System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting bulletin board information; means for categorizing the collected information; means for collecting user hobbies and business-related information; means for filtering the collected information based on the user hobbies and business-related information; means for notifying the user of the filtered information; and means for displaying the filtered information as recommended information.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] Conventional in-house bulletin board systems have the problem that users miss information that interests them or is necessary for their work because the information they see is so diverse, making it difficult for them to quickly access the information they need. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user's hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information, thereby enabling users to quickly and easily obtain information that interests them or is necessary for their work.

[0006] "Means for collecting bulletin board information" refers to a function for automatically obtaining necessary information from internal bulletin boards.

[0007] The "means for categorizing by category" refers to a function for grouping acquired bulletin board information based on specific categories.

[0008] "Means for collecting user's hobbies, preferences and work-related information" refers to a function for collecting information about personal interests and work entered by the user.

[0009] "Means for filtering information" refers to a function for selecting highly relevant information from collected information based on the user's hobbies and preferences or business-related information.

[0010] The term "means for notifying the user of filtered information" refers to a function for notifying the user of filtered information.

[0011] "Means for displaying recommended information" refers to a function for presenting filtered information to users in a prominent manner on the bulletin board.

[0012] "Natural language processing technology" refers to technology for processing human language using a computer, such as analyzing and classifying text.

[0013] A "user profile" refers to a data set containing a user's attribute information, including hobbies, preferences, and work-related information.

[0014] "Recommendation score" refers to an index that quantifies the relevance of information filtered based on the user's profile information. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is an in-house bulletin board system that allows users to efficiently obtain information of interest or information necessary for their work without missing it. This system is composed of a server, terminals, and users. The roles of each and the program processing are explained in detail below.

[0037] server

[0038] Program processing

[0039] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, by retrieving all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." The server also collects user profile information. The profile includes the user's hobbies, preferences, and information necessary for work.

[0040] The server then filters the collected information based on the user profile. For example, for a user interested in a tennis club, it will pick out information related to the tennis club. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[0041] Terminal

[0042] Program processing

[0043] When a user first uses the system, the device displays a profile setting screen. Here, the user enters their hobbies and work-related information. This data is sent to the server and saved as a profile. The device receives notification data from the server in real time and displays it to the user in the form of a pop-up or alert. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[0044] User

[0045] How to use

[0046] During initial setup, users enter information related to their hobbies and work. This creates a profile that is saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, notifications of upcoming technical seminars or upcoming tennis club activities may be displayed. When users access the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[0047] Specific examples

[0048] Example 1: User A is interested in the tennis club

[0049] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[0050] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[0051] 3. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[0052] 4. Device: Receives notifications in real time and displays them to User A in a pop-up.

[0053] 5. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[0054] Example 2: User B is interested in technical seminars

[0055] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[0056] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[0057] 3. Server: Generates the filtered "Notification of Technical Seminar" as notification data and sends it to User B's terminal.

[0058] 4. Device: Receives notifications in real time and displays them to User B as an alert.

[0059] 5. Bulletin board access: When user B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0060] In this way, this system allows users to efficiently obtain information related to their interests and work, improving work efficiency and simplifying information gathering.

[0061] The processing flow will be explained below.

[0062] server

[0063] Step 1:

[0064] Periodically retrieve bulletin board information, which involves accessing the bulletin board API and retrieving all content in JSON format.

[0065] Step 2:

[0066] The fetched data is saved in the database. This is to persist the data and use it in subsequent processing.

[0067] Step 3:

[0068] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[0069] Step 4:

[0070] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[0071] Step 5:

[0072] Analyze user profiles and extract categories and keywords of interest to each user.

[0073] Step 6:

[0074] Based on each user's profile, relevant information is filtered from the bulletin board database. For example, if a user is interested in tennis clubs, posts related to tennis clubs are selected.

[0075] Step 7:

[0076] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests.

[0077] Step 8:

[0078] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[0079] Terminal

[0080] Step 1:

[0081] As an initial setting, an interface is displayed that allows the user to input hobbies, preferences, and business-related information.

[0082] Step 2:

[0083] The hobby and work-related information entered by the user is sent to the server and saved as a profile.

[0084] Step 3:

[0085] Receive notification data sent from the server in real time, including push notifications and alerts.

[0086] Step 4:

[0087] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[0088] Step 5:

[0089] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[0090] User

[0091] Step 1:

[0092] When using the service for the first time, users enter information about their hobbies and work, which is then saved on the server as a user profile.

[0093] Step 2:

[0094] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[0095] Step 3:

[0096] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminars," "New Product Development," and "Tennis Clubs" displayed on the bulletin board's homepage.

[0097] Example 1

[0098] 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."

[0099] In today's information society, it is important for users to efficiently obtain information necessary for their work or that interests them. However, with so much information available, there is an increasing risk of missing or overlooking necessary information. For this reason, users are required to efficiently collect information related to their interests and work, and to take appropriate action based on that information. Conventional bulletin board systems require users to collect information manually, which requires time and effort. There is also the risk of missing relevant information. A new system is needed to solve these issues and improve users' information gathering and work efficiency.

[0100] 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.

[0101] In this invention, the server includes means for collecting bulletin board information, means for categorizing the collected information using natural language processing technology, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for assigning a recommendation score to the filtered information, means for formatting information with a high recommendation score as notification data and transmitting it to the user terminal, and means for displaying the filtered information as recommended information. This enables users to efficiently and accurately obtain the information they need based on their hobbies, preferences, and work-related information.

[0102] "Bulletin board information" refers to a series of posts and notices shared within a company or organization, including business communications, event notices, club activities, and the like.

[0103] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language, and includes technologies such as text analysis, category classification, and entity recognition.

[0104] "Categorizing" refers to the act of grouping collected information based on specific criteria or themes to make the information easier to organize and access.

[0105] "User hobbies and preferences" refer to specific activities or topics that a user is personally interested in, and are primarily related to recreation, learning, etc.

[0106] "Work-related information" refers to information that a user needs to perform their work, including news, notices, guidance, and the like related to their work.

[0107] "Filtering" refers to the process of sorting collected information based on specific criteria or conditions and excluding unnecessary information.

[0108] The "recommendation score" is a numerical representation of the relevance and importance of filtered information, and is an index that indicates the usefulness of the information to the user.

[0109] "Notification data" refers to data that has been formatted to provide specific information to the user and has been converted into a format that can be displayed on the terminal.

[0110] "Sending to the user terminal" refers to the act of delivering the notification data generated by the server to the device used by the user (PC, smartphone, tablet, etc.) using a communication means.

[0111] "Recommended information" refers to information that is determined to be particularly relevant to the user based on filtering and recommendation scores, and is presented to the user preferentially.

[0112] This invention is a system that enables users to efficiently obtain information that interests them or is necessary for their work without missing anything. This system is composed of a server, a terminal, and a user. The roles of each component and the detailed process will be explained below.

[0113] server

[0114] The server first collects information from the company's internal bulletin board. This collection occurs every hour via the bulletin board's API. The server runs a script written in Python to retrieve all post data from the bulletin board's API endpoint and save it in JSON format. The collected information is then analyzed using natural language processing (NLP) technology. The NLP library used here is NLTK. As a result of the analysis, posts are classified into categories, such as "business announcements," "events," and "club activities."

[0115] Next, the server collects the user's profile information. The profile includes the user's hobbies, preferences, and information necessary for work. The profile setting data sent from the device is saved in a database. The user profile is stored in the database along with the user ID and is used for subsequent filtering processes.

[0116] The server filters the collected information based on the user profile. For example, if a user is interested in tennis clubs, it will pick up information related to tennis clubs. During the filtering process, the server queries posts that match the profile information and stores the results in a temporary filtered list.

[0117] Furthermore, the server assigns a recommendation score to the filtered information. This score is assigned based on the importance and relevance of the information. A scoring algorithm is used to calculate a score for each post, and the score is stored in a database. Information with a high score is formatted as notification data and sent to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it to the device.

[0118] Finally, the selected information is compiled into data that is displayed as "recommended information" when users access the bulletin board. This allows information that interests users to be displayed on the top page when they visit the bulletin board.

[0119] Terminal

[0120] When a user first uses the system, the device displays a profile setting screen where the user enters their hobbies and work-related information. The entered data is sent to the server via an HTTP POST request. A front-end using React is launched, and a form input screen is displayed.

[0121] Notification data is received from the server in real time. Socket.IO is used for real-time communication to receive notification data. The received notification data is displayed to the user in the form of a popup or alert. When a notification is received, the information is displayed to the user using the notification function of the browser or app.

[0122] When a user accesses the bulletin board, the recommended information is displayed as a list of data received from the server. When the page is loaded, the data retrieved from the server is rendered and displayed as an HTML list.

[0123] User

[0124] When a user first uses the system, they enter information related to their hobbies and work. For example, they enter topics of interest such as "tennis" or "technical seminars." This creates a profile that is then saved on the server.

[0125] Users check the notifications displayed on their devices and obtain the information they need. For example, User A checks the "next scheduled tennis club activity" in a pop-up notification. When accessing the bulletin board, they can efficiently check the recommended information displayed on the top page. When User A accesses the bulletin board, "next scheduled tennis club activity" is displayed at the top, allowing them to check it immediately.

[0126] Examples of specific examples and prompts

[0127] Specific examples

[0128] Example 1: User A is interested in the tennis club

[0129] 1. The server collects information about the "tennis club's next scheduled activity" from the company bulletin board and categorizes it.

[0130] 2. The server extracts "Interested in tennis clubs" from User A's profile and filters related information.

[0131] 3. The server generates the filtered "next tennis club activity schedule" as notification data and sends it to User A's device.

[0132] 4. The device receives the notification in real time and displays it to User A in a pop-up.

[0133] 5. When User A accesses the bulletin board, recommended information for the "Tennis Club's next scheduled activity" is displayed on the top page.

[0134] Example 2: User B is interested in technical seminars

[0135] 1. The server collects information about "technical seminar announcements" from the internal bulletin board and categorizes it.

[0136] 2. The server extracts "Interested in technical seminars" from User B's profile and filters out related information.

[0137] 3. The server generates the filtered "Technical Seminar Notification" as notification data and sends it to User B's terminal.

[0138] 4. The device receives the notification in real time and displays it to User B as an alert.

[0139] 5. When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0140] Prompt Sentence Examples

[0141] "Please tell us what information you have collected and filtered about tennis clubs."

[0142] "Find out about upcoming technical seminars and add them to your recommendation list."

[0143] The present invention enables users to efficiently and accurately obtain information related to their hobbies, preferences, and business, thereby improving business efficiency and simplifying information gathering.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1:

[0146] The server collects information from the bulletin board API. This collection occurs every hour. Specifically, the server uses a Python script to access the bulletin board's API endpoint and retrieves all post data in JSON format. The input is the response data from the bulletin board API, and the output is the collected post data saved in JSON format.

[0147] Step 2:

[0148] The server analyzes the collected JSON-formatted post data using natural language processing (NLP) technology and classifies it by category. Specifically, it performs text analysis using the NLTK library and classifies it into categories such as "business announcements," "events," and "club activities." The input is JSON-formatted post data, and the output is post data classified by category.

[0149] Step 3:

[0150] The server collects user profile information. Specifically, it stores the hobbies, preferences, and work-related information entered by the user from their device in a database. The input is the HTTP POST request data sent from the device, and the output is the user profile information stored in the database.

[0151] Step 4:

[0152] The server filters the collected message board information based on the user profile. The server queries for posts that match the user profile information and stores the results in a temporary filtered list. The input is the user profile information and the post data categorized by category, and the output is the filtered post list.

[0153] Step 5:

[0154] The server calculates a recommendation score for the filtered posts. It uses a scoring algorithm to assign a score to each post and stores the scores in a database. The input is the filtered list of posts, and the output is the list of posts with their recommendation scores.

[0155] Step 6:

[0156] The server formats information with high recommendation scores as notification data and sends it to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it. The input is a list of posts with assigned recommendation scores, and the output is JSON data formatted as notification data.

[0157] Step 7:

[0158] The terminal receives notification data sent from the server in real time. Specifically, it uses Socket.IO for real-time communication to receive notification data. The input is notification data from the server, and the output is a notification displayed on the terminal in the form of a popup or alert.

[0159] Step 8:

[0160] The user checks the notification displayed on the device and obtains the necessary information. Specifically, the user clicks on a pop-up notification or alert to check detailed information. The input is the notification displayed on the device, and the output is the detailed information obtained by the user.

[0161] Step 9:

[0162] When a user accesses a bulletin board, recommended information is displayed on the top page. The data retrieved from the server is rendered into an HTML list and displayed as the page loads. The input is the recommended information data retrieved from the server, and the output is the recommended information displayed on the bulletin board top page.

[0163] (Application example 1)

[0164] 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."

[0165] In information sharing within a company, the challenge is to efficiently collect information that users need or are interested in and notify them in real time. In particular, in factory work, it is necessary to obtain important information such as production schedules and maintenance information without missing anything and to respond quickly. For this reason, there is a need for a system that can appropriately notify information filtered based on the user's profile and improve work efficiency.

[0166] 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.

[0167] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user hobbies, preferences, and work-related information, means for notifying the user of the filtered information, means for displaying the filtered information as recommended information, and means for linking with equipment in the factory and notifying and displaying information related to the equipment in real time, thereby enabling users to efficiently obtain information related to their interests and work without missing out.

[0168] "Bulletin board information" refers to various information posted on an internal company bulletin board, including notifications related to the user's work and event information.

[0169] "Means for categorizing" refers to methods or techniques for dividing collected bulletin board information into specific categories, with the aim of efficient information management.

[0170] "User's hobbies, preferences and work-related information" refers to items in which the user is interested and information necessary for work, and is stored on the server as profile-based data.

[0171] "Filtering means" refers to methods or techniques for selecting highly relevant information from the collected bulletin board information based on the user's profile information.

[0172] "Means for notifying" refers to methods or technologies for quickly and appropriately transmitting filtered information to the user's terminal.

[0173] "Means for displaying recommended information" refers to methods or techniques for displaying filtered information in a prominent manner on the top page of a bulletin board or the like, in accordance with the user's interests.

[0174] "Means of linking with equipment within a factory and notifying information" refers to methods and technologies that collect information on the status and schedule of equipment within a factory in real time and notify users at the appropriate time.

[0175] This invention is an in-factory communication system that allows users to efficiently obtain information of interest or information necessary for work without missing anything. This system is composed of a server, terminals, and users, and the roles of each and the program processing will be explained in detail below.

[0176] server

[0177] First, the server periodically collects information from the factory bulletin board. This is done, for example, every hour by retrieving all posts from the bulletin board's API. The collected information is analyzed using natural language processing (NLP) technology and classified into categories such as "production schedule," "maintenance information," and "work instructions." The server also collects user profile information, which includes information about the user's interests and work needs.

[0178] The server then filters the collected information based on the user profile. For example, if a user is interested in maintenance information for a specific device, relevant information will be picked out. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This selected information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[0179] Terminal

[0180] When a user first uses the system, the terminal displays a profile setting screen. Here, the user enters their interests and work-related information. This data is sent to the server and saved as a profile. The terminal receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. In addition, when the bulletin board is accessed, a list of data received from the server as recommended information is displayed. The terminal also has the function of linking to equipment in the factory and obtaining and notifying equipment status and maintenance information in real time.

[0181] User

[0182] During initial setup, users create a profile by entering information related to their interests and work, which is then saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, information such as "next scheduled maintenance for equipment A" or "schedule changes for production line B" may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[0183] Specific examples

[0184] Example 1: For users interested in equipment maintenance

[0185] 1. The server collects information about the next scheduled equipment maintenance from the factory bulletin board and categorizes it.

[0186] 2. The server extracts "interest in equipment maintenance" from the user profile and filters the relevant information.

[0187] 3. The server generates the filtered "next scheduled maintenance for the device" as notification data and sends it to the user's device.

[0188] 4. The device receives the notification in real time and displays it to the user in a pop-up.

[0189] 5. Bulletin board access: When a user accesses the bulletin board, recommended information about the next scheduled maintenance for the equipment is displayed on the top page.

[0190] Example 2: For users interested in production schedules

[0191] 1. The server collects information about "changes in production line schedules" from the factory bulletin board and categorizes it.

[0192] 2. The server extracts the user profile information that states "I'm interested in production schedules" and filters out the relevant information.

[0193] 3. The server generates the filtered "production line schedule change" as notification data and sends it to the user's terminal.

[0194] 4. The device receives the notification in real time and displays it to the user as an alert.

[0195] 5. Bulletin board access: When a user accesses the bulletin board, the recommended information for "changing the production line schedule" is displayed on the top page.

[0196] Example prompt sentence:

[0197] "Please create a description of a system that allows factory robots to efficiently obtain maintenance information."

[0198] As described above, this invention allows users to quickly and efficiently obtain information related to their interests and work, which is expected to improve the efficiency and productivity of factory operations.

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1:

[0201] The server collects information from bulletin boards in factories. The server connects to the bulletin board's API and periodically retrieves posted content (for example, every hour). The retrieved data is in JSON format and includes the post ID, post content, posting date and time, etc. As a result, the input is the bulletin board post information, and the output is the collected raw data.

[0202] Step 2:

[0203] The server analyzes the collected information using natural language processing (NLP) technology and classifies it into categories. For example, the server uses a TF-IDF vectorizer and a KMeans clustering algorithm to classify the information into categories such as "production schedule," "maintenance information," and "work instructions." The input is the collected raw data, and the output is information classified by category.

[0204] Step 3:

[0205] The server collects the user's profile information, which includes user-entered interests and business-related information, such as "I'm interested in maintenance information for a specific piece of equipment." The input is the user's profile information, and the output is the data stored on the server as a profile.

[0206] Step 4:

[0207] The server filters the collected information based on the user profile. The server refers to the profile information and extracts information of related categories. For example, for a user who is "interested in maintenance information," maintenance-related information is picked up. The input is the categorized information and the user profile, and the output is the filtered information.

[0208] Step 5:

[0209] The server assigns a recommendation score to the filtered information. The recommendation score indicates the importance and relevance of the information, and the server selects information based on this score. The input is the filtered information, and the output is the scored information.

[0210] Step 6:

[0211] The server formats the filtered and scored information as notification data and sends it to the user's device. The input is the scored information, and the output is the formatted notification data.

[0212] Step 7:

[0213] The terminal receives notification data from the server in real time and displays it to the user in the form of a popup or alert. For example, when the terminal receives a notification, it displays a popup on the display to inform the user of the latest information. The input is the formatted notification data, and the output is the notification that is displayed to the user.

[0214] Step 8:

[0215] When a terminal accesses a bulletin board, the data received from the server as recommended information is displayed in a list. For example, recommended information is displayed prominently on the bulletin board's top page. The input is notification data, and the output is the recommended information on the bulletin board.

