowledge base. The input is the collected information, and the output is the updated knowledge base. Specifically, the server sends requests to various API endpoints, analyzes and processes the received data, and passes it to the AI ​​engine.

[0242] Step 3:

[0243] The new participant wears a head-mounted display and sends questions from their device while working. The device transmits the question to the server, which passes it on to the AI ​​engine. The AI ​​engine references the knowledge base to generate an appropriate answer and returns it to the server. The server sends the generated answer to the new participant's device. The input is the user's question, and the output is the answer from the AI ​​engine. Specifically, a question entered in voice or text format is sent to the server as an API request, and the AI ​​engine processes it and returns an answer.

[0244] Step 4:

[0245] New participants input feedback on the answers and support provided. The device sends the feedback information to the server, which then stores it in a database. The server then analyzes the stored feedback information and reflects it in the AI ​​engine. The input is the user's feedback, and the output is updated learning data for the AI ​​engine. Specifically, the device receives input form or voice feedback, sends it to the server, which analyzes it and reflects it in the database.

[0246] Step 5:

[0247] The server periodically collects new information and supplies it to the AI ​​engine. Each time new information is collected, the AI ​​engine keeps updating the knowledge base. This allows new participants to always have access to the latest information. The input is new information data, and the output is an updated knowledge base. Specifically, the server executes scheduled tasks, collects the latest information using APIs and scraping technology, adds it to the database, and the AI ​​engine learns from it and reflects it in the knowledge base.

[0248] Through this process, newcomers can acquire and learn information in real time, enabling them to efficiently master their work.By using a generative AI model to process prompts, the system is able to respond immediately to user questions.

[0249] 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.

[0250] This invention is an onboarding support system that combines an AI engine and an emotion engine to help newcomers learn their jobs quickly and efficiently. This system reduces the burden on accepting members and allows newcomers to start work with a sense of psychological safety.

[0251] Program processing

[0252] 1. New member registration

[0253] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0254] Device: Sends the entered profile information to the server.

[0255] Server: Registers the received profile information in a database.

[0256] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[0257] 2. Learning and updating information

[0258] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0259] Server: Collects general information such as industry standards and guidelines from online resources.

[0260] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0261] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[0262] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0263] 3. Providing support

[0264] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0265] Terminal: Sends the entered question to the server.

[0266] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0267] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0268] 4. Feedback and Improvement

[0269] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0270] Device: Sends feedback information to the server.

[0271] Server: Stores the received feedback information in a database.

[0272] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0273] 5. Emotion Recognition and Analysis

[0274] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[0275] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[0276] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[0277] 6. Use of Emotional Data

[0278] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[0279] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[0280] Specific examples

[0281] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[0282] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[0283] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[0284] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[0285] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[0286] 5. Emotion recognition: When Taro Tanaka types a question, the emotion engine detects "confusion" through his facial expression and sends that information to the server.

[0287] 6. Use of emotional data: Based on the emotional data of "confusion," the server instructs the AI ​​engine to provide assistance appropriate to Taro Tanaka's condition. The AI ​​engine generates assistance that explains the information in more detail so that Taro Tanaka can easily understand it, and sends the answer to his device.

[0288] In this way, by combining an AI engine and an emotion engine, the present invention provides an environment in which newcomers can efficiently learn their jobs, significantly reducing the burden on accepting members.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0292] Step 2:

[0293] Device: Sends the entered profile information to the server.

[0294] Step 3:

[0295] Server: Registers the received profile information in a database.

[0296] Step 4:

[0297] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[0298] Step 5:

[0299] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0300] Step 6:

[0301] Server: Collects general information such as industry standards and guidelines from online resources.

[0302] Step 7:

[0303] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0304] Step 8:

[0305] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[0306] Step 9:

[0307] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0308] Step 10:

[0309] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0310] Step 11:

[0311] Terminal: Sends the entered question to the server.

[0312] Step 12:

[0313] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0314] Step 13:

[0315] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0316] Step 14:

[0317] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0318] Step 15:

[0319] Device: Sends feedback information to the server.

[0320] Step 16:

[0321] Server: Stores the received feedback information in a database.

[0322] Step 17:

[0323] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0324] Step 18:

[0325] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[0326] Step 19:

[0327] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[0328] Step 20:

[0329] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[0330] Step 21:

[0331] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[0332] Step 22:

[0333] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[0334] Example 2

[0335] 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."

[0336] In modern companies, it is an important challenge for newcomers to quickly and efficiently learn about business processes and corporate culture. However, training and supporting newcomers requires a great deal of time and effort, which often places a burden on the team members. Providing appropriate information and psychological support is also important for newcomers to begin their work effectively, but providing these in a balanced manner can be difficult. Furthermore, in addition to providing standard information, newcomers also need support tailored to their emotions and circumstances. A system that can address this is needed.

[0337] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0338] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest information and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers, analyzing it, and reflecting it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and adjusting support content based on that data, and means for monitoring using the emotion engine and optimizing support content based on historical data. This allows newcomers to learn their jobs quickly and efficiently, reduces the burden on accepting members, and further increases the psychological safety of newcomers.

[0339] "Profile information" is individual attribute information such as the new participant's name, position, and responsibilities.

[0340] "Database" refers to an information management system for storing and managing received profile information.

[0341] "Internal documents" are documents such as procedures, policies, and product information created within a company.

[0342] "Industry standard knowledge" is information about widely recognized guidelines and best practices within a particular industry.

[0343] "Latest news" refers to the latest information on ongoing news, industry trends, product releases, and more.

[0344] An "AI engine" is a system that uses artificial intelligence technology to analyze collected information and build and update a knowledge base.

[0345] A "knowledge base" is a collection of information built by an AI engine that is used to provide answers and support to newcomers.

[0346] "Feedback" refers to the evaluation and opinions that new participants give about the answers and support provided.

[0347] The "emotion engine" is a technology that analyzes the emotions of new participants in real time and reflects the results of that analysis in the support provided.

[0348] "Monitoring" is the process of continuously observing the status of new participants and collecting and analyzing that data.

[0349] This invention is an onboarding system that helps new employees learn their jobs quickly and efficiently. This system functions through the cooperation of three entities: a server, a terminal, and a user. The specific hardware and software configurations and data processing methods are described below.

[0350] Components and technologies used

[0351] server

[0352] The server works with software modules including a database, AI engine, and emotion engine. This server is responsible for managing profile information and collecting, analyzing, and updating information. Specifically, it uses the following technologies:

[0353] Database system: MySQL, PostgreSQL, etc.

[0354] AI engines: generative AI models such as TensorFlow and PyTorch

[0355] Emotion engine: Facial recognition API (e.g., Microsoft Azure Face API), speech recognition system (e.g., Amazon Polly)

[0356] Terminal

[0357] Terminals are devices used by new participants and administrators, including smartphones, PCs, tablets, etc. Terminals communicate with the server using the following software:

[0358] Web browser: Google Chrome, Mozilla Firefox

[0359] Dedicated application: Custom app (compatible with iOS and Android)

[0360] User

[0361] Users are both new participants and administrators who handle them. Users use their devices to enter profile information, submit questions, and provide feedback.

[0362] Specific operation explanation

[0363] New Member Registration

[0364] User (Administrator): The HR department administrator enters the new employee's profile information (e.g., name, position, and responsibilities) from the terminal.

[0365] Terminal: The terminal sends the entered information to the server, which registers it in a database.

[0366] Learning and updating information

[0367] Server: The server calls APIs or uses web scraping techniques to collect internal documents, industry standard knowledge, and the latest information. For example, it uses the Google Scholar API to obtain industry standard knowledge.

[0368] AI engine: Analyzes collected information using natural language processing (NLP) technology to build and update the knowledge base.

[0369] Providing support

[0370] User (newcomer): The newcomer uses the terminal to input and submit a question. For example, they input a prompt such as, "When is the next sales meeting?"

[0371] Server: The server sends this question to the AI ​​engine, which generates an appropriate answer from its knowledge base and sends it back to the device.

[0372] Feedback and Improvements

[0373] User (newcomer): Enter and submit feedback on the answers received by newcomers. Provide specific feedback such as "The explanation was easy to understand."

[0374] Server: The server collects feedback information and reflects it in the AI ​​engine.

[0375] Emotion Recognition and Analysis

[0376] User (newcomer): When a user inputs a question from a device, the emotion engine performs real-time analysis using the camera and microphone.

[0377] Emotion Engine: The emotion engine analyzes the newcomer's facial expressions and tone of voice and sends the emotional data to the server.

[0378] Server: Based on the emotional data, the server instructs the AI ​​engine on the appropriate response.

[0379] Use of Emotional Data

[0380] Server: Reflects the analyzed emotional data in the knowledge base and adjusts and provides individual support content.

[0381] The emotion engine also continuously monitors the state of newcomers and uses that data along with feedback data.

[0382] Specific examples

[0383] Example 1: When a new employee is assigned to the sales department, if the new employee inputs the question, "What is the schedule for the next sales meeting?", the AI ​​engine will generate an answer from the knowledge base, such as, "The next sales meeting will be held next Monday at 10:00," and send it to the new employee's device. In addition, if the new employee's confused feelings are analyzed, the AI ​​engine will provide assistance by providing additional detailed information.

[0384] In this way, the present invention allows newcomers to learn the job quickly and effectively, reduces the burden on host team members, and provides psychological safety.

[0385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0386] Step 1:

[0387] The user (administrator) enters the profile information of the new participant into the terminal. The input items include name, position, and responsibilities. Once the input is complete, the user presses the send button.

[0388] Input: Name, position, and duties of new participant

[0389] Output: The entered profile information is sent from the device to the server.

[0390] Step 2:

[0391] The terminal transmits the profile information input by the user to the server using a secure communication protocol (for example, HTTPS).

[0392] Input: Profile information (name, job title, responsibilities)

[0393] Output: Profile information is sent to the server.

[0394] Step 3:

[0395] The server registers the received profile information in a database, checking for duplicates against existing records and creating new records.

[0396] Input: Profile information (name, job title, responsibilities)

[0397] Output: The new participant's information is registered in the database and a unique ID is generated.

[0398] Step 4:

[0399] The server instructs the AI ​​engine to learn information related to the new entrant based on the unique ID and profile information.

[0400] Input: Profile information (including unique ID)

[0401] Output: Learning instructions are sent to the AI ​​engine.

[0402] Step 5:

[0403] The server collects internal documents, industry standard knowledge, and up-to-date information from various sources, including data retrieval from internal systems, web scraping, and various APIs.

[0404] Inputs: Learning instructions, internal document management systems, online resources, APIs

[0405] Output: The collected information is stored on the server.

[0406] Step 6:

[0407] The AI ​​engine analyzes the collected information to build and update its internal knowledge base, using natural language processing techniques to extract important information and store it as structured data.

[0408] Input: Collected information (documents, guidelines, news, etc.)

[0409] Output: An updated knowledge base is generated.

[0410] Step 7:

[0411] The user (newcomer) uses a chat tool or a dedicated app to enter a question and press the send button. For example, they send a prompt such as, "What is the schedule for the next sales meeting?"

[0412] Input: Question (prompt)

[0413] Output: The question is sent from the terminal to the server.

[0414] Step 8:

[0415] The terminal transmits the entered question to the server.

[0416] Input: Question (prompt)

[0417] Output: The query data is sent to the server.

[0418] Step 9:

[0419] The server transfers the received question data to the AI ​​engine and instructs it to generate an answer.

[0420] Input: Question (prompt)

[0421] Output: Instructions are sent to the AI ​​engine for processing.

[0422] Step 10:

[0423] The AI ​​engine searches its knowledge base for an appropriate answer and generates it, for example, "The next sales meeting will be held next Monday at 10:00."

[0424] Input: Question, Knowledge Base

[0425] Output: The generated answer

[0426] Step 11:

[0427] The server sends the generated answer to the new participant's terminal.

[0428] Input: Generated answer

[0429] Output: The answer is displayed on the new participant's device.

[0430] Step 12:

[0431] The user (newcomer) enters feedback on the answers and support provided and presses the send button. Specifically, the user enters feedback such as "The explanation was easy to understand."

[0432] Input: Feedback

[0433] Output: Feedback data is sent from the device to the server.

[0434] Step 13:

[0435] The terminal transmits the feedback data to the server.

[0436] Input: Feedback

[0437] Output: Feedback data is sent to the server.

[0438] Step 14:

[0439] The server stores the received feedback data in a database and analyzes it, and the analysis results are reflected in the AI ​​engine's learning data.

[0440] Input: Feedback data

[0441] Output: Updated training data

[0442] Step 15:

[0443] Users (new participants) use the device's camera and microphone to input questions, and the emotion engine analyzes facial expressions and voice in real time.

[0444] Input: facial expression data, voice data

[0445] Output: Parsed emotion data

[0446] Step 16:

[0447] The emotion engine analyzes the emotions of new participants and sends the results to the server.

[0448] Input: facial expression data, voice data

[0449] Output: Analysis results (emotion data)

[0450] Step 17:

[0451] The server then instructs the AI ​​engine on the appropriate support content based on the received emotional data. For example, it provides a more detailed explanation to a newcomer who is feeling confused.

[0452] Input: Emotion data

[0453] Output: Support content instructions

[0454] Step 18:

[0455] The server performs continuous monitoring and provides optimal support based on past data.

[0456] Input: Newcomer history data, emotion data

[0457] Output: Optimized support content

[0458] (Application example 2)

[0459] 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."

[0460] There is a need for an onboarding system that helps new employees learn their jobs quickly and efficiently. However, existing systems lack the ability to reduce the psychological burden on new employees and provide support that takes their emotions into consideration. This has led to early employee turnover, delays in job acquisition, and increased burdens on the new employees.

[0461] 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.

[0462] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting information resources and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving inquiries from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers and analyzing it to reflect it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and supplying emotional data to the AI ​​engine, and means for adjusting support content based on the emotional data. This enables newcomers to learn their jobs efficiently and with less psychological burden.

[0463] "Newcomers" are employees or staff who join a company or organization and begin work.

[0464] "Profile information" refers to personal information such as name, job title, and job responsibilities, as well as information related to work.

[0465] A "database" is a data structure that organizes, stores, and easily searches and retrieves information.

[0466] "Information resources" refers to information sources such as internal company information, general information, and the latest information.

[0467] An "AI engine" is a system that uses artificial intelligence to analyze data, learn, and provide appropriate answers and support.

[0468] A "knowledge base" is a collection of information learned by an AI engine, and is a database that serves as the basis for generating answers to questions.

[0469] An "inquiry" is a question that a newcomer asks for information or support.

[0470] "Feedback" refers to the opinions and evaluations that new participants give about the answers and support provided.

[0471] "Emotional data" refers to emotional information obtained through new participants' facial expressions, text, etc.

[0472] "Support content" refers to the guidance and assistance provided to newcomers to help them learn their jobs efficiently.

[0473] This invention is an onboarding system that helps new employees quickly and efficiently learn their jobs. The system is implemented using specific hardware and software.

[0474] System configuration

[0475] The system includes the following components:

[0476] Server: The primary computing resource for managing the database and running the AI ​​engine and emotion engine.

[0477] Terminal (user): A device through which a user interacts with the system, such as a mobile terminal such as a smartphone or smart glasses, or a PC.

[0478] AI engine: A software component that analyzes and learns from collected information and builds and updates the knowledge base.

[0479] Emotion engine: A software component that analyzes user emotions in real time and adjusts support content.

[0480] Database: Data storage for saving new participants' profile information and feedback information.

[0481] System program processing

[0482] 1. New participant registration

[0483] Server: Receives profile information of new participants and registers them in the database. Profile information includes name, job title, responsibilities, etc.

[0484] Server: Based on the registered information, instructs the AI ​​engine to learn related information.

[0485] 2. Learning and updating information

[0486] Server: Collects internal information, general information, and the latest information and supplies it to the AI ​​engine.

[0487] AI engine: Analyzes collected information and builds and updates the knowledge base.

[0488] 3. Providing support

[0489] Terminal (user): The user inputs a question from the terminal. The question is about business.

[0490] Server: Passes the received question to the AI ​​engine.

[0491] AI engine: Generates appropriate answers from the knowledge base and sends them to the user's device via the server.

[0492] 4. Feedback and Improvement

[0493] Terminal (user): Enters feedback on the answers provided.

[0494] Server: Receives feedback information and stores it in a database.

[0495] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0496] 5. Emotion Recognition and Analysis

[0497] Terminal (user): When entering a question, the emotion engine analyzes the user's emotions in real time.

[0498] Emotion engine: Sends emotion data to the server.

[0499] Server: Based on the emotional data, the AI ​​engine instructs it to generate appropriate support content.

[0500] Hardware and software used

[0501] Hardware: Servers, smartphones, smart glasses, PCs

[0502] software:

[0503] emotion_recognition library: Used to analyze user emotions.

