System

Abstract

A system is provided.SOLUTION: A system comprising: means for registering team member information in a database; means for monitoring online status of members in real-time; means for determining when members are not busy; means for creating bots to facilitate communication and join chat rooms; means for bots to provide questions and topics to members; means for collecting user responses and storing chat history; means for adjusting bot algorithms based on collected data; and means for evaluating system performance and adding new topics and questions.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] While communication, including appropriate small talk, is important for improving team productivity and the quality of communication, the problem is that it can be difficult to engage in spontaneous small talk at the right time. In particular, in remote work environments, natural interactions between team members decrease, often leading to feelings of isolation. This can lead to a decline in team cohesion and trust, ultimately negatively impacting work results. Furthermore, bringing up topics at inappropriate times during busy periods can also risk reducing work efficiency. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means. First, it includes means for registering information about team members in a database and monitoring their online status in real time. It also provides means for determining when members are not busy, and based on this, it generates a bot that promotes communication and has it participate in a chat room. The bot poses questions and topics to members at appropriate times, collects user responses, and saves the chat history. It then adjusts the bot's algorithm based on the collected data, and adds new topics and questions while evaluating the system's performance. By continuously supporting effective communication in this way, it is possible to improve team productivity and cohesion.

[0006] "Team Member" means an individual member selected to work on a particular project or assignment.

[0007] "Information" is data about team members, including names, titles, schedules, online status, etc.

[0008] A "database" is a system that systematically stores information and allows it to be quickly accessed, updated, and deleted as needed.

[0009] "Online status" is information indicating the online or offline status and activity status of a team member.

[0010] "Real-time monitoring" means constantly checking ongoing status and events, and immediately updating any changes.

[0011] "Off-peak hours" are times when members are not overwhelmed with work or have relatively free time.

[0012] "Communication" is the act of exchanging information, feelings, and opinions, and can be done through various means, such as verbal, written, or electronic tools.

[0013] A "bot" is a program that automatically executes specific tasks, and in the present invention, it plays a role in promoting team communication.

[0014] A "chat room" is a virtual conversation space where members can exchange text messages online in real time.

[0015] "Questions and Topics" are sentences or topics that are intended to spark conversation and promote communication between members.

[0016] "User responses" refer to team members' responses and actions to questions or topics posed by the bot.

[0017] "Chat history" means a record of messages exchanged within a chat room that is saved for later reference or analysis.

[0018] An "algorithm" is a procedure or computational method for solving a specific problem, which in this invention is used to improve the quality of a bot's conversation.

[0019] "System performance" is an indicator of how efficiently and effectively a system can perform its expected functions and processes.

[0020] A "topic" is a subject or theme of a conversation, used to stimulate communication between members. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention is directed to a system designed to promote communication within a team and improve business results. The system includes a server, a terminal, and a user.

[0043] Overall system configuration

[0044] 1. Server configuration

[0045] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[0046] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[0047] 2. Monitor your online status

[0048] The server monitors the online status of members in real time. For example, the server periodically checks the online status of member A and uses chat activity and calendar events as metrics to determine whether they are busy.

[0049] 3. Determine the less busy times

[0050] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[0051] 4. Creating a bot and joining a chat room

[0052] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[0053] 5. Asking questions and providing topics

[0054] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[0055] 6. Collecting and analyzing user responses

[0056] The server collects user responses to questions and topics provided by the bot and saves the chat history. For example, if member A replies, "I spent the weekend with my family," the server saves this comment for later analysis.

[0057] 7. Algorithm Adjustments

[0058] The server adjusts the bot's algorithm based on the collected data, optimizing the timing and topic selection for future chats. For example, if member A often responds positively to a particular topic, the server will adjust the bot to cover that topic more frequently.

[0059] Specific examples

[0060] Example 1: Monday Chat

[0061] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened each weekend, and a natural conversation begins.

[0062] Example 2: Project progress check

[0063] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[0064] The above is a specific embodiment of the present invention. This system promotes timely communication, improving team cohesion and productivity.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The server stores team member information in a database, including each member's name, job title, schedule, and online status.

[0068] Step 2:

[0069] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[0070] Step 3:

[0071] The server determines when members are free by looking at calendar events and chat activity, for example by looking at the times when a particular member is free and marking them as "free."

[0072] Step 4:

[0073] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to the specified chat room.

[0074] Step 5:

[0075] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss. For example, it might ask, "How was your weekend?"

[0076] Step 6:

[0077] Users respond to the bot's questions. For example, User A might reply, "I had a great time with my family," and User B might respond, "I went to watch a sports game."

[0078] Step 7:

[0079] The server collects user responses and stores the chat history for future reference and analysis.

[0080] Step 8:

[0081] The server adjusts the bot's algorithm based on the collected data: for example, if a particular topic gets a good response, it changes the settings to throw more of that topic at it.

[0082] Step 9:

[0083] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[0084] Step 10:

[0085] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[0086] Example 1

[0087] 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."

[0088] In today's work environment, effective communication between team members is often lacking. This lack of communication leads to delays in information sharing and misunderstandings, resulting in reduced productivity. Especially with the increasing trend toward online work, casual but important communication such as daily chats and progress checks is declining. This calls for an effective system that monitors online status in real time and promotes timely communication.

[0089] 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.

[0090] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program that promotes dialogue and having members participate in a dialogue environment, means for the program to provide questions and topics to members, means for collecting user responses and saving a dialogue history, means for adjusting the operation of the program based on the collected data, means for evaluating system performance and adding new topics and questions, and means for running the system in a cloud computing environment, which promotes communication at appropriate times and makes it possible to improve team cohesion and productivity.

[0091] "Team members" refers to a set of individuals working together to achieve a goal.

[0092] "Database" refers to a system for efficiently storing, retrieving, and managing structured information in digital form.

[0093] "Online status" refers to a state that indicates in real time whether a user is connected to the Internet and is active.

[0094] "Real-time" refers to processing or communication occurring immediately without delay.

[0095] An "interactive environment" refers to a virtual or physical space in which users communicate.

[0096] A "program" refers to a set of instructions designed to perform a particular function.

[0097] "Questions and Topics" refers to topics and questions provided to stimulate communication.

[0098] "User" refers to an individual or organization that uses the system or service.

[0099] "Reaction" refers to the response or feedback a user gives to a question or topic.

[0100] "Dialogue history" refers to a record of previous communications.

[0101] "Adjusting behavior" refers to changing algorithms or settings to improve system performance.

[0102] A "cloud computing environment" refers to an environment in which computing resources and services are used via the Internet.

[0103] The present invention provides a system for promoting communication within a team and improving business results. The system includes a server, a terminal, and a user.

[0104] Server Configuration

[0105] The server acts as a central point for managing all data and processes. It uses a database management system (e.g., MySQL) to register team member information in a database. A server running on the cloud (e.g., AWS EC2) is used. The server then monitors the online status of team members in real time. Specifically, the server regularly monitors users' chat activity and calendar appointments, and updates their online status accordingly.

[0106] Registering user information and monitoring online status

[0107] Users log in to the system using a dedicated terminal, and the server saves information such as the user's name, job title, schedule, and online status in the database. The server saves the information by executing an SQL statement such as INSERT INTO users. The server also monitors the online status in real time by executing the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to obtain recent activity.

[0108] Determining non-busy times

[0109] The server determines non-busy times based on the user's schedule information and chat activity. It lists available time slots by calling the calendar API and checking the busy status. For example, if member A has no plans between 10:00 and 11:00 AM, the server determines this time slot as a "non-busy time slot."

[0110] Creating a bot and joining a chat room

[0111] The server creates a dedicated bot to facilitate communication between users. The created bot automatically joins the configured chat room. The server executes bot = BotFactory.createBot(), and the created bot calls bot.joinChatroom('general') to join the chat room.

[0112] Asking questions and providing topics

[0113] The bot provides users with questions and topics based on pre-defined timing and conditions. For example, the bot might send the message "Good morning everyone. How was your weekend?" at 10:00 AM. A concrete example would be the bot executing bot.sendMessage(chatroom='general', message='How was your weekend?').

[0114] Collecting and analyzing user responses

[0115] The server collects user reactions to questions and topics provided by the bot and stores them in a database. For example, the server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) to store the reactions.

[0116] Bot algorithm adjustments

[0117] The server adjusts the bot's algorithm based on the collected data to optimize future questions and topics. The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[0118] Specific examples

[0119] Chat every Monday: The server checks the online status of members every Monday at 9:00 AM, and the bot posts to the chat room, "Good morning everyone. How was your weekend?" User A replies, "I went on a picnic with my family," and the server saves this in the database.

[0120] Project progress check: The server checks the user's online status every day at 3:00 PM, and the bot asks, "How is the current project progressing?" User B answers, "I'm a little behind," which the server collects and uses for analysis.

[0121] Example prompts for generative AI models

[0122] "Create a bot message at 9am on Monday morning to check your team's online status and ask how their weekend is going."

[0123] "Send a bot message every day at 3 PM to remind you to check in on your project progress."

[0124] This promotes timely communication and improves team cohesion and productivity.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1:

[0127] Starting and initializing the server

[0128] The server starts the server machine, connects to the database management system (e.g. MySQL), reads the configuration files (e.g. config files, API keys, etc.) and performs any necessary initial configuration.

[0129] Input: Server startup request, configuration file

[0130] Output: Server environment with initial settings completed

[0131] Specific operation: The server reads the config.yaml file and sets the database connection information.

[0132] Step 2:

[0133] Registering user information

[0134] Users log in to the system using a dedicated terminal, which sends the user's basic information to the server, which then registers the information in a database.

[0135] Input: User login information (name, job title, schedule, online status)

[0136] Output: User information registered in the database

[0137] Specific operation: When a user fills in the login form and clicks the "Login" button, the terminal sends an API request, and the server executes the SQL statement INSERT INTO users to save the information.

[0138] Step 3:

[0139] Real-time online status monitoring

[0140] The server checks each user's online status at a specified interval (e.g., every minute), looking at chat activity and calendar appointments.

[0141] Input: User activity data, calendar data

[0142] Output: Latest online status

[0143] Specific operation: The server executes the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to get the most recent activity.

[0144] Step 4:

[0145] Determining non-busy times

[0146] The server determines the times when the user is not busy based on the collected schedule information and chat activity.

[0147] Input: Schedule information and activity data from the Calendar API

[0148] Output: A list of available time slots for each user

[0149] Specific operation: Calls the calendar API to check the busy status and lists available time slots.

[0150] Step 5:

[0151] Creating a bot and joining a chat room

[0152] The server generates a dedicated bot to promote communication between users and has it participate in a set chat room.

[0153] Input: bot generated request

[0154] Output: Bots that joined the chat room

[0155] Specific operation: The server executes bot = BotFactory.createBot() and calls bot.joinChatroom('general') to join the chat room.

[0156] Step 6:

[0157] Asking questions and providing topics

[0158] The bot provides questions and topics to users based on set timing and conditions.

[0159] Input: Pre-set prompt, current date and time

[0160] Output: Questions and topics posted in the chat room

[0161] Specific behavior: The bot executes bot.sendMessage(chatroom='general', message='How was your weekend?').

[0162] Step 7:

[0163] Collecting and analyzing user responses

[0164] The server collects user responses to questions and topics provided by the bot and stores them in a database.

[0165] Input: User's chat message

[0166] Output: Reaction data stored in a database

[0167] Specific operation: The server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) and saves the reactions.

[0168] Step 8:

[0169] Bot algorithm adjustments

[0170] The server adjusts the bot's algorithm based on the collected data to optimize questions and topics for future questions.

[0171] Input: Collected reaction data, analysis results

[0172] Output: Adjusted bot algorithm

[0173] Specific operation: The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[0174] (Application example 1)

[0175] 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."

[0176] The goal is to eliminate the decline in work efficiency and lack of understanding caused by a lack of communication within a team. In particular, in content distribution services, it is often the case that project progress is not checked smoothly or ideas are not shared smoothly, which can have a negative impact on the quality of deliverables and delivery dates. In addition, as more work is done online, it is necessary to understand the status of team members in real time and promote communication at the appropriate time.

[0177] 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.

[0178] In this invention, the server includes means for registering information of team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating an autoresponder and having it participate in a conversation room, means for the autoresponder to provide questions and topics to members, means for collecting user responses and saving the conversation history, means for adjusting the autoresponder algorithm based on the collected data, and means for evaluating system performance and adding new topics and questions, thereby promoting communication within the team and enabling improved work efficiency and smoother work.

[0179] A "team member" is an individual registered in the system, and is a member of a group who works with their own role.

[0180] A "database" is a collection of data that organizes, stores, accesses, and manages information about team members.

[0181] "Online status" indicates whether a team member is currently online or offline.

[0182] "Real-time" means that data and information are updated and reflected immediately, without delay.

[0183] "Quiet times" are times when team members are available for other instructions or communication given their current schedules and activities.

[0184] An "automatic responder" is a virtual questioner or interlocutor generated by the system, and has the function of facilitating communication with team members.

[0185] A "chat room" is a virtual place where team members can gather and communicate via text and voice.

[0186] "Questions and topics" are conversation starters that the autoresponder provides to team members.

[0187] "User responses" are the responses and reactions that team members give to questions and topics posed by the autoresponder.

[0188] "Conversation history" is a record of conversations and chats between team members.

[0189] "Adjusting the algorithm" means processing the collected data to optimize the operation of the automated responder and promote more effective communication.

[0190] "System performance" is an index that evaluates the effectiveness of overall operation and communication, and indicates how efficient the system is.

[0191] "New topics and questions" are content that the system generates additionally and that can trigger new dialogue with the user.

[0192] MODE FOR CARRYING OUT THE INVENTION

[0193] The following describes in detail the mode for carrying out the present invention. The present invention is a system for promoting communication within a team and improving work efficiency. This system includes elements of a server, a terminal, and a user, and realizes effective communication using an automatic responder.

[0194] 1. Server configuration

[0195] The server is the central point that manages all data and processes, and provides the following means:

[0196] A means of registering team member information in a database

[0197] A way to monitor members' online status in real time

[0198] A way to determine when members are not busy

[0199] The server stores the names, titles, schedules, and online status of members A and B in a database, allowing the server to constantly monitor the status of members and determine the appropriate timing for communication.

[0200] 2. Creating and joining an autoresponder

[0201] To promote team communication, the server has a means of generating auto-responders and having them join conversation rooms. The auto-responders provide questions and topics to members at appropriate times. For example, the server might have an auto-responder join a "chat room" at 10 a.m. and ask a question such as, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[0202] 3. Collecting user responses and adjusting the algorithm

[0203] The server has a means to collect user responses to questions and topics provided by the autoresponder and store the conversation history. For example, if member A replies, "I spent the weekend with my family," the server stores this comment for later analysis.

[0204] The server then uses the collected data to adjust the autoresponder algorithm and add new topics and questions to optimize future communications. By repeating this process, the server evaluates the system's performance and improves its overall effectiveness.

[0205] Specific examples

[0206] Example 1: Monday Chat

[0207] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the autoresponder posts a message to the chat room saying, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened on their respective weekends, and a natural conversation begins.

[0208] Example 2: Project progress check

[0209] The server checks the online status of members every day at 3:00 p.m. If it determines that a member is not busy, the autoresponder asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[0210] Hardware and software used

[0211] Specifically, the following hardware and software are used:

[0212] Server (Linux or Windows server)

[0213] A database management system (MySQL, PostgreSQL, or MongoDB)

[0214] Chatbot building framework (Rasa, Dialogflow, etc.)

[0215] Python scripts for data collection and analysis

[0216] Prompt Sentence Examples

[0217] prompt:

[0218] Create a bot that facilitates communication within your team by prompting team members with questions and topics at the right time to facilitate smooth communication.

[0219] Required features:

[0220] 1. Monitor members' online status

[0221] 2. Determine the less busy times

[0222] 3. Create a bot and join the chat room

[0223] 4. Asking questions and providing topics

[0224] 5. Collecting and storing user responses

[0225] Output format:

[0226] 1. Class Blueprints

[0227] 2. Code for each class

[0228] Product usage:

[0229] An application that promotes communication among content production teams

[0230] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0231] Processing steps of the system that realizes the application example

[0232] Step 1:

[0233] The server registers the team member information in a database.

[0234] Input: Member A, Member B names, titles, schedules, online status

[0235] Data processing / calculation: Writing member information to the database

[0236] Output: Complete member information stored in the database.

[0237] Step 2:

[0238] The server monitors the online status of members in real time.

[0239] Input: Member's schedule and current time

[0240] Data processing / calculation: Comparing the current time with the member's schedule and determining their online status

[0241] Output: Member's online status is updated as "online" or "offline".

[0242] Step 3:

[0243] The server determines when members are not busy.

[0244] Input: Member schedule, online status, chat activity

[0245] Data processing / calculation: Predicting the next quietest time slot based on schedules and activities

[0246] Output: The quiet times are identified and stored.

[0247] Step 4:

[0248] The server generates autoresponders to participate in conversation rooms to facilitate communication.

[0249] Input: Off-peak hours

[0250] Data processing / calculation: Creating an instance of an autoresponder and adding it to a specified conversation room

[0251] Output: The autoresponder joins the conversation room.

[0252] Step 5:

[0253] The autoresponder provides members with questions and topics at the appropriate time.

[0254] Input: Member's online status and off-peak hours

[0255] Data processing / calculation: Randomly selecting questions from a pre-defined list and posting them to the chat room

[0256] Output: The question or topic is displayed in the conversation room.

[0257] Step 6:

[0258] The server collects user responses and stores the conversation history.

[0259] Input: Member response

[0260] Data processing / calculation: Analyzing the response content and adding it to the conversation history

[0261] Output: A new entry is added to the conversation history.

[0262] Step 7:

[0263] The server uses the collected data to adjust the autoresponder's algorithm and add new topics and questions.

[0264] Input: Stored conversation history and user reaction data

[0265] Data processing / calculation: Analyzing reaction data and optimizing the algorithm of the automatic responder

[0266] Output: An updated list of topics and questions provided by the autoresponder.

[0267] Step 8:

[0268] The server evaluates the system's performance and generates new topics and questions to promote effective communication.

