System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting information; means for transmitting the inputted information to a server; means for generating questions based on the information received on the server; means for transmitting the generated questions to a player; means for receiving answers from the player; means for organizing the received answers to generate a report; and means for transmitting the generated report to a manager.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In traditional management work, the information exchange between managers and players was often complicated, slowing down the overall speed of the work. The underlying cause of this problem was the difference in the granularity of the information provided by players, which required managers to organize the information, and players tended to neglect to report progress and status. Therefore, there was a need to smoothly exchange information between managers and players and improve work efficiency. [Means for solving the problem]

[0005] The present invention provides a system including a means for inputting information, a means for transmitting the input information to a server, a means for generating questions based on the information received on the server, a means for transmitting the generated questions to players, a means for receiving answers from players, a means for organizing the received answers and creating a report, and a means for transmitting the created report to a manager. In particular, by including a means for using a generation AI when generating questions on the server and a means for sending questions to players in chat format, a system is constructed that can efficiently collect, organize, and report information that managers want to confirm. This facilitates information sharing between managers and players, improving the overall speed of operations.

[0006] The "means for inputting information" is an interface for inputting information that the user wants to check into the system.

[0007] "Means for transmitting input information to a server" refers to a mechanism for transferring information input by a user to a server.

[0008] The "means for generating questions based on information received on the server" is a function for automatically generating necessary questions based on information received by the server.

[0009] A "means for generating questions" is an algorithm or program that generates appropriate questions based on specific information.

[0010] The "means for transmitting the generated question to the player" refers to a communication means for notifying the player of the generated question.

[0011] "Means for receiving answers from players" refers to the mechanism by which the system receives answers from players.

[0012] "Means for organizing received responses and creating a report" refers to a function that analyzes received response data and compiles it into a report in a set format.

[0013] The "means for sending the created report to the manager" refers to a communication means or display means for providing the organized report to the manager.

[0014] "Generative AI" is a technology that uses machine learning models and natural language processing models to automatically generate questions and answers based on user input.

[0015] "Chat format" is a method of communicating with users in the form of text-based messages. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system for improving the efficiency of information sharing between a manager and players. Specific embodiments of the present invention will be described below.

[0038] The system includes "means for inputting information," "means for transmitting input information to a server," "means for generating questions based on information received on the server," "means for transmitting the generated questions to players," "means for receiving answers from players," "means for organizing received answers and creating reports," and "means for transmitting the created reports to a manager."

[0039] How to enter information

[0040] The user (manager) inputs the information they want to check into the device (app). Specifically, they can input information such as "task progress" and "current issues" through a project management app or a dedicated interface.

[0041] A means of sending input information to the server

[0042] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[0043] means for generating questions based on the information received on the server;

[0044] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What percentage of your task is complete?" or "What are your current challenges?"

[0045] A means of sending generated questions to players

[0046] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[0047] A means of receiving answers from players

[0048] The user (player) inputs answers to questions displayed on the terminal. Answers can be entered in multiple choice format or free text format, and the terminal sends the answers to the server in real time.

[0049] A means of organizing and reporting on the responses received

[0050] The server analyzes the responses received and generates formatted reports. A database is used to manage the responses, and dashboards and report generation tools allow managers to understand the overall situation at a glance.

[0051] A means of sending the generated report to a manager

[0052] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[0053] Specific examples

[0054] For example, consider a scenario where a manager is checking the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues." The device sends this information to a server, which then uses a generative AI to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[0055] The generated questions are sent to each player's device in chat format. The player then responds with specific answers, such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to a server, which analyzes the received data and creates a standardized report. This report is then sent to the manager's device, allowing them to centrally monitor the project's progress.

[0056] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[0057] The processing flow will be explained below.

[0058] Understood. Below is a detailed explanation of the program processing of this system.

[0059] Step 1:

[0060] The user (manager) enters the progress of the task or questions they want to check into the device (app). For example, they enter "progress of task A" or "current issues of task B" in text format.

[0061] Step 2:

[0062] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[0063] Step 3:

[0064] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (e.g., progress or problem).

[0065] Step 4:

[0066] The server uses a generative AI model to generate questions based on the information it receives, such as "What percentage of task A is complete?" or "What are the current challenges for task B?"

[0067] Step 5:

[0068] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[0069] Step 6:

[0070] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[0071] Step 7:

[0072] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[0073] Step 8:

[0074] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[0075] Step 9:

[0076] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.).

[0077] Step 10:

[0078] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[0079] Step 11:

[0080] The user (manager) can check the progress and a summary of responses from the players through the terminal. Based on the displayed report, they can consider the progress of the project and measures to address issues.

[0081] Through these steps, the system can streamline information sharing between managers and players, improving the speed of the entire operation.

[0082] Example 1

[0083] 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."

[0084] In conventional project management systems, information sharing between managers and players is inefficient, making it difficult to accurately grasp progress and issues. In particular, manual information input and data organization are cumbersome and time-consuming, which can slow down the overall speed of work. Furthermore, because the generated data is not managed centrally, it is difficult to check progress in real time or share issues.

[0085] 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.

[0086] In this invention, the server includes means for analyzing information and generating appropriate questions using a generative AI model, means for sending the generated questions to players' devices in chat format, and means for receiving and analyzing answers sent by players in real time. This makes it possible to streamline information sharing between managers and players and improve the speed of the entire business.

[0087] The "means for inputting information" refers to an interface that allows a manager to use a terminal to input information such as the progress of a task or issues.

[0088] "Structuring as data packets" refers to a technical technique that converts input information into a uniform format so that it can be efficiently transmitted and received.

[0089] A "communications network" is an infrastructure for sending and receiving data, such as the Internet or an internal network.

[0090] A "server" is a computer system that receives requests from clients via a network and processes them appropriately.

[0091] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate appropriate questions and answers based on input data.

[0092] The "means for generating questions" is a function that uses a generative AI model to create questions based on the information received by the server.

[0093] "Chat format" is a user interface format that supports interactive messaging.

[0094] The "means for transmitting to the terminal" is a mechanism for distributing the generated question to the device used by the player via a network.

[0095] A "player" is a user who is in charge of a task in a project and reports on progress and issues.

[0096] The "means for receiving answers" is a process for the server to obtain and analyze the answers entered by the player.

[0097] A "means for analyzing answers" is a system that processes received player answers and converts them into meaningful information.

[0098] A "formatted report" is a report with a set format that is generated based on collected and analyzed data.

[0099] "Electronic format" refers to a document format that can be stored and viewed digitally, such as PDF or Excel.

[0100] The "means for sending to the manager's terminal" is a function for distributing the report created by the server to the manager's device.

[0101] "Real-time" refers to the property that data processing and communication are instantaneous and without delay.

[0102] The present invention is a system for improving the efficiency of information sharing between a manager and players. A specific embodiment of the present invention will be described.

[0103] First, the user (manager) enters information such as the "progress of tasks" or "current issues" they want to check into their device through a project management app or dedicated interface. This information is structured as a data packet and sent to the server via the Internet or an internal network. HTTP or HTTPS is used as the communication network.

[0104] The server receives the information sent from the device, stores it in a database, and prepares it for further analysis, which may include data integrity checks and data validation.

[0105] Based on the received information, the server uses a generative AI model (for example, OpenAI's GPT-4 (registered trademark)) to generate appropriate questions. This generation uses prompt sentences. Prompt sentences are sentences that instruct the AI ​​model on what questions it should generate. For example, "What is the progress rate for Task A?" or "Are there any problems with Task A right now?"

[0106] The generated questions are sent from the server to the player's device in chat format, allowing players to respond intuitively. This interface is displayed on the mobile and desktop apps.

[0107] Players enter answers to questions displayed on their terminals. Answers can be multiple choice or free description. The terminals send the answers entered by the players to the server in real time. Real-time transmission means that answers are instantly updated on the server.

[0108] The server analyzes the received responses and generates formatted reports, which are then managed using a database and a dashboard and report generation tool, allowing managers to see the overall situation at a glance.

[0109] Finally, the server sends the created report to the manager's terminal in electronic format such as PDF or Excel, and the manager is notified of the report via email or in-system notification.

[0110] Specific examples

[0111] For example, when a manager checks the progress of a new project, they use a device to input the "task progress rate" and "current issues." The device then sends this information to the server, which uses a generative AI model to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[0112] The generated questions are sent to each player's device in chat format, and the player responds with specific answers such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to the server, which analyzes the received data and creates a standardized report.

