System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving work style and work experience information input by a user; means for storing the received work style and work experience information in a database; means for using AI to analyze the stored work style and work experience information to find other users having a common topic or interest; means for sending a notification to users matched by the AI; and means for providing a chat function to support communication between the matched users.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] Traditional personnel placements often lead to dissatisfaction due to mismatches between departments and the so-called "department gacha." While there is a demand for improvements in the work environment and the quality of work, people are often placed solely on their background and skills without considering their individual work styles, interests, and concerns. This can lead to a lack of communication within the organization and poor progress in work. Therefore, there is a need for appropriate matching that takes into account users' work styles, interests, and concerns. [Means for solving the problem]

[0005] The present invention provides a system that receives work style and work history data entered by users and stores this data in a database. It also includes a system that uses artificial intelligence (AI) to analyze the stored data and find other users with common topics and interests. This system sends notifications to users matched by the AI ​​and provides a chat function to facilitate communication between matched users. The system also includes a system that receives feedback data from users and analyzes the received feedback data to adjust the AI ​​algorithm, thereby improving the accuracy of the system. The algorithm also takes into account users' hobbies, interests, work history, and work style, achieving more accurate matching.

[0006] "User" refers to an individual who uses the system and has a role of superior or subordinate.

[0007] "Work style" refers to the specific characteristics of how a user works at the workplace, such as data-driven or team-oriented.

[0008] "Work history data" refers to historical information such as a user's past work experience, achievements, and job titles.

[0009] "Database" refers to an information system for storing and managing user profile data.

[0010] "Artificial intelligence (AI)" refers to algorithms or software that help perform specific tasks by analyzing large amounts of data and identifying patterns and trends.

[0011] "Analysis" refers to the process of evaluating received data and processing it to identify commonalities and differences.

[0012] "Matching" refers to the process of finding users with common topics or interests and forming appropriate pairs or groups.

[0013] "Notification" refers to a message or alert that notifies the user of a match result.

[0014] "Chat function" refers to a communication tool that enables instant message exchange between matched users.

[0015] "Feedback Data" refers to user-provided opinions and ratings of the system and the matching experience.

[0016] An "algorithm" is a set of well-defined steps or computational methods for solving a particular task or problem. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that uses AI to match superiors and subordinates, taking into account the user's work style, work history, and interests. The system consists of user registration and profile setup, data storage, AI-based matching process, match notification, chat function, feedback collection, and algorithm adjustment.

[0039] Program processing

[0040] User registration and profile settings

[0041] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[0042] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[0043] The user enters the necessary information into each form and presses the submit button.

[0044] The terminal transmits the input data to the server.

[0045] Data storage

[0046] 2. The server receives the received user data and stores it in a database.

[0047] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0048] AI-powered matching process

[0049] 3. The server periodically retrieves the profiles of newly registered users from the database.

[0050] The server passes the acquired profile data to an AI algorithm and begins analysis.

[0051] The AI ​​evaluates similarities in work history, work style, hobbies, and interests.

[0052] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[0053] The server generates the matching results and stores them in a matching table.

[0054] Matching notification

[0055] 4. The server checks the matching results and sends notifications to each user.

[0056] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0057] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0058] Providing chat functionality

[0059] 5. The user taps the notification to proceed to the chat screen.

[0060] The terminal activates a chat function and supports sending and receiving messages between users.

[0061] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0062] The server receives the message and forwards it to the other user.

[0063] The terminal displays the forwarded message to the user.

[0064] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[0065] Additional feedback and adjustments

[0066] 6. Users provide feedback about their matching and chat experiences.

[0067] The terminal transmits the feedback data to the server.

[0068] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[0069] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0070] Specific examples

[0071] User A registration and profile settings

[0072] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[0073] The terminal receives this data and transmits it to the server.

[0074] The server stores the data in a database.

[0075] User B registration and profile settings

[0076] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[0077] The terminal receives this data and transmits it to the server.

[0078] The server stores the data in a database.

[0079] Matching Process and Notifications

[0080] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection) and generates matching results.

[0081] The server sends a matching notification to User A and User B.

[0082] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[0083] Start chatting

[0084] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[0085] The terminal sends a message to the server, which forwards the message to user B.

[0086] The device displays the message to User B, who then replies.

[0087] This system is expected to reduce the mismatch known as departmental gacha, improving the work environment and the quality of work.

[0088] The processing flow will be explained below.

[0089] Step 1:

[0090] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[0091] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[0092] Step 2:

[0093] The user enters the necessary information into each form and presses the submit button.

[0094] The terminal transmits the input data to the server.

[0095] Step 3:

[0096] The server receives the received user data and stores it in a database.

[0097] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0098] Step 4:

[0099] The server periodically retrieves the profiles of newly registered users from the database.

[0100] The server passes the acquired profile data to an AI algorithm and begins analysis.

[0101] Step 5:

[0102] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, and interests.

[0103] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[0104] Step 6:

[0105] The server generates the matching results and stores them in a matching table.

[0106] Step 7:

[0107] The server verifies the match results and sends notifications to the respective users.

[0108] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0109] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0110] Step 8:

[0111] The user taps the notification to proceed to the chat screen.

[0112] The terminal activates a chat function and supports sending and receiving messages between users.

[0113] Step 9:

[0114] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0115] The server receives the message and forwards it to the other user.

[0116] The terminal displays the forwarded message to the user.

[0117] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[0118] Step 10:

[0119] Users provide feedback about their matching and chat experiences.

[0120] The terminal transmits the feedback data to the server.

[0121] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[0122] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0123] Example 1

[0124] 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."

[0125] In traditional workplaces, matching between superiors and subordinates was problematic in that it was not possible to take into account not only work style and work history, but also personal preferences such as hobbies and interests. This led to frequent mismatches known as "department gacha," which led to problems of reduced workplace efficiency and atmosphere. Furthermore, it was difficult to automatically improve the matching algorithm based on feedback, making it difficult to improve the quality of service.

[0126] 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.

[0127] In this invention, the server includes: means for receiving attribute data entered by a user; means for storing the received attribute data in a data storage device; means for analyzing the stored attribute data and using artificial intelligence (AI) to find other users with common topics or interests; means for sending notifications to users matched by the AI; means for providing an information exchange function to support communication between matched users; and means for receiving evaluation data from users, analyzing the received evaluation data, and adjusting the AI ​​algorithm. This enables appropriate matching of superiors and subordinates while taking personal preferences into consideration, thereby improving the efficiency and atmosphere of the work environment. Furthermore, it is possible to continuously improve the algorithm by utilizing feedback and thereby improve the quality of service.

[0128] "Attribute data" refers to information such as a user's work style, work history, hobbies, and interests.

[0129] "Data storage device" refers to a storage medium or database for storing received user data.

[0130] "Artificial intelligence (AI)" refers to algorithms and programs that analyze user data and find other users with common topics and interests.

[0131] "Notification" refers to a message sent to inform a user of a match result.

[0132] "Information exchange function" refers to chat and message sending functions provided as a means of communication between matched users.

[0133] "Evaluation data" refers to feedback information provided by users.

[0134] "Algorithm" refers to the mathematical methods and processes used to match users based on factors such as work style, work history, hobbies, and interests.

[0135] This invention is a system that uses artificial intelligence (AI) to match superiors and subordinates, taking into account the user's work style, work history, hobbies, and interests. The system consists of user registration and profile setup, data storage in a data storage device, the AI-based matching process, match notification, information exchange functionality, additional evaluation data collection, and algorithm adjustment.

[0136] User registration and profile settings

[0137] The user first installs the application and enters attribute data such as name, email address, work history, work style, hobbies and interests when launching it for the first time. The device receives this data and sends it to the server. The device in question is an Android or iOS smartphone.

[0138] Data storage

[0139] The server stores the received attribute data in a data storage device, specifically a database such as MySQL, which stores each user's attribute data in an appropriate format for later use in the process.

[0140] AI-powered matching process

[0141] The server periodically retrieves the profiles of newly registered users from the database and passes the retrieved data to an AI algorithm for analysis. The algorithm is written in a programming language such as Python and executed via a REST API. The AI ​​algorithm evaluates similarities in the users' work history, work style, hobbies, and interests to generate appropriate matches. For example, it compares the profiles of User A (a superior) and User B (a subordinate) to find common interests.

[0142] Matching notification

[0143] The server checks the matching results and sends a notification to each user. The notification is sent using a push notification service such as Firebase Cloud Messaging (FCM). The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[0144] Providing information exchange functions

[0145] The user taps the notification to go to the chat screen, and the device launches the information exchange function. The information exchange function uses WebSocket or other methods to achieve real-time communication and supports the sending and receiving of messages between users. For example, when user A sends a message saying, "Hello, let's talk about outdoor activities," the message is forwarded to user B via the server.

[0146] Additional evaluation data collection and algorithm adjustments

[0147] Users provide evaluation data about their matchmaking and chat experiences, which is then sent to the server. The server analyzes the received evaluation data and adjusts the AI ​​algorithm. This analysis is done by adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[0148] Prompt Sentence Examples

[0149] The following prompts can be input into the generative AI model to obtain information that supports detailed matching algorithms and feedback analysis methods.

[0150] Example prompt 1:

[0151] "What is the optimal AI algorithm for matching superiors and subordinates based on analyzing users' work history, work style, and interests? Please also tell us how to implement it specifically."

[0152] Example prompt 2:

[0153] "How can we improve the accuracy of matching by adjusting the AI ​​algorithm based on user feedback? Please also tell us the specific analysis method and implementation example."

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1:

[0156] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[0157] Enter: Installed apps

[0158] Output: New registration screen

[0159] Step 2:

[0160] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[0161] Input: Trigger for new registration screen transition

[0162] Output: Input form

[0163] Step 3:

[0164] The user enters the necessary information into each form and presses the submit button.

[0165] Input: Name, email address, work history, work style, hobbies, interests

[0166] Output: Send input data

[0167] Step 4:

[0168] The terminal transmits the input data to the server.

[0169] Input: User-entered data

[0170] Output: Send data to the server

[0171] Step 5:

[0172] The server stores the received data in a data store, specifically by executing an SQL query to insert the data into a "user table" using a database such as MySQL.

[0173] Input: Input data

[0174] Output: Saving to a data storage device

[0175] Step 6:

[0176] The server periodically retrieves the profiles of newly registered users from the database. Specifically, a Cron job is set up to periodically retrieve newly registered user data from the database using a SELECT query.

[0177] Input: Cron job trigger

[0178] Output: Newly registered user data

[0179] Step 7:

[0180] The server passes the acquired profile data to an AI algorithm for analysis, specifically by sending a REST API request to an AI model written in Python.

[0181] Input: Newly registered user data

[0182] Output: Analysis results (matching result candidates)

[0183] Step 8:

[0184] The AI ​​algorithm evaluates similarities in work history, work style, hobbies, and interests to generate matching results. Example: User A (superior) and User B (subordinate) discover that they share common interests in "marketing" and "outdoor activities."

[0185] Input: Profile data

[0186] Output: Matching results

[0187] Step 9:

[0188] The server saves the matching results in a matching table. Specifically, it executes an INSERT query to the "matching table" to save the matching information.

[0189] Input: Matching results

[0190] Output: Save to Matching Table

[0191] Step 10:

[0192] The server notifies each user of the matching results by sending a push notification using services such as Firebase Cloud Messaging (FCM).

[0193] Input: Matching results

[0194] Output: Notification message

[0195] Step 11:

[0196] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0197] Input: Notification message

[0198] Output: Notification screen

[0199] Step 12:

[0200] The user taps the notification to proceed to the chat screen.

[0201] Input: Notification tap

[0202] Output: Chat screen

[0203] Step 13:

[0204] The device will launch a chat function and support the sending and receiving of messages between users, specifically using WebSocket and other protocols to achieve real-time communication.

[0205] Input: Chat screen

[0206] Output: Chat function activated

[0207] Step 14:

[0208] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0209] Input: Message input

[0210] Output: Message sent

[0211] Step 15:

[0212] The server receives the message and forwards it to the other user.

[0213] Input: Incoming message

[0214] Output: Forward message to other user

[0215] Step 16:

[0216] The device displays the forwarded message to the user. Example: User A sends a message saying "Hello, let's talk about outdoor activities!", and User B receives the message and replies.

[0217] Input: Forwarded message

[0218] Output: Message display

[0219] Step 17:

[0220] Users provide evaluation data about their matching and chat experiences.

[0221] Input: Feedback information

[0222] Output: Evaluation data

[0223] Step 18:

[0224] The terminal transmits the evaluation data to the server.

[0225] Input: Evaluation data

[0226] Output: Send data to the server

[0227] Step 19:

[0228] The server analyzes the evaluation data and adjusts the AI ​​algorithm, specifically adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[0229] Input: Evaluation data

[0230] Output: Tuned AI algorithm

[0231] (Application example 1)

[0232] 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."

[0233] With conventional systems, it was difficult to properly match staff and efficiently manage shifts in physical stores. If shifts were arranged without taking into account the compatibility and interests of staff members, the workplace atmosphere and work efficiency were likely to decline.

[0234] 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.

[0235] In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for sending notifications to users matched by the AI, means for providing a chat function to support communication between matched users, means for receiving and analyzing feedback data and adjusting the AI ​​algorithm, and means for enabling optimal staff pairing and shift management in physical stores. This enables appropriate staff matching and efficient shift management in physical stores, and is expected to improve the work environment and work efficiency.

[0236] "Work style" refers to the specific methods, practices, and systems by which a user carries out their work.

[0237] "Work history" is information indicating the occupations the user has had up to now, the duration of those occupations, and the details of those occupations.

[0238] A "database" is a system that systematically integrates multiple data and stores them in a form that is easy to manage and use.

[0239] Artificial intelligence (AI) is a technology that supports and automates human intellectual tasks by learning, analyzing, and inferring from large amounts of data.

[0240] A "notification" is a message from the system to the user conveying specific information or results.

[0241] The "chat function" is a function that supports real-time text communication between users.

[0242] "Feedback data" refers to data regarding opinions and evaluations provided by users regarding their experience and results using the system.

[0243] An "algorithm" is a set of rules that defines a series of processing steps or calculation methods.

[0244] A "brick and mortar store" is an actual sales or service point that is located in a physical location and offers goods or services.

[0245] "Matching" is the process of finding and combining other users and resources that are most suitable based on the user's characteristics and data.

[0246] "Pairing" is the act of linking two or more users together as a team based on their mutual characteristics and compatibility.

[0247] "Shift management" is the process of efficiently allocating and managing the working hours and responsibilities of each staff member in the workplace.

[0248] This invention is a system for efficiently performing optimal staff matching and shift management in a physical store. Specific embodiments for realizing this system will be described below.

[0249] System Configuration

[0250] This system is built using a smartphone application and a cloud server. The smartphone application is used by staff to register and manage their own information, and the cloud server stores and analyzes the registered data.

[0251] Hardware and software used

[0252] Hardware: Smartphones (iOS, Android), cloud infrastructure (AWS, GCP)

[0253] Software: App development frameworks (React Native, Flutter), AI algorithms (TensorFlow, PyTorch), API servers (Node.js, Express), databases (MySQL, PostgreSQL)

[0254] Data registration

[0255] The user installs the smartphone app and, when launching it for the first time, fills in an input form with information such as their name, work history, work style, hobbies, and interests. The device then sends the data entered by the user to the server, which then stores it in a database.

[0256] Data analysis

[0257] The server periodically retrieves the profile data of newly registered users from the database and passes it to an AI algorithm for analysis. The AI ​​algorithm evaluates similarities based on the user's work history, work style, hobbies, interests, etc., and matches them with the most suitable staff.

[0258] Notifications and chat features

[0259] Once a match is generated, the server sends a notification to each user, such as "We've found a colleague who shares your interests." Users can tap the notification to go to the chat screen and exchange messages in real time.

[0260] Feedback and algorithm adjustments

[0261] Users can provide feedback on their matching and chat experiences, which the server then analyzes to optimize the AI ​​algorithm and achieve even more accurate matching.

[0262] Specific examples

[0263] Specifically, the following scenarios are possible:

[0264] 1. User A: "4 years of experience in customer service, hobby: cooking, interest: knowledge of ingredients."

[0265] 2. User B: "2 years of experience in customer service, hobbies: travel, interests: tourist information."

[0266] The AI ​​algorithm can find that User A and User B share a common work history in customer service and that their hobbies and interests are related, making it possible to optimally pair them.

[0267] Example prompt statement:

[0268] "Staff member A has four years of experience in customer service, and his hobby is cooking, so he is interested in learning about ingredients. Staff member B has two years of experience, and his hobby is traveling, so he is interested in information about tourist spots. Use AI to analyze what commonalities can be found."

[0269] In this way, this system enables appropriate staff matching and efficient shift management, which is expected to improve the work environment and work efficiency.

[0270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0271] Step 1:

[0272] The user installs the app and, when launching it for the first time, enters their name, work history, work style, hobbies, interests, etc. into the input form. This input data becomes the initial data for the application. Input: Name, work history, work style, hobbies, interests. Output: Entered data. Specific operation: The device displays the input form, and once the user has entered the required information, they tap the send button to send the data to the server.

[0273] Step 2:

[0274] The device sends the entered data to the server. Input: Data entered by the user. Output: Data sent to the server. Specific operation: The device sends data to the cloud server via REST API.

[0275] Step 3:

[0276] The server stores the received user data in the database. Input: Data received by the server. Output: Data stored in the database. Specific operation: The server executes an INSERT statement in the database (e.g., MySQL, PostgreSQL) to store the data.

[0277] Step 4:

[0278] The server periodically retrieves profile data of newly registered users from the database. Input: Timer event. Output: Retrieved profile data. Specific operation: The server periodically executes an SQL query to retrieve data of newly registered users.

[0279] Step 5:

[0280] The server passes the acquired profile data to an AI algorithm for analysis. Input: Acquired profile data. Output: Analysis results. Specific operation: The server inputs the profile data into an AI model (e.g., TensorFlow, PyTorch) and uses a generative AI model to calculate similarities based on work history and interests.