[0504] ai_engine library: Used to generate appropriate answers to questions from collected information.

[0505] database module: Used to store profile and feedback information.

[0506] Specific examples

[0507] When a new employee, "Mr. A," is assigned to a store, the store manager uses a smartphone app to enter Mr. A's profile information and sends it to the server. The server registers that information in a database and instructs the AI ​​engine to learn information related to Mr. A's position. When Mr. A asks, "Please tell me how to display this product," the AI ​​engine generates an appropriate answer from its knowledge base, replying, "This product is available in sizes S, M, and L." When Mr. A enters feedback, the server collects that information and reflects it in the AI ​​engine's learning data.

[0508] Example prompts for generative AI models

[0509] We are developing an assistant to support new employee onboarding at an apparel shop. This assistant uses an AI engine and an emotion engine to answer employees' questions and provide appropriate support depending on their emotions at the time. New employees ask questions through a smartphone app, and the AI ​​answers the questions, collects feedback, and learns. For example, if a question asks, "What sizes are available for this product?", the AI ​​engine will respond, "This product is available in sizes S, M, and L." Please tell us the specific implementation method for this assistant.

[0510] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0511] Step 1:

[0512] Input: New participant profile information (name, position, responsibilities, etc.)

[0513] Operation: The user (person in charge) enters profile information on a smartphone or PC.

[0514] Output: Profile information is sent to the server

[0515] Server operation: Register profile information in the database

[0516] Step 2:

[0517] Input: Registered profile information

[0518] Operation: The server instructs the AI ​​engine to learn relevant information based on the profile information.

[0519] Output: Instructions for the AI ​​engine to start learning

[0520] How the AI ​​engine works: Learns internal information, general information, and industry trend information to build a knowledge base

[0521] Step 3:

[0522] Input: Internal information, industry standard guidelines, latest news

[0523] How it works: The server continuously collects this information and provides it to the AI ​​engine.

[0524] Output: Updated information is fed to the AI ​​engine

[0525] How the AI ​​engine works: Analyzes the information provided and updates its knowledge base

[0526] Step 4:

[0527] Input: Inquiry from new participant

[0528] How it works: A user (newcomer) enters a question on a smartphone or PC.

[0529] Output: The query is sent to the server

[0530] Server operation: Passes the query to the AI ​​engine

[0531] Step 5:

[0532] Input: Inquiry details

[0533] How it works: The AI ​​engine searches the knowledge base and generates the appropriate answer

[0534] Output: The generated answer is returned to the server

[0535] Server action: Send the answer to the user's device

[0536] Step 6:

[0537] Input: Feedback from newcomers

[0538] Action: User (newcomer) enters feedback on provided answer

[0539] Output: Feedback is sent to the server

[0540] Server operation: The feedback information is saved in a database and reflected in the AI ​​engine's learning data.

[0541] Step 7:

[0542] Input: facial expressions and text when new participants type in their questions

[0543] How it works: The emotion engine acquires emotion data through the device's camera or text input.

[0544] Output: Emotion data is sent to the server

[0545] How the emotion engine works: Analyzes emotions from facial expressions and text in real time and sends the results to the server.

[0546] Step 8:

[0547] Input: Emotion data

[0548] How it works: The server instructs the AI ​​engine to generate appropriate support content based on the emotion data.

[0549] Output: Adjusted support content is generated

[0550] How the AI ​​engine works: Adjusts assistance content based on emotional data and generates the best answer for the user

[0551] 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.

[0552] 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.

[0553] 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.

[0554] [Second embodiment]

[0555] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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."

[0567] The present invention is an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, a terminal, and a user (newcomer) as follows.

[0568] Program processing

[0569] 1. New member registration

[0570] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0571] Terminal: Sends the entered information to the server.

[0572] Server: Registers the received profile information in a database and instructs the AI ​​engine to learn appropriate information depending on the new participant's role.

[0573] 2. Learning and updating information

[0574] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0575] Server: Collects general information such as industry standards and guidelines from online resources.

[0576] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0577] AI engine: Analyzes collected information and builds an internal knowledge base.

[0578] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0579] 3. Providing support

[0580] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0581] Terminal: Sends the entered question to the server.

[0582] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0583] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0584] 4. Feedback and Improvement

[0585] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0586] Device: Sends feedback information to the server.

[0587] Server: Stores the received feedback information in a database.

[0588] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0589] Specific examples

[0590] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[0591] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[0592] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[0593] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[0594] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[0595] In this way, the present invention provides an environment in which newcomers can efficiently learn the business, and significantly reduces the burden on the accepting members.

[0596] The processing flow will be explained below.

[0597] Step 1:

[0598] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0599] Step 2:

[0600] Device: Sends the entered profile information to the server.

[0601] Step 3:

[0602] Server: Registers the received profile information in a database.

[0603] Step 4:

[0604] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[0605] Step 5:

[0606] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0607] Step 6:

[0608] Server: Collects general information such as industry standards and guidelines from online resources.

[0609] Step 7:

[0610] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0611] Step 8:

[0612] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[0613] Step 9:

[0614] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0615] Step 10:

[0616] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0617] Step 11:

[0618] Terminal: Sends the entered question to the server.

[0619] Step 12:

[0620] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0621] Step 13:

[0622] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0623] Step 14:

[0624] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0625] Step 15:

[0626] Device: Sends feedback information to the server.

[0627] Step 16:

[0628] Server: Stores the received feedback information in a database.

[0629] Step 17:

[0630] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0631] Example 1

[0632] 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."

[0633] It is important for new entrants to companies and organizations to quickly and efficiently learn their work. However, the onboarding process for new entrants is often time-consuming and laborious, placing a burden on existing members as well. Furthermore, if the information required for onboarding is scattered, new entrants have difficulty obtaining the information they need. There is a need for a system that can solve these issues and enable new entrants to begin work smoothly.

[0634] 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.

[0635] In this invention, the server includes means for receiving profile information of new entrants and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest industry trend information and supplying them to an AI engine, means for analyzing the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from new entrants and generating and sending answers from the knowledge base, and means for receiving feedback from new entrants, analyzing it, and reflecting it in the AI ​​engine. This enables new entrants to quickly obtain the information they need and achieves effective onboarding.

[0636] "Newcomers" refer to new members who join a company or organization.

[0637] "Profile Information" refers to personal information about a Newcomer, such as their name, job title, and responsibilities.

[0638] "Database" refers to a system for efficiently storing, managing, and accessing information.

[0639] "Internal documents" refers to information such as documents, policies, procedures, etc. used within a company.

[0640] "Industry standard knowledge" refers to knowledge of generally recognized standards and guidelines in a particular industry.

[0641] "Industry trend information" refers to information about the latest trends and news within an industry.

[0642] "AI engine" refers to a system that uses artificial intelligence to analyze information and build and update a knowledge base.

[0643] A "knowledge base" refers to a collection of information collected and organized by an AI engine.

[0644] "Questions" refer to any doubts or clarifications that newcomers have during the onboarding process.

[0645] "Answer" refers to the answer to a newcomer's question that the AI ​​engine generates based on its knowledge base.

[0646] "Feedback" refers to the evaluation and opinions that newcomers give of the answers and support provided.

[0647] "Server" refers to a central computer for processing information and communicating with multiple terminals.

[0648] "Terminals" refer to devices such as PCs and smartphones used by new entrants and administrators.

[0649] MODE FOR CARRYING OUT THE INVENTION

[0650] The present invention provides an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, terminals, and users.

[0651] The system consists of the following main components:

[0652] 1. Server: A central computer that processes information and accesses databases and AI engines.

[0653] 2. Terminals: Devices used by newcomers and administrators, including PCs and smartphones.

[0654] 3. Database: A system for storing profile and feedback information.

[0655] 4. AI Engine: Artificial intelligence for analyzing information, building and updating knowledge bases, and generating answers to questions.

[0656] Specific hardware and software

[0657] Server: Uses a Linux-based operating system, a database management system such as MySQL, and a programming language such as Python or Java.

[0658] Device: PC or smartphone with operating system such as Windows, macOS, Android, iOS.

[0659] AI engine: Machine learning frameworks such as TensorFlow and PyTorch are used to implement NLP models, and GPT-3 and other models are used as generative AI models.

[0660] System processing overview

[0661] The device (user) enters the profile information of new entrants (such as name, job title, and responsibilities) using a dedicated input form. The entered information is sent from the device to the server, which stores the received information in a database. The server then collects internal company documents, industry standard knowledge, and the latest industry trend information, and supplies this information to the AI ​​engine.

[0662] The AI ​​engine analyzes the collected information and builds and updates its internal knowledge base. When a newcomer enters a question using a dedicated app or web chat tool, the device sends the question to the server. The server passes the question to the AI ​​engine, which generates an answer from the knowledge base. The answer is then sent to the user's device via the server.

[0663] Furthermore, when newcomers provide feedback, that feedback is sent from the device to the server, which stores the information in a database. The saved feedback is analyzed and reflected in the AI ​​engine's learning data, allowing it to generate even more accurate answers.

[0664] Specific examples

[0665] Example 1: New employee "A" is assigned to the sales department

[0666] 1. Profile registration: A human resources officer uses a PC to enter A's name, job title, and job duties (sales) into a dedicated input form and submits it. The server saves this information in a new employee information table and instructs the AI ​​engine on learning tasks appropriate to A's role.

[0667] 2. Information learning: The server retrieves sales process and product information from the company's network drive and collects industry-standard guidelines from online resources via API. The AI ​​engine analyzes this information and builds a knowledge base.

[0668] 3. Providing support: Person A enters a question into a dedicated app on their smartphone, such as "What is the schedule for the next sales meeting?", and sends it. The server passes the question to the AI ​​engine, which generates an answer, "The next sales meeting will be held at 10:00 next Monday," and sends it to Person A's smartphone.

[0669] 4. Feedback and Improvement: User A enters and sends feedback on their smartphone, saying, "The explanation was easy to understand." The server saves the feedback in a feedback information table and reflects it in the AI ​​engine's learning data.

[0670] Prompt Sentence Examples

[0671] Prompt for newcomers to register their profile: "Please register your profile as a newcomer. Your name is A, your job title is sales, and your job is product sales."

[0672] Information learning prompt: "Gather internal sales information and industry-standard sales guidelines."

[0673] Prompt for support question: "When is the next sales meeting?"

[0674] Feedback prompt: "The support instructions were easy to understand."

[0675] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0676] Step 1: Complete and submit your newcomer profile information

[0677] Terminal (user): The user uses a dedicated input form to enter profile information such as the new entrant's name, job title, and responsibilities (input: profile information).

[0678] Terminal: Send the entered profile information to the server as an HTTP POST request (Output: Send profile information).

[0679] Step 2: Receive profile information and register in database

[0680] Server: Receives the transmitted profile information (input: profile information).

[0681] Server: Save the profile information to the database using the INSERT statement (data processing: save information, output: register to database).

[0682] Step 3: Information Collection Instructions

[0683] Server: Based on the profile information, instructs the AI ​​engine on information gathering tasks that correspond to the new entrant's role (input: profile information, output: information gathering task).

[0684] Step 4: Gathering internal information

[0685] Server: Retrieves internal documents, policies, procedures, etc. through the company's internal network drive or internal system API (input: information gathering tasks, data processing: information gathering, output: internal information).

[0686] Server: Analyzes the acquired internal information using a text analysis engine and organizes it by category (data processing: text analysis, output: organized information).

[0687] Step 5: Gathering external resources

[0688] Server: Collects industry standard knowledge and guidelines from online resources via API (input: information gathering task, data processing: information gathering, output: industry standard knowledge).

[0689] Server: Obtains the latest industry trend information from various RSS feeds and news feeds and extracts the necessary information (data processing: information extraction, output: industry trend information).

[0690] Step 6: Build and update your knowledge base

[0691] AI engine: The NLP engine analyzes collected internal information, industry standard knowledge, and industry trend information and converts it into meaningful structured data (input: collected information, data processing: NLP analysis, output: structured data).

[0692] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base (data processing: periodic updates, output: updated knowledge base).

[0693] Step 7: Enter and submit newcomer questions

[0694] Terminal (user): Newcomers use a dedicated app or web chat tool to input questions (input: question).

[0695] Terminal: Sends the entered question to the server via an HTTP POST request (Output: Send question).

[0696] Step 8: Parsing the question and generating an answer

[0697] Server: Receives questions from users and requests the AI ​​engine to analyze them (input: question).

[0698] AI engine: Analyzes the question using a natural language processing engine, searches for relevant information from a knowledge base, and generates an appropriate answer (data calculation: question analysis, data processing: answer generation, output: generated answer).

[0699] Server: Sends the generated answer to the user's terminal (output: Send answer).

[0700] Step 9: Enter and submit your feedback

[0701] Terminal (user): The newcomer inputs feedback on the answers and support provided (input:feedback).

[0702] Terminal: Send the entered feedback to the server via an HTTP POST request (Output: Send Feedback).

[0703] Step 10: Store and analyze feedback

[0704] Server: Stores the received feedback information in a database (input: feedback, data processing: information storage, output: storage in database).

[0705] Server: Passes the saved feedback to the AI ​​engine and analyzes it using a machine learning algorithm (data calculation: feedback analysis, output: analysis results).

[0706] AI engine: Reflects the analysis results in the learning data and performs model tuning to improve answer accuracy (data processing: model tuning, output: improved answer accuracy).

[0707] (Application example 1)

[0708] 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."

[0709] New employees need an onboarding support system to quickly and efficiently learn their jobs. In particular, advanced support is needed to help them adapt to the work environment and work procedures at factories and other work sites. There is also a demand for devices that display work procedures and safety measures in real time, along with related AI-based support.

[0710] 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.

[0711] In this invention, the server includes a means for receiving new employee profile information and registering it in a database; a means for collecting internal company information, general information, and the latest information and providing it to an AI engine; and a means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base. This allows new employees to learn work procedures and safety measures in real time and efficiently master their work. Furthermore, the use of a head-mounted display allows for immediate support on-site. Furthermore, prompt sentence processing utilizing a generative AI model enables real-time question resolution.

[0712] "Newcomers" refers to employees or staff who are joining an organization or project for the first time.

[0713] "Profile Information" refers to data about an individual, such as name, job title, and job responsibilities.

[0714] "Database" refers to a system for systematically storing user profile information and other related data.

[0715] "Internal information" refers to documents and data created and held within a company or organization.

[0716] "General information" refers to information that is commonly used outside the company, such as industry standards and guidelines.

[0717] "Latest information" refers to news and trend information that is updated in real time.

[0718] "AI engine" refers to artificial intelligence technology that analyzes collected data and builds and updates a knowledge base.

[0719] A "knowledge base" refers to a collection of data that systematically organizes and provides information learned by an AI engine.

[0720] "Means for receiving questions and generating answers" refers to the process of receiving questions from new participants and generating appropriate answers from the knowledge base.

[0721] "Feedback" refers to the opinions and evaluations that new participants enter regarding the support provided.

[0722] "Analyzing" refers to processing the feedback information and using it to improve the AI ​​engine.

[0723] A "head-mounted display" refers to a device that can display visual information when worn.

[0724] "Real-time" refers to a time frame in which processing and response occur instantaneously.

[0725] A "generative AI model" refers to a model that uses artificial intelligence to generate text and data.

[0726] A "prompt" refers to an instruction or question input to a generative AI model.

[0727] To implement this invention, it is necessary to build a system that is mainly comprised of a server, terminals, and users (new participants). A specific embodiment of this system is shown below.

[0728] System configuration

[0729] Server: Mainly responsible for the database, AI engine, and information collection and update functions.

[0730] Terminal: Mainly used to input information for new participants, receive questions, display answers, and collect feedback. A head-mounted display (HMD) is worn to display real-time work instructions.

[0731] User: The new participant operates the device (HMD) to acquire and learn the necessary information.

[0732] Hardware and software used

[0733] Hardware:

[0734] Head-mounted displays (HMDs): Examples include Oculus Rift and HTC Vive.

[0735] Server: A powerful cloud server (e.g., an AWS EC2 instance).

[0736] software:

[0737] Database management system (e.g. MySQL)

[0738] AI engines (e.g., Google Cloud AI, IBM Watson)

[0739] API communication libraries (e.g., Python's Requests library)

[0740] Explanation of each process

[0741] 1. New participant registration:

[0742] New participants' profile information (such as name, position, and responsibilities) is entered on a terminal and sent to the server, which then registers it in the database.

[0743] 2. Information Collection and Update:

[0744] The server collects internal documents, industry standard knowledge, the latest news, etc. and supplies them to the AI ​​engine, which then learns from this information and builds and updates its knowledge base.

[0745] 3. Real-time support:

[0746] When a new participant wears an HMD and sends a question from their device while working, the server passes the question to the AI ​​engine, which generates an appropriate answer from the knowledge base. For example, if the question is, "When is the next maintenance work scheduled?", the AI ​​engine generates the answer, "The next maintenance is scheduled for tomorrow at 2 p.m." The generative AI model processes the prompt sentence and resolves the question in real time.