[0113] Prompt Sentence Examples

[0114] Below are some example prompts to input to a generative AI model:

[0115] "What is the progress percentage of Task A?"

[0116] "Are you currently having any problems with Task A?"

[0117] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] The user (manager) enters information such as "task progress" and "current issues" that they want to check into their device through a project management app or a dedicated interface.

[0121] The information entered is specific data such as "Task A's progress rate is 50%" and "Task B's progress rate is 75%."

[0122] This information is input to the terminal, which then structures it into data packets.

[0123] Step 2:

[0124] The terminal sends structured data packets to the server via the Internet or an internal network.

[0125] In this case, HTTP or HTTPS is used as the communication protocol, and the data packets are in JSON or XML format.

[0126] Stable and secure data transfer is achieved.

[0127] Step 3:

[0128] The server receives the data packets sent from the terminal.

[0129] The received information is stored in a database and prepared for analysis.

[0130] Specifically, the received data is checked for consistency and validated, and then formatted into an analyzable format.

[0131] Step 4:

[0132] The server generates prompt sentences for a generative AI model (e.g., OpenAI's GPT-4) based on the received information.

[0133] Prompts are specific questions such as "What percentage of task A is complete?" or "What are your current challenges?"

[0134] This generates appropriate questions based on the information entered.

[0135] Step 5:

[0136] The server sends the generated questions to the player's terminal in chat format.

[0137] Specifically, questions are delivered in real time using WebSocket and API.

[0138] The questions are displayed in a chat box so that players can intuitively enter their answers.

[0139] Step 6:

[0140] The user (player) inputs answers to questions displayed on the terminal.

[0141] The answers are specific data such as "Progress: 80%" or "Challenges: None in particular."

[0142] The terminal transmits this response to the server in real time.

[0143] Step 7:

[0144] The server receives the responses sent by the players.

[0145] The received data is stored in a database and preparations for analysis are underway.

[0146] Specifically, the response data is checked for consistency and formatted.

[0147] Step 8:

[0148] The server analyzes the received responses and generates a formatted report.

[0149] Response data is aggregated from the database and visualized using dashboards and report generation tools.

[0150] View average progress and a list of assignments.

[0151] Step 9:

[0152] The server sends the generated report to the manager's terminal in an electronic format such as PDF or Excel.

[0153] Notify managers of the report's existence via email and in-system notifications.

[0154] This allows managers to see the progress of projects from a single location.

[0155] (Application example 1)

[0156] 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."

[0157] In the past, information sharing regarding work progress and issues within factories often relied on manual labor, resulting in frequent delays and miscommunication of information, lower productivity, and delayed problem resolution. It was also difficult to grasp the actual work status of each robot in real time, and it took a long time for managers to grasp the overall situation. As a result, efficient work management and rapid response are required.

[0158] 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.

[0159] In this invention, the server includes a means for inputting information, a means for transmitting the input information to the server via a communication network, a means for generating questions using a generative AI model based on the information received on the server, a means for transmitting the generated questions to players in chat format, a means for receiving answers from players in real time, a means for analyzing the received answers and creating reports on progress and issues, and a means for sending the created reports in PDF or Excel format to a manager. This automates the process from inputting information to generating questions, collecting answers, and creating and sending reports, making it possible to grasp the progress and issues of work in the factory in real time.

[0160] "Means for inputting information" refers to the function that allows the user (manager) to input information such as progress and tasks into the terminal.

[0161] "Means for transmitting input information to a server via a communication network" refers to a communication function for transmitting data from a terminal to a server.

[0162] "Means for generating questions using a generative AI model" refers to a function that automatically generates appropriate questions using generative AI technology based on received information.

[0163] "Means for sending to players in chat format" refers to a function for sending generated questions to players' terminals in chat format.

[0164] "Means for receiving answers from players in real time" refers to a function for instantly transmitting answers from players to a server and receiving them in real time.

[0165] "Means for analyzing the received responses and generating progress and assignment reports" refers to a function that analyzes the responses received by the server and generates reports on progress and current assignments.

[0166] "A means of sending the created report to the manager in PDF or Excel format" refers to the function of converting the created report into PDF or Excel format and sending it to the manager.

[0167] The present invention provides a system that improves the efficiency of information sharing between managers and players and enables real-time understanding of work progress and issues within a factory. Specific embodiments of the present invention will be described below.

[0168] Hardware and software used

[0169] Hardware:

[0170] User device: The smartphone, tablet, or PC used by the manager.

[0171] Player terminal: The control terminal for the robots working in the factory.

[0172] Communication networks: Internal networks, including Wi-Fi and wired LAN, and the Internet.

[0173] software:

[0174] Server: A server on the cloud or a local server in the factory.

[0175] Generative AI models: For example, using generative AI technologies such as OpenAI's GPT series.

[0176] Chat-style interface: Chat applications (e.g. Telegram, Slack, etc.) built into the user and robot devices.

[0177] Data processing and calculation flow

[0178] 1. Enter and submit information:

[0179] Users use smartphones, tablets, or PCs to input information about work progress and current issues. For example, they input specific data such as a task's progress rate of "60%" or a current issue of "missing parts." This information is sent to the server as structured data packets over a communications network.

[0180] 2. Question generation:

[0181] The server analyzes the received data and uses a generative AI model to generate appropriate questions, such as "What is the progress rate of the current task?" or "What is the current challenge?"

[0182] 3. Submitting Questions and Receiving Answers:

[0183] The generated questions are sent to the players' (robots') terminals in chat format. Each robot responds to the questions in real time, and the answers are immediately sent to the server.

[0184] 4. Response analysis and reporting:

[0185] The server analyzes the received responses and creates a progress and task report. This report is converted into a format such as PDF or Excel and sent to the user's device. The report visualizes each robot's progress and tasks so that they can be understood at a glance.

[0186] Specific examples

[0187] For example, consider the case where a manager wants to check the progress of a new project. The manager uses his or her smartphone to input information such as "Progress rate: 60%" and "Current issue: Parts missing." This information is sent to a server, which uses a generative AI model to generate questions such as "What is the progress rate of the current task?" and "What is the current issue?" The generated questions are sent to the chat-style interface of each robot in the factory, and the robot responds in real time with "Progress rate: 75%" and "Issue: New parts have arrived." Based on these responses, the server creates a report and sends it in PDF format to the manager's device.

[0188] Prompt Sentence Examples

[0189] Below are some examples of prompt sentences to input to the generative AI model.

[0190] Problem-solving question generation:

[0191] input:

[0192] Task progress: 60%

[0193] Current issue: Parts shortage

[0194] output:

[0195] What is the current task progress percentage?

[0196] What are your current challenges?

[0197] In this way, the present invention improves the efficiency of information sharing between managers and players, making it possible to grasp the progress and issues of work within the factory in real time.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] The user (manager) enters information. Specifically, they log in to the application using a smartphone, tablet, or PC and enter specific data such as "Task progress: 60%" and "Current issue: missing parts." This becomes the input data.

[0201] Step 2:

[0202] The device sends the information entered by the user to the server in real time. The input data is sent as structured data packets using a communication network (Wi-Fi or wired LAN), and the server receives the input data.

[0203] Step 3:

[0204] The server analyzes the received data. Based on the analyzed data, it activates a generative AI model and generates questions. For example, if the prompts "Task progress rate: 60%" and "Current issue: missing parts" are input, the generative AI model will generate questions such as "What is the current task progress rate?" and "What is the current issue?" This becomes the output data.

[0205] Step 4:

[0206] The server sends the generated questions to each robot's terminal in chat format. The robot receives the questions using a chat application and displays the answers. This interface is visual and intuitive.

[0207] Step 5:

[0208] The player (robot) inputs answers to questions sent from the server in real time. For example, they input information such as "Task progress: 75%" or "Current issue: New parts have arrived." This becomes the input data.

[0209] Step 6:

[0210] The terminal transmits the answers entered by the player to the server in real time. The answer data is transmitted to the server as a structured data packet using a communication network, and the server receives the answer data.

[0211] Step 7:

[0212] The server analyzes the received response data. Based on the analyzed data, it generates progress and assignment reports. The reports are created in PDF or Excel format. This report generation process also accesses the database, compares the data with past data, and visualizes it on a dashboard.