[0216] Step 9:

[0217] The terminal links to the equipment in the factory and obtains and notifies the equipment status and maintenance information in real time. For example, this involves obtaining information from the equipment's sensors and immediately notifying the user if an abnormality is detected. The input is the equipment's sensor information, and the output is a notification that is displayed in real time.

[0218] Step 10:

[0219] The user checks the notification and takes the necessary action. The user receives the notification from the terminal and, for example, checks the maintenance schedule or responds to changes in the production schedule. The input is the terminal notification, and the output is the user's response action.

[0220] 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.

[0221] This invention is an in-house bulletin board system that incorporates an emotion engine that recognizes the user's emotions. This enables more personalized information to be provided according to the user's emotional state. The system consists of a server, terminals, and users. The roles of each component and the program processing are explained in detail below.

[0222] server

[0223] Program processing

[0224] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, and retrieves all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." An emotion engine then analyzes user emotion data in real time, and the information is recorded in a database.

[0225] The server then filters the collected information based on the user profile and emotional data. For example, if a user is interested in a tennis club, not only will it pick up information related to the tennis club, but if the user is in a positive emotional state, it will also add more information. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. The selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[0226] Terminal

[0227] Program processing

[0228] When a user first uses the system, the device displays a profile setting screen. Here, the user can enter information about their hobbies and work, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The device receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[0229] User

[0230] How to use

[0231] During initial setup, the user enters information related to their hobbies and work. They also input their current emotional state using the emotion input means or capture it via sensors. This creates a profile and emotional data, which are then stored in the system. The user can periodically check notifications on the device to obtain the latest information. For example, notifications about upcoming technical seminars or the next tennis club activity schedule may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[0232] Specific examples

[0233] Example 1: User A is interested in the tennis club and is in a positive emotional state

[0234] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[0235] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[0236] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[0237] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[0238] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[0239] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[0240] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[0241] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[0242] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[0243] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[0244] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[0245] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[0246] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0247] In this way, this system not only allows users to efficiently obtain information related to their interests and work, but also provides more appropriate information because it is personalized according to their emotional state.

[0248] The processing flow will be explained below.

[0249] server

[0250] Step 1:

[0251] Periodically retrieve bulletin board information. The server accesses the bulletin board API and retrieves all content in JSON format.

[0252] Step 2:

[0253] The retrieved data is saved in a database. The data is persisted for use in subsequent processing.

[0254] Step 3:

[0255] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[0256] Step 4:

[0257] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[0258] Step 5:

[0259] The emotion engine analyzes the user's emotional data in real time and stores the information in a database. Emotion data is collected using technologies such as facial recognition and text analysis.

[0260] Step 6:

[0261] By analyzing user profiles and emotional data, the system filters collected information based on each user's interests and emotional state. For example, for a user who is interested in a tennis club and has a positive emotional state, the system will pick out relevant information and increase the amount of information available.

[0262] Step 7:

[0263] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests and emotions.

[0264] Step 8:

[0265] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[0266] Terminal

[0267] Step 1:

[0268] As an initial setting, the system presents an interface for users to input their hobbies, preferences, and work-related information, while also configuring the system to use sensors (e.g., camera and microphone) to track their emotional state in real time.

[0269] Step 2:

[0270] The hobby and work-related information and emotional data entered by the user are sent to a server and saved as a profile and emotional data.

[0271] Step 3:

[0272] Receive notification data sent from the server in real time, including push notifications and alerts.

[0273] Step 4:

[0274] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[0275] Step 5:

[0276] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[0277] User

[0278] Step 1:

[0279] When using the robot for the first time, users input information about their hobbies and work. Using the emotion input means, users' current emotional state is also input or captured by a sensor.

[0280] Step 2:

[0281] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[0282] Step 3:

[0283] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminar," "New Product Development," and "Tennis Club" displayed on the bulletin board homepage. The amount and content of information displayed is adjusted according to the user's emotional state.

[0284] Example 2

[0285] 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."

[0286] Conventional in-house bulletin board systems filter information based on users' hobbies, preferences, and work-related information, but do not provide information that takes into account the user's emotional state. As a result, users receive information that does not match their emotional state, and they may feel stressed by information overload or notifications of unnecessary information. Another problem is that users cannot quickly access the information they really need. Therefore, there is a need for personalized information provision that takes into account not only users' hobbies, preferences, and work-related information, but also their emotional state.

[0287] 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.

[0288] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user's hobbies, preferences, and work-related information, means for analyzing the user's emotional state, means for recording the analyzed emotional state in a database, means for filtering the collected information based on the user's hobbies, preferences, work-related information, and emotional state, means for assigning a recommendation score to the filtered information, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information, thereby enabling more personalized information to be provided according to the user's emotional state.

[0289] The "means of collecting bulletin board information" refers to a system that periodically obtains posted information via the API of the internal bulletin board and stores it in a database.

[0290] "Means for categorizing by category" is a function that uses natural language processing technology to analyze collected bulletin board information and sort it into different categories.

[0291] "Means for collecting user's hobbies, preferences and work-related information" is a function that collects information entered by the user on the profile setting screen and past browsing history and stores them in a database.

[0292] The "means for analyzing the user's emotional state" is a mechanism that uses an emotion engine to analyze the user's emotional data in real time and record the results.

[0293] The "means for recording the analyzed emotional state in a database" is a function for storing the user's emotional data analyzed by the emotion engine in a database.

[0294] The "means for filtering information" is a mechanism for narrowing down the collected bulletin board information based on the user's hobbies, preferences, work-related information, and emotional state.

[0295] The "means for assigning a recommendation score" is a function that scores filtered information using a machine learning model.

[0296] The "notification means" is a mechanism that sends filtered information and information with high recommendation scores to the user's device in real time and displays a notification.

[0297] "Means for displaying recommended information" is a function that displays filtered information received from the server in a prominent manner on the top page when accessing the bulletin board.

[0298] The present invention is an in-house bulletin board system that can provide personalized information according to the emotional state of a user. The present invention is implemented using the following hardware and software.

[0299] server

[0300] The server has the specific role of collecting and analyzing bulletin board information, categorizing the information, and then sending notifications based on the user's profile and emotional data. The server periodically calls the bulletin board API to collect the latest posts. The collected information is stored in a database and analyzed using natural language processing technology such as Google Cloud Natural Language API. This analysis results in the information being classified into categories such as "business announcements," "events," and "club activities."

[0301] The server then uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time. The analysis results are recorded in a database. The collected information is filtered based on the analyzed emotional state, as well as the user's hobbies, preferences, and work-related information. The filtered information is assigned a recommendation score using a machine learning model, and information with a high score is formatted as notification data. This notification data is sent to the device in real time and displayed as "recommended information" when the bulletin board is accessed.

[0302] Terminal

[0303] When a user first uses the system, the device displays a profile setup screen where the user enters information about their hobbies and work, and uses sensors (e.g., facial expression recognition cameras and heart rate sensors) and input fields to track their emotional state in real time. This data is then sent to the server and stored in a database.

[0304] The device receives notification data from the server in real time and displays it in the form of a pop-up or alert. When the user accesses the bulletin board, filtered data is displayed as recommended information.

[0305] User

[0306] During initial setup, users input information related to their hobbies and work, and also input their current emotional state or capture it using sensors. Users can periodically check notifications on their device to receive the latest information. For example, they may receive notifications about upcoming technical seminars or the next tennis club activity. When accessing the bulletin board, information of interest is displayed prominently, allowing users to obtain information efficiently.

[0307] Specific examples

[0308] Example 1: User A is interested in the tennis club and is in a positive emotional state

[0309] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[0310] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[0311] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[0312] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[0313] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[0314] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[0315] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[0316] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[0317] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[0318] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[0319] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[0320] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[0321] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0322] Examples of prompt statements

[0323] Example prompt sentence:

[0324] "Create notifications for upcoming technical seminars and provide information that will interest users. Adjust the filtering and notification content based on the user's emotional state, both positive and negative."

[0325] In this way, the present invention provides more personalized information provision according to the user's emotional state.

[0326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0327] Program processing flow

[0328] Server-side processing

[0329] Step 1:

[0330] The server collects bulletin board information. It calls the bulletin board API to retrieve post data from the last hour. This post data is retrieved in JSON format and stored in a database.

[0331] Input: Post data obtained from the bulletin board API

[0332] Output: Post data stored in the database

[0333] Specific behavior:

[0334] The server schedules a periodic job to call the API every hour, stores the retrieved data in a buffer, and then writes it to the database.

[0335] Step 2:

[0336] The server analyzes the collected information using natural language processing technology, using the Google Cloud Natural Language API to analyze the content of each post and classify it into categories such as "business announcements," "events," and "club activities."

[0337] Input: Post data stored in the database

[0338] Output: Post data with categories

[0339] Specific behavior:

[0340] The server reads the unparsed post data from the database, sends a parsing request to the Google Cloud Natural Language API, and writes the returned parsed results back to the database.

[0341] Step 3:

[0342] The server analyzes the user's emotional data, using an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time and record the information in a database.

[0343] Input: User emotion data (real-time data from sensors and user input)

[0344] Output: Sentiment analysis data recorded in a database

[0345] Specific behavior:

[0346] The server receives the data from the emotion sensor and sends an analysis request to the IBM Watson Tone Analyzer API. The returned analysis results are stored in a database.

[0347] Step 4:

[0348] The server filters the collected information based on the user profile and emotional data, extracting information of interest based on the user's hobbies, preferences, work-related information, and emotional state.

[0349] Input: User profile data, emotion data, and categorized post data

[0350] Output: Filtered post data

[0351] Specific behavior:

[0352] The server reads user profiles and sentiment data from a database and filters the posted data based on pre-defined rules and machine learning models.

[0353] Step 5:

[0354] The server assigns a recommendation score to the filtered information, using a machine learning model to score each piece of information and select the information with the highest score.

[0355] Input: Filtered post data

[0356] Output: Filtered data with recommendation scores

[0357] Specific behavior:

[0358] The server uses a machine learning model to score each post and store the results in a database.

[0359] Step 6:

[0360] The server formats the information with the highest score as notification data and sends it to the user's device. It also generates data to be displayed as "recommended information" when the user accesses the bulletin board.

[0361] Input: Filtered data with recommendation scores

[0362] Output: Notification data sent to your device and data displayed as "Recommended Information"

[0363] Specific behavior:

[0364] The server formats the notification data into JSON format and sends it to the user's device in real time. It also generates data for the bulletin board's top page and stores it in a database.

[0365] Terminal side processing

[0366] Step 1:

[0367] The device displays a profile setup screen when first used, collects information about the user's hobbies and work, and also collects sensor information for real-time tracking of emotional data.

[0368] Input: User-entered data (hobbies, work-related information, emotional data)

[0369] Output: Profile data and emotion data sent to the server

[0370] Specific behavior:

[0371] The device displays a profile setting screen, and once the user has completed their input, the information is sent to the server. Sensor information is also sent to the server in real time.

[0372] Step 2:

[0373] The terminal receives the notification data sent from the server in real time and displays it to the user in the form of a pop-up or alert.

[0374] Input: Notification data from the server

[0375] Output: Popups or alerts that are displayed to the user

[0376] Specific behavior:

[0377] When the terminal receives the notification data, it immediately displays a pop-up to the user and sounds an alert sound if necessary.

[0378] Step 3:

[0379] When the terminal accesses the bulletin board, it displays a list of data received from the server as recommended information.

[0380] Input: Recommendation data from the server

[0381] Output: A list of recommendations displayed on the bulletin board

[0382] Specific behavior:

[0383] When the terminal accesses the bulletin board, it receives the recommended information data sent from the server and displays it on the user interface.

[0384] User-side processing

[0385] Step 1:

[0386] During initial setup, the user inputs information related to their hobbies and work, and their emotional state is also input or captured by sensors.

[0387] Input: Hobbies, work-related information, emotional state

[0388] Output: Profile data and emotion data sent to the server

[0389] Specific behavior:

[0390] The user enters the necessary information on the device's profile setting screen and sets the sensor to be used.

[0391] Step 2:

[0392] Users can periodically check notifications on their devices to get the latest information.

[0393] Input: Notification data from the device

[0394] Output: Check for the latest information

[0395] Specific behavior:

[0396] Users can check pop-ups and alerts displayed on their devices to obtain the latest information.

[0397] Step 3:

[0398] When a user accesses the bulletin board, information of particular interest to the user is displayed in a prominent manner, allowing the user to obtain information efficiently.

[0399] Input: Forum recommendations

[0400] Output: Get the information you are interested in

[0401] Specific behavior:

[0402] Users access the bulletin board, check the content displayed as recommended information, and obtain information that interests them.

[0403] (Application example 2)

[0404] 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."

[0405] In today's information-saturated society, it is difficult to effectively sort and provide the information that each user needs. Furthermore, while systems that can appropriately customize information based on the user's emotional state could provide a more satisfying experience, there are few systems that can do this. This poses a challenge: users may be overwhelmed by the amount of information available, and may not be able to obtain the appropriate information, which can lead to stress.

[0406] 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 collecting bulletin board information, means for classifying the collected information by category, means for collecting a user's hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for recognizing a user's emotional state in real time, means for adjusting the filtered information based on the recognized emotional state, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information. This makes it possible to personalize information according to the user's emotional state and provide more appropriate and satisfying information.

[0407] "Bulletin Board Information" means the content of messages or notices posted on internal bulletin boards or online forums.

[0408] A "category" is a category for classifying bulletin board information based on a specific topic or theme.

[0409] "Hobbies" refers to activities and interests that a user is personally interested in.

[0410] "Business-related information" refers to information related to a user's job or work.

[0411] "Emotional state" refers to a user's real-time psychological emotional state, which may be classified as positive or negative.

[0412] "Filtering" is the process of sorting out information based on specific criteria.

[0413] "Notification" is a means of notifying the user of filtered information in real time.

[0414] "Recommended information" is information that is determined to be particularly important to notify based on the user's interests and emotional state.

[0415] This invention is a virtual store system that combines an emotion engine that recognizes the user's emotions. This system makes it possible to provide a more personalized shopping experience according to the user's emotional state. The system consists of a server, smart glasses (terminals), and a user. Details are described below.

[0416] server

[0417] Program processing

[0418] The server first collects product information from the virtual store. This is done periodically, for example, every hour, to retrieve all product data from the store's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "fashion," "electronics," and "sports goods." An emotion engine then analyzes the user's emotional data in real time and records the information in a database.

[0419] The server then filters the product information based on the user profile and emotional data. For example, if a user is interested in sports equipment, the server will not only provide sports equipment-related information but also provide additional promotional information if the user is in a positive emotional state. The server then assigns a recommendation score to the filtered information and selects the information with the highest score. This information is then formatted as notification data and sent to the smart glasses. The selected information is then also summarized in the recommendations displayed on the smart glasses.

[0420] Smart glasses (terminal)

[0421] Program processing

[0422] The smart glasses display a profile setting screen when the user first uses the system. Here, the user can enter their hobbies and interests, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The smart glasses receive notification data from the server in real time and display it to the user in the form of pop-ups or alerts. When the user accesses a virtual store, the glasses display a list of recommended information received from the server.

[0423] User

[0424] How to use

[0425] During initial setup, users input information related to their hobbies and interests. Their current emotional state is also input using an emotion input means or captured by sensors. This creates a profile and emotional data, which are then stored in the system. Users can periodically check notifications on the smart glasses to obtain the latest information. For example, "sale information for fashion categories" or "new sports goods" are displayed. When users access a virtual store, information of particular interest is prominently displayed, allowing them to efficiently select products.

[0426] Hardware and software used

[0427] Hardware: Smart glasses (built-in camera, microphone, display)

[0428] Server: Cloud service (e.g. AWS, Google Cloud)

[0429] Software and Libraries

[0430] Emotion Engine API: Affect Recognition Engine (e.g. Affectiva)

[0431] Face recognition library: OpenCV, dlib

[0432] Natural language processing libraries: NLTK, spaCy

[0433] Real-time communication: WebSocket

[0434] Specific examples

[0435] Example 1: User A is interested in fashion and in a positive emotional state

[0436] 1. The server collects and categorizes products in the fashion category.

[0437] 2. The server extracts "Interested in fashion" from User A's profile and filters related information.

[0438] 3. The server adjusts the emotion engine to recognize User A's positive emotional state and provide additional promotional information.

[0439] 4. The server generates the filtered "fashion sale information" as notification data and sends it to the smart glasses.

[0440] 5. The smart glasses receive the notification in real time and display it to User A in a pop-up.

[0441] Example 2: User B is interested in sports equipment and is in a negative emotional state

[0442] 1. The server collects and categorizes products in the sporting goods category.

[0443] 2. The server extracts "Interested in sporting goods" from User B's profile and filters out related information.

[0444] 3. The server adjusts the emotion engine to recognize user B's negative emotional state and provide information sparingly.

[0445] 4. The server generates the filtered "new sports goods" as notification data and sends it to the smart glasses.

[0446] 5. The smart glasses receive the notification in real time and display it to User B as an alert.

[0447] Prompt Sentence Examples

[0448] "Now explain to me what data the emotion engine uses and how it can help personalize the virtual shopping experience."

[0449] "Please tell us more about how the smart glasses' built-in camera and microphone can be used to analyze the user's emotions in real time and recommend products based on that state."

[0450] The above description clearly shows the specific embodiments for carrying out the present invention.

[0451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0452] Step 1:

[0453] Input: A user puts on smart glasses and logs into the system.

[0454] How it works: The user enters their hobbies and interests on the smart glasses' initial setup screen. The camera and microphone are then activated to collect emotional data in real time.

[0455] Output: The user's interest, preference and emotional data are sent from the device to the server and saved in a profile.

[0456] Step 2:

[0457] Input: User profile and collected emotion data.

[0458] How it works: The server periodically collects all necessary information via the internal bulletin board and the virtual store's API.

[0459] Output: Collected product information and bulletin board information is saved in a database.

[0460] Step 3:

[0461] Input: Collected product information and bulletin board information.

[0462] How it works: The server uses natural language processing (NLP) techniques to categorize the collected information. It uses the OpenAI GPT model to analyze the meaning of each piece of information and classify it into categories such as "fashion," "electronics," and "sports goods."

[0463] Output: Product information classified by category is generated and stored in a database.

[0464] Step 4:

[0465] Input: User profile information and product information organized by category.

[0466] How it works: The server generates a filtered list of information based on the hobbies and interests registered in the user's profile.

[0467] Output: A list of product information that matches the user's hobbies and preferences is generated.

[0468] Step 5:

[0469] Input: User sentiment data and filtered product information.

[0470] How it works: The server uses an emotion engine to analyze the user's real-time emotional state. It uses Affectiva and Microsoft Azure emotion recognition APIs to assess a positive or negative emotional state.

[0471] Output: A list of information tailored to the emotional state is generated.