[0747] 4. Feedback and Improvement:

[0748] New participants enter feedback on the support provided through their devices and send it to the server, which then stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[0749] Specific examples

[0750] Example: New factory worker

[0751] When a new employee is assigned to a factory production line, he or she puts on the HMD and asks, "When is the next maintenance work scheduled?" The server uses an AI engine to instantly respond. For example, it might generate a response such as, "The next maintenance work is scheduled for 2:00 PM tomorrow." Actual work procedures and safety measures are also displayed in real time through the HMD.

[0752] Example prompt for a generative AI model:

[0753] "A new employee has been assigned to work as a production line operator in a factory. He puts on the HMD and asks, 'When is the next maintenance task scheduled?' Please generate an appropriate answer."

[0754] In this way, the system provides an environment in which newcomers can quickly and efficiently learn their jobs, significantly reducing the burden on the accepting party.

[0755] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0756] Step 1:

[0757] A new participant enters their profile information (name, job title, and responsibilities) through a terminal. The terminal sends this profile information to the server, which then registers the received profile information in a database. The input is the new participant's profile information, and the output is the registered profile information in the database. Specifically, the terminal receives the form input and sends it to the server in JSON format as a POST request, which the server receives and saves in the database.

[0758] Step 2:

[0759] The server collects internal information, general information, and the latest information. The server uses APIs to obtain information from internal databases, industry-standard knowledge bases, and news feeds. The obtained information is supplied to the AI ​​engine, which learns from this information and builds and updates its internal knowledge base. The input is the collected information, and the output is the updated knowledge base. Specifically, the server sends requests to various API endpoints, analyzes and processes the received data, and passes it to the AI ​​engine.

[0760] Step 3:

[0761] The new participant wears a head-mounted display and sends questions from their device while working. The device transmits the question to the server, which passes it on to the AI ​​engine. The AI ​​engine references the knowledge base to generate an appropriate answer and returns it to the server. The server sends the generated answer to the new participant's device. The input is the user's question, and the output is the answer from the AI ​​engine. Specifically, a question entered in voice or text format is sent to the server as an API request, and the AI ​​engine processes it and returns an answer.

[0762] Step 4:

[0763] New participants input feedback on the answers and support provided. The device sends the feedback information to the server, which then stores it in a database. The server then analyzes the stored feedback information and reflects it in the AI ​​engine. The input is the user's feedback, and the output is updated learning data for the AI ​​engine. Specifically, the device receives input form or voice feedback, sends it to the server, which analyzes it and reflects it in the database.

[0764] Step 5:

[0765] The server periodically collects new information and supplies it to the AI ​​engine. Each time new information is collected, the AI ​​engine keeps updating the knowledge base. This allows new participants to always have access to the latest information. The input is new information data, and the output is an updated knowledge base. Specifically, the server executes scheduled tasks, collects the latest information using APIs and scraping technology, adds it to the database, and the AI ​​engine learns from it and reflects it in the knowledge base.

[0766] Through this process, newcomers can acquire and learn information in real time, enabling them to efficiently master their work.By using a generative AI model to process prompts, the system is able to respond immediately to user questions.

[0767] 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.

[0768] This invention is an onboarding support system that combines an AI engine and an emotion engine to help newcomers learn their jobs quickly and efficiently. This system reduces the burden on accepting members and allows newcomers to start work with a sense of psychological safety.

[0769] Program processing

[0770] 1. New member registration

[0771] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0772] Device: Sends the entered profile information to the server.

[0773] Server: Registers the received profile information in a database.

[0774] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[0775] 2. Learning and updating information

[0776] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0777] Server: Collects general information such as industry standards and guidelines from online resources.

[0778] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0779] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[0780] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0781] 3. Providing support

[0782] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0783] Terminal: Sends the entered question to the server.

[0784] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0785] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0786] 4. Feedback and Improvement

[0787] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0788] Device: Sends feedback information to the server.

[0789] Server: Stores the received feedback information in a database.

[0790] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0791] 5. Emotion Recognition and Analysis

[0792] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[0793] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[0794] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[0795] 6. Use of Emotional Data

[0796] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[0797] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[0798] Specific examples

[0799] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[0800] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[0801] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[0802] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[0803] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[0804] 5. Emotion recognition: When Taro Tanaka types a question, the emotion engine detects "confusion" through his facial expression and sends that information to the server.

[0805] 6. Use of emotional data: Based on the emotional data of "confusion," the server instructs the AI ​​engine to provide assistance appropriate to Taro Tanaka's condition. The AI ​​engine generates assistance that explains the information in more detail so that Taro Tanaka can easily understand it, and sends the answer to his device.

[0806] In this way, by combining an AI engine and an emotion engine, the present invention provides an environment in which newcomers can efficiently learn their jobs, significantly reducing the burden on accepting members.

[0807] The processing flow will be explained below.

[0808] Step 1:

[0809] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[0810] Step 2:

[0811] Device: Sends the entered profile information to the server.

[0812] Step 3:

[0813] Server: Registers the received profile information in a database.

[0814] Step 4:

[0815] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[0816] Step 5:

[0817] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[0818] Step 6:

[0819] Server: Collects general information such as industry standards and guidelines from online resources.

[0820] Step 7:

[0821] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[0822] Step 8:

[0823] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[0824] Step 9:

[0825] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[0826] Step 10:

[0827] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[0828] Step 11:

[0829] Terminal: Sends the entered question to the server.

[0830] Step 12:

[0831] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[0832] Step 13:

[0833] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[0834] Step 14:

[0835] Terminal (user): Newcomers enter feedback on the answers and support provided.

[0836] Step 15:

[0837] Device: Sends feedback information to the server.

[0838] Step 16:

[0839] Server: Stores the received feedback information in a database.

[0840] Step 17:

[0841] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[0842] Step 18:

[0843] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[0844] Step 19:

[0845] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[0846] Step 20:

[0847] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[0848] Step 21:

[0849] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[0850] Step 22:

[0851] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[0852] Example 2

[0853] 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."

[0854] In modern companies, it is an important challenge for newcomers to quickly and efficiently learn about business processes and corporate culture. However, training and supporting newcomers requires a great deal of time and effort, which often places a burden on the team members. Providing appropriate information and psychological support is also important for newcomers to begin their work effectively, but providing these in a balanced manner can be difficult. Furthermore, in addition to providing standard information, newcomers also need support tailored to their emotions and circumstances. A system that can address this is needed.

[0855] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0856] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest information and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers, analyzing it, and reflecting it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and adjusting support content based on that data, and means for monitoring using the emotion engine and optimizing support content based on historical data. This allows newcomers to learn their jobs quickly and efficiently, reduces the burden on accepting members, and further increases the psychological safety of newcomers.

[0857] "Profile information" is individual attribute information such as the new participant's name, position, and responsibilities.

[0858] "Database" refers to an information management system for storing and managing received profile information.

[0859] "Internal documents" are documents such as procedures, policies, and product information created within a company.

[0860] "Industry standard knowledge" is information about widely recognized guidelines and best practices within a particular industry.

[0861] "Latest news" refers to the latest information on ongoing news, industry trends, product releases, and more.

[0862] An "AI engine" is a system that uses artificial intelligence technology to analyze collected information and build and update a knowledge base.

[0863] A "knowledge base" is a collection of information built by an AI engine that is used to provide answers and support to newcomers.

[0864] "Feedback" refers to the evaluation and opinions that new participants give about the answers and support provided.

[0865] The "emotion engine" is a technology that analyzes the emotions of new participants in real time and reflects the results of that analysis in the support provided.

[0866] "Monitoring" is the process of continuously observing the status of new participants and collecting and analyzing that data.

[0867] This invention is an onboarding system that helps new employees learn their jobs quickly and efficiently. This system functions through the cooperation of three entities: a server, a terminal, and a user. The specific hardware and software configurations and data processing methods are described below.

[0868] Components and technologies used

[0869] server

[0870] The server works with software modules including a database, AI engine, and emotion engine. This server is responsible for managing profile information and collecting, analyzing, and updating information. Specifically, it uses the following technologies:

[0871] Database system: MySQL, PostgreSQL, etc.

[0872] AI engines: generative AI models such as TensorFlow and PyTorch

[0873] Emotion engine: Facial recognition API (e.g., Microsoft Azure Face API), speech recognition system (e.g., Amazon Polly)

[0874] Terminal

[0875] Terminals are devices used by new participants and administrators, including smartphones, PCs, tablets, etc. Terminals communicate with the server using the following software:

[0876] Web browser: Google Chrome, Mozilla Firefox

[0877] Dedicated application: Custom app (compatible with iOS and Android)

[0878] User

[0879] Users are both new participants and administrators who handle them. Users use their devices to enter profile information, submit questions, and provide feedback.

[0880] Specific operation explanation

[0881] New Member Registration

[0882] User (Administrator): The HR department administrator enters the new employee's profile information (e.g., name, position, and responsibilities) from the terminal.

[0883] Terminal: The terminal sends the entered information to the server, which registers it in a database.

[0884] Learning and updating information

[0885] Server: The server calls APIs or uses web scraping techniques to collect internal documents, industry standard knowledge, and the latest information. For example, it uses the Google Scholar API to obtain industry standard knowledge.

[0886] AI engine: Analyzes collected information using natural language processing (NLP) technology to build and update the knowledge base.

[0887] Providing support

[0888] User (newcomer): The newcomer uses the terminal to input and submit a question. For example, they input a prompt such as, "When is the next sales meeting?"

[0889] Server: The server sends this question to the AI ​​engine, which generates an appropriate answer from its knowledge base and sends it back to the device.

[0890] Feedback and Improvements

[0891] User (newcomer): Enter and submit feedback on the answers received by newcomers. Provide specific feedback such as "The explanation was easy to understand."

[0892] Server: The server collects feedback information and reflects it in the AI ​​engine.

[0893] Emotion Recognition and Analysis

[0894] User (newcomer): When a user inputs a question from a device, the emotion engine performs real-time analysis using the camera and microphone.

[0895] Emotion Engine: The emotion engine analyzes the newcomer's facial expressions and tone of voice and sends the emotional data to the server.

[0896] Server: Based on the emotional data, the server instructs the AI ​​engine on the appropriate response.

[0897] Use of Emotional Data

[0898] Server: Reflects the analyzed emotional data in the knowledge base and adjusts and provides individual support content.

[0899] The emotion engine also continuously monitors the state of newcomers and uses that data along with feedback data.

[0900] Specific examples

[0901] Example 1: When a new employee is assigned to the sales department, if the new employee inputs the question, "What is the schedule for the next sales meeting?", the AI ​​engine will generate an answer from the knowledge base, such as, "The next sales meeting will be held next Monday at 10:00," and send it to the new employee's device. In addition, if the new employee's confused feelings are analyzed, the AI ​​engine will provide assistance by providing additional detailed information.

[0902] In this way, the present invention allows newcomers to learn the job quickly and effectively, reduces the burden on host team members, and provides psychological safety.

[0903] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0904] Step 1:

[0905] The user (administrator) enters the profile information of the new participant into the terminal. The input items include name, position, and responsibilities. Once the input is complete, the user presses the send button.

[0906] Input: Name, position, and duties of new participant

[0907] Output: The entered profile information is sent from the device to the server.

[0908] Step 2:

[0909] The terminal transmits the profile information input by the user to the server using a secure communication protocol (for example, HTTPS).

[0910] Input: Profile information (name, job title, responsibilities)

[0911] Output: Profile information is sent to the server.

[0912] Step 3:

[0913] The server registers the received profile information in a database, checking for duplicates against existing records and creating new records.

[0914] Input: Profile information (name, job title, responsibilities)

[0915] Output: The new participant's information is registered in the database and a unique ID is generated.

[0916] Step 4:

[0917] The server instructs the AI ​​engine to learn information related to the new entrant based on the unique ID and profile information.

[0918] Input: Profile information (including unique ID)

[0919] Output: Learning instructions are sent to the AI ​​engine.

[0920] Step 5:

[0921] The server collects internal documents, industry standard knowledge, and up-to-date information from various sources, including data retrieval from internal systems, web scraping, and various APIs.

[0922] Inputs: Learning instructions, internal document management systems, online resources, APIs

[0923] Output: The collected information is stored on the server.

[0924] Step 6:

[0925] The AI ​​engine analyzes the collected information to build and update its internal knowledge base, using natural language processing techniques to extract important information and store it as structured data.

[0926] Input: Collected information (documents, guidelines, news, etc.)

[0927] Output: An updated knowledge base is generated.

[0928] Step 7:

[0929] The user (newcomer) uses a chat tool or a dedicated app to enter a question and press the send button. For example, they send a prompt such as, "What is the schedule for the next sales meeting?"

[0930] Input: Question (prompt)

[0931] Output: The question is sent from the terminal to the server.

[0932] Step 8:

[0933] The terminal transmits the entered question to the server.

[0934] Input: Question (prompt)

[0935] Output: The query data is sent to the server.

[0936] Step 9:

[0937] The server transfers the received question data to the AI ​​engine and instructs it to generate an answer.

[0938] Input: Question (prompt)

[0939] Output: Instructions are sent to the AI ​​engine for processing.

[0940] Step 10:

[0941] The AI ​​engine searches its knowledge base for an appropriate answer and generates it, for example, "The next sales meeting will be held next Monday at 10:00."

[0942] Input: Question, Knowledge Base

[0943] Output: The generated answer

[0944] Step 11:

[0945] The server sends the generated answer to the new participant's terminal.

[0946] Input: Generated answer

[0947] Output: The answer is displayed on the new participant's device.

[0948] Step 12:

[0949] The user (newcomer) enters feedback on the answers and support provided and presses the send button. Specifically, the user enters feedback such as "The explanation was easy to understand."

[0950] Input: Feedback

[0951] Output: Feedback data is sent from the device to the server.

[0952] Step 13:

[0953] The terminal transmits the feedback data to the server.

[0954] Input: Feedback

[0955] Output: Feedback data is sent to the server.

[0956] Step 14:

[0957] The server stores the received feedback data in a database and analyzes it, and the analysis results are reflected in the AI ​​engine's learning data.

[0958] Input: Feedback data

[0959] Output: Updated training data

[0960] Step 15:

[0961] Users (new participants) use the device's camera and microphone to input questions, and the emotion engine analyzes facial expressions and voice in real time.

[0962] Input: facial expression data, voice data

[0963] Output: Parsed emotion data

[0964] Step 16:

[0965] The emotion engine analyzes the emotions of new participants and sends the results to the server.

[0966] Input: facial expression data, voice data

[0967] Output: Analysis results (emotion data)

[0968] Step 17:

[0969] The server then instructs the AI ​​engine on the appropriate support content based on the received emotional data. For example, it provides a more detailed explanation to a newcomer who is feeling confused.

[0970] Input: Emotion data

[0971] Output: Support content instructions

[0972] Step 18:

[0973] The server performs continuous monitoring and provides optimal support based on past data.

[0974] Input: Newcomer history data, emotion data

[0975] Output: Optimized support content

[0976] (Application example 2)

[0977] 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."

[0978] There is a need for an onboarding system that helps new employees learn their jobs quickly and efficiently. However, existing systems lack the ability to reduce the psychological burden on new employees and provide support that takes their emotions into consideration. This has led to early employee turnover, delays in job acquisition, and increased burdens on the new employees.

[0979] 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.

[0980] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting information resources and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving inquiries from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers and analyzing it to reflect it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and supplying emotional data to the AI ​​engine, and means for adjusting support content based on the emotional data. This enables newcomers to learn their jobs efficiently and with less psychological burden.

[0981] "Newcomers" are employees or staff who join a company or organization and begin work.

[0982] "Profile information" refers to personal information such as name, job title, and job responsibilities, as well as information related to work.

[0983] A "database" is a data structure that organizes, stores, and easily searches and retrieves information.

[0984] "Information resources" refers to information sources such as internal company information, general information, and the latest information.

[0985] An "AI engine" is a system that uses artificial intelligence to analyze data, learn, and provide appropriate answers and support.

[0986] A "knowledge base" is a collection of information learned by an AI engine, and is a database that serves as the basis for generating answers to questions.

[0987] An "inquiry" is a question that a newcomer asks for information or support.

[0988] "Feedback" refers to the opinions and evaluations that new participants give about the answers and support provided.

[0989] "Emotional data" refers to emotional information obtained through new participants' facial expressions, text, etc.

[0990] "Support content" refers to the guidance and assistance provided to newcomers to help them learn their jobs efficiently.

[0991] This invention is an onboarding system that helps new employees quickly and efficiently learn their jobs. The system is implemented using specific hardware and software.

[0992] System configuration

[0993] The system includes the following components:

[0994] Server: The primary computing resource for managing the database and running the AI ​​engine and emotion engine.