[0213] Step 8:

[0214] The server sends the created report to the manager's terminal. The report is communicated via email and in-system notifications, allowing the manager to grasp the project's progress and current issues in real time.

[0215] 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.

[0216] The present invention is a system for improving the efficiency of information sharing between managers and players, and by combining it with an emotion engine that recognizes the emotions of users, it achieves more accurate information gathering and communication. Specific embodiments of the present invention will be described below.

[0217] The system comprises a "means for inputting information," a "means for transmitting input information to a server," a "means for generating questions based on information received on the server," a "means for transmitting the generated questions to the player," a "means for receiving answers from the player," a "means for organizing the received answers and creating a report," a "means for sending the created report to a manager," and an "emotion engine."

[0218] How to enter information

[0219] The user (manager) inputs the information they want to check into the device (app). For example, they can input "progress of task A" or "current issues of task B" through a project management app or a dedicated interface.

[0220] A means of sending input information to the server

[0221] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[0222] means for generating questions based on the information received on the server;

[0223] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?"

[0224] Use of emotion engine

[0225] The emotion engine recognizes emotions from user (player) input and dialogue and analyzes that data. The recognized emotion data is incorporated into the question generation process and used to adjust the tone and specificity of the questions. For example, if the player is feeling stressed, the tone of the questions will be softened or changed to show more interest.

[0226] A means of sending generated questions to players

[0227] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[0228] A means of receiving answers from players

[0229] The user (player) inputs answers to questions displayed on the device. For example, answers can be entered in the form of "Progress: 80%" or "Challenges: None in particular," and the device can send these to the server in real time.

[0230] A means of organizing and reporting on the responses received

[0231] The server analyzes the received response data and creates standardized reports. The database manages responses, and dashboards and report generation tools allow managers to grasp the overall situation at a glance. Analysis of emotional data also allows for the creation of detailed reports that include players' mental states.

[0232] A means of sending the generated report to a manager

[0233] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[0234] Specific examples

[0235] For example, consider a scenario in which a manager checks the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues," and the device sends this information to the server. The server analyzes the received information and uses a generative AI and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player responds with specific answers such as "Progress rate: 80%" and "Issues: None in particular."

[0236] Emotional data is also collected along with the player's responses. For example, if the player is feeling anxious, that emotional data is also sent to the server. The server analyzes the received data and creates a report such as, "The player's progress rate is 80%, there are no particular issues, but he is feeling anxious." Finally, this report is sent to the manager's device, allowing them to understand the player's mental state as well as the project's progress.

[0237] In this way, the present invention is a system that realizes more comprehensive management by streamlining information sharing between managers and players and utilizing emotional data.

[0238] The processing flow will be explained below.

[0239] Understood. The processing of this system will be explained in detail below.

[0240] Step 1:

[0241] The user (manager) inputs the progress of the task or questions they want to check into the device (app). For example, they input information such as "progress of task A" or "current issues of task B."

[0242] Step 2:

[0243] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[0244] Step 3:

[0245] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (progress or problem).

[0246] Step 4:

[0247] The server starts the emotion engine and collects data to recognize the user's (player's) emotions. The emotion engine analyzes the player's past chat history and current input data to estimate their emotional state.

[0248] Step 5:

[0249] The server uses a generative AI model to generate questions based on the information and emotional data it receives, adjusting the tone of the questions to soften them if the player is feeling stressed, for example.

[0250] Step 6:

[0251] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[0252] Step 7:

[0253] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[0254] Step 8:

[0255] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[0256] Step 9:

[0257] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[0258] Step 10:

[0259] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.). It also analyzes emotional data and reflects the player's mental state in the report.

[0260] Step 11:

[0261] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[0262] Step 12:

[0263] The user (manager) can check the progress and summary of responses from the players through the device. Based on the displayed report, they can consider the progress of the project and measures to address issues. In addition, by understanding the emotional state of the players, they can manage the project more appropriately.

[0264] Through these steps, this system streamlines information sharing between managers and players and utilizes emotional data to achieve more comprehensive management.

[0265] Example 2

[0266] 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."

[0267] Previous information sharing systems lacked efficient communication between managers and players, making it particularly difficult to grasp the players' emotional states. This meant that managers were unable to properly manage players' performance and psychological state. Furthermore, the quality and tone of the questions generated did not match the players' emotional states, resulting in answers that lacked accuracy and candor.

[0268] 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.

[0269] In this invention, the server includes means for generating questions using an emotion engine that recognizes the user's emotions, means for organizing the received answers and emotion data to create a report, and means for sending the created report to the manager. This makes it possible to generate questions that reflect the player's emotional state, increasing the player's response rate and accuracy and enabling the manager to appropriately manage the player's performance and psychological state.

[0270] The "means for inputting information" refers to a means for providing information that the user wants to check through an input device such as a terminal.

[0271] The "means for transmitting input information to a server" refers to a means for structuring information input by a user into a data packet and transmitting it to a server via the Internet or an internal network.

[0272] "Means for generating questions based on information received on the server" refers to means for the server to analyze the information received and automatically create appropriate questions using a generative AI model.

[0273] The "emotion engine that recognizes user emotions" is an engine that recognizes emotions from user input and dialogue data and analyzes that data.

[0274] The "means for sending generated questions to players" refers to a means for the server to send automatically generated questions to the player's terminal, and utilizes a chat-style interface.

[0275] The "means for receiving answers from players" refers to the means by which players answer questions through their terminals and transmit the answers to the server in real time.

[0276] The "means for organizing received answer and emotion data and creating a report" is a means for the server to analyze the answer data and emotion data received from the players and create a standardized report.

[0277] The "means for sending the created report to the manager" is a means for sending the report created by the server to the manager's terminal so that the manager can check the status of the player.

[0278] A "generative AI model" is an artificial intelligence model that automatically generates questions based on received information and data.

[0279] A "prompt" is text that is input into a generative AI model and serves as the basis for the model to generate appropriate questions and responses.

[0280] The present invention is a system that improves the efficiency of information sharing between managers and players and combines it with an emotion engine that recognizes user emotions to achieve more accurate information gathering and communication. This system includes the following components:

[0281] Hardware and software used

[0282] Hardware: Servers, devices (smartphones and PCs)

[0283] Software: Project management apps, emotion engines, generative AI models, database management systems, chat interfaces

[0284] How to enter information

[0285] The user (manager) enters the information they want to check into the device through a project management app or a dedicated interface. Examples of information that a user enters include "progress of task A" and "current issues for task B." For example, they can enter "progress rate: 80%" and "current issues: none" into the text boxes of a project management app.

[0286] A means of sending input information to the server

[0287] The device structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. Protocols such as HTTP and HTTPS are used for communication, and the data is sent while checking its integrity.

[0288] means for generating questions based on the information received on the server;

[0289] The server analyzes the received information and automatically generates appropriate questions using a generative AI model. For example, questions such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?" are generated. The server temporarily stores the generated questions in a database.

[0290] Use of emotion engine

[0291] The server uses an emotion engine to recognize emotions from the user's (player's) input and dialogue, and analyzes the data. For example, if the player is feeling stressed, the server softens the tone of the questions or adjusts the content. The questions generated in this way take into account the player's psychological state.

[0292] A means of sending generated questions to players

[0293] The server then sends the generated questions to the player's device, where they can intuitively answer them using a chat-style interface that is integrated into the mobile and desktop apps.

[0294] A means of receiving answers from players

[0295] The user (player) answers questions displayed on the device. For example, specific answers such as "Progress rate: 80%" or "Challenges: None in particular" are possible. The answers are sent to the server in real time, allowing for efficient data collection.

[0296] A means to organize and report on received response and sentiment data

[0297] The server analyzes the received response data and emotional data and creates a formatted report that details the player's task progress and emotional state, allowing managers to grasp the overall situation at a glance. The report can be output in PDF or Excel format.

[0298] A means of sending the generated report to a manager

[0299] The server then sends the generated report to the manager's device, where it is provided via email and in-system notifications, allowing the manager to check the player's status.

[0300] Specific examples

[0301] For example, if a manager wants to check the progress of a new project, they can use their device to input the "task progress rate" and "current issues." The device sends this information to the server, which analyzes the received information and uses a generative AI model and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player replies with "Progress rate: 80%" and "Issues: None in particular." A report summarizing these answers and emotion data is created by the server and ultimately sent to the manager's device.