[0281] Step 6:

[0282] The AI ​​evaluates the similarities of the profiles and generates matching results. Input: Profile data. Output: Generated matching results. Specific operation: The AI ​​uses a clustering algorithm (e.g., K-means) to find commonalities in each user's data.

[0283] Step 7:

[0284] The server checks the matching results and sends notifications to each user. Input: Matching results. Output: Sent notifications. Specific behavior: The server uses the notification API to send notification messages to matched users.

[0285] Step 8:

[0286] The user taps the notification to proceed to the chat screen. Input: Notification message. Output: Chat screen. Specific operation: The device receives the notification, and when the user taps the notification, the chat function is launched and messages can be sent and received with the other party.

[0287] Step 9:

[0288] The user provides feedback and sends it to the server. Input: Feedback data. Output: Feedback sent to the server. Specific operations: The terminal displays a feedback form and sends the data entered by the user to the server.

[0289] Step 10:

[0290] The server analyzes the feedback data and adjusts the AI ​​algorithm. Input: Feedback data. Output: Optimized AI algorithm. Specific operation: Based on the feedback data, the server readjusts the learning parameters of the AI ​​model to improve the accuracy of the next match.

[0291] 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.

[0292] This invention is a system that matches superiors and subordinates based on the user's work style, work history, hobbies, and interests, as well as their emotional data, using an emotion engine. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition and utilization using the emotion engine, match notification, provision of a chat function, and feedback collection and algorithm adjustment.

[0293] Program processing

[0294] User registration and profile settings

[0295] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[0296] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[0297] The user enters the necessary information into each form and presses the submit button.

[0298] The terminal transmits the input data to the server.

[0299] Data storage

[0300] 2. The server receives the received user data and stores it in a database.

[0301] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0302] Emotion recognition by emotion engine

[0303] 3. The server uses an emotion engine to extract emotion data from the user's input data.

[0304] The emotion engine uses natural language processing technology to recognize emotions from text data entered by the user. For example, if a user enters "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[0305] AI-powered matching process

[0306] 4. The server periodically retrieves the profile and emotion data of newly registered users from the database.

[0307] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[0308] The AI ​​evaluates similarities in work history, work style, hobbies, interests, and emotions.

[0309] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[0310] The server generates the matching results and stores them in a matching table.

[0311] Matching notification

[0312] 5. The server checks the matching results and sends notifications to each user.

[0313] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0314] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0315] Providing chat functionality

[0316] 6. The user taps the notification to proceed to the chat screen.

[0317] The terminal activates a chat function and supports sending and receiving messages between users.

[0318] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0319] The server receives the message and forwards it to the other user.

[0320] The terminal displays the forwarded message to the user.

[0321] Example: User A sends a message saying "Hi, would you like to talk about outdoor activities?" User B receives the message and replies.

[0322] Additional feedback and adjustments

[0323] 7. Users provide feedback about their matching and chat experiences.

[0324] The terminal transmits the feedback data to the server.

[0325] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[0326] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0327] Specific examples

[0328] User A registration and profile settings

[0329] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[0330] The terminal receives this data and transmits it to the server.

[0331] The server stores the data in a database.

[0332] Emotional Data Recognition

[0333] 1. The server obtains the sentence "I've been busy and stressed lately" from User A's input data.

[0334] The emotion engine analyzes this sentence and recognizes the emotion "stress."

[0335] User B registration and profile settings

[0336] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[0337] The terminal receives this data and transmits it to the server.

[0338] The server stores the data in a database.

[0339] Matching Process and Notifications

[0340] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection, low stress) and generates matching results.

[0341] The server sends a matching notification to User A and User B.

[0342] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[0343] Start chatting

[0344] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[0345] The terminal sends a message to the server, which forwards the message to user B.

[0346] The device displays the message to User B, who then replies.

[0347] This system is expected to reduce the mismatches known as "department gacha," improving the work environment and the quality of work. In addition, by utilizing an emotion engine, more detailed matching based on the user's psychological state becomes possible, and it is expected to provide a more comfortable work environment.

[0348] The processing flow will be explained below.

[0349] Step 1:

[0350] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[0351] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[0352] Step 2:

[0353] The user enters the necessary information into each form and presses the submit button.

[0354] The terminal transmits the input data to the server.

[0355] Step 3:

[0356] The server receives the received user data and stores it in a database.

[0357] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0358] Step 4:

[0359] The server extracts emotion data from the user's input data using an emotion engine.

[0360] The emotion engine uses natural language processing techniques to recognize emotions from text data entered by the user.

[0361] Example: If a user types, "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[0362] Step 5:

[0363] The server periodically retrieves the profile and emotion data of newly registered users from the database.

[0364] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[0365] Step 6:

[0366] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, interests, and emotions.

[0367] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[0368] Step 7:

[0369] The server generates the matching results and stores them in a matching table.

[0370] Step 8:

[0371] The server verifies the match results and sends notifications to the respective users.

[0372] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0373] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0374] Step 9:

[0375] The user taps the notification to proceed to the chat screen.

[0376] The terminal activates a chat function and supports sending and receiving messages between users.

[0377] Step 10:

[0378] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0379] The server receives the message and forwards it to the other user.

[0380] The terminal displays the forwarded message to the user.

[0381] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[0382] Step 11:

[0383] Users provide feedback about their matching and chat experiences.

[0384] The terminal transmits the feedback data to the server.

[0385] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[0386] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0387] Example 2

[0388] 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."

[0389] Conventional matching systems match users based on their work history, work style, hobbies, and interests, but lack a mechanism to consider the user's emotional state. As a result, the user's psychological compatibility is not fully taken into account, limiting the improvement of the work environment and work quality. Furthermore, they lacked functionality to facilitate smooth communication after matching. This resulted in problems such as insufficient user satisfaction and insufficient matching results.

[0390] 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.

[0391] In this invention, the server includes: means for receiving work style, work history data, and emotional data input by a user; means for storing the received work style, work history data, and emotional data in a database; means for analyzing the stored work style, work history data, and emotional data and using artificial intelligence (AI) to find other users who share common topics, interests, and emotional states; means for sending notifications to users matched by the AI; and means for providing a chat function to support communication between matched users. This enables more precise matching that takes into account the user's emotional state, which is expected to improve the work environment and work quality.

[0392] "Work style" refers to the characteristics of an individual user that indicate the method and attitude with which they proceed with their work.

[0393] "Work history data" is information that encompasses a user's past and present work experience and career history.

[0394] "Emotion data" is data that represents the psychological state and emotional status of the user.

[0395] "Database" means a system for systematically storing, accessing and managing received data.

[0396] "Artificial intelligence (AI)" is a technology that mimics human intelligence and performs data analysis and decision-making.

[0397] "Means for sending notifications" refers to functions and technologies for sending information or messages to users.

[0398] The "chat function" is a communication means that enables users to send and receive messages in real time.

[0399] "Feedback data" refers to information including evaluations, opinions, and impressions provided by users through their use of the system.

[0400] An "emotion engine" is a technology that uses natural language processing technology to extract emotions from user text data.

[0401] An "algorithm" is a logical method that describes a series of steps or calculations to solve a particular problem.

[0402] This invention is a system that matches superiors and subordinates based on users' work style, work history, and emotional data. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition using an emotion engine, match notification, chat functionality, and feedback collection and algorithm adjustment.

[0403] User registration and profile settings

[0404] The user first installs the application and proceeds to the registration screen when launching it for the first time. The device then displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Once the user enters the information in each field and presses the submit button, the device sends the data to the server.

[0405] Data storage

[0406] The server stores the received user data in a database, including name, email address, work history, work style, hobbies, and interests, using a relational database such as MySQL or PostgreSQL.

[0407] Emotion recognition by emotion engine

[0408] The server passes the user's input data to the emotion engine, which extracts the emotion data. The emotion engine uses natural language processing technology to recognize emotions from the text data. This process uses NLP libraries such as spaCy and NLTK. For example, the input "I've been busy and stressed lately" is analyzed and the emotion "stress" is extracted.

[0409] AI-powered matching process

[0410] The server periodically retrieves the profile data and emotional data of newly registered users from the database and passes them to an AI algorithm for analysis. AI models used include Scikit-learn and TensorFlow. The AI ​​algorithm evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. For example, it can match users who share marketing experience and outdoor activities, or who share low-stress emotional states.

[0411] Matching notification

[0412] The server checks the matching results and sends a notification to the matched users. The notification is sent using a notification service such as Firebase Cloud Messaging. The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[0413] Providing chat functionality

[0414] The user taps the notification to go to the chat screen, enters a message, and presses the send button. The device sends the message to the server, which then forwards it to the other user. This allows real-time message exchange between users.

[0415] Additional feedback and adjustments

[0416] Users can provide feedback on their matching and chat experiences via their devices, and the server analyzes this feedback data and adjusts the AI ​​algorithm and emotion engine to improve system accuracy and user satisfaction.

[0417] Specific examples (prompt sentence examples)

[0418] For example, if User A installs the app and enters "Name: User A", "Email: usera@example.com", "Work Experience: 9 years in marketing", "Work Style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection", these data will be stored in the database. Similarly, if User B enters "Name: User B", "Email: userb@example.com", "Work Experience: 2 years in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection", these will also be stored. The server then matches User A and User B based on their shared hobbies and emotional state and sends them notifications.

[0419] This system will help reduce mismatches in the workplace and provide a better working environment.

[0420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0421] Step 1:

[0422] The user installs the application and proceeds to the registration screen. The device loads the user interface and displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Input: User's personal information. Output: Data entered in the input form.

[0423] Step 2:

[0424] The user enters the required information and presses the send button. The terminal captures the entered data, packages it in JSON format, and sends it to the server using the HTTPS protocol. Input: Data entered by the user. Output: JSON data sent to the server.

[0425] Step 3:

[0426] The server receives the received user data, performs data integrity checks, and then saves the data in a database (e.g., MySQL, PostgreSQL). Input: User data in JSON format. Output: User profile information saved in the database.

[0427] Step 4:

[0428] The server passes the user's text data to the emotion engine, which extracts emotion data. The emotion engine (e.g., spaCy, NLTK) uses natural language processing techniques to recognize emotions from the text data. Input: User's text data. Output: Extracted emotion data.

[0429] Step 5:

[0430] The server periodically retrieves profile data and emotion data of newly registered users from the database. The server sets up a scheduled task (e.g., a Cron job) to execute the database query. Input: User data from the database. Output: Retrieved profile data and emotion data.

[0431] Step 6:

[0432] The server passes the acquired data to an AI algorithm for analysis. The AI ​​model (e.g., Scikit-learn, TensorFlow) evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. Input: Profile data and emotion data. Output: Matching results.

[0433] Step 7:

[0434] The server checks the generated matching results and stores them in a database as a matching table. Input: Matching results from the AI ​​algorithm. Output: Stored matching table.

[0435] Step 8:

[0436] The server sends notifications to the matched users based on the matching results. The server uses a notification service (e.g., Firebase Cloud Messaging) to send notifications. Input: Matching results. Output: Notification message sent to the user.

[0437] Step 9:

[0438] The user taps the notification to proceed to the chat screen. The device launches the chat function and displays an interface where messages can be sent and received. Input: Notification message. Output: Chat screen.

[0439] Step 10:

[0440] The user inputs a message and presses the send button. The terminal sends the message to the server, which then forwards the message to the other user. Input: User's message. Output: Message forwarded to the other user.

[0441] Step 11:

[0442] Users provide feedback about their matching and chat experiences. The device sends the feedback data to the server, which then analyzes it. Input: Feedback from the user. Output: Feedback data sent to the server.

[0443] Step 12:

[0444] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine, thereby improving the system's accuracy and user satisfaction. Input: Feedback data. Output: Adjusted AI algorithm and emotion engine.

[0445] (Application example 2)

[0446] 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."

[0447] Conventional factory robot management systems take into account robot profile data, but do not use emotional data to match robots or optimize work teams. As a result, robot stress and declining efficiency are often overlooked, negatively impacting work efficiency and quality. Furthermore, the inability to form appropriate work pairs has led to increased robot breakdowns and errors, which has become an issue.

[0448] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for analyzing emotion data and combining it with profile data to perform matching, means for sending notifications to matched users, means for providing a chat function to support communication between matched users, and means for forming an optimal work team for matched users. This makes it possible to form an optimal work team based on the profile data and emotion data, thereby improving work efficiency and reducing stress on the robot.

[0449] "User" refers to a person who uses the system or a robot administrator.

[0450] "Work style" is information that indicates the tendencies and characteristics of the user or robot regarding how they proceed with work.

[0451] "Work history data" is career information about jobs and projects that a user or robot has worked on in the past.

[0452] A "database" is a collection of information for storing and managing received data.

[0453] "Artificial intelligence (AI)" refers to machine learning models and related technologies for analyzing and matching data.

[0454] "Emotion data" is information obtained by analyzing the current emotional state of the user or robot.

[0455] "Profile data" is data that compiles personal information and characteristics of a user or robot, such as work style, work history, hobbies, and interests.

[0456] "Matching" is the process of forming optimal work teams or pairs based on profile data and emotional data.

[0457] A "notification" is a message from the system to convey information to a user or administrator.

[0458] The "chat function" is a message exchange function to support communication between matched users or robots.

[0459] A "work team" is a group of robots or users who work together and are organized based on profile data and emotion data.

[0460] "Work efficiency" is an index that indicates how much work a user or a robot can effectively perform within a certain amount of time.

[0461] "Stress" is an emotional state that represents the strain or tension felt by a user or a robot.

[0462] This invention realizes optimal robot matching and work team formation based on profile data and emotion data in a factory robot management system. To implement this, a system with the following functions is constructed.

[0463] Program processing

[0464] The system's programming is divided into the following steps:

[0465] 1. User registration and profile setup:

[0466] The user registers basic information about the robot (ID, work history, working style, emotional state, etc.) through smart glasses. The device receives this information and sends it to the server.

[0467] 2. Data storage:

[0468] The server stores the received robot profile data in a database, which is used for future analysis and matching.

[0469] 3. Emotion Recognition with Emotion Engine:

[0470] The server collects the robot's work data and operation logs and analyzes its emotional state using an emotion engine that utilizes the emotion recognition API of Microsoft Azure Cognitive Services.

[0471] 4. AI Matching Process:

[0472] The server passes the stored profile data and emotion data to an AI algorithm built with AWS SageMaker to find the best match.

[0473] 5. Match Notification:

[0474] The matching results are then communicated to the factory manager via smart glasses, allowing the manager to make appropriate task assignments.

[0475] 6. Providing chat functionality:

[0476] If necessary, a chat function will be provided to support communication between robots, which will be implemented using the Slack API.

[0477] 7. Additional feedback and adjustments:

[0478] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, thereby improving the accuracy of matching.

[0479] Hardware and software used

[0480] Smart glasses: High-performance smart glasses (e.g. HoloLens 2)

[0481] Emotion Engine: Emotion Recognition API from Microsoft Azure Cognitive Services

[0482] Database: AWS RDS (Relational Database Service)

[0483] AI Algorithms: Machine Learning Models with AWS SageMaker

[0484] Chat function: Slack API

[0485] Specific examples

[0486] The factory manager puts on the HoloLens 2 and checks the information about Robot A (ID: R001, work history: assembly, emotional state: stress). The HoloLens 2 display shows a matching result recommending collaboration with Robot B (ID: R002, work history: assembly, emotional state: relaxed).

[0487] Prompt Sentence Examples

[0488] plaintext

[0489] You are a factory manager. Use your smart glasses to check the following information:

[0490] Robot ID R001: Assembly work, high stress.

[0491] Robot ID R002: Assembly task, relaxed state.

[0492] Use this information to create the optimal work team and allocate tasks efficiently.

[0493] This system makes it possible to form optimal work teams based on profile data and emotional data, thereby improving work efficiency and reducing stress for robots.

[0494] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0495] Step 1:

[0496] The user registers basic information about the robot through smart glasses. The user inputs data such as the robot's ID, work history, working style, and emotional state. The terminal receives this information and sends it to the server. The input in this step is the robot information entered by the user, and the output is the basic information data sent to the server.

[0497] Step 2:

[0498] The server stores the received robot basic information data in a database, ensuring data for later analysis and matching. The input in this step is the basic information data received from the device, and the output is the profile data stored in the database.

[0499] Step 3:

[0500] The server acquires the robot's work data and operation logs and analyzes its emotional state using an emotion engine. The emotion engine uses the emotion recognition API of Microsoft Azure Cognitive Services to extract the robot's emotional state, such as stress or reduced efficiency, from the work data. The input in this step is the work data and operation logs, and the output is the analyzed emotional data.

[0501] Step 4:

[0502] The server then passes the stored profile data and emotion data to an AI algorithm for analysis. The AI ​​algorithm, built on AWS SageMaker, discovers commonalities between robots and determines optimal matching. The inputs for this step are profile data and emotion data, and the output is the optimal matching result.

[0503] Step 5:

[0504] The server notifies the smart glasses of the optimal matching result. The user can then use the smart glasses to check the optimal working pairing and team composition between the robots. The input in this step is the AI ​​matching result, and the output is a notification message for the user.

[0505] Step 6:

[0506] The user assigns appropriate tasks based on the matching results on the smart glasses display. If necessary, communication between the robots is supported via a chat function. This chat function is implemented using the Slack API. The input in this step is the user's task assignment instructions and chat messages, and the output is communication data between the robots.

[0507] Step 7:

[0508] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, which improves the accuracy of matching and improves work efficiency in the future. The input in this step is the feedback data from users, and the output is the adjusted AI algorithm.

[0509] By following the above steps, the system can create an optimal work team based on profile and emotion data, improving work efficiency and reducing stress for robots.

[0510] 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.

[0511] 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.

[0512] 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.

[0513] [Second embodiment]

[0514] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0515] 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.

[0516] 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).

[0517] 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.

[0518] 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.

[0519] 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).

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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."

[0526] This invention is a system that uses AI to match superiors and subordinates, taking into account the user's work style, work history, and interests. The system consists of user registration and profile setup, data storage, AI-based matching process, match notification, chat function, feedback collection, and algorithm adjustment.