[0269] Input: System log data, conversation history, user response data

[0270] Data processing / calculation: The process of integrating data, calculating performance indicators, and generating more effective questions and topics.

[0271] Output: New topics or questions are added to the algorithm's configuration.

[0272] 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.

[0273] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion engine that recognizes the emotions of users. This system includes elements of a server, a terminal, and a user.

[0274] Overall system configuration

[0275] 1. Server configuration

[0276] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[0277] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[0278] 2. Monitor your online status

[0279] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[0280] 3. Determine the less busy times

[0281] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[0282] 4. Creating a bot and joining a chat room

[0283] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[0284] 5. Asking questions and providing topics

[0285] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[0286] 6. Emotion Recognition by Emotion Engine

[0287] The bot uses an emotion engine to analyze emotions from users' text messages. For example, if User A replies "I'm a little tired," the emotion engine analyzes this message and recognizes that User A is tired.

[0288] 7. Collecting and analyzing user responses

[0289] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[0290] 8. Algorithm Adjustments

[0291] The server adjusts the bot's algorithm based on the collected data. For example, if a particular topic receives a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[0292] 9. Adding new topics and questions

[0293] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[0294] Specific examples

[0295] Example 1: Monday Chat

[0296] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning everyone. How was your weekend?". As Member A and Member B start sharing what happened over the weekend, a natural conversation begins. At the same time, the emotion engine analyzes the members' messages and understands their emotional state.

[0297] Example 2: Project progress check

[0298] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A responds, "It's going well," while member B continues, "It's a little behind schedule," creating an opportunity for regular progress checks. The emotion engine analyzes these messages and recognizes that member A's answers are positive, while member B's answers are partly negative.

[0299] In this way, the system based on the present invention can further improve team cohesion and productivity by promoting communication at the right time and providing responses that take into account the user's emotions.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] The server stores team member information in a database, including each member's name, job title, schedule, and online status. For example, member A's name, job title "Engineer," and working hours are stored in the database.

[0303] Step 2:

[0304] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database. For example, if member A becomes online at 9:00 a.m., that information is immediately updated in the database.

[0305] Step 3:

[0306] The server determines when a member is not busy by looking at calendar appointments and chat activity. For example, since member A has no appointments between 10:00 and 11:00 AM, the server determines this time period as a "non-busy time period."

[0307] Step 4:

[0308] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to a specified chat room. For example, the bot joins a "chat room."

[0309] Step 5:

[0310] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss, such as, "How was your weekend?"

[0311] Step 6:

[0312] Users respond to the bot's questions. For example, User A replies, "I had a great time with my family," and User B follows with, "I went to watch a sports game." The bot responds with an appropriate response.

[0313] Step 7:

[0314] The emotion engine analyzes emotions from user text messages. For example, it analyzes the message "I'm a little tired" from user A and recognizes the emotional state as "fatigue."

[0315] Step 8:

[0316] The bot's response is adjusted based on the results of the emotion engine. For example, if user A says "I'm tired," the bot will respond by saying "Take care and rest."

[0317] Step 9:

[0318] The server collects user responses and stores the chat history for future reference and analysis. For example, the responses of User A and User B are stored in a database.

[0319] Step 10:

[0320] The server uses the collected data to adjust the bot's algorithms: if a particular topic is well-received, it changes its settings to target that topic more frequently.

[0321] Step 11:

[0322] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database based on the evaluation results to improve communication effectiveness.

[0323] Step 12:

[0324] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[0325] Example 2

[0326] 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."

[0327] Modern teams require more efficient communication and interactions that take into account the emotional state of members, but existing tools and systems do not adequately address these needs. While basic functions such as monitoring online status and determining when members are not busy are provided, they lack the ability to promote effective communication or recognize emotions. This leads to a decline in team cohesion and productivity, and a lack of work efficiency.

[0328] 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.

[0329] In this invention, the server includes means for registering information about team members in a storage device, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program to promote communication and for allowing members to participate in the communication area, means for the program to provide questions and topics to members, means for collecting user responses and saving communication history, means for adjusting the program's operation procedure based on the collected data, means for evaluating system performance and adding new topics and questions, and means for analyzing user emotions using an emotion recognition engine. This enables effective communication that takes into account the emotional states of members, thereby improving team cohesion and productivity.

[0330] "Team member information" refers to data such as a user's name, title, schedule, and online status.

[0331] "Storage device" refers to hardware or software for storing data.

[0332] "Online state" refers to a state in which a user is connected to a network.

[0333] "Real-time monitoring" refers to instantly checking and updating the current situation and status.

[0334] "Off-peak hours" refers to times when the user is not tied down to a specific task or schedule.

[0335] "Programs that facilitate communication" refers to software designed to support and stimulate interaction between users.

[0336] A "communication realm" refers to a virtual or physical space in which users exchange information.

[0337] "The program provides questions or topics to members" means that the software presents specific topics or questions to users.

[0338] "Collecting user responses and saving communication history" refers to recording the actions and comments made by the user and making them available for later reference.

[0339] "Adjusting the program's operating procedures based on collected data" refers to analyzing stored data and changing the software's behavior based on the results.

[0340] "Evaluate the system's performance and add new topics and questions" refers to evaluating the system's operation and introducing new topics and questions as a way to improve it.

[0341] An "emotion recognition engine" refers to software that analyzes text and voice to estimate a user's emotions.

[0342] "Analyzing the user's emotions" refers to determining the user's emotional state based on input data.

[0343] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion recognition engine that recognizes the emotions of a user. The system includes elements of a server, a terminal, and a user.

[0344] Overall system configuration

[0345] The server first sets up a database and registers team member information. This registration is done using Python and a MySQL database. Specifically, the server stores user names, job titles, schedules, and online status in the database. This allows the server to centrally manage all data and processes and constantly monitor the status of team members.

[0346] For example, the server stores in the database the name of member A as "Yamada Taro," his position as "team leader," his schedule as "meeting from 10:00 AM to 11:00 AM," and his online status as "online."

[0347] The server runs a script to check the online status of members at regular intervals and updates the results to the database. Here, we will create a monitoring script using Python. The server checks which members are currently online and updates the results to the database.

[0348] The server also uses the collected data to determine when a member is not busy. This determination is made using the Python Pandas library. For example, the server checks member A's schedule and determines that he has no appointments between 10:00 AM and 11:00 AM, and determines this time period as a "non-busy time period."

[0349] The server then generates a program (here called a bot) that facilitates communication and has it participate in the specified chat room. This bot is generated using Node.js and the Bot Framework, and the bot participates in communication areas such as Slack and Microsoft Teams.

[0350] For example, the server creates a bot that participates in a "chat room" and has the bot send the message "Good morning. How are you today?"

[0351] The bot prompts members with questions and topics at appropriate times, using generative AI models (such as OpenAI's GPT-3) to generate natural-sounding dialogue. For example, at 10 a.m., the bot posts to the chat room, "Good morning, everyone. How was your weekend?" The following is an example of a prompt:

[0352] "How was your weekend?"

[0353] The bot uses an emotion recognition engine to analyze users' text messages and recognize their emotions. It uses Google Cloud Natural Language API and IBM Watson's NLP API. For example, if User A replies, "I'm a little tired," the emotion recognition engine will tag this message as "fatigue" and understand User A's emotional state.

[0354] The server collects user responses to questions and topics provided by the bot and stores the communication history. It uses Python and MySQL to store the chat history in a database. For example, it logs the conversation between users A and B and evaluates their responses.

[0355] The server uses the collected data to adjust the bot's algorithms. Based on the topics that generated the most responses, it changes its settings to offer more new questions and topics. In addition, based on the results of an emotion recognition engine, it adjusts its responses to match the user's emotional state.

[0356] Finally, the server evaluates the system's performance and adds new topics and questions, further improving the effectiveness of communication. For example, the server adds new topics such as "recently read books" and "favorite places" to the database, and the bot uses these topics to advance the conversation.

[0357] Through this system, the server, terminals, and users cooperate to realize more effective and emotionally sensitive communication, improving team cohesion and productivity.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1: The server registers team member information in a storage device. Specifically, the server stores the user's (e.g., "Yamada Taro") name, job title ("Team Leader"), schedule ("Meeting from 10:00 to 11:00"), and online status ("Online") in the database. The input of this step is user information, and the output is the user information stored in the database.

[0360] Step 2: The server monitors the online status of members in real time at regular intervals. It runs a Python script to check the current online status and update the database. Specifically, the script checks the online status of members and updates the database. The input of this step is the current online status of members, and the output is the updated database contents.

[0361] Step 3: The server determines the non-busy time periods for members based on the collected data. Using Python's Pandas library, it analyzes members' schedule data and finds free time periods. Specifically, it determines the time periods that do not include events such as "meetings" or "work" as "non-busy time periods." The input for this step is the members' schedule data, and the output is the determined "non-busy time periods."

[0362] Step 4: The server generates a program (bot) to facilitate communication and has it join the specified communication area. The bot is generated using Node.js and the Bot Framework, and joins a chat room such as Slack or Microsoft Teams. Specifically, the bot joins a "chat room" and posts an initial greeting message. The input to this step is an instance of the generated bot, and the output is the bot that has joined the chat room.

[0363] Step 5: The bot presents questions and topics to members at the specified times. A generative AI model (e.g., OpenAI's GPT-3) is used to generate natural dialogue. The prompt sentence is "How was your weekend?" and the generated message is sent to the member. The input for this step is the prompt sentence, and the output is the generated question or topic message.

[0364] Step 6: The bot uses an emotion recognition engine to analyze the user's message and recognize emotions. It uses Google Cloud Natural Language API or IBM Watson's NLP API to analyze the user's text message. Specifically, if the user replies "I'm a little tired," the emotion recognition engine detects "fatigue." The input for this step is the user's text message, and the output is the analyzed emotion data.

[0365] Step 7: The server collects user responses to the questions and topics provided by the bot and saves the communication history. Using Python and MySQL, the server records the user's messages and responses in a database. Specifically, it saves the conversation between users A and B as a log. The input to this step is the user's response data, and the output is the communication history saved in the database.

[0366] Step 8: The server adjusts the bot's calculation procedures based on the collected data. It analyzes the data and changes the settings to prioritize topics that users responded well to. It also adjusts responses taking into account the results of the emotion recognition engine. The input to this step is the collected communication history data and emotion data, and the output is the adjusted bot's calculation procedures.

[0367] Step 9: The server evaluates the system's performance and adds new topics and questions to the database. Specifically, based on the system's evaluation results, new topics such as "recently read books" and "favorite places" are added to the database, and the bot uses these topics to advance the conversation. The input to this step is the system's evaluation data, and the output is the new topics and questions that have been added.

[0368] (Application example 2)

[0369] 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."

[0370] The present invention aims to solve problems related to online communication between teams and individuals. Conventional systems are limited to providing standard questions and topics without considering the user's emotions, which can lead to low user satisfaction. Furthermore, even in systems that utilize emotion recognition technology, the analysis results are often not effectively utilized, making it difficult to provide optimal information to users. This leads to issues such as a decline in the quality of communication and a lack of effectiveness in business and experience.

[0371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0372] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a bot to promote communication and having it participate in a chat room, means for the bot to provide questions and topics to members, means for collecting user responses and saving chat histories, means for adjusting the bot algorithm based on the collected data, means for evaluating system performance and adding new topics and questions, means for analyzing user emotions and recommending products and information based on the analysis results, and means for promoting communication at optimal times based on the emotion analysis results. This enables flexible and effective information provision and communication that takes into account the user's emotional state.

[0373] "Means for registering team member information in a database" refers to the ability to store information such as each team member's name, job title, schedule, and online status in a database.

[0374] "Means of monitoring members' online status in real time" refers to the ability to constantly check the online status of team members and update that status with the latest information.

[0375] "Means for determining when members are not busy" refers to a function that automatically identifies when team members are not busy based on their schedules and activities.

[0376] "A means of generating bots that promote communication and having them participate in chat rooms" refers to the function of creating bots that automatically engage in conversations and have them participate in designated chat rooms in order to stimulate communication within a team.

[0377] "A means for the bot to provide questions and topics to members" refers to the function whereby the generated bot provides questions and topics to team members at appropriate times, promoting interaction.

[0378] "Means for collecting user responses and saving chat history" refers to a function for collecting team members' responses to chats and saving the content in a database.

[0379] "Means for adjusting the bot's algorithm based on collected data" refers to a function for analyzing saved chat data to optimize the bot's dialogue algorithm.

[0380] "Means for evaluating the system's performance and adding new topics and questions" refers to the ability to evaluate the system's functionality and effectiveness and, if necessary, add new topics and questions to improve its performance.

[0381] "Means of analyzing user emotions and recommending products and information based on the analysis results" refers to a function that uses emotion recognition technology to analyze user emotions and recommend appropriate products and information based on the results.

[0382] "Means to promote communication at the optimal time based on the results of emotion analysis" refers to a function that provides questions and topics at the most effective time based on the results of emotion analysis of the user, thereby promoting communication.

[0383] The following describes in detail the mode for carrying out the present invention. The present invention is a system that uses a smartphone application to promote communication within a team or in a virtual store and combines it with an emotion engine that analyzes user emotions. This system combines the elements of a server, terminals, and users to provide optimal information and communication.

[0384] 1. Server settings

[0385] The server is the centralized center for all data and processes. It registers team member and user information in a database and monitors their online status in real time. It also utilizes an emotion engine and generative AI model to analyze user messages and provide appropriate information.

[0386] 2.Register team member information

[0387] The server registers information such as team member names, job titles, schedules, and online status in a database, allowing the status of each member to be monitored in real time.

[0388] 3. Monitor your online status

[0389] The server periodically checks the online status of all members and updates the database based on this information, allowing you to know exactly when members are available.

[0390] 4. Determining the right timing

[0391] The server uses the collected data to determine when members are less busy, for example, when they are not tied down with meetings or other tasks.

[0392] 5. Creating a bot and joining a chat room

[0393] The server generates a bot to facilitate communication and invites it into a designated chat room. The bot stimulates interaction among team members through casual conversation and questions.

[0394] 6. Ask questions and share topics

[0395] The generated bot will then prompt members with questions and topics at the appropriate time, such as "Good morning everyone. How was your weekend?"

[0396] 7. Emotion analysis using an emotion engine

[0397] The bot uses an emotion engine to analyze emotions from users' text messages. The emotion engine analyzes messages sent by users and recognizes, for example, a message such as "I'm a little tired" as "tired."

[0398] 8.Collecting and analyzing user responses

[0399] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[0400] 9. Algorithm Adjustments

[0401] The server adjusts the bot's algorithm based on the collected data. If a particular topic generates a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[0402] 10. Add new topics or questions

[0403] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database to improve communication effectiveness.

[0404] 11. User sentiment analysis and product recommendation

[0405] The server analyzes the user's emotions and recommends products and information based on the analysis results. For example, if the server determines that the user is feeling a little depressed, it will recommend products that will lift the user's spirits.

[0406] 12. Promoting communication based on emotion analysis results

[0407] Based on the results of sentiment analysis, questions and topics are presented at the optimal time to promote effective communication. For example, if the user has a positive reaction, an additional comment expressing gratitude is provided.

[0408] Specific examples

[0409] For example, if a user types, "I haven't been feeling well lately," the emotion engine will recognize this as "tired" or "depressed," and the bot will ask, "Would you like to see your favorite products?" and recommend products that will help refresh them.

[0410] Prompt Sentence Examples

[0411] Design an application that performs sentiment analysis on text entered by a user and recommends appropriate products.

[0412] If the emotional state is "happy", recommend a product from product list A.

[0413] If the emotional state is "neutral," recommend a product from product list B.

[0414] If the emotional state is "sad", recommend a product from product list C.

[0415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0416] Step 1:

[0417] The server registers team member information in a database. Specifically, it receives individual information such as team member name, job title, schedule, and online status as input data and saves it in the database. This process centralizes the management of each member's basic information.

[0418] Step 2:

[0419] The server monitors the online status of team members in real time at regular intervals. It receives online status check requests as input data, obtains the current online status of each member, and updates the database. This process ensures that the latest online status is always available.

[0420] Step 3:

[0421] The server determines when members are not busy. It receives the members' schedule information as input, executes logic to determine "not busy times" based on that information, and saves the output in a database. This process allows the optimal communication timing for each member to be determined.

[0422] Step 4:

[0423] The server generates a bot to facilitate communication and has it join the specified chat room. It receives a bot creation request as input, creates a corresponding bot, and has it join the chat room. Through this process, the bot plays the role of facilitating communication.

[0424] Step 5:

[0425] The server then has the generated bot provide questions and topics at the appropriate time. It references the user's online status and quiet times, sends a request to the bot to generate questions and topics, and posts the results in the chat room. This process promotes natural communication.

[0426] Step 6:

[0427] The server collects user responses to questions and topics posed by the bot, stores the chat history, and takes user replies as input data and stores them in a database. This processing allows for future data analysis and trend identification.

[0428] Step 7:

[0429] The server adjusts the bot's algorithm based on the collected data. It uses the collected chat history as input data to evaluate and optimize the algorithm's performance. This process improves the quality of the bot and enables more effective communication.

[0430] Step 8:

[0431] The server evaluates the system's performance and adds new topics and questions. It uses the performance data for system evaluation as input, executes new topic and question generation requests based on the evaluation results, and stores them in the database. This process allows users to receive the latest and most relevant topics.

[0432] Step 9:

[0433] The server analyzes the text entered by the user using an emotion engine and recommends products and information based on the analysis results. The server receives the user's message content as input data, analyzes it using the emotion engine, executes product recommendation logic based on the analysis results, and outputs the most suitable products and information. This process allows for recommendations that are individually customized for the user.

[0434] Step 10:

[0435] The server promotes communication at the optimal timing based on the results of emotion analysis. It receives the emotion analysis results as input, executes logic to optimize the timing of communication based on those results, and instructs the bot on the results. This process enables questions and topics to be asked at the optimal timing according to the user's emotional state.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Second embodiment]

[0440] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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."

[0452] The present invention is directed to a system designed to promote communication within a team and improve business results. The system includes a server, a terminal, and a user.