[0302] Prompt Sentence Examples

[0303] "Please tell me the progress of your current task."

[0304] "What are your current challenges with the task?"

[0305] The above is an embodiment of the present invention, which makes the information sharing between managers and players more efficient and realizes comprehensive management that utilizes emotion data.

[0306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0307] Step 1: Enter your information

[0308] The user (manager) uses a project management app or a dedicated interface to input the information they want to check into their device. The information they input includes "progress of task A" and "current issues for task B." Specifically, the user inputs "progress rate: 80%" or "current issues: none" into the app's text boxes. This input data is temporarily stored in the device's internal storage area.

[0309] Step 2: Send the information to the server

[0310] The terminal structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. HTTP or HTTPS is used as the protocol. At this time, the data integrity is checked to ensure reliable communication. The input is the task information entered by the user, and the output is the data packet sent to the server.

[0311] Step 3: Generate questions based on the received information

[0312] The server analyzes the received data packets and extracts the necessary information. Based on this information, the server uses a generative AI model to automatically generate appropriate questions. For example, based on the extracted progress information, questions such as "What is the progress rate for task A?" or "What are the current challenges for task B?" are generated. The input is the task information sent to the server, and the output is the generated question.

[0313] Step 4: Use the emotion engine to tailor your questions

[0314] The server uses an emotion engine to recognize emotions from the user's (player's) past inputs and interactions. For example, if it is determined that the player has been feeling stressed recently, the emotion data is reflected in the question generation process. The tone and content of the question are adjusted to take the player's emotions into consideration. The input is the generated question and emotion data, and the output is the adjusted question.

[0315] Step 5: Send the generated questions to the player

[0316] The server sends the generated question to the player's device through a chat-style interface, through which the player can answer the question. Specifically, the server converts the question into a message format and sends a notification to the player's app. The input is the adjusted question, and the output is the question displayed on the player's device.

[0317] Step 6: Player answers the question

[0318] The user (player) answers questions displayed on the device. For example, they input specific answers such as "Progress: 80%" or "Challenges: None in particular." The input data is sent to the server in real time. The input is the displayed question, and the output is the input answer.

[0319] Step 7: Send the answer to the server

[0320] The terminal structures the answers entered by the user and sends them to the server in real time. The communication is done using HTTP or HTTPS and the data integrity is checked. The input is the answer entered by the user and the output is the answer sent to the server.

[0321] Step 8: Organize response and sentiment data and create reports

[0322] The server analyzes the received response data and emotion data and creates a formatted report. This report details the player's task progress and emotional state. Specifically, the server manages the response data using a database and creates dashboard-style reports using a report generation tool. The input is the response data and emotion data, and the output is the generated report.

[0323] Step 9: Send the generated report to your manager

[0324] The server sends the created report to the manager's terminal. The report is output in PDF or Excel format and provided to the manager via email or internal system notification. The input is the generated report, and the output is the report displayed on the manager's terminal.

[0325] (Application example 2)

[0326] 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."

[0327] In modern factories, information sharing between workers and managers is not yet efficient, and in particular, there is a lack of systems that can take into account the emotional state of workers. As a result, awareness of work progress and problems can be insufficient, leading to reduced work efficiency and safety issues. The present invention aims to solve these problems and provide an efficient information sharing system that takes into account the emotions of workers.

[0328] 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.

[0329] In this invention, the server includes means for inputting information, means for transmitting the input information to the server, means for generating questions based on the information received on the server, means for transmitting the generated questions to players, means for receiving answers from players, means for organizing the received answers and creating a report, means for transmitting the created report to a manager, means for recognizing player emotions using an emotion engine, and means for incorporating recognized emotion data into the question generation process. This improves the efficiency of information sharing between workers and managers and enables management that takes into account the emotional states of workers.

[0330] The "means for inputting information" refers to a means for the user to input information that the user wants to check into the terminal.

[0331] The "means for transmitting input information to a server" is a means for structuring information input from a terminal into a data packet and transmitting it to a server.

[0332] The "means for generating a question based on the information received on the server" refers to a means for analyzing the information received by the server and generating an appropriate question based on that information.

[0333] The "means for transmitting the generated question to the player" refers to a means for transmitting the question generated by the server to the player's terminal.

[0334] The "means for receiving answers from the player" refers to the means by which the server receives answers entered by the player using the terminal.

[0335] The "means for organizing received answers and creating reports" refers to the means by which the server stores the player's answers in a database, analyzes them, and creates standard reports.

[0336] The "means for transmitting the created report to the manager" is a means for transmitting the report created by the server to the manager's terminal.

[0337] "Means for recognizing player emotions using an emotion engine" refers to means for recognizing emotions from player inputs and dialogues and analyzing the data.

[0338] The "means for incorporating recognized emotional data into the question generation process" refers to a means for adjusting the tone and specificity of a question based on the emotional data recognized by the emotion engine.

[0339] A "generative AI model" is an artificial intelligence model used to generate questions on the server.

[0340] A "prompt" is a document to be input into a generative AI model, and is a guided sentence to support the generation of appropriate questions.

[0341] The system for implementing this invention is constructed based on a smartphone application, a cloud server, and an emotion engine. The specific configuration and operation of the system are described below.

[0342] First, the user, a worker, uses a smartphone application to input information, such as the "current task progress" or "recognized problems." This input method is usually implemented by text input using a touchscreen or voice input. Furthermore, the application has a built-in emotion engine that also collects emotional data through facial expression and voice analysis of the worker.

[0343] The smartphone application then transmits the input information and emotion data to a cloud server over Wi-Fi or 4G / 5G networks, where the server receives the information and stores it in a database.

[0344] The cloud server uses the generative AI model to generate appropriate questions based on the received information. For example, if the received information is "Current task progress: 60%", the problem is "Machine malfunction", and the emotion is "Anxiety", the server inputs the following prompt sentence into the generative AI model:

[0345] To the question generation AI system:

[0346] Use the information below to generate appropriate questions.

[0347] Work progress: 60%

[0348] Problem: Mechanical malfunction

[0349] Emotion: Anxiety

[0350] Expected output:

[0351] Questions asking for details about the machine malfunction (e.g., What caused the machine malfunction?).

[0352] A generative AI model (e.g., OpenAI GPT-3®) generates an appropriate question based on this prompt, such as "What is the cause of the machine malfunction?"

[0353] The generated questions are then sent to the smartphone application in chat format, allowing workers to answer questions intuitively, and the response data is sent back to the cloud server in real time.

[0354] The server analyzes the received response data and generates a report that includes progress and issues, as well as worker emotion data. The report can also be used to perform more detailed analysis by referencing information in the database.

[0355] Finally, the cloud server sends the created report to the manager's device, where it is provided in PDF or dashboard format, allowing the manager to comprehensively grasp the progress of the work, any issues, and the emotional state of the workers.

[0356] In this way, the present invention provides a more comprehensive work management system by realizing efficient information sharing between workers and managers and utilizing emotion data.

[0357] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0358] Step 1:

[0359] The user (worker) opens the smartphone application and inputs information such as progress and problems. The emotion engine analyzes the worker's facial expressions and voice to recognize emotional data. At this stage, inputs include "Task progress: 60%", "Problem: machine malfunction", and "Emotion: anxiety". This data is output and temporarily saved within the application.

[0360] Step 2:

[0361] The device structures the input information and emotion data into data packets and sends them to the cloud server. Communication is via Wi-Fi or 4G / 5G networks, and the data is securely encrypted before transmission. The input is the information saved in step 1, and the cloud server receives this data as output.

[0362] Step 3:

[0363] The server parses the received information and stores it in a database. The parsing process involves data format conversion and normalization. The input is the data set sent in step 2, and the output is structured data stored in the server's database.

[0364] Step 4:

[0365] The server generates prompts to be input to the generative AI model based on the stored data. The prompts include progress, problems, and emotional data. As a specific example, the following prompt is generated: "To the question generation AI system: Please generate an appropriate question based on the following information. Work progress: 60%, Problem: Machine malfunction, Emotion: Anxiety." The input is data stored in the database on the server, and the output is the prompt to be sent to the generative AI model.

[0366] Step 5:

[0367] A generative AI model (OpenAI GPT-3) generates an appropriate question based on the prompt. The generated question takes the form, for example, "What is the cause of the machine malfunction?" The input is the prompt generated in step 4, and the output is a specific question.