[0527] Program processing

[0528] User registration and profile settings

[0529] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[0530] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[0531] The user enters the necessary information into each form and presses the submit button.

[0532] The terminal transmits the input data to the server.

[0533] Data storage

[0534] 2. The server receives the received user data and stores it in a database.

[0535] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0536] AI-powered matching process

[0537] 3. The server periodically retrieves the profiles of newly registered users from the database.

[0538] The server passes the acquired profile data to an AI algorithm and begins analysis.

[0539] The AI ​​evaluates similarities in work history, work style, hobbies, and interests.

[0540] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[0541] The server generates the matching results and stores them in a matching table.

[0542] Matching notification

[0543] 4. The server checks the matching results and sends notifications to each user.

[0544] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0545] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0546] Providing chat functionality

[0547] 5. The user taps the notification to proceed to the chat screen.

[0548] The terminal activates a chat function and supports sending and receiving messages between users.

[0549] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0550] The server receives the message and forwards it to the other user.

[0551] The terminal displays the forwarded message to the user.

[0552] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[0553] Additional feedback and adjustments

[0554] 6. Users provide feedback about their matching and chat experiences.

[0555] The terminal transmits the feedback data to the server.

[0556] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[0557] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0558] Specific examples

[0559] User A registration and profile settings

[0560] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[0561] The terminal receives this data and transmits it to the server.

[0562] The server stores the data in a database.

[0563] User B registration and profile settings

[0564] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[0565] The terminal receives this data and transmits it to the server.

[0566] The server stores the data in a database.

[0567] Matching Process and Notifications

[0568] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection) and generates matching results.

[0569] The server sends a matching notification to User A and User B.

[0570] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[0571] Start chatting

[0572] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[0573] The terminal sends a message to the server, which forwards the message to user B.

[0574] The device displays the message to User B, who then replies.

[0575] This system is expected to reduce the mismatch known as departmental gacha, improving the work environment and the quality of work.

[0576] The processing flow will be explained below.

[0577] Step 1:

[0578] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[0579] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[0580] Step 2:

[0581] The user enters the necessary information into each form and presses the submit button.

[0582] The terminal transmits the input data to the server.

[0583] Step 3:

[0584] The server receives the received user data and stores it in a database.

[0585] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0586] Step 4:

[0587] The server periodically retrieves the profiles of newly registered users from the database.

[0588] The server passes the acquired profile data to an AI algorithm and begins analysis.

[0589] Step 5:

[0590] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, and interests.

[0591] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[0592] Step 6:

[0593] The server generates the matching results and stores them in a matching table.

[0594] Step 7:

[0595] The server verifies the match results and sends notifications to the respective users.

[0596] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0597] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0598] Step 8:

[0599] The user taps the notification to proceed to the chat screen.

[0600] The terminal activates a chat function and supports sending and receiving messages between users.

[0601] Step 9:

[0602] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0603] The server receives the message and forwards it to the other user.

[0604] The terminal displays the forwarded message to the user.

[0605] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[0606] Step 10:

[0607] Users provide feedback about their matching and chat experiences.

[0608] The terminal transmits the feedback data to the server.

[0609] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[0610] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0611] Example 1

[0612] 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."

[0613] In traditional workplaces, matching between superiors and subordinates was problematic in that it was not possible to take into account not only work style and work history, but also personal preferences such as hobbies and interests. This led to frequent mismatches known as "department gacha," which led to problems of reduced workplace efficiency and atmosphere. Furthermore, it was difficult to automatically improve the matching algorithm based on feedback, making it difficult to improve the quality of service.

[0614] 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.

[0615] In this invention, the server includes: means for receiving attribute data entered by a user; means for storing the received attribute data in a data storage device; means for analyzing the stored attribute data and using artificial intelligence (AI) to find other users with common topics or interests; means for sending notifications to users matched by the AI; means for providing an information exchange function to support communication between matched users; and means for receiving evaluation data from users, analyzing the received evaluation data, and adjusting the AI ​​algorithm. This enables appropriate matching of superiors and subordinates while taking personal preferences into consideration, thereby improving the efficiency and atmosphere of the work environment. Furthermore, it is possible to continuously improve the algorithm by utilizing feedback and thereby improve the quality of service.

[0616] "Attribute data" refers to information such as a user's work style, work history, hobbies, and interests.

[0617] "Data storage device" refers to a storage medium or database for storing received user data.

[0618] "Artificial intelligence (AI)" refers to algorithms and programs that analyze user data and find other users with common topics and interests.

[0619] "Notification" refers to a message sent to inform a user of a match result.

[0620] "Information exchange function" refers to chat and message sending functions provided as a means of communication between matched users.

[0621] "Evaluation data" refers to feedback information provided by users.

[0622] "Algorithm" refers to the mathematical methods and processes used to match users based on factors such as work style, work history, hobbies, and interests.

[0623] This invention is a system that uses artificial intelligence (AI) to match superiors and subordinates, taking into account the user's work style, work history, hobbies, and interests. The system consists of user registration and profile setup, data storage in a data storage device, the AI-based matching process, match notification, information exchange functionality, additional evaluation data collection, and algorithm adjustment.

[0624] User registration and profile settings

[0625] The user first installs the application and enters attribute data such as name, email address, work history, work style, hobbies and interests when launching it for the first time. The device receives this data and sends it to the server. The device in question is an Android or iOS smartphone.

[0626] Data storage

[0627] The server stores the received attribute data in a data storage device, specifically a database such as MySQL, which stores each user's attribute data in an appropriate format for later use in the process.

[0628] AI-powered matching process

[0629] The server periodically retrieves the profiles of newly registered users from the database and passes the retrieved data to an AI algorithm for analysis. The algorithm is written in a programming language such as Python and executed via a REST API. The AI ​​algorithm evaluates similarities in the users' work history, work style, hobbies, and interests to generate appropriate matches. For example, it compares the profiles of User A (a superior) and User B (a subordinate) to find common interests.

[0630] Matching notification

[0631] The server checks the matching results and sends a notification to each user. The notification is sent using a push notification service such as Firebase Cloud Messaging (FCM). The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[0632] Providing information exchange functions

[0633] The user taps the notification to go to the chat screen, and the device launches the information exchange function. The information exchange function uses WebSocket or other methods to achieve real-time communication and supports the sending and receiving of messages between users. For example, when user A sends a message saying, "Hello, let's talk about outdoor activities," the message is forwarded to user B via the server.

[0634] Additional evaluation data collection and algorithm adjustments

[0635] Users provide evaluation data about their matchmaking and chat experiences, which is then sent to the server. The server analyzes the received evaluation data and adjusts the AI ​​algorithm. This analysis is done by adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[0636] Prompt Sentence Examples

[0637] The following prompts can be input into the generative AI model to obtain information that supports detailed matching algorithms and feedback analysis methods.

[0638] Example prompt 1:

[0639] "What is the optimal AI algorithm for matching superiors and subordinates based on analyzing users' work history, work style, and interests? Please also tell us how to implement it specifically."

[0640] Example prompt 2:

[0641] "How can we improve the accuracy of matching by adjusting the AI ​​algorithm based on user feedback? Please also tell us the specific analysis method and implementation example."

[0642] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0643] Step 1:

[0644] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[0645] Enter: Installed apps

[0646] Output: New registration screen

[0647] Step 2:

[0648] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[0649] Input: Trigger for new registration screen transition

[0650] Output: Input form

[0651] Step 3:

[0652] The user enters the necessary information into each form and presses the submit button.

[0653] Input: Name, email address, work history, work style, hobbies, interests

[0654] Output: Send input data

[0655] Step 4:

[0656] The terminal transmits the input data to the server.

[0657] Input: User-entered data

[0658] Output: Send data to the server

[0659] Step 5:

[0660] The server stores the received data in a data store, specifically by executing an SQL query to insert the data into a "user table" using a database such as MySQL.

[0661] Input: Input data

[0662] Output: Saving to a data storage device

[0663] Step 6:

[0664] The server periodically retrieves the profiles of newly registered users from the database. Specifically, a Cron job is set up to periodically retrieve newly registered user data from the database using a SELECT query.

[0665] Input: Cron job trigger

[0666] Output: Newly registered user data

[0667] Step 7:

[0668] The server passes the acquired profile data to an AI algorithm for analysis, specifically by sending a REST API request to an AI model written in Python.

[0669] Input: Newly registered user data

[0670] Output: Analysis results (matching result candidates)

[0671] Step 8:

[0672] The AI ​​algorithm evaluates similarities in work history, work style, hobbies, and interests to generate matching results. Example: User A (superior) and User B (subordinate) discover that they share common interests in "marketing" and "outdoor activities."

[0673] Input: Profile data

[0674] Output: Matching results

[0675] Step 9:

[0676] The server saves the matching results in a matching table. Specifically, it executes an INSERT query to the "matching table" to save the matching information.

[0677] Input: Matching results

[0678] Output: Save to Matching Table

[0679] Step 10:

[0680] The server notifies each user of the matching results by sending a push notification using services such as Firebase Cloud Messaging (FCM).

[0681] Input: Matching results

[0682] Output: Notification message

[0683] Step 11:

[0684] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0685] Input: Notification message

[0686] Output: Notification screen

[0687] Step 12:

[0688] The user taps the notification to proceed to the chat screen.

[0689] Input: Notification tap

[0690] Output: Chat screen

[0691] Step 13:

[0692] The device will launch a chat function and support the sending and receiving of messages between users, specifically using WebSocket and other protocols to achieve real-time communication.

[0693] Input: Chat screen

[0694] Output: Chat function activated

[0695] Step 14:

[0696] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0697] Input: Message input

[0698] Output: Message sent

[0699] Step 15:

[0700] The server receives the message and forwards it to the other user.

[0701] Input: Incoming message

[0702] Output: Forward message to other user

[0703] Step 16:

[0704] The device displays the forwarded message to the user. Example: User A sends a message saying "Hello, let's talk about outdoor activities!", and User B receives the message and replies.

[0705] Input: Forwarded message

[0706] Output: Message display

[0707] Step 17:

[0708] Users provide evaluation data about their matching and chat experiences.

[0709] Input: Feedback information

[0710] Output: Evaluation data

[0711] Step 18:

[0712] The terminal transmits the evaluation data to the server.

[0713] Input: Evaluation data

[0714] Output: Send data to the server

[0715] Step 19:

[0716] The server analyzes the evaluation data and adjusts the AI ​​algorithm, specifically adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[0717] Input: Evaluation data

[0718] Output: Tuned AI algorithm

[0719] (Application example 1)

[0720] 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."

[0721] With conventional systems, it was difficult to properly match staff and efficiently manage shifts in physical stores. If shifts were arranged without taking into account the compatibility and interests of staff members, the workplace atmosphere and work efficiency were likely to decline.

[0722] 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.

[0723] In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for sending notifications to users matched by the AI, means for providing a chat function to support communication between matched users, means for receiving and analyzing feedback data and adjusting the AI ​​algorithm, and means for enabling optimal staff pairing and shift management in physical stores. This enables appropriate staff matching and efficient shift management in physical stores, and is expected to improve the work environment and work efficiency.

[0724] "Work style" refers to the specific methods, practices, and systems by which a user carries out their work.

[0725] "Work history" is information indicating the occupations the user has had up to now, the duration of those occupations, and the details of those occupations.

[0726] A "database" is a system that systematically integrates multiple data and stores them in a form that is easy to manage and use.

[0727] Artificial intelligence (AI) is a technology that supports and automates human intellectual tasks by learning, analyzing, and inferring from large amounts of data.

[0728] A "notification" is a message from the system to the user conveying specific information or results.

[0729] The "chat function" is a function that supports real-time text communication between users.

[0730] "Feedback data" refers to data regarding opinions and evaluations provided by users regarding their experience and results using the system.

[0731] An "algorithm" is a set of rules that defines a series of processing steps or calculation methods.

[0732] A "brick and mortar store" is an actual sales or service point that is located in a physical location and offers goods or services.

[0733] "Matching" is the process of finding and combining other users and resources that are most suitable based on the user's characteristics and data.

[0734] "Pairing" is the act of linking two or more users together as a team based on their mutual characteristics and compatibility.

[0735] "Shift management" is the process of efficiently allocating and managing the working hours and responsibilities of each staff member in the workplace.

[0736] This invention is a system for efficiently performing optimal staff matching and shift management in a physical store. Specific embodiments for realizing this system will be described below.

[0737] System Configuration

[0738] This system is built using a smartphone application and a cloud server. The smartphone application is used by staff to register and manage their own information, and the cloud server stores and analyzes the registered data.

[0739] Hardware and software used

[0740] Hardware: Smartphones (iOS, Android), cloud infrastructure (AWS, GCP)

[0741] Software: App development frameworks (React Native, Flutter), AI algorithms (TensorFlow, PyTorch), API servers (Node.js, Express), databases (MySQL, PostgreSQL)

[0742] Data registration

[0743] The user installs the smartphone app and, when launching it for the first time, fills in an input form with information such as their name, work history, work style, hobbies, and interests. The device then sends the data entered by the user to the server, which then stores it in a database.

[0744] Data analysis

[0745] The server periodically retrieves the profile data of newly registered users from the database and passes it to an AI algorithm for analysis. The AI ​​algorithm evaluates similarities based on the user's work history, work style, hobbies, interests, etc., and matches them with the most suitable staff.

[0746] Notifications and chat features

[0747] Once a match is generated, the server sends a notification to each user, such as "We've found a colleague who shares your interests." Users can tap the notification to go to the chat screen and exchange messages in real time.

[0748] Feedback and algorithm adjustments

[0749] Users can provide feedback on their matching and chat experiences, which the server then analyzes to optimize the AI ​​algorithm and achieve even more accurate matching.

[0750] Specific examples

[0751] Specifically, the following scenarios are possible:

[0752] 1. User A: "4 years of experience in customer service, hobby: cooking, interest: knowledge of ingredients."

[0753] 2. User B: "2 years of experience in customer service, hobbies: travel, interests: tourist information."

[0754] The AI ​​algorithm can find that User A and User B share a common work history in customer service and that their hobbies and interests are related, making it possible to optimally pair them.

[0755] Example prompt statement:

[0756] "Staff member A has four years of experience in customer service, and his hobby is cooking, so he is interested in learning about ingredients. Staff member B has two years of experience, and his hobby is traveling, so he is interested in information about tourist spots. Use AI to analyze what commonalities can be found."

[0757] In this way, this system enables appropriate staff matching and efficient shift management, which is expected to improve the work environment and work efficiency.

[0758] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0759] Step 1:

[0760] The user installs the app and, when launching it for the first time, enters their name, work history, work style, hobbies, interests, etc. into the input form. This input data becomes the initial data for the application. Input: Name, work history, work style, hobbies, interests. Output: Entered data. Specific operation: The device displays the input form, and once the user has entered the required information, they tap the send button to send the data to the server.

[0761] Step 2:

[0762] The device sends the entered data to the server. Input: Data entered by the user. Output: Data sent to the server. Specific operation: The device sends data to the cloud server via REST API.

[0763] Step 3:

[0764] The server stores the received user data in the database. Input: Data received by the server. Output: Data stored in the database. Specific operation: The server executes an INSERT statement in the database (e.g., MySQL, PostgreSQL) to store the data.

[0765] Step 4:

[0766] The server periodically retrieves profile data of newly registered users from the database. Input: Timer event. Output: Retrieved profile data. Specific operation: The server periodically executes an SQL query to retrieve data of newly registered users.

[0767] Step 5:

[0768] The server passes the acquired profile data to an AI algorithm for analysis. Input: Acquired profile data. Output: Analysis results. Specific operation: The server inputs the profile data into an AI model (e.g., TensorFlow, PyTorch) and uses a generative AI model to calculate similarities based on work history and interests.

[0769] Step 6:

[0770] The AI ​​evaluates the similarities of the profiles and generates matching results. Input: Profile data. Output: Generated matching results. Specific operation: The AI ​​uses a clustering algorithm (e.g., K-means) to find commonalities in each user's data.

[0771] Step 7:

[0772] The server checks the matching results and sends notifications to each user. Input: Matching results. Output: Sent notifications. Specific behavior: The server uses the notification API to send notification messages to matched users.

[0773] Step 8:

[0774] The user taps the notification to proceed to the chat screen. Input: Notification message. Output: Chat screen. Specific operation: The device receives the notification, and when the user taps the notification, the chat function is launched and messages can be sent and received with the other party.

[0775] Step 9:

[0776] The user provides feedback and sends it to the server. Input: Feedback data. Output: Feedback sent to the server. Specific operations: The terminal displays a feedback form and sends the data entered by the user to the server.

[0777] Step 10:

[0778] The server analyzes the feedback data and adjusts the AI ​​algorithm. Input: Feedback data. Output: Optimized AI algorithm. Specific operation: Based on the feedback data, the server readjusts the learning parameters of the AI ​​model to improve the accuracy of the next match.

[0779] 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.

[0780] This invention is a system that matches superiors and subordinates based on the user's work style, work history, hobbies, and interests, as well as their emotional data, using an emotion engine. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition and utilization using the emotion engine, match notification, provision of a chat function, and feedback collection and algorithm adjustment.

[0781] Program processing

[0782] User registration and profile settings

[0783] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[0784] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[0785] The user enters the necessary information into each form and presses the submit button.

[0786] The terminal transmits the input data to the server.

[0787] Data storage

[0788] 2. The server receives the received user data and stores it in a database.

[0789] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0790] Emotion recognition by emotion engine

[0791] 3. The server uses an emotion engine to extract emotion data from the user's input data.

[0792] The emotion engine uses natural language processing technology to recognize emotions from text data entered by the user. For example, if a user enters "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[0793] AI-powered matching process

[0794] 4. The server periodically retrieves the profile and emotion data of newly registered users from the database.

[0795] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[0796] The AI ​​evaluates similarities in work history, work style, hobbies, interests, and emotions.

[0797] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[0798] The server generates the matching results and stores them in a matching table.

[0799] Matching notification

[0800] 5. The server checks the matching results and sends notifications to each user.

[0801] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0802] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0803] Providing chat functionality

[0804] 6. The user taps the notification to proceed to the chat screen.