[0453] Overall system configuration

[0454] 1. Server configuration

[0455] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[0456] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[0457] 2. Monitor your online status

[0458] The server monitors the online status of members in real time. For example, the server periodically checks the online status of member A and uses chat activity and calendar events as metrics to determine whether they are busy.

[0459] 3. Determine the less busy times

[0460] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[0461] 4. Creating a bot and joining a chat room

[0462] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[0463] 5. Asking questions and providing topics

[0464] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[0465] 6. Collecting and analyzing user responses

[0466] The server collects user responses to questions and topics provided by the bot and saves the chat history. For example, if member A replies, "I spent the weekend with my family," the server saves this comment for later analysis.

[0467] 7. Algorithm Adjustments

[0468] The server adjusts the bot's algorithm based on the collected data, optimizing the timing and topic selection for future chats. For example, if member A often responds positively to a particular topic, the server will adjust the bot to cover that topic more frequently.

[0469] Specific examples

[0470] Example 1: Monday Chat

[0471] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened each weekend, and a natural conversation begins.

[0472] Example 2: Project progress check

[0473] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[0474] The above is a specific embodiment of the present invention. This system promotes timely communication, improving team cohesion and productivity.

[0475] The processing flow will be explained below.

[0476] Step 1:

[0477] The server stores team member information in a database, including each member's name, job title, schedule, and online status.

[0478] Step 2:

[0479] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[0480] Step 3:

[0481] The server determines when members are free by looking at calendar events and chat activity, for example by looking at the times when a particular member is free and marking them as "free."

[0482] Step 4:

[0483] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to the specified chat room.

[0484] Step 5:

[0485] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss. For example, it might ask, "How was your weekend?"

[0486] Step 6:

[0487] Users respond to the bot's questions. For example, User A might reply, "I had a great time with my family," and User B might respond, "I went to watch a sports game."

[0488] Step 7:

[0489] The server collects user responses and stores the chat history for future reference and analysis.

[0490] Step 8:

[0491] The server adjusts the bot's algorithm based on the collected data: for example, if a particular topic gets a good response, it changes the settings to throw more of that topic at it.

[0492] Step 9:

[0493] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[0494] Step 10:

[0495] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[0496] Example 1

[0497] 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."

[0498] In today's work environment, effective communication between team members is often lacking. This lack of communication leads to delays in information sharing and misunderstandings, resulting in reduced productivity. Especially with the increasing trend toward online work, casual but important communication such as daily chats and progress checks is declining. This calls for an effective system that monitors online status in real time and promotes timely communication.

[0499] 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.

[0500] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program that promotes dialogue and having members participate in a dialogue environment, means for the program to provide questions and topics to members, means for collecting user responses and saving a dialogue history, means for adjusting the operation of the program based on the collected data, means for evaluating system performance and adding new topics and questions, and means for running the system in a cloud computing environment, which promotes communication at appropriate times and makes it possible to improve team cohesion and productivity.

[0501] "Team members" refers to a set of individuals working together to achieve a goal.

[0502] "Database" refers to a system for efficiently storing, retrieving, and managing structured information in digital form.

[0503] "Online status" refers to a state that indicates in real time whether a user is connected to the Internet and is active.

[0504] "Real-time" refers to processing or communication occurring immediately without delay.

[0505] An "interactive environment" refers to a virtual or physical space in which users communicate.

[0506] A "program" refers to a set of instructions designed to perform a particular function.

[0507] "Questions and Topics" refers to topics and questions provided to stimulate communication.

[0508] "User" refers to an individual or organization that uses the system or service.

[0509] "Reaction" refers to the response or feedback a user gives to a question or topic.

[0510] "Dialogue history" refers to a record of previous communications.

[0511] "Adjusting behavior" refers to changing algorithms or settings to improve system performance.

[0512] A "cloud computing environment" refers to an environment in which computing resources and services are used via the Internet.

[0513] The present invention provides a system for promoting communication within a team and improving business results. The system includes a server, a terminal, and a user.

[0514] Server Configuration

[0515] The server acts as a central point for managing all data and processes. It uses a database management system (e.g., MySQL) to register team member information in a database. A server running on the cloud (e.g., AWS EC2) is used. The server then monitors the online status of team members in real time. Specifically, the server regularly monitors users' chat activity and calendar appointments, and updates their online status accordingly.

[0516] Registering user information and monitoring online status

[0517] Users log in to the system using a dedicated terminal, and the server saves information such as the user's name, job title, schedule, and online status in the database. The server saves the information by executing an SQL statement such as INSERT INTO users. The server also monitors the online status in real time by executing the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to obtain recent activity.

[0518] Determining non-busy times

[0519] The server determines non-busy times based on the user's schedule information and chat activity. It lists available time slots by calling the calendar API and checking the busy status. For example, if member A has no plans between 10:00 and 11:00 AM, the server determines this time slot as a "non-busy time slot."

[0520] Creating a bot and joining a chat room

[0521] The server creates a dedicated bot to facilitate communication between users. The created bot automatically joins the configured chat room. The server executes bot = BotFactory.createBot(), and the created bot calls bot.joinChatroom('general') to join the chat room.

[0522] Asking questions and providing topics

[0523] The bot provides users with questions and topics based on pre-defined timing and conditions. For example, the bot might send the message "Good morning everyone. How was your weekend?" at 10:00 AM. A concrete example would be the bot executing bot.sendMessage(chatroom='general', message='How was your weekend?').

[0524] Collecting and analyzing user responses

[0525] The server collects user reactions to questions and topics provided by the bot and stores them in a database. For example, the server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) to store the reactions.

[0526] Bot algorithm adjustments

[0527] The server adjusts the bot's algorithm based on the collected data to optimize future questions and topics. The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[0528] Specific examples

[0529] Chat every Monday: The server checks the online status of members every Monday at 9:00 AM, and the bot posts to the chat room, "Good morning everyone. How was your weekend?" User A replies, "I went on a picnic with my family," and the server saves this in the database.

[0530] Project progress check: The server checks the user's online status every day at 3:00 PM, and the bot asks, "How is the current project progressing?" User B answers, "I'm a little behind," which the server collects and uses for analysis.

[0531] Example prompts for generative AI models

[0532] "Create a bot message at 9am on Monday morning to check your team's online status and ask how their weekend is going."

[0533] "Send a bot message every day at 3 PM to remind you to check in on your project progress."

[0534] This promotes timely communication and improves team cohesion and productivity.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1:

[0537] Starting and initializing the server

[0538] The server starts the server machine, connects to the database management system (e.g. MySQL), reads the configuration files (e.g. config files, API keys, etc.) and performs any necessary initial configuration.

[0539] Input: Server startup request, configuration file

[0540] Output: Server environment with initial settings completed

[0541] Specific operation: The server reads the config.yaml file and sets the database connection information.

[0542] Step 2:

[0543] Registering user information

[0544] Users log in to the system using a dedicated terminal, which sends the user's basic information to the server, which then registers the information in a database.

[0545] Input: User login information (name, job title, schedule, online status)

[0546] Output: User information registered in the database

[0547] Specific operation: When a user fills in the login form and clicks the "Login" button, the terminal sends an API request, and the server executes the SQL statement INSERT INTO users to save the information.

[0548] Step 3:

[0549] Real-time online status monitoring

[0550] The server checks each user's online status at a specified interval (e.g., every minute), looking at chat activity and calendar appointments.

[0551] Input: User activity data, calendar data

[0552] Output: Latest online status

[0553] Specific operation: The server executes the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to get the most recent activity.

[0554] Step 4:

[0555] Determining non-busy times

[0556] The server determines the times when the user is not busy based on the collected schedule information and chat activity.

[0557] Input: Schedule information and activity data from the Calendar API

[0558] Output: A list of available time slots for each user

[0559] Specific operation: Calls the calendar API to check the busy status and lists available time slots.

[0560] Step 5:

[0561] Creating a bot and joining a chat room

[0562] The server generates a dedicated bot to promote communication between users and has it participate in a set chat room.

[0563] Input: bot generated request

[0564] Output: Bots that joined the chat room

[0565] Specific operation: The server executes bot = BotFactory.createBot() and calls bot.joinChatroom('general') to join the chat room.

[0566] Step 6:

[0567] Asking questions and providing topics

[0568] The bot provides questions and topics to users based on set timing and conditions.

[0569] Input: Pre-set prompt, current date and time

[0570] Output: Questions and topics posted in the chat room

[0571] Specific behavior: The bot executes bot.sendMessage(chatroom='general', message='How was your weekend?').

[0572] Step 7:

[0573] Collecting and analyzing user responses

[0574] The server collects user responses to questions and topics provided by the bot and stores them in a database.

[0575] Input: User's chat message

[0576] Output: Reaction data stored in a database

[0577] Specific operation: The server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) and saves the reactions.

[0578] Step 8:

[0579] Bot algorithm adjustments

[0580] The server adjusts the bot's algorithm based on the collected data to optimize questions and topics for future questions.

[0581] Input: Collected reaction data, analysis results

[0582] Output: Adjusted bot algorithm

[0583] Specific operation: The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[0584] (Application example 1)

[0585] 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."

[0586] The goal is to eliminate the decline in work efficiency and lack of understanding caused by a lack of communication within a team. In particular, in content distribution services, it is often the case that project progress is not checked smoothly or ideas are not shared smoothly, which can have a negative impact on the quality of deliverables and delivery dates. In addition, as more work is done online, it is necessary to understand the status of team members in real time and promote communication at the appropriate time.

[0587] 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.

[0588] In this invention, the server includes means for registering information of team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating an autoresponder and having it participate in a conversation room, means for the autoresponder to provide questions and topics to members, means for collecting user responses and saving the conversation history, means for adjusting the autoresponder algorithm based on the collected data, and means for evaluating system performance and adding new topics and questions, thereby promoting communication within the team and enabling improved work efficiency and smoother work.

[0589] A "team member" is an individual registered in the system, and is a member of a group who works with their own role.

[0590] A "database" is a collection of data that organizes, stores, accesses, and manages information about team members.

[0591] "Online status" indicates whether a team member is currently online or offline.

[0592] "Real-time" means that data and information are updated and reflected immediately, without delay.

[0593] "Quiet times" are times when team members are available for other instructions or communication given their current schedules and activities.

[0594] An "automatic responder" is a virtual questioner or interlocutor generated by the system, and has the function of facilitating communication with team members.

[0595] A "chat room" is a virtual place where team members can gather and communicate via text and voice.

[0596] "Questions and topics" are conversation starters that the autoresponder provides to team members.

[0597] "User responses" are the responses and reactions that team members give to questions and topics posed by the autoresponder.

[0598] "Conversation history" is a record of conversations and chats between team members.

[0599] "Adjusting the algorithm" means processing the collected data to optimize the operation of the automated responder and promote more effective communication.

[0600] "System performance" is an index that evaluates the effectiveness of overall operation and communication, and indicates how efficient the system is.

[0601] "New topics and questions" are content that the system generates additionally and that can trigger new dialogue with the user.

[0602] MODE FOR CARRYING OUT THE INVENTION

[0603] The following describes in detail the mode for carrying out the present invention. The present invention is a system for promoting communication within a team and improving work efficiency. This system includes elements of a server, a terminal, and a user, and realizes effective communication using an automatic responder.

[0604] 1. Server configuration

[0605] The server is the central point that manages all data and processes, and provides the following means:

[0606] A means of registering team member information in a database

[0607] A way to monitor members' online status in real time

[0608] A way to determine when members are not busy

[0609] The server stores the names, titles, schedules, and online status of members A and B in a database, allowing the server to constantly monitor the status of members and determine the appropriate timing for communication.

[0610] 2. Creating and joining an autoresponder

[0611] To promote team communication, the server has a means of generating auto-responders and having them join conversation rooms. The auto-responders provide questions and topics to members at appropriate times. For example, the server might have an auto-responder join a "chat room" at 10 a.m. and ask a question such as, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[0612] 3. Collecting user responses and adjusting the algorithm

[0613] The server has a means to collect user responses to questions and topics provided by the autoresponder and store the conversation history. For example, if member A replies, "I spent the weekend with my family," the server stores this comment for later analysis.

[0614] The server then uses the collected data to adjust the autoresponder algorithm and add new topics and questions to optimize future communications. By repeating this process, the server evaluates the system's performance and improves its overall effectiveness.

[0615] Specific examples

[0616] Example 1: Monday Chat

[0617] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the autoresponder posts a message to the chat room saying, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened on their respective weekends, and a natural conversation begins.

[0618] Example 2: Project progress check

[0619] The server checks the online status of members every day at 3:00 p.m. If it determines that a member is not busy, the autoresponder asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[0620] Hardware and software used

[0621] Specifically, the following hardware and software are used:

[0622] Server (Linux or Windows server)

[0623] A database management system (MySQL, PostgreSQL, or MongoDB)

[0624] Chatbot building framework (Rasa, Dialogflow, etc.)

[0625] Python scripts for data collection and analysis

[0626] Prompt Sentence Examples

[0627] prompt:

[0628] Create a bot that facilitates communication within your team by prompting team members with questions and topics at the right time to facilitate smooth communication.

[0629] Required features:

[0630] 1. Monitor members' online status

[0631] 2. Determine the less busy times

[0632] 3. Create a bot and join the chat room

[0633] 4. Asking questions and providing topics

[0634] 5. Collecting and storing user responses

[0635] Output format:

[0636] 1. Class Blueprints

[0637] 2. Code for each class

[0638] Product usage:

[0639] An application that promotes communication among content production teams

[0640] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0641] Processing steps of the system that realizes the application example

[0642] Step 1:

[0643] The server registers the team member information in a database.

[0644] Input: Member A, Member B names, titles, schedules, online status

[0645] Data processing / calculation: Writing member information to the database

[0646] Output: Complete member information stored in the database.

[0647] Step 2:

[0648] The server monitors the online status of members in real time.

[0649] Input: Member's schedule and current time

[0650] Data processing / calculation: Comparing the current time with the member's schedule and determining their online status

[0651] Output: Member's online status is updated as "online" or "offline".

[0652] Step 3:

[0653] The server determines when members are not busy.

[0654] Input: Member schedule, online status, chat activity

[0655] Data processing / calculation: Predicting the next quietest time slot based on schedules and activities

[0656] Output: The quiet times are identified and stored.

[0657] Step 4:

[0658] The server generates autoresponders to participate in conversation rooms to facilitate communication.

[0659] Input: Off-peak hours

[0660] Data processing / calculation: Creating an instance of an autoresponder and adding it to a specified conversation room

[0661] Output: The autoresponder joins the conversation room.

[0662] Step 5:

[0663] The autoresponder provides members with questions and topics at the appropriate time.

[0664] Input: Member's online status and off-peak hours

[0665] Data processing / calculation: Randomly selecting questions from a pre-defined list and posting them to the chat room

[0666] Output: The question or topic is displayed in the conversation room.

[0667] Step 6:

[0668] The server collects user responses and stores the conversation history.

[0669] Input: Member response

[0670] Data processing / calculation: Analyzing the response content and adding it to the conversation history

[0671] Output: A new entry is added to the conversation history.

[0672] Step 7:

[0673] The server uses the collected data to adjust the autoresponder's algorithm and add new topics and questions.

[0674] Input: Stored conversation history and user reaction data

[0675] Data processing / calculation: Analyzing reaction data and optimizing the algorithm of the automatic responder

[0676] Output: An updated list of topics and questions provided by the autoresponder.

[0677] Step 8:

[0678] The server evaluates the system's performance and generates new topics and questions to promote effective communication.

[0679] Input: System log data, conversation history, user response data

[0680] Data processing / calculation: The process of integrating data, calculating performance indicators, and generating more effective questions and topics.

[0681] Output: New topics or questions are added to the algorithm's configuration.

[0682] 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.

[0683] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion engine that recognizes the emotions of users. This system includes elements of a server, a terminal, and a user.

[0684] Overall system configuration

[0685] 1. Server configuration

[0686] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[0687] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[0688] 2. Monitor your online status

[0689] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[0690] 3. Determine the less busy times

[0691] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[0692] 4. Creating a bot and joining a chat room

[0693] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[0694] 5. Asking questions and providing topics

[0695] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[0696] 6. Emotion Recognition by Emotion Engine

[0697] The bot uses an emotion engine to analyze emotions from users' text messages. For example, if User A replies "I'm a little tired," the emotion engine analyzes this message and recognizes that User A is tired.

[0698] 7. Collecting and analyzing user responses

[0699] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[0700] 8. Algorithm Adjustments

[0701] The server adjusts the bot's algorithm based on the collected data. For example, if a particular topic receives a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[0702] 9. Adding new topics and questions

[0703] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[0704] Specific examples

[0705] Example 1: Monday Chat

[0706] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning everyone. How was your weekend?". As Member A and Member B start sharing what happened over the weekend, a natural conversation begins. At the same time, the emotion engine analyzes the members' messages and understands their emotional state.

[0707] Example 2: Project progress check

[0708] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A responds, "It's going well," while member B continues, "It's a little behind schedule," creating an opportunity for regular progress checks. The emotion engine analyzes these messages and recognizes that member A's answers are positive, while member B's answers are partly negative.

[0709] In this way, the system based on the present invention can further improve team cohesion and productivity by promoting communication at the right time and providing responses that take into account the user's emotions.

[0710] The processing flow will be explained below.

[0711] Step 1:

[0712] The server stores team member information in a database, including each member's name, job title, schedule, and online status. For example, member A's name, job title "Engineer," and working hours are stored in the database.

[0713] Step 2:

[0714] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database. For example, if member A becomes online at 9:00 a.m., that information is immediately updated in the database.

[0715] Step 3:

[0716] The server determines when a member is not busy by looking at calendar appointments and chat activity. For example, since member A has no appointments between 10:00 and 11:00 AM, the server determines this time period as a "non-busy time period."

[0717] Step 4:

[0718] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to a specified chat room. For example, the bot joins a "chat room."

[0719] Step 5:

[0720] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss, such as, "How was your weekend?"

[0721] Step 6:

[0722] Users respond to the bot's questions. For example, User A replies, "I had a great time with my family," and User B follows with, "I went to watch a sports game." The bot responds with an appropriate response.