[0995] Terminal (user): A device through which a user interacts with the system, such as a mobile terminal such as a smartphone or smart glasses, or a PC.

[0996] AI engine: A software component that analyzes and learns from collected information and builds and updates the knowledge base.

[0997] Emotion engine: A software component that analyzes user emotions in real time and adjusts support content.

[0998] Database: Data storage for saving new participants' profile information and feedback information.

[0999] System program processing

[1000] 1. New participant registration

[1001] Server: Receives profile information of new participants and registers them in the database. Profile information includes name, job title, responsibilities, etc.

[1002] Server: Based on the registered information, instructs the AI ​​engine to learn related information.

[1003] 2. Learning and updating information

[1004] Server: Collects internal information, general information, and the latest information and supplies it to the AI ​​engine.

[1005] AI engine: Analyzes collected information and builds and updates the knowledge base.

[1006] 3. Providing support

[1007] Terminal (user): The user inputs a question from the terminal. The question is about business.

[1008] Server: Passes the received question to the AI ​​engine.

[1009] AI engine: Generates appropriate answers from the knowledge base and sends them to the user's device via the server.

[1010] 4. Feedback and Improvement

[1011] Terminal (user): Enters feedback on the answers provided.

[1012] Server: Receives feedback information and stores it in a database.

[1013] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1014] 5. Emotion Recognition and Analysis

[1015] Terminal (user): When entering a question, the emotion engine analyzes the user's emotions in real time.

[1016] Emotion engine: Sends emotion data to the server.

[1017] Server: Based on the emotional data, the AI ​​engine instructs it to generate appropriate support content.

[1018] Hardware and software used

[1019] Hardware: Servers, smartphones, smart glasses, PCs

[1020] software:

[1021] emotion_recognition library: Used to analyze user emotions.

[1022] ai_engine library: Used to generate appropriate answers to questions from collected information.

[1023] database module: Used to store profile and feedback information.

[1024] Specific examples

[1025] When a new employee, "Mr. A," is assigned to a store, the store manager uses a smartphone app to enter Mr. A's profile information and sends it to the server. The server registers that information in a database and instructs the AI ​​engine to learn information related to Mr. A's position. When Mr. A asks, "Please tell me how to display this product," the AI ​​engine generates an appropriate answer from its knowledge base, replying, "This product is available in sizes S, M, and L." When Mr. A enters feedback, the server collects that information and reflects it in the AI ​​engine's learning data.

[1026] Example prompts for generative AI models

[1027] We are developing an assistant to support new employee onboarding at an apparel shop. This assistant uses an AI engine and an emotion engine to answer employees' questions and provide appropriate support depending on their emotions at the time. New employees ask questions through a smartphone app, and the AI ​​answers the questions, collects feedback, and learns. For example, if a question asks, "What sizes are available for this product?", the AI ​​engine will respond, "This product is available in sizes S, M, and L." Please tell us the specific implementation method for this assistant.

[1028] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1029] Step 1:

[1030] Input: New participant profile information (name, position, responsibilities, etc.)

[1031] Operation: The user (person in charge) enters profile information on a smartphone or PC.

[1032] Output: Profile information is sent to the server

[1033] Server operation: Register profile information in the database

[1034] Step 2:

[1035] Input: Registered profile information

[1036] Operation: The server instructs the AI ​​engine to learn relevant information based on the profile information.

[1037] Output: Instructions for the AI ​​engine to start learning

[1038] How the AI ​​engine works: Learns internal information, general information, and industry trend information to build a knowledge base

[1039] Step 3:

[1040] Input: Internal information, industry standard guidelines, latest news

[1041] How it works: The server continuously collects this information and provides it to the AI ​​engine.

[1042] Output: Updated information is fed to the AI ​​engine

[1043] How the AI ​​engine works: Analyzes the information provided and updates its knowledge base

[1044] Step 4:

[1045] Input: Inquiry from new participant

[1046] How it works: A user (newcomer) enters a question on a smartphone or PC.

[1047] Output: The query is sent to the server

[1048] Server operation: Passes the query to the AI ​​engine

[1049] Step 5:

[1050] Input: Inquiry details

[1051] How it works: The AI ​​engine searches the knowledge base and generates the appropriate answer

[1052] Output: The generated answer is returned to the server

[1053] Server action: Send the answer to the user's device

[1054] Step 6:

[1055] Input: Feedback from newcomers

[1056] Action: User (newcomer) enters feedback on provided answer

[1057] Output: Feedback is sent to the server

[1058] Server operation: The feedback information is saved in a database and reflected in the AI ​​engine's learning data.

[1059] Step 7:

[1060] Input: facial expressions and text when new participants type in their questions

[1061] How it works: The emotion engine acquires emotion data through the device's camera or text input.

[1062] Output: Emotion data is sent to the server

[1063] How the emotion engine works: Analyzes emotions from facial expressions and text in real time and sends the results to the server.

[1064] Step 8:

[1065] Input: Emotion data

[1066] How it works: The server instructs the AI ​​engine to generate appropriate support content based on the emotion data.

[1067] Output: Adjusted support content is generated

[1068] How the AI ​​engine works: Adjusts assistance content based on emotional data and generates the best answer for the user

[1069] 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.

[1070] 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.

[1071] 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.

[1072] [Third embodiment]

[1073] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1074] 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.

[1075] 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).

[1076] 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.

[1077] 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.

[1078] 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).

[1079] 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.

[1080] 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.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] 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."

[1085] The present invention is an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, a terminal, and a user (newcomer) as follows.

[1086] Program processing

[1087] 1. New member registration

[1088] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1089] Terminal: Sends the entered information to the server.

[1090] Server: Registers the received profile information in a database and instructs the AI ​​engine to learn appropriate information depending on the new participant's role.

[1091] 2. Learning and updating information

[1092] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1093] Server: Collects general information such as industry standards and guidelines from online resources.

[1094] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1095] AI engine: Analyzes collected information and builds an internal knowledge base.

[1096] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1097] 3. Providing support

[1098] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1099] Terminal: Sends the entered question to the server.

[1100] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1101] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1102] 4. Feedback and Improvement

[1103] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1104] Device: Sends feedback information to the server.

[1105] Server: Stores the received feedback information in a database.

[1106] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1107] Specific examples

[1108] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[1109] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[1110] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[1111] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[1112] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[1113] In this way, the present invention provides an environment in which newcomers can efficiently learn the business, and significantly reduces the burden on the accepting members.

[1114] The processing flow will be explained below.

[1115] Step 1:

[1116] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1117] Step 2:

[1118] Device: Sends the entered profile information to the server.

[1119] Step 3:

[1120] Server: Registers the received profile information in a database.

[1121] Step 4:

[1122] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[1123] Step 5:

[1124] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1125] Step 6:

[1126] Server: Collects general information such as industry standards and guidelines from online resources.

[1127] Step 7:

[1128] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1129] Step 8:

[1130] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[1131] Step 9:

[1132] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1133] Step 10:

[1134] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1135] Step 11:

[1136] Terminal: Sends the entered question to the server.

[1137] Step 12:

[1138] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1139] Step 13:

[1140] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1141] Step 14:

[1142] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1143] Step 15:

[1144] Device: Sends feedback information to the server.

[1145] Step 16:

[1146] Server: Stores the received feedback information in a database.

[1147] Step 17:

[1148] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1149] Example 1

[1150] 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."

[1151] It is important for new entrants to companies and organizations to quickly and efficiently learn their work. However, the onboarding process for new entrants is often time-consuming and laborious, placing a burden on existing members as well. Furthermore, if the information required for onboarding is scattered, new entrants have difficulty obtaining the information they need. There is a need for a system that can solve these issues and enable new entrants to begin work smoothly.

[1152] 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.

[1153] In this invention, the server includes means for receiving profile information of new entrants and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest industry trend information and supplying them to an AI engine, means for analyzing the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from new entrants and generating and sending answers from the knowledge base, and means for receiving feedback from new entrants, analyzing it, and reflecting it in the AI ​​engine. This enables new entrants to quickly obtain the information they need and achieves effective onboarding.

[1154] "Newcomers" refer to new members who join a company or organization.

[1155] "Profile Information" refers to personal information about a Newcomer, such as their name, job title, and responsibilities.

[1156] "Database" refers to a system for efficiently storing, managing, and accessing information.

[1157] "Internal documents" refers to information such as documents, policies, procedures, etc. used within a company.

[1158] "Industry standard knowledge" refers to knowledge of generally recognized standards and guidelines in a particular industry.

[1159] "Industry trend information" refers to information about the latest trends and news within an industry.

[1160] "AI engine" refers to a system that uses artificial intelligence to analyze information and build and update a knowledge base.

[1161] A "knowledge base" refers to a collection of information collected and organized by an AI engine.

[1162] "Questions" refer to any doubts or clarifications that newcomers have during the onboarding process.

[1163] "Answer" refers to the answer to a newcomer's question that the AI ​​engine generates based on its knowledge base.

[1164] "Feedback" refers to the evaluation and opinions that newcomers give of the answers and support provided.

[1165] "Server" refers to a central computer for processing information and communicating with multiple terminals.

[1166] "Terminals" refer to devices such as PCs and smartphones used by new entrants and administrators.

[1167] MODE FOR CARRYING OUT THE INVENTION

[1168] The present invention provides an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, terminals, and users.

[1169] The system consists of the following main components:

[1170] 1. Server: A central computer that processes information and accesses databases and AI engines.

[1171] 2. Terminals: Devices used by newcomers and administrators, including PCs and smartphones.

[1172] 3. Database: A system for storing profile and feedback information.

[1173] 4. AI Engine: Artificial intelligence for analyzing information, building and updating knowledge bases, and generating answers to questions.

[1174] Specific hardware and software

[1175] Server: Uses a Linux-based operating system, a database management system such as MySQL, and a programming language such as Python or Java.

[1176] Device: PC or smartphone with operating system such as Windows, macOS, Android, iOS.

[1177] AI engine: Machine learning frameworks such as TensorFlow and PyTorch are used to implement NLP models, and GPT-3 and other models are used as generative AI models.

[1178] System processing overview

[1179] The device (user) enters the profile information of new entrants (such as name, job title, and responsibilities) using a dedicated input form. The entered information is sent from the device to the server, which stores the received information in a database. The server then collects internal company documents, industry standard knowledge, and the latest industry trend information, and supplies this information to the AI ​​engine.

[1180] The AI ​​engine analyzes the collected information and builds and updates its internal knowledge base. When a newcomer enters a question using a dedicated app or web chat tool, the device sends the question to the server. The server passes the question to the AI ​​engine, which generates an answer from the knowledge base. The answer is then sent to the user's device via the server.

[1181] Furthermore, when newcomers provide feedback, that feedback is sent from the device to the server, which stores the information in a database. The saved feedback is analyzed and reflected in the AI ​​engine's learning data, allowing it to generate even more accurate answers.

[1182] Specific examples

[1183] Example 1: New employee "A" is assigned to the sales department

[1184] 1. Profile registration: A human resources officer uses a PC to enter A's name, job title, and job duties (sales) into a dedicated input form and submits it. The server saves this information in a new employee information table and instructs the AI ​​engine on learning tasks appropriate to A's role.

[1185] 2. Information learning: The server retrieves sales process and product information from the company's network drive and collects industry-standard guidelines from online resources via API. The AI ​​engine analyzes this information and builds a knowledge base.

[1186] 3. Providing support: Person A enters a question into a dedicated app on their smartphone, such as "What is the schedule for the next sales meeting?", and sends it. The server passes the question to the AI ​​engine, which generates an answer, "The next sales meeting will be held at 10:00 next Monday," and sends it to Person A's smartphone.

[1187] 4. Feedback and Improvement: User A enters and sends feedback on their smartphone, saying, "The explanation was easy to understand." The server saves the feedback in a feedback information table and reflects it in the AI ​​engine's learning data.

[1188] Prompt Sentence Examples

[1189] Prompt for newcomers to register their profile: "Please register your profile as a newcomer. Your name is A, your job title is sales, and your job is product sales."

[1190] Information learning prompt: "Gather internal sales information and industry-standard sales guidelines."

[1191] Prompt for support question: "When is the next sales meeting?"

[1192] Feedback prompt: "The support instructions were easy to understand."

[1193] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1194] Step 1: Complete and submit your newcomer profile information

[1195] Terminal (user): The user uses a dedicated input form to enter profile information such as the new entrant's name, job title, and responsibilities (input: profile information).

[1196] Terminal: Send the entered profile information to the server as an HTTP POST request (Output: Send profile information).

[1197] Step 2: Receive profile information and register in database

[1198] Server: Receives the transmitted profile information (input: profile information).

[1199] Server: Save the profile information to the database using the INSERT statement (data processing: save information, output: register to database).

[1200] Step 3: Information Collection Instructions

[1201] Server: Based on the profile information, instructs the AI ​​engine on information gathering tasks that correspond to the new entrant's role (input: profile information, output: information gathering task).

[1202] Step 4: Gathering internal information

[1203] Server: Retrieves internal documents, policies, procedures, etc. through the company's internal network drive or internal system API (input: information gathering tasks, data processing: information gathering, output: internal information).

[1204] Server: Analyzes the acquired internal information using a text analysis engine and organizes it by category (data processing: text analysis, output: organized information).

[1205] Step 5: Gathering external resources

[1206] Server: Collects industry standard knowledge and guidelines from online resources via API (input: information gathering task, data processing: information gathering, output: industry standard knowledge).

[1207] Server: Obtains the latest industry trend information from various RSS feeds and news feeds and extracts the necessary information (data processing: information extraction, output: industry trend information).

[1208] Step 6: Build and update your knowledge base

[1209] AI engine: The NLP engine analyzes collected internal information, industry standard knowledge, and industry trend information and converts it into meaningful structured data (input: collected information, data processing: NLP analysis, output: structured data).

[1210] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base (data processing: periodic updates, output: updated knowledge base).

[1211] Step 7: Enter and submit newcomer questions

[1212] Terminal (user): Newcomers use a dedicated app or web chat tool to input questions (input: question).

[1213] Terminal: Sends the entered question to the server via an HTTP POST request (Output: Send question).

[1214] Step 8: Parsing the question and generating an answer

[1215] Server: Receives questions from users and requests the AI ​​engine to analyze them (input: question).

[1216] AI engine: Analyzes the question using a natural language processing engine, searches for relevant information from a knowledge base, and generates an appropriate answer (data calculation: question analysis, data processing: answer generation, output: generated answer).

[1217] Server: Sends the generated answer to the user's terminal (output: Send answer).

[1218] Step 9: Enter and submit your feedback

[1219] Terminal (user): The newcomer inputs feedback on the answers and support provided (input:feedback).

[1220] Terminal: Send the entered feedback to the server via an HTTP POST request (Output: Send Feedback).

[1221] Step 10: Store and analyze feedback

[1222] Server: Stores the received feedback information in a database (input: feedback, data processing: information storage, output: storage in database).

[1223] Server: Passes the saved feedback to the AI ​​engine and analyzes it using a machine learning algorithm (data calculation: feedback analysis, output: analysis results).

[1224] AI engine: Reflects the analysis results in the learning data and performs model tuning to improve answer accuracy (data processing: model tuning, output: improved answer accuracy).

[1225] (Application example 1)

[1226] 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."

[1227] New employees need an onboarding support system to quickly and efficiently learn their jobs. In particular, advanced support is needed to help them adapt to the work environment and work procedures at factories and other work sites. There is also a demand for devices that display work procedures and safety measures in real time, along with related AI-based support.

[1228] 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.

[1229] In this invention, the server includes a means for receiving new employee profile information and registering it in a database; a means for collecting internal company information, general information, and the latest information and providing it to an AI engine; and a means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base. This allows new employees to learn work procedures and safety measures in real time and efficiently master their work. Furthermore, the use of a head-mounted display allows for immediate support on-site. Furthermore, prompt sentence processing utilizing a generative AI model enables real-time question resolution.

[1230] "Newcomers" refers to employees or staff who are joining an organization or project for the first time.

[1231] "Profile Information" refers to data about an individual, such as name, job title, and job responsibilities.

[1232] "Database" refers to a system for systematically storing user profile information and other related data.

[1233] "Internal information" refers to documents and data created and held within a company or organization.

[1234] "General information" refers to information that is commonly used outside the company, such as industry standards and guidelines.

[1235] "Latest information" refers to news and trend information that is updated in real time.

[1236] "AI engine" refers to artificial intelligence technology that analyzes collected data and builds and updates a knowledge base.

[1237] A "knowledge base" refers to a collection of data that systematically organizes and provides information learned by an AI engine.

[1238] "Means for receiving questions and generating answers" refers to the process of receiving questions from new participants and generating appropriate answers from the knowledge base.

[1239] "Feedback" refers to the opinions and evaluations that new participants enter regarding the support provided.

[1240] "Analyzing" refers to processing the feedback information and using it to improve the AI ​​engine.