[0805] The terminal activates a chat function and supports sending and receiving messages between users.

[0806] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0807] The server receives the message and forwards it to the other user.

[0808] The terminal displays the forwarded message to the user.

[0809] Example: User A sends a message saying "Hi, would you like to talk about outdoor activities?" User B receives the message and replies.

[0810] Additional feedback and adjustments

[0811] 7. Users provide feedback about their matching and chat experiences.

[0812] The terminal transmits the feedback data to the server.

[0813] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[0814] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0815] Specific examples

[0816] User A registration and profile settings

[0817] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[0818] The terminal receives this data and transmits it to the server.

[0819] The server stores the data in a database.

[0820] Emotional Data Recognition

[0821] 1. The server obtains the sentence "I've been busy and stressed lately" from User A's input data.

[0822] The emotion engine analyzes this sentence and recognizes the emotion "stress."

[0823] User B registration and profile settings

[0824] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[0825] The terminal receives this data and transmits it to the server.

[0826] The server stores the data in a database.

[0827] Matching Process and Notifications

[0828] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection, low stress) and generates matching results.

[0829] The server sends a matching notification to User A and User B.

[0830] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[0831] Start chatting

[0832] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[0833] The terminal sends a message to the server, which forwards the message to user B.

[0834] The device displays the message to User B, who then replies.

[0835] This system is expected to reduce the mismatches known as "department gacha," improving the work environment and the quality of work. In addition, by utilizing an emotion engine, more detailed matching based on the user's psychological state becomes possible, and it is expected to provide a more comfortable work environment.

[0836] The processing flow will be explained below.

[0837] Step 1:

[0838] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[0839] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[0840] Step 2:

[0841] The user enters the necessary information into each form and presses the submit button.

[0842] The terminal transmits the input data to the server.

[0843] Step 3:

[0844] The server receives the received user data and stores it in a database.

[0845] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[0846] Step 4:

[0847] The server extracts emotion data from the user's input data using an emotion engine.

[0848] The emotion engine uses natural language processing techniques to recognize emotions from text data entered by the user.

[0849] Example: If a user types, "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[0850] Step 5:

[0851] The server periodically retrieves the profile and emotion data of newly registered users from the database.

[0852] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[0853] Step 6:

[0854] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, interests, and emotions.

[0855] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[0856] Step 7:

[0857] The server generates the matching results and stores them in a matching table.

[0858] Step 8:

[0859] The server verifies the match results and sends notifications to the respective users.

[0860] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[0861] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[0862] Step 9:

[0863] The user taps the notification to proceed to the chat screen.

[0864] The terminal activates a chat function and supports sending and receiving messages between users.

[0865] Step 10:

[0866] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[0867] The server receives the message and forwards it to the other user.

[0868] The terminal displays the forwarded message to the user.

[0869] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[0870] Step 11:

[0871] Users provide feedback about their matching and chat experiences.

[0872] The terminal transmits the feedback data to the server.

[0873] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[0874] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[0875] Example 2

[0876] 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."

[0877] Conventional matching systems match users based on their work history, work style, hobbies, and interests, but lack a mechanism to consider the user's emotional state. As a result, the user's psychological compatibility is not fully taken into account, limiting the improvement of the work environment and work quality. Furthermore, they lacked functionality to facilitate smooth communication after matching. This resulted in problems such as insufficient user satisfaction and insufficient matching results.

[0878] 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.

[0879] In this invention, the server includes: means for receiving work style, work history data, and emotional data input by a user; means for storing the received work style, work history data, and emotional data in a database; means for analyzing the stored work style, work history data, and emotional data and using artificial intelligence (AI) to find other users who share common topics, interests, and emotional states; means for sending notifications to users matched by the AI; and means for providing a chat function to support communication between matched users. This enables more precise matching that takes into account the user's emotional state, which is expected to improve the work environment and work quality.

[0880] "Work style" refers to the characteristics of an individual user that indicate the method and attitude with which they proceed with their work.

[0881] "Work history data" is information that encompasses a user's past and present work experience and career history.

[0882] "Emotion data" is data that represents the psychological state and emotional status of the user.

[0883] "Database" means a system for systematically storing, accessing and managing received data.

[0884] "Artificial intelligence (AI)" is a technology that mimics human intelligence and performs data analysis and decision-making.

[0885] "Means for sending notifications" refers to functions and technologies for sending information or messages to users.

[0886] The "chat function" is a communication means that enables users to send and receive messages in real time.

[0887] "Feedback data" refers to information including evaluations, opinions, and impressions provided by users through their use of the system.

[0888] An "emotion engine" is a technology that uses natural language processing technology to extract emotions from user text data.

[0889] An "algorithm" is a logical method that describes a series of steps or calculations to solve a particular problem.

[0890] This invention is a system that matches superiors and subordinates based on users' work style, work history, and emotional data. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition using an emotion engine, match notification, chat functionality, and feedback collection and algorithm adjustment.

[0891] User registration and profile settings

[0892] The user first installs the application and proceeds to the registration screen when launching it for the first time. The device then displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Once the user enters the information in each field and presses the submit button, the device sends the data to the server.

[0893] Data storage

[0894] The server stores the received user data in a database, including name, email address, work history, work style, hobbies, and interests, using a relational database such as MySQL or PostgreSQL.

[0895] Emotion recognition by emotion engine

[0896] The server passes the user's input data to the emotion engine, which extracts the emotion data. The emotion engine uses natural language processing technology to recognize emotions from the text data. This process uses NLP libraries such as spaCy and NLTK. For example, the input "I've been busy and stressed lately" is analyzed and the emotion "stress" is extracted.

[0897] AI-powered matching process

[0898] The server periodically retrieves the profile data and emotional data of newly registered users from the database and passes them to an AI algorithm for analysis. AI models used include Scikit-learn and TensorFlow. The AI ​​algorithm evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. For example, it can match users who share marketing experience and outdoor activities, or who share low-stress emotional states.

[0899] Matching notification

[0900] The server checks the matching results and sends a notification to the matched users. The notification is sent using a notification service such as Firebase Cloud Messaging. The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[0901] Providing chat functionality

[0902] The user taps the notification to go to the chat screen, enters a message, and presses the send button. The device sends the message to the server, which then forwards it to the other user. This allows real-time message exchange between users.

[0903] Additional feedback and adjustments

[0904] Users can provide feedback on their matching and chat experiences via their devices, and the server analyzes this feedback data and adjusts the AI ​​algorithm and emotion engine to improve system accuracy and user satisfaction.

[0905] Specific examples (prompt sentence examples)

[0906] For example, if User A installs the app and enters "Name: User A", "Email: usera@example.com", "Work Experience: 9 years in marketing", "Work Style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection", these data will be stored in the database. Similarly, if User B enters "Name: User B", "Email: userb@example.com", "Work Experience: 2 years in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection", these will also be stored. The server then matches User A and User B based on their shared hobbies and emotional state and sends them notifications.

[0907] This system will help reduce mismatches in the workplace and provide a better working environment.

[0908] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0909] Step 1:

[0910] The user installs the application and proceeds to the registration screen. The device loads the user interface and displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Input: User's personal information. Output: Data entered in the input form.

[0911] Step 2:

[0912] The user enters the required information and presses the send button. The terminal captures the entered data, packages it in JSON format, and sends it to the server using the HTTPS protocol. Input: Data entered by the user. Output: JSON data sent to the server.

[0913] Step 3:

[0914] The server receives the received user data, performs data integrity checks, and then saves the data in a database (e.g., MySQL, PostgreSQL). Input: User data in JSON format. Output: User profile information saved in the database.

[0915] Step 4:

[0916] The server passes the user's text data to the emotion engine, which extracts emotion data. The emotion engine (e.g., spaCy, NLTK) uses natural language processing techniques to recognize emotions from the text data. Input: User's text data. Output: Extracted emotion data.

[0917] Step 5:

[0918] The server periodically retrieves profile data and emotion data of newly registered users from the database. The server sets up a scheduled task (e.g., a Cron job) to execute the database query. Input: User data from the database. Output: Retrieved profile data and emotion data.

[0919] Step 6:

[0920] The server passes the acquired data to an AI algorithm for analysis. The AI ​​model (e.g., Scikit-learn, TensorFlow) evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. Input: Profile data and emotion data. Output: Matching results.

[0921] Step 7:

[0922] The server checks the generated matching results and stores them in a database as a matching table. Input: Matching results from the AI ​​algorithm. Output: Stored matching table.

[0923] Step 8:

[0924] The server sends notifications to the matched users based on the matching results. The server uses a notification service (e.g., Firebase Cloud Messaging) to send notifications. Input: Matching results. Output: Notification message sent to the user.

[0925] Step 9:

[0926] The user taps the notification to proceed to the chat screen. The device launches the chat function and displays an interface where messages can be sent and received. Input: Notification message. Output: Chat screen.

[0927] Step 10:

[0928] The user inputs a message and presses the send button. The terminal sends the message to the server, which then forwards the message to the other user. Input: User's message. Output: Message forwarded to the other user.

[0929] Step 11:

[0930] Users provide feedback about their matching and chat experiences. The device sends the feedback data to the server, which then analyzes it. Input: Feedback from the user. Output: Feedback data sent to the server.

[0931] Step 12:

[0932] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine, thereby improving the system's accuracy and user satisfaction. Input: Feedback data. Output: Adjusted AI algorithm and emotion engine.

[0933] (Application example 2)

[0934] 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."

[0935] Conventional factory robot management systems take into account robot profile data, but do not use emotional data to match robots or optimize work teams. As a result, robot stress and declining efficiency are often overlooked, negatively impacting work efficiency and quality. Furthermore, the inability to form appropriate work pairs has led to increased robot breakdowns and errors, which has become an issue.

[0936] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for analyzing emotion data and combining it with profile data to perform matching, means for sending notifications to matched users, means for providing a chat function to support communication between matched users, and means for forming an optimal work team for matched users. This makes it possible to form an optimal work team based on the profile data and emotion data, thereby improving work efficiency and reducing stress on the robot.

[0937] "User" refers to a person who uses the system or a robot administrator.

[0938] "Work style" is information that indicates the tendencies and characteristics of the user or robot regarding how they proceed with work.

[0939] "Work history data" is career information about jobs and projects that a user or robot has worked on in the past.

[0940] A "database" is a collection of information for storing and managing received data.

[0941] "Artificial intelligence (AI)" refers to machine learning models and related technologies for analyzing and matching data.

[0942] "Emotion data" is information obtained by analyzing the current emotional state of the user or robot.

[0943] "Profile data" is data that compiles personal information and characteristics of a user or robot, such as work style, work history, hobbies, and interests.

[0944] "Matching" is the process of forming optimal work teams or pairs based on profile data and emotional data.

[0945] A "notification" is a message from the system to convey information to a user or administrator.

[0946] The "chat function" is a message exchange function to support communication between matched users or robots.

[0947] A "work team" is a group of robots or users who work together and are organized based on profile data and emotion data.

[0948] "Work efficiency" is an index that indicates how much work a user or a robot can effectively perform within a certain amount of time.

[0949] "Stress" is an emotional state that represents the strain or tension felt by a user or a robot.

[0950] This invention realizes optimal robot matching and work team formation based on profile data and emotion data in a factory robot management system. To implement this, a system with the following functions is constructed.

[0951] Program processing

[0952] The system's programming is divided into the following steps:

[0953] 1. User registration and profile setup:

[0954] The user registers basic information about the robot (ID, work history, working style, emotional state, etc.) through smart glasses. The device receives this information and sends it to the server.

[0955] 2. Data storage:

[0956] The server stores the received robot profile data in a database, which is used for future analysis and matching.

[0957] 3. Emotion Recognition with Emotion Engine:

[0958] The server collects the robot's work data and operation logs and analyzes its emotional state using an emotion engine that utilizes the emotion recognition API of Microsoft Azure Cognitive Services.

[0959] 4. AI Matching Process:

[0960] The server passes the stored profile data and emotion data to an AI algorithm built with AWS SageMaker to find the best match.

[0961] 5. Match Notification:

[0962] The matching results are then communicated to the factory manager via smart glasses, allowing the manager to make appropriate task assignments.

[0963] 6. Providing chat functionality:

[0964] If necessary, a chat function will be provided to support communication between robots, which will be implemented using the Slack API.

[0965] 7. Additional feedback and adjustments:

[0966] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, thereby improving the accuracy of matching.

[0967] Hardware and software used

[0968] Smart glasses: High-performance smart glasses (e.g. HoloLens 2)

[0969] Emotion Engine: Emotion Recognition API from Microsoft Azure Cognitive Services

[0970] Database: AWS RDS (Relational Database Service)

[0971] AI Algorithms: Machine Learning Models with AWS SageMaker

[0972] Chat function: Slack API

[0973] Specific examples

[0974] The factory manager puts on the HoloLens 2 and checks the information about Robot A (ID: R001, work history: assembly, emotional state: stress). The HoloLens 2 display shows a matching result recommending collaboration with Robot B (ID: R002, work history: assembly, emotional state: relaxed).

[0975] Prompt Sentence Examples

[0976] plaintext

[0977] You are a factory manager. Use your smart glasses to check the following information:

[0978] Robot ID R001: Assembly work, high stress.

[0979] Robot ID R002: Assembly task, relaxed state.

[0980] Use this information to create the optimal work team and allocate tasks efficiently.

[0981] This system makes it possible to form optimal work teams based on profile data and emotional data, thereby improving work efficiency and reducing stress for robots.

[0982] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0983] Step 1:

[0984] The user registers basic information about the robot through smart glasses. The user inputs data such as the robot's ID, work history, working style, and emotional state. The terminal receives this information and sends it to the server. The input in this step is the robot information entered by the user, and the output is the basic information data sent to the server.

[0985] Step 2:

[0986] The server stores the received robot basic information data in a database, ensuring data for later analysis and matching. The input in this step is the basic information data received from the device, and the output is the profile data stored in the database.

[0987] Step 3:

[0988] The server acquires the robot's work data and operation logs and analyzes its emotional state using an emotion engine. The emotion engine uses the emotion recognition API of Microsoft Azure Cognitive Services to extract the robot's emotional state, such as stress or reduced efficiency, from the work data. The input in this step is the work data and operation logs, and the output is the analyzed emotional data.

[0989] Step 4:

[0990] The server then passes the stored profile data and emotion data to an AI algorithm for analysis. The AI ​​algorithm, built on AWS SageMaker, discovers commonalities between robots and determines optimal matching. The inputs for this step are profile data and emotion data, and the output is the optimal matching result.

[0991] Step 5:

[0992] The server notifies the smart glasses of the optimal matching result. The user can then use the smart glasses to check the optimal working pairing and team composition between the robots. The input in this step is the AI ​​matching result, and the output is a notification message for the user.

[0993] Step 6:

[0994] The user assigns appropriate tasks based on the matching results on the smart glasses display. If necessary, communication between the robots is supported via a chat function. This chat function is implemented using the Slack API. The input in this step is the user's task assignment instructions and chat messages, and the output is communication data between the robots.

[0995] Step 7:

[0996] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, which improves the accuracy of matching and improves work efficiency in the future. The input in this step is the feedback data from users, and the output is the adjusted AI algorithm.

[0997] By following the above steps, the system can create an optimal work team based on profile and emotion data, improving work efficiency and reducing stress for robots.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] [Third embodiment]

[1002] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1003] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1004] 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).

[1005] 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.

[1006] 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.

[1007] 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).

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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."

[1014] This invention is a system that uses AI to match superiors and subordinates, taking into account the user's work style, work history, and interests. The system consists of user registration and profile setup, data storage, AI-based matching process, match notification, chat function, feedback collection, and algorithm adjustment.

[1015] Program processing

[1016] User registration and profile settings

[1017] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[1018] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[1019] The user enters the necessary information into each form and presses the submit button.

[1020] The terminal transmits the input data to the server.

[1021] Data storage

[1022] 2. The server receives the received user data and stores it in a database.

[1023] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1024] AI-powered matching process

[1025] 3. The server periodically retrieves the profiles of newly registered users from the database.

[1026] The server passes the acquired profile data to an AI algorithm and begins analysis.

[1027] The AI ​​evaluates similarities in work history, work style, hobbies, and interests.

[1028] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[1029] The server generates the matching results and stores them in a matching table.

[1030] Matching notification

[1031] 4. The server checks the matching results and sends notifications to each user.

[1032] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1033] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1034] Providing chat functionality

[1035] 5. The user taps the notification to proceed to the chat screen.

[1036] The terminal activates a chat function and supports sending and receiving messages between users.

[1037] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1038] The server receives the message and forwards it to the other user.

[1039] The terminal displays the forwarded message to the user.

[1040] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[1041] Additional feedback and adjustments

[1042] 6. Users provide feedback about their matching and chat experiences.

[1043] The terminal transmits the feedback data to the server.

[1044] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[1045] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1046] Specific examples

[1047] User A registration and profile settings

[1048] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[1049] The terminal receives this data and transmits it to the server.

[1050] The server stores the data in a database.

[1051] User B registration and profile settings

[1052] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[1053] The terminal receives this data and transmits it to the server.

[1054] The server stores the data in a database.

[1055] Matching Process and Notifications

[1056] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection) and generates matching results.

[1057] The server sends a matching notification to User A and User B.

[1058] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[1059] Start chatting

[1060] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[1061] The terminal sends a message to the server, which forwards the message to user B.

[1062] The device displays the message to User B, who then replies.

[1063] This system is expected to reduce the mismatch known as departmental gacha, improving the work environment and the quality of work.

[1064] The processing flow will be explained below.

[1065] Step 1:

[1066] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[1067] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[1068] Step 2:

[1069] The user enters the necessary information into each form and presses the submit button.

[1070] The terminal transmits the input data to the server.

[1071] Step 3:

[1072] The server receives the received user data and stores it in a database.

[1073] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1074] Step 4:

[1075] The server periodically retrieves the profiles of newly registered users from the database.

[1076] The server passes the acquired profile data to an AI algorithm and begins analysis.

[1077] Step 5:

[1078] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, and interests.

[1079] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[1080] Step 6:

[1081] The server generates the matching results and stores them in a matching table.