[0723] Step 7:

[0724] The emotion engine analyzes emotions from user text messages. For example, it analyzes the message "I'm a little tired" from user A and recognizes the emotional state as "fatigue."

[0725] Step 8:

[0726] The bot's response is adjusted based on the results of the emotion engine. For example, if user A says "I'm tired," the bot will respond by saying "Take care and rest."

[0727] Step 9:

[0728] The server collects user responses and stores the chat history for future reference and analysis. For example, the responses of User A and User B are stored in a database.

[0729] Step 10:

[0730] The server uses the collected data to adjust the bot's algorithms: if a particular topic is well-received, it changes its settings to target that topic more frequently.

[0731] Step 11:

[0732] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database based on the evaluation results to improve communication effectiveness.

[0733] Step 12:

[0734] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[0735] Example 2

[0736] 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."

[0737] Modern teams require more efficient communication and interactions that take into account the emotional state of members, but existing tools and systems do not adequately address these needs. While basic functions such as monitoring online status and determining when members are not busy are provided, they lack the ability to promote effective communication or recognize emotions. This leads to a decline in team cohesion and productivity, and a lack of work efficiency.

[0738] 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.

[0739] In this invention, the server includes means for registering information about team members in a storage device, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program to promote communication and for allowing members to participate in the communication area, means for the program to provide questions and topics to members, means for collecting user responses and saving communication history, means for adjusting the program's operation procedure based on the collected data, means for evaluating system performance and adding new topics and questions, and means for analyzing user emotions using an emotion recognition engine. This enables effective communication that takes into account the emotional states of members, thereby improving team cohesion and productivity.

[0740] "Team member information" refers to data such as a user's name, title, schedule, and online status.

[0741] "Storage device" refers to hardware or software for storing data.

[0742] "Online state" refers to a state in which a user is connected to a network.

[0743] "Real-time monitoring" refers to instantly checking and updating the current situation and status.

[0744] "Off-peak hours" refers to times when the user is not tied down to a specific task or schedule.

[0745] "Programs that facilitate communication" refers to software designed to support and stimulate interaction between users.

[0746] A "communication realm" refers to a virtual or physical space in which users exchange information.

[0747] "The program provides questions or topics to members" means that the software presents specific topics or questions to users.

[0748] "Collecting user responses and saving communication history" refers to recording the actions and comments made by the user and making them available for later reference.

[0749] "Adjusting the program's operating procedures based on collected data" refers to analyzing stored data and changing the software's behavior based on the results.

[0750] "Evaluate the system's performance and add new topics and questions" refers to evaluating the system's operation and introducing new topics and questions as a way to improve it.

[0751] An "emotion recognition engine" refers to software that analyzes text and voice to estimate a user's emotions.

[0752] "Analyzing the user's emotions" refers to determining the user's emotional state based on input data.

[0753] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion recognition engine that recognizes the emotions of a user. The system includes elements of a server, a terminal, and a user.

[0754] Overall system configuration

[0755] The server first sets up a database and registers team member information. This registration is done using Python and a MySQL database. Specifically, the server stores user names, job titles, schedules, and online status in the database. This allows the server to centrally manage all data and processes and constantly monitor the status of team members.

[0756] For example, the server stores in the database the name of member A as "Yamada Taro," his position as "team leader," his schedule as "meeting from 10:00 AM to 11:00 AM," and his online status as "online."

[0757] The server runs a script to check the online status of members at regular intervals and updates the results to the database. Here, we will create a monitoring script using Python. The server checks which members are currently online and updates the results to the database.

[0758] The server also uses the collected data to determine when a member is not busy. This determination is made using the Python Pandas library. For example, the server checks member A's schedule and determines that he has no appointments between 10:00 AM and 11:00 AM, and determines this time period as a "non-busy time period."

[0759] The server then generates a program (here called a bot) that facilitates communication and has it participate in the specified chat room. This bot is generated using Node.js and the Bot Framework, and the bot participates in communication areas such as Slack and Microsoft Teams.

[0760] For example, the server creates a bot that participates in a "chat room" and has the bot send the message "Good morning. How are you today?"

[0761] The bot prompts members with questions and topics at appropriate times, using generative AI models (such as OpenAI's GPT-3) to generate natural-sounding dialogue. For example, at 10 a.m., the bot posts to the chat room, "Good morning, everyone. How was your weekend?" The following is an example of a prompt:

[0762] "How was your weekend?"

[0763] The bot uses an emotion recognition engine to analyze users' text messages and recognize their emotions. It uses Google Cloud Natural Language API and IBM Watson's NLP API. For example, if User A replies, "I'm a little tired," the emotion recognition engine will tag this message as "fatigue" and understand User A's emotional state.

[0764] The server collects user responses to questions and topics provided by the bot and stores the communication history. It uses Python and MySQL to store the chat history in a database. For example, it logs the conversation between users A and B and evaluates their responses.

[0765] The server uses the collected data to adjust the bot's algorithms. Based on the topics that generated the most responses, it changes its settings to offer more new questions and topics. In addition, based on the results of an emotion recognition engine, it adjusts its responses to match the user's emotional state.

[0766] Finally, the server evaluates the system's performance and adds new topics and questions, further improving the effectiveness of communication. For example, the server adds new topics such as "recently read books" and "favorite places" to the database, and the bot uses these topics to advance the conversation.

[0767] Through this system, the server, terminals, and users cooperate to realize more effective and emotionally sensitive communication, improving team cohesion and productivity.

[0768] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0769] Step 1: The server registers team member information in a storage device. Specifically, the server stores the user's (e.g., "Yamada Taro") name, job title ("Team Leader"), schedule ("Meeting from 10:00 to 11:00"), and online status ("Online") in the database. The input of this step is user information, and the output is the user information stored in the database.

[0770] Step 2: The server monitors the online status of members in real time at regular intervals. It runs a Python script to check the current online status and update the database. Specifically, the script checks the online status of members and updates the database. The input of this step is the current online status of members, and the output is the updated database contents.

[0771] Step 3: The server determines the non-busy time periods for members based on the collected data. Using Python's Pandas library, it analyzes members' schedule data and finds free time periods. Specifically, it determines the time periods that do not include events such as "meetings" or "work" as "non-busy time periods." The input for this step is the members' schedule data, and the output is the determined "non-busy time periods."

[0772] Step 4: The server generates a program (bot) to facilitate communication and has it join the specified communication area. The bot is generated using Node.js and the Bot Framework, and joins a chat room such as Slack or Microsoft Teams. Specifically, the bot joins a "chat room" and posts an initial greeting message. The input to this step is an instance of the generated bot, and the output is the bot that has joined the chat room.

[0773] Step 5: The bot presents questions and topics to members at the specified times. A generative AI model (e.g., OpenAI's GPT-3) is used to generate natural dialogue. The prompt sentence is "How was your weekend?" and the generated message is sent to the member. The input for this step is the prompt sentence, and the output is the generated question or topic message.

[0774] Step 6: The bot uses an emotion recognition engine to analyze the user's message and recognize emotions. It uses Google Cloud Natural Language API or IBM Watson's NLP API to analyze the user's text message. Specifically, if the user replies "I'm a little tired," the emotion recognition engine detects "fatigue." The input for this step is the user's text message, and the output is the analyzed emotion data.

[0775] Step 7: The server collects user responses to the questions and topics provided by the bot and saves the communication history. Using Python and MySQL, the server records the user's messages and responses in a database. Specifically, it saves the conversation between users A and B as a log. The input to this step is the user's response data, and the output is the communication history saved in the database.

[0776] Step 8: The server adjusts the bot's calculation procedures based on the collected data. It analyzes the data and changes the settings to prioritize topics that users responded well to. It also adjusts responses taking into account the results of the emotion recognition engine. The input to this step is the collected communication history data and emotion data, and the output is the adjusted bot's calculation procedures.

[0777] Step 9: The server evaluates the system's performance and adds new topics and questions to the database. Specifically, based on the system's evaluation results, new topics such as "recently read books" and "favorite places" are added to the database, and the bot uses these topics to advance the conversation. The input to this step is the system's evaluation data, and the output is the new topics and questions that have been added.

[0778] (Application example 2)

[0779] 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."

[0780] The present invention aims to solve problems related to online communication between teams and individuals. Conventional systems are limited to providing standard questions and topics without considering the user's emotions, which can lead to low user satisfaction. Furthermore, even in systems that utilize emotion recognition technology, the analysis results are often not effectively utilized, making it difficult to provide optimal information to users. This leads to issues such as a decline in the quality of communication and a lack of effectiveness in business and experience.

[0781] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0782] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a bot to promote communication and having it participate in a chat room, means for the bot to provide questions and topics to members, means for collecting user responses and saving chat histories, means for adjusting the bot algorithm based on the collected data, means for evaluating system performance and adding new topics and questions, means for analyzing user emotions and recommending products and information based on the analysis results, and means for promoting communication at optimal times based on the emotion analysis results. This enables flexible and effective information provision and communication that takes into account the user's emotional state.

[0783] "Means for registering team member information in a database" refers to the ability to store information such as each team member's name, job title, schedule, and online status in a database.

[0784] "Means of monitoring members' online status in real time" refers to the ability to constantly check the online status of team members and update that status with the latest information.

[0785] "Means for determining when members are not busy" refers to a function that automatically identifies when team members are not busy based on their schedules and activities.

[0786] "A means of generating bots that promote communication and having them participate in chat rooms" refers to the function of creating bots that automatically engage in conversations and have them participate in designated chat rooms in order to stimulate communication within a team.

[0787] "A means for the bot to provide questions and topics to members" refers to the function whereby the generated bot provides questions and topics to team members at appropriate times, promoting interaction.

[0788] "Means for collecting user responses and saving chat history" refers to a function for collecting team members' responses to chats and saving the content in a database.

[0789] "Means for adjusting the bot's algorithm based on collected data" refers to a function for analyzing saved chat data to optimize the bot's dialogue algorithm.

[0790] "Means for evaluating the system's performance and adding new topics and questions" refers to the ability to evaluate the system's functionality and effectiveness and, if necessary, add new topics and questions to improve its performance.

[0791] "Means of analyzing user emotions and recommending products and information based on the analysis results" refers to a function that uses emotion recognition technology to analyze user emotions and recommend appropriate products and information based on the results.

[0792] "Means to promote communication at the optimal time based on the results of emotion analysis" refers to a function that provides questions and topics at the most effective time based on the results of emotion analysis of the user, thereby promoting communication.

[0793] The following describes in detail the mode for carrying out the present invention. The present invention is a system that uses a smartphone application to promote communication within a team or in a virtual store and combines it with an emotion engine that analyzes user emotions. This system combines the elements of a server, a terminal, and a user to provide optimal information and communication.

[0794] 1. Server configuration

[0795] The server is the centralized center for all data and processes. It registers team member and user information in a database and monitors their online status in real time. It also utilizes an emotion engine and generative AI model to analyze user messages and provide appropriate information.

[0796] 2.Register team member information

[0797] The server registers information such as team member names, job titles, schedules, and online status in a database, allowing the status of each member to be monitored in real time.

[0798] 3. Monitor your online status

[0799] The server periodically checks the online status of all members and updates the database based on this information, allowing you to know exactly when members are available.

[0800] 4. Determining the right timing

[0801] The server uses the collected data to determine when members are less busy, for example, when they are not tied down with meetings or other tasks.

[0802] 5. Creating a bot and joining a chat room

[0803] The server generates a bot to facilitate communication and invites it into a designated chat room. The bot stimulates interaction among team members through casual conversation and questions.

[0804] 6. Ask questions and share topics

[0805] The generated bot will then prompt members with questions and topics at the appropriate time, such as "Good morning everyone. How was your weekend?"

[0806] 7. Emotion analysis using an emotion engine

[0807] The bot uses an emotion engine to analyze emotions from users' text messages. The emotion engine analyzes messages sent by users and recognizes, for example, a message such as "I'm a little tired" as "tired."

[0808] 8.Collecting and analyzing user responses

[0809] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[0810] 9. Algorithm Adjustments

[0811] The server adjusts the bot's algorithm based on the collected data. If a particular topic generates a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[0812] 10. Add new topics or questions

[0813] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database to improve communication effectiveness.

[0814] 11. User sentiment analysis and product recommendation

[0815] The server analyzes the user's emotions and recommends products and information based on the analysis results. For example, if the server determines that the user is feeling a little depressed, it will recommend products that will lift the user's spirits.

[0816] 12. Promoting communication based on emotion analysis results

[0817] Based on the results of sentiment analysis, questions and topics are presented at the optimal time to promote effective communication. For example, if the user has a positive reaction, an additional comment expressing gratitude is provided.

[0818] Specific examples

[0819] For example, if a user types, "I haven't been feeling well lately," the emotion engine will recognize this as "tired" or "depressed," and the bot will ask, "Would you like to see your favorite products?" and recommend products that will help refresh them.

[0820] Prompt Sentence Examples

[0821] Design an application that performs sentiment analysis on user-entered text and recommends appropriate products.

[0822] If the emotional state is "happy", recommend a product from product list A.

[0823] If the emotional state is "neutral," recommend a product from product list B.

[0824] If the emotional state is "sad", recommend a product from product list C.

[0825] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0826] Step 1:

[0827] The server registers team member information in a database. Specifically, it receives individual information such as team member name, job title, schedule, and online status as input data and saves it in the database. This process centralizes the management of each member's basic information.

[0828] Step 2:

[0829] The server monitors the online status of team members in real time at regular intervals. It receives online status check requests as input data, obtains the current online status of each member, and updates the database. This process ensures that the latest online status is always available.

[0830] Step 3:

[0831] The server determines when members are not busy. It receives the members' schedule information as input, executes logic to determine "not busy times" based on that information, and saves the output in a database. This process allows the optimal communication timing for each member to be determined.

[0832] Step 4:

[0833] The server generates a bot to facilitate communication and has it join the specified chat room. It receives a bot creation request as input, creates a corresponding bot, and has it join the chat room. Through this process, the bot plays the role of facilitating communication.

[0834] Step 5:

[0835] The server then has the generated bot provide questions and topics at the appropriate time. It references the user's online status and quiet times, sends a request to the bot to generate questions and topics, and posts the results in the chat room. This process promotes natural communication.

[0836] Step 6:

[0837] The server collects user responses to questions and topics posed by the bot, stores the chat history, and takes user replies as input data and stores them in a database. This processing allows for future data analysis and trend identification.

[0838] Step 7:

[0839] The server adjusts the bot's algorithm based on the collected data. It uses the collected chat history as input data to evaluate and optimize the algorithm's performance. This process improves the quality of the bot and enables more effective communication.

[0840] Step 8:

[0841] The server evaluates the system's performance and adds new topics and questions. It uses the performance data for system evaluation as input, executes new topic and question generation requests based on the evaluation results, and stores them in the database. This process allows users to receive the latest and most relevant topics.

[0842] Step 9:

[0843] The server analyzes the text entered by the user using an emotion engine and recommends products and information based on the analysis results. The server receives the user's message content as input data, analyzes it using the emotion engine, executes product recommendation logic based on the analysis results, and outputs the most suitable products and information. This process allows for recommendations that are individually customized for the user.

[0844] Step 10:

[0845] The server promotes communication at the optimal timing based on the results of emotion analysis. It receives the emotion analysis results as input, executes logic to optimize the timing of communication based on those results, and instructs the bot on the results. This process enables questions and topics to be asked at the optimal timing according to the user's emotional state.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] [Third embodiment]

[0850] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0851] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0852] 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).

[0853] 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.

[0854] 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.

[0855] 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).

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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."

[0862] The present invention is directed to a system designed to promote communication within a team and improve business results. The system includes a server, a terminal, and a user.

[0863] Overall system configuration

[0864] 1. Server configuration

[0865] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[0866] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[0867] 2. Monitor your online status

[0868] The server monitors the online status of members in real time. For example, the server periodically checks the online status of member A and uses chat activity and calendar events as metrics to determine whether they are busy.

[0869] 3. Determine the less busy times

[0870] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[0871] 4. Creating a bot and joining a chat room

[0872] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[0873] 5. Asking questions and providing topics

[0874] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[0875] 6. Collecting and analyzing user responses

[0876] The server collects user responses to questions and topics provided by the bot and saves the chat history. For example, if member A replies, "I spent the weekend with my family," the server saves this comment for later analysis.

[0877] 7. Algorithm Adjustments

[0878] The server adjusts the bot's algorithm based on the collected data, optimizing the timing and topic selection for future chats. For example, if member A often responds positively to a particular topic, the server will adjust the bot to cover that topic more frequently.

[0879] Specific examples

[0880] Example 1: Monday Chat

[0881] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened each weekend, and a natural conversation begins.

[0882] Example 2: Project progress check

[0883] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[0884] The above is a specific embodiment of the present invention. This system promotes timely communication, improving team cohesion and productivity.

[0885] The processing flow will be explained below.

[0886] Step 1:

[0887] The server stores team member information in a database, including each member's name, job title, schedule, and online status.

[0888] Step 2:

[0889] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[0890] Step 3:

[0891] The server determines when members are free by looking at calendar events and chat activity, for example by looking at the times when a particular member is free and marking them as "free."

[0892] Step 4:

[0893] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to the specified chat room.

[0894] Step 5:

[0895] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss. For example, it might ask, "How was your weekend?"

[0896] Step 6:

[0897] Users respond to the bot's questions. For example, User A might reply, "I had a great time with my family," and User B might respond, "I went to watch a sports game."

[0898] Step 7:

[0899] The server collects user responses and stores the chat history for future reference and analysis.

[0900] Step 8:

[0901] The server adjusts the bot's algorithm based on the collected data: for example, if a particular topic gets a good response, it changes the settings to throw more of that topic at it.

[0902] Step 9:

[0903] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[0904] Step 10:

[0905] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[0906] Example 1

[0907] 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."

[0908] In today's work environment, effective communication between team members is often lacking. This lack of communication leads to delays in information sharing and misunderstandings, resulting in reduced productivity. Especially with the increasing trend toward online work, casual but important communication such as daily chats and progress checks is declining. This calls for an effective system that monitors online status in real time and promotes timely communication.