[0472] Step 6:

[0473] Input: Reconciled information list.

[0474] How it works: The server calculates a recommendation score for the filtered information, assigning a priority and interest score to each information item.

[0475] Output: A list of information with a recommendation score is generated.

[0476] Step 7:

[0477] Input: A list of information with recommendation scores.

[0478] How it works: The server formats the notification data and sends it to the user's smart glasses. Real-time notifications are sent using WebSocket.

[0479] Output: Notification data is sent to the device.

[0480] Step 8:

[0481] Input: Notification data.

[0482] How it works: The smart glasses receive notification data in real time and display it to the user in the form of a popup or alert, or it can use eye tracking or voice commands.

[0483] Output: The recommended information is visually presented to the user.

[0484] Through these steps, users can receive personalized information in real time that is tailored to their emotional state, enabling them to enjoy an efficient and satisfying shopping experience.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] [Second embodiment]

[0489] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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).

[0495] 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.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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."

[0501] This invention is an in-house bulletin board system that allows users to efficiently obtain information of interest or information necessary for their work without missing it. This system is composed of a server, terminals, and users. The roles of each and the program processing are explained in detail below.

[0502] server

[0503] Program processing

[0504] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, by retrieving all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." The server also collects user profile information. The profile includes the user's hobbies, preferences, and information necessary for work.

[0505] The server then filters the collected information based on the user profile. For example, for a user interested in a tennis club, it will pick out information related to the tennis club. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[0506] Terminal

[0507] Program processing

[0508] When a user first uses the system, the device displays a profile setting screen. Here, the user enters their hobbies and work-related information. This data is sent to the server and saved as a profile. The device receives notification data from the server in real time and displays it to the user in the form of a pop-up or alert. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[0509] User

[0510] How to use

[0511] During initial setup, users enter information related to their hobbies and work. This creates a profile that is saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, notifications of upcoming technical seminars or upcoming tennis club activities may be displayed. When users access the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[0512] Specific examples

[0513] Example 1: User A is interested in the tennis club

[0514] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[0515] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[0516] 3. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[0517] 4. Device: Receives notifications in real time and displays them to User A in a pop-up.

[0518] 5. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[0519] Example 2: User B is interested in technical seminars

[0520] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[0521] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[0522] 3. Server: Generates the filtered "Notification of Technical Seminar" as notification data and sends it to User B's terminal.

[0523] 4. Device: Receives notifications in real time and displays them to User B as an alert.

[0524] 5. Bulletin board access: When user B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0525] In this way, this system allows users to efficiently obtain information related to their interests and work, improving work efficiency and simplifying information gathering.

[0526] The processing flow will be explained below.

[0527] server

[0528] Step 1:

[0529] Periodically retrieve bulletin board information, which involves accessing the bulletin board API and retrieving all content in JSON format.

[0530] Step 2:

[0531] The fetched data is saved in the database. This is to persist the data and use it in subsequent processing.

[0532] Step 3:

[0533] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[0534] Step 4:

[0535] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[0536] Step 5:

[0537] Analyze user profiles and extract categories and keywords of interest to each user.

[0538] Step 6:

[0539] Based on each user's profile, relevant information is filtered from the bulletin board database. For example, if a user is interested in tennis clubs, posts related to tennis clubs are selected.

[0540] Step 7:

[0541] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests.

[0542] Step 8:

[0543] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[0544] Terminal

[0545] Step 1:

[0546] As an initial setting, an interface is displayed that allows the user to input hobbies, preferences, and business-related information.

[0547] Step 2:

[0548] The hobby and work-related information entered by the user is sent to the server and saved as a profile.

[0549] Step 3:

[0550] Receive notification data sent from the server in real time, including push notifications and alerts.

[0551] Step 4:

[0552] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[0553] Step 5:

[0554] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[0555] User

[0556] Step 1:

[0557] When using the service for the first time, users enter information about their hobbies and work, which is then saved on the server as a user profile.

[0558] Step 2:

[0559] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[0560] Step 3:

[0561] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminars," "New Product Development," and "Tennis Clubs" displayed on the bulletin board's homepage.

[0562] Example 1

[0563] 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."

[0564] In today's information society, it is important for users to efficiently obtain information necessary for their work or that interests them. However, with so much information available, there is an increasing risk of missing or overlooking necessary information. For this reason, users are required to efficiently collect information related to their interests and work, and to take appropriate action based on that information. Conventional bulletin board systems require users to collect information manually, which requires time and effort. There is also the risk of missing relevant information. A new system is needed to solve these issues and improve users' information gathering and work efficiency.

[0565] 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.

[0566] In this invention, the server includes means for collecting bulletin board information, means for categorizing the collected information using natural language processing technology, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for assigning a recommendation score to the filtered information, means for formatting information with a high recommendation score as notification data and transmitting it to the user terminal, and means for displaying the filtered information as recommended information. This enables users to efficiently and accurately obtain the information they need based on their hobbies, preferences, and work-related information.

[0567] "Bulletin board information" refers to a series of posts and notices shared within a company or organization, including business communications, event notices, club activities, and the like.

[0568] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language, and includes technologies such as text analysis, category classification, and entity recognition.

[0569] "Categorizing" refers to the act of grouping collected information based on specific criteria or themes to make the information easier to organize and access.

[0570] "User hobbies and preferences" refer to specific activities or topics that a user is personally interested in, and are primarily related to recreation, learning, etc.

[0571] "Work-related information" refers to information that a user needs to perform their work, including news, notices, guidance, and the like related to their work.

[0572] "Filtering" refers to the process of sorting collected information based on specific criteria or conditions and excluding unnecessary information.

[0573] The "recommendation score" is a numerical representation of the relevance and importance of filtered information, and is an index that indicates the usefulness of the information to the user.

[0574] "Notification data" refers to data that has been formatted to provide specific information to the user and has been converted into a format that can be displayed on the terminal.

[0575] "Sending to the user terminal" refers to the act of delivering the notification data generated by the server to the device used by the user (PC, smartphone, tablet, etc.) using a communication means.

[0576] "Recommended information" refers to information that is determined to be particularly relevant to the user based on filtering and recommendation scores, and is presented to the user preferentially.

[0577] This invention is a system that enables users to efficiently obtain information that interests them or is necessary for their work without missing anything. This system is composed of a server, a terminal, and a user. The roles of each component and the detailed process will be explained below.

[0578] server

[0579] The server first collects information from the company's internal bulletin board. This collection occurs every hour via the bulletin board's API. The server runs a script written in Python to retrieve all post data from the bulletin board's API endpoint and save it in JSON format. The collected information is then analyzed using natural language processing (NLP) technology. The NLP library used here is NLTK. As a result of the analysis, posts are classified into categories, such as "business announcements," "events," and "club activities."

[0580] Next, the server collects the user's profile information. The profile includes the user's hobbies, preferences, and information necessary for work. The profile setting data sent from the device is saved in a database. The user profile is stored in the database along with the user ID and is used for subsequent filtering processes.

[0581] The server filters the collected information based on the user profile. For example, if a user is interested in tennis clubs, it will pick up information related to tennis clubs. During the filtering process, the server queries posts that match the profile information and stores the results in a temporary filtered list.

[0582] Furthermore, the server assigns a recommendation score to the filtered information. This score is assigned based on the importance and relevance of the information. A scoring algorithm is used to calculate a score for each post, and the score is stored in a database. Information with a high score is formatted as notification data and sent to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it to the device.

[0583] Finally, the selected information is compiled into data that is displayed as "recommended information" when users access the bulletin board. This allows information that interests users to be displayed on the top page when they visit the bulletin board.

[0584] Terminal

[0585] When a user first uses the system, the device displays a profile setting screen where the user enters their hobbies and work-related information. The entered data is sent to the server via an HTTP POST request. A front-end using React is launched, and a form input screen is displayed.

[0586] Notification data is received from the server in real time. Socket.IO is used for real-time communication to receive notification data. The received notification data is displayed to the user in the form of a popup or alert. When a notification is received, the information is displayed to the user using the notification function of the browser or app.

[0587] When a user accesses the bulletin board, the recommended information is displayed as a list of data received from the server. When the page is loaded, the data retrieved from the server is rendered and displayed as an HTML list.

[0588] User

[0589] When a user first uses the system, they enter information related to their hobbies and work. For example, they enter topics of interest such as "tennis" or "technical seminars." This creates a profile that is then saved on the server.

[0590] Users check the notifications displayed on their devices and obtain the information they need. For example, User A checks the "next scheduled tennis club activity" in a pop-up notification. When accessing the bulletin board, they can efficiently check the recommended information displayed on the top page. When User A accesses the bulletin board, "next scheduled tennis club activity" is displayed at the top, allowing them to check it immediately.

[0591] Examples of specific examples and prompts

[0592] Specific examples

[0593] Example 1: User A is interested in the tennis club

[0594] 1. The server collects information about the "tennis club's next scheduled activity" from the company bulletin board and categorizes it.

[0595] 2. The server extracts "Interested in tennis clubs" from User A's profile and filters related information.

[0596] 3. The server generates the filtered "next tennis club activity schedule" as notification data and sends it to User A's device.

[0597] 4. The device receives the notification in real time and displays it to User A in a pop-up.

[0598] 5. When User A accesses the bulletin board, recommended information for the "Tennis Club's next scheduled activity" is displayed on the top page.

[0599] Example 2: User B is interested in technical seminars

[0600] 1. The server collects information about "technical seminar announcements" from the internal bulletin board and categorizes it.

[0601] 2. The server extracts "Interested in technical seminars" from User B's profile and filters out related information.

[0602] 3. The server generates the filtered "Technical Seminar Notification" as notification data and sends it to User B's terminal.

[0603] 4. The device receives the notification in real time and displays it to User B as an alert.

[0604] 5. When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0605] Prompt Sentence Examples

[0606] "Please tell us what information you have collected and filtered about tennis clubs."

[0607] "Find out about upcoming technical seminars and add them to your recommendation list."

[0608] The present invention enables users to efficiently and accurately obtain information related to their hobbies, preferences, and business, thereby improving business efficiency and simplifying information gathering.

[0609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] The server collects information from the bulletin board API. This collection occurs every hour. Specifically, the server uses a Python script to access the bulletin board's API endpoint and retrieves all post data in JSON format. The input is the response data from the bulletin board API, and the output is the collected post data saved in JSON format.

[0612] Step 2:

[0613] The server analyzes the collected JSON-formatted post data using natural language processing (NLP) technology and classifies it by category. Specifically, it performs text analysis using the NLTK library and classifies it into categories such as "business announcements," "events," and "club activities." The input is JSON-formatted post data, and the output is post data classified by category.

[0614] Step 3:

[0615] The server collects user profile information. Specifically, it stores the hobbies, preferences, and work-related information entered by the user from their device in a database. The input is the HTTP POST request data sent from the device, and the output is the user profile information stored in the database.

[0616] Step 4:

[0617] The server filters the collected message board information based on the user profile. The server queries for posts that match the user profile information and stores the results in a temporary filtered list. The input is the user profile information and the post data categorized by category, and the output is the filtered post list.

[0618] Step 5:

[0619] The server calculates a recommendation score for the filtered posts. It uses a scoring algorithm to assign a score to each post and stores the scores in a database. The input is the filtered list of posts, and the output is the list of posts with their recommendation scores.

[0620] Step 6:

[0621] The server formats information with high recommendation scores as notification data and sends it to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it. The input is a list of posts with assigned recommendation scores, and the output is JSON data formatted as notification data.

[0622] Step 7:

[0623] The terminal receives notification data sent from the server in real time. Specifically, it uses Socket.IO for real-time communication to receive notification data. The input is notification data from the server, and the output is a notification displayed on the terminal in the form of a popup or alert.

[0624] Step 8:

[0625] The user checks the notification displayed on the device and obtains the necessary information. Specifically, the user clicks on a pop-up notification or alert to check detailed information. The input is the notification displayed on the device, and the output is the detailed information obtained by the user.

[0626] Step 9:

[0627] When a user accesses a bulletin board, recommended information is displayed on the top page. The data retrieved from the server is rendered into an HTML list and displayed as the page loads. The input is the recommended information data retrieved from the server, and the output is the recommended information displayed on the bulletin board top page.

[0628] (Application example 1)

[0629] 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."

[0630] In information sharing within a company, the challenge is to efficiently collect information that users need or are interested in and notify them in real time. In particular, in factory work, it is necessary to obtain important information such as production schedules and maintenance information without missing anything and to respond quickly. For this reason, there is a need for a system that can appropriately notify information filtered based on the user's profile and improve work efficiency.

[0631] 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.

[0632] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user hobbies, preferences, and work-related information, means for notifying the user of the filtered information, means for displaying the filtered information as recommended information, and means for linking with equipment in the factory and notifying and displaying information related to the equipment in real time, thereby enabling users to efficiently obtain information related to their interests and work without missing out.

[0633] "Bulletin board information" refers to various information posted on an internal company bulletin board, including notifications related to the user's work and event information.

[0634] "Means for categorizing" refers to methods or techniques for dividing collected bulletin board information into specific categories, with the aim of efficient information management.

[0635] "User's hobbies, preferences and work-related information" refers to items in which the user is interested and information necessary for work, and is stored on the server as profile-based data.

[0636] "Filtering means" refers to methods or techniques for selecting highly relevant information from the collected bulletin board information based on the user's profile information.

[0637] "Means for notifying" refers to methods or technologies for quickly and appropriately transmitting filtered information to the user's terminal.

[0638] "Means for displaying recommended information" refers to methods or techniques for displaying filtered information in a prominent manner on the top page of a bulletin board or the like, in accordance with the user's interests.

[0639] "Means of linking with equipment within a factory and notifying information" refers to methods and technologies that collect information on the status and schedule of equipment within a factory in real time and notify users at the appropriate time.

[0640] This invention is an in-factory communication system that allows users to efficiently obtain information of interest or information necessary for work without missing anything. This system is composed of a server, terminals, and users, and the roles of each and the program processing will be explained in detail below.

[0641] server

[0642] First, the server periodically collects information from the factory bulletin board. This is done, for example, every hour by retrieving all posts from the bulletin board's API. The collected information is analyzed using natural language processing (NLP) technology and classified into categories such as "production schedule," "maintenance information," and "work instructions." The server also collects user profile information, which includes information about the user's interests and work needs.

[0643] The server then filters the collected information based on the user profile. For example, if a user is interested in maintenance information for a specific device, relevant information will be picked out. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This selected information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[0644] Terminal

[0645] When a user first uses the system, the terminal displays a profile setting screen. Here, the user enters their interests and work-related information. This data is sent to the server and saved as a profile. The terminal receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. In addition, when the bulletin board is accessed, a list of data received from the server as recommended information is displayed. The terminal also has the function of linking to equipment in the factory and obtaining and notifying equipment status and maintenance information in real time.

[0646] User

[0647] During initial setup, users create a profile by entering information related to their interests and work, which is then saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, information such as "next scheduled maintenance for equipment A" or "schedule changes for production line B" may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[0648] Specific examples

[0649] Example 1: For users interested in equipment maintenance

[0650] 1. The server collects information about the next scheduled equipment maintenance from the factory bulletin board and categorizes it.

[0651] 2. The server extracts "interest in equipment maintenance" from the user profile and filters the relevant information.

[0652] 3. The server generates the filtered "next scheduled maintenance for the device" as notification data and sends it to the user's device.

[0653] 4. The device receives the notification in real time and displays it to the user in a pop-up.

[0654] 5. Bulletin board access: When a user accesses the bulletin board, recommended information about the next scheduled maintenance for the equipment is displayed on the top page.

[0655] Example 2: For users interested in production schedules

[0656] 1. The server collects information about "changes in production line schedules" from the factory bulletin board and categorizes it.

[0657] 2. The server extracts the user profile information that states "I'm interested in production schedules" and filters out the relevant information.

[0658] 3. The server generates the filtered "production line schedule change" as notification data and sends it to the user's terminal.

[0659] 4. The device receives the notification in real time and displays it to the user as an alert.

[0660] 5. Bulletin board access: When a user accesses the bulletin board, the recommended information for "changing the production line schedule" is displayed on the top page.

[0661] Example prompt sentence:

[0662] "Please create a description of a system that allows factory robots to efficiently obtain maintenance information."

[0663] As described above, this invention allows users to quickly and efficiently obtain information related to their interests and work, which is expected to improve the efficiency and productivity of factory operations.

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1:

[0666] The server collects information from bulletin boards in factories. The server connects to the bulletin board's API and periodically retrieves posted content (for example, every hour). The retrieved data is in JSON format and includes the post ID, post content, posting date and time, etc. As a result, the input is the bulletin board post information, and the output is the collected raw data.

[0667] Step 2:

[0668] The server analyzes the collected information using natural language processing (NLP) technology and classifies it into categories. For example, the server uses a TF-IDF vectorizer and a KMeans clustering algorithm to classify the information into categories such as "production schedule," "maintenance information," and "work instructions." The input is the collected raw data, and the output is information classified by category.

[0669] Step 3:

[0670] The server collects the user's profile information, which includes user-entered interests and business-related information, such as "I'm interested in maintenance information for a specific piece of equipment." The input is the user's profile information, and the output is the data stored on the server as a profile.

[0671] Step 4:

[0672] The server filters the collected information based on the user profile. The server refers to the profile information and extracts information of related categories. For example, for a user who is "interested in maintenance information," maintenance-related information is picked up. The input is the categorized information and the user profile, and the output is the filtered information.

[0673] Step 5:

[0674] The server assigns a recommendation score to the filtered information. The recommendation score indicates the importance and relevance of the information, and the server selects information based on this score. The input is the filtered information, and the output is the scored information.

[0675] Step 6:

[0676] The server formats the filtered and scored information as notification data and sends it to the user's device. The input is the scored information, and the output is the formatted notification data.

[0677] Step 7:

[0678] The terminal receives notification data from the server in real time and displays it to the user in the form of a popup or alert. For example, when the terminal receives a notification, it displays a popup on the display to inform the user of the latest information. The input is the formatted notification data, and the output is the notification that is displayed to the user.

[0679] Step 8:

[0680] When a terminal accesses a bulletin board, the data received from the server as recommended information is displayed in a list. For example, recommended information is displayed prominently on the bulletin board's top page. The input is notification data, and the output is the recommended information on the bulletin board.

[0681] Step 9:

[0682] The terminal links to the equipment in the factory and obtains and notifies the equipment status and maintenance information in real time. For example, this involves obtaining information from the equipment's sensors and immediately notifying the user if an abnormality is detected. The input is the equipment's sensor information, and the output is a notification that is displayed in real time.

[0683] Step 10:

[0684] The user checks the notification and takes the necessary action. The user receives the notification from the terminal and, for example, checks the maintenance schedule or responds to changes in the production schedule. The input is the terminal notification, and the output is the user's response action.

[0685] 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.