[1082] Step 7:

[1083] The server verifies the match results and sends notifications to the respective users.

[1084] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1085] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1086] Step 8:

[1087] The user taps the notification to proceed to the chat screen.

[1088] The terminal activates a chat function and supports sending and receiving messages between users.

[1089] Step 9:

[1090] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1091] The server receives the message and forwards it to the other user.

[1092] The terminal displays the forwarded message to the user.

[1093] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[1094] Step 10:

[1095] Users provide feedback about their matching and chat experiences.

[1096] The terminal transmits the feedback data to the server.

[1097] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[1098] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1099] Example 1

[1100] 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."

[1101] In traditional workplaces, matching between superiors and subordinates was problematic in that it was not possible to take into account not only work style and work history, but also personal preferences such as hobbies and interests. This led to frequent mismatches known as "department gacha," which led to problems of reduced workplace efficiency and atmosphere. Furthermore, it was difficult to automatically improve the matching algorithm based on feedback, making it difficult to improve the quality of service.

[1102] 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.

[1103] In this invention, the server includes: means for receiving attribute data entered by a user; means for storing the received attribute data in a data storage device; means for analyzing the stored attribute data and using artificial intelligence (AI) to find other users with common topics or interests; means for sending notifications to users matched by the AI; means for providing an information exchange function to support communication between matched users; and means for receiving evaluation data from users, analyzing the received evaluation data, and adjusting the AI ​​algorithm. This enables appropriate matching of superiors and subordinates while taking personal preferences into consideration, thereby improving the efficiency and atmosphere of the work environment. Furthermore, it is possible to continuously improve the algorithm by utilizing feedback and thereby improve the quality of service.

[1104] "Attribute data" refers to information such as a user's work style, work history, hobbies, and interests.

[1105] "Data storage device" refers to a storage medium or database for storing received user data.

[1106] "Artificial intelligence (AI)" refers to algorithms and programs that analyze user data and find other users with common topics and interests.

[1107] "Notification" refers to a message sent to inform a user of a match result.

[1108] "Information exchange function" refers to chat and message sending functions provided as a means of communication between matched users.

[1109] "Evaluation data" refers to feedback information provided by users.

[1110] "Algorithm" refers to the mathematical methods and processes used to match users based on factors such as work style, work history, hobbies, and interests.

[1111] This invention is a system that uses artificial intelligence (AI) to match superiors and subordinates, taking into account the user's work style, work history, hobbies, and interests. The system consists of user registration and profile setup, data storage in a data storage device, the AI-based matching process, match notification, information exchange functionality, additional evaluation data collection, and algorithm adjustment.

[1112] User registration and profile settings

[1113] The user first installs the application and enters attribute data such as name, email address, work history, work style, hobbies and interests when launching it for the first time. The device receives this data and sends it to the server. The device in question is an Android or iOS smartphone.

[1114] Data storage

[1115] The server stores the received attribute data in a data storage device, specifically a database such as MySQL, which stores each user's attribute data in an appropriate format for later use in the process.

[1116] AI-powered matching process

[1117] The server periodically retrieves the profiles of newly registered users from the database and passes the retrieved data to an AI algorithm for analysis. The algorithm is written in a programming language such as Python and executed via a REST API. The AI ​​algorithm evaluates similarities in the users' work history, work style, hobbies, and interests to generate appropriate matches. For example, it compares the profiles of User A (a superior) and User B (a subordinate) to find common interests.

[1118] Matching notification

[1119] The server checks the matching results and sends a notification to each user. The notification is sent using a push notification service such as Firebase Cloud Messaging (FCM). The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[1120] Providing information exchange functions

[1121] The user taps the notification to go to the chat screen, and the device launches the information exchange function. The information exchange function uses WebSocket or other methods to achieve real-time communication and supports the sending and receiving of messages between users. For example, when user A sends a message saying, "Hello, let's talk about outdoor activities," the message is forwarded to user B via the server.

[1122] Additional evaluation data collection and algorithm adjustments

[1123] Users provide evaluation data about their matchmaking and chat experiences, which is then sent to the server. The server analyzes the received evaluation data and adjusts the AI ​​algorithm. This analysis is done by adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[1124] Prompt Sentence Examples

[1125] The following prompts can be input into the generative AI model to obtain information that supports detailed matching algorithms and feedback analysis methods.

[1126] Example prompt 1:

[1127] "What is the optimal AI algorithm for matching superiors and subordinates based on analyzing users' work history, work style, and interests? Please also tell us how to implement it specifically."

[1128] Example prompt 2:

[1129] "How can we improve the accuracy of matching by adjusting the AI ​​algorithm based on user feedback? Please also tell us the specific analysis method and implementation example."

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[1133] Enter: Installed apps

[1134] Output: New registration screen

[1135] Step 2:

[1136] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[1137] Input: Trigger for new registration screen transition

[1138] Output: Input form

[1139] Step 3:

[1140] The user enters the necessary information into each form and presses the submit button.

[1141] Input: Name, email address, work history, work style, hobbies, interests

[1142] Output: Send input data

[1143] Step 4:

[1144] The terminal transmits the input data to the server.

[1145] Input: User-entered data

[1146] Output: Send data to the server

[1147] Step 5:

[1148] The server stores the received data in a data store, specifically by executing an SQL query to insert the data into a "user table" using a database such as MySQL.

[1149] Input: Input data

[1150] Output: Saving to a data storage device

[1151] Step 6:

[1152] The server periodically retrieves the profiles of newly registered users from the database. Specifically, a Cron job is set up to periodically retrieve newly registered user data from the database using a SELECT query.

[1153] Input: Cron job trigger

[1154] Output: Newly registered user data

[1155] Step 7:

[1156] The server passes the acquired profile data to an AI algorithm for analysis, specifically by sending a REST API request to an AI model written in Python.

[1157] Input: Newly registered user data

[1158] Output: Analysis results (matching result candidates)

[1159] Step 8:

[1160] The AI ​​algorithm evaluates similarities in work history, work style, hobbies, and interests to generate matching results. Example: User A (superior) and User B (subordinate) discover that they share common interests in "marketing" and "outdoor activities."

[1161] Input: Profile data

[1162] Output: Matching results

[1163] Step 9:

[1164] The server saves the matching results in a matching table. Specifically, it executes an INSERT query to the "matching table" to save the matching information.

[1165] Input: Matching results

[1166] Output: Save to Matching Table

[1167] Step 10:

[1168] The server notifies each user of the matching results by sending a push notification using services such as Firebase Cloud Messaging (FCM).

[1169] Input: Matching results

[1170] Output: Notification message

[1171] Step 11:

[1172] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1173] Input: Notification message

[1174] Output: Notification screen

[1175] Step 12:

[1176] The user taps the notification to proceed to the chat screen.

[1177] Input: Notification tap

[1178] Output: Chat screen

[1179] Step 13:

[1180] The device will launch a chat function and support the sending and receiving of messages between users, specifically using WebSocket and other protocols to achieve real-time communication.

[1181] Input: Chat screen

[1182] Output: Chat function activated

[1183] Step 14:

[1184] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1185] Input: Message input

[1186] Output: Message sent

[1187] Step 15:

[1188] The server receives the message and forwards it to the other user.

[1189] Input: Incoming message

[1190] Output: Forward message to other user

[1191] Step 16:

[1192] The device displays the forwarded message to the user. Example: User A sends a message saying "Hello, let's talk about outdoor activities!", and User B receives the message and replies.

[1193] Input: Forwarded message

[1194] Output: Message display

[1195] Step 17:

[1196] Users provide evaluation data about their matching and chat experiences.

[1197] Input: Feedback information

[1198] Output: Evaluation data

[1199] Step 18:

[1200] The terminal transmits the evaluation data to the server.

[1201] Input: Evaluation data

[1202] Output: Send data to the server

[1203] Step 19:

[1204] The server analyzes the evaluation data and adjusts the AI ​​algorithm, specifically adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[1205] Input: Evaluation data

[1206] Output: Tuned AI algorithm

[1207] (Application example 1)

[1208] 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."

[1209] With conventional systems, it was difficult to properly match staff and efficiently manage shifts in physical stores. If shifts were arranged without taking into account the compatibility and interests of staff members, the workplace atmosphere and work efficiency were likely to decline.

[1210] 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.

[1211] In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for sending notifications to users matched by the AI, means for providing a chat function to support communication between matched users, means for receiving and analyzing feedback data and adjusting the AI ​​algorithm, and means for enabling optimal staff pairing and shift management in physical stores. This enables appropriate staff matching and efficient shift management in physical stores, and is expected to improve the work environment and work efficiency.

[1212] "Work style" refers to the specific methods, practices, and systems by which a user carries out their work.

[1213] "Work history" is information indicating the occupations the user has had up to now, the duration of those occupations, and the details of those occupations.

[1214] A "database" is a system that systematically integrates multiple data and stores them in a form that is easy to manage and use.

[1215] Artificial intelligence (AI) is a technology that supports and automates human intellectual tasks by learning, analyzing, and inferring from large amounts of data.

[1216] A "notification" is a message from the system to the user conveying specific information or results.

[1217] The "chat function" is a function that supports real-time text communication between users.

[1218] "Feedback data" refers to data regarding opinions and evaluations provided by users regarding their experience and results using the system.

[1219] An "algorithm" is a set of rules that defines a series of processing steps or calculation methods.

[1220] A "brick and mortar store" is an actual sales or service point that is located in a physical location and offers goods or services.

[1221] "Matching" is the process of finding and combining other users and resources that are most suitable based on the user's characteristics and data.

[1222] "Pairing" is the act of linking two or more users together as a team based on their mutual characteristics and compatibility.

[1223] "Shift management" is the process of efficiently allocating and managing the working hours and responsibilities of each staff member in the workplace.

[1224] This invention is a system for efficiently performing optimal staff matching and shift management in a physical store. Specific embodiments for realizing this system will be described below.

[1225] System Configuration

[1226] This system is built using a smartphone application and a cloud server. The smartphone application is used by staff to register and manage their own information, and the cloud server stores and analyzes the registered data.

[1227] Hardware and software used

[1228] Hardware: Smartphones (iOS, Android), cloud infrastructure (AWS, GCP)

[1229] Software: App development frameworks (React Native, Flutter), AI algorithms (TensorFlow, PyTorch), API servers (Node.js, Express), databases (MySQL, PostgreSQL)

[1230] Data registration

[1231] The user installs the smartphone app and, when launching it for the first time, fills in an input form with information such as their name, work history, work style, hobbies, and interests. The device then sends the data entered by the user to the server, which then stores it in a database.

[1232] Data analysis

[1233] The server periodically retrieves the profile data of newly registered users from the database and passes it to an AI algorithm for analysis. The AI ​​algorithm evaluates similarities based on the user's work history, work style, hobbies, interests, etc., and matches them with the most suitable staff.

[1234] Notifications and chat features

[1235] Once a match is generated, the server sends a notification to each user, such as "We've found a colleague who shares your interests." Users can tap the notification to go to the chat screen and exchange messages in real time.

[1236] Feedback and algorithm adjustments

[1237] Users can provide feedback on their matching and chat experiences, which the server then analyzes to optimize the AI ​​algorithm and achieve even more accurate matching.

[1238] Specific examples

[1239] Specifically, the following scenarios are possible:

[1240] 1. User A: "4 years of experience in customer service, hobby: cooking, interest: knowledge of ingredients."

[1241] 2. User B: "2 years of experience in customer service, hobbies: travel, interests: tourist information."

[1242] The AI ​​algorithm can find that User A and User B share a common work history in customer service and that their hobbies and interests are related, making it possible to optimally pair them.

[1243] Example prompt statement:

[1244] "Staff member A has four years of experience in customer service, and his hobby is cooking, so he is interested in learning about ingredients. Staff member B has two years of experience, and his hobby is traveling, so he is interested in information about tourist spots. Use AI to analyze what commonalities can be found."

[1245] In this way, this system enables appropriate staff matching and efficient shift management, which is expected to improve the work environment and work efficiency.

[1246] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1247] Step 1:

[1248] The user installs the app and, when launching it for the first time, enters their name, work history, work style, hobbies, interests, etc. into the input form. This input data becomes the initial data for the application. Input: Name, work history, work style, hobbies, interests. Output: Entered data. Specific operation: The device displays the input form, and once the user has entered the required information, they tap the send button to send the data to the server.

[1249] Step 2:

[1250] The device sends the entered data to the server. Input: Data entered by the user. Output: Data sent to the server. Specific operation: The device sends data to the cloud server via REST API.

[1251] Step 3:

[1252] The server stores the received user data in the database. Input: Data received by the server. Output: Data stored in the database. Specific operation: The server executes an INSERT statement in the database (e.g., MySQL, PostgreSQL) to store the data.

[1253] Step 4:

[1254] The server periodically retrieves profile data of newly registered users from the database. Input: Timer event. Output: Retrieved profile data. Specific operation: The server periodically executes an SQL query to retrieve data of newly registered users.

[1255] Step 5:

[1256] The server passes the acquired profile data to an AI algorithm for analysis. Input: Acquired profile data. Output: Analysis results. Specific operation: The server inputs the profile data into an AI model (e.g., TensorFlow, PyTorch) and uses a generative AI model to calculate similarities based on work history and interests.

[1257] Step 6:

[1258] The AI ​​evaluates the similarities of the profiles and generates matching results. Input: Profile data. Output: Generated matching results. Specific operation: The AI ​​uses a clustering algorithm (e.g., K-means) to find commonalities in each user's data.

[1259] Step 7:

[1260] The server checks the matching results and sends notifications to each user. Input: Matching results. Output: Sent notifications. Specific behavior: The server uses the notification API to send notification messages to matched users.

[1261] Step 8:

[1262] The user taps the notification to proceed to the chat screen. Input: Notification message. Output: Chat screen. Specific operation: The device receives the notification, and when the user taps the notification, the chat function is launched and messages can be sent and received with the other party.

[1263] Step 9:

[1264] The user provides feedback and sends it to the server. Input: Feedback data. Output: Feedback sent to the server. Specific operations: The terminal displays a feedback form and sends the data entered by the user to the server.

[1265] Step 10:

[1266] The server analyzes the feedback data and adjusts the AI ​​algorithm. Input: Feedback data. Output: Optimized AI algorithm. Specific operation: Based on the feedback data, the server readjusts the learning parameters of the AI ​​model to improve the accuracy of the next match.

[1267] 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.

[1268] This invention is a system that matches superiors and subordinates based on the user's work style, work history, hobbies, and interests, as well as their emotional data, using an emotion engine. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition and utilization using the emotion engine, match notification, provision of a chat function, and feedback collection and algorithm adjustment.

[1269] Program processing

[1270] User registration and profile settings

[1271] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[1272] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[1273] The user enters the necessary information into each form and presses the submit button.

[1274] The terminal transmits the input data to the server.

[1275] Data storage

[1276] 2. The server receives the received user data and stores it in a database.

[1277] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1278] Emotion recognition by emotion engine

[1279] 3. The server uses an emotion engine to extract emotion data from the user's input data.

[1280] The emotion engine uses natural language processing technology to recognize emotions from text data entered by the user. For example, if a user enters "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[1281] AI-powered matching process

[1282] 4. The server periodically retrieves the profile and emotion data of newly registered users from the database.

[1283] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[1284] The AI ​​evaluates similarities in work history, work style, hobbies, interests, and emotions.

[1285] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[1286] The server generates the matching results and stores them in a matching table.

[1287] Matching notification

[1288] 5. The server checks the matching results and sends notifications to each user.

[1289] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1290] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1291] Providing chat functionality

[1292] 6. The user taps the notification to proceed to the chat screen.

[1293] The terminal activates a chat function and supports sending and receiving messages between users.

[1294] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1295] The server receives the message and forwards it to the other user.

[1296] The terminal displays the forwarded message to the user.

[1297] Example: User A sends a message saying "Hi, would you like to talk about outdoor activities?" User B receives the message and replies.

[1298] Additional feedback and adjustments

[1299] 7. Users provide feedback about their matching and chat experiences.

[1300] The terminal transmits the feedback data to the server.

[1301] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[1302] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1303] Specific examples

[1304] User A registration and profile settings

[1305] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[1306] The terminal receives this data and transmits it to the server.

[1307] The server stores the data in a database.

[1308] Emotional Data Recognition

[1309] 1. The server obtains the sentence "I've been busy and stressed lately" from User A's input data.

[1310] The emotion engine analyzes this sentence and recognizes the emotion "stress."

[1311] User B registration and profile settings

[1312] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[1313] The terminal receives this data and transmits it to the server.

[1314] The server stores the data in a database.

[1315] Matching Process and Notifications

[1316] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection, low stress) and generates matching results.

[1317] The server sends a matching notification to User A and User B.

[1318] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[1319] Start chatting

[1320] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[1321] The terminal sends a message to the server, which forwards the message to user B.

[1322] The device displays the message to User B, who then replies.

[1323] This system is expected to reduce the mismatches known as "department gacha," improving the work environment and the quality of work. In addition, by utilizing an emotion engine, more detailed matching based on the user's psychological state becomes possible, and it is expected to provide a more comfortable work environment.

[1324] The processing flow will be explained below.

[1325] Step 1:

[1326] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[1327] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[1328] Step 2:

[1329] The user enters the necessary information into each form and presses the submit button.

[1330] The terminal transmits the input data to the server.

[1331] Step 3:

[1332] The server receives the received user data and stores it in a database.

[1333] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1334] Step 4:

[1335] The server extracts emotion data from the user's input data using an emotion engine.

[1336] The emotion engine uses natural language processing techniques to recognize emotions from text data entered by the user.

[1337] Example: If a user types, "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[1338] Step 5:

[1339] The server periodically retrieves the profile and emotion data of newly registered users from the database.

[1340] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[1341] Step 6:

[1342] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, interests, and emotions.

[1343] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[1344] Step 7:

[1345] The server generates the matching results and stores them in a matching table.

[1346] Step 8:

[1347] The server verifies the match results and sends notifications to the respective users.

[1348] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1349] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1350] Step 9:

[1351] The user taps the notification to proceed to the chat screen.