[0368] Step 6:

[0369] The server sends the generated question to the player's (worker's) smartphone application. This is done in a chat format, allowing the worker to respond intuitively. The input is the question generated in step 5, and the output is the question displayed on the worker's device.

[0370] Step 7:

[0371] The user (worker) enters an answer to the question displayed on the terminal. For example, the answer is "The bolt is loose." The input is the question displayed in step 6, and the output is the answer re-entered into the terminal.

[0372] Step 8:

[0373] The terminal transmits the worker's response to the cloud server in real time. The communication is secure. The input is the response entered in step 7, and the output is the response data received by the cloud server.

[0374] Step 9:

[0375] The server again analyzes the received response data and generates a report, which includes progress, problem details, and sentiment data. This analysis includes structuring and statistical analysis of the data. The input is the response data received in step 8, and the output is the generated report.

[0376] Step 10:

[0377] The server sends the generated report to the manager's terminal. The report is provided in PDF or dashboard format and is sent via email or system notification. The input is the report generated in step 9, and the output is the report that arrives on the manager's terminal.

[0378] 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.

[0379] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] 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.

[0381] [Second embodiment]

[0382] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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."

[0394] The present invention is a system for improving the efficiency of information sharing between a manager and players. Specific embodiments of the present invention will be described below.

[0395] The system includes "means for inputting information," "means for transmitting input information to a server," "means for generating questions based on information received on the server," "means for transmitting the generated questions to players," "means for receiving answers from players," "means for organizing received answers and creating reports," and "means for transmitting the created reports to a manager."

[0396] How to enter information

[0397] The user (manager) inputs the information they want to check into the device (app). Specifically, they can input information such as "task progress" and "current issues" through a project management app or a dedicated interface.

[0398] A means of sending input information to the server

[0399] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[0400] means for generating questions based on the information received on the server;

[0401] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What percentage of your task is complete?" or "What are your current challenges?"

[0402] A means of sending generated questions to players

[0403] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[0404] A means of receiving answers from players

[0405] The user (player) inputs answers to questions displayed on the terminal. Answers can be entered in multiple choice format or free text format, and the terminal sends the answers to the server in real time.

[0406] A means of organizing and reporting on the responses received

[0407] The server analyzes the responses received and generates formatted reports. A database is used to manage the responses, and dashboards and report generation tools allow managers to understand the overall situation at a glance.

[0408] A means of sending the generated report to a manager

[0409] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[0410] Specific examples

[0411] For example, consider a scenario where a manager is checking the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues." The device sends this information to a server, which then uses a generative AI to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[0412] The generated questions are sent to each player's device in chat format. The player then responds with specific answers, such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to a server, which analyzes the received data and creates a standardized report. This report is then sent to the manager's device, allowing them to centrally monitor the project's progress.

[0413] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[0414] The processing flow will be explained below.

[0415] Understood. Below is a detailed explanation of the program processing of this system.

[0416] Step 1:

[0417] The user (manager) enters the progress of the task or questions they want to check into the device (app). For example, they enter "progress of task A" or "current issues of task B" in text format.

[0418] Step 2:

[0419] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[0420] Step 3:

[0421] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (e.g., progress or problem).

[0422] Step 4:

[0423] The server uses a generative AI model to generate questions based on the information it receives, such as "What percentage of task A is complete?" or "What are the current challenges for task B?"

[0424] Step 5:

[0425] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[0426] Step 6:

[0427] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[0428] Step 7:

[0429] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[0430] Step 8:

[0431] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[0432] Step 9:

[0433] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.).

[0434] Step 10:

[0435] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[0436] Step 11:

[0437] The user (manager) can check the progress and a summary of responses from the players through the terminal. Based on the displayed report, they can consider the progress of the project and measures to address issues.

[0438] Through these steps, the system can streamline information sharing between managers and players, improving the speed of the entire operation.

[0439] Example 1

[0440] 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."

[0441] In conventional project management systems, information sharing between managers and players is inefficient, making it difficult to accurately grasp progress and issues. In particular, manual information input and data organization are cumbersome and time-consuming, which can slow down the overall speed of work. Furthermore, because the generated data is not managed centrally, it is difficult to check progress in real time or share issues.

[0442] 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.

[0443] In this invention, the server includes means for analyzing information and generating appropriate questions using a generative AI model, means for sending the generated questions to players' devices in chat format, and means for receiving and analyzing answers sent by players in real time. This makes it possible to streamline information sharing between managers and players and improve the speed of the entire business.

[0444] The "means for inputting information" refers to an interface that allows a manager to use a terminal to input information such as the progress of a task or issues.

[0445] "Structuring as data packets" refers to a technical technique that converts input information into a uniform format so that it can be efficiently transmitted and received.

[0446] A "communications network" is an infrastructure for sending and receiving data, such as the Internet or an internal network.

[0447] A "server" is a computer system that receives requests from clients via a network and processes them appropriately.

[0448] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate appropriate questions and answers based on input data.

[0449] The "means for generating questions" is a function that uses a generative AI model to create questions based on the information received by the server.

[0450] "Chat format" is a user interface format that supports interactive messaging.

[0451] The "means for transmitting to the terminal" is a mechanism for distributing the generated question to the device used by the player via a network.

[0452] A "player" is a user who is in charge of a task in a project and reports on progress and issues.

[0453] The "means for receiving answers" is a process for the server to obtain and analyze the answers entered by the player.

[0454] A "means for analyzing answers" is a system that processes received player answers and converts them into meaningful information.

[0455] A "formatted report" is a report with a set format that is generated based on collected and analyzed data.

[0456] "Electronic format" refers to a document format that can be stored and viewed digitally, such as PDF or Excel.

[0457] The "means for sending to the manager's terminal" is a function for distributing the report created by the server to the manager's device.

[0458] "Real-time" refers to the property that data processing and communication are instantaneous and without delay.

[0459] The present invention is a system for improving the efficiency of information sharing between a manager and players. A specific embodiment of the present invention will be described.

[0460] First, the user (manager) enters information such as the "progress of tasks" or "current issues" they want to check into their device through a project management app or dedicated interface. This information is structured as a data packet and sent to the server via the Internet or an internal network. HTTP or HTTPS is used as the communication network.

[0461] The server receives the information sent from the device, stores it in a database, and prepares it for further analysis, which may include data integrity checks and data validation.

[0462] Based on the received information, the server uses a generative AI model (for example, OpenAI's GPT-4) to generate appropriate questions. This generation uses prompts. A prompt is a sentence that instructs the AI ​​model on what questions it should generate. For example, "What percentage of the task A is complete?" or "Are there any problems with task A right now?"

[0463] The generated questions are sent from the server to the player's device in chat format, allowing players to respond intuitively. This interface is displayed on the mobile and desktop apps.

[0464] Players enter answers to questions displayed on their terminals. Answers can be multiple choice or free description. The terminals send the answers entered by the players to the server in real time. Real-time transmission means that answers are instantly updated on the server.

[0465] The server analyzes the received responses and generates formatted reports, which are then managed using a database and a dashboard and report generation tool, allowing managers to see the overall situation at a glance.

[0466] Finally, the server sends the created report to the manager's terminal in electronic format such as PDF or Excel, and the manager is notified of the report via email or in-system notification.

[0467] Specific examples

[0468] For example, when a manager checks the progress of a new project, they use a device to input the "task progress rate" and "current issues." The device then sends this information to the server, which uses a generative AI model to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[0469] The generated questions are sent to each player's device in chat format, and the player responds with specific answers such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to the server, which analyzes the received data and creates a standardized report.

[0470] Prompt Sentence Examples

[0471] Below are some example prompts to input to a generative AI model:

[0472] "What is the progress percentage of Task A?"

[0473] "Are you currently having any problems with Task A?"

[0474] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[0475] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0476] Step 1:

[0477] The user (manager) enters information such as "task progress" and "current issues" that they want to check into their device through a project management app or a dedicated interface.

[0478] The information entered is specific data such as "Task A's progress rate is 50%" and "Task B's progress rate is 75%."

[0479] This information is input to the terminal, which then structures it into data packets.

[0480] Step 2:

[0481] The terminal sends structured data packets to the server via the Internet or an internal network.

[0482] In this case, HTTP or HTTPS is used as the communication protocol, and the data packets are in JSON or XML format.

[0483] Stable and secure data transfer is achieved.

[0484] Step 3:

[0485] The server receives the data packets sent from the terminal.