[0686] This invention is an in-house bulletin board system that incorporates an emotion engine that recognizes the user's emotions. This enables more personalized information to be provided according to the user's emotional state. The system consists of a server, terminals, and users. The roles of each component and the program processing are explained in detail below.

[0687] server

[0688] Program processing

[0689] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, and retrieves all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." An emotion engine then analyzes user emotion data in real time, and the information is recorded in a database.

[0690] The server then filters the collected information based on the user profile and emotional data. For example, if a user is interested in a tennis club, not only will it pick up information related to the tennis club, but if the user is in a positive emotional state, it will also add more information. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. The selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[0691] Terminal

[0692] Program processing

[0693] When a user first uses the system, the device displays a profile setting screen. Here, the user can enter information about their hobbies and work, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The device receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[0694] User

[0695] How to use

[0696] During initial setup, the user enters information related to their hobbies and work. They also input their current emotional state using the emotion input means or capture it via sensors. This creates a profile and emotional data, which are then stored in the system. The user can periodically check notifications on the device to obtain the latest information. For example, notifications about upcoming technical seminars or the next tennis club activity schedule may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[0697] Specific examples

[0698] Example 1: User A is interested in the tennis club and is in a positive emotional state

[0699] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[0700] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[0701] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[0702] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[0703] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[0704] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[0705] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[0706] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[0707] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[0708] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[0709] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[0710] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[0711] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0712] In this way, this system not only allows users to efficiently obtain information related to their interests and work, but also provides more appropriate information because it is personalized according to their emotional state.

[0713] The processing flow will be explained below.

[0714] server

[0715] Step 1:

[0716] Periodically retrieve bulletin board information. The server accesses the bulletin board API and retrieves all content in JSON format.

[0717] Step 2:

[0718] The retrieved data is saved in a database. The data is persisted for use in subsequent processing.

[0719] Step 3:

[0720] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[0721] Step 4:

[0722] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[0723] Step 5:

[0724] The emotion engine analyzes the user's emotional data in real time and stores the information in a database. Emotion data is collected using technologies such as facial recognition and text analysis.

[0725] Step 6:

[0726] By analyzing user profiles and emotional data, the system filters collected information based on each user's interests and emotional state. For example, for a user who is interested in a tennis club and has a positive emotional state, the system will pick out relevant information and increase the amount of information available.

[0727] Step 7:

[0728] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests and emotions.

[0729] Step 8:

[0730] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[0731] Terminal

[0732] Step 1:

[0733] As an initial setting, the system presents an interface for users to input their hobbies, preferences, and work-related information, while also configuring the system to use sensors (e.g., camera and microphone) to track their emotional state in real time.

[0734] Step 2:

[0735] The hobby and work-related information and emotional data entered by the user are sent to a server and saved as a profile and emotional data.

[0736] Step 3:

[0737] Receive notification data sent from the server in real time, including push notifications and alerts.

[0738] Step 4:

[0739] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[0740] Step 5:

[0741] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[0742] User

[0743] Step 1:

[0744] When using the robot for the first time, users input information about their hobbies and work. Using the emotion input means, users' current emotional state is also input or captured by a sensor.

[0745] Step 2:

[0746] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[0747] Step 3:

[0748] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminar," "New Product Development," and "Tennis Club" displayed on the bulletin board homepage. The amount and content of information displayed is adjusted according to the user's emotional state.

[0749] Example 2

[0750] 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."

[0751] Conventional in-house bulletin board systems filter information based on users' hobbies, preferences, and work-related information, but do not provide information that takes into account the user's emotional state. As a result, users receive information that does not match their emotional state, and they may feel stressed by information overload or notifications of unnecessary information. Another problem is that users cannot quickly access the information they really need. Therefore, there is a need for personalized information provision that takes into account not only users' hobbies, preferences, and work-related information, but also their emotional state.

[0752] 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.

[0753] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user's hobbies, preferences, and work-related information, means for analyzing the user's emotional state, means for recording the analyzed emotional state in a database, means for filtering the collected information based on the user's hobbies, preferences, work-related information, and emotional state, means for assigning a recommendation score to the filtered information, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information, thereby enabling more personalized information to be provided according to the user's emotional state.

[0754] The "means of collecting bulletin board information" refers to a system that periodically obtains posted information via the API of the internal bulletin board and stores it in a database.

[0755] "Means for categorizing by category" is a function that uses natural language processing technology to analyze collected bulletin board information and sort it into different categories.

[0756] "Means for collecting user's hobbies, preferences and work-related information" is a function that collects information entered by the user on the profile setting screen and past browsing history and stores them in a database.

[0757] The "means for analyzing the user's emotional state" is a mechanism that uses an emotion engine to analyze the user's emotional data in real time and record the results.

[0758] The "means for recording the analyzed emotional state in a database" is a function for storing the user's emotional data analyzed by the emotion engine in a database.

[0759] The "means for filtering information" is a mechanism for narrowing down the collected bulletin board information based on the user's hobbies, preferences, work-related information, and emotional state.

[0760] The "means for assigning a recommendation score" is a function that scores filtered information using a machine learning model.

[0761] The "notification means" is a mechanism that sends filtered information and information with high recommendation scores to the user's device in real time and displays a notification.

[0762] "Means for displaying recommended information" is a function that displays filtered information received from the server in a prominent manner on the top page when accessing the bulletin board.

[0763] The present invention is an in-house bulletin board system that can provide personalized information according to the emotional state of a user. The present invention is implemented using the following hardware and software.

[0764] server

[0765] The server has the specific role of collecting and analyzing bulletin board information, categorizing the information, and then sending notifications based on the user's profile and emotional data. The server periodically calls the bulletin board API to collect the latest posts. The collected information is stored in a database and analyzed using natural language processing technology such as Google Cloud Natural Language API. This analysis results in the information being classified into categories such as "business announcements," "events," and "club activities."

[0766] The server then uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time. The analysis results are recorded in a database. The collected information is filtered based on the analyzed emotional state, as well as the user's hobbies, preferences, and work-related information. The filtered information is assigned a recommendation score using a machine learning model, and information with a high score is formatted as notification data. This notification data is sent to the device in real time and displayed as "recommended information" when the bulletin board is accessed.

[0767] Terminal

[0768] When a user first uses the system, the device displays a profile setup screen where the user enters information about their hobbies and work, and uses sensors (e.g., facial expression recognition cameras and heart rate sensors) and input fields to track their emotional state in real time. This data is then sent to the server and stored in a database.

[0769] The device receives notification data from the server in real time and displays it in the form of a pop-up or alert. When the user accesses the bulletin board, filtered data is displayed as recommended information.

[0770] User

[0771] During initial setup, users input information related to their hobbies and work, and also input their current emotional state or capture it using sensors. Users can periodically check notifications on their device to receive the latest information. For example, they may receive notifications about upcoming technical seminars or the next tennis club activity. When accessing the bulletin board, information of interest is displayed prominently, allowing users to obtain information efficiently.

[0772] Specific examples

[0773] Example 1: User A is interested in the tennis club and is in a positive emotional state

[0774] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[0775] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[0776] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[0777] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[0778] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[0779] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[0780] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[0781] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[0782] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[0783] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[0784] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[0785] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[0786] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0787] Examples of prompt statements

[0788] Example prompt sentence:

[0789] "Create notifications for upcoming technical seminars and provide information that will interest users. Adjust the filtering and notification content based on the user's emotional state, both positive and negative."

[0790] In this way, the present invention provides more personalized information provision according to the user's emotional state.

[0791] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0792] Program processing flow

[0793] Server-side processing

[0794] Step 1:

[0795] The server collects bulletin board information. It calls the bulletin board API to retrieve post data from the last hour. This post data is retrieved in JSON format and stored in a database.

[0796] Input: Post data obtained from the bulletin board API

[0797] Output: Post data stored in the database

[0798] Specific behavior:

[0799] The server schedules a periodic job to call the API every hour, stores the retrieved data in a buffer, and then writes it to the database.

[0800] Step 2:

[0801] The server analyzes the collected information using natural language processing technology, using the Google Cloud Natural Language API to analyze the content of each post and classify it into categories such as "business announcements," "events," and "club activities."

[0802] Input: Post data stored in the database

[0803] Output: Post data with categories

[0804] Specific behavior:

[0805] The server reads the unparsed post data from the database, sends a parsing request to the Google Cloud Natural Language API, and writes the returned parsed results back to the database.

[0806] Step 3:

[0807] The server analyzes the user's emotional data, using an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time and record the information in a database.

[0808] Input: User emotion data (real-time data from sensors and user input)

[0809] Output: Sentiment analysis data recorded in a database

[0810] Specific behavior:

[0811] The server receives the data from the emotion sensor and sends an analysis request to the IBM Watson Tone Analyzer API. The returned analysis results are stored in a database.

[0812] Step 4:

[0813] The server filters the collected information based on the user profile and emotional data, extracting information of interest based on the user's hobbies, preferences, work-related information, and emotional state.

[0814] Input: User profile data, emotion data, and categorized post data

[0815] Output: Filtered post data

[0816] Specific behavior:

[0817] The server reads user profiles and sentiment data from a database and filters the posted data based on pre-defined rules and machine learning models.

[0818] Step 5:

[0819] The server assigns a recommendation score to the filtered information, using a machine learning model to score each piece of information and select the information with the highest score.

[0820] Input: Filtered post data

[0821] Output: Filtered data with recommendation scores

[0822] Specific behavior:

[0823] The server uses a machine learning model to score each post and store the results in a database.

[0824] Step 6:

[0825] The server formats the information with the highest score as notification data and sends it to the user's device. It also generates data to be displayed as "recommended information" when the user accesses the bulletin board.

[0826] Input: Filtered data with recommendation scores

[0827] Output: Notification data sent to your device and data displayed as "Recommended Information"

[0828] Specific behavior:

[0829] The server formats the notification data into JSON format and sends it to the user's device in real time. It also generates data for the bulletin board's top page and stores it in a database.

[0830] Terminal side processing

[0831] Step 1:

[0832] The device displays a profile setup screen when first used, collects information about the user's hobbies and work, and also collects sensor information for real-time tracking of emotional data.

[0833] Input: User-entered data (hobbies, work-related information, emotional data)

[0834] Output: Profile data and emotion data sent to the server

[0835] Specific behavior:

[0836] The device displays a profile setting screen, and once the user has completed their input, the information is sent to the server. Sensor information is also sent to the server in real time.

[0837] Step 2:

[0838] The terminal receives the notification data sent from the server in real time and displays it to the user in the form of a pop-up or alert.

[0839] Input: Notification data from the server

[0840] Output: Popups or alerts that are displayed to the user

[0841] Specific behavior:

[0842] When the terminal receives the notification data, it immediately displays a pop-up to the user and sounds an alert sound if necessary.

[0843] Step 3:

[0844] When the terminal accesses the bulletin board, it displays a list of data received from the server as recommended information.

[0845] Input: Recommendation data from the server

[0846] Output: A list of recommendations displayed on the bulletin board

[0847] Specific behavior:

[0848] When the terminal accesses the bulletin board, it receives the recommended information data sent from the server and displays it on the user interface.

[0849] User-side processing

[0850] Step 1:

[0851] During initial setup, the user inputs information related to their hobbies and work, and their emotional state is also input or captured by sensors.

[0852] Input: Hobbies, work-related information, emotional state

[0853] Output: Profile data and emotion data sent to the server

[0854] Specific behavior:

[0855] The user enters the necessary information on the device's profile setting screen and sets the sensor to be used.

[0856] Step 2:

[0857] Users can periodically check notifications on their devices to get the latest information.

[0858] Input: Notification data from the device

[0859] Output: Check for the latest information

[0860] Specific behavior:

[0861] Users can check pop-ups and alerts displayed on their devices to obtain the latest information.

[0862] Step 3:

[0863] When a user accesses the bulletin board, information of particular interest to the user is displayed in a prominent manner, allowing the user to obtain information efficiently.

[0864] Input: Forum recommendations

[0865] Output: Get the information you are interested in

[0866] Specific behavior:

[0867] Users access the bulletin board, check the content displayed as recommended information, and obtain information that interests them.

[0868] (Application example 2)

[0869] 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."

[0870] In today's information-saturated society, it is difficult to effectively sort and provide the information that each user needs. Furthermore, while systems that can appropriately customize information based on the user's emotional state could provide a more satisfying experience, there are few systems that can do this. This poses a challenge: users may be overwhelmed by the amount of information available, and may not be able to obtain the appropriate information, which can lead to stress.

[0871] 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 collecting bulletin board information, means for classifying the collected information by category, means for collecting a user's hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for recognizing a user's emotional state in real time, means for adjusting the filtered information based on the recognized emotional state, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information. This makes it possible to personalize information according to the user's emotional state and provide more appropriate and satisfying information.

[0872] "Bulletin Board Information" means the content of messages or notices posted on internal bulletin boards or online forums.

[0873] A "category" is a category for classifying bulletin board information based on a specific topic or theme.

[0874] "Hobbies" refers to activities and interests that a user is personally interested in.

[0875] "Business-related information" refers to information related to a user's job or work.

[0876] "Emotional state" refers to a user's real-time psychological emotional state, which may be classified as positive or negative.

[0877] "Filtering" is the process of sorting out information based on specific criteria.

[0878] "Notification" is a means of notifying the user of filtered information in real time.

[0879] "Recommended information" is information that is determined to be particularly important to notify based on the user's interests and emotional state.

[0880] This invention is a virtual store system that combines an emotion engine that recognizes the user's emotions. This system makes it possible to provide a more personalized shopping experience according to the user's emotional state. The system consists of a server, smart glasses (terminals), and a user. Details are described below.

[0881] server

[0882] Program processing

[0883] The server first collects product information from the virtual store. This is done periodically, for example, every hour, to retrieve all product data from the store's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "fashion," "electronics," and "sports goods." An emotion engine then analyzes the user's emotional data in real time and records the information in a database.

[0884] The server then filters the product information based on the user profile and emotional data. For example, if a user is interested in sports equipment, the server will not only provide sports equipment-related information but also provide additional promotional information if the user is in a positive emotional state. The server then assigns a recommendation score to the filtered information and selects the information with the highest score. This information is then formatted as notification data and sent to the smart glasses. The selected information is then also summarized in the recommendations displayed on the smart glasses.

[0885] Smart glasses (terminal)

[0886] Program processing

[0887] The smart glasses display a profile setting screen when the user first uses the system. Here, the user can enter their hobbies and interests, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The smart glasses receive notification data from the server in real time and display it to the user in the form of pop-ups or alerts. When the user accesses a virtual store, the glasses display a list of recommended information received from the server.

[0888] User

[0889] How to use

[0890] During initial setup, users input information related to their hobbies and interests. Their current emotional state is also input using an emotion input means or captured by sensors. This creates a profile and emotional data, which are then stored in the system. Users can periodically check notifications on the smart glasses to obtain the latest information. For example, "sale information for fashion categories" or "new sports goods" are displayed. When users access a virtual store, information of particular interest is prominently displayed, allowing them to efficiently select products.

[0891] Hardware and software used

[0892] Hardware: Smart glasses (built-in camera, microphone, display)

[0893] Server: Cloud service (e.g. AWS, Google Cloud)

[0894] Software and Libraries

[0895] Emotion Engine API: Affect Recognition Engine (e.g. Affectiva)

[0896] Face recognition library: OpenCV, dlib

[0897] Natural language processing libraries: NLTK, spaCy

[0898] Real-time communication: WebSocket

[0899] Specific examples

[0900] Example 1: User A is interested in fashion and in a positive emotional state

[0901] 1. The server collects and categorizes products in the fashion category.

[0902] 2. The server extracts "Interested in fashion" from User A's profile and filters related information.

[0903] 3. The server adjusts the emotion engine to recognize User A's positive emotional state and provide additional promotional information.

[0904] 4. The server generates the filtered "fashion sale information" as notification data and sends it to the smart glasses.

[0905] 5. The smart glasses receive the notification in real time and display it to User A in a pop-up.

[0906] Example 2: User B is interested in sports equipment and is in a negative emotional state

[0907] 1. The server collects and categorizes products in the sporting goods category.

[0908] 2. The server extracts "Interested in sporting goods" from User B's profile and filters out related information.

[0909] 3. The server adjusts the emotion engine to recognize user B's negative emotional state and provide information sparingly.

[0910] 4. The server generates the filtered "new sports goods" as notification data and sends it to the smart glasses.

[0911] 5. The smart glasses receive the notification in real time and display it to User B as an alert.

[0912] Prompt Sentence Examples

[0913] "Now explain to me what data the emotion engine uses and how it can help personalize the virtual shopping experience."

[0914] "Please tell us more about how the smart glasses' built-in camera and microphone can be used to analyze the user's emotions in real time and recommend products based on that state."

[0915] The above description clearly shows the specific embodiments for carrying out the present invention.

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] Input: A user puts on smart glasses and logs into the system.

[0919] How it works: The user enters their hobbies and interests on the smart glasses' initial setup screen. The camera and microphone are then activated to collect emotional data in real time.

[0920] Output: The user's interest, preference and emotional data are sent from the device to the server and saved in a profile.

[0921] Step 2:

[0922] Input: User profile and collected emotion data.

[0923] How it works: The server periodically collects all necessary information via the internal bulletin board and the virtual store's API.

[0924] Output: Collected product information and bulletin board information is saved in a database.

[0925] Step 3:

[0926] Input: Collected product information and bulletin board information.

[0927] How it works: The server uses natural language processing (NLP) techniques to categorize the collected information. It uses the OpenAI GPT model to analyze the meaning of each piece of information and classify it into categories such as "fashion," "electronics," and "sports goods."

[0928] Output: Product information classified by category is generated and stored in a database.

[0929] Step 4:

[0930] Input: User profile information and product information organized by category.

[0931] How it works: The server generates a filtered list of information based on the hobbies and interests registered in the user's profile.

[0932] Output: A list of product information that matches the user's hobbies and preferences is generated.

[0933] Step 5:

[0934] Input: User sentiment data and filtered product information.

[0935] How it works: The server uses an emotion engine to analyze the user's real-time emotional state. It uses Affectiva and Microsoft Azure emotion recognition APIs to assess a positive or negative emotional state.

[0936] Output: A list of information tailored to the emotional state is generated.

[0937] Step 6:

[0938] Input: Reconciled information list.

[0939] How it works: The server calculates a recommendation score for the filtered information, assigning a priority and interest score to each information item.

[0940] Output: A list of information with a recommendation score is generated.

[0941] Step 7:

[0942] Input: A list of information with recommendation scores.

[0943] How it works: The server formats the notification data and sends it to the user's smart glasses. Real-time notifications are sent using WebSocket.

[0944] Output: Notification data is sent to the device.

[0945] Step 8:

[0946] Input: Notification data.