[0909] 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.

[0910] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program that promotes dialogue and having members participate in a dialogue environment, means for the program to provide questions and topics to members, means for collecting user responses and saving a dialogue history, means for adjusting the operation of the program based on the collected data, means for evaluating system performance and adding new topics and questions, and means for running the system in a cloud computing environment, which promotes communication at appropriate times and makes it possible to improve team cohesion and productivity.

[0911] "Team members" refers to a set of individuals working together to achieve a goal.

[0912] "Database" refers to a system for efficiently storing, retrieving, and managing structured information in digital form.

[0913] "Online status" refers to a state that indicates in real time whether a user is connected to the Internet and is active.

[0914] "Real-time" refers to processing or communication occurring immediately without delay.

[0915] An "interactive environment" refers to a virtual or physical space in which users communicate.

[0916] A "program" refers to a set of instructions designed to perform a particular function.

[0917] "Questions and Topics" refers to topics and questions provided to stimulate communication.

[0918] "User" refers to an individual or organization that uses the system or service.

[0919] "Reaction" refers to the response or feedback a user gives to a question or topic.

[0920] "Dialogue history" refers to a record of previous communications.

[0921] "Adjusting behavior" refers to changing algorithms or settings to improve system performance.

[0922] A "cloud computing environment" refers to an environment in which computing resources and services are used via the Internet.

[0923] The present invention provides a system for promoting communication within a team and improving business results. The system includes a server, a terminal, and a user.

[0924] Server Configuration

[0925] The server acts as a central point for managing all data and processes. It uses a database management system (e.g., MySQL) to register team member information in a database. A server running on the cloud (e.g., AWS EC2) is used. The server then monitors the online status of team members in real time. Specifically, the server regularly monitors users' chat activity and calendar appointments, and updates their online status accordingly.

[0926] Registering user information and monitoring online status

[0927] Users log in to the system using a dedicated terminal, and the server saves information such as the user's name, job title, schedule, and online status in the database. The server saves the information by executing an SQL statement such as INSERT INTO users. The server also monitors the online status in real time by executing the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to obtain recent activity.

[0928] Determining non-busy times

[0929] The server determines non-busy times based on the user's schedule information and chat activity. It lists available time slots by calling the calendar API and checking the busy status. For example, if member A has no plans between 10:00 and 11:00 AM, the server determines this time slot as a "non-busy time slot."

[0930] Creating a bot and joining a chat room

[0931] The server creates a dedicated bot to facilitate communication between users. The created bot automatically joins the configured chat room. The server executes bot = BotFactory.createBot(), and the created bot calls bot.joinChatroom('general') to join the chat room.

[0932] Asking questions and providing topics

[0933] The bot provides users with questions and topics based on pre-defined timing and conditions. For example, the bot might send the message "Good morning everyone. How was your weekend?" at 10:00 AM. A concrete example would be the bot executing bot.sendMessage(chatroom='general', message='How was your weekend?').

[0934] Collecting and analyzing user responses

[0935] The server collects user reactions to questions and topics provided by the bot and stores them in a database. For example, the server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) to store the reactions.

[0936] Bot algorithm adjustments

[0937] The server adjusts the bot's algorithm based on the collected data to optimize future questions and topics. The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[0938] Specific examples

[0939] Chat every Monday: The server checks the online status of members every Monday at 9:00 AM, and the bot posts to the chat room, "Good morning everyone. How was your weekend?" User A replies, "I went on a picnic with my family," and the server saves this in the database.

[0940] Project progress check: The server checks the user's online status every day at 3:00 PM, and the bot asks, "How is the current project progressing?" User B answers, "I'm a little behind," which the server collects and uses for analysis.

[0941] Example prompts for generative AI models

[0942] "Create a bot message at 9am on Monday morning to check your team's online status and ask how their weekend is going."

[0943] "Send a bot message every day at 3 PM to remind you to check in on your project progress."

[0944] This promotes timely communication and improves team cohesion and productivity.

[0945] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0946] Step 1:

[0947] Starting and initializing the server

[0948] The server starts the server machine, connects to the database management system (e.g. MySQL), reads the configuration files (e.g. config files, API keys, etc.) and performs any necessary initial configuration.

[0949] Input: Server startup request, configuration file

[0950] Output: Server environment with initial settings completed

[0951] Specific operation: The server reads the config.yaml file and sets the database connection information.

[0952] Step 2:

[0953] Registering user information

[0954] Users log in to the system using a dedicated terminal, which sends the user's basic information to the server, which then registers the information in a database.

[0955] Input: User login information (name, job title, schedule, online status)

[0956] Output: User information registered in the database

[0957] Specific operation: When a user fills in the login form and clicks the "Login" button, the terminal sends an API request, and the server executes the SQL statement INSERT INTO users to save the information.

[0958] Step 3:

[0959] Real-time online status monitoring

[0960] The server checks each user's online status at a specified interval (e.g., every minute), looking at chat activity and calendar appointments.

[0961] Input: User activity data, calendar data

[0962] Output: Latest online status

[0963] Specific operation: The server executes the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to get the most recent activity.

[0964] Step 4:

[0965] Determining non-busy times

[0966] The server determines the times when the user is not busy based on the collected schedule information and chat activity.

[0967] Input: Schedule information and activity data from the Calendar API

[0968] Output: A list of available time slots for each user

[0969] Specific operation: Calls the calendar API to check the busy status and lists available time slots.

[0970] Step 5:

[0971] Creating a bot and joining a chat room

[0972] The server generates a dedicated bot to promote communication between users and has it participate in a set chat room.

[0973] Input: bot generated request

[0974] Output: Bots that joined the chat room

[0975] Specific operation: The server executes bot = BotFactory.createBot() and calls bot.joinChatroom('general') to join the chat room.

[0976] Step 6:

[0977] Asking questions and providing topics

[0978] The bot provides questions and topics to users based on set timing and conditions.

[0979] Input: Pre-set prompt, current date and time

[0980] Output: Questions and topics posted in the chat room

[0981] Specific behavior: The bot executes bot.sendMessage(chatroom='general', message='How was your weekend?').

[0982] Step 7:

[0983] Collecting and analyzing user responses

[0984] The server collects user responses to questions and topics provided by the bot and stores them in a database.

[0985] Input: User's chat message

[0986] Output: Reaction data stored in a database

[0987] Specific operation: The server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) and saves the reactions.

[0988] Step 8:

[0989] Bot algorithm adjustments

[0990] The server adjusts the bot's algorithm based on the collected data to optimize questions and topics for future questions.

[0991] Input: Collected reaction data, analysis results

[0992] Output: Adjusted bot algorithm

[0993] Specific operation: The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[0994] (Application example 1)

[0995] 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."

[0996] The goal is to eliminate the decline in work efficiency and lack of understanding caused by a lack of communication within a team. In particular, in content distribution services, it is often the case that project progress is not checked smoothly or ideas are not shared smoothly, which can have a negative impact on the quality of deliverables and delivery dates. In addition, as more work is done online, it is necessary to understand the status of team members in real time and promote communication at the appropriate time.

[0997] 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.

[0998] In this invention, the server includes means for registering information of team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating an autoresponder and having it participate in a conversation room, means for the autoresponder to provide questions and topics to members, means for collecting user responses and saving the conversation history, means for adjusting the autoresponder algorithm based on the collected data, and means for evaluating system performance and adding new topics and questions, thereby promoting communication within the team and enabling improved work efficiency and smoother work.

[0999] A "team member" is an individual registered in the system, and is a member of a group who works with their own role.

[1000] A "database" is a collection of data that organizes, stores, accesses, and manages information about team members.

[1001] "Online status" indicates whether a team member is currently online or offline.

[1002] "Real-time" means that data and information are updated and reflected immediately, without delay.

[1003] "Quiet times" are times when team members are available for other instructions or communication given their current schedules and activities.

[1004] An "automatic responder" is a virtual questioner or interlocutor generated by the system, and has the function of facilitating communication with team members.

[1005] A "chat room" is a virtual place where team members can gather and communicate via text and voice.

[1006] "Questions and topics" are conversation starters that the autoresponder provides to team members.

[1007] "User responses" are the responses and reactions that team members give to questions and topics posed by the autoresponder.

[1008] "Conversation history" is a record of conversations and chats between team members.

[1009] "Adjusting the algorithm" means processing the collected data to optimize the operation of the automated responder and promote more effective communication.

[1010] "System performance" is an index that evaluates the effectiveness of overall operation and communication, and indicates how efficient the system is.

[1011] "New topics and questions" are content that the system generates additionally and that can trigger new dialogue with the user.

[1012] MODE FOR CARRYING OUT THE INVENTION

[1013] The following describes in detail the mode for carrying out the present invention. The present invention is a system for promoting communication within a team and improving work efficiency. This system includes elements of a server, a terminal, and a user, and realizes effective communication using an automatic responder.

[1014] 1. Server configuration

[1015] The server is the central point that manages all data and processes, and provides the following means:

[1016] A means of registering team member information in a database

[1017] A way to monitor members' online status in real time

[1018] A way to determine when members are not busy

[1019] The server stores the names, titles, schedules, and online status of members A and B in a database, allowing the server to constantly monitor the status of members and determine the appropriate timing for communication.

[1020] 2. Creating and joining an autoresponder

[1021] To promote team communication, the server has a means of generating auto-responders and having them join conversation rooms. The auto-responders provide questions and topics to members at appropriate times. For example, the server might have an auto-responder join a "chat room" at 10 a.m. and ask a question such as, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[1022] 3. Collecting user responses and adjusting the algorithm

[1023] The server has a means to collect user responses to questions and topics provided by the autoresponder and store the conversation history. For example, if member A replies, "I spent the weekend with my family," the server stores this comment for later analysis.

[1024] The server then uses the collected data to adjust the autoresponder algorithm and add new topics and questions to optimize future communications. By repeating this process, the server evaluates the system's performance and improves its overall effectiveness.

[1025] Specific examples

[1026] Example 1: Monday Chat

[1027] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the autoresponder posts a message to the chat room saying, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened on their respective weekends, and a natural conversation begins.

[1028] Example 2: Project progress check

[1029] The server checks the online status of members every day at 3:00 p.m. If it determines that a member is not busy, the autoresponder asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[1030] Hardware and software used

[1031] Specifically, the following hardware and software are used:

[1032] Server (Linux or Windows server)

[1033] A database management system (MySQL, PostgreSQL, or MongoDB)

[1034] Chatbot building framework (Rasa, Dialogflow, etc.)

[1035] Python scripts for data collection and analysis

[1036] Prompt Sentence Examples

[1037] prompt:

[1038] Create a bot that facilitates communication within your team by prompting team members with questions and topics at the right time to facilitate smooth communication.

[1039] Required features:

[1040] 1. Monitor members' online status

[1041] 2. Determine the less busy times

[1042] 3. Create a bot and join the chat room

[1043] 4. Asking questions and providing topics

[1044] 5. Collecting and storing user responses

[1045] Output format:

[1046] 1. Class Blueprints

[1047] 2. Code for each class

[1048] Product usage:

[1049] An application that promotes communication among content production teams

[1050] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1051] Processing steps of the system that realizes the application example

[1052] Step 1:

[1053] The server registers the team member information in a database.

[1054] Input: Member A, Member B names, titles, schedules, online status

[1055] Data processing / calculation: Writing member information to the database

[1056] Output: Complete member information stored in the database.

[1057] Step 2:

[1058] The server monitors the online status of members in real time.

[1059] Input: Member's schedule and current time

[1060] Data processing / calculation: Comparing the current time with the member's schedule and determining their online status

[1061] Output: Member's online status is updated as "online" or "offline".

[1062] Step 3:

[1063] The server determines when members are not busy.

[1064] Input: Member schedule, online status, chat activity

[1065] Data processing / calculation: Predicting the next quietest time slot based on schedules and activities

[1066] Output: The quiet times are identified and stored.

[1067] Step 4:

[1068] The server generates autoresponders to participate in conversation rooms to facilitate communication.

[1069] Input: Off-peak hours

[1070] Data processing / calculation: Creating an instance of an autoresponder and adding it to a specified conversation room

[1071] Output: The autoresponder joins the conversation room.

[1072] Step 5:

[1073] The autoresponder provides members with questions and topics at the appropriate time.

[1074] Input: Member's online status and off-peak hours

[1075] Data processing / calculation: Randomly selecting questions from a pre-defined list and posting them to the chat room

[1076] Output: The question or topic is displayed in the conversation room.

[1077] Step 6:

[1078] The server collects user responses and stores the conversation history.

[1079] Input: Member response

[1080] Data processing / calculation: Analyzing the response content and adding it to the conversation history

[1081] Output: A new entry is added to the conversation history.

[1082] Step 7:

[1083] The server uses the collected data to adjust the autoresponder's algorithm and add new topics and questions.

[1084] Input: Stored conversation history and user reaction data

[1085] Data processing / calculation: Analyzing reaction data and optimizing the algorithm of the automatic responder

[1086] Output: An updated list of topics and questions provided by the autoresponder.

[1087] Step 8:

[1088] The server evaluates the system's performance and generates new topics and questions to promote effective communication.

[1089] Input: System log data, conversation history, user response data

[1090] Data processing / calculation: The process of integrating data, calculating performance indicators, and generating more effective questions and topics.

[1091] Output: New topics or questions are added to the algorithm's configuration.

[1092] 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.

[1093] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion engine that recognizes the emotions of users. This system includes elements of a server, a terminal, and a user.

[1094] Overall system configuration

[1095] 1. Server configuration

[1096] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[1097] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[1098] 2. Monitor your online status

[1099] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[1100] 3. Determine the less busy times

[1101] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[1102] 4. Creating a bot and joining a chat room

[1103] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[1104] 5. Asking questions and providing topics

[1105] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[1106] 6. Emotion Recognition by Emotion Engine

[1107] The bot uses an emotion engine to analyze emotions from users' text messages. For example, if User A replies "I'm a little tired," the emotion engine analyzes this message and recognizes that User A is tired.

[1108] 7. Collecting and analyzing user responses

[1109] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[1110] 8. Algorithm Adjustments

[1111] The server adjusts the bot's algorithm based on the collected data. For example, if a particular topic receives a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[1112] 9. Adding new topics and questions

[1113] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[1114] Specific examples

[1115] Example 1: Monday Chat

[1116] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning everyone. How was your weekend?". As Member A and Member B start sharing what happened over the weekend, a natural conversation begins. At the same time, the emotion engine analyzes the members' messages and understands their emotional state.

[1117] Example 2: Project progress check

[1118] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A responds, "It's going well," while member B continues, "It's a little behind schedule," creating an opportunity for regular progress checks. The emotion engine analyzes these messages and recognizes that member A's answers are positive, while member B's answers are partly negative.

[1119] In this way, the system based on the present invention can further improve team cohesion and productivity by promoting communication at the right time and providing responses that take into account the user's emotions.

[1120] The processing flow will be explained below.

[1121] Step 1:

[1122] The server stores team member information in a database, including each member's name, job title, schedule, and online status. For example, member A's name, job title "Engineer," and working hours are stored in the database.

[1123] Step 2:

[1124] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database. For example, if member A becomes online at 9:00 a.m., that information is immediately updated in the database.

[1125] Step 3:

[1126] The server determines when a member is not busy by looking at calendar appointments and chat activity. For example, since member A has no appointments between 10:00 and 11:00 AM, the server determines this time period as a "non-busy time period."

[1127] Step 4:

[1128] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to a specified chat room. For example, the bot joins a "chat room."

[1129] Step 5:

[1130] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss, such as, "How was your weekend?"

[1131] Step 6:

[1132] Users respond to the bot's questions. For example, User A replies, "I had a great time with my family," and User B follows with, "I went to watch a sports game." The bot responds with an appropriate response.

[1133] Step 7:

[1134] The emotion engine analyzes emotions from user text messages. For example, it analyzes the message "I'm a little tired" from user A and recognizes the emotional state as "fatigue."

[1135] Step 8:

[1136] The bot's response is adjusted based on the results of the emotion engine. For example, if user A says "I'm tired," the bot will respond by saying "Take care and rest."

[1137] Step 9:

[1138] The server collects user responses and stores the chat history for future reference and analysis. For example, the responses of User A and User B are stored in a database.

[1139] Step 10:

[1140] The server uses the collected data to adjust the bot's algorithms: if a particular topic is well-received, it changes its settings to target that topic more frequently.

[1141] Step 11:

[1142] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database based on the evaluation results to improve communication effectiveness.

[1143] Step 12:

[1144] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[1145] Example 2

[1146] 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."

[1147] Modern teams require more efficient communication and interactions that take into account the emotional state of members, but existing tools and systems do not adequately address these needs. While basic functions such as monitoring online status and determining when members are not busy are provided, they lack the ability to promote effective communication or recognize emotions. This leads to a decline in team cohesion and productivity, and a lack of work efficiency.

[1148] 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.

[1149] In this invention, the server includes means for registering information about team members in a storage device, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program to promote communication and for allowing members to participate in the communication area, means for the program to provide questions and topics to members, means for collecting user responses and saving communication history, means for adjusting the program's operation procedure based on the collected data, means for evaluating system performance and adding new topics and questions, and means for analyzing user emotions using an emotion recognition engine. This enables effective communication that takes into account the emotional states of members, thereby improving team cohesion and productivity.

[1150] "Team member information" refers to data such as a user's name, title, schedule, and online status.

[1151] "Storage device" refers to hardware or software for storing data.

[1152] "Online state" refers to a state in which a user is connected to a network.

[1153] "Real-time monitoring" refers to instantly checking and updating the current situation and status.

[1154] "Off-peak hours" refers to times when the user is not tied down to a specific task or schedule.

[1155] "Programs that facilitate communication" refers to software designed to support and stimulate interaction between users.

[1156] A "communication realm" refers to a virtual or physical space in which users exchange information.

[1157] "The program provides questions or topics to members" means that the software presents specific topics or questions to users.

[1158] "Collecting user responses and saving communication history" refers to recording the actions and comments made by the user and making them available for later reference.

[1159] "Adjusting the program's operating procedures based on collected data" refers to analyzing stored data and changing the software's behavior based on the results.