[1352] The terminal activates a chat function and supports sending and receiving messages between users.

[1353] Step 10:

[1354] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1355] The server receives the message and forwards it to the other user.

[1356] The terminal displays the forwarded message to the user.

[1357] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[1358] Step 11:

[1359] Users provide feedback about their matching and chat experiences.

[1360] The terminal transmits the feedback data to the server.

[1361] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[1362] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1363] Example 2

[1364] 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."

[1365] Conventional matching systems match users based on their work history, work style, hobbies, and interests, but lack a mechanism to consider the user's emotional state. As a result, the user's psychological compatibility is not fully taken into account, limiting the improvement of the work environment and work quality. Furthermore, they lacked functionality to facilitate smooth communication after matching. This resulted in problems such as insufficient user satisfaction and insufficient matching results.

[1366] 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.

[1367] In this invention, the server includes: means for receiving work style, work history data, and emotional data input by a user; means for storing the received work style, work history data, and emotional data in a database; means for analyzing the stored work style, work history data, and emotional data and using artificial intelligence (AI) to find other users who share common topics, interests, and emotional states; means for sending notifications to users matched by the AI; and means for providing a chat function to support communication between matched users. This enables more precise matching that takes into account the user's emotional state, which is expected to improve the work environment and work quality.

[1368] "Work style" refers to the characteristics of an individual user that indicate the method and attitude with which they proceed with their work.

[1369] "Work history data" is information that encompasses a user's past and present work experience and career history.

[1370] "Emotion data" is data that represents the psychological state and emotional status of the user.

[1371] "Database" means a system for systematically storing, accessing and managing received data.

[1372] "Artificial intelligence (AI)" is a technology that mimics human intelligence and performs data analysis and decision-making.

[1373] "Means for sending notifications" refers to functions and technologies for sending information or messages to users.

[1374] The "chat function" is a communication means that enables users to send and receive messages in real time.

[1375] "Feedback data" refers to information including evaluations, opinions, and impressions provided by users through their use of the system.

[1376] An "emotion engine" is a technology that uses natural language processing technology to extract emotions from user text data.

[1377] An "algorithm" is a logical method that describes a series of steps or calculations to solve a particular problem.

[1378] This invention is a system that matches superiors and subordinates based on users' work style, work history, and emotional data. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition using an emotion engine, match notification, chat functionality, and feedback collection and algorithm adjustment.

[1379] User registration and profile settings

[1380] The user first installs the application and proceeds to the registration screen when launching it for the first time. The device then displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Once the user enters the information in each field and presses the submit button, the device sends the data to the server.

[1381] Data storage

[1382] The server stores the received user data in a database, including name, email address, work history, work style, hobbies, and interests, using a relational database such as MySQL or PostgreSQL.

[1383] Emotion recognition by emotion engine

[1384] The server passes the user's input data to the emotion engine, which extracts the emotion data. The emotion engine uses natural language processing technology to recognize emotions from the text data. This process uses NLP libraries such as spaCy and NLTK. For example, the input "I've been busy and stressed lately" is analyzed and the emotion "stress" is extracted.

[1385] AI-powered matching process

[1386] The server periodically retrieves the profile data and emotional data of newly registered users from the database and passes them to an AI algorithm for analysis. AI models used include Scikit-learn and TensorFlow. The AI ​​algorithm evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. For example, it can match users who share marketing experience and outdoor activities, or who share low-stress emotional states.

[1387] Matching notification

[1388] The server checks the matching results and sends a notification to the matched users. The notification is sent using a notification service such as Firebase Cloud Messaging. The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[1389] Providing chat functionality

[1390] The user taps the notification to go to the chat screen, enters a message, and presses the send button. The device sends the message to the server, which then forwards it to the other user. This allows real-time message exchange between users.

[1391] Additional feedback and adjustments

[1392] Users can provide feedback on their matching and chat experiences via their devices, and the server analyzes this feedback data and adjusts the AI ​​algorithm and emotion engine to improve system accuracy and user satisfaction.

[1393] Specific examples (prompt sentence examples)

[1394] For example, if User A installs the app and enters "Name: User A", "Email: usera@example.com", "Work Experience: 9 years in marketing", "Work Style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection", these data will be stored in the database. Similarly, if User B enters "Name: User B", "Email: userb@example.com", "Work Experience: 2 years in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection", these will also be stored. The server then matches User A and User B based on their shared hobbies and emotional state and sends them notifications.

[1395] This system will help reduce mismatches in the workplace and provide a better working environment.

[1396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1397] Step 1:

[1398] The user installs the application and proceeds to the registration screen. The device loads the user interface and displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Input: User's personal information. Output: Data entered in the input form.

[1399] Step 2:

[1400] The user enters the required information and presses the send button. The terminal captures the entered data, packages it in JSON format, and sends it to the server using the HTTPS protocol. Input: Data entered by the user. Output: JSON data sent to the server.

[1401] Step 3:

[1402] The server receives the received user data, performs data integrity checks, and then saves the data in a database (e.g., MySQL, PostgreSQL). Input: User data in JSON format. Output: User profile information saved in the database.

[1403] Step 4:

[1404] The server passes the user's text data to the emotion engine, which extracts emotion data. The emotion engine (e.g., spaCy, NLTK) uses natural language processing techniques to recognize emotions from the text data. Input: User's text data. Output: Extracted emotion data.

[1405] Step 5:

[1406] The server periodically retrieves profile data and emotion data of newly registered users from the database. The server sets up a scheduled task (e.g., a Cron job) to execute the database query. Input: User data from the database. Output: Retrieved profile data and emotion data.

[1407] Step 6:

[1408] The server passes the acquired data to an AI algorithm for analysis. The AI ​​model (e.g., Scikit-learn, TensorFlow) evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. Input: Profile data and emotion data. Output: Matching results.

[1409] Step 7:

[1410] The server checks the generated matching results and stores them in a database as a matching table. Input: Matching results from the AI ​​algorithm. Output: Stored matching table.

[1411] Step 8:

[1412] The server sends notifications to the matched users based on the matching results. The server uses a notification service (e.g., Firebase Cloud Messaging) to send notifications. Input: Matching results. Output: Notification message sent to the user.

[1413] Step 9:

[1414] The user taps the notification to proceed to the chat screen. The device launches the chat function and displays an interface where messages can be sent and received. Input: Notification message. Output: Chat screen.

[1415] Step 10:

[1416] The user inputs a message and presses the send button. The terminal sends the message to the server, which then forwards the message to the other user. Input: User's message. Output: Message forwarded to the other user.

[1417] Step 11:

[1418] Users provide feedback about their matching and chat experiences. The device sends the feedback data to the server, which then analyzes it. Input: Feedback from the user. Output: Feedback data sent to the server.

[1419] Step 12:

[1420] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine, thereby improving the system's accuracy and user satisfaction. Input: Feedback data. Output: Adjusted AI algorithm and emotion engine.

[1421] (Application example 2)

[1422] 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."

[1423] Conventional factory robot management systems take into account robot profile data, but do not use emotional data to match robots or optimize work teams. As a result, robot stress and declining efficiency are often overlooked, negatively impacting work efficiency and quality. Furthermore, the inability to form appropriate work pairs has led to increased robot breakdowns and errors, which has become an issue.

[1424] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for analyzing emotion data and combining it with profile data to perform matching, means for sending notifications to matched users, means for providing a chat function to support communication between matched users, and means for forming an optimal work team for matched users. This makes it possible to form an optimal work team based on the profile data and emotion data, thereby improving work efficiency and reducing stress on the robot.

[1425] "User" refers to a person who uses the system or a robot administrator.

[1426] "Work style" is information that indicates the tendencies and characteristics of the user or robot regarding how they proceed with work.

[1427] "Work history data" is career information about jobs and projects that a user or robot has worked on in the past.

[1428] A "database" is a collection of information for storing and managing received data.

[1429] "Artificial intelligence (AI)" refers to machine learning models and related technologies for analyzing and matching data.

[1430] "Emotion data" is information obtained by analyzing the current emotional state of the user or robot.

[1431] "Profile data" is data that compiles personal information and characteristics of a user or robot, such as work style, work history, hobbies, and interests.

[1432] "Matching" is the process of forming optimal work teams or pairs based on profile data and emotional data.

[1433] A "notification" is a message from the system to convey information to a user or administrator.

[1434] The "chat function" is a message exchange function to support communication between matched users or robots.

[1435] A "work team" is a group of robots or users who work together and are organized based on profile data and emotion data.

[1436] "Work efficiency" is an index that indicates how much work a user or a robot can effectively perform within a certain amount of time.

[1437] "Stress" is an emotional state that represents the strain or tension felt by a user or a robot.

[1438] This invention realizes optimal robot matching and work team formation based on profile data and emotion data in a factory robot management system. To implement this, a system with the following functions is constructed.

[1439] Program processing

[1440] The system's programming is divided into the following steps:

[1441] 1. User registration and profile setup:

[1442] The user registers basic information about the robot (ID, work history, working style, emotional state, etc.) through smart glasses. The device receives this information and sends it to the server.

[1443] 2. Data storage:

[1444] The server stores the received robot profile data in a database, which is used for future analysis and matching.

[1445] 3. Emotion Recognition with Emotion Engine:

[1446] The server collects the robot's work data and operation logs and analyzes its emotional state using an emotion engine that utilizes the emotion recognition API of Microsoft Azure Cognitive Services.

[1447] 4. AI Matching Process:

[1448] The server passes the stored profile data and emotion data to an AI algorithm built with AWS SageMaker to find the best match.

[1449] 5. Match Notification:

[1450] The matching results are then communicated to the factory manager via smart glasses, allowing the manager to make appropriate task assignments.

[1451] 6. Providing chat functionality:

[1452] If necessary, a chat function will be provided to support communication between robots, which will be implemented using the Slack API.

[1453] 7. Additional feedback and adjustments:

[1454] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, thereby improving the accuracy of matching.

[1455] Hardware and software used

[1456] Smart glasses: High-performance smart glasses (e.g. HoloLens 2)

[1457] Emotion Engine: Emotion Recognition API from Microsoft Azure Cognitive Services

[1458] Database: AWS RDS (Relational Database Service)

[1459] AI Algorithms: Machine Learning Models with AWS SageMaker

[1460] Chat function: Slack API

[1461] Specific examples

[1462] The factory manager puts on the HoloLens 2 and checks the information about Robot A (ID: R001, work history: assembly, emotional state: stress). The HoloLens 2 display shows a matching result recommending collaboration with Robot B (ID: R002, work history: assembly, emotional state: relaxed).

[1463] Prompt Sentence Examples

[1464] plaintext

[1465] You are a factory manager. Use your smart glasses to check the following information:

[1466] Robot ID R001: Assembly work, high stress.

[1467] Robot ID R002: Assembly task, relaxed state.

[1468] Use this information to create the optimal work team and allocate tasks efficiently.

[1469] This system makes it possible to form optimal work teams based on profile data and emotional data, thereby improving work efficiency and reducing stress for robots.

[1470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1471] Step 1:

[1472] The user registers basic information about the robot through smart glasses. The user inputs data such as the robot's ID, work history, working style, and emotional state. The terminal receives this information and sends it to the server. The input in this step is the robot information entered by the user, and the output is the basic information data sent to the server.

[1473] Step 2:

[1474] The server stores the received robot basic information data in a database, ensuring data for later analysis and matching. The input in this step is the basic information data received from the device, and the output is the profile data stored in the database.

[1475] Step 3:

[1476] The server acquires the robot's work data and operation logs and analyzes its emotional state using an emotion engine. The emotion engine uses the emotion recognition API of Microsoft Azure Cognitive Services to extract the robot's emotional state, such as stress or reduced efficiency, from the work data. The input in this step is the work data and operation logs, and the output is the analyzed emotional data.

[1477] Step 4:

[1478] The server then passes the stored profile data and emotion data to an AI algorithm for analysis. The AI ​​algorithm, built on AWS SageMaker, discovers commonalities between robots and determines optimal matching. The inputs for this step are profile data and emotion data, and the output is the optimal matching result.

[1479] Step 5:

[1480] The server notifies the smart glasses of the optimal matching result. The user can then use the smart glasses to check the optimal working pairing and team composition between the robots. The input in this step is the AI ​​matching result, and the output is a notification message for the user.

[1481] Step 6:

[1482] The user assigns appropriate tasks based on the matching results on the smart glasses display. If necessary, communication between the robots is supported via a chat function. This chat function is implemented using the Slack API. The input in this step is the user's task assignment instructions and chat messages, and the output is communication data between the robots.

[1483] Step 7:

[1484] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, which improves the accuracy of matching and improves work efficiency in the future. The input in this step is the feedback data from users, and the output is the adjusted AI algorithm.

[1485] By following the above steps, the system can create an optimal work team based on profile and emotion data, improving work efficiency and reducing stress for robots.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] [Fourth embodiment]

[1490] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1491] 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.

[1492] 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).

[1493] 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.

[1494] 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.

[1495] 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).

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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."

[1503] This invention is a system that uses AI to match superiors and subordinates, taking into account the user's work style, work history, and interests. The system consists of user registration and profile setup, data storage, AI-based matching process, match notification, chat function, feedback collection, and algorithm adjustment.

[1504] Program processing

[1505] User registration and profile settings

[1506] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[1507] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[1508] The user enters the necessary information into each form and presses the submit button.

[1509] The terminal transmits the input data to the server.

[1510] Data storage

[1511] 2. The server receives the received user data and stores it in a database.

[1512] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1513] AI-powered matching process

[1514] 3. The server periodically retrieves the profiles of newly registered users from the database.

[1515] The server passes the acquired profile data to an AI algorithm and begins analysis.

[1516] The AI ​​evaluates similarities in work history, work style, hobbies, and interests.

[1517] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[1518] The server generates the matching results and stores them in a matching table.

[1519] Matching notification

[1520] 4. The server checks the matching results and sends notifications to each user.

[1521] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1522] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1523] Providing chat functionality

[1524] 5. The user taps the notification to proceed to the chat screen.

[1525] The terminal activates a chat function and supports sending and receiving messages between users.

[1526] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1527] The server receives the message and forwards it to the other user.

[1528] The terminal displays the forwarded message to the user.

[1529] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[1530] Additional feedback and adjustments

[1531] 6. Users provide feedback about their matching and chat experiences.

[1532] The terminal transmits the feedback data to the server.

[1533] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[1534] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1535] Specific examples

[1536] User A registration and profile settings

[1537] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[1538] The terminal receives this data and transmits it to the server.

[1539] The server stores the data in a database.

[1540] User B registration and profile settings

[1541] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[1542] The terminal receives this data and transmits it to the server.

[1543] The server stores the data in a database.

[1544] Matching Process and Notifications

[1545] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection) and generates matching results.

[1546] The server sends a matching notification to User A and User B.

[1547] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[1548] Start chatting

[1549] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[1550] The terminal sends a message to the server, which forwards the message to user B.

[1551] The device displays the message to User B, who then replies.

[1552] This system is expected to reduce the mismatch known as departmental gacha, improving the work environment and the quality of work.

[1553] The processing flow will be explained below.

[1554] Step 1:

[1555] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[1556] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[1557] Step 2:

[1558] The user enters the necessary information into each form and presses the submit button.

[1559] The terminal transmits the input data to the server.

[1560] Step 3:

[1561] The server receives the received user data and stores it in a database.

[1562] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1563] Step 4:

[1564] The server periodically retrieves the profiles of newly registered users from the database.

[1565] The server passes the acquired profile data to an AI algorithm and begins analysis.

[1566] Step 5:

[1567] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, and interests.

[1568] Example: Compare the profiles of User A (superior) and User B (subordinate) and discover that they share common interests in "marketing" and "outdoor activities."

[1569] Step 6:

[1570] The server generates the matching results and stores them in a matching table.

[1571] Step 7:

[1572] The server verifies the match results and sends notifications to the respective users.

[1573] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1574] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1575] Step 8:

[1576] The user taps the notification to proceed to the chat screen.

[1577] The terminal activates a chat function and supports sending and receiving messages between users.

[1578] Step 9:

[1579] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1580] The server receives the message and forwards it to the other user.

[1581] The terminal displays the forwarded message to the user.

[1582] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[1583] Step 10:

[1584] Users provide feedback about their matching and chat experiences.

[1585] The terminal transmits the feedback data to the server.

[1586] The server analyzes the received feedback data and adjusts the AI ​​algorithm.

[1587] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1588] Example 1

[1589] 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."

[1590] In traditional workplaces, matching between superiors and subordinates was problematic in that it was not possible to take into account not only work style and work history, but also personal preferences such as hobbies and interests. This led to frequent mismatches known as "department gacha," which led to problems of reduced workplace efficiency and atmosphere. Furthermore, it was difficult to automatically improve the matching algorithm based on feedback, making it difficult to improve the quality of service.

[1591] 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.

[1592] In this invention, the server includes: means for receiving attribute data entered by a user; means for storing the received attribute data in a data storage device; means for analyzing the stored attribute data and using artificial intelligence (AI) to find other users with common topics or interests; means for sending notifications to users matched by the AI; means for providing an information exchange function to support communication between matched users; and means for receiving evaluation data from users, analyzing the received evaluation data, and adjusting the AI ​​algorithm. This enables appropriate matching of superiors and subordinates while taking personal preferences into consideration, thereby improving the efficiency and atmosphere of the work environment. Furthermore, it is possible to continuously improve the algorithm by utilizing feedback and thereby improve the quality of service.

[1593] "Attribute data" refers to information such as a user's work style, work history, hobbies, and interests.

[1594] "Data storage device" refers to a storage medium or database for storing received user data.

[1595] "Artificial intelligence (AI)" refers to algorithms and programs that analyze user data and find other users with common topics and interests.

[1596] "Notification" refers to a message sent to inform a user of a match result.

[1597] "Information exchange function" refers to chat and message sending functions provided as a means of communication between matched users.

[1598] "Evaluation data" refers to feedback information provided by users.

[1599] "Algorithm" refers to the mathematical methods and processes used to match users based on factors such as work style, work history, hobbies, and interests.