[1241] A "head-mounted display" refers to a device that can display visual information when worn.

[1242] "Real-time" refers to a time frame in which processing and response occur instantaneously.

[1243] A "generative AI model" refers to a model that uses artificial intelligence to generate text and data.

[1244] A "prompt" refers to an instruction or question input to a generative AI model.

[1245] To implement this invention, it is necessary to build a system that is mainly comprised of a server, terminals, and users (new participants). A specific embodiment of this system is shown below.

[1246] System configuration

[1247] Server: Mainly responsible for the database, AI engine, and information collection and update functions.

[1248] Terminal: Mainly used to input information for new participants, receive questions, display answers, and collect feedback. A head-mounted display (HMD) is worn to display real-time work instructions.

[1249] User: The new participant operates the device (HMD) to acquire and learn the necessary information.

[1250] Hardware and software used

[1251] Hardware:

[1252] Head-mounted displays (HMDs): Examples include Oculus Rift and HTC Vive.

[1253] Server: A powerful cloud server (e.g., an AWS EC2 instance).

[1254] software:

[1255] Database management system (e.g. MySQL)

[1256] AI engines (e.g., Google Cloud AI, IBM Watson)

[1257] API communication libraries (e.g., Python's Requests library)

[1258] Explanation of each process

[1259] 1. New participant registration:

[1260] New participants' profile information (such as name, position, and responsibilities) is entered on a terminal and sent to the server, which then registers it in the database.

[1261] 2. Information Collection and Update:

[1262] The server collects internal documents, industry standard knowledge, the latest news, etc. and supplies them to the AI ​​engine, which then learns from this information and builds and updates its knowledge base.

[1263] 3. Real-time support:

[1264] When a new participant wears an HMD and sends a question from their device while working, the server passes the question to the AI ​​engine, which generates an appropriate answer from the knowledge base. For example, if the question is, "When is the next maintenance work scheduled?", the AI ​​engine generates the answer, "The next maintenance is scheduled for tomorrow at 2 p.m." The generative AI model processes the prompt sentence and resolves the question in real time.

[1265] 4. Feedback and Improvement:

[1266] New participants enter feedback on the support provided through their devices and send it to the server, which then stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[1267] Specific examples

[1268] Example: New factory worker

[1269] When a new employee is assigned to a factory production line, he or she puts on the HMD and asks, "When is the next maintenance work scheduled?" The server uses an AI engine to instantly respond. For example, it might generate a response such as, "The next maintenance work is scheduled for 2:00 PM tomorrow." Actual work procedures and safety measures are also displayed in real time through the HMD.

[1270] Example prompt for a generative AI model:

[1271] "A new employee has been assigned to work as a production line operator in a factory. He puts on the HMD and asks, 'When is the next maintenance task scheduled?' Please generate an appropriate answer."

[1272] In this way, the system provides an environment in which newcomers can quickly and efficiently learn their jobs, significantly reducing the burden on the accepting party.

[1273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1274] Step 1:

[1275] A new participant enters their profile information (name, job title, and responsibilities) through a terminal. The terminal sends this profile information to the server, which then registers the received profile information in a database. The input is the new participant's profile information, and the output is the registered profile information in the database. Specifically, the terminal receives the form input and sends it to the server in JSON format as a POST request, which the server receives and saves in the database.

[1276] Step 2:

[1277] The server collects internal information, general information, and the latest information. The server uses APIs to obtain information from internal databases, industry-standard knowledge bases, and news feeds. The obtained information is supplied to the AI ​​engine, which learns from this information and builds and updates its internal knowledge base. The input is the collected information, and the output is the updated knowledge base. Specifically, the server sends requests to various API endpoints, analyzes and processes the received data, and passes it to the AI ​​engine.

[1278] Step 3:

[1279] The new participant wears a head-mounted display and sends questions from their device while working. The device transmits the question to the server, which passes it on to the AI ​​engine. The AI ​​engine references the knowledge base to generate an appropriate answer and returns it to the server. The server sends the generated answer to the new participant's device. The input is the user's question, and the output is the answer from the AI ​​engine. Specifically, a question entered in voice or text format is sent to the server as an API request, and the AI ​​engine processes it and returns an answer.

[1280] Step 4:

[1281] New participants input feedback on the answers and support provided. The device sends the feedback information to the server, which then stores it in a database. The server then analyzes the stored feedback information and reflects it in the AI ​​engine. The input is the user's feedback, and the output is updated learning data for the AI ​​engine. Specifically, the device receives input form or voice feedback, sends it to the server, which analyzes it and reflects it in the database.

[1282] Step 5:

[1283] The server periodically collects new information and supplies it to the AI ​​engine. Each time new information is collected, the AI ​​engine keeps updating the knowledge base. This allows new participants to always have access to the latest information. The input is new information data, and the output is an updated knowledge base. Specifically, the server executes scheduled tasks, collects the latest information using APIs and scraping technology, adds it to the database, and the AI ​​engine learns from it and reflects it in the knowledge base.

[1284] Through this process, newcomers can acquire and learn information in real time, enabling them to efficiently master their work.By using a generative AI model to process prompts, the system is able to respond immediately to user questions.

[1285] 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.

[1286] This invention is an onboarding support system that combines an AI engine and an emotion engine to help newcomers learn their jobs quickly and efficiently. This system reduces the burden on accepting members and allows newcomers to start work with a sense of psychological safety.

[1287] Program processing

[1288] 1. New member registration

[1289] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1290] Device: Sends the entered profile information to the server.

[1291] Server: Registers the received profile information in a database.

[1292] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[1293] 2. Learning and updating information

[1294] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1295] Server: Collects general information such as industry standards and guidelines from online resources.

[1296] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1297] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[1298] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1299] 3. Providing support

[1300] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1301] Terminal: Sends the entered question to the server.

[1302] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1303] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1304] 4. Feedback and Improvement

[1305] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1306] Device: Sends feedback information to the server.

[1307] Server: Stores the received feedback information in a database.

[1308] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1309] 5. Emotion Recognition and Analysis

[1310] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[1311] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[1312] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[1313] 6. Use of Emotional Data

[1314] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[1315] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[1316] Specific examples

[1317] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[1318] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[1319] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[1320] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[1321] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[1322] 5. Emotion recognition: When Taro Tanaka types a question, the emotion engine detects "confusion" through his facial expression and sends that information to the server.

[1323] 6. Use of emotional data: Based on the emotional data of "confusion," the server instructs the AI ​​engine to provide assistance appropriate to Taro Tanaka's condition. The AI ​​engine generates assistance that explains the information in more detail so that Taro Tanaka can easily understand it, and sends the answer to his device.

[1324] In this way, by combining an AI engine and an emotion engine, the present invention provides an environment in which newcomers can efficiently learn their jobs, significantly reducing the burden on accepting members.

[1325] The processing flow will be explained below.

[1326] Step 1:

[1327] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1328] Step 2:

[1329] Device: Sends the entered profile information to the server.

[1330] Step 3:

[1331] Server: Registers the received profile information in a database.

[1332] Step 4:

[1333] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[1334] Step 5:

[1335] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1336] Step 6:

[1337] Server: Collects general information such as industry standards and guidelines from online resources.

[1338] Step 7:

[1339] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1340] Step 8:

[1341] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[1342] Step 9:

[1343] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1344] Step 10:

[1345] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1346] Step 11:

[1347] Terminal: Sends the entered question to the server.

[1348] Step 12:

[1349] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1350] Step 13:

[1351] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1352] Step 14:

[1353] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1354] Step 15:

[1355] Device: Sends feedback information to the server.

[1356] Step 16:

[1357] Server: Stores the received feedback information in a database.

[1358] Step 17:

[1359] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1360] Step 18:

[1361] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[1362] Step 19:

[1363] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[1364] Step 20:

[1365] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[1366] Step 21:

[1367] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[1368] Step 22:

[1369] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[1370] Example 2

[1371] 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."

[1372] In modern companies, it is an important challenge for newcomers to quickly and efficiently learn about business processes and corporate culture. However, training and supporting newcomers requires a great deal of time and effort, which often places a burden on the team members. Providing appropriate information and psychological support is also important for newcomers to begin their work effectively, but providing these in a balanced manner can be difficult. Furthermore, in addition to providing standard information, newcomers also need support tailored to their emotions and circumstances. A system that can address this is needed.

[1373] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1374] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest information and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers, analyzing it, and reflecting it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and adjusting support content based on that data, and means for monitoring using the emotion engine and optimizing support content based on historical data. This allows newcomers to learn their jobs quickly and efficiently, reduces the burden on accepting members, and further increases the psychological safety of newcomers.

[1375] "Profile information" is individual attribute information such as the new participant's name, position, and responsibilities.

[1376] "Database" refers to an information management system for storing and managing received profile information.

[1377] "Internal documents" are documents such as procedures, policies, and product information created within a company.

[1378] "Industry standard knowledge" is information about widely recognized guidelines and best practices within a particular industry.

[1379] "Latest news" refers to the latest information on ongoing news, industry trends, product releases, and more.

[1380] An "AI engine" is a system that uses artificial intelligence technology to analyze collected information and build and update a knowledge base.

[1381] A "knowledge base" is a collection of information built by an AI engine that is used to provide answers and support to newcomers.

[1382] "Feedback" refers to the evaluation and opinions that new participants give about the answers and support provided.

[1383] The "emotion engine" is a technology that analyzes the emotions of new participants in real time and reflects the results of that analysis in the support provided.

[1384] "Monitoring" is the process of continuously observing the status of new participants and collecting and analyzing that data.

[1385] This invention is an onboarding system that helps new employees learn their jobs quickly and efficiently. This system functions through the cooperation of three entities: a server, a terminal, and a user. The specific hardware and software configurations and data processing methods are described below.

[1386] Components and technologies used

[1387] server

[1388] The server works with software modules including a database, AI engine, and emotion engine. This server is responsible for managing profile information and collecting, analyzing, and updating information. Specifically, it uses the following technologies:

[1389] Database system: MySQL, PostgreSQL, etc.

[1390] AI engines: generative AI models such as TensorFlow and PyTorch

[1391] Emotion engine: Facial recognition API (e.g., Microsoft Azure Face API), speech recognition system (e.g., Amazon Polly)

[1392] Terminal

[1393] Terminals are devices used by new participants and administrators, including smartphones, PCs, tablets, etc. Terminals communicate with the server using the following software:

[1394] Web browser: Google Chrome, Mozilla Firefox

[1395] Dedicated application: Custom app (compatible with iOS and Android)

[1396] User

[1397] Users are both new participants and administrators who handle them. Users use their devices to enter profile information, submit questions, and provide feedback.

[1398] Specific operation explanation

[1399] New Member Registration

[1400] User (Administrator): The HR department administrator enters the new employee's profile information (e.g., name, position, and responsibilities) from the terminal.

[1401] Terminal: The terminal sends the entered information to the server, which registers it in a database.

[1402] Learning and updating information

[1403] Server: The server calls APIs or uses web scraping techniques to collect internal documents, industry standard knowledge, and the latest information. For example, it uses the Google Scholar API to obtain industry standard knowledge.

[1404] AI engine: Analyzes collected information using natural language processing (NLP) technology to build and update the knowledge base.

[1405] Providing support

[1406] User (newcomer): The newcomer uses the terminal to input and submit a question. For example, they input a prompt such as, "When is the next sales meeting?"

[1407] Server: The server sends this question to the AI ​​engine, which generates an appropriate answer from its knowledge base and sends it back to the device.

[1408] Feedback and Improvements

[1409] User (newcomer): Enter and submit feedback on the answers received by newcomers. Provide specific feedback such as "The explanation was easy to understand."

[1410] Server: The server collects feedback information and reflects it in the AI ​​engine.

[1411] Emotion Recognition and Analysis

[1412] User (newcomer): When a user inputs a question from a device, the emotion engine performs real-time analysis using the camera and microphone.

[1413] Emotion Engine: The emotion engine analyzes the newcomer's facial expressions and tone of voice and sends the emotional data to the server.

[1414] Server: Based on the emotional data, the server instructs the AI ​​engine on the appropriate response.

[1415] Use of Emotional Data

[1416] Server: Reflects the analyzed emotional data in the knowledge base and adjusts and provides individual support content.

[1417] The emotion engine also continuously monitors the state of newcomers and uses that data along with feedback data.

[1418] Specific examples

[1419] Example 1: When a new employee is assigned to the sales department, if the new employee inputs the question, "What is the schedule for the next sales meeting?", the AI ​​engine will generate an answer from the knowledge base, such as, "The next sales meeting will be held next Monday at 10:00," and send it to the new employee's device. In addition, if the new employee's confused feelings are analyzed, the AI ​​engine will provide assistance by providing additional detailed information.

[1420] In this way, the present invention allows newcomers to learn the job quickly and effectively, reduces the burden on host team members, and provides psychological safety.

[1421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1422] Step 1:

[1423] The user (administrator) enters the profile information of the new participant into the terminal. The input items include name, position, and responsibilities. Once the input is complete, the user presses the send button.

[1424] Input: Name, position, and duties of new participant

[1425] Output: The entered profile information is sent from the device to the server.

[1426] Step 2:

[1427] The terminal transmits the profile information input by the user to the server using a secure communication protocol (for example, HTTPS).

[1428] Input: Profile information (name, job title, responsibilities)

[1429] Output: Profile information is sent to the server.

[1430] Step 3:

[1431] The server registers the received profile information in a database, checking for duplicates against existing records and creating new records.

[1432] Input: Profile information (name, job title, responsibilities)

[1433] Output: The new participant's information is registered in the database and a unique ID is generated.

[1434] Step 4:

[1435] The server instructs the AI ​​engine to learn information related to the new entrant based on the unique ID and profile information.

[1436] Input: Profile information (including unique ID)

[1437] Output: Learning instructions are sent to the AI ​​engine.

[1438] Step 5:

[1439] The server collects internal documents, industry standard knowledge, and up-to-date information from various sources, including data retrieval from internal systems, web scraping, and various APIs.

[1440] Inputs: Learning instructions, internal document management systems, online resources, APIs

[1441] Output: The collected information is stored on the server.

[1442] Step 6:

[1443] The AI ​​engine analyzes the collected information to build and update its internal knowledge base, using natural language processing techniques to extract important information and store it as structured data.

[1444] Input: Collected information (documents, guidelines, news, etc.)

[1445] Output: An updated knowledge base is generated.

[1446] Step 7:

[1447] The user (newcomer) uses a chat tool or a dedicated app to enter a question and press the send button. For example, they send a prompt such as, "What is the schedule for the next sales meeting?"

[1448] Input: Question (prompt)

[1449] Output: The question is sent from the terminal to the server.

[1450] Step 8:

[1451] The terminal transmits the entered question to the server.

[1452] Input: Question (prompt)

[1453] Output: The query data is sent to the server.

[1454] Step 9:

[1455] The server transfers the received question data to the AI ​​engine and instructs it to generate an answer.

[1456] Input: Question (prompt)

[1457] Output: Instructions are sent to the AI ​​engine for processing.

[1458] Step 10:

[1459] The AI ​​engine searches its knowledge base for an appropriate answer and generates it, for example, "The next sales meeting will be held next Monday at 10:00."

[1460] Input: Question, Knowledge Base

[1461] Output: The generated answer

[1462] Step 11:

[1463] The server sends the generated answer to the new participant's terminal.

[1464] Input: Generated answer

[1465] Output: The answer is displayed on the new participant's device.

[1466] Step 12:

[1467] The user (newcomer) enters feedback on the answers and support provided and presses the send button. Specifically, the user enters feedback such as "The explanation was easy to understand."

[1468] Input: Feedback

[1469] Output: Feedback data is sent from the device to the server.

[1470] Step 13:

[1471] The terminal transmits the feedback data to the server.

[1472] Input: Feedback

[1473] Output: Feedback data is sent to the server.

[1474] Step 14:

[1475] The server stores the received feedback data in a database and analyzes it, and the analysis results are reflected in the AI ​​engine's learning data.

[1476] Input: Feedback data

[1477] Output: Updated training data

[1478] Step 15:

[1479] Users (new participants) use the device's camera and microphone to input questions, and the emotion engine analyzes facial expressions and voice in real time.

[1480] Input: facial expression data, voice data

[1481] Output: Parsed emotion data

[1482] Step 16:

[1483] The emotion engine analyzes the emotions of new participants and sends the results to the server.

[1484] Input: facial expression data, voice data

[1485] Output: Analysis results (emotion data)

[1486] Step 17:

[1487] The server then instructs the AI ​​engine on the appropriate support content based on the received emotional data. For example, it provides a more detailed explanation to a newcomer who is feeling confused.

[1488] Input: Emotion data

[1489] Output: Support content instructions

[1490] Step 18:

[1491] The server performs continuous monitoring and provides optimal support based on past data.