[0486] The received information is stored in a database and prepared for analysis.

[0487] Specifically, the received data is checked for consistency and validated, and then formatted into an analyzable format.

[0488] Step 4:

[0489] The server generates prompt sentences for a generative AI model (e.g., OpenAI's GPT-4) based on the received information.

[0490] Prompts are specific questions such as "What percentage of task A is complete?" or "What are your current challenges?"

[0491] This generates appropriate questions based on the information entered.

[0492] Step 5:

[0493] The server sends the generated questions to the player's terminal in chat format.

[0494] Specifically, questions are delivered in real time using WebSocket and API.

[0495] The questions are displayed in a chat box so that players can intuitively enter their answers.

[0496] Step 6:

[0497] The user (player) inputs answers to questions displayed on the terminal.

[0498] The answers are specific data such as "Progress: 80%" or "Challenges: None in particular."

[0499] The terminal transmits this response to the server in real time.

[0500] Step 7:

[0501] The server receives the responses sent by the players.

[0502] The received data is stored in a database and preparations for analysis are underway.

[0503] Specifically, the response data is checked for consistency and formatted.

[0504] Step 8:

[0505] The server analyzes the received responses and generates a formatted report.

[0506] Response data is aggregated from the database and visualized using dashboards and report generation tools.

[0507] View average progress and a list of assignments.

[0508] Step 9:

[0509] The server sends the generated report to the manager's terminal in an electronic format such as PDF or Excel.

[0510] Notify managers of the report's existence via email and in-system notifications.

[0511] This allows managers to see the progress of projects from a single location.

[0512] (Application example 1)

[0513] 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."

[0514] In the past, information sharing regarding work progress and issues within factories often relied on manual labor, resulting in frequent delays and miscommunication of information, lower productivity, and delayed problem resolution. It was also difficult to grasp the actual work status of each robot in real time, and it took a long time for managers to grasp the overall situation. As a result, efficient work management and rapid response are required.

[0515] 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.

[0516] In this invention, the server includes a means for inputting information, a means for transmitting the input information to the server via a communication network, a means for generating questions using a generative AI model based on the information received on the server, a means for transmitting the generated questions to players in chat format, a means for receiving answers from players in real time, a means for analyzing the received answers and creating reports on progress and issues, and a means for sending the created reports in PDF or Excel format to a manager. This automates the process from inputting information to generating questions, collecting answers, and creating and sending reports, making it possible to grasp the progress and issues of work in the factory in real time.

[0517] "Means for inputting information" refers to the function that allows the user (manager) to input information such as progress and tasks into the terminal.

[0518] "Means for transmitting input information to a server via a communication network" refers to a communication function for transmitting data from a terminal to a server.

[0519] "Means for generating questions using a generative AI model" refers to a function that automatically generates appropriate questions using generative AI technology based on received information.

[0520] "Means for sending to players in chat format" refers to a function for sending generated questions to players' terminals in chat format.

[0521] "Means for receiving answers from players in real time" refers to a function for instantly transmitting answers from players to a server and receiving them in real time.

[0522] "Means for analyzing the received responses and generating progress and assignment reports" refers to a function that analyzes the responses received by the server and generates reports on progress and current assignments.

[0523] "A means of sending the created report to the manager in PDF or Excel format" refers to the function of converting the created report into PDF or Excel format and sending it to the manager.

[0524] The present invention provides a system that improves the efficiency of information sharing between managers and players and enables real-time understanding of work progress and issues within a factory. Specific embodiments of the present invention will be described below.

[0525] Hardware and software used

[0526] Hardware:

[0527] User device: The smartphone, tablet, or PC used by the manager.

[0528] Player terminal: The control terminal for the robots working in the factory.

[0529] Communication networks: Internal networks, including Wi-Fi and wired LAN, and the Internet.

[0530] software:

[0531] Server: A server on the cloud or a local server in the factory.

[0532] Generative AI models: For example, using generative AI technologies such as OpenAI's GPT series.

[0533] Chat-style interface: Chat applications (e.g. Telegram, Slack, etc.) built into the user and robot devices.

[0534] Data processing and calculation flow

[0535] 1. Enter and submit information:

[0536] Users use smartphones, tablets, or PCs to input information about work progress and current issues. For example, they input specific data such as a task's progress rate of "60%" or a current issue of "missing parts." This information is sent to the server as structured data packets over a communications network.

[0537] 2. Question generation:

[0538] The server analyzes the received data and uses a generative AI model to generate appropriate questions, such as "What is the progress rate of the current task?" or "What is the current challenge?"

[0539] 3. Submitting Questions and Receiving Answers:

[0540] The generated questions are sent to the players' (robots') terminals in chat format. Each robot responds to the questions in real time, and the answers are immediately sent to the server.

[0541] 4. Response analysis and reporting:

[0542] The server analyzes the received responses and creates a progress and task report. This report is converted into a format such as PDF or Excel and sent to the user's device. The report visualizes each robot's progress and tasks so that they can be understood at a glance.

[0543] Specific examples

[0544] For example, consider the case where a manager wants to check the progress of a new project. The manager uses his or her smartphone to input information such as "Progress rate: 60%" and "Current issue: Parts missing." This information is sent to a server, which uses a generative AI model to generate questions such as "What is the progress rate of the current task?" and "What is the current issue?" The generated questions are sent to the chat-style interface of each robot in the factory, and the robot responds in real time with "Progress rate: 75%" and "Issue: New parts have arrived." Based on these responses, the server creates a report and sends it in PDF format to the manager's device.

[0545] Prompt Sentence Examples

[0546] Below are some examples of prompt sentences to input to the generative AI model.

[0547] Problem-solving question generation:

[0548] input:

[0549] Task progress: 60%

[0550] Current issue: Parts shortage

[0551] output:

[0552] What is the current task progress percentage?

[0553] What are your current challenges?

[0554] In this way, the present invention improves the efficiency of information sharing between managers and players, making it possible to grasp the progress and issues of work within the factory in real time.

[0555] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0556] Step 1:

[0557] The user (manager) enters information. Specifically, they log in to the application using a smartphone, tablet, or PC and enter specific data such as "Task progress: 60%" and "Current issue: missing parts." This becomes the input data.

[0558] Step 2:

[0559] The device sends the information entered by the user to the server in real time. The input data is sent as structured data packets using a communication network (Wi-Fi or wired LAN), and the server receives the input data.

[0560] Step 3:

[0561] The server analyzes the received data. Based on the analyzed data, it activates a generative AI model and generates questions. For example, if the prompts "Task progress rate: 60%" and "Current issue: missing parts" are input, the generative AI model will generate questions such as "What is the current task progress rate?" and "What is the current issue?" This becomes the output data.

[0562] Step 4:

[0563] The server sends the generated questions to each robot's terminal in chat format. The robot receives the questions using a chat application and displays the answers. This interface is visual and intuitive.

[0564] Step 5:

[0565] The player (robot) inputs answers to questions sent from the server in real time. For example, they input information such as "Task progress: 75%" or "Current issue: New parts have arrived." This becomes the input data.

[0566] Step 6:

[0567] The terminal transmits the answers entered by the player to the server in real time. The answer data is transmitted to the server as a structured data packet using a communication network, and the server receives the answer data.

[0568] Step 7:

[0569] The server analyzes the received response data. Based on the analyzed data, it generates progress and assignment reports. The reports are created in PDF or Excel format. This report generation process also accesses the database, compares the data with past data, and visualizes it on a dashboard.

[0570] Step 8:

[0571] The server sends the created report to the manager's terminal. The report is communicated via email and in-system notifications, allowing the manager to grasp the project's progress and current issues in real time.

[0572] 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.

[0573] The present invention is a system for improving the efficiency of information sharing between managers and players, and by combining it with an emotion engine that recognizes the emotions of users, it achieves more accurate information gathering and communication. Specific embodiments of the present invention will be described below.

[0574] The system comprises a "means for inputting information," a "means for transmitting input information to a server," a "means for generating questions based on information received on the server," a "means for transmitting the generated questions to the player," a "means for receiving answers from the player," a "means for organizing the received answers and creating a report," a "means for sending the created report to a manager," and an "emotion engine."

[0575] How to enter information

[0576] The user (manager) inputs the information they want to check into the device (app). For example, they can input "progress of task A" or "current issues of task B" through a project management app or a dedicated interface.