[1160] "Evaluate the system's performance and add new topics and questions" refers to evaluating the system's operation and introducing new topics and questions as a way to improve it.

[1161] An "emotion recognition engine" refers to software that analyzes text and voice to estimate a user's emotions.

[1162] "Analyzing the user's emotions" refers to determining the user's emotional state based on input data.

[1163] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion recognition engine that recognizes the emotions of a user. The system includes elements of a server, a terminal, and a user.

[1164] Overall system configuration

[1165] The server first sets up a database and registers team member information. This registration is done using Python and a MySQL database. Specifically, the server stores user names, job titles, schedules, and online status in the database. This allows the server to centrally manage all data and processes and constantly monitor the status of team members.

[1166] For example, the server stores in the database the name of member A as "Yamada Taro," his position as "team leader," his schedule as "meeting from 10:00 AM to 11:00 AM," and his online status as "online."

[1167] The server runs a script to check the online status of members at regular intervals and updates the results to the database. Here, we will create a monitoring script using Python. The server checks which members are currently online and updates the results to the database.

[1168] The server also uses the collected data to determine when a member is not busy. This determination is made using the Python Pandas library. For example, the server checks member A's schedule and determines that he has no appointments between 10:00 AM and 11:00 AM, and determines this time period as a "non-busy time period."

[1169] The server then generates a program (here called a bot) that facilitates communication and has it participate in the specified chat room. This bot is generated using Node.js and the Bot Framework, and the bot participates in communication areas such as Slack and Microsoft Teams.

[1170] For example, the server creates a bot that participates in a "chat room" and has the bot send the message "Good morning. How are you today?"

[1171] The bot prompts members with questions and topics at appropriate times, using generative AI models (such as OpenAI's GPT-3) to generate natural-sounding dialogue. For example, at 10 a.m., the bot posts to the chat room, "Good morning, everyone. How was your weekend?" The following is an example of a prompt:

[1172] "How was your weekend?"

[1173] The bot uses an emotion recognition engine to analyze users' text messages and recognize their emotions. It uses Google Cloud Natural Language API and IBM Watson's NLP API. For example, if User A replies, "I'm a little tired," the emotion recognition engine will tag this message as "fatigue" and understand User A's emotional state.

[1174] The server collects user responses to questions and topics provided by the bot and stores the communication history. It uses Python and MySQL to store the chat history in a database. For example, it logs the conversation between users A and B and evaluates their responses.

[1175] The server uses the collected data to adjust the bot's algorithms. Based on the topics that generated the most responses, it changes its settings to offer more new questions and topics. In addition, based on the results of an emotion recognition engine, it adjusts its responses to match the user's emotional state.

[1176] Finally, the server evaluates the system's performance and adds new topics and questions, further improving the effectiveness of communication. For example, the server adds new topics such as "recently read books" and "favorite places" to the database, and the bot uses these topics to advance the conversation.

[1177] Through this system, the server, terminals, and users cooperate to realize more effective and emotionally sensitive communication, improving team cohesion and productivity.

[1178] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1179] Step 1: The server registers team member information in a storage device. Specifically, the server stores the user's (e.g., "Yamada Taro") name, job title ("Team Leader"), schedule ("Meeting from 10:00 to 11:00"), and online status ("Online") in the database. The input of this step is user information, and the output is the user information stored in the database.

[1180] Step 2: The server monitors the online status of members in real time at regular intervals. It runs a Python script to check the current online status and update the database. Specifically, the script checks the online status of members and updates the database. The input of this step is the current online status of members, and the output is the updated database contents.

[1181] Step 3: The server determines the non-busy time periods for members based on the collected data. Using Python's Pandas library, it analyzes members' schedule data and finds free time periods. Specifically, it determines the time periods that do not include events such as "meetings" or "work" as "non-busy time periods." The input for this step is the members' schedule data, and the output is the determined "non-busy time periods."

[1182] Step 4: The server generates a program (bot) to facilitate communication and has it join the specified communication area. The bot is generated using Node.js and the Bot Framework, and joins a chat room such as Slack or Microsoft Teams. Specifically, the bot joins a "chat room" and posts an initial greeting message. The input to this step is an instance of the generated bot, and the output is the bot that has joined the chat room.

[1183] Step 5: The bot presents questions and topics to members at the specified times. A generative AI model (e.g., OpenAI's GPT-3) is used to generate natural dialogue. The prompt sentence is "How was your weekend?" and the generated message is sent to the member. The input for this step is the prompt sentence, and the output is the generated question or topic message.

[1184] Step 6: The bot uses an emotion recognition engine to analyze the user's message and recognize emotions. It uses Google Cloud Natural Language API or IBM Watson's NLP API to analyze the user's text message. Specifically, if the user replies "I'm a little tired," the emotion recognition engine detects "fatigue." The input for this step is the user's text message, and the output is the analyzed emotion data.

[1185] Step 7: The server collects user responses to the questions and topics provided by the bot and saves the communication history. Using Python and MySQL, the server records the user's messages and responses in a database. Specifically, it saves the conversation between users A and B as a log. The input to this step is the user's response data, and the output is the communication history saved in the database.

[1186] Step 8: The server adjusts the bot's calculation procedures based on the collected data. It analyzes the data and changes the settings to prioritize topics that users responded well to. It also adjusts responses taking into account the results of the emotion recognition engine. The input to this step is the collected communication history data and emotion data, and the output is the adjusted bot's calculation procedures.

[1187] Step 9: The server evaluates the system's performance and adds new topics and questions to the database. Specifically, based on the system's evaluation results, new topics such as "recently read books" and "favorite places" are added to the database, and the bot uses these topics to advance the conversation. The input to this step is the system's evaluation data, and the output is the new topics and questions that have been added.

[1188] (Application example 2)

[1189] 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."

[1190] The present invention aims to solve problems related to online communication between teams and individuals. Conventional systems are limited to providing standard questions and topics without considering the user's emotions, which can lead to low user satisfaction. Furthermore, even in systems that utilize emotion recognition technology, the analysis results are often not effectively utilized, making it difficult to provide optimal information to users. This leads to issues such as a decline in the quality of communication and a lack of effectiveness in business and experience.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1192] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a bot to promote communication and having it participate in a chat room, means for the bot to provide questions and topics to members, means for collecting user responses and saving chat histories, means for adjusting the bot algorithm based on the collected data, means for evaluating system performance and adding new topics and questions, means for analyzing user emotions and recommending products and information based on the analysis results, and means for promoting communication at optimal times based on the emotion analysis results. This enables flexible and effective information provision and communication that takes into account the user's emotional state.

[1193] "Means for registering team member information in a database" refers to the ability to store information such as each team member's name, job title, schedule, and online status in a database.

[1194] "Means of monitoring members' online status in real time" refers to the ability to constantly check the online status of team members and update that status with the latest information.

[1195] "Means for determining when members are not busy" refers to a function that automatically identifies when team members are not busy based on their schedules and activities.

[1196] "A means of generating bots that promote communication and having them participate in chat rooms" refers to the function of creating bots that automatically engage in conversations and have them participate in designated chat rooms in order to stimulate communication within a team.

[1197] "A means for the bot to provide questions and topics to members" refers to the function whereby the generated bot provides questions and topics to team members at appropriate times, promoting interaction.

[1198] "Means for collecting user responses and saving chat history" refers to a function for collecting team members' responses to chats and saving the content in a database.

[1199] "Means for adjusting the bot's algorithm based on collected data" refers to a function for analyzing saved chat data to optimize the bot's dialogue algorithm.

[1200] "Means for evaluating the system's performance and adding new topics and questions" refers to the ability to evaluate the system's functionality and effectiveness and, if necessary, add new topics and questions to improve its performance.

[1201] "Means of analyzing user emotions and recommending products and information based on the analysis results" refers to a function that uses emotion recognition technology to analyze user emotions and recommend appropriate products and information based on the results.

[1202] "Means to promote communication at the optimal time based on the results of emotion analysis" refers to a function that provides questions and topics at the most effective time based on the results of emotion analysis of the user, thereby promoting communication.

[1203] The following describes in detail the mode for carrying out the present invention. The present invention is a system that uses a smartphone application to promote communication within a team or in a virtual store and combines it with an emotion engine that analyzes user emotions. This system combines the elements of a server, a terminal, and a user to provide optimal information and communication.

[1204] 1. Server configuration

[1205] The server is the centralized center for all data and processes. It registers team member and user information in a database and monitors their online status in real time. It also utilizes an emotion engine and generative AI model to analyze user messages and provide appropriate information.

[1206] 2.Register team member information

[1207] The server registers information such as team member names, job titles, schedules, and online status in a database, allowing the status of each member to be monitored in real time.

[1208] 3. Monitor your online status

[1209] The server periodically checks the online status of all members and updates the database based on this information, allowing you to know exactly when members are available.

[1210] 4. Determining the right timing

[1211] The server uses the collected data to determine when members are less busy, for example, when they are not tied down with meetings or other tasks.

[1212] 5. Creating a bot and joining a chat room

[1213] The server generates a bot to facilitate communication and invites it into a designated chat room. The bot stimulates interaction among team members through casual conversation and questions.

[1214] 6. Ask questions and share topics

[1215] The generated bot will then prompt members with questions and topics at the appropriate time, such as "Good morning everyone. How was your weekend?"

[1216] 7. Emotion analysis using an emotion engine

[1217] The bot uses an emotion engine to analyze emotions from users' text messages. The emotion engine analyzes messages sent by users and recognizes, for example, a message such as "I'm a little tired" as "tired."

[1218] 8.Collecting and analyzing user responses

[1219] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[1220] 9. Algorithm Adjustments

[1221] The server adjusts the bot's algorithm based on the collected data. If a particular topic generates a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[1222] 10. Add new topics or questions

[1223] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database to improve communication effectiveness.

[1224] 11. User sentiment analysis and product recommendation

[1225] The server analyzes the user's emotions and recommends products and information based on the analysis results. For example, if the server determines that the user is feeling a little depressed, it will recommend products that will lift the user's spirits.

[1226] 12. Promoting communication based on emotion analysis results

[1227] Based on the results of sentiment analysis, questions and topics are presented at the optimal time to promote effective communication. For example, if the user has a positive reaction, an additional comment expressing gratitude is provided.

[1228] Specific examples

[1229] For example, if a user types, "I haven't been feeling well lately," the emotion engine will recognize this as "tired" or "depressed," and the bot will ask, "Would you like to see your favorite products?" and recommend products that will help refresh them.

[1230] Prompt Sentence Examples

[1231] Design an application that performs sentiment analysis on user-entered text and recommends appropriate products.

[1232] If the emotional state is "happy", recommend a product from product list A.

[1233] If the emotional state is "neutral," recommend a product from product list B.

[1234] If the emotional state is "sad", recommend a product from product list C.

[1235] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1236] Step 1:

[1237] The server registers team member information in a database. Specifically, it receives individual information such as team member name, job title, schedule, and online status as input data and saves it in the database. This process centralizes the management of each member's basic information.

[1238] Step 2:

[1239] The server monitors the online status of team members in real time at regular intervals. It receives online status check requests as input data, obtains the current online status of each member, and updates the database. This process ensures that the latest online status is always available.

[1240] Step 3:

[1241] The server determines when members are not busy. It receives the members' schedule information as input, executes logic to determine "not busy times" based on that information, and saves the output in a database. This process allows the optimal communication timing for each member to be determined.

[1242] Step 4:

[1243] The server generates a bot to facilitate communication and has it join the specified chat room. It receives a bot creation request as input, creates a corresponding bot, and has it join the chat room. Through this process, the bot plays the role of facilitating communication.

[1244] Step 5:

[1245] The server then has the generated bot provide questions and topics at the appropriate time. It references the user's online status and quiet times, sends a request to the bot to generate questions and topics, and posts the results in the chat room. This process promotes natural communication.

[1246] Step 6:

[1247] The server collects user responses to questions and topics posed by the bot, stores the chat history, and takes user replies as input data and stores them in a database. This processing allows for future data analysis and trend identification.

[1248] Step 7:

[1249] The server adjusts the bot's algorithm based on the collected data. It uses the collected chat history as input data to evaluate and optimize the algorithm's performance. This process improves the quality of the bot and enables more effective communication.

[1250] Step 8:

[1251] The server evaluates the system's performance and adds new topics and questions. It uses the performance data for system evaluation as input, executes new topic and question generation requests based on the evaluation results, and stores them in the database. This process allows users to receive the latest and most relevant topics.

[1252] Step 9:

[1253] The server analyzes the text entered by the user using an emotion engine and recommends products and information based on the analysis results. The server receives the user's message content as input data, analyzes it using the emotion engine, executes product recommendation logic based on the analysis results, and outputs the most suitable products and information. This process allows for recommendations that are individually customized for the user.

[1254] Step 10:

[1255] The server promotes communication at the optimal timing based on the results of emotion analysis. It receives the emotion analysis results as input, executes logic to optimize the timing of communication based on those results, and instructs the bot on the results. This process enables questions and topics to be asked at the optimal timing according to the user's emotional state.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] [Fourth embodiment]

[1260] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1261] 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.

[1262] 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).

[1263] 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.

[1264] 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.

[1265] 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).

[1266] 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.

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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."

[1273] The present invention is directed to a system designed to promote communication within a team and improve business results. The system includes a server, a terminal, and a user.

[1274] Overall system configuration

[1275] 1. Server configuration

[1276] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[1277] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[1278] 2. Monitor your online status

[1279] The server monitors the online status of members in real time. For example, the server periodically checks the online status of member A and uses chat activity and calendar events as metrics to determine whether they are busy.

[1280] 3. Determine the less busy times

[1281] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[1282] 4. Creating a bot and joining a chat room

[1283] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[1284] 5. Asking questions and providing topics

[1285] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[1286] 6. Collecting and analyzing user responses

[1287] The server collects user responses to questions and topics provided by the bot and saves the chat history. For example, if member A replies, "I spent the weekend with my family," the server saves this comment for later analysis.

[1288] 7. Algorithm Adjustments

[1289] The server adjusts the bot's algorithm based on the collected data, optimizing the timing and topic selection for future chats. For example, if member A often responds positively to a particular topic, the server will adjust the bot to cover that topic more frequently.

[1290] Specific examples

[1291] Example 1: Monday Chat

[1292] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened each weekend, and a natural conversation begins.

[1293] Example 2: Project progress check

[1294] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[1295] The above is a specific embodiment of the present invention. This system promotes timely communication, improving team cohesion and productivity.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The server stores team member information in a database, including each member's name, job title, schedule, and online status.

[1299] Step 2:

[1300] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[1301] Step 3:

[1302] The server determines when members are free by looking at calendar events and chat activity, for example by looking at the times when a particular member is free and marking them as "free."

[1303] Step 4:

[1304] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to the specified chat room.

[1305] Step 5:

[1306] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss. For example, it might ask, "How was your weekend?"

[1307] Step 6:

[1308] Users respond to the bot's questions. For example, User A might reply, "I had a great time with my family," and User B might respond, "I went to watch a sports game."

[1309] Step 7:

[1310] The server collects user responses and stores the chat history for future reference and analysis.

[1311] Step 8:

[1312] The server adjusts the bot's algorithm based on the collected data: for example, if a particular topic gets a good response, it changes the settings to throw more of that topic at it.

[1313] Step 9:

[1314] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[1315] Step 10:

[1316] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[1317] Example 1

[1318] 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."

[1319] In today's work environment, effective communication between team members is often lacking. This lack of communication leads to delays in information sharing and misunderstandings, resulting in reduced productivity. Especially with the increasing trend toward online work, casual but important communication such as daily chats and progress checks is declining. This calls for an effective system that monitors online status in real time and promotes timely communication.

[1320] 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.

[1321] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program that promotes dialogue and having members participate in a dialogue environment, means for the program to provide questions and topics to members, means for collecting user responses and saving a dialogue history, means for adjusting the operation of the program based on the collected data, means for evaluating system performance and adding new topics and questions, and means for running the system in a cloud computing environment, which promotes communication at appropriate times and makes it possible to improve team cohesion and productivity.

[1322] "Team members" refers to a set of individuals working together to achieve a goal.

[1323] "Database" refers to a system for efficiently storing, retrieving, and managing structured information in digital form.

[1324] "Online status" refers to a state that indicates in real time whether a user is connected to the Internet and is active.

[1325] "Real-time" refers to processing or communication occurring immediately without delay.

[1326] An "interactive environment" refers to a virtual or physical space in which users communicate.

[1327] A "program" refers to a set of instructions designed to perform a particular function.

[1328] "Questions and Topics" refers to topics and questions provided to stimulate communication.

[1329] "User" refers to an individual or organization that uses the system or service.

[1330] "Reaction" refers to the response or feedback a user gives to a question or topic.

[1331] "Dialogue history" refers to a record of previous communications.

[1332] "Adjusting behavior" refers to changing algorithms or settings to improve system performance.

[1333] A "cloud computing environment" refers to an environment in which computing resources and services are used via the Internet.

[1334] The present invention provides a system for promoting communication within a team and improving business results. The system includes a server, a terminal, and a user.

[1335] Server Configuration

[1336] The server acts as a central point for managing all data and processes. It uses a database management system (e.g., MySQL) to register team member information in a database. A server running on the cloud (e.g., AWS EC2) is used. The server then monitors the online status of team members in real time. Specifically, the server regularly monitors users' chat activity and calendar appointments, and updates their online status accordingly.

[1337] Registering user information and monitoring online status

[1338] Users log in to the system using a dedicated terminal, and the server saves information such as the user's name, job title, schedule, and online status in the database. The server saves the information by executing an SQL statement such as INSERT INTO users. The server also monitors the online status in real time by executing the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to obtain recent activity.

[1339] Determining non-busy times

[1340] The server determines non-busy times based on the user's schedule information and chat activity. It lists available time slots by calling the calendar API and checking the busy status. For example, if member A has no plans between 10:00 and 11:00 AM, the server determines this time slot as a "non-busy time slot."

[1341] Creating a bot and joining a chat room

[1342] The server creates a dedicated bot to facilitate communication between users. The created bot automatically joins the configured chat room. The server executes bot = BotFactory.createBot(), and the created bot calls bot.joinChatroom('general') to join the chat room.