[0947] How it works: The smart glasses receive notification data in real time and display it to the user in the form of a popup or alert, or it can use eye tracking or voice commands.

[0948] Output: The recommended information is visually presented to the user.

[0949] Through these steps, users can receive personalized information in real time that is tailored to their emotional state, enabling them to enjoy an efficient and satisfying shopping experience.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] [Third embodiment]

[0954] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0955] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0956] 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).

[0957] 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.

[0958] 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.

[0959] 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).

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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."

[0966] This invention is an in-house bulletin board system that allows users to efficiently obtain information of interest or information necessary for their work without missing it. This system is composed of a server, terminals, and users. The roles of each and the program processing are explained in detail below.

[0967] server

[0968] Program processing

[0969] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, by retrieving all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." The server also collects user profile information. The profile includes the user's hobbies, preferences, and information necessary for work.

[0970] The server then filters the collected information based on the user profile. For example, for a user interested in a tennis club, it will pick out information related to the tennis club. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[0971] Terminal

[0972] Program processing

[0973] When a user first uses the system, the device displays a profile setting screen. Here, the user enters their hobbies and work-related information. This data is sent to the server and saved as a profile. The device receives notification data from the server in real time and displays it to the user in the form of a pop-up or alert. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[0974] User

[0975] How to use

[0976] During initial setup, users enter information related to their hobbies and work. This creates a profile that is saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, notifications of upcoming technical seminars or upcoming tennis club activities may be displayed. When users access the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[0977] Specific examples

[0978] Example 1: User A is interested in the tennis club

[0979] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[0980] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[0981] 3. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[0982] 4. Device: Receives notifications in real time and displays them to User A in a pop-up.

[0983] 5. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[0984] Example 2: User B is interested in technical seminars

[0985] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[0986] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[0987] 3. Server: Generates the filtered "Notification of Technical Seminar" as notification data and sends it to User B's terminal.

[0988] 4. Device: Receives notifications in real time and displays them to User B as an alert.

[0989] 5. Bulletin board access: When user B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[0990] In this way, this system allows users to efficiently obtain information related to their interests and work, improving work efficiency and simplifying information gathering.

[0991] The processing flow will be explained below.

[0992] server

[0993] Step 1:

[0994] Periodically retrieve bulletin board information, which involves accessing the bulletin board API and retrieving all content in JSON format.

[0995] Step 2:

[0996] The fetched data is saved in the database. This is to persist the data and use it in subsequent processing.

[0997] Step 3:

[0998] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[0999] Step 4:

[1000] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[1001] Step 5:

[1002] Analyze user profiles and extract categories and keywords of interest to each user.

[1003] Step 6:

[1004] Based on each user's profile, relevant information is filtered from the bulletin board database. For example, if a user is interested in tennis clubs, posts related to tennis clubs are selected.

[1005] Step 7:

[1006] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests.

[1007] Step 8:

[1008] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[1009] Terminal

[1010] Step 1:

[1011] As an initial setting, an interface is displayed that allows the user to input hobbies, preferences, and business-related information.

[1012] Step 2:

[1013] The hobby and work-related information entered by the user is sent to the server and saved as a profile.

[1014] Step 3:

[1015] Receive notification data sent from the server in real time, including push notifications and alerts.

[1016] Step 4:

[1017] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[1018] Step 5:

[1019] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[1020] User

[1021] Step 1:

[1022] When using the service for the first time, users enter information about their hobbies and work, which is then saved on the server as a user profile.

[1023] Step 2:

[1024] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[1025] Step 3:

[1026] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminars," "New Product Development," and "Tennis Clubs" displayed on the bulletin board's homepage.

[1027] Example 1

[1028] 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."

[1029] In today's information society, it is important for users to efficiently obtain information necessary for their work or that interests them. However, with so much information available, there is an increasing risk of missing or overlooking necessary information. For this reason, users are required to efficiently collect information related to their interests and work, and to take appropriate action based on that information. Conventional bulletin board systems require users to collect information manually, which requires time and effort. There is also the risk of missing relevant information. A new system is needed to solve these issues and improve users' information gathering and work efficiency.

[1030] 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.

[1031] In this invention, the server includes means for collecting bulletin board information, means for categorizing the collected information using natural language processing technology, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for assigning a recommendation score to the filtered information, means for formatting information with a high recommendation score as notification data and transmitting it to the user terminal, and means for displaying the filtered information as recommended information. This enables users to efficiently and accurately obtain the information they need based on their hobbies, preferences, and work-related information.

[1032] "Bulletin board information" refers to a series of posts and notices shared within a company or organization, including business communications, event notices, club activities, and the like.

[1033] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language, and includes technologies such as text analysis, category classification, and entity recognition.

[1034] "Categorizing" refers to the act of grouping collected information based on specific criteria or themes to make the information easier to organize and access.

[1035] "User hobbies and preferences" refer to specific activities or topics that a user is personally interested in, and are primarily related to recreation, learning, etc.

[1036] "Work-related information" refers to information that a user needs to perform their work, including news, notices, guidance, and the like related to their work.

[1037] "Filtering" refers to the process of sorting collected information based on specific criteria or conditions and excluding unnecessary information.

[1038] The "recommendation score" is a numerical representation of the relevance and importance of filtered information, and is an index that indicates the usefulness of the information to the user.

[1039] "Notification data" refers to data that has been formatted to provide specific information to the user and has been converted into a format that can be displayed on the terminal.

[1040] "Sending to the user terminal" refers to the act of delivering the notification data generated by the server to the device used by the user (PC, smartphone, tablet, etc.) using a communication means.

[1041] "Recommended information" refers to information that is determined to be particularly relevant to the user based on filtering and recommendation scores, and is presented to the user preferentially.

[1042] This invention is a system that enables users to efficiently obtain information that interests them or is necessary for their work without missing anything. This system is composed of a server, a terminal, and a user. The roles of each component and the detailed process will be explained below.

[1043] server

[1044] The server first collects information from the company's internal bulletin board. This collection occurs every hour via the bulletin board's API. The server runs a script written in Python to retrieve all post data from the bulletin board's API endpoint and save it in JSON format. The collected information is then analyzed using natural language processing (NLP) technology. The NLP library used here is NLTK. As a result of the analysis, posts are classified into categories, such as "business announcements," "events," and "club activities."

[1045] Next, the server collects the user's profile information. The profile includes the user's hobbies, preferences, and information necessary for work. The profile setting data sent from the device is saved in a database. The user profile is stored in the database along with the user ID and is used for subsequent filtering processes.

[1046] The server filters the collected information based on the user profile. For example, if a user is interested in tennis clubs, it will pick up information related to tennis clubs. During the filtering process, the server queries posts that match the profile information and stores the results in a temporary filtered list.

[1047] Furthermore, the server assigns a recommendation score to the filtered information. This score is assigned based on the importance and relevance of the information. A scoring algorithm is used to calculate a score for each post, and the score is stored in a database. Information with a high score is formatted as notification data and sent to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it to the device.

[1048] Finally, the selected information is compiled into data that is displayed as "recommended information" when users access the bulletin board. This allows information that interests users to be displayed on the top page when they visit the bulletin board.

[1049] Terminal

[1050] When a user first uses the system, the device displays a profile setting screen where the user enters their hobbies and work-related information. The entered data is sent to the server via an HTTP POST request. A front-end using React is launched, and a form input screen is displayed.

[1051] Notification data is received from the server in real time. Socket.IO is used for real-time communication to receive notification data. The received notification data is displayed to the user in the form of a popup or alert. When a notification is received, the information is displayed to the user using the notification function of the browser or app.

[1052] When a user accesses the bulletin board, the recommended information is displayed as a list of data received from the server. When the page is loaded, the data retrieved from the server is rendered and displayed as an HTML list.

[1053] User

[1054] When a user first uses the system, they enter information related to their hobbies and work. For example, they enter topics of interest such as "tennis" or "technical seminars." This creates a profile that is then saved on the server.

[1055] Users check the notifications displayed on their devices and obtain the information they need. For example, User A checks the "next scheduled tennis club activity" in a pop-up notification. When accessing the bulletin board, they can efficiently check the recommended information displayed on the top page. When User A accesses the bulletin board, "next scheduled tennis club activity" is displayed at the top, allowing them to check it immediately.

[1056] Examples of specific examples and prompts

[1057] Specific examples

[1058] Example 1: User A is interested in the tennis club

[1059] 1. The server collects information about the "tennis club's next scheduled activity" from the company bulletin board and categorizes it.

[1060] 2. The server extracts "Interested in tennis clubs" from User A's profile and filters related information.

[1061] 3. The server generates the filtered "next tennis club activity schedule" as notification data and sends it to User A's device.

[1062] 4. The device receives the notification in real time and displays it to User A in a pop-up.

[1063] 5. When User A accesses the bulletin board, recommended information for the "Tennis Club's next scheduled activity" is displayed on the top page.

[1064] Example 2: User B is interested in technical seminars

[1065] 1. The server collects information about "technical seminar announcements" from the internal bulletin board and categorizes it.

[1066] 2. The server extracts "Interested in technical seminars" from User B's profile and filters out related information.

[1067] 3. The server generates the filtered "Technical Seminar Notification" as notification data and sends it to User B's terminal.

[1068] 4. The device receives the notification in real time and displays it to User B as an alert.

[1069] 5. When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[1070] Prompt Sentence Examples

[1071] "Please tell us what information you have collected and filtered about tennis clubs."

[1072] "Find out about upcoming technical seminars and add them to your recommendation list."

[1073] The present invention enables users to efficiently and accurately obtain information related to their hobbies, preferences, and business, thereby improving business efficiency and simplifying information gathering.

[1074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1075] Step 1:

[1076] The server collects information from the bulletin board API. This collection occurs every hour. Specifically, the server uses a Python script to access the bulletin board's API endpoint and retrieves all post data in JSON format. The input is the response data from the bulletin board API, and the output is the collected post data saved in JSON format.

[1077] Step 2:

[1078] The server analyzes the collected JSON-formatted post data using natural language processing (NLP) technology and classifies it by category. Specifically, it performs text analysis using the NLTK library and classifies it into categories such as "business announcements," "events," and "club activities." The input is JSON-formatted post data, and the output is post data classified by category.

[1079] Step 3:

[1080] The server collects user profile information. Specifically, it stores the hobbies, preferences, and work-related information entered by the user from their device in a database. The input is the HTTP POST request data sent from the device, and the output is the user profile information stored in the database.

[1081] Step 4:

[1082] The server filters the collected message board information based on the user profile. The server queries for posts that match the user profile information and stores the results in a temporary filtered list. The input is the user profile information and the post data categorized by category, and the output is the filtered post list.

[1083] Step 5:

[1084] The server calculates a recommendation score for the filtered posts. It uses a scoring algorithm to assign a score to each post and stores the scores in a database. The input is the filtered list of posts, and the output is the list of posts with their recommendation scores.

[1085] Step 6:

[1086] The server formats information with high recommendation scores as notification data and sends it to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it. The input is a list of posts with assigned recommendation scores, and the output is JSON data formatted as notification data.

[1087] Step 7:

[1088] The terminal receives notification data sent from the server in real time. Specifically, it uses Socket.IO for real-time communication to receive notification data. The input is notification data from the server, and the output is a notification displayed on the terminal in the form of a popup or alert.

[1089] Step 8:

[1090] The user checks the notification displayed on the device and obtains the necessary information. Specifically, the user clicks on a pop-up notification or alert to check detailed information. The input is the notification displayed on the device, and the output is the detailed information obtained by the user.

[1091] Step 9:

[1092] When a user accesses a bulletin board, recommended information is displayed on the top page. The data retrieved from the server is rendered into an HTML list and displayed as the page loads. The input is the recommended information data retrieved from the server, and the output is the recommended information displayed on the bulletin board top page.

[1093] (Application example 1)

[1094] 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."

[1095] In information sharing within a company, the challenge is to efficiently collect information that users need or are interested in and notify them in real time. In particular, in factory work, it is necessary to obtain important information such as production schedules and maintenance information without missing anything and to respond quickly. For this reason, there is a need for a system that can appropriately notify information filtered based on the user's profile and improve work efficiency.

[1096] 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.

[1097] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user hobbies, preferences, and work-related information, means for notifying the user of the filtered information, means for displaying the filtered information as recommended information, and means for linking with equipment in the factory and notifying and displaying information related to the equipment in real time, thereby enabling users to efficiently obtain information related to their interests and work without missing out.

[1098] "Bulletin board information" refers to various information posted on an internal company bulletin board, including notifications related to the user's work and event information.

[1099] "Means for categorizing" refers to methods or techniques for dividing collected bulletin board information into specific categories, with the aim of efficient information management.

[1100] "User's hobbies, preferences and work-related information" refers to items in which the user is interested and information necessary for work, and is stored on the server as profile-based data.

[1101] "Filtering means" refers to methods or techniques for selecting highly relevant information from the collected bulletin board information based on the user's profile information.

[1102] "Means for notifying" refers to methods or technologies for quickly and appropriately transmitting filtered information to the user's terminal.

[1103] "Means for displaying recommended information" refers to methods or techniques for displaying filtered information in a prominent manner on the top page of a bulletin board or the like, in accordance with the user's interests.

[1104] "Means of linking with equipment within a factory and notifying information" refers to methods and technologies that collect information on the status and schedule of equipment within a factory in real time and notify users at the appropriate time.

[1105] This invention is an in-factory communication system that allows users to efficiently obtain information of interest or information necessary for work without missing anything. This system is composed of a server, terminals, and users, and the roles of each and the program processing will be explained in detail below.

[1106] server

[1107] First, the server periodically collects information from the factory bulletin board. This is done, for example, every hour by retrieving all posts from the bulletin board's API. The collected information is analyzed using natural language processing (NLP) technology and classified into categories such as "production schedule," "maintenance information," and "work instructions." The server also collects user profile information, which includes information about the user's interests and work needs.

[1108] The server then filters the collected information based on the user profile. For example, if a user is interested in maintenance information for a specific device, relevant information will be picked out. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This selected information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[1109] Terminal

[1110] When a user first uses the system, the terminal displays a profile setting screen. Here, the user enters their interests and work-related information. This data is sent to the server and saved as a profile. The terminal receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. In addition, when the bulletin board is accessed, a list of data received from the server as recommended information is displayed. The terminal also has the function of linking to equipment in the factory and obtaining and notifying equipment status and maintenance information in real time.

[1111] User

[1112] During initial setup, users create a profile by entering information related to their interests and work, which is then saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, information such as "next scheduled maintenance for equipment A" or "schedule changes for production line B" may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[1113] Specific examples

[1114] Example 1: For users interested in equipment maintenance

[1115] 1. The server collects information about the next scheduled equipment maintenance from the factory bulletin board and categorizes it.

[1116] 2. The server extracts "interest in equipment maintenance" from the user profile and filters the relevant information.

[1117] 3. The server generates the filtered "next scheduled maintenance for the device" as notification data and sends it to the user's device.

[1118] 4. The device receives the notification in real time and displays it to the user in a pop-up.

[1119] 5. Bulletin board access: When a user accesses the bulletin board, recommended information about the next scheduled maintenance for the equipment is displayed on the top page.

[1120] Example 2: For users interested in production schedules

[1121] 1. The server collects information about "changes in production line schedules" from the factory bulletin board and categorizes it.

[1122] 2. The server extracts the user profile information that states "I'm interested in production schedules" and filters out the relevant information.

[1123] 3. The server generates the filtered "production line schedule change" as notification data and sends it to the user's terminal.

[1124] 4. The device receives the notification in real time and displays it to the user as an alert.

[1125] 5. Bulletin board access: When a user accesses the bulletin board, the recommended information for "changing the production line schedule" is displayed on the top page.

[1126] Example prompt sentence:

[1127] "Please create a description of a system that allows factory robots to efficiently obtain maintenance information."

[1128] As described above, this invention allows users to quickly and efficiently obtain information related to their interests and work, which is expected to improve the efficiency and productivity of factory operations.

[1129] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1130] Step 1:

[1131] The server collects information from bulletin boards in factories. The server connects to the bulletin board's API and periodically retrieves posted content (for example, every hour). The retrieved data is in JSON format and includes the post ID, post content, posting date and time, etc. As a result, the input is the bulletin board post information, and the output is the collected raw data.

[1132] Step 2:

[1133] The server analyzes the collected information using natural language processing (NLP) technology and classifies it into categories. For example, the server uses a TF-IDF vectorizer and a KMeans clustering algorithm to classify the information into categories such as "production schedule," "maintenance information," and "work instructions." The input is the collected raw data, and the output is information classified by category.

[1134] Step 3:

[1135] The server collects the user's profile information, which includes user-entered interests and business-related information, such as "I'm interested in maintenance information for a specific piece of equipment." The input is the user's profile information, and the output is the data stored on the server as a profile.

[1136] Step 4:

[1137] The server filters the collected information based on the user profile. The server refers to the profile information and extracts information of related categories. For example, for a user who is "interested in maintenance information," maintenance-related information is picked up. The input is the categorized information and the user profile, and the output is the filtered information.

[1138] Step 5:

[1139] The server assigns a recommendation score to the filtered information. The recommendation score indicates the importance and relevance of the information, and the server selects information based on this score. The input is the filtered information, and the output is the scored information.

[1140] Step 6:

[1141] The server formats the filtered and scored information as notification data and sends it to the user's device. The input is the scored information, and the output is the formatted notification data.

[1142] Step 7:

[1143] The terminal receives notification data from the server in real time and displays it to the user in the form of a popup or alert. For example, when the terminal receives a notification, it displays a popup on the display to inform the user of the latest information. The input is the formatted notification data, and the output is the notification that is displayed to the user.

[1144] Step 8:

[1145] When a terminal accesses a bulletin board, the data received from the server as recommended information is displayed in a list. For example, recommended information is displayed prominently on the bulletin board's top page. The input is notification data, and the output is the recommended information on the bulletin board.

[1146] Step 9:

[1147] The terminal links to the equipment in the factory and obtains and notifies the equipment status and maintenance information in real time. For example, this involves obtaining information from the equipment's sensors and immediately notifying the user if an abnormality is detected. The input is the equipment's sensor information, and the output is a notification that is displayed in real time.

[1148] Step 10:

[1149] The user checks the notification and takes the necessary action. The user receives the notification from the terminal and, for example, checks the maintenance schedule or responds to changes in the production schedule. The input is the terminal notification, and the output is the user's response action.

[1150] 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.

[1151] This invention is an in-house bulletin board system that incorporates an emotion engine that recognizes the user's emotions. This enables more personalized information to be provided according to the user's emotional state. The system consists of a server, terminals, and users. The roles of each component and the program processing are explained in detail below.

[1152] server

[1153] Program processing

[1154] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, and retrieves all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." An emotion engine then analyzes user emotion data in real time, and the information is recorded in a database.