[0577] A means of sending input information to the server

[0578] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[0579] means for generating questions based on the information received on the server;

[0580] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?"

[0581] Use of emotion engine

[0582] The emotion engine recognizes emotions from user (player) input and dialogue and analyzes that data. The recognized emotion data is incorporated into the question generation process and used to adjust the tone and specificity of the questions. For example, if the player is feeling stressed, the tone of the questions will be softened or changed to show more interest.

[0583] A means of sending generated questions to players

[0584] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[0585] A means of receiving answers from players

[0586] The user (player) inputs answers to questions displayed on the device. For example, answers can be entered in the form of "Progress: 80%" or "Challenges: None in particular," and the device can send these to the server in real time.

[0587] A means of organizing and reporting on the responses received

[0588] The server analyzes the received response data and creates standardized reports. The database manages responses, and dashboards and report generation tools allow managers to grasp the overall situation at a glance. Analysis of emotional data also allows for the creation of detailed reports that include players' mental states.

[0589] A means of sending the generated report to a manager

[0590] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[0591] Specific examples

[0592] For example, consider a scenario in which a manager checks the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues," and the device sends this information to the server. The server analyzes the received information and uses a generative AI and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player responds with specific answers such as "Progress rate: 80%" and "Issues: None in particular."

[0593] Emotional data is also collected along with the player's responses. For example, if the player is feeling anxious, that emotional data is also sent to the server. The server analyzes the received data and creates a report such as, "The player's progress rate is 80%, there are no particular issues, but he is feeling anxious." Finally, this report is sent to the manager's device, allowing them to understand the player's mental state as well as the project's progress.

[0594] In this way, the present invention is a system that realizes more comprehensive management by streamlining information sharing between managers and players and utilizing emotional data.

[0595] The processing flow will be explained below.

[0596] Understood. The processing of this system will be explained in detail below.

[0597] Step 1:

[0598] The user (manager) inputs the progress of the task or questions they want to check into the device (app). For example, they input information such as "progress of task A" or "current issues of task B."

[0599] Step 2:

[0600] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[0601] Step 3:

[0602] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (progress or problem).

[0603] Step 4:

[0604] The server starts the emotion engine and collects data to recognize the user's (player's) emotions. The emotion engine analyzes the player's past chat history and current input data to estimate their emotional state.

[0605] Step 5:

[0606] The server uses a generative AI model to generate questions based on the information and emotional data it receives, adjusting the tone of the questions to soften them if the player is feeling stressed, for example.

[0607] Step 6:

[0608] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[0609] Step 7:

[0610] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[0611] Step 8:

[0612] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[0613] Step 9:

[0614] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[0615] Step 10:

[0616] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.). It also analyzes emotional data and reflects the player's mental state in the report.

[0617] Step 11:

[0618] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[0619] Step 12:

[0620] The user (manager) can check the progress and summary of responses from the players through the device. Based on the displayed report, they can consider the progress of the project and measures to address issues. In addition, by understanding the emotional state of the players, they can manage the project more appropriately.

[0621] Through these steps, this system streamlines information sharing between managers and players and utilizes emotional data to achieve more comprehensive management.

[0622] Example 2

[0623] 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."

[0624] Previous information sharing systems lacked efficient communication between managers and players, making it particularly difficult to grasp the players' emotional states. This meant that managers were unable to properly manage players' performance and psychological state. Furthermore, the quality and tone of the questions generated did not match the players' emotional states, resulting in answers that lacked accuracy and candor.

[0625] 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.

[0626] In this invention, the server includes means for generating questions using an emotion engine that recognizes the user's emotions, means for organizing the received answers and emotion data to create a report, and means for sending the created report to the manager. This makes it possible to generate questions that reflect the player's emotional state, increasing the player's response rate and accuracy and enabling the manager to appropriately manage the player's performance and psychological state.

[0627] The "means for inputting information" refers to a means for providing information that the user wants to check through an input device such as a terminal.

[0628] The "means for transmitting input information to a server" refers to a means for structuring information input by a user into a data packet and transmitting it to a server via the Internet or an internal network.

[0629] "Means for generating questions based on information received on the server" refers to means for the server to analyze the information received and automatically create appropriate questions using a generative AI model.

[0630] The "emotion engine that recognizes user emotions" is an engine that recognizes emotions from user input and dialogue data and analyzes that data.

[0631] The "means for sending generated questions to players" refers to a means for the server to send automatically generated questions to the player's terminal, and utilizes a chat-style interface.

[0632] The "means for receiving answers from players" refers to the means by which players answer questions through their terminals and transmit the answers to the server in real time.

[0633] The "means for organizing received answer and emotion data and creating a report" is a means for the server to analyze the answer data and emotion data received from the players and create a standardized report.

[0634] The "means for sending the created report to the manager" is a means for sending the report created by the server to the manager's terminal so that the manager can check the status of the player.

[0635] A "generative AI model" is an artificial intelligence model that automatically generates questions based on received information and data.

[0636] A "prompt" is text that is input into a generative AI model and serves as the basis for the model to generate appropriate questions and responses.

[0637] The present invention is a system that improves the efficiency of information sharing between managers and players and combines it with an emotion engine that recognizes user emotions to achieve more accurate information gathering and communication. This system includes the following components:

[0638] Hardware and software used

[0639] Hardware: Servers, devices (smartphones and PCs)

[0640] Software: Project management apps, emotion engines, generative AI models, database management systems, chat interfaces

[0641] How to enter information

[0642] The user (manager) enters the information they want to check into the device through a project management app or a dedicated interface. Examples of information that a user enters include "progress of task A" and "current issues for task B." For example, they can enter "progress rate: 80%" and "current issues: none" into the text boxes of a project management app.

[0643] A means of sending input information to the server

[0644] The device structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. Protocols such as HTTP and HTTPS are used for communication, and the data is sent while checking its integrity.

[0645] means for generating questions based on the information received on the server;

[0646] The server analyzes the received information and automatically generates appropriate questions using a generative AI model. For example, questions such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?" are generated. The server temporarily stores the generated questions in a database.

[0647] Use of emotion engine

[0648] The server uses an emotion engine to recognize emotions from the user's (player's) input and dialogue, and analyzes the data. For example, if the player is feeling stressed, the server softens the tone of the questions or adjusts the content. The questions generated in this way take into account the player's psychological state.

[0649] A means of sending generated questions to players

[0650] The server then sends the generated questions to the player's device, where they can intuitively answer them using a chat-style interface that is integrated into the mobile and desktop apps.

[0651] A means of receiving answers from players

[0652] The user (player) answers questions displayed on the device. For example, specific answers such as "Progress rate: 80%" or "Challenges: None in particular" are possible. The answers are sent to the server in real time, allowing for efficient data collection.

[0653] A means to organize and report on received response and sentiment data

[0654] The server analyzes the received response data and emotional data and creates a formatted report that details the player's task progress and emotional state, allowing managers to grasp the overall situation at a glance. The report can be output in PDF or Excel format.

[0655] A means of sending the generated report to a manager

[0656] The server then sends the generated report to the manager's device, where it is provided via email and in-system notifications, allowing the manager to check the player's status.

[0657] Specific examples

[0658] For example, if a manager wants to check the progress of a new project, they can use their device to input the "task progress rate" and "current issues." The device sends this information to the server, which analyzes the received information and uses a generative AI model and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player replies with "Progress rate: 80%" and "Issues: None in particular." A report summarizing these answers and emotion data is created by the server and ultimately sent to the manager's device.

[0659] Prompt Sentence Examples

[0660] "Please tell me the progress of your current task."

[0661] "What are your current challenges with the task?"

[0662] The above is an embodiment of the present invention, which makes the information sharing between managers and players more efficient and realizes comprehensive management that utilizes emotion data.

[0663] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0664] Step 1: Enter your information

[0665] The user (manager) uses a project management app or a dedicated interface to input the information they want to check into their device. The information they input includes "progress of task A" and "current issues for task B." Specifically, the user inputs "progress rate: 80%" or "current issues: none" into the app's text boxes. This input data is temporarily stored in the device's internal storage area.

[0666] Step 2: Send the information to the server

[0667] The terminal structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. HTTP or HTTPS is used as the protocol. At this time, the data integrity is checked to ensure reliable communication. The input is the task information entered by the user, and the output is the data packet sent to the server.