[1600] This invention is a system that uses artificial intelligence (AI) to match superiors and subordinates, taking into account the user's work style, work history, hobbies, and interests. The system consists of user registration and profile setup, data storage in a data storage device, the AI-based matching process, match notification, information exchange functionality, additional evaluation data collection, and algorithm adjustment.

[1601] User registration and profile settings

[1602] The user first installs the application and enters attribute data such as name, email address, work history, work style, hobbies and interests when launching it for the first time. The device receives this data and sends it to the server. The device in question is an Android or iOS smartphone.

[1603] Data storage

[1604] The server stores the received attribute data in a data storage device, specifically a database such as MySQL, which stores each user's attribute data in an appropriate format for later use in the process.

[1605] AI-powered matching process

[1606] The server periodically retrieves the profiles of newly registered users from the database and passes the retrieved data to an AI algorithm for analysis. The algorithm is written in a programming language such as Python and executed via a REST API. The AI ​​algorithm evaluates similarities in the users' work history, work style, hobbies, and interests to generate appropriate matches. For example, it compares the profiles of User A (a superior) and User B (a subordinate) to find common interests.

[1607] Matching notification

[1608] The server checks the matching results and sends a notification to each user. The notification is sent using a push notification service such as Firebase Cloud Messaging (FCM). The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[1609] Providing information exchange functions

[1610] The user taps the notification to go to the chat screen, and the device launches the information exchange function. The information exchange function uses WebSocket or other methods to achieve real-time communication and supports the sending and receiving of messages between users. For example, when user A sends a message saying, "Hello, let's talk about outdoor activities," the message is forwarded to user B via the server.

[1611] Additional evaluation data collection and algorithm adjustments

[1612] Users provide evaluation data about their matchmaking and chat experiences, which is then sent to the server. The server analyzes the received evaluation data and adjusts the AI ​​algorithm. This analysis is done by adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[1613] Prompt Sentence Examples

[1614] The following prompts can be input into the generative AI model to obtain information that supports detailed matching algorithms and feedback analysis methods.

[1615] Example prompt 1:

[1616] "What is the optimal AI algorithm for matching superiors and subordinates based on analyzing users' work history, work style, and interests? Please also tell us how to implement it specifically."

[1617] Example prompt 2:

[1618] "How can we improve the accuracy of matching by adjusting the AI ​​algorithm based on user feedback? Please also tell us the specific analysis method and implementation example."

[1619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1620] Step 1:

[1621] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[1622] Enter: Installed apps

[1623] Output: New registration screen

[1624] Step 2:

[1625] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[1626] Input: Trigger for new registration screen transition

[1627] Output: Input form

[1628] Step 3:

[1629] The user enters the necessary information into each form and presses the submit button.

[1630] Input: Name, email address, work history, work style, hobbies, interests

[1631] Output: Send input data

[1632] Step 4:

[1633] The terminal transmits the input data to the server.

[1634] Input: User-entered data

[1635] Output: Send data to the server

[1636] Step 5:

[1637] The server stores the received data in a data store, specifically by executing an SQL query to insert the data into a "user table" using a database such as MySQL.

[1638] Input: Input data

[1639] Output: Saving to a data storage device

[1640] Step 6:

[1641] The server periodically retrieves the profiles of newly registered users from the database. Specifically, a Cron job is set up to periodically retrieve newly registered user data from the database using a SELECT query.

[1642] Input: Cron job trigger

[1643] Output: Newly registered user data

[1644] Step 7:

[1645] The server passes the acquired profile data to an AI algorithm for analysis, specifically by sending a REST API request to an AI model written in Python.

[1646] Input: Newly registered user data

[1647] Output: Analysis results (matching result candidates)

[1648] Step 8:

[1649] The AI ​​algorithm evaluates similarities in work history, work style, hobbies, and interests to generate matching results. Example: User A (superior) and User B (subordinate) discover that they share common interests in "marketing" and "outdoor activities."

[1650] Input: Profile data

[1651] Output: Matching results

[1652] Step 9:

[1653] The server saves the matching results in a matching table. Specifically, it executes an INSERT query to the "matching table" to save the matching information.

[1654] Input: Matching results

[1655] Output: Save to Matching Table

[1656] Step 10:

[1657] The server notifies each user of the matching results by sending a push notification using services such as Firebase Cloud Messaging (FCM).

[1658] Input: Matching results

[1659] Output: Notification message

[1660] Step 11:

[1661] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1662] Input: Notification message

[1663] Output: Notification screen

[1664] Step 12:

[1665] The user taps the notification to proceed to the chat screen.

[1666] Input: Notification tap

[1667] Output: Chat screen

[1668] Step 13:

[1669] The device will launch a chat function and support the sending and receiving of messages between users, specifically using WebSocket and other protocols to achieve real-time communication.

[1670] Input: Chat screen

[1671] Output: Chat function activated

[1672] Step 14:

[1673] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1674] Input: Message input

[1675] Output: Message sent

[1676] Step 15:

[1677] The server receives the message and forwards it to the other user.

[1678] Input: Incoming message

[1679] Output: Forward message to other user

[1680] Step 16:

[1681] The device displays the forwarded message to the user. Example: User A sends a message saying "Hello, let's talk about outdoor activities!", and User B receives the message and replies.

[1682] Input: Forwarded message

[1683] Output: Message display

[1684] Step 17:

[1685] Users provide evaluation data about their matching and chat experiences.

[1686] Input: Feedback information

[1687] Output: Evaluation data

[1688] Step 18:

[1689] The terminal transmits the evaluation data to the server.

[1690] Input: Evaluation data

[1691] Output: Send data to the server

[1692] Step 19:

[1693] The server analyzes the evaluation data and adjusts the AI ​​algorithm, specifically adjusting the hyperparameters of the machine learning algorithm and retraining the model.

[1694] Input: Evaluation data

[1695] Output: Tuned AI algorithm

[1696] (Application example 1)

[1697] 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."

[1698] With conventional systems, it was difficult to properly match staff and efficiently manage shifts in physical stores. If shifts were arranged without taking into account the compatibility and interests of staff members, the workplace atmosphere and work efficiency were likely to decline.

[1699] 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.

[1700] In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for sending notifications to users matched by the AI, means for providing a chat function to support communication between matched users, means for receiving and analyzing feedback data and adjusting the AI ​​algorithm, and means for enabling optimal staff pairing and shift management in physical stores. This enables appropriate staff matching and efficient shift management in physical stores, and is expected to improve the work environment and work efficiency.

[1701] "Work style" refers to the specific methods, practices, and systems by which a user carries out their work.

[1702] "Work history" is information indicating the occupations the user has had up to now, the duration of those occupations, and the details of those occupations.

[1703] A "database" is a system that systematically integrates multiple data and stores them in a form that is easy to manage and use.

[1704] Artificial intelligence (AI) is a technology that supports and automates human intellectual tasks by learning, analyzing, and inferring from large amounts of data.

[1705] A "notification" is a message from the system to the user conveying specific information or results.

[1706] The "chat function" is a function that supports real-time text communication between users.

[1707] "Feedback data" refers to data regarding opinions and evaluations provided by users regarding their experience and results using the system.

[1708] An "algorithm" is a set of rules that defines a series of processing steps or calculation methods.

[1709] A "brick and mortar store" is an actual sales or service point that is located in a physical location and offers goods or services.

[1710] "Matching" is the process of finding and combining other users and resources that are most suitable based on the user's characteristics and data.

[1711] "Pairing" is the act of linking two or more users together as a team based on their mutual characteristics and compatibility.

[1712] "Shift management" is the process of efficiently allocating and managing the working hours and responsibilities of each staff member in the workplace.

[1713] This invention is a system for efficiently performing optimal staff matching and shift management in a physical store. Specific embodiments for realizing this system will be described below.

[1714] System Configuration

[1715] This system is built using a smartphone application and a cloud server. The smartphone application is used by staff to register and manage their own information, and the cloud server stores and analyzes the registered data.

[1716] Hardware and software used

[1717] Hardware: Smartphones (iOS, Android), cloud infrastructure (AWS, GCP)

[1718] Software: App development frameworks (React Native, Flutter), AI algorithms (TensorFlow, PyTorch), API servers (Node.js, Express), databases (MySQL, PostgreSQL)

[1719] Data registration

[1720] The user installs the smartphone app and, when launching it for the first time, fills in an input form with information such as their name, work history, work style, hobbies, and interests. The device then sends the data entered by the user to the server, which then stores it in a database.

[1721] Data analysis

[1722] The server periodically retrieves the profile data of newly registered users from the database and passes it to an AI algorithm for analysis. The AI ​​algorithm evaluates similarities based on the user's work history, work style, hobbies, interests, etc., and matches them with the most suitable staff.

[1723] Notifications and chat features

[1724] Once a match is generated, the server sends a notification to each user, such as "We've found a colleague who shares your interests." Users can tap the notification to go to the chat screen and exchange messages in real time.

[1725] Feedback and algorithm adjustments

[1726] Users can provide feedback on their matching and chat experiences, which the server then analyzes to optimize the AI ​​algorithm and achieve even more accurate matching.

[1727] Specific examples

[1728] Specifically, the following scenarios are possible:

[1729] 1. User A: "4 years of experience in customer service, hobby: cooking, interest: knowledge of ingredients."

[1730] 2. User B: "2 years of experience in customer service, hobbies: travel, interests: tourist information."

[1731] The AI ​​algorithm can find that User A and User B share a common work history in customer service and that their hobbies and interests are related, making it possible to optimally pair them.

[1732] Example prompt statement:

[1733] "Staff member A has four years of experience in customer service, and his hobby is cooking, so he is interested in learning about ingredients. Staff member B has two years of experience, and his hobby is traveling, so he is interested in information about tourist spots. Use AI to analyze what commonalities can be found."

[1734] In this way, this system enables appropriate staff matching and efficient shift management, which is expected to improve the work environment and work efficiency.

[1735] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1736] Step 1:

[1737] The user installs the app and, when launching it for the first time, enters their name, work history, work style, hobbies, interests, etc. into the input form. This input data becomes the initial data for the application. Input: Name, work history, work style, hobbies, interests. Output: Entered data. Specific operation: The device displays the input form, and once the user has entered the required information, they tap the send button to send the data to the server.

[1738] Step 2:

[1739] The device sends the entered data to the server. Input: Data entered by the user. Output: Data sent to the server. Specific operation: The device sends data to the cloud server via REST API.

[1740] Step 3:

[1741] The server stores the received user data in the database. Input: Data received by the server. Output: Data stored in the database. Specific operation: The server executes an INSERT statement in the database (e.g., MySQL, PostgreSQL) to store the data.

[1742] Step 4:

[1743] The server periodically retrieves profile data of newly registered users from the database. Input: Timer event. Output: Retrieved profile data. Specific operation: The server periodically executes an SQL query to retrieve data of newly registered users.

[1744] Step 5:

[1745] The server passes the acquired profile data to an AI algorithm for analysis. Input: Acquired profile data. Output: Analysis results. Specific operation: The server inputs the profile data into an AI model (e.g., TensorFlow, PyTorch) and uses a generative AI model to calculate similarities based on work history and interests.

[1746] Step 6:

[1747] The AI ​​evaluates the similarities of the profiles and generates matching results. Input: Profile data. Output: Generated matching results. Specific operation: The AI ​​uses a clustering algorithm (e.g., K-means) to find commonalities in each user's data.

[1748] Step 7:

[1749] The server checks the matching results and sends notifications to each user. Input: Matching results. Output: Sent notifications. Specific behavior: The server uses the notification API to send notification messages to matched users.

[1750] Step 8:

[1751] The user taps the notification to proceed to the chat screen. Input: Notification message. Output: Chat screen. Specific operation: The device receives the notification, and when the user taps the notification, the chat function is launched and messages can be sent and received with the other party.

[1752] Step 9:

[1753] The user provides feedback and sends it to the server. Input: Feedback data. Output: Feedback sent to the server. Specific operations: The terminal displays a feedback form and sends the data entered by the user to the server.

[1754] Step 10:

[1755] The server analyzes the feedback data and adjusts the AI ​​algorithm. Input: Feedback data. Output: Optimized AI algorithm. Specific operation: Based on the feedback data, the server readjusts the learning parameters of the AI ​​model to improve the accuracy of the next match.

[1756] 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.

[1757] This invention is a system that matches superiors and subordinates based on the user's work style, work history, hobbies, and interests, as well as their emotional data, using an emotion engine. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition and utilization using the emotion engine, match notification, provision of a chat function, and feedback collection and algorithm adjustment.

[1758] Program processing

[1759] User registration and profile settings

[1760] 1. The user installs the app and proceeds to the new registration screen when they launch it for the first time.

[1761] The terminal displays a form for the user to enter their name, email address, work history, work style, hobbies and interests.

[1762] The user enters the necessary information into each form and presses the submit button.

[1763] The terminal transmits the input data to the server.

[1764] Data storage

[1765] 2. The server receives the received user data and stores it in a database.

[1766] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1767] Emotion recognition by emotion engine

[1768] 3. The server uses an emotion engine to extract emotion data from the user's input data.

[1769] The emotion engine uses natural language processing technology to recognize emotions from text data entered by the user. For example, if a user enters "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[1770] AI-powered matching process

[1771] 4. The server periodically retrieves the profile and emotion data of newly registered users from the database.

[1772] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[1773] The AI ​​evaluates similarities in work history, work style, hobbies, interests, and emotions.

[1774] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[1775] The server generates the matching results and stores them in a matching table.

[1776] Matching notification

[1777] 5. The server checks the matching results and sends notifications to each user.

[1778] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1779] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1780] Providing chat functionality

[1781] 6. The user taps the notification to proceed to the chat screen.

[1782] The terminal activates a chat function and supports sending and receiving messages between users.

[1783] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1784] The server receives the message and forwards it to the other user.

[1785] The terminal displays the forwarded message to the user.

[1786] Example: User A sends a message saying "Hi, would you like to talk about outdoor activities?" User B receives the message and replies.

[1787] Additional feedback and adjustments

[1788] 7. Users provide feedback about their matching and chat experiences.

[1789] The terminal transmits the feedback data to the server.

[1790] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[1791] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1792] Specific examples

[1793] User A registration and profile settings

[1794] 1. User A installs the app and, when launching it for the first time, enters the following information: "Name: User A", "Email: usera@example.com", "Work experience: 9 years in marketing", "Work style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection".

[1795] The terminal receives this data and transmits it to the server.

[1796] The server stores the data in a database.

[1797] Emotional Data Recognition

[1798] 1. The server obtains the sentence "I've been busy and stressed lately" from User A's input data.

[1799] The emotion engine analyzes this sentence and recognizes the emotion "stress."

[1800] User B registration and profile settings

[1801] 1. User B installs the app and enters the following information when launching it for the first time: "Name: User B", "Email: userb@example.com", "Work Experience: 2 years of experience in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection".

[1802] The terminal receives this data and transmits it to the server.

[1803] The server stores the data in a database.

[1804] Matching Process and Notifications

[1805] 1. The server finds commonalities between User A and User B (marketing experience, outdoor activities, interest in environmental protection, low stress) and generates matching results.

[1806] The server sends a matching notification to User A and User B.

[1807] The device will display a notification to User A and User B saying, "You have found someone with common interests."

[1808] Start chatting

[1809] 1. User A taps the notification to open the chat screen and sends a message saying, "Hello, would you like to talk about outdoor activities?"

[1810] The terminal sends a message to the server, which forwards the message to user B.

[1811] The device displays the message to User B, who then replies.

[1812] This system is expected to reduce the mismatches known as "department gacha," improving the work environment and the quality of work. In addition, by utilizing an emotion engine, more detailed matching based on the user's psychological state becomes possible, and it is expected to provide a more comfortable work environment.

[1813] The processing flow will be explained below.

[1814] Step 1:

[1815] The user installs the app and proceeds to the new registration screen when launching it for the first time.

[1816] The device displays a form for the user to enter their name, email address, work history, work style, and interests.

[1817] Step 2:

[1818] The user enters the necessary information into each form and presses the submit button.

[1819] The terminal transmits the input data to the server.

[1820] Step 3:

[1821] The server receives the received user data and stores it in a database.

[1822] Examples include information such as "name," "email address," "work history," "work style," "hobbies," and "interests."

[1823] Step 4:

[1824] The server extracts emotion data from the user's input data using an emotion engine.

[1825] The emotion engine uses natural language processing techniques to recognize emotions from text data entered by the user.

[1826] Example: If a user types, "I've been busy and stressed lately," the emotion engine will recognize the emotion "stress."

[1827] Step 5:

[1828] The server periodically retrieves the profile and emotion data of newly registered users from the database.

[1829] The server passes the acquired profile data and emotional data to an AI algorithm and begins analysis.

[1830] Step 6:

[1831] The server uses AI algorithms to evaluate similarities in work history, work style, hobbies, interests, and emotions.

[1832] Example: Comparing the profiles of User A (superior) and User B (subordinate), we discover that they share interests in "marketing" and "outdoor activities" and have an emotional state of "low stress."

[1833] Step 7:

[1834] The server generates the matching results and stores them in a matching table.

[1835] Step 8:

[1836] The server verifies the match results and sends notifications to the respective users.

[1837] The terminal receives a notification from the server and displays to the user, "A superior / subordinate with common interests has been found."

[1838] Example: User A and User B will receive a notification saying, "A superior / subordinate who shares common interests with you has been found."

[1839] Step 9:

[1840] The user taps the notification to proceed to the chat screen.

[1841] The terminal activates a chat function and supports sending and receiving messages between users.

[1842] Step 10:

[1843] When the user inputs a message and presses the send button, the terminal sends the message to the server.

[1844] The server receives the message and forwards it to the other user.

[1845] The terminal displays the forwarded message to the user.

[1846] Example: User A sends a message saying "Hi, let's talk about outdoor activities!" User B receives the message and replies.

[1847] Step 11:

[1848] Users provide feedback about their matching and chat experiences.

[1849] The terminal transmits the feedback data to the server.

[1850] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine.

[1851] For example, after receiving certain feedback, we adjust the AI ​​algorithm to improve the accuracy of matching.