[1155] The server then filters the collected information based on the user profile and emotional data. For example, if a user is interested in a tennis club, not only will it pick up information related to the tennis club, but if the user is in a positive emotional state, it will also add more information. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. The selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[1156] Terminal

[1157] Program processing

[1158] When a user first uses the system, the device displays a profile setting screen. Here, the user can enter information about their hobbies and work, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The device receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[1159] User

[1160] How to use

[1161] During initial setup, the user enters information related to their hobbies and work. They also input their current emotional state using the emotion input means or capture it via sensors. This creates a profile and emotional data, which are then stored in the system. The user can periodically check notifications on the device to obtain the latest information. For example, notifications about upcoming technical seminars or the next tennis club activity schedule may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[1162] Specific examples

[1163] Example 1: User A is interested in the tennis club and is in a positive emotional state

[1164] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[1165] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[1166] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[1167] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[1168] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[1169] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[1170] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[1171] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[1172] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[1173] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[1174] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[1175] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[1176] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[1177] In this way, this system not only allows users to efficiently obtain information related to their interests and work, but also provides more appropriate information because it is personalized according to their emotional state.

[1178] The processing flow will be explained below.

[1179] server

[1180] Step 1:

[1181] Periodically retrieve bulletin board information. The server accesses the bulletin board API and retrieves all content in JSON format.

[1182] Step 2:

[1183] The retrieved data is saved in a database. The data is persisted for use in subsequent processing.

[1184] Step 3:

[1185] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[1186] Step 4:

[1187] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[1188] Step 5:

[1189] The emotion engine analyzes the user's emotional data in real time and stores the information in a database. Emotion data is collected using technologies such as facial recognition and text analysis.

[1190] Step 6:

[1191] By analyzing user profiles and emotional data, the system filters collected information based on each user's interests and emotional state. For example, for a user who is interested in a tennis club and has a positive emotional state, the system will pick out relevant information and increase the amount of information available.

[1192] Step 7:

[1193] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests and emotions.

[1194] Step 8:

[1195] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[1196] Terminal

[1197] Step 1:

[1198] As an initial setting, the system presents an interface for users to input their hobbies, preferences, and work-related information, while also configuring the system to use sensors (e.g., camera and microphone) to track their emotional state in real time.

[1199] Step 2:

[1200] The hobby and work-related information and emotional data entered by the user are sent to a server and saved as a profile and emotional data.

[1201] Step 3:

[1202] Receive notification data sent from the server in real time, including push notifications and alerts.

[1203] Step 4:

[1204] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[1205] Step 5:

[1206] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[1207] User

[1208] Step 1:

[1209] When using the robot for the first time, users input information about their hobbies and work. Using the emotion input means, users' current emotional state is also input or captured by a sensor.

[1210] Step 2:

[1211] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[1212] Step 3:

[1213] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminar," "New Product Development," and "Tennis Club" displayed on the bulletin board homepage. The amount and content of information displayed is adjusted according to the user's emotional state.

[1214] Example 2

[1215] 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."

[1216] Conventional in-house bulletin board systems filter information based on users' hobbies, preferences, and work-related information, but do not provide information that takes into account the user's emotional state. As a result, users receive information that does not match their emotional state, and they may feel stressed by information overload or notifications of unnecessary information. Another problem is that users cannot quickly access the information they really need. Therefore, there is a need for personalized information provision that takes into account not only users' hobbies, preferences, and work-related information, but also their emotional state.

[1217] 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.

[1218] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user's hobbies, preferences, and work-related information, means for analyzing the user's emotional state, means for recording the analyzed emotional state in a database, means for filtering the collected information based on the user's hobbies, preferences, work-related information, and emotional state, means for assigning a recommendation score to the filtered information, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information, thereby enabling more personalized information to be provided according to the user's emotional state.

[1219] The "means of collecting bulletin board information" refers to a system that periodically obtains posted information via the API of the internal bulletin board and stores it in a database.

[1220] "Means for categorizing by category" is a function that uses natural language processing technology to analyze collected bulletin board information and sort it into different categories.

[1221] "Means for collecting user's hobbies, preferences and work-related information" is a function that collects information entered by the user on the profile setting screen and past browsing history and stores them in a database.

[1222] The "means for analyzing the user's emotional state" is a mechanism that uses an emotion engine to analyze the user's emotional data in real time and record the results.

[1223] The "means for recording the analyzed emotional state in a database" is a function for storing the user's emotional data analyzed by the emotion engine in a database.

[1224] The "means for filtering information" is a mechanism for narrowing down the collected bulletin board information based on the user's hobbies, preferences, work-related information, and emotional state.

[1225] The "means for assigning a recommendation score" is a function that scores filtered information using a machine learning model.

[1226] The "notification means" is a mechanism that sends filtered information and information with high recommendation scores to the user's device in real time and displays a notification.

[1227] "Means for displaying recommended information" is a function that displays filtered information received from the server in a prominent manner on the top page when accessing the bulletin board.

[1228] The present invention is an in-house bulletin board system that can provide personalized information according to the emotional state of a user. The present invention is implemented using the following hardware and software.

[1229] server

[1230] The server has the specific role of collecting and analyzing bulletin board information, categorizing the information, and then sending notifications based on the user's profile and emotional data. The server periodically calls the bulletin board API to collect the latest posts. The collected information is stored in a database and analyzed using natural language processing technology such as Google Cloud Natural Language API. This analysis results in the information being classified into categories such as "business announcements," "events," and "club activities."

[1231] The server then uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time. The analysis results are recorded in a database. The collected information is filtered based on the analyzed emotional state, as well as the user's hobbies, preferences, and work-related information. The filtered information is assigned a recommendation score using a machine learning model, and information with a high score is formatted as notification data. This notification data is sent to the device in real time and displayed as "recommended information" when the bulletin board is accessed.

[1232] Terminal

[1233] When a user first uses the system, the device displays a profile setup screen where the user enters information about their hobbies and work, and uses sensors (e.g., facial expression recognition cameras and heart rate sensors) and input fields to track their emotional state in real time. This data is then sent to the server and stored in a database.

[1234] The device receives notification data from the server in real time and displays it in the form of a pop-up or alert. When the user accesses the bulletin board, filtered data is displayed as recommended information.

[1235] User

[1236] During initial setup, users input information related to their hobbies and work, and also input their current emotional state or capture it using sensors. Users can periodically check notifications on their device to receive the latest information. For example, they may receive notifications about upcoming technical seminars or the next tennis club activity. When accessing the bulletin board, information of interest is displayed prominently, allowing users to obtain information efficiently.

[1237] Specific examples

[1238] Example 1: User A is interested in the tennis club and is in a positive emotional state

[1239] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[1240] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[1241] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[1242] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[1243] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[1244] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[1245] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[1246] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[1247] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[1248] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[1249] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[1250] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[1251] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[1252] Examples of prompt statements

[1253] Example prompt sentence:

[1254] "Create notifications for upcoming technical seminars and provide information that will interest users. Adjust the filtering and notification content based on the user's emotional state, both positive and negative."

[1255] In this way, the present invention provides more personalized information provision according to the user's emotional state.

[1256] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1257] Program processing flow

[1258] Server-side processing

[1259] Step 1:

[1260] The server collects bulletin board information. It calls the bulletin board API to retrieve post data from the last hour. This post data is retrieved in JSON format and stored in a database.

[1261] Input: Post data obtained from the bulletin board API

[1262] Output: Post data stored in the database

[1263] Specific behavior:

[1264] The server schedules a periodic job to call the API every hour, stores the retrieved data in a buffer, and then writes it to the database.

[1265] Step 2:

[1266] The server analyzes the collected information using natural language processing technology, using the Google Cloud Natural Language API to analyze the content of each post and classify it into categories such as "business announcements," "events," and "club activities."

[1267] Input: Post data stored in the database

[1268] Output: Post data with categories

[1269] Specific behavior:

[1270] The server reads the unparsed post data from the database, sends a parsing request to the Google Cloud Natural Language API, and writes the returned parsed results back to the database.

[1271] Step 3:

[1272] The server analyzes the user's emotional data, using an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time and record the information in a database.

[1273] Input: User emotion data (real-time data from sensors and user input)

[1274] Output: Sentiment analysis data recorded in a database

[1275] Specific behavior:

[1276] The server receives the data from the emotion sensor and sends an analysis request to the IBM Watson Tone Analyzer API. The returned analysis results are stored in a database.

[1277] Step 4:

[1278] The server filters the collected information based on the user profile and emotional data, extracting information of interest based on the user's hobbies, preferences, work-related information, and emotional state.

[1279] Input: User profile data, emotion data, and categorized post data

[1280] Output: Filtered post data

[1281] Specific behavior:

[1282] The server reads user profiles and sentiment data from a database and filters the posted data based on pre-defined rules and machine learning models.

[1283] Step 5:

[1284] The server assigns a recommendation score to the filtered information, using a machine learning model to score each piece of information and select the information with the highest score.

[1285] Input: Filtered post data

[1286] Output: Filtered data with recommendation scores

[1287] Specific behavior:

[1288] The server uses a machine learning model to score each post and store the results in a database.

[1289] Step 6:

[1290] The server formats the information with the highest score as notification data and sends it to the user's device. It also generates data to be displayed as "recommended information" when the user accesses the bulletin board.

[1291] Input: Filtered data with recommendation scores

[1292] Output: Notification data sent to your device and data displayed as "Recommended Information"

[1293] Specific behavior:

[1294] The server formats the notification data into JSON format and sends it to the user's device in real time. It also generates data for the bulletin board's top page and stores it in a database.

[1295] Terminal side processing

[1296] Step 1:

[1297] The device displays a profile setup screen when first used, collects information about the user's hobbies and work, and also collects sensor information for real-time tracking of emotional data.

[1298] Input: User-entered data (hobbies, work-related information, emotional data)

[1299] Output: Profile data and emotion data sent to the server

[1300] Specific behavior:

[1301] The device displays a profile setting screen, and once the user has completed their input, the information is sent to the server. Sensor information is also sent to the server in real time.

[1302] Step 2:

[1303] The terminal receives the notification data sent from the server in real time and displays it to the user in the form of a pop-up or alert.

[1304] Input: Notification data from the server

[1305] Output: Popups or alerts that are displayed to the user

[1306] Specific behavior:

[1307] When the terminal receives the notification data, it immediately displays a pop-up to the user and sounds an alert sound if necessary.

[1308] Step 3:

[1309] When the terminal accesses the bulletin board, it displays a list of data received from the server as recommended information.

[1310] Input: Recommendation data from the server

[1311] Output: A list of recommendations displayed on the bulletin board

[1312] Specific behavior:

[1313] When the terminal accesses the bulletin board, it receives the recommended information data sent from the server and displays it on the user interface.

[1314] User-side processing

[1315] Step 1:

[1316] During initial setup, the user inputs information related to their hobbies and work, and their emotional state is also input or captured by sensors.

[1317] Input: Hobbies, work-related information, emotional state

[1318] Output: Profile data and emotion data sent to the server

[1319] Specific behavior:

[1320] The user enters the necessary information on the device's profile setting screen and sets the sensor to be used.

[1321] Step 2:

[1322] Users can periodically check notifications on their devices to get the latest information.

[1323] Input: Notification data from the device

[1324] Output: Check for the latest information

[1325] Specific behavior:

[1326] Users can check pop-ups and alerts displayed on their devices to obtain the latest information.

[1327] Step 3:

[1328] When a user accesses the bulletin board, information of particular interest to the user is displayed in a prominent manner, allowing the user to obtain information efficiently.

[1329] Input: Forum recommendations

[1330] Output: Get the information you are interested in

[1331] Specific behavior:

[1332] Users access the bulletin board, check the content displayed as recommended information, and obtain information that interests them.

[1333] (Application example 2)

[1334] 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."

[1335] In today's information-saturated society, it is difficult to effectively sort and provide the information that each user needs. Furthermore, while systems that can appropriately customize information based on the user's emotional state could provide a more satisfying experience, there are few systems that can do this. This poses a challenge: users may be overwhelmed by the amount of information available, and may not be able to obtain the appropriate information, which can lead to stress.

[1336] 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 collecting bulletin board information, means for classifying the collected information by category, means for collecting a user's hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for recognizing a user's emotional state in real time, means for adjusting the filtered information based on the recognized emotional state, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information. This makes it possible to personalize information according to the user's emotional state and provide more appropriate and satisfying information.

[1337] "Bulletin Board Information" means the content of messages or notices posted on internal bulletin boards or online forums.

[1338] A "category" is a category for classifying bulletin board information based on a specific topic or theme.

[1339] "Hobbies" refers to activities and interests that a user is personally interested in.

[1340] "Business-related information" refers to information related to a user's job or work.

[1341] "Emotional state" refers to a user's real-time psychological emotional state, which may be classified as positive or negative.

[1342] "Filtering" is the process of sorting out information based on specific criteria.

[1343] "Notification" is a means of notifying the user of filtered information in real time.

[1344] "Recommended information" is information that is determined to be particularly important to notify based on the user's interests and emotional state.

[1345] This invention is a virtual store system that combines an emotion engine that recognizes the user's emotions. This system makes it possible to provide a more personalized shopping experience according to the user's emotional state. The system consists of a server, smart glasses (terminals), and a user. Details are described below.

[1346] server

[1347] Program processing

[1348] The server first collects product information from the virtual store. This is done periodically, for example, every hour, to retrieve all product data from the store's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "fashion," "electronics," and "sports goods." An emotion engine then analyzes the user's emotional data in real time and records the information in a database.

[1349] The server then filters the product information based on the user profile and emotional data. For example, if a user is interested in sports equipment, the server will not only provide sports equipment-related information but also provide additional promotional information if the user is in a positive emotional state. The server then assigns a recommendation score to the filtered information and selects the information with the highest score. This information is then formatted as notification data and sent to the smart glasses. The selected information is then also summarized in the recommendations displayed on the smart glasses.

[1350] Smart glasses (terminal)

[1351] Program processing

[1352] The smart glasses display a profile setting screen when the user first uses the system. Here, the user can enter their hobbies and interests, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The smart glasses receive notification data from the server in real time and display it to the user in the form of pop-ups or alerts. When the user accesses a virtual store, the glasses display a list of recommended information received from the server.

[1353] User

[1354] How to use

[1355] During initial setup, users input information related to their hobbies and interests. Their current emotional state is also input using an emotion input means or captured by sensors. This creates a profile and emotional data, which are then stored in the system. Users can periodically check notifications on the smart glasses to obtain the latest information. For example, "sale information for fashion categories" or "new sports goods" are displayed. When users access a virtual store, information of particular interest is prominently displayed, allowing them to efficiently select products.

[1356] Hardware and software used

[1357] Hardware: Smart glasses (built-in camera, microphone, display)

[1358] Server: Cloud service (e.g. AWS, Google Cloud)

[1359] Software and Libraries

[1360] Emotion Engine API: Affect Recognition Engine (e.g. Affectiva)

[1361] Face recognition library: OpenCV, dlib

[1362] Natural language processing libraries: NLTK, spaCy

[1363] Real-time communication: WebSocket

[1364] Specific examples

[1365] Example 1: User A is interested in fashion and in a positive emotional state

[1366] 1. The server collects and categorizes products in the fashion category.

[1367] 2. The server extracts "Interested in fashion" from User A's profile and filters related information.

[1368] 3. The server adjusts the emotion engine to recognize User A's positive emotional state and provide additional promotional information.

[1369] 4. The server generates the filtered "fashion sale information" as notification data and sends it to the smart glasses.

[1370] 5. The smart glasses receive the notification in real time and display it to User A in a pop-up.

[1371] Example 2: User B is interested in sports equipment and is in a negative emotional state

[1372] 1. The server collects and categorizes products in the sporting goods category.

[1373] 2. The server extracts "Interested in sporting goods" from User B's profile and filters out related information.

[1374] 3. The server adjusts the emotion engine to recognize user B's negative emotional state and provide information sparingly.

[1375] 4. The server generates the filtered "new sports goods" as notification data and sends it to the smart glasses.

[1376] 5. The smart glasses receive the notification in real time and display it to User B as an alert.

[1377] Prompt Sentence Examples

[1378] "Now explain to me what data the emotion engine uses and how it can help personalize the virtual shopping experience."

[1379] "Please tell us more about how the smart glasses' built-in camera and microphone can be used to analyze the user's emotions in real time and recommend products based on that state."

[1380] The above description clearly shows the specific embodiments for carrying out the present invention.

[1381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1382] Step 1:

[1383] Input: A user puts on smart glasses and logs into the system.

[1384] How it works: The user enters their hobbies and interests on the smart glasses' initial setup screen. The camera and microphone are then activated to collect emotional data in real time.

[1385] Output: The user's interest, preference and emotional data are sent from the device to the server and saved in a profile.

[1386] Step 2:

[1387] Input: User profile and collected emotion data.

[1388] How it works: The server periodically collects all necessary information via the internal bulletin board and the virtual store's API.

[1389] Output: Collected product information and bulletin board information is saved in a database.

[1390] Step 3:

[1391] Input: Collected product information and bulletin board information.

[1392] How it works: The server uses natural language processing (NLP) techniques to categorize the collected information. It uses the OpenAI GPT model to analyze the meaning of each piece of information and classify it into categories such as "fashion," "electronics," and "sports goods."

[1393] Output: Product information classified by category is generated and stored in a database.

[1394] Step 4:

[1395] Input: User profile information and product information organized by category.

[1396] How it works: The server generates a filtered list of information based on the hobbies and interests registered in the user's profile.

[1397] Output: A list of product information that matches the user's hobbies and preferences is generated.

[1398] Step 5:

[1399] Input: User sentiment data and filtered product information.

[1400] How it works: The server uses an emotion engine to analyze the user's real-time emotional state. It uses Affectiva and Microsoft Azure emotion recognition APIs to assess a positive or negative emotional state.

[1401] Output: A list of information tailored to the emotional state is generated.

[1402] Step 6:

[1403] Input: Reconciled information list.

[1404] How it works: The server calculates a recommendation score for the filtered information, assigning a priority and interest score to each information item.

[1405] Output: A list of information with a recommendation score is generated.

[1406] Step 7:

[1407] Input: A list of information with recommendation scores.

[1408] How it works: The server formats the notification data and sends it to the user's smart glasses. Real-time notifications are sent using WebSocket.

[1409] Output: Notification data is sent to the device.

[1410] Step 8:

[1411] Input: Notification data.

[1412] How it works: The smart glasses receive notification data in real time and display it to the user in the form of a popup or alert, or it can use eye tracking or voice commands.

[1413] Output: The recommended information is visually presented to the user.

[1414] Through these steps, users can receive personalized information in real time that is tailored to their emotional state, enabling them to enjoy an efficient and satisfying shopping experience.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] [Fourth embodiment]

[1419] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1420] 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.

[1421] 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).

[1422] 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.

[1423] 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.

[1424] 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).