[1492] Input: Newcomer history data, emotion data

[1493] Output: Optimized support content

[1494] (Application example 2)

[1495] 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."

[1496] There is a need for an onboarding system that helps new employees learn their jobs quickly and efficiently. However, existing systems lack the ability to reduce the psychological burden on new employees and provide support that takes their emotions into consideration. This has led to early employee turnover, delays in job acquisition, and increased burdens on the new employees.

[1497] 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.

[1498] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting information resources and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving inquiries from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers and analyzing it to reflect it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and supplying emotional data to the AI ​​engine, and means for adjusting support content based on the emotional data. This enables newcomers to learn their jobs efficiently and with less psychological burden.

[1499] "Newcomers" are employees or staff who join a company or organization and begin work.

[1500] "Profile information" refers to personal information such as name, job title, and job responsibilities, as well as information related to work.

[1501] A "database" is a data structure that organizes, stores, and easily searches and retrieves information.

[1502] "Information resources" refers to information sources such as internal company information, general information, and the latest information.

[1503] An "AI engine" is a system that uses artificial intelligence to analyze data, learn, and provide appropriate answers and support.

[1504] A "knowledge base" is a collection of information learned by an AI engine, and is a database that serves as the basis for generating answers to questions.

[1505] An "inquiry" is a question that a newcomer asks for information or support.

[1506] "Feedback" refers to the opinions and evaluations that new participants give about the answers and support provided.

[1507] "Emotional data" refers to emotional information obtained through new participants' facial expressions, text, etc.

[1508] "Support content" refers to the guidance and assistance provided to newcomers to help them learn their jobs efficiently.

[1509] This invention is an onboarding system that helps new employees quickly and efficiently learn their jobs. The system is implemented using specific hardware and software.

[1510] System configuration

[1511] The system includes the following components:

[1512] Server: The primary computing resource for managing the database and running the AI ​​engine and emotion engine.

[1513] Terminal (user): A device through which a user interacts with the system, such as a mobile terminal such as a smartphone or smart glasses, or a PC.

[1514] AI engine: A software component that analyzes and learns from collected information and builds and updates the knowledge base.

[1515] Emotion engine: A software component that analyzes user emotions in real time and adjusts support content.

[1516] Database: Data storage for saving new participants' profile information and feedback information.

[1517] System program processing

[1518] 1. New participant registration

[1519] Server: Receives profile information of new participants and registers them in the database. Profile information includes name, job title, responsibilities, etc.

[1520] Server: Based on the registered information, instructs the AI ​​engine to learn related information.

[1521] 2. Learning and updating information

[1522] Server: Collects internal information, general information, and the latest information and supplies it to the AI ​​engine.

[1523] AI engine: Analyzes collected information and builds and updates the knowledge base.

[1524] 3. Providing support

[1525] Terminal (user): The user inputs a question from the terminal. The question is about business.

[1526] Server: Passes the received question to the AI ​​engine.

[1527] AI engine: Generates appropriate answers from the knowledge base and sends them to the user's device via the server.

[1528] 4. Feedback and Improvement

[1529] Terminal (user): Enters feedback on the answers provided.

[1530] Server: Receives feedback information and stores it in a database.

[1531] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1532] 5. Emotion Recognition and Analysis

[1533] Terminal (user): When entering a question, the emotion engine analyzes the user's emotions in real time.

[1534] Emotion engine: Sends emotion data to the server.

[1535] Server: Based on the emotional data, the AI ​​engine instructs it to generate appropriate support content.

[1536] Hardware and software used

[1537] Hardware: Servers, smartphones, smart glasses, PCs

[1538] software:

[1539] emotion_recognition library: Used to analyze user emotions.

[1540] ai_engine library: Used to generate appropriate answers to questions from collected information.

[1541] database module: Used to store profile and feedback information.

[1542] Specific examples

[1543] When a new employee, "Mr. A," is assigned to a store, the store manager uses a smartphone app to enter Mr. A's profile information and sends it to the server. The server registers that information in a database and instructs the AI ​​engine to learn information related to Mr. A's position. When Mr. A asks, "Please tell me how to display this product," the AI ​​engine generates an appropriate answer from its knowledge base, replying, "This product is available in sizes S, M, and L." When Mr. A enters feedback, the server collects that information and reflects it in the AI ​​engine's learning data.

[1544] Example prompts for generative AI models

[1545] We are developing an assistant to support new employee onboarding at an apparel shop. This assistant uses an AI engine and an emotion engine to answer employees' questions and provide appropriate support depending on their emotions at the time. New employees ask questions through a smartphone app, and the AI ​​answers the questions, collects feedback, and learns. For example, if a question asks, "What sizes are available for this product?", the AI ​​engine will respond, "This product is available in sizes S, M, and L." Please tell us the specific implementation method for this assistant.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] Input: New participant profile information (name, position, responsibilities, etc.)

[1549] Operation: The user (person in charge) enters profile information on a smartphone or PC.

[1550] Output: Profile information is sent to the server

[1551] Server operation: Register profile information in the database

[1552] Step 2:

[1553] Input: Registered profile information

[1554] Operation: The server instructs the AI ​​engine to learn relevant information based on the profile information.

[1555] Output: Instructions for the AI ​​engine to start learning

[1556] How the AI ​​engine works: Learns internal information, general information, and industry trend information to build a knowledge base

[1557] Step 3:

[1558] Input: Internal information, industry standard guidelines, latest news

[1559] How it works: The server continuously collects this information and provides it to the AI ​​engine.

[1560] Output: Updated information is fed to the AI ​​engine

[1561] How the AI ​​engine works: Analyzes the information provided and updates its knowledge base

[1562] Step 4:

[1563] Input: Inquiry from new participant

[1564] How it works: A user (newcomer) enters a question on a smartphone or PC.

[1565] Output: The query is sent to the server

[1566] Server operation: Passes the query to the AI ​​engine

[1567] Step 5:

[1568] Input: Inquiry details

[1569] How it works: The AI ​​engine searches the knowledge base and generates the appropriate answer

[1570] Output: The generated answer is returned to the server

[1571] Server action: Send the answer to the user's device

[1572] Step 6:

[1573] Input: Feedback from newcomers

[1574] Action: User (newcomer) enters feedback on provided answer

[1575] Output: Feedback is sent to the server

[1576] Server operation: The feedback information is saved in a database and reflected in the AI ​​engine's learning data.

[1577] Step 7:

[1578] Input: facial expressions and text when new participants type in their questions

[1579] How it works: The emotion engine acquires emotion data through the device's camera or text input.

[1580] Output: Emotion data is sent to the server

[1581] How the emotion engine works: Analyzes emotions from facial expressions and text in real time and sends the results to the server.

[1582] Step 8:

[1583] Input: Emotion data

[1584] How it works: The server instructs the AI ​​engine to generate appropriate support content based on the emotion data.

[1585] Output: Adjusted support content is generated

[1586] How the AI ​​engine works: Adjusts assistance content based on emotional data and generates the best answer for the user

[1587] 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.

[1588] 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.

[1589] 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.

[1590] [Fourth embodiment]

[1591] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1592] 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.

[1593] 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).

[1594] 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.

[1595] 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.

[1596] 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).

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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.

[1602] 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.

[1603] 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."

[1604] The present invention is an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, a terminal, and a user (newcomer) as follows.

[1605] Program processing

[1606] 1. New member registration

[1607] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1608] Terminal: Sends the entered information to the server.

[1609] Server: Registers the received profile information in a database and instructs the AI ​​engine to learn appropriate information depending on the new participant's role.

[1610] 2. Learning and updating information

[1611] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1612] Server: Collects general information such as industry standards and guidelines from online resources.

[1613] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1614] AI engine: Analyzes collected information and builds an internal knowledge base.

[1615] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1616] 3. Providing support

[1617] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1618] Terminal: Sends the entered question to the server.

[1619] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1620] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1621] 4. Feedback and Improvement

[1622] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1623] Device: Sends feedback information to the server.

[1624] Server: Stores the received feedback information in a database.

[1625] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1626] Specific examples

[1627] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[1628] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[1629] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[1630] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[1631] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[1632] In this way, the present invention provides an environment in which newcomers can efficiently learn the business, and significantly reduces the burden on the accepting members.

[1633] The processing flow will be explained below.

[1634] Step 1:

[1635] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1636] Step 2:

[1637] Device: Sends the entered profile information to the server.

[1638] Step 3:

[1639] Server: Registers the received profile information in a database.

[1640] Step 4:

[1641] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[1642] Step 5:

[1643] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1644] Step 6:

[1645] Server: Collects general information such as industry standards and guidelines from online resources.

[1646] Step 7:

[1647] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1648] Step 8:

[1649] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[1650] Step 9:

[1651] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1652] Step 10:

[1653] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1654] Step 11:

[1655] Terminal: Sends the entered question to the server.

[1656] Step 12:

[1657] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1658] Step 13:

[1659] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1660] Step 14:

[1661] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1662] Step 15:

[1663] Device: Sends feedback information to the server.

[1664] Step 16:

[1665] Server: Stores the received feedback information in a database.

[1666] Step 17:

[1667] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1668] Example 1

[1669] 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."

[1670] It is important for new entrants to companies and organizations to quickly and efficiently learn their work. However, the onboarding process for new entrants is often time-consuming and laborious, placing a burden on existing members as well. Furthermore, if the information required for onboarding is scattered, new entrants have difficulty obtaining the information they need. There is a need for a system that can solve these issues and enable new entrants to begin work smoothly.

[1671] 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.

[1672] In this invention, the server includes means for receiving profile information of new entrants and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest industry trend information and supplying them to an AI engine, means for analyzing the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from new entrants and generating and sending answers from the knowledge base, and means for receiving feedback from new entrants, analyzing it, and reflecting it in the AI ​​engine. This enables new entrants to quickly obtain the information they need and achieves effective onboarding.

[1673] "Newcomers" refer to new members who join a company or organization.

[1674] "Profile Information" refers to personal information about a Newcomer, such as their name, job title, and responsibilities.

[1675] "Database" refers to a system for efficiently storing, managing, and accessing information.

[1676] "Internal documents" refers to information such as documents, policies, procedures, etc. used within a company.

[1677] "Industry standard knowledge" refers to knowledge of generally recognized standards and guidelines in a particular industry.

[1678] "Industry trend information" refers to information about the latest trends and news within an industry.

[1679] "AI engine" refers to a system that uses artificial intelligence to analyze information and build and update a knowledge base.

[1680] A "knowledge base" refers to a collection of information collected and organized by an AI engine.

[1681] "Questions" refer to any doubts or clarifications that newcomers have during the onboarding process.

[1682] "Answer" refers to the answer to a newcomer's question that the AI ​​engine generates based on its knowledge base.

[1683] "Feedback" refers to the evaluation and opinions that newcomers give of the answers and support provided.

[1684] "Server" refers to a central computer for processing information and communicating with multiple terminals.

[1685] "Terminals" refer to devices such as PCs and smartphones used by new entrants and administrators.

[1686] MODE FOR CARRYING OUT THE INVENTION

[1687] The present invention provides an onboarding support system that enables newcomers to quickly and efficiently learn their jobs. This system is mainly composed of a server, terminals, and users.

[1688] The system consists of the following main components:

[1689] 1. Server: A central computer that processes information and accesses databases and AI engines.

[1690] 2. Terminals: Devices used by newcomers and administrators, including PCs and smartphones.

[1691] 3. Database: A system for storing profile and feedback information.

[1692] 4. AI Engine: Artificial intelligence for analyzing information, building and updating knowledge bases, and generating answers to questions.

[1693] Specific hardware and software

[1694] Server: Uses a Linux-based operating system, a database management system such as MySQL, and a programming language such as Python or Java.

[1695] Device: PC or smartphone with operating system such as Windows, macOS, Android, iOS.

[1696] AI engine: Machine learning frameworks such as TensorFlow and PyTorch are used to implement NLP models, and GPT-3 and other models are used as generative AI models.

[1697] System processing overview

[1698] The device (user) enters the profile information of new entrants (such as name, job title, and responsibilities) using a dedicated input form. The entered information is sent from the device to the server, which stores the received information in a database. The server then collects internal company documents, industry standard knowledge, and the latest industry trend information, and supplies this information to the AI ​​engine.

[1699] The AI ​​engine analyzes the collected information and builds and updates its internal knowledge base. When a newcomer enters a question using a dedicated app or web chat tool, the device sends the question to the server. The server passes the question to the AI ​​engine, which generates an answer from the knowledge base. The answer is then sent to the user's device via the server.

[1700] Furthermore, when newcomers provide feedback, that feedback is sent from the device to the server, which stores the information in a database. The saved feedback is analyzed and reflected in the AI ​​engine's learning data, allowing it to generate even more accurate answers.

[1701] Specific examples

[1702] Example 1: New employee "A" is assigned to the sales department

[1703] 1. Profile registration: A human resources officer uses a PC to enter A's name, job title, and job duties (sales) into a dedicated input form and submits it. The server saves this information in a new employee information table and instructs the AI ​​engine on learning tasks appropriate to A's role.

[1704] 2. Information learning: The server retrieves sales process and product information from the company's network drive and collects industry-standard guidelines from online resources via API. The AI ​​engine analyzes this information and builds a knowledge base.

[1705] 3. Providing support: Person A enters a question into a dedicated app on their smartphone, such as "What is the schedule for the next sales meeting?", and sends it. The server passes the question to the AI ​​engine, which generates an answer, "The next sales meeting will be held at 10:00 next Monday," and sends it to Person A's smartphone.

[1706] 4. Feedback and Improvement: User A enters and sends feedback on their smartphone, saying, "The explanation was easy to understand." The server saves the feedback in a feedback information table and reflects it in the AI ​​engine's learning data.

[1707] Prompt Sentence Examples

[1708] Prompt for newcomers to register their profile: "Please register your profile as a newcomer. Your name is A, your job title is sales, and your job is product sales."

[1709] Information learning prompt: "Gather internal sales information and industry-standard sales guidelines."

[1710] Prompt for support question: "When is the next sales meeting?"

[1711] Feedback prompt: "The support instructions were easy to understand."

[1712] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1713] Step 1: Complete and submit your newcomer profile information

[1714] Terminal (user): The user uses a dedicated input form to enter profile information such as the new entrant's name, job title, and responsibilities (input: profile information).

[1715] Terminal: Send the entered profile information to the server as an HTTP POST request (Output: Send profile information).

[1716] Step 2: Receive profile information and register in database

[1717] Server: Receives the transmitted profile information (input: profile information).

[1718] Server: Save the profile information to the database using the INSERT statement (data processing: save information, output: register to database).

[1719] Step 3: Information Collection Instructions

[1720] Server: Based on the profile information, instructs the AI ​​engine on information gathering tasks that correspond to the new entrant's role (input: profile information, output: information gathering task).

[1721] Step 4: Gathering internal information

[1722] Server: Retrieves internal documents, policies, procedures, etc. through the company's internal network drive or internal system API (input: information gathering tasks, data processing: information gathering, output: internal information).

[1723] Server: Analyzes the acquired internal information using a text analysis engine and organizes it by category (data processing: text analysis, output: organized information).

[1724] Step 5: Gathering external resources

[1725] Server: Collects industry standard knowledge and guidelines from online resources via API (input: information gathering task, data processing: information gathering, output: industry standard knowledge).

[1726] Server: Obtains the latest industry trend information from various RSS feeds and news feeds and extracts the necessary information (data processing: information extraction, output: industry trend information).

[1727] Step 6: Build and update your knowledge base

[1728] AI engine: The NLP engine analyzes collected internal information, industry standard knowledge, and industry trend information and converts it into meaningful structured data (input: collected information, data processing: NLP analysis, output: structured data).

[1729] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base (data processing: periodic updates, output: updated knowledge base).

[1730] Step 7: Enter and submit newcomer questions

[1731] Terminal (user): Newcomers use a dedicated app or web chat tool to input questions (input: question).

[1732] Terminal: Sends the entered question to the server via an HTTP POST request (Output: Send question).

[1733] Step 8: Parsing the question and generating an answer

[1734] Server: Receives questions from users and requests the AI ​​engine to analyze them (input: question).

[1735] AI engine: Analyzes the question using a natural language processing engine, searches for relevant information from a knowledge base, and generates an appropriate answer (data calculation: question analysis, data processing: answer generation, output: generated answer).

[1736] Server: Sends the generated answer to the user's terminal (output: Send answer).

[1737] Step 9: Enter and submit your feedback

[1738] Terminal (user): The newcomer inputs feedback on the answers and support provided (input:feedback).

[1739] Terminal: Send the entered feedback to the server via an HTTP POST request (Output: Send Feedback).

[1740] Step 10: Store and analyze feedback

[1741] Server: Stores the received feedback information in a database (input: feedback, data processing: information storage, output: storage in database).

[1742] Server: Passes the saved feedback to the AI ​​engine and analyzes it using a machine learning algorithm (data calculation: feedback analysis, output: analysis results).