[1852] Example 2

[1853] 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."

[1854] Conventional matching systems match users based on their work history, work style, hobbies, and interests, but lack a mechanism to consider the user's emotional state. As a result, the user's psychological compatibility is not fully taken into account, limiting the improvement of the work environment and work quality. Furthermore, they lacked functionality to facilitate smooth communication after matching. This resulted in problems such as insufficient user satisfaction and insufficient matching results.

[1855] 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.

[1856] In this invention, the server includes: means for receiving work style, work history data, and emotional data input by a user; means for storing the received work style, work history data, and emotional data in a database; means for analyzing the stored work style, work history data, and emotional data and using artificial intelligence (AI) to find other users who share common topics, interests, and emotional states; means for sending notifications to users matched by the AI; and means for providing a chat function to support communication between matched users. This enables more precise matching that takes into account the user's emotional state, which is expected to improve the work environment and work quality.

[1857] "Work style" refers to the characteristics of an individual user that indicate the method and attitude with which they proceed with their work.

[1858] "Work history data" is information that encompasses a user's past and present work experience and career history.

[1859] "Emotion data" is data that represents the psychological state and emotional status of the user.

[1860] "Database" means a system for systematically storing, accessing and managing received data.

[1861] "Artificial intelligence (AI)" is a technology that mimics human intelligence and performs data analysis and decision-making.

[1862] "Means for sending notifications" refers to functions and technologies for sending information or messages to users.

[1863] The "chat function" is a communication means that enables users to send and receive messages in real time.

[1864] "Feedback data" refers to information including evaluations, opinions, and impressions provided by users through their use of the system.

[1865] An "emotion engine" is a technology that uses natural language processing technology to extract emotions from user text data.

[1866] An "algorithm" is a logical method that describes a series of steps or calculations to solve a particular problem.

[1867] This invention is a system that matches superiors and subordinates based on users' work style, work history, and emotional data. The system consists of user registration and profile setup, data storage, an AI-based matching process, emotion recognition using an emotion engine, match notification, chat functionality, and feedback collection and algorithm adjustment.

[1868] User registration and profile settings

[1869] The user first installs the application and proceeds to the registration screen when launching it for the first time. The device then displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Once the user enters the information in each field and presses the submit button, the device sends the data to the server.

[1870] Data storage

[1871] The server stores the received user data in a database, including name, email address, work history, work style, hobbies, and interests, using a relational database such as MySQL or PostgreSQL.

[1872] Emotion recognition by emotion engine

[1873] The server passes the user's input data to the emotion engine, which extracts the emotion data. The emotion engine uses natural language processing technology to recognize emotions from the text data. This process uses NLP libraries such as spaCy and NLTK. For example, the input "I've been busy and stressed lately" is analyzed and the emotion "stress" is extracted.

[1874] AI-powered matching process

[1875] The server periodically retrieves the profile data and emotional data of newly registered users from the database and passes them to an AI algorithm for analysis. AI models used include Scikit-learn and TensorFlow. The AI ​​algorithm evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. For example, it can match users who share marketing experience and outdoor activities, or who share low-stress emotional states.

[1876] Matching notification

[1877] The server checks the matching results and sends a notification to the matched users. The notification is sent using a notification service such as Firebase Cloud Messaging. The device receives the notification from the server and displays a message to the user saying, "A superior / subordinate with common interests has been found."

[1878] Providing chat functionality

[1879] The user taps the notification to go to the chat screen, enters a message, and presses the send button. The device sends the message to the server, which then forwards it to the other user. This allows real-time message exchange between users.

[1880] Additional feedback and adjustments

[1881] Users can provide feedback on their matching and chat experiences via their devices, and the server analyzes this feedback data and adjusts the AI ​​algorithm and emotion engine to improve system accuracy and user satisfaction.

[1882] Specific examples (prompt sentence examples)

[1883] For example, if User A installs the app and enters "Name: User A", "Email: usera@example.com", "Work Experience: 9 years in marketing", "Work Style: Data-driven", "Hobbies: Mountain climbing", and "Interests: Environmental protection", these data will be stored in the database. Similarly, if User B enters "Name: User B", "Email: userb@example.com", "Work Experience: 2 years in digital marketing", "Work Style: Team-oriented", "Hobbies: Kayaking", and "Interests: Environmental protection", these will also be stored. The server then matches User A and User B based on their shared hobbies and emotional state and sends them notifications.

[1884] This system will help reduce mismatches in the workplace and provide a better working environment.

[1885] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1886] Step 1:

[1887] The user installs the application and proceeds to the registration screen. The device loads the user interface and displays a form for the user to enter their name, email address, work history, work style, hobbies and interests. Input: User's personal information. Output: Data entered in the input form.

[1888] Step 2:

[1889] The user enters the required information and presses the send button. The terminal captures the entered data, packages it in JSON format, and sends it to the server using the HTTPS protocol. Input: Data entered by the user. Output: JSON data sent to the server.

[1890] Step 3:

[1891] The server receives the received user data, performs data integrity checks, and then saves the data in a database (e.g., MySQL, PostgreSQL). Input: User data in JSON format. Output: User profile information saved in the database.

[1892] Step 4:

[1893] The server passes the user's text data to the emotion engine, which extracts emotion data. The emotion engine (e.g., spaCy, NLTK) uses natural language processing techniques to recognize emotions from the text data. Input: User's text data. Output: Extracted emotion data.

[1894] Step 5:

[1895] The server periodically retrieves profile data and emotion data of newly registered users from the database. The server sets up a scheduled task (e.g., a Cron job) to execute the database query. Input: User data from the database. Output: Retrieved profile data and emotion data.

[1896] Step 6:

[1897] The server passes the acquired data to an AI algorithm for analysis. The AI ​​model (e.g., Scikit-learn, TensorFlow) evaluates similarities in users' work history, work style, hobbies, interests, and emotions to generate matching results. Input: Profile data and emotion data. Output: Matching results.

[1898] Step 7:

[1899] The server checks the generated matching results and stores them in a database as a matching table. Input: Matching results from the AI ​​algorithm. Output: Stored matching table.

[1900] Step 8:

[1901] The server sends notifications to the matched users based on the matching results. The server uses a notification service (e.g., Firebase Cloud Messaging) to send notifications. Input: Matching results. Output: Notification message sent to the user.

[1902] Step 9:

[1903] The user taps the notification to proceed to the chat screen. The device launches the chat function and displays an interface where messages can be sent and received. Input: Notification message. Output: Chat screen.

[1904] Step 10:

[1905] The user inputs a message and presses the send button. The terminal sends the message to the server, which then forwards the message to the other user. Input: User's message. Output: Message forwarded to the other user.

[1906] Step 11:

[1907] Users provide feedback about their matching and chat experiences. The device sends the feedback data to the server, which then analyzes it. Input: Feedback from the user. Output: Feedback data sent to the server.

[1908] Step 12:

[1909] The server analyzes the received feedback data and adjusts the AI ​​algorithm and emotion engine, thereby improving the system's accuracy and user satisfaction. Input: Feedback data. Output: Adjusted AI algorithm and emotion engine.

[1910] (Application example 2)

[1911] 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."

[1912] Conventional factory robot management systems take into account robot profile data, but do not use emotional data to match robots or optimize work teams. As a result, robot stress and declining efficiency are often overlooked, negatively impacting work efficiency and quality. Furthermore, the inability to form appropriate work pairs has led to increased robot breakdowns and errors, which has become an issue.

[1913] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving work style and work history data input by a user, means for storing the received work style and work history data in a database, means for analyzing the stored work style and work history data and using artificial intelligence (AI) to find other users with common topics and interests, means for analyzing emotion data and combining it with profile data to perform matching, means for sending notifications to matched users, means for providing a chat function to support communication between matched users, and means for forming an optimal work team for matched users. This makes it possible to form an optimal work team based on the profile data and emotion data, thereby improving work efficiency and reducing stress on the robot.

[1914] "User" refers to a person who uses the system or a robot administrator.

[1915] "Work style" is information that indicates the tendencies and characteristics of the user or robot regarding how they proceed with work.

[1916] "Work history data" is career information about jobs and projects that a user or robot has worked on in the past.

[1917] A "database" is a collection of information for storing and managing received data.

[1918] "Artificial intelligence (AI)" refers to machine learning models and related technologies for analyzing and matching data.

[1919] "Emotion data" is information obtained by analyzing the current emotional state of the user or robot.

[1920] "Profile data" is data that compiles personal information and characteristics of a user or robot, such as work style, work history, hobbies, and interests.

[1921] "Matching" is the process of forming optimal work teams or pairs based on profile data and emotional data.

[1922] A "notification" is a message from the system to convey information to a user or administrator.

[1923] The "chat function" is a message exchange function to support communication between matched users or robots.

[1924] A "work team" is a group of robots or users who work together and are organized based on profile data and emotion data.

[1925] "Work efficiency" is an index that indicates how much work a user or a robot can effectively perform within a certain amount of time.

[1926] "Stress" is an emotional state that represents the strain or tension felt by a user or a robot.

[1927] This invention realizes optimal robot matching and work team formation based on profile data and emotion data in a factory robot management system. To implement this, a system with the following functions is constructed.

[1928] Program processing

[1929] The system's programming is divided into the following steps:

[1930] 1. User registration and profile setup:

[1931] The user registers basic information about the robot (ID, work history, working style, emotional state, etc.) through smart glasses. The device receives this information and sends it to the server.

[1932] 2. Data storage:

[1933] The server stores the received robot profile data in a database, which is used for future analysis and matching.

[1934] 3. Emotion Recognition with Emotion Engine:

[1935] The server collects the robot's work data and operation logs and analyzes its emotional state using an emotion engine that utilizes the emotion recognition API of Microsoft Azure Cognitive Services.

[1936] 4. AI Matching Process:

[1937] The server passes the stored profile data and emotion data to an AI algorithm built with AWS SageMaker to find the best match.

[1938] 5. Match Notification:

[1939] The matching results are then communicated to the factory manager via smart glasses, allowing the manager to make appropriate task assignments.

[1940] 6. Providing chat functionality:

[1941] If necessary, a chat function will be provided to support communication between robots, which will be implemented using the Slack API.

[1942] 7. Additional feedback and adjustments:

[1943] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, thereby improving the accuracy of matching.

[1944] Hardware and software used

[1945] Smart glasses: High-performance smart glasses (e.g. HoloLens 2)

[1946] Emotion Engine: Emotion Recognition API from Microsoft Azure Cognitive Services

[1947] Database: AWS RDS (Relational Database Service)

[1948] AI Algorithms: Machine Learning Models with AWS SageMaker

[1949] Chat function: Slack API

[1950] Specific examples

[1951] The factory manager puts on the HoloLens 2 and checks the information about Robot A (ID: R001, work history: assembly, emotional state: stress). The HoloLens 2 display shows a matching result recommending collaboration with Robot B (ID: R002, work history: assembly, emotional state: relaxed).

[1952] Prompt Sentence Examples

[1953] plaintext

[1954] You are a factory manager. Use your smart glasses to check the following information:

[1955] Robot ID R001: Assembly work, high stress.

[1956] Robot ID R002: Assembly task, relaxed state.

[1957] Use this information to create the optimal work team and allocate tasks efficiently.

[1958] This system makes it possible to form optimal work teams based on profile data and emotional data, thereby improving work efficiency and reducing stress for robots.

[1959] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1960] Step 1:

[1961] The user registers basic information about the robot through smart glasses. The user inputs data such as the robot's ID, work history, working style, and emotional state. The terminal receives this information and sends it to the server. The input in this step is the robot information entered by the user, and the output is the basic information data sent to the server.

[1962] Step 2:

[1963] The server stores the received robot basic information data in a database, ensuring data for later analysis and matching. The input in this step is the basic information data received from the device, and the output is the profile data stored in the database.

[1964] Step 3:

[1965] The server acquires the robot's work data and operation logs and analyzes its emotional state using an emotion engine. The emotion engine uses the emotion recognition API of Microsoft Azure Cognitive Services to extract the robot's emotional state, such as stress or reduced efficiency, from the work data. The input in this step is the work data and operation logs, and the output is the analyzed emotional data.

[1966] Step 4:

[1967] The server then passes the stored profile data and emotion data to an AI algorithm for analysis. The AI ​​algorithm, built on AWS SageMaker, discovers commonalities between robots and determines optimal matching. The inputs for this step are profile data and emotion data, and the output is the optimal matching result.

[1968] Step 5:

[1969] The server notifies the smart glasses of the optimal matching result. The user can then use the smart glasses to check the optimal working pairing and team composition between the robots. The input in this step is the AI ​​matching result, and the output is a notification message for the user.

[1970] Step 6:

[1971] The user assigns appropriate tasks based on the matching results on the smart glasses display. If necessary, communication between the robots is supported via a chat function. This chat function is implemented using the Slack API. The input in this step is the user's task assignment instructions and chat messages, and the output is communication data between the robots.

[1972] Step 7:

[1973] The server receives feedback data from users and analyzes it to adjust the AI ​​algorithm, which improves the accuracy of matching and improves work efficiency in the future. The input in this step is the feedback data from users, and the output is the adjusted AI algorithm.

[1974] By following the above steps, the system can create an optimal work team based on profile and emotion data, improving work efficiency and reducing stress for robots.

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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).

[1982] 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.

[1983] 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."

[1984] 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.

[1985] 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).

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] The following is further disclosed regarding the above embodiment.

[1997] (Claim 1)

[1998] means for receiving work style and work history data input by a user;

[1999] means for storing the received work style and work history data in a database;

[2000] means for using artificial intelligence (AI) to analyze stored work style and work history data to find other users with common topics or interests;

[2001] A means of sending notifications to users who have been matched by AI;

[2002] A means for providing a chat function that supports communication between matched users;

[2003] A system including:

[2004] (Claim 2)

[2005] 10. The system of claim 1, further comprising means for receiving feedback data from a user and analyzing the received feedback data to adjust an artificial intelligence (AI) algorithm.

[2006] (Claim 3)

[2007] 10. The system of claim 1, wherein the algorithm for finding users with common topics and interests based on work style and work history data takes into account the user's hobbies and interests, work history and work style.

[2008] "Example 1"

[2009] (Claim 1)

[2010] means for receiving attribute data input by a user;

[2011] means for storing the received attribute data in a data storage device;

[2012] using artificial intelligence (AI) to analyze the stored attribute data and find other users with common topics or interests;

[2013] A means of sending notifications to users who have been matched by AI;

[2014] A means for providing an information exchange function that supports communication between matched users;

[2015] A system including:

[2016] (Claim 2)

[2017] 10. The system of claim 1, further comprising means for receiving evaluation data from a user and analyzing the received evaluation data to adjust an artificial intelligence (AI) algorithm.

[2018] (Claim 3)

[2019] 10. The system of claim 1, wherein the algorithm for finding users with common topics or interests based on attribute data takes into account user preferences, interests, history, and style.

[2020] "Application Example 1"

[2021] (Claim 1)

[2022] means for receiving work style and work history data input by a user;

[2023] means for storing the received work style and work history data in a database;

[2024] means for using artificial intelligence (AI) to analyze stored work style and work history data to find other users with common topics or interests;

[2025] A means of sending notifications to users who have been matched by AI;

[2026] A means for providing a chat function that supports communication between matched users;

[2027] means for receiving and analyzing feedback data to adjust the artificial intelligence algorithm;

[2028] A means to enable optimal staff pairing and shift management in physical stores,

[2029] A system including:

[2030] (Claim 2)

[2031] 10. The system of claim 1, characterized by an algorithm for finding users with common topics and interests, taking into account users' hobbies and interests, work history and work style.

[2032] (Claim 3)

[2033] The system of claim 1, further comprising means for evaluating compatibility between staff members within a physical store and making optimal pairings.

[2034] "Example 2: Combining Emotion Engines"

[2035] (Claim 1)

[2036] means for receiving work style, work history data, and emotion data input by a user;

[2037] means for storing the received work style, work history data, and emotion data in a database;

[2038] means for using artificial intelligence (AI) to analyze the stored work style, work history data, and emotional data to find other users with common topics, interests, and emotional states;

[2039] A means of sending notifications to users who have been matched by AI;

[2040] A means for providing a chat function that supports communication between matched users;

[2041] A system including:

[2042] (Claim 2)

[2043] 10. The system of claim 1, further comprising means for receiving feedback data from a user and analyzing the received feedback data to adjust the artificial intelligence (AI) algorithm and the emotion engine.

[2044] (Claim 3)

[2045] The system of claim 1, wherein the algorithm for finding users with common topics, interests, and emotional states based on work style, work history data, and emotional data takes into account the user's hobbies and interests, work history, work style, and emotional state.

[2046] "Application example 2 when combining emotion engines"

[2047] (Claim 1)

[2048] means for receiving work style and work history data input by a user;

[2049] means for storing the received work style and work history data in a database;

[2050] means for using artificial intelligence (AI) to analyze stored work style and work history data to find other users with common topics or interests;

[2051] A means of sending notifications to users who have been matched by AI;

[2052] A means for providing a chat function that supports communication between matched users;

[2053] A means for analyzing emotion data and combining it with profile data to perform matching;

[2054] The system includes a means for forming an optimal work team for matched users.

[2055] (Claim 2)

[2056] 10. The system of claim 1, further comprising means for receiving feedback data from a user and analyzing the received feedback data to adjust an artificial intelligence (AI) algorithm.

[2057] (Claim 3)

[2058] 2. The system according to claim 1, wherein an optimal work team is formed based on profile data and emotion data. [Explanation of symbols]

[2059] 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 work style and work history data input by a user; means for storing the received work style and work history data in a database; means for using artificial intelligence (AI) to analyze the stored work style and work history data to find other users with common topics and interests; A means for sending a notification to a user matched by the AI; A means for providing a chat function that supports communication between matched users; A system including:

2. The system of claim 1 , further comprising means for receiving feedback data from a user and analyzing the received feedback data to adjust an artificial intelligence (AI) algorithm.

3. The system of claim 1, wherein the algorithm for finding users with common topics and interests based on work style and work history data takes into account the user's hobbies and interests, work history and work style.

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