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] 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."

[1432] This invention is an in-house bulletin board system that allows users to efficiently obtain information of interest or information necessary for their work without missing it. This system is composed of a server, terminals, and users. The roles of each and the program processing are explained in detail below.

[1433] server

[1434] Program processing

[1435] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, by retrieving all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." The server also collects user profile information. The profile includes the user's hobbies, preferences, and information necessary for work.

[1436] The server then filters the collected information based on the user profile. For example, for a user interested in a tennis club, it will pick out information related to the tennis club. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[1437] Terminal

[1438] Program processing

[1439] When a user first uses the system, the device displays a profile setting screen. Here, the user enters their hobbies and work-related information. This data is sent to the server and saved as a profile. The device receives notification data from the server in real time and displays it to the user in the form of a pop-up or alert. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[1440] User

[1441] How to use

[1442] During initial setup, users enter information related to their hobbies and work. This creates a profile that is saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, notifications of upcoming technical seminars or upcoming tennis club activities may be displayed. When users access the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[1443] Specific examples

[1444] Example 1: User A is interested in the tennis club

[1445] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[1446] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[1447] 3. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[1448] 4. Device: Receives notifications in real time and displays them to User A in a pop-up.

[1449] 5. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[1450] Example 2: User B is interested in technical seminars

[1451] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[1452] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[1453] 3. Server: Generates the filtered "Notification of Technical Seminar" as notification data and sends it to User B's terminal.

[1454] 4. Device: Receives notifications in real time and displays them to User B as an alert.

[1455] 5. Bulletin board access: When user B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[1456] In this way, this system allows users to efficiently obtain information related to their interests and work, improving work efficiency and simplifying information gathering.

[1457] The processing flow will be explained below.

[1458] server

[1459] Step 1:

[1460] Periodically retrieve bulletin board information, which involves accessing the bulletin board API and retrieving all content in JSON format.

[1461] Step 2:

[1462] The fetched data is saved in the database. This is to persist the data and use it in subsequent processing.

[1463] Step 3:

[1464] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[1465] Step 4:

[1466] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[1467] Step 5:

[1468] Analyze user profiles and extract categories and keywords of interest to each user.

[1469] Step 6:

[1470] Based on each user's profile, relevant information is filtered from the bulletin board database. For example, if a user is interested in tennis clubs, posts related to tennis clubs are selected.

[1471] Step 7:

[1472] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests.

[1473] Step 8:

[1474] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[1475] Terminal

[1476] Step 1:

[1477] As an initial setting, an interface is displayed that allows the user to input hobbies, preferences, and business-related information.

[1478] Step 2:

[1479] The hobby and work-related information entered by the user is sent to the server and saved as a profile.

[1480] Step 3:

[1481] Receive notification data sent from the server in real time, including push notifications and alerts.

[1482] Step 4:

[1483] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[1484] Step 5:

[1485] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[1486] User

[1487] Step 1:

[1488] When using the service for the first time, users enter information about their hobbies and work, which is then saved on the server as a user profile.

[1489] Step 2:

[1490] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[1491] Step 3:

[1492] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminars," "New Product Development," and "Tennis Clubs" displayed on the bulletin board's homepage.

[1493] Example 1

[1494] 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."

[1495] In today's information society, it is important for users to efficiently obtain information necessary for their work or that interests them. However, with so much information available, there is an increasing risk of missing or overlooking necessary information. For this reason, users are required to efficiently collect information related to their interests and work, and to take appropriate action based on that information. Conventional bulletin board systems require users to collect information manually, which requires time and effort. There is also the risk of missing relevant information. A new system is needed to solve these issues and improve users' information gathering and work efficiency.

[1496] 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.

[1497] In this invention, the server includes means for collecting bulletin board information, means for categorizing the collected information using natural language processing technology, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for assigning a recommendation score to the filtered information, means for formatting information with a high recommendation score as notification data and transmitting it to the user terminal, and means for displaying the filtered information as recommended information. This enables users to efficiently and accurately obtain the information they need based on their hobbies, preferences, and work-related information.

[1498] "Bulletin board information" refers to a series of posts and notices shared within a company or organization, including business communications, event notices, club activities, and the like.

[1499] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language, and includes technologies such as text analysis, category classification, and entity recognition.

[1500] "Categorizing" refers to the act of grouping collected information based on specific criteria or themes to make the information easier to organize and access.

[1501] "User hobbies and preferences" refer to specific activities or topics that a user is personally interested in, and are primarily related to recreation, learning, etc.

[1502] "Work-related information" refers to information that a user needs to perform their work, including news, notices, guidance, and the like related to their work.

[1503] "Filtering" refers to the process of sorting collected information based on specific criteria or conditions and excluding unnecessary information.

[1504] The "recommendation score" is a numerical representation of the relevance and importance of filtered information, and is an index that indicates the usefulness of the information to the user.

[1505] "Notification data" refers to data that has been formatted to provide specific information to the user and has been converted into a format that can be displayed on the terminal.

[1506] "Sending to the user terminal" refers to the act of delivering the notification data generated by the server to the device used by the user (PC, smartphone, tablet, etc.) using a communication means.

[1507] "Recommended information" refers to information that is determined to be particularly relevant to the user based on filtering and recommendation scores, and is presented to the user preferentially.

[1508] This invention is a system that enables users to efficiently obtain information that interests them or is necessary for their work without missing anything. This system is composed of a server, a terminal, and a user. The roles of each component and the detailed process will be explained below.

[1509] server

[1510] The server first collects information from the company's internal bulletin board. This collection occurs every hour via the bulletin board's API. The server runs a script written in Python to retrieve all post data from the bulletin board's API endpoint and save it in JSON format. The collected information is then analyzed using natural language processing (NLP) technology. The NLP library used here is NLTK. As a result of the analysis, posts are classified into categories, such as "business announcements," "events," and "club activities."

[1511] Next, the server collects the user's profile information. The profile includes the user's hobbies, preferences, and information necessary for work. The profile setting data sent from the device is saved in a database. The user profile is stored in the database along with the user ID and is used for subsequent filtering processes.

[1512] The server filters the collected information based on the user profile. For example, if a user is interested in tennis clubs, it will pick up information related to tennis clubs. During the filtering process, the server queries posts that match the profile information and stores the results in a temporary filtered list.

[1513] Furthermore, the server assigns a recommendation score to the filtered information. This score is assigned based on the importance and relevance of the information. A scoring algorithm is used to calculate a score for each post, and the score is stored in a database. Information with a high score is formatted as notification data and sent to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it to the device.

[1514] Finally, the selected information is compiled into data that is displayed as "recommended information" when users access the bulletin board. This allows information that interests users to be displayed on the top page when they visit the bulletin board.

[1515] Terminal

[1516] When a user first uses the system, the device displays a profile setting screen where the user enters their hobbies and work-related information. The entered data is sent to the server via an HTTP POST request. A front-end using React is launched, and a form input screen is displayed.

[1517] Notification data is received from the server in real time. Socket.IO is used for real-time communication to receive notification data. The received notification data is displayed to the user in the form of a popup or alert. When a notification is received, the information is displayed to the user using the notification function of the browser or app.

[1518] When a user accesses the bulletin board, the recommended information is displayed as a list of data received from the server. When the page is loaded, the data retrieved from the server is rendered and displayed as an HTML list.

[1519] User

[1520] When a user first uses the system, they enter information related to their hobbies and work. For example, they enter topics of interest such as "tennis" or "technical seminars." This creates a profile that is then saved on the server.

[1521] Users check the notifications displayed on their devices and obtain the information they need. For example, User A checks the "next scheduled tennis club activity" in a pop-up notification. When accessing the bulletin board, they can efficiently check the recommended information displayed on the top page. When User A accesses the bulletin board, "next scheduled tennis club activity" is displayed at the top, allowing them to check it immediately.

[1522] Examples of specific examples and prompts

[1523] Specific examples

[1524] Example 1: User A is interested in the tennis club

[1525] 1. The server collects information about the "tennis club's next scheduled activity" from the company bulletin board and categorizes it.

[1526] 2. The server extracts "Interested in tennis clubs" from User A's profile and filters related information.

[1527] 3. The server generates the filtered "next tennis club activity schedule" as notification data and sends it to User A's device.

[1528] 4. The device receives the notification in real time and displays it to User A in a pop-up.

[1529] 5. When User A accesses the bulletin board, recommended information for the "Tennis Club's next scheduled activity" is displayed on the top page.

[1530] Example 2: User B is interested in technical seminars

[1531] 1. The server collects information about "technical seminar announcements" from the internal bulletin board and categorizes it.

[1532] 2. The server extracts "Interested in technical seminars" from User B's profile and filters out related information.

[1533] 3. The server generates the filtered "Technical Seminar Notification" as notification data and sends it to User B's terminal.

[1534] 4. The device receives the notification in real time and displays it to User B as an alert.

[1535] 5. When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[1536] Prompt Sentence Examples

[1537] "Please tell us what information you have collected and filtered about tennis clubs."

[1538] "Find out about upcoming technical seminars and add them to your recommendation list."

[1539] The present invention enables users to efficiently and accurately obtain information related to their hobbies, preferences, and business, thereby improving business efficiency and simplifying information gathering.

[1540] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1541] Step 1:

[1542] The server collects information from the bulletin board API. This collection occurs every hour. Specifically, the server uses a Python script to access the bulletin board's API endpoint and retrieves all post data in JSON format. The input is the response data from the bulletin board API, and the output is the collected post data saved in JSON format.

[1543] Step 2:

[1544] The server analyzes the collected JSON-formatted post data using natural language processing (NLP) technology and classifies it by category. Specifically, it performs text analysis using the NLTK library and classifies it into categories such as "business announcements," "events," and "club activities." The input is JSON-formatted post data, and the output is post data classified by category.

[1545] Step 3:

[1546] The server collects user profile information. Specifically, it stores the hobbies, preferences, and work-related information entered by the user from their device in a database. The input is the HTTP POST request data sent from the device, and the output is the user profile information stored in the database.

[1547] Step 4:

[1548] The server filters the collected message board information based on the user profile. The server queries for posts that match the user profile information and stores the results in a temporary filtered list. The input is the user profile information and the post data categorized by category, and the output is the filtered post list.

[1549] Step 5:

[1550] The server calculates a recommendation score for the filtered posts. It uses a scoring algorithm to assign a score to each post and stores the scores in a database. The input is the filtered list of posts, and the output is the list of posts with their recommendation scores.

[1551] Step 6:

[1552] The server formats information with high recommendation scores as notification data and sends it to the user's device. The server selects posts with high scores from the filtered list, encodes the information in JSON format, and sends it. The input is a list of posts with assigned recommendation scores, and the output is JSON data formatted as notification data.

[1553] Step 7:

[1554] The terminal receives notification data sent from the server in real time. Specifically, it uses Socket.IO for real-time communication to receive notification data. The input is notification data from the server, and the output is a notification displayed on the terminal in the form of a popup or alert.

[1555] Step 8:

[1556] The user checks the notification displayed on the device and obtains the necessary information. Specifically, the user clicks on a pop-up notification or alert to check detailed information. The input is the notification displayed on the device, and the output is the detailed information obtained by the user.

[1557] Step 9:

[1558] When a user accesses a bulletin board, recommended information is displayed on the top page. The data retrieved from the server is rendered into an HTML list and displayed as the page loads. The input is the recommended information data retrieved from the server, and the output is the recommended information displayed on the bulletin board top page.

[1559] (Application example 1)

[1560] 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."

[1561] In information sharing within a company, the challenge is to efficiently collect information that users need or are interested in and notify them in real time. In particular, in factory work, it is necessary to obtain important information such as production schedules and maintenance information without missing anything and to respond quickly. For this reason, there is a need for a system that can appropriately notify information filtered based on the user's profile and improve work efficiency.

[1562] 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.

[1563] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user hobbies, preferences, and work-related information, means for filtering the collected information based on the user hobbies, preferences, and work-related information, means for notifying the user of the filtered information, means for displaying the filtered information as recommended information, and means for linking with equipment in the factory and notifying and displaying information related to the equipment in real time, thereby enabling users to efficiently obtain information related to their interests and work without missing out.

[1564] "Bulletin board information" refers to various information posted on an internal company bulletin board, including notifications related to the user's work and event information.

[1565] "Means for categorizing" refers to methods or techniques for dividing collected bulletin board information into specific categories, with the aim of efficient information management.

[1566] "User's hobbies, preferences and work-related information" refers to items in which the user is interested and information necessary for work, and is stored on the server as profile-based data.

[1567] "Filtering means" refers to methods or techniques for selecting highly relevant information from the collected bulletin board information based on the user's profile information.

[1568] "Means for notifying" refers to methods or technologies for quickly and appropriately transmitting filtered information to the user's terminal.

[1569] "Means for displaying recommended information" refers to methods or techniques for displaying filtered information in a prominent manner on the top page of a bulletin board or the like, in accordance with the user's interests.

[1570] "Means of linking with equipment within a factory and notifying information" refers to methods and technologies that collect information on the status and schedule of equipment within a factory in real time and notify users at the appropriate time.

[1571] This invention is an in-factory communication system that allows users to efficiently obtain information of interest or information necessary for work without missing anything. This system is composed of a server, terminals, and users, and the roles of each and the program processing will be explained in detail below.

[1572] server

[1573] First, the server periodically collects information from the factory bulletin board. This is done, for example, every hour by retrieving all posts from the bulletin board's API. The collected information is analyzed using natural language processing (NLP) technology and classified into categories such as "production schedule," "maintenance information," and "work instructions." The server also collects user profile information, which includes information about the user's interests and work needs.

[1574] The server then filters the collected information based on the user profile. For example, if a user is interested in maintenance information for a specific device, relevant information will be picked out. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This selected information is formatted as notification data and sent to the user's device. Furthermore, the selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[1575] Terminal

[1576] When a user first uses the system, the terminal displays a profile setting screen. Here, the user enters their interests and work-related information. This data is sent to the server and saved as a profile. The terminal receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. In addition, when the bulletin board is accessed, a list of data received from the server as recommended information is displayed. The terminal also has the function of linking to equipment in the factory and obtaining and notifying equipment status and maintenance information in real time.

[1577] User

[1578] During initial setup, users create a profile by entering information related to their interests and work, which is then saved in the system. Users can periodically check notifications on their devices to obtain the latest information. For example, information such as "next scheduled maintenance for equipment A" or "schedule changes for production line B" may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[1579] Specific examples

[1580] Example 1: For users interested in equipment maintenance

[1581] 1. The server collects information about the next scheduled equipment maintenance from the factory bulletin board and categorizes it.

[1582] 2. The server extracts "interest in equipment maintenance" from the user profile and filters the relevant information.

[1583] 3. The server generates the filtered "next scheduled maintenance for the device" as notification data and sends it to the user's device.

[1584] 4. The device receives the notification in real time and displays it to the user in a pop-up.

[1585] 5. Bulletin board access: When a user accesses the bulletin board, recommended information about the next scheduled maintenance for the equipment is displayed on the top page.

[1586] Example 2: For users interested in production schedules

[1587] 1. The server collects information about "changes in production line schedules" from the factory bulletin board and categorizes it.

[1588] 2. The server extracts the user profile information that states "I'm interested in production schedules" and filters out the relevant information.

[1589] 3. The server generates the filtered "production line schedule change" as notification data and sends it to the user's terminal.

[1590] 4. The device receives the notification in real time and displays it to the user as an alert.

[1591] 5. Bulletin board access: When a user accesses the bulletin board, the recommended information for "changing the production line schedule" is displayed on the top page.

[1592] Example prompt sentence:

[1593] "Please create a description of a system that allows factory robots to efficiently obtain maintenance information."

[1594] As described above, this invention allows users to quickly and efficiently obtain information related to their interests and work, which is expected to improve the efficiency and productivity of factory operations.

[1595] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1596] Step 1:

[1597] The server collects information from bulletin boards in factories. The server connects to the bulletin board's API and periodically retrieves posted content (for example, every hour). The retrieved data is in JSON format and includes the post ID, post content, posting date and time, etc. As a result, the input is the bulletin board post information, and the output is the collected raw data.

[1598] Step 2:

[1599] The server analyzes the collected information using natural language processing (NLP) technology and classifies it into categories. For example, the server uses a TF-IDF vectorizer and a KMeans clustering algorithm to classify the information into categories such as "production schedule," "maintenance information," and "work instructions." The input is the collected raw data, and the output is information classified by category.

[1600] Step 3:

[1601] The server collects the user's profile information, which includes user-entered interests and business-related information, such as "I'm interested in maintenance information for a specific piece of equipment." The input is the user's profile information, and the output is the data stored on the server as a profile.

[1602] Step 4:

[1603] The server filters the collected information based on the user profile. The server refers to the profile information and extracts information of related categories. For example, for a user who is "interested in maintenance information," maintenance-related information is picked up. The input is the categorized information and the user profile, and the output is the filtered information.

[1604] Step 5:

[1605] The server assigns a recommendation score to the filtered information. The recommendation score indicates the importance and relevance of the information, and the server selects information based on this score. The input is the filtered information, and the output is the scored information.

[1606] Step 6:

[1607] The server formats the filtered and scored information as notification data and sends it to the user's device. The input is the scored information, and the output is the formatted notification data.

[1608] Step 7:

[1609] The terminal receives notification data from the server in real time and displays it to the user in the form of a popup or alert. For example, when the terminal receives a notification, it displays a popup on the display to inform the user of the latest information. The input is the formatted notification data, and the output is the notification that is displayed to the user.

[1610] Step 8:

[1611] When a terminal accesses a bulletin board, the data received from the server as recommended information is displayed in a list. For example, recommended information is displayed prominently on the bulletin board's top page. The input is notification data, and the output is the recommended information on the bulletin board.

[1612] Step 9:

[1613] The terminal links to the equipment in the factory and obtains and notifies the equipment status and maintenance information in real time. For example, this involves obtaining information from the equipment's sensors and immediately notifying the user if an abnormality is detected. The input is the equipment's sensor information, and the output is a notification that is displayed in real time.

[1614] Step 10:

[1615] The user checks the notification and takes the necessary action. The user receives the notification from the terminal and, for example, checks the maintenance schedule or responds to changes in the production schedule. The input is the terminal notification, and the output is the user's response action.

[1616] 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.

[1617] This invention is an in-house bulletin board system that incorporates an emotion engine that recognizes the user's emotions. This enables more personalized information to be provided according to the user's emotional state. The system consists of a server, terminals, and users. The roles of each component and the program processing are explained in detail below.

[1618] server

[1619] Program processing

[1620] The server first collects information from the company's internal bulletin board. This is done periodically, for example, every hour, and retrieves all posts from the bulletin board's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "business announcements," "events," and "club activities." An emotion engine then analyzes user emotion data in real time, and the information is recorded in a database.