[1743] AI engine: Reflects the analysis results in the learning data and performs model tuning to improve answer accuracy (data processing: model tuning, output: improved answer accuracy).

[1744] (Application example 1)

[1745] 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."

[1746] New employees need an onboarding support system to quickly and efficiently learn their jobs. In particular, advanced support is needed to help them adapt to the work environment and work procedures at factories and other work sites. There is also a demand for devices that display work procedures and safety measures in real time, along with related AI-based support.

[1747] 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.

[1748] In this invention, the server includes a means for receiving new employee profile information and registering it in a database; a means for collecting internal company information, general information, and the latest information and providing it to an AI engine; and a means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base. This allows new employees to learn work procedures and safety measures in real time and efficiently master their work. Furthermore, the use of a head-mounted display allows for immediate support on-site. Furthermore, prompt sentence processing utilizing a generative AI model enables real-time question resolution.

[1749] "Newcomers" refers to employees or staff who are joining an organization or project for the first time.

[1750] "Profile Information" refers to data about an individual, such as name, job title, and job responsibilities.

[1751] "Database" refers to a system for systematically storing user profile information and other related data.

[1752] "Internal information" refers to documents and data created and held within a company or organization.

[1753] "General information" refers to information that is commonly used outside the company, such as industry standards and guidelines.

[1754] "Latest information" refers to news and trend information that is updated in real time.

[1755] "AI engine" refers to artificial intelligence technology that analyzes collected data and builds and updates a knowledge base.

[1756] A "knowledge base" refers to a collection of data that systematically organizes and provides information learned by an AI engine.

[1757] "Means for receiving questions and generating answers" refers to the process of receiving questions from new participants and generating appropriate answers from the knowledge base.

[1758] "Feedback" refers to the opinions and evaluations that new participants enter regarding the support provided.

[1759] "Analyzing" refers to processing the feedback information and using it to improve the AI ​​engine.

[1760] A "head-mounted display" refers to a device that can display visual information when worn.

[1761] "Real-time" refers to a time frame in which processing and response occur instantaneously.

[1762] A "generative AI model" refers to a model that uses artificial intelligence to generate text and data.

[1763] A "prompt" refers to an instruction or question input to a generative AI model.

[1764] To implement this invention, it is necessary to build a system that is mainly comprised of a server, terminals, and users (new participants). A specific embodiment of this system is shown below.

[1765] System configuration

[1766] Server: Mainly responsible for the database, AI engine, and information collection and update functions.

[1767] Terminal: Mainly used to input information for new participants, receive questions, display answers, and collect feedback. A head-mounted display (HMD) is worn to display real-time work instructions.

[1768] User: The new participant operates the device (HMD) to acquire and learn the necessary information.

[1769] Hardware and software used

[1770] Hardware:

[1771] Head-mounted displays (HMDs): Examples include Oculus Rift and HTC Vive.

[1772] Server: A powerful cloud server (e.g., an AWS EC2 instance).

[1773] software:

[1774] Database management system (e.g. MySQL)

[1775] AI engines (e.g., Google Cloud AI, IBM Watson)

[1776] API communication libraries (e.g., Python's Requests library)

[1777] Explanation of each process

[1778] 1. New participant registration:

[1779] New participants' profile information (such as name, position, and responsibilities) is entered on a terminal and sent to the server, which then registers it in the database.

[1780] 2. Information Collection and Update:

[1781] The server collects internal documents, industry standard knowledge, the latest news, etc. and supplies them to the AI ​​engine, which then learns from this information and builds and updates its knowledge base.

[1782] 3. Real-time support:

[1783] When a new participant wears an HMD and sends a question from their device while working, the server passes the question to the AI ​​engine, which generates an appropriate answer from the knowledge base. For example, if the question is, "When is the next maintenance work scheduled?", the AI ​​engine generates the answer, "The next maintenance is scheduled for tomorrow at 2 p.m." The generative AI model processes the prompt sentence and resolves the question in real time.

[1784] 4. Feedback and Improvement:

[1785] New participants enter feedback on the support provided through their devices and send it to the server, which then stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[1786] Specific examples

[1787] Example: New factory worker

[1788] When a new employee is assigned to a factory production line, he or she puts on the HMD and asks, "When is the next maintenance work scheduled?" The server uses an AI engine to instantly respond. For example, it might generate a response such as, "The next maintenance work is scheduled for 2:00 PM tomorrow." Actual work procedures and safety measures are also displayed in real time through the HMD.

[1789] Example prompt for a generative AI model:

[1790] "A new employee has been assigned to work as a production line operator in a factory. He puts on the HMD and asks, 'When is the next maintenance task scheduled?' Please generate an appropriate answer."

[1791] In this way, the system provides an environment in which newcomers can quickly and efficiently learn their jobs, significantly reducing the burden on the accepting party.

[1792] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1793] Step 1:

[1794] A new participant enters their profile information (name, job title, and responsibilities) through a terminal. The terminal sends this profile information to the server, which then registers the received profile information in a database. The input is the new participant's profile information, and the output is the registered profile information in the database. Specifically, the terminal receives the form input and sends it to the server in JSON format as a POST request, which the server receives and saves in the database.

[1795] Step 2:

[1796] The server collects internal information, general information, and the latest information. The server uses APIs to obtain information from internal databases, industry-standard knowledge bases, and news feeds. The obtained information is supplied to the AI ​​engine, which learns from this information and builds and updates its internal knowledge base. The input is the collected information, and the output is the updated knowledge base. Specifically, the server sends requests to various API endpoints, analyzes and processes the received data, and passes it to the AI ​​engine.

[1797] Step 3:

[1798] The new participant wears a head-mounted display and sends questions from their device while working. The device transmits the question to the server, which passes it on to the AI ​​engine. The AI ​​engine references the knowledge base to generate an appropriate answer and returns it to the server. The server sends the generated answer to the new participant's device. The input is the user's question, and the output is the answer from the AI ​​engine. Specifically, a question entered in voice or text format is sent to the server as an API request, and the AI ​​engine processes it and returns an answer.

[1799] Step 4:

[1800] New participants input feedback on the answers and support provided. The device sends the feedback information to the server, which then stores it in a database. The server then analyzes the stored feedback information and reflects it in the AI ​​engine. The input is the user's feedback, and the output is updated learning data for the AI ​​engine. Specifically, the device receives input form or voice feedback, sends it to the server, which analyzes it and reflects it in the database.

[1801] Step 5:

[1802] The server periodically collects new information and supplies it to the AI ​​engine. Each time new information is collected, the AI ​​engine keeps updating the knowledge base. This allows new participants to always have access to the latest information. The input is new information data, and the output is an updated knowledge base. Specifically, the server executes scheduled tasks, collects the latest information using APIs and scraping technology, adds it to the database, and the AI ​​engine learns from it and reflects it in the knowledge base.

[1803] Through this process, newcomers can acquire and learn information in real time, enabling them to efficiently master their work.By using a generative AI model to process prompts, the system is able to respond immediately to user questions.

[1804] 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.

[1805] This invention is an onboarding support system that combines an AI engine and an emotion engine to help newcomers learn their jobs quickly and efficiently. This system reduces the burden on accepting members and allows newcomers to start work with a sense of psychological safety.

[1806] Program processing

[1807] 1. New member registration

[1808] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1809] Device: Sends the entered profile information to the server.

[1810] Server: Registers the received profile information in a database.

[1811] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[1812] 2. Learning and updating information

[1813] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1814] Server: Collects general information such as industry standards and guidelines from online resources.

[1815] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1816] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[1817] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1818] 3. Providing support

[1819] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1820] Terminal: Sends the entered question to the server.

[1821] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1822] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1823] 4. Feedback and Improvement

[1824] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1825] Device: Sends feedback information to the server.

[1826] Server: Stores the received feedback information in a database.

[1827] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1828] 5. Emotion Recognition and Analysis

[1829] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[1830] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[1831] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[1832] 6. Use of Emotional Data

[1833] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[1834] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[1835] Specific examples

[1836] Example 1: When new employee "Taro Tanaka" is assigned to the sales department

[1837] 1. Profile registration: Taro Tanaka's name, job title, and job responsibilities (sales) are entered on the terminal and sent to the server. The server registers this information in the database.

[1838] 2. Information learning: The server collects internal sales information (sales process, product information), general information (industry sales guidelines), and the latest information (latest product release information) and supplies it to the AI ​​engine. The AI ​​engine learns from this information and builds a knowledge base.

[1839] 3. Providing support: When Taro Tanaka sends a question from his device, such as "What is the schedule for the next sales meeting?", the server passes the question to the AI ​​engine. The AI ​​engine generates an answer from its knowledge base, such as "The next sales meeting will be held at 10:00 next Monday," and sends it to Taro Tanaka's device via the server.

[1840] 4. Feedback and Improvement: Taro Tanaka enters feedback such as "The explanation was easy to understand" and sends it from his device to the server. The server stores the feedback information in a database and reflects it in the AI ​​engine's learning data.

[1841] 5. Emotion recognition: When Taro Tanaka types a question, the emotion engine detects "confusion" through his facial expression and sends that information to the server.

[1842] 6. Use of emotional data: Based on the emotional data of "confusion," the server instructs the AI ​​engine to provide assistance appropriate to Taro Tanaka's condition. The AI ​​engine generates assistance that explains the information in more detail so that Taro Tanaka can easily understand it, and sends the answer to his device.

[1843] In this way, by combining an AI engine and an emotion engine, the present invention provides an environment in which newcomers can efficiently learn their jobs, significantly reducing the burden on accepting members.

[1844] The processing flow will be explained below.

[1845] Step 1:

[1846] Terminal (user): The HR department or administrator enters the new employee's profile information (name, position, responsibilities, etc.) on the terminal.

[1847] Step 2:

[1848] Device: Sends the entered profile information to the server.

[1849] Step 3:

[1850] Server: Registers the received profile information in a database.

[1851] Step 4:

[1852] Server: Based on the registered information, instructs the AI ​​engine to learn information related to the role of the new participant.

[1853] Step 5:

[1854] Server: Searches and collects internal company information such as company documents, policies, and procedures.

[1855] Step 6:

[1856] Server: Collects general information such as industry standards and guidelines from online resources.

[1857] Step 7:

[1858] Server: Uses various APIs and news feeds to gather the latest company news and industry trend information.

[1859] Step 8:

[1860] AI engine: Analyzes collected internal information, general information, and the latest information to build an internal knowledge base.

[1861] Step 9:

[1862] Server: Periodically supplies new information to the AI ​​engine and updates the knowledge base.

[1863] Step 10:

[1864] Device (user): New participants input questions into the AI ​​engine using a chat tool or dedicated app from a device such as a smartphone or PC.

[1865] Step 11:

[1866] Terminal: Sends the entered question to the server.

[1867] Step 12:

[1868] AI engine: Analyzes incoming questions and generates appropriate answers from a knowledge base.

[1869] Step 13:

[1870] Server: Sends the answer generated by the AI ​​engine to the new participant's device.

[1871] Step 14:

[1872] Terminal (user): Newcomers enter feedback on the answers and support provided.

[1873] Step 15:

[1874] Device: Sends feedback information to the server.

[1875] Step 16:

[1876] Server: Stores the received feedback information in a database.

[1877] Step 17:

[1878] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[1879] Step 18:

[1880] Terminal (user): When a new participant enters a question, the emotion engine analyzes emotions from facial expressions and text in real time.

[1881] Step 19:

[1882] Emotion engine: Analyzes the emotions of new participants and sends the results to the server.

[1883] Step 20:

[1884] Server: Based on the received emotional data, the AI ​​engine is instructed to generate appropriate support content.

[1885] Step 21:

[1886] Server: Reflects the analyzed emotional data in the knowledge base and adjusts the support content.

[1887] Step 22:

[1888] Server: Continuously monitors using the emotion engine and provides support appropriate to the newcomer's condition based on historical data.

[1889] Example 2

[1890] 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."

[1891] In modern companies, it is an important challenge for newcomers to quickly and efficiently learn about business processes and corporate culture. However, training and supporting newcomers requires a great deal of time and effort, which often places a burden on the team members. Providing appropriate information and psychological support is also important for newcomers to begin their work effectively, but providing these in a balanced manner can be difficult. Furthermore, in addition to providing standard information, newcomers also need support tailored to their emotions and circumstances. A system that can address this is needed.

[1892] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1893] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting internal documents, industry standard knowledge, and the latest information and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving questions from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers, analyzing it, and reflecting it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and adjusting support content based on that data, and means for monitoring using the emotion engine and optimizing support content based on historical data. This allows newcomers to learn their jobs quickly and efficiently, reduces the burden on accepting members, and further increases the psychological safety of newcomers.

[1894] "Profile information" is individual attribute information such as the new participant's name, position, and responsibilities.

[1895] "Database" refers to an information management system for storing and managing received profile information.

[1896] "Internal documents" are documents such as procedures, policies, and product information created within a company.

[1897] "Industry standard knowledge" is information about widely recognized guidelines and best practices within a particular industry.

[1898] "Latest news" refers to the latest information on ongoing news, industry trends, product releases, and more.

[1899] An "AI engine" is a system that uses artificial intelligence technology to analyze collected information and build and update a knowledge base.

[1900] A "knowledge base" is a collection of information built by an AI engine that is used to provide answers and support to newcomers.

[1901] "Feedback" refers to the evaluation and opinions that new participants give about the answers and support provided.

[1902] The "emotion engine" is a technology that analyzes the emotions of new participants in real time and reflects the results of that analysis in the support provided.

[1903] "Monitoring" is the process of continuously observing the status of new participants and collecting and analyzing that data.

[1904] This invention is an onboarding system that helps new employees learn their jobs quickly and efficiently. This system functions through the cooperation of three entities: a server, a terminal, and a user. The specific hardware and software configurations and data processing methods are described below.

[1905] Components and technologies used

[1906] server

[1907] The server works with software modules including a database, AI engine, and emotion engine. This server is responsible for managing profile information and collecting, analyzing, and updating information. Specifically, it uses the following technologies:

[1908] Database system: MySQL, PostgreSQL, etc.

[1909] AI engines: generative AI models such as TensorFlow and PyTorch

[1910] Emotion engine: Facial recognition API (e.g., Microsoft Azure Face API), speech recognition system (e.g., Amazon Polly)

[1911] Terminal

[1912] Terminals are devices used by new participants and administrators, including smartphones, PCs, tablets, etc. Terminals communicate with the server using the following software:

[1913] Web browser: Google Chrome, Mozilla Firefox

[1914] Dedicated application: Custom app (compatible with iOS and Android)

[1915] User

[1916] Users are both new participants and administrators who handle them. Users use their devices to enter profile information, submit questions, and provide feedback.

[1917] Specific operation explanation

[1918] New Member Registration

[1919] User (Administrator): The HR department administrator enters the new employee's profile information (e.g., name, position, and responsibilities) from the terminal.

[1920] Terminal: The terminal sends the entered information to the server, which registers it in a database.

[1921] Learning and updating information

[1922] Server: The server calls APIs or uses web scraping techniques to collect internal documents, industry standard knowledge, and the latest information. For example, it uses the Google Scholar API to obtain industry standard knowledge.

[1923] AI engine: Analyzes collected information using natural language processing (NLP) technology to build and update the knowledge base.

[1924] Providing support

[1925] User (newcomer): The newcomer uses the terminal to input and submit a question. For example, they input a prompt such as, "When is the next sales meeting?"

[1926] Server: The server sends this question to the AI ​​engine, which generates an appropriate answer from its knowledge base and sends it back to the device.

[1927] Feedback and Improvements

[1928] User (newcomer): Enter and submit feedback on the answers received by newcomers. Provide specific feedback such as "The explanation was easy to understand."

[1929] Server: The server collects feedback information and reflects it in the AI ​​engine.

[1930] Emotion Recognition and Analysis

[1931] User (newcomer): When a user inputs a question from a device, the emotion engine performs real-time analysis using the camera and microphone.

[1932] Emotion Engine: The emotion engine analyzes the newcomer's facial expressions and tone of voice and sends the emotional data to the server.

[1933] Server: Based on the emotional data, the server instructs the AI ​​engine on the appropriate response.

[1934] Use of Emotional Data

[1935] Server: Reflects the analyzed emotional data in the knowledge base and adjusts and provides individual support content.

[1936] The emotion engine also continuously monitors the state of newcomers and uses that data along with feedback data.

[1937] Specific examples

[1938] Example 1: When a new employee is assigned to the sales department, if the new employee inputs the question, "What is the schedule for the next sales meeting?", the AI ​​engine will generate an answer from the knowledge base, such as, "The next sales meeting will be held next Monday at 10:00," and send it to the new employee's device. In addition, if the new employee's confused feelings are analyzed, the AI ​​engine will provide assistance by providing additional detailed information.