[1343] Asking questions and providing topics

[1344] The bot provides users with questions and topics based on pre-defined timing and conditions. For example, the bot might send the message "Good morning everyone. How was your weekend?" at 10:00 AM. A concrete example would be the bot executing bot.sendMessage(chatroom='general', message='How was your weekend?').

[1345] Collecting and analyzing user responses

[1346] The server collects user reactions to questions and topics provided by the bot and stores them in a database. For example, the server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) to store the reactions.

[1347] Bot algorithm adjustments

[1348] The server adjusts the bot's algorithm based on the collected data to optimize future questions and topics. The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[1349] Specific examples

[1350] Chat every Monday: The server checks the online status of members every Monday at 9:00 AM, and the bot posts to the chat room, "Good morning everyone. How was your weekend?" User A replies, "I went on a picnic with my family," and the server saves this in the database.

[1351] Project progress check: The server checks the user's online status every day at 3:00 PM, and the bot asks, "How is the current project progressing?" User B answers, "I'm a little behind," which the server collects and uses for analysis.

[1352] Example prompts for generative AI models

[1353] "Create a bot message at 9am on Monday morning to check your team's online status and ask how their weekend is going."

[1354] "Send a bot message every day at 3 PM to remind you to check in on your project progress."

[1355] This promotes timely communication and improves team cohesion and productivity.

[1356] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1357] Step 1:

[1358] Starting and initializing the server

[1359] The server starts the server machine, connects to the database management system (e.g. MySQL), reads the configuration files (e.g. config files, API keys, etc.) and performs any necessary initial configuration.

[1360] Input: Server startup request, configuration file

[1361] Output: Server environment with initial settings completed

[1362] Specific operation: The server reads the config.yaml file and sets the database connection information.

[1363] Step 2:

[1364] Registering user information

[1365] Users log in to the system using a dedicated terminal, which sends the user's basic information to the server, which then registers the information in a database.

[1366] Input: User login information (name, job title, schedule, online status)

[1367] Output: User information registered in the database

[1368] Specific operation: When a user fills in the login form and clicks the "Login" button, the terminal sends an API request, and the server executes the SQL statement INSERT INTO users to save the information.

[1369] Step 3:

[1370] Real-time online status monitoring

[1371] The server checks each user's online status at a specified interval (e.g., every minute), looking at chat activity and calendar appointments.

[1372] Input: User activity data, calendar data

[1373] Output: Latest online status

[1374] Specific operation: The server executes the SQL statement SELECT FROM user_activity WHERE timestamp > NOW() - INTERVAL 1 MINUTE to get the most recent activity.

[1375] Step 4:

[1376] Determining non-busy times

[1377] The server determines the times when the user is not busy based on the collected schedule information and chat activity.

[1378] Input: Schedule information and activity data from the Calendar API

[1379] Output: A list of available time slots for each user

[1380] Specific operation: Calls the calendar API to check the busy status and lists available time slots.

[1381] Step 5:

[1382] Creating a bot and joining a chat room

[1383] The server generates a dedicated bot to promote communication between users and has it participate in a set chat room.

[1384] Input: bot generated request

[1385] Output: Bots that joined the chat room

[1386] Specific operation: The server executes bot = BotFactory.createBot() and calls bot.joinChatroom('general') to join the chat room.

[1387] Step 6:

[1388] Asking questions and providing topics

[1389] The bot provides questions and topics to users based on set timing and conditions.

[1390] Input: Pre-set prompt, current date and time

[1391] Output: Questions and topics posted in the chat room

[1392] Specific behavior: The bot executes bot.sendMessage(chatroom='general', message='How was your weekend?').

[1393] Step 7:

[1394] Collecting and analyzing user responses

[1395] The server collects user responses to questions and topics provided by the bot and stores them in a database.

[1396] Input: User's chat message

[1397] Output: Reaction data stored in a database

[1398] Specific operation: The server executes the SQL statement INSERT INTO reactions (user_id, message) VALUES (?, ?) and saves the reactions.

[1399] Step 8:

[1400] Bot algorithm adjustments

[1401] The server adjusts the bot's algorithm based on the collected data to optimize questions and topics for future questions.

[1402] Input: Collected reaction data, analysis results

[1403] Output: Adjusted bot algorithm

[1404] Specific operation: The server analyzes the data using an analytical model (e.g., a machine learning model) and executes bot.updateAlgorithm(new_parameters) based on the results.

[1405] (Application example 1)

[1406] 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."

[1407] The goal is to eliminate the decline in work efficiency and lack of understanding caused by a lack of communication within a team. In particular, in content distribution services, it is often the case that project progress is not checked smoothly or ideas are not shared smoothly, which can have a negative impact on the quality of deliverables and delivery dates. In addition, as more work is done online, it is necessary to understand the status of team members in real time and promote communication at the appropriate time.

[1408] 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.

[1409] In this invention, the server includes means for registering information of team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating an autoresponder and having it participate in a conversation room, means for the autoresponder to provide questions and topics to members, means for collecting user responses and saving the conversation history, means for adjusting the autoresponder algorithm based on the collected data, and means for evaluating system performance and adding new topics and questions, thereby promoting communication within the team and enabling improved work efficiency and smoother work.

[1410] A "team member" is an individual registered in the system, and is a member of a group who works with their own role.

[1411] A "database" is a collection of data that organizes, stores, accesses, and manages information about team members.

[1412] "Online status" indicates whether a team member is currently online or offline.

[1413] "Real-time" means that data and information are updated and reflected immediately, without delay.

[1414] "Quiet times" are times when team members are available for other instructions or communication given their current schedules and activities.

[1415] An "automatic responder" is a virtual questioner or interlocutor generated by the system, and has the function of facilitating communication with team members.

[1416] A "chat room" is a virtual place where team members can gather and communicate via text and voice.

[1417] "Questions and topics" are conversation starters that the autoresponder provides to team members.

[1418] "User responses" are the responses and reactions that team members give to questions and topics posed by the autoresponder.

[1419] "Conversation history" is a record of conversations and chats between team members.

[1420] "Adjusting the algorithm" means processing the collected data to optimize the operation of the automated responder and promote more effective communication.

[1421] "System performance" is an index that evaluates the effectiveness of overall operation and communication, and indicates how efficient the system is.

[1422] "New topics and questions" are content that the system generates additionally and that can trigger new dialogue with the user.

[1423] MODE FOR CARRYING OUT THE INVENTION

[1424] The following describes in detail the mode for carrying out the present invention. The present invention is a system for promoting communication within a team and improving work efficiency. This system includes elements of a server, a terminal, and a user, and realizes effective communication using an automatic responder.

[1425] 1. Server configuration

[1426] The server is the central point that manages all data and processes, and provides the following means:

[1427] A means of registering team member information in a database

[1428] A way to monitor members' online status in real time

[1429] A way to determine when members are not busy

[1430] The server stores the names, titles, schedules, and online status of members A and B in a database, allowing the server to constantly monitor the status of members and determine the appropriate timing for communication.

[1431] 2. Creating and joining an autoresponder

[1432] To promote team communication, the server has a means of generating auto-responders and having them join conversation rooms. The auto-responders provide questions and topics to members at appropriate times. For example, the server might have an auto-responder join a "chat room" at 10 a.m. and ask a question such as, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[1433] 3. Collecting user responses and adjusting the algorithm

[1434] The server has a means to collect user responses to questions and topics provided by the autoresponder and store the conversation history. For example, if member A replies, "I spent the weekend with my family," the server stores this comment for later analysis.

[1435] The server then uses the collected data to adjust the autoresponder algorithm and add new topics and questions to optimize future communications. By repeating this process, the server evaluates the system's performance and improves its overall effectiveness.

[1436] Specific examples

[1437] Example 1: Monday Chat

[1438] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the autoresponder posts a message to the chat room saying, "Good morning, everyone. How was your weekend?" Member A and Member B begin sharing what happened on their respective weekends, and a natural conversation begins.

[1439] Example 2: Project progress check

[1440] The server checks the online status of members every day at 3:00 p.m. If it determines that a member is not busy, the autoresponder asks, "How is the current project progressing?" To this, member A might respond, "It's going well," while member B might respond, "It's a little behind schedule," creating a forum for regular progress checks.

[1441] Hardware and software used

[1442] Specifically, the following hardware and software are used:

[1443] Server (Linux or Windows server)

[1444] A database management system (MySQL, PostgreSQL, or MongoDB)

[1445] Chatbot building framework (Rasa, Dialogflow, etc.)

[1446] Python scripts for data collection and analysis

[1447] Prompt Sentence Examples

[1448] prompt:

[1449] Create a bot that facilitates communication within your team by prompting team members with questions and topics at the right time to facilitate smooth communication.

[1450] Required features:

[1451] 1. Monitor members' online status

[1452] 2. Determine the less busy times

[1453] 3. Create a bot and join the chat room

[1454] 4. Asking questions and providing topics

[1455] 5. Collecting and storing user responses

[1456] Output format:

[1457] 1. Class Blueprints

[1458] 2. Code for each class

[1459] Product usage:

[1460] An application that promotes communication among content production teams

[1461] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1462] Processing steps of the system that realizes the application example

[1463] Step 1:

[1464] The server registers the team member information in a database.

[1465] Input: Member A, Member B names, titles, schedules, online status

[1466] Data processing / calculation: Writing member information to the database

[1467] Output: Complete member information stored in the database.

[1468] Step 2:

[1469] The server monitors the online status of members in real time.

[1470] Input: Member's schedule and current time

[1471] Data processing / calculation: Comparing the current time with the member's schedule and determining their online status

[1472] Output: Member's online status is updated as "online" or "offline".

[1473] Step 3:

[1474] The server determines when members are not busy.

[1475] Input: Member schedule, online status, chat activity

[1476] Data processing / calculation: Predicting the next quietest time slot based on schedules and activities

[1477] Output: The quiet times are identified and stored.

[1478] Step 4:

[1479] The server generates autoresponders to participate in conversation rooms to facilitate communication.

[1480] Input: Off-peak hours

[1481] Data processing / calculation: Creating an instance of an autoresponder and adding it to a specified conversation room

[1482] Output: The autoresponder joins the conversation room.

[1483] Step 5:

[1484] The autoresponder provides members with questions and topics at the appropriate time.

[1485] Input: Member's online status and off-peak hours

[1486] Data processing / calculation: Randomly selecting questions from a pre-defined list and posting them to the chat room

[1487] Output: The question or topic is displayed in the conversation room.

[1488] Step 6:

[1489] The server collects user responses and stores the conversation history.

[1490] Input: Member response

[1491] Data processing / calculation: Analyzing the response content and adding it to the conversation history

[1492] Output: A new entry is added to the conversation history.

[1493] Step 7:

[1494] The server uses the collected data to adjust the autoresponder's algorithm and add new topics and questions.

[1495] Input: Stored conversation history and user reaction data

[1496] Data processing / calculation: Analyzing reaction data and optimizing the algorithm of the automatic responder

[1497] Output: An updated list of topics and questions provided by the autoresponder.

[1498] Step 8:

[1499] The server evaluates the system's performance and generates new topics and questions to promote effective communication.

[1500] Input: System log data, conversation history, user response data

[1501] Data processing / calculation: The process of integrating data, calculating performance indicators, and generating more effective questions and topics.

[1502] Output: New topics or questions are added to the algorithm's configuration.

[1503] 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.

[1504] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion engine that recognizes the emotions of users. This system includes elements of a server, a terminal, and a user.

[1505] Overall system configuration

[1506] 1. Server configuration

[1507] The server acts as a central point for all data and processes, registering team member information in a database and monitoring their online status in real time.

[1508] For example, the server stores the name, title, schedule, and online status of member A in a database, allowing the server to constantly monitor the status of members and make decisions based on appropriate information.

[1509] 2. Monitor your online status

[1510] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database.

[1511] 3. Determine the less busy times

[1512] The server determines the time periods when a member is not busy based on the collected data. For example, if member A has no plans between 10:00 and 11:00 a.m., the server determines this time period as a "non-busy time period."

[1513] 4. Creating a bot and joining a chat room

[1514] The server generates a bot to facilitate communication and then joins the generated bot in a specified chat room. For example, the bot joins a "chat room" and monitors the conversations there.

[1515] 5. Asking questions and providing topics

[1516] The bot will provide members with questions and topics at the appropriate time. For example, at 10 a.m., the bot will ask a question like, "Good morning, everyone. How was your weekend?" This question allows member A and member B to casually start a conversation.

[1517] 6. Emotion Recognition by Emotion Engine

[1518] The bot uses an emotion engine to analyze emotions from users' text messages. For example, if User A replies "I'm a little tired," the emotion engine analyzes this message and recognizes that User A is tired.

[1519] 7. Collecting and analyzing user responses

[1520] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[1521] 8. Algorithm Adjustments

[1522] The server adjusts the bot's algorithm based on the collected data. For example, if a particular topic receives a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[1523] 9. Adding new topics and questions

[1524] The server evaluates the system's performance and adds new topics and questions. For example, based on the system's evaluation results, new chat topics can be added to the database to improve communication effectiveness.

[1525] Specific examples

[1526] Example 1: Monday Chat

[1527] The server checks the online status of members every Monday morning at 9:00 AM. After confirming that everyone is online, the bot posts to the chat room, "Good morning everyone. How was your weekend?". As Member A and Member B start sharing what happened over the weekend, a natural conversation begins. At the same time, the emotion engine analyzes the members' messages and understands their emotional state.

[1528] Example 2: Project progress check

[1529] The server checks the online status of members every day at 3:00 p.m. Once it determines that a member is not busy, the bot asks, "How is the current project progressing?" To this, member A responds, "It's going well," while member B continues, "It's a little behind schedule," creating an opportunity for regular progress checks. The emotion engine analyzes these messages and recognizes that member A's answers are positive, while member B's answers are partly negative.

[1530] In this way, the system based on the present invention can further improve team cohesion and productivity by promoting communication at the right time and providing responses that take into account the user's emotions.

[1531] The processing flow will be explained below.

[1532] Step 1:

[1533] The server stores team member information in a database, including each member's name, job title, schedule, and online status. For example, member A's name, job title "Engineer," and working hours are stored in the database.

[1534] Step 2:

[1535] The server monitors the online status of team members in real time. The server checks the online status of team members at regular intervals and updates the results to the database. For example, if member A becomes online at 9:00 a.m., that information is immediately updated in the database.

[1536] Step 3:

[1537] The server determines when a member is not busy by looking at calendar appointments and chat activity. For example, since member A has no appointments between 10:00 and 11:00 AM, the server determines this time period as a "non-busy time period."

[1538] Step 4:

[1539] When the server determines that the timing is appropriate, it creates a bot and invites it to join the chat room. The server creates an instance of the bot and invites it to a specified chat room. For example, the bot joins a "chat room."

[1540] Step 5:

[1541] The bot monitors interactions between members in a chat room and, when it determines that members are not busy, offers questions or topics to discuss, such as, "How was your weekend?"

[1542] Step 6:

[1543] Users respond to the bot's questions. For example, User A replies, "I had a great time with my family," and User B follows with, "I went to watch a sports game." The bot responds with an appropriate response.

[1544] Step 7:

[1545] The emotion engine analyzes emotions from user text messages. For example, it analyzes the message "I'm a little tired" from user A and recognizes the emotional state as "fatigue."

[1546] Step 8:

[1547] The bot's response is adjusted based on the results of the emotion engine. For example, if user A says "I'm tired," the bot will respond by saying "Take care and rest."

[1548] Step 9:

[1549] The server collects user responses and stores the chat history for future reference and analysis. For example, the responses of User A and User B are stored in a database.

[1550] Step 10:

[1551] The server uses the collected data to adjust the bot's algorithms: if a particular topic is well-received, it changes its settings to target that topic more frequently.

[1552] Step 11:

[1553] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database based on the evaluation results to improve communication effectiveness.

[1554] Step 12:

[1555] The server repeats this process periodically to continually support team communication, keeping the team cohesive and productive.

[1556] Example 2

[1557] 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."

[1558] Modern teams require more efficient communication and interactions that take into account the emotional state of members, but existing tools and systems do not adequately address these needs. While basic functions such as monitoring online status and determining when members are not busy are provided, they lack the ability to promote effective communication or recognize emotions. This leads to a decline in team cohesion and productivity, and a lack of work efficiency.

[1559] 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.

[1560] In this invention, the server includes means for registering information about team members in a storage device, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a program to promote communication and for allowing members to participate in the communication area, means for the program to provide questions and topics to members, means for collecting user responses and saving communication history, means for adjusting the program's operation procedure based on the collected data, means for evaluating system performance and adding new topics and questions, and means for analyzing user emotions using an emotion recognition engine. This enables effective communication that takes into account the emotional states of members, thereby improving team cohesion and productivity.

[1561] "Team member information" refers to data such as a user's name, title, schedule, and online status.

[1562] "Storage device" refers to hardware or software for storing data.

[1563] "Online state" refers to a state in which a user is connected to a network.

[1564] "Real-time monitoring" refers to instantly checking and updating the current situation and status.

[1565] "Off-peak hours" refers to times when the user is not tied down to a specific task or schedule.

[1566] "Programs that facilitate communication" refers to software designed to support and stimulate interaction between users.

[1567] A "communication realm" refers to a virtual or physical space in which users exchange information.

[1568] "The program provides questions or topics to members" means that the software presents specific topics or questions to users.

[1569] "Collecting user responses and saving communication history" refers to recording the actions and comments made by the user and making them available for later reference.

[1570] "Adjusting the program's operating procedures based on collected data" refers to analyzing stored data and changing the software's behavior based on the results.

[1571] "Evaluate the system's performance and add new topics and questions" refers to evaluating the system's operation and introducing new topics and questions as a way to improve it.

[1572] An "emotion recognition engine" refers to software that analyzes text and voice to estimate a user's emotions.

[1573] "Analyzing the user's emotions" refers to determining the user's emotional state based on input data.

[1574] The following describes in detail the mode for carrying out the present invention. The present invention is a system designed to promote communication within a team and improve business results, and incorporates an emotion recognition engine that recognizes the emotions of a user. The system includes elements of a server, a terminal, and a user.