[1621] The server then filters the collected information based on the user profile and emotional data. For example, if a user is interested in a tennis club, not only will it pick up information related to the tennis club, but if the user is in a positive emotional state, it will also add more information. The filtered information is then assigned a recommendation score, and information with the highest score is selected. This information is formatted as notification data and sent to the user's device. The selected information is also compiled into the data displayed as "recommended information" when the user accesses the bulletin board.

[1622] Terminal

[1623] Program processing

[1624] When a user first uses the system, the device displays a profile setting screen. Here, the user can enter information about their hobbies and work, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The device receives notification data from the server in real time and displays it to the user in the form of pop-ups or alerts. When the user accesses the bulletin board, the device displays a list of recommended information received from the server.

[1625] User

[1626] How to use

[1627] During initial setup, the user enters information related to their hobbies and work. They also input their current emotional state using the emotion input means or capture it via sensors. This creates a profile and emotional data, which are then stored in the system. The user can periodically check notifications on the device to obtain the latest information. For example, notifications about upcoming technical seminars or the next tennis club activity schedule may be displayed. When accessing the bulletin board, information of particular interest is displayed prominently, allowing users to obtain information efficiently.

[1628] Specific examples

[1629] Example 1: User A is interested in the tennis club and is in a positive emotional state

[1630] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[1631] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[1632] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[1633] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[1634] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[1635] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[1636] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[1637] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[1638] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[1639] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[1640] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[1641] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[1642] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[1643] In this way, this system not only allows users to efficiently obtain information related to their interests and work, but also provides more appropriate information because it is personalized according to their emotional state.

[1644] The processing flow will be explained below.

[1645] server

[1646] Step 1:

[1647] Periodically retrieve bulletin board information. The server accesses the bulletin board API and retrieves all content in JSON format.

[1648] Step 2:

[1649] The retrieved data is saved in a database. The data is persisted for use in subsequent processing.

[1650] Step 3:

[1651] Natural language processing (NLP) is applied to the saved data to analyze each post, and based on the analysis results, posts are automatically classified into categories such as "business announcements," "events," and "club activities."

[1652] Step 4:

[1653] The user profile is retrieved from the database. The user profile includes information about the user's interests and preferences, as well as information necessary for the user's work.

[1654] Step 5:

[1655] The emotion engine analyzes the user's emotional data in real time and stores the information in a database. Emotion data is collected using technologies such as facial recognition and text analysis.

[1656] Step 6:

[1657] By analyzing user profiles and emotional data, the system filters collected information based on each user's interests and emotional state. For example, for a user who is interested in a tennis club and has a positive emotional state, the system will pick out relevant information and increase the amount of information available.

[1658] Step 7:

[1659] A recommendation score is calculated for the filtered information and sorted in descending order. The recommendation score indicates the degree to which the information matches the user's interests and emotions.

[1660] Step 8:

[1661] The information with the highest recommendation scores is formatted as notification data and sent to the user's device. It also generates "recommended information" to be displayed when the bulletin board is accessed and sends it to the bulletin board system.

[1662] Terminal

[1663] Step 1:

[1664] As an initial setting, the system presents an interface for users to input their hobbies, preferences, and work-related information, while also configuring the system to use sensors (e.g., camera and microphone) to track their emotional state in real time.

[1665] Step 2:

[1666] The hobby and work-related information and emotional data entered by the user are sent to a server and saved as a profile and emotional data.

[1667] Step 3:

[1668] Receive notification data sent from the server in real time, including push notifications and alerts.

[1669] Step 4:

[1670] The received notification is displayed in the user interface. For example, a notification of the next scheduled tennis club activity is displayed.

[1671] Step 5:

[1672] When a user accesses the bulletin board, recommended information from the server is displayed. For example, information such as "Technical Seminars," "New Product Development," and "Tennis Clubs" is displayed in a list.

[1673] User

[1674] Step 1:

[1675] When using the robot for the first time, users input information about their hobbies and work. Using the emotion input means, users' current emotional state is also input or captured by a sensor.

[1676] Step 2:

[1677] Check the notification that has arrived on your device. For example, check the "Notification of a technical seminar."

[1678] Step 3:

[1679] Access the internal bulletin board and check recommended information. For example, check the information on "Technical Seminar," "New Product Development," and "Tennis Club" displayed on the bulletin board homepage. The amount and content of information displayed is adjusted according to the user's emotional state.

[1680] Example 2

[1681] 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."

[1682] Conventional in-house bulletin board systems filter information based on users' hobbies, preferences, and work-related information, but do not provide information that takes into account the user's emotional state. As a result, users receive information that does not match their emotional state, and they may feel stressed by information overload or notifications of unnecessary information. Another problem is that users cannot quickly access the information they really need. Therefore, there is a need for personalized information provision that takes into account not only users' hobbies, preferences, and work-related information, but also their emotional state.

[1683] 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.

[1684] In this invention, the server includes means for collecting bulletin board information, means for classifying the collected information by category, means for collecting user's hobbies, preferences, and work-related information, means for analyzing the user's emotional state, means for recording the analyzed emotional state in a database, means for filtering the collected information based on the user's hobbies, preferences, work-related information, and emotional state, means for assigning a recommendation score to the filtered information, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information, thereby enabling more personalized information to be provided according to the user's emotional state.

[1685] The "means of collecting bulletin board information" refers to a system that periodically obtains posted information via the API of the internal bulletin board and stores it in a database.

[1686] "Means for categorizing by category" is a function that uses natural language processing technology to analyze collected bulletin board information and sort it into different categories.

[1687] "Means for collecting user's hobbies, preferences and work-related information" is a function that collects information entered by the user on the profile setting screen and past browsing history and stores them in a database.

[1688] The "means for analyzing the user's emotional state" is a mechanism that uses an emotion engine to analyze the user's emotional data in real time and record the results.

[1689] The "means for recording the analyzed emotional state in a database" is a function for storing the user's emotional data analyzed by the emotion engine in a database.

[1690] The "means for filtering information" is a mechanism for narrowing down the collected bulletin board information based on the user's hobbies, preferences, work-related information, and emotional state.

[1691] The "means for assigning a recommendation score" is a function that scores filtered information using a machine learning model.

[1692] The "notification means" is a mechanism that sends filtered information and information with high recommendation scores to the user's device in real time and displays a notification.

[1693] "Means for displaying recommended information" is a function that displays filtered information received from the server in a prominent manner on the top page when accessing the bulletin board.

[1694] The present invention is an in-house bulletin board system that can provide personalized information according to the emotional state of a user. The present invention is implemented using the following hardware and software.

[1695] server

[1696] The server has the specific role of collecting and analyzing bulletin board information, categorizing the information, and then sending notifications based on the user's profile and emotional data. The server periodically calls the bulletin board API to collect the latest posts. The collected information is stored in a database and analyzed using natural language processing technology such as Google Cloud Natural Language API. This analysis results in the information being classified into categories such as "business announcements," "events," and "club activities."

[1697] The server then uses an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time. The analysis results are recorded in a database. The collected information is filtered based on the analyzed emotional state, as well as the user's hobbies, preferences, and work-related information. The filtered information is assigned a recommendation score using a machine learning model, and information with a high score is formatted as notification data. This notification data is sent to the device in real time and displayed as "recommended information" when the bulletin board is accessed.

[1698] Terminal

[1699] When a user first uses the system, the device displays a profile setup screen where the user enters information about their hobbies and work, and uses sensors (e.g., facial expression recognition cameras and heart rate sensors) and input fields to track their emotional state in real time. This data is then sent to the server and stored in a database.

[1700] The device receives notification data from the server in real time and displays it in the form of a pop-up or alert. When the user accesses the bulletin board, filtered data is displayed as recommended information.

[1701] User

[1702] During initial setup, users input information related to their hobbies and work, and also input their current emotional state or capture it using sensors. Users can periodically check notifications on their device to receive the latest information. For example, they may receive notifications about upcoming technical seminars or the next tennis club activity. When accessing the bulletin board, information of interest is displayed prominently, allowing users to obtain information efficiently.

[1703] Specific examples

[1704] Example 1: User A is interested in the tennis club and is in a positive emotional state

[1705] 1. Server: Collect information about the "next tennis club activity schedule" from the company bulletin board and categorize it.

[1706] 2. Server: Extract "Interested in tennis club" from User A's profile and filter related information.

[1707] 3. Server: The emotion engine recognizes User A's positive emotional state and adjusts to provide more detailed information.

[1708] 4. Server: Generate the filtered "next tennis club activity schedule" as notification data and send it to User A's device.

[1709] 5. Device: Receives notifications in real time and displays them to User A in a pop-up.

[1710] 6. Bulletin board access: When user A accesses the bulletin board, recommended information about the "Tennis Club's next scheduled activity" is displayed on the top page.

[1711] Example 2: User B is interested in a technical seminar and is in a negative emotional state

[1712] 1. Server: Collects information about "technical seminar notifications" from the internal bulletin board and categorizes it.

[1713] 2. Server: Extract "Interested in technical seminars" from User B's profile and filter related information.

[1714] 3. Server: The emotion engine recognizes User B's negative emotional state and moderates the information provided.

[1715] 4. Server: Generates the filtered "Notice of Technical Seminar" as notification data and sends it to User B's terminal.

[1716] 5. Device: Receives notifications in real time and displays them to User B as an alert.

[1717] 6. Bulletin board access: When User B accesses the bulletin board, the recommended information "Notification of Technical Seminar" is displayed on the top page.

[1718] Examples of prompt statements

[1719] Example prompt sentence:

[1720] "Create notifications for upcoming technical seminars and provide information that will interest users. Adjust the filtering and notification content based on the user's emotional state, both positive and negative."

[1721] In this way, the present invention provides more personalized information provision according to the user's emotional state.

[1722] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1723] Program processing flow

[1724] Server-side processing

[1725] Step 1:

[1726] The server collects bulletin board information. It calls the bulletin board API to retrieve post data from the last hour. This post data is retrieved in JSON format and stored in a database.

[1727] Input: Post data obtained from the bulletin board API

[1728] Output: Post data stored in the database

[1729] Specific behavior:

[1730] The server schedules a periodic job to call the API every hour, stores the retrieved data in a buffer, and then writes it to the database.

[1731] Step 2:

[1732] The server analyzes the collected information using natural language processing technology, using the Google Cloud Natural Language API to analyze the content of each post and classify it into categories such as "business announcements," "events," and "club activities."

[1733] Input: Post data stored in the database

[1734] Output: Post data with categories

[1735] Specific behavior:

[1736] The server reads the unparsed post data from the database, sends a parsing request to the Google Cloud Natural Language API, and writes the returned parsed results back to the database.

[1737] Step 3:

[1738] The server analyzes the user's emotional data, using an emotion engine such as IBM Watson Tone Analyzer to analyze the user's emotional state in real time and record the information in a database.

[1739] Input: User emotion data (real-time data from sensors and user input)

[1740] Output: Sentiment analysis data recorded in a database

[1741] Specific behavior:

[1742] The server receives the data from the emotion sensor and sends an analysis request to the IBM Watson Tone Analyzer API. The returned analysis results are stored in a database.

[1743] Step 4:

[1744] The server filters the collected information based on the user profile and emotional data, extracting information of interest based on the user's hobbies, preferences, work-related information, and emotional state.

[1745] Input: User profile data, emotion data, and categorized post data

[1746] Output: Filtered post data

[1747] Specific behavior:

[1748] The server reads user profiles and sentiment data from a database and filters the posted data based on pre-defined rules and machine learning models.

[1749] Step 5:

[1750] The server assigns a recommendation score to the filtered information, using a machine learning model to score each piece of information and select the information with the highest score.

[1751] Input: Filtered post data

[1752] Output: Filtered data with recommendation scores

[1753] Specific behavior:

[1754] The server uses a machine learning model to score each post and store the results in a database.

[1755] Step 6:

[1756] The server formats the information with the highest score as notification data and sends it to the user's device. It also generates data to be displayed as "recommended information" when the user accesses the bulletin board.

[1757] Input: Filtered data with recommendation scores

[1758] Output: Notification data sent to your device and data displayed as "Recommended Information"

[1759] Specific behavior:

[1760] The server formats the notification data into JSON format and sends it to the user's device in real time. It also generates data for the bulletin board's top page and stores it in a database.

[1761] Terminal side processing

[1762] Step 1:

[1763] The device displays a profile setup screen when first used, collects information about the user's hobbies and work, and also collects sensor information for real-time tracking of emotional data.

[1764] Input: User-entered data (hobbies, work-related information, emotional data)

[1765] Output: Profile data and emotion data sent to the server

[1766] Specific behavior:

[1767] The device displays a profile setting screen, and once the user has completed their input, the information is sent to the server. Sensor information is also sent to the server in real time.

[1768] Step 2:

[1769] The terminal receives the notification data sent from the server in real time and displays it to the user in the form of a pop-up or alert.

[1770] Input: Notification data from the server

[1771] Output: Popups or alerts that are displayed to the user

[1772] Specific behavior:

[1773] When the terminal receives the notification data, it immediately displays a pop-up to the user and sounds an alert sound if necessary.

[1774] Step 3:

[1775] When the terminal accesses the bulletin board, it displays a list of data received from the server as recommended information.

[1776] Input: Recommendation data from the server

[1777] Output: A list of recommendations displayed on the bulletin board

[1778] Specific behavior:

[1779] When the terminal accesses the bulletin board, it receives the recommended information data sent from the server and displays it on the user interface.

[1780] User-side processing

[1781] Step 1:

[1782] During initial setup, the user inputs information related to their hobbies and work, and their emotional state is also input or captured by sensors.

[1783] Input: Hobbies, work-related information, emotional state

[1784] Output: Profile data and emotion data sent to the server

[1785] Specific behavior:

[1786] The user enters the necessary information on the device's profile setting screen and sets the sensor to be used.

[1787] Step 2:

[1788] Users can periodically check notifications on their devices to get the latest information.

[1789] Input: Notification data from the device

[1790] Output: Check for the latest information

[1791] Specific behavior:

[1792] Users can check pop-ups and alerts displayed on their devices to obtain the latest information.

[1793] Step 3:

[1794] When a user accesses the bulletin board, information of particular interest to the user is displayed in a prominent manner, allowing the user to obtain information efficiently.

[1795] Input: Forum recommendations

[1796] Output: Get the information you are interested in

[1797] Specific behavior:

[1798] Users access the bulletin board, check the content displayed as recommended information, and obtain information that interests them.

[1799] (Application example 2)

[1800] 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."

[1801] In today's information-saturated society, it is difficult to effectively sort and provide the information that each user needs. Furthermore, while systems that can appropriately customize information based on the user's emotional state could provide a more satisfying experience, there are few systems that can do this. This poses a challenge: users may be overwhelmed by the amount of information available, and may not be able to obtain the appropriate information, which can lead to stress.

[1802] 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 collecting bulletin board information, means for classifying the collected information by category, means for collecting a user's hobbies, preferences, and work-related information, means for filtering the collected information based on the user's hobbies, preferences, and work-related information, means for recognizing a user's emotional state in real time, means for adjusting the filtered information based on the recognized emotional state, means for notifying the user of the filtered information, and means for displaying the filtered information as recommended information. This makes it possible to personalize information according to the user's emotional state and provide more appropriate and satisfying information.

[1803] "Bulletin Board Information" means the content of messages or notices posted on internal bulletin boards or online forums.

[1804] A "category" is a category for classifying bulletin board information based on a specific topic or theme.

[1805] "Hobbies" refers to activities and interests that a user is personally interested in.

[1806] "Business-related information" refers to information related to a user's job or work.

[1807] "Emotional state" refers to a user's real-time psychological emotional state, which may be classified as positive or negative.

[1808] "Filtering" is the process of sorting out information based on specific criteria.

[1809] "Notification" is a means of notifying the user of filtered information in real time.

[1810] "Recommended information" is information that is determined to be particularly important to notify based on the user's interests and emotional state.

[1811] This invention is a virtual store system that combines an emotion engine that recognizes the user's emotions. This system makes it possible to provide a more personalized shopping experience according to the user's emotional state. The system consists of a server, smart glasses (terminals), and a user. Details are described below.

[1812] server

[1813] Program processing

[1814] The server first collects product information from the virtual store. This is done periodically, for example, every hour, to retrieve all product data from the store's API. The collected information is then analyzed using natural language processing (NLP) technology and classified into categories, such as "fashion," "electronics," and "sports goods." An emotion engine then analyzes the user's emotional data in real time and records the information in a database.

[1815] The server then filters the product information based on the user profile and emotional data. For example, if a user is interested in sports equipment, the server will not only provide sports equipment-related information but also provide additional promotional information if the user is in a positive emotional state. The server then assigns a recommendation score to the filtered information and selects the information with the highest score. This information is then formatted as notification data and sent to the smart glasses. The selected information is then also summarized in the recommendations displayed on the smart glasses.

[1816] Smart glasses (terminal)

[1817] Program processing

[1818] The smart glasses display a profile setting screen when the user first uses the system. Here, the user can enter their hobbies and interests, and can also use sensors and input fields to track their emotional state in real time. The data is sent to the server and saved as profile and emotional data. The smart glasses receive notification data from the server in real time and display it to the user in the form of pop-ups or alerts. When the user accesses a virtual store, the glasses display a list of recommended information received from the server.

[1819] User

[1820] How to use

[1821] During initial setup, users input information related to their hobbies and interests. Their current emotional state is also input using an emotion input means or captured by sensors. This creates a profile and emotional data, which are then stored in the system. Users can periodically check notifications on the smart glasses to obtain the latest information. For example, "sale information for fashion categories" or "new sports goods" are displayed. When users access a virtual store, information of particular interest is prominently displayed, allowing them to efficiently select products.

[1822] Hardware and software used

[1823] Hardware: Smart glasses (built-in camera, microphone, display)

[1824] Server: Cloud service (e.g. AWS, Google Cloud)

[1825] Software and Libraries

[1826] Emotion Engine API: Affect Recognition Engine (e.g. Affectiva)

[1827] Face recognition library: OpenCV, dlib

[1828] Natural language processing libraries: NLTK, spaCy

[1829] Real-time communication: WebSocket

[1830] Specific examples

[1831] Example 1: User A is interested in fashion and in a positive emotional state

[1832] 1. The server collects and categorizes products in the fashion category.

[1833] 2. The server extracts "Interested in fashion" from User A's profi...

Claims

1. a means for collecting bulletin board information; A means of categorizing the information collected; and A means for collecting user's hobbies, preferences and business-related information; means for filtering the collected information based on the user's interests, preferences and business-related information; means for notifying a user of the filtered information; a means for displaying the filtered information as recommended information; A system including:

2. 2. The system according to claim 1, wherein the bulletin board information is analyzed and classified into categories using natural language processing technology.

3. The system of claim 1 , wherein a recommendation score is calculated for the filtered information based on the user profile.

Images