[1939] In this way, the present invention allows newcomers to learn the job quickly and effectively, reduces the burden on host team members, and provides psychological safety.

[1940] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1941] Step 1:

[1942] The user (administrator) enters the profile information of the new participant into the terminal. The input items include name, position, and responsibilities. Once the input is complete, the user presses the send button.

[1943] Input: Name, position, and duties of new participant

[1944] Output: The entered profile information is sent from the device to the server.

[1945] Step 2:

[1946] The terminal transmits the profile information input by the user to the server using a secure communication protocol (for example, HTTPS).

[1947] Input: Profile information (name, job title, responsibilities)

[1948] Output: Profile information is sent to the server.

[1949] Step 3:

[1950] The server registers the received profile information in a database, checking for duplicates against existing records and creating new records.

[1951] Input: Profile information (name, job title, responsibilities)

[1952] Output: The new participant's information is registered in the database and a unique ID is generated.

[1953] Step 4:

[1954] The server instructs the AI ​​engine to learn information related to the new entrant based on the unique ID and profile information.

[1955] Input: Profile information (including unique ID)

[1956] Output: Learning instructions are sent to the AI ​​engine.

[1957] Step 5:

[1958] The server collects internal documents, industry standard knowledge, and up-to-date information from various sources, including data retrieval from internal systems, web scraping, and various APIs.

[1959] Inputs: Learning instructions, internal document management systems, online resources, APIs

[1960] Output: The collected information is stored on the server.

[1961] Step 6:

[1962] The AI ​​engine analyzes the collected information to build and update its internal knowledge base, using natural language processing techniques to extract important information and store it as structured data.

[1963] Input: Collected information (documents, guidelines, news, etc.)

[1964] Output: An updated knowledge base is generated.

[1965] Step 7:

[1966] The user (newcomer) uses a chat tool or a dedicated app to enter a question and press the send button. For example, they send a prompt such as, "What is the schedule for the next sales meeting?"

[1967] Input: Question (prompt)

[1968] Output: The question is sent from the terminal to the server.

[1969] Step 8:

[1970] The terminal transmits the entered question to the server.

[1971] Input: Question (prompt)

[1972] Output: The query data is sent to the server.

[1973] Step 9:

[1974] The server transfers the received question data to the AI ​​engine and instructs it to generate an answer.

[1975] Input: Question (prompt)

[1976] Output: Instructions are sent to the AI ​​engine for processing.

[1977] Step 10:

[1978] The AI ​​engine searches its knowledge base for an appropriate answer and generates it, for example, "The next sales meeting will be held next Monday at 10:00."

[1979] Input: Question, Knowledge Base

[1980] Output: The generated answer

[1981] Step 11:

[1982] The server sends the generated answer to the new participant's terminal.

[1983] Input: Generated answer

[1984] Output: The answer is displayed on the new participant's device.

[1985] Step 12:

[1986] The user (newcomer) enters feedback on the answers and support provided and presses the send button. Specifically, the user enters feedback such as "The explanation was easy to understand."

[1987] Input: Feedback

[1988] Output: Feedback data is sent from the device to the server.

[1989] Step 13:

[1990] The terminal transmits the feedback data to the server.

[1991] Input: Feedback

[1992] Output: Feedback data is sent to the server.

[1993] Step 14:

[1994] The server stores the received feedback data in a database and analyzes it, and the analysis results are reflected in the AI ​​engine's learning data.

[1995] Input: Feedback data

[1996] Output: Updated training data

[1997] Step 15:

[1998] Users (new participants) use the device's camera and microphone to input questions, and the emotion engine analyzes facial expressions and voice in real time.

[1999] Input: facial expression data, voice data

[2000] Output: Parsed emotion data

[2001] Step 16:

[2002] The emotion engine analyzes the emotions of new participants and sends the results to the server.

[2003] Input: facial expression data, voice data

[2004] Output: Analysis results (emotion data)

[2005] Step 17:

[2006] The server then instructs the AI ​​engine on the appropriate support content based on the received emotional data. For example, it provides a more detailed explanation to a newcomer who is feeling confused.

[2007] Input: Emotion data

[2008] Output: Support content instructions

[2009] Step 18:

[2010] The server performs continuous monitoring and provides optimal support based on past data.

[2011] Input: Newcomer history data, emotion data

[2012] Output: Optimized support content

[2013] (Application example 2)

[2014] 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."

[2015] There is a need for an onboarding system that helps new employees learn their jobs quickly and efficiently. However, existing systems lack the ability to reduce the psychological burden on new employees and provide support that takes their emotions into consideration. This has led to early employee turnover, delays in job acquisition, and increased burdens on the new employees.

[2016] 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.

[2017] In this invention, the server includes means for receiving profile information of newcomers and registering it in a database, means for collecting information resources and supplying them to an AI engine, means for learning the information collected by the AI ​​engine and building and updating an internal knowledge base, means for receiving inquiries from newcomers and generating and sending answers from the knowledge base, means for receiving feedback from newcomers and analyzing it to reflect it in the AI ​​engine, means for analyzing the emotions of newcomers in real time and supplying emotional data to the AI ​​engine, and means for adjusting support content based on the emotional data. This enables newcomers to learn their jobs efficiently and with less psychological burden.

[2018] "Newcomers" are employees or staff who join a company or organization and begin work.

[2019] "Profile information" refers to personal information such as name, job title, and job responsibilities, as well as information related to work.

[2020] A "database" is a data structure that organizes, stores, and easily searches and retrieves information.

[2021] "Information resources" refers to information sources such as internal company information, general information, and the latest information.

[2022] An "AI engine" is a system that uses artificial intelligence to analyze data, learn, and provide appropriate answers and support.

[2023] A "knowledge base" is a collection of information learned by an AI engine, and is a database that serves as the basis for generating answers to questions.

[2024] An "inquiry" is a question that a newcomer asks for information or support.

[2025] "Feedback" refers to the opinions and evaluations that new participants give about the answers and support provided.

[2026] "Emotional data" refers to emotional information obtained through new participants' facial expressions, text, etc.

[2027] "Support content" refers to the guidance and assistance provided to newcomers to help them learn their jobs efficiently.

[2028] This invention is an onboarding system that helps new employees quickly and efficiently learn their jobs. The system is implemented using specific hardware and software.

[2029] System configuration

[2030] The system includes the following components:

[2031] Server: The primary computing resource for managing the database and running the AI ​​engine and emotion engine.

[2032] Terminal (user): A device through which a user interacts with the system, such as a mobile terminal such as a smartphone or smart glasses, or a PC.

[2033] AI engine: A software component that analyzes and learns from collected information and builds and updates the knowledge base.

[2034] Emotion engine: A software component that analyzes user emotions in real time and adjusts support content.

[2035] Database: Data storage for saving new participants' profile information and feedback information.

[2036] System program processing

[2037] 1. New participant registration

[2038] Server: Receives profile information of new participants and registers them in the database. Profile information includes name, job title, responsibilities, etc.

[2039] Server: Based on the registered information, instructs the AI ​​engine to learn related information.

[2040] 2. Learning and updating information

[2041] Server: Collects internal information, general information, and the latest information and supplies it to the AI ​​engine.

[2042] AI engine: Analyzes collected information and builds and updates the knowledge base.

[2043] 3. Providing support

[2044] Terminal (user): The user inputs a question from the terminal. The question is about business.

[2045] Server: Passes the received question to the AI ​​engine.

[2046] AI engine: Generates appropriate answers from the knowledge base and sends them to the user's device via the server.

[2047] 4. Feedback and Improvement

[2048] Terminal (user): Enters feedback on the answers provided.

[2049] Server: Receives feedback information and stores it in a database.

[2050] Server: Analyzes the saved feedback information and reflects it in the AI ​​engine's learning data.

[2051] 5. Emotion Recognition and Analysis

[2052] Terminal (user): When entering a question, the emotion engine analyzes the user's emotions in real time.

[2053] Emotion engine: Sends emotion data to the server.

[2054] Server: Based on the emotional data, the AI ​​engine instructs it to generate appropriate support content.

[2055] Hardware and software used

[2056] Hardware: Servers, smartphones, smart glasses, PCs

[2057] software:

[2058] emotion_recognition library: Used to analyze user emotions.

[2059] ai_engine library: Used to generate appropriate answers to questions from collected information.

[2060] database module: Used to store profile and feedback information.

[2061] Specific examples

[2062] When a new employee, "Mr. A," is assigned to a store, the store manager uses a smartphone app to enter Mr. A's profile information and sends it to the server. The server registers that information in a database and instructs the AI ​​engine to learn information related to Mr. A's position. When Mr. A asks, "Please tell me how to display this product," the AI ​​engine generates an appropriate answer from its knowledge base, replying, "This product is available in sizes S, M, and L." When Mr. A enters feedback, the server collects that information and reflects it in the AI ​​engine's learning data.

[2063] Example prompts for generative AI models

[2064] We are developing an assistant to support new employee onboarding at an apparel shop. This assistant uses an AI engine and an emotion engine to answer employees' questions and provide appropriate support depending on their emotions at the time. New employees ask questions through a smartphone app, and the AI ​​answers the questions, collects feedback, and learns. For example, if a question asks, "What sizes are available for this product?", the AI ​​engine will respond, "This product is available in sizes S, M, and L." Please tell us the specific implementation method for this assistant.

[2065] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2066] Step 1:

[2067] Input: New participant profile information (name, position, responsibilities, etc.)

[2068] Operation: The user (person in charge) enters profile information on a smartphone or PC.

[2069] Output: Profile information is sent to the server

[2070] Server operation: Register profile information in the database

[2071] Step 2:

[2072] Input: Registered profile information

[2073] Operation: The server instructs the AI ​​engine to learn relevant information based on the profile information.

[2074] Output: Instructions for the AI ​​engine to start learning

[2075] How the AI ​​engine works: Learns internal information, general information, and industry trend information to build a knowledge base

[2076] Step 3:

[2077] Input: Internal information, industry standard guidelines, latest news

[2078] How it works: The server continuously collects this information and provides it to the AI ​​engine.

[2079] Output: Updated information is fed to the AI ​​engine

[2080] How the AI ​​engine works: Analyzes the information provided and updates its knowledge base

[2081] Step 4:

[2082] Input: Inquiry from new participant

[2083] How it works: A user (newcomer) enters a question on a smartphone or PC.

[2084] Output: The query is sent to the server

[2085] Server operation: Passes the query to the AI ​​engine

[2086] Step 5:

[2087] Input: Inquiry details

[2088] How it works: The AI ​​engine searches the knowledge base and generates the appropriate answer

[2089] Output: The generated answer is returned to the server

[2090] Server action: Send the answer to the user's device

[2091] Step 6:

[2092] Input: Feedback from newcomers

[2093] Action: User (newcomer) enters feedback on provided answer

[2094] Output: Feedback is sent to the server

[2095] Server operation: The feedback information is saved in a database and reflected in the AI ​​engine's learning data.

[2096] Step 7:

[2097] Input: facial expressions and text when new participants type in their questions

[2098] How it works: The emotion engine acquires emotion data through the device's camera or text input.

[2099] Output: Emotion data is sent to the server

[2100] How the emotion engine works: Analyzes emotions from facial expressions and text in real time and sends the results to the server.

[2101] Step 8:

[2102] Input: Emotion data

[2103] How it works: The server instructs the AI ​​engine to generate appropriate support content based on the emotion data.

[2104] Output: Adjusted support content is generated

[2105] How the AI ​​engine works: Adjusts assistance content based on emotional data and generates the best answer for the user

[2106] 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.

[2107] 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.

[2108] 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.

[2109] 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.

[2110] 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.

[2111] 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.

[2112] 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).

[2113] 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.

[2114] 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."

[2115] 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.

[2116] 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).

[2117] 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.

[2118] 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.

[2119] 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.

[2120] 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.

[2121] 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.

[2122] 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.

[2123] 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.

[2124] 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.

[2125] 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.

[2126] 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.

[2127] The following is further disclosed regarding the above embodiment.

[2128] (Claim 1)

[2129] means for receiving and registering profile information of new participants in a database;

[2130] A means of collecting internal information, general information, and the latest information and supplying it to the AI ​​engine;

[2131] A means of learning from the information collected by the AI ​​engine and building and updating its internal knowledge base;

[2132] a means for receiving questions from new participants and generating and transmitting answers from a knowledge base;

[2133] A means to receive feedback from new participants, analyze it, and reflect it in the AI ​​engine;

[2134] A system including:

[2135] (Claim 2)

[2136] 10. The system of claim 1, further comprising means for directing the learning of relevant information based on the newcomer's profile information.

[2137] (Claim 3)

[2138] 10. The system of claim 1, further comprising means for periodically checking for updates and updating the knowledge base.

[2139] "Example 1"

[2140] (Claim 1)

[2141] means for receiving profile information of new entrants and registering them in a database;

[2142] A means to collect internal documents, industry standard knowledge, and the latest industry trend information and feed it to the AI ​​engine;

[2143] A means for analyzing the information collected by the AI ​​engine and building and updating its internal knowledge base;

[2144] a means for receiving questions from newcomers and generating and transmitting answers from a knowledge base;

[2145] A means to receive feedback from new entrants, analyze it, and reflect it in the AI ​​engine;

[2146] A system including:

[2147] (Claim 2)

[2148] 10. The system of claim 1, further comprising means for directing the learning of relevant information based on the newcomer's profile information.

[2149] (Claim 3)

[2150] 10. The system of claim 1, further comprising means for periodically checking for updates and updating the knowledge base.

[2151] "Application Example 1"

[2152] (Claim 1)

[2153] means for receiving and registering profile information of new participants in a database;

[2154] A means of collecting internal information, general information, and the latest information and supplying it to the AI ​​engine;

[2155] A means of learning from the information collected by the AI ​​engine and building and updating its internal knowledge base;

[2156] a means for receiving questions from new participants and generating and transmitting answers from a knowledge base;

[2157] A means to receive feedback from new participants, analyze it, and reflect it in the AI ​​engine;

[2158] A method using a head-mounted display that displays work procedures and safety measures in real time, and

[2159] A means to utilize generative AI models to process prompts and resolve questions for newcomers;

[2160] A system including:

[2161] (Claim 2)

[2162] 10. The system of claim 1, further comprising means for directing the learning of relevant information based on the newcomer's profile information.

[2163] (Claim 3)

[2164] 10. The system of claim 1, further comprising means for periodically checking for updates and updating the knowledge base.

[2165] "Example 2: Combining Emotion Engines"

[2166] (Claim 1)

[2167] means for receiving and registering profile information of new participants in a database;

[2168] A means to collect internal documents, industry standard knowledge, and up-to-date information to feed the AI ​​engine;

[2169] A means of learning from the information collected by the AI ​​engine and building and updating its internal knowledge base;

[2170] a means for receiving questions from new participants and generating and transmitting answers from a knowledge base;

[2171] A means to receive feedback from new participants, analyze it, and reflect it in the AI ​​engine;

[2172] A method to analyze newcomers' emotions in real time and adjust support content based on that data,

[2173] Monitoring using an emotion engine and optimizing support content based on historical data;

[2174] A system including:

[2175] (Claim 2)

[2176] 10. The system of claim 1, further comprising means for directing the learning of relevant information based on the newcomer's profile information.

[2177] (Claim 3)

[2178] 10. The system of claim 1, further comprising means for periodically checking for updates and updating the knowledge base.

[2179] "Application example 2 when combining emotion engines"

[2180] (Claim 1)

[2181] means for receiving and registering profile information of new participants in a database;

[2182] A means for collecting information resources and supplying them to the AI ​​engine;

[2183] A means of learning from the information collected by the AI ​​engine and building and updating its internal knowledge base;

[2184] means for receiving inquiries from new participants and generating and transmitting answers from a knowledge base;

[2185] A means to receive feedback from new participants, analyze it, and reflect it in the AI ​​engine;

[2186] A means to analyze the emotions of newcomers in real time and provide the emotion data to the AI ​​engine,

[2187] A means for adjusting support content based on emotion data;

[2188] A system including:

[2189] (Claim 2)

[2190] 10. The system of claim 1, further comprising means for directing the learning of relevant information based on the newcomer's profile information.

[2191] (Claim 3)

[2192] 10. The system of claim 1, further comprising means for periodically checking for updates and updating the knowledge base. [Explanation of symbols]

[2193] 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. means for receiving and registering profile information of new participants in a database; A means of collecting internal information, general information, and the latest information and supplying it to the AI ​​engine; A means of learning from the information collected by the AI ​​engine and building and updating its internal knowledge base; a means for receiving questions from new participants and generating and transmitting answers from a knowledge base; A means to receive feedback from new participants, analyze it, and reflect it in the AI ​​engine; A system including:

2. 10. The system of claim 1, further comprising means for directing the learning of related information based on the newcomer's profile information.

3. 2. The system of claim 1, further comprising means for periodically checking for updates and updating the knowledge base.

Images