[1575] Overall system configuration

[1576] The server first sets up a database and registers team member information. This registration is done using Python and a MySQL database. Specifically, the server stores user names, job titles, schedules, and online status in the database. This allows the server to centrally manage all data and processes and constantly monitor the status of team members.

[1577] For example, the server stores in the database the name of member A as "Yamada Taro," his position as "team leader," his schedule as "meeting from 10:00 AM to 11:00 AM," and his online status as "online."

[1578] The server runs a script to check the online status of members at regular intervals and updates the results to the database. Here, we will create a monitoring script using Python. The server checks which members are currently online and updates the results to the database.

[1579] The server also uses the collected data to determine when a member is not busy. This determination is made using the Python Pandas library. For example, the server checks member A's schedule and determines that he has no appointments between 10:00 AM and 11:00 AM, and determines this time period as a "non-busy time period."

[1580] The server then generates a program (here called a bot) that facilitates communication and has it participate in the specified chat room. This bot is generated using Node.js and the Bot Framework, and the bot participates in communication areas such as Slack and Microsoft Teams.

[1581] For example, the server creates a bot that participates in a "chat room" and has the bot send the message "Good morning. How are you today?"

[1582] The bot prompts members with questions and topics at appropriate times, using generative AI models (such as OpenAI's GPT-3) to generate natural-sounding dialogue. For example, at 10 a.m., the bot posts to the chat room, "Good morning, everyone. How was your weekend?" The following is an example of a prompt:

[1583] "How was your weekend?"

[1584] The bot uses an emotion recognition engine to analyze users' text messages and recognize their emotions. It uses Google Cloud Natural Language API and IBM Watson's NLP API. For example, if User A replies, "I'm a little tired," the emotion recognition engine will tag this message as "fatigue" and understand User A's emotional state.

[1585] The server collects user responses to questions and topics provided by the bot and stores the communication history. It uses Python and MySQL to store the chat history in a database. For example, it logs the conversation between users A and B and evaluates their responses.

[1586] The server uses the collected data to adjust the bot's algorithms. Based on the topics that generated the most responses, it changes its settings to offer more new questions and topics. In addition, based on the results of an emotion recognition engine, it adjusts its responses to match the user's emotional state.

[1587] Finally, the server evaluates the system's performance and adds new topics and questions, further improving the effectiveness of communication. For example, the server adds new topics such as "recently read books" and "favorite places" to the database, and the bot uses these topics to advance the conversation.

[1588] Through this system, the server, terminals, and users cooperate to realize more effective and emotionally sensitive communication, improving team cohesion and productivity.

[1589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1590] Step 1: The server registers team member information in a storage device. Specifically, the server stores the user's (e.g., "Yamada Taro") name, job title ("Team Leader"), schedule ("Meeting from 10:00 to 11:00"), and online status ("Online") in the database. The input of this step is user information, and the output is the user information stored in the database.

[1591] Step 2: The server monitors the online status of members in real time at regular intervals. It runs a Python script to check the current online status and update the database. Specifically, the script checks the online status of members and updates the database. The input of this step is the current online status of members, and the output is the updated database contents.

[1592] Step 3: The server determines the non-busy time periods for members based on the collected data. Using Python's Pandas library, it analyzes members' schedule data and finds free time periods. Specifically, it determines the time periods that do not include events such as "meetings" or "work" as "non-busy time periods." The input for this step is the members' schedule data, and the output is the determined "non-busy time periods."

[1593] Step 4: The server generates a program (bot) to facilitate communication and has it join the specified communication area. The bot is generated using Node.js and the Bot Framework, and joins a chat room such as Slack or Microsoft Teams. Specifically, the bot joins a "chat room" and posts an initial greeting message. The input to this step is an instance of the generated bot, and the output is the bot that has joined the chat room.

[1594] Step 5: The bot presents questions and topics to members at the specified times. A generative AI model (e.g., OpenAI's GPT-3) is used to generate natural dialogue. The prompt sentence is "How was your weekend?" and the generated message is sent to the member. The input for this step is the prompt sentence, and the output is the generated question or topic message.

[1595] Step 6: The bot uses an emotion recognition engine to analyze the user's message and recognize emotions. It uses Google Cloud Natural Language API or IBM Watson's NLP API to analyze the user's text message. Specifically, if the user replies "I'm a little tired," the emotion recognition engine detects "fatigue." The input for this step is the user's text message, and the output is the analyzed emotion data.

[1596] Step 7: The server collects user responses to the questions and topics provided by the bot and saves the communication history. Using Python and MySQL, the server records the user's messages and responses in a database. Specifically, it saves the conversation between users A and B as a log. The input to this step is the user's response data, and the output is the communication history saved in the database.

[1597] Step 8: The server adjusts the bot's calculation procedures based on the collected data. It analyzes the data and changes the settings to prioritize topics that users responded well to. It also adjusts responses taking into account the results of the emotion recognition engine. The input to this step is the collected communication history data and emotion data, and the output is the adjusted bot's calculation procedures.

[1598] Step 9: The server evaluates the system's performance and adds new topics and questions to the database. Specifically, based on the system's evaluation results, new topics such as "recently read books" and "favorite places" are added to the database, and the bot uses these topics to advance the conversation. The input to this step is the system's evaluation data, and the output is the new topics and questions that have been added.

[1599] (Application example 2)

[1600] 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."

[1601] The present invention aims to solve problems related to online communication between teams and individuals. Conventional systems are limited to providing standard questions and topics without considering the user's emotions, which can lead to low user satisfaction. Furthermore, even in systems that utilize emotion recognition technology, the analysis results are often not effectively utilized, making it difficult to provide optimal information to users. This leads to issues such as a decline in the quality of communication and a lack of effectiveness in business and experience.

[1602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1603] In this invention, the server includes means for registering information about team members in a database, means for monitoring the online status of members in real time, means for determining time periods when members are not busy, means for generating a bot to promote communication and having it participate in a chat room, means for the bot to provide questions and topics to members, means for collecting user responses and saving chat histories, means for adjusting the bot algorithm based on the collected data, means for evaluating system performance and adding new topics and questions, means for analyzing user emotions and recommending products and information based on the analysis results, and means for promoting communication at optimal times based on the emotion analysis results. This enables flexible and effective information provision and communication that takes into account the user's emotional state.

[1604] "Means for registering team member information in a database" refers to the ability to store information such as each team member's name, job title, schedule, and online status in a database.

[1605] "Means of monitoring members' online status in real time" refers to the ability to constantly check the online status of team members and update that status with the latest information.

[1606] "Means for determining when members are not busy" refers to a function that automatically identifies when team members are not busy based on their schedules and activities.

[1607] "A means of generating bots that promote communication and having them participate in chat rooms" refers to the function of creating bots that automatically engage in conversations and have them participate in designated chat rooms in order to stimulate communication within a team.

[1608] "A means for the bot to provide questions and topics to members" refers to the function whereby the generated bot provides questions and topics to team members at appropriate times, promoting interaction.

[1609] "Means for collecting user responses and saving chat history" refers to a function for collecting team members' responses to chats and saving the content in a database.

[1610] "Means for adjusting the bot's algorithm based on collected data" refers to a function for analyzing saved chat data to optimize the bot's dialogue algorithm.

[1611] "Means for evaluating the system's performance and adding new topics and questions" refers to the ability to evaluate the system's functionality and effectiveness and, if necessary, add new topics and questions to improve its performance.

[1612] "Means of analyzing user emotions and recommending products and information based on the analysis results" refers to a function that uses emotion recognition technology to analyze user emotions and recommend appropriate products and information based on the results.

[1613] "Means to promote communication at the optimal time based on the results of emotion analysis" refers to a function that provides questions and topics at the most effective time based on the results of emotion analysis of the user, thereby promoting communication.

[1614] The following describes in detail the mode for carrying out the present invention. The present invention is a system that uses a smartphone application to promote communication within a team or in a virtual store and combines it with an emotion engine that analyzes user emotions. This system combines the elements of a server, a terminal, and a user to provide optimal information and communication.

[1615] 1. Server configuration

[1616] The server is the centralized center for all data and processes. It registers team member and user information in a database and monitors their online status in real time. It also utilizes an emotion engine and generative AI model to analyze user messages and provide appropriate information.

[1617] 2.Register team member information

[1618] The server registers information such as team member names, job titles, schedules, and online status in a database, allowing the status of each member to be monitored in real time.

[1619] 3. Monitor your online status

[1620] The server periodically checks the online status of all members and updates the database based on this information, allowing you to know exactly when members are available.

[1621] 4. Determining the right timing

[1622] The server uses the collected data to determine when members are less busy, for example, when they are not tied down with meetings or other tasks.

[1623] 5. Creating a bot and joining a chat room

[1624] The server generates a bot to facilitate communication and invites it into a designated chat room. The bot stimulates interaction among team members through casual conversation and questions.

[1625] 6. Ask questions and share topics

[1626] The generated bot will then prompt members with questions and topics at the appropriate time, such as "Good morning everyone. How was your weekend?"

[1627] 7. Emotion analysis using an emotion engine

[1628] The bot uses an emotion engine to analyze emotions from users' text messages. The emotion engine analyzes messages sent by users and recognizes, for example, a message such as "I'm a little tired" as "tired."

[1629] 8.Collecting and analyzing user responses

[1630] The server collects user responses to questions and topics posed by the bot and stores the chat history for future reference and analysis.

[1631] 9. Algorithm Adjustments

[1632] The server adjusts the bot's algorithm based on the collected data. If a particular topic generates a positive response, the server changes the settings to address that topic more frequently. It also adjusts responses based on the results of the emotion engine to match the user's emotional state.

[1633] 10. Add new topics or questions

[1634] The server evaluates the system's performance and adds new topics and questions, for example, adding new chat topics to the database to improve communication effectiveness.

[1635] 11. User sentiment analysis and product recommendation

[1636] The server analyzes the user's emotions and recommends products and information based on the analysis results. For example, if the server determines that the user is feeling a little depressed, it will recommend products that will lift the user's spirits.

[1637] 12. Promoting communication based on emotion analysis results

[1638] Based on the results of sentiment analysis, questions and topics are presented at the optimal time to promote effective communication. For example, if the user has a positive reaction, an additional comment expressing gratitude is provided.

[1639] Specific examples

[1640] For example, if a user types, "I haven't been feeling well lately," the emotion engine will recognize this as "tired" or "depressed," and the bot will ask, "Would you like to see your favorite products?" and recommend products that will help refresh them.

[1641] Prompt Sentence Examples

[1642] Design an application that performs sentiment analysis on text entered by a user and recommends appropriate products.

[1643] If the emotional state is "happy", recommend a product from product list A.

[1644] If the emotional state is "neutral," recommend a product from product list B.

[1645] If the emotional state is "sad", recommend a product from product list C.

[1646] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1647] Step 1:

[1648] The server registers team member information in a database. Specifically, it receives individual information such as team member name, job title, schedule, and online status as input data and saves it in the database. This process centralizes the management of each member's basic information.

[1649] Step 2:

[1650] The server monitors the online status of team members in real time at regular intervals. It receives online status check requests as input data, obtains the current online status of each member, and updates the database. This process ensures that the latest online status is always available.

[1651] Step 3:

[1652] The server determines when members are not busy. It receives the members' schedule information as input, executes logic to determine "not busy times" based on that information, and saves the output in a database. This process allows the optimal communication timing for each member to be determined.

[1653] Step 4:

[1654] The server generates a bot to facilitate communication and has it join the specified chat room. It receives a bot creation request as input, creates a corresponding bot, and has it join the chat room. Through this process, the bot plays the role of facilitating communication.

[1655] Step 5:

[1656] The server then has the generated bot provide questions and topics at the appropriate time. It references the user's online status and quiet times, sends a request to the bot to generate questions and topics, and posts the results in the chat room. This process promotes natural communication.

[1657] Step 6:

[1658] The server collects user responses to questions and topics posed by the bot, stores the chat history, and takes user replies as input data and stores them in a database. This processing allows for future data analysis and trend identification.

[1659] Step 7:

[1660] The server adjusts the bot's algorithm based on the collected data. It uses the collected chat history as input data to evaluate and optimize the algorithm's performance. This process improves the quality of the bot and enables more effective communication.

[1661] Step 8:

[1662] The server evaluates the system's performance and adds new topics and questions. It uses the performance data for system evaluation as input, executes new topic and question generation requests based on the evaluation results, and stores them in the database. This process allows users to receive the latest and most relevant topics.

[1663] Step 9:

[1664] The server analyzes the text entered by the user using an emotion engine and recommends products and information based on the analysis results. The server receives the user's message content as input data, analyzes it using the emotion engine, executes product recommendation logic based on the analysis results, and outputs the most suitable products and information. This process allows for recommendations that are individually customized for the user.

[1665] Step 10:

[1666] The server promotes communication at the optimal timing based on the results of emotion analysis. It receives the emotion analysis results as input, executes logic to optimize the timing of communication based on those results, and instructs the bot on the results. This process enables questions and topics to be asked at the optimal timing according to the user's emotional state.

[1667] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1668] 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.

[1669] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1670] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1671] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1672] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1673] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1674] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1675] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1676] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1677] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1678] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1679] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1680] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1681] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1682] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1683] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1684] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1685] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1686] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1687] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1688] The following is further disclosed regarding the above embodiment.

[1689] (Claim 1)

[1690] A means for registering team member information in a database;

[1691] A means of monitoring members' online status in real time;

[1692] A means of determining when members are not busy; and

[1693] A means to generate bots to facilitate communication and have them participate in chat rooms;

[1694] The bot provides members with questions and topics to discuss.

[1695] a means for collecting user responses and storing chat history;

[1696] A way to adjust the bot's algorithm based on the collected data, and

[1697] a means of evaluating the system's performance and adding new topics and questions;

[1698] A system including:

[1699] (Claim 2)

[1700] 10. The system of claim 1, further comprising means for indicating when to check the online status of a member.

[1701] (Claim 3)

[1702] 2. The system according to claim 1, further comprising means for analyzing chat histories between members to optimize timing for the next chat.

[1703] "Example 1"

[1704] (Claim 1)

[1705] A means for registering team member information in a database;

[1706] A means of monitoring members' online status in real time;

[1707] A means of determining when members are not busy; and

[1708] means for generating programs that facilitate interaction and participating in an interactive environment;

[1709] The program provides a means for members to ask questions and share topics.

[1710] a means for collecting user responses and storing a dialogue history;

[1711] a means of adjusting program behavior based on the collected data; and

[1712] a means of evaluating the system's performance and adding new topics and questions;

[1713] A system including means for executing the system in a cloud computing environment.

[1714] (Claim 2)

[1715] 10. The system of claim 1, further comprising means for indicating when to check the online status of a member.

[1716] (Claim 3)

[1717] 2. The system according to claim 1, further comprising means for analyzing a history of interactions between members to optimize the timing of the next interaction.

[1718] "Application Example 1"

[1719] (Claim 1)

[1720] A means for registering team member information in a database;

[1721] A means of monitoring members' online status in real time;

[1722] A means of determining when members are not busy; and

[1723] A means for generating an automatic responder that promotes communication and for participating in a conversation room;

[1724] A means for an automated responder to provide questions and topics to members;

[1725] a means for collecting user responses and storing conversation history;

[1726] a means for adjusting the algorithm of the automatic responder based on the collected data;

[1727] a means of evaluating the system's performance and adding new topics and questions;

[1728] A system including:

[1729] (Claim 2)

[1730] 10. The system of claim 1, further comprising means for indicating when to check the online status of a member.

[1731] (Claim 3)

[1732] 2. The system according to claim 1, further comprising means for analyzing a conversation history between members and optimizing the timing of the next chat.

[1733] "Example 2: Combining Emotion Engines"

[1734] (Claim 1)

[1735] A means for registering information of team members in a storage device;

[1736] A means of monitoring members' online status in real time;

[1737] A means of determining when members are not busy; and

[1738] means for generating programs that facilitate communication and participate in the communication field;

[1739] The program provides a means for members to ask questions and share topics.

[1740] a means for collecting user responses and storing communication history;

[1741] a means for adjusting the program's computational procedures based on the collected data; and

[1742] a means of evaluating the system's performance and adding new topics and questions;

[1743] means for analyzing a user's emotions using an emotion recognition engine;

[1744] A system including:

[1745] (Claim 2)

[1746] 10. The system of claim 1, further comprising means for indicating when to check the online status of the member.

[1747] (Claim 3)

[1748] 2. The system according to claim 1, further comprising means for analyzing communication history between members and optimizing the timing of the next chat.

[1749] "Application example 2 when combining emotion engines"

[1750] (Claim 1)

[1751] A means for registering team member information in a database;

[1752] A means of monitoring members' online status in real time;

[1753] A means of determining when members are not busy; and

[1754] A means to generate bots to facilitate communication and have them participate in chat rooms;

[1755] The bot provides members with questions and topics to discuss.

[1756] a means for collecting user responses and storing chat history;

[1757] A way to adjust the bot's algorithm based on the collected data, and

[1758] a means of evaluating the system's performance and adding new topics and questions;

[1759] A means for analyzing user emotions and recommending products and information based on the analysis results;

[1760] A means to promote communication at the optimal time based on the results of sentiment analysis, and

[1761] A system including:

[1762] (Claim 2)

[1763] 10. The system of claim 1, further comprising means for indicating when to check the online status of a member.

[1764] (Claim 3)

[1765] 2. The system according to claim 1, further comprising means for analyzing chat histories between members to optimize timing for the next chat. [Explanation of symbols]

[1766] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for registering team member information in a database; A means of monitoring members' online status in real time; A means of determining when members are not busy; and A means to generate bots to facilitate communication and have them participate in chat rooms; The bot provides members with questions and topics to discuss. a means for collecting user responses and storing chat history; A way to adjust the bot's algorithm based on the collected data, and a means of evaluating the system's performance and adding new topics and questions; A system including:

2. 10. The system of claim 1, further comprising means for indicating when to check the online status of members.

3. The system according to claim 1, further comprising means for analyzing chat histories between members to optimize timing for the next chat.

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