[0668] Step 3: Generate questions based on the received information

[0669] The server analyzes the received data packets and extracts the necessary information. Based on this information, the server uses a generative AI model to automatically generate appropriate questions. For example, based on the extracted progress information, questions such as "What is the progress rate for task A?" or "What are the current challenges for task B?" are generated. The input is the task information sent to the server, and the output is the generated question.

[0670] Step 4: Use the emotion engine to tailor your questions

[0671] The server uses an emotion engine to recognize emotions from the user's (player's) past inputs and interactions. For example, if it is determined that the player has been feeling stressed recently, the emotion data is reflected in the question generation process. The tone and content of the question are adjusted to take the player's emotions into consideration. The input is the generated question and emotion data, and the output is the adjusted question.

[0672] Step 5: Send the generated questions to the player

[0673] The server sends the generated question to the player's device through a chat-style interface, through which the player can answer the question. Specifically, the server converts the question into a message format and sends a notification to the player's app. The input is the adjusted question, and the output is the question displayed on the player's device.

[0674] Step 6: Player answers the question

[0675] The user (player) answers questions displayed on the device. For example, they input specific answers such as "Progress: 80%" or "Challenges: None in particular." The input data is sent to the server in real time. The input is the displayed question, and the output is the input answer.

[0676] Step 7: Send the answer to the server

[0677] The terminal structures the answers entered by the user and sends them to the server in real time. The communication is done using HTTP or HTTPS and the data integrity is checked. The input is the answer entered by the user and the output is the answer sent to the server.

[0678] Step 8: Organize response and sentiment data and create reports

[0679] The server analyzes the received response data and emotion data and creates a formatted report. This report details the player's task progress and emotional state. Specifically, the server manages the response data using a database and creates dashboard-style reports using a report generation tool. The input is the response data and emotion data, and the output is the generated report.

[0680] Step 9: Send the generated report to your manager

[0681] The server sends the created report to the manager's terminal. The report is output in PDF or Excel format and provided to the manager via email or internal system notification. The input is the generated report, and the output is the report displayed on the manager's terminal.

[0682] (Application example 2)

[0683] 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."

[0684] In modern factories, information sharing between workers and managers is not yet efficient, and in particular, there is a lack of systems that can take into account the emotional state of workers. As a result, awareness of work progress and problems can be insufficient, leading to reduced work efficiency and safety issues. The present invention aims to solve these problems and provide an efficient information sharing system that takes into account the emotions of workers.

[0685] 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.

[0686] In this invention, the server includes means for inputting information, means for transmitting the input information to the server, means for generating questions based on the information received on the server, means for transmitting the generated questions to players, means for receiving answers from players, means for organizing the received answers and creating a report, means for transmitting the created report to a manager, means for recognizing player emotions using an emotion engine, and means for incorporating recognized emotion data into the question generation process. This improves the efficiency of information sharing between workers and managers and enables management that takes into account the emotional states of workers.

[0687] The "means for inputting information" refers to a means for the user to input information that the user wants to check into the terminal.

[0688] The "means for transmitting input information to a server" is a means for structuring information input from a terminal into a data packet and transmitting it to a server.

[0689] The "means for generating a question based on the information received on the server" refers to a means for analyzing the information received by the server and generating an appropriate question based on that information.

[0690] The "means for transmitting the generated question to the player" refers to a means for transmitting the question generated by the server to the player's terminal.

[0691] The "means for receiving answers from the player" refers to the means by which the server receives answers entered by the player using the terminal.

[0692] The "means for organizing received answers and creating reports" refers to the means by which the server stores the player's answers in a database, analyzes them, and creates standard reports.

[0693] The "means for transmitting the created report to the manager" is a means for transmitting the report created by the server to the manager's terminal.

[0694] "Means for recognizing player emotions using an emotion engine" refers to means for recognizing emotions from player inputs and dialogues and analyzing the data.

[0695] The "means for incorporating recognized emotional data into the question generation process" refers to a means for adjusting the tone and specificity of a question based on the emotional data recognized by the emotion engine.

[0696] A "generative AI model" is an artificial intelligence model used to generate questions on the server.

[0697] A "prompt" is a document to be input into a generative AI model, and is a guided sentence to support the generation of appropriate questions.

[0698] The system for implementing this invention is constructed based on a smartphone application, a cloud server, and an emotion engine. The specific configuration and operation of the system are described below.

[0699] First, the user, a worker, uses a smartphone application to input information, such as the "current task progress" or "recognized problems." This input method is usually implemented by text input using a touchscreen or voice input. Furthermore, the application has a built-in emotion engine that also collects emotional data through facial expression and voice analysis of the worker.

[0700] The smartphone application then transmits the input information and emotion data to a cloud server over Wi-Fi or 4G / 5G networks, where the server receives the information and stores it in a database.

[0701] The cloud server uses the generative AI model to generate appropriate questions based on the received information. For example, if the received information is "Current task progress: 60%", the problem is "Machine malfunction", and the emotion is "Anxiety", the server inputs the following prompt sentence into the generative AI model:

[0702] To the question generation AI system:

[0703] Use the information below to generate appropriate questions.

[0704] Work progress: 60%

[0705] Problem: Mechanical malfunction

[0706] Emotion: Anxiety

[0707] Expected output:

[0708] Questions asking for details about the machine malfunction (e.g., What caused the machine malfunction?).

[0709] A generative AI model (e.g., OpenAI GPT-3) uses this prompt to generate an appropriate question, such as "What caused the machine to malfunction?"

[0710] The generated questions are then sent to the smartphone application in chat format, allowing workers to answer questions intuitively, and the response data is sent back to the cloud server in real time.

[0711] The server analyzes the received response data and generates a report that includes progress and issues, as well as worker emotion data. The report can also be used to perform more detailed analysis by referencing information in the database.

[0712] Finally, the cloud server sends the created report to the manager's device, where it is provided in PDF or dashboard format, allowing the manager to comprehensively grasp the progress of the work, any issues, and the emotional state of the workers.

[0713] In this way, the present invention provides a more comprehensive work management system by realizing efficient information sharing between workers and managers and utilizing emotion data.

[0714] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0715] Step 1:

[0716] The user (worker) opens the smartphone application and inputs information such as progress and problems. The emotion engine analyzes the worker's facial expressions and voice to recognize emotional data. At this stage, inputs include "Task progress: 60%", "Problem: machine malfunction", and "Emotion: anxiety". This data is output and temporarily saved within the application.

[0717] Step 2:

[0718] The device structures the input information and emotion data into data packets and sends them to the cloud server. Communication is via Wi-Fi or 4G / 5G networks, and the data is securely encrypted before transmission. The input is the information saved in step 1, and the cloud server receives this data as output.

[0719] Step 3:

[0720] The server parses the received information and stores it in a database. The parsing process involves data format conversion and normalization. The input is the data set sent in step 2, and the output is structured data stored in the server's database.

[0721] Step 4:

[0722] The server generates prompts to be input to the generative AI model based on the stored data. The prompts include progress, problems, and emotional data. As a specific example, the following prompt is generated: "To the question generation AI system: Please generate an appropriate question based on the following information. Work progress: 60%, Problem: Machine malfunction, Emotion: Anxiety." The input is data stored in the database on the server, and the output is the prompt to be sent to the generative AI model.

[0723] Step 5:

[0724] A generative AI model (OpenAI GPT-3) generates an appropriate question based on the prompt. The generated question takes the form, for example, "What is the cause of the machine malfunction?" The input is the prompt generated in step 4, and the output is a specific question.

[0725] Step 6:

[0726] The server sends the generated question to the player's (worker's) smartphone application. This is done in a chat format, allowing the worker to respond intuitively. The input is the question generated in step 5, and the output is the question displayed on the worker's device.

[0727] Step 7:

[0728] The user (worker) enters an answer to the question displayed on the terminal. For example, the answer is "The bolt is loose." The input is the question displayed in step 6, and the output is the answer re-entered into the terminal.

[0729] Step 8:

[0730] The terminal transmits the worker's response to the cloud server in real time. The communication is secure. The input is the response entered in step 7, and the output is the response data received by the cloud server.

[0731] Step 9:

[0732] The server again analyzes the received response data and generates a report, which includes progress, problem details, and sentiment data. This analysis includes structuring and statistical analysis of the data. The input is the response data received in step 8, and the output is the generated report.

[0733] Step 10:

[0734] The server sends the generated report to the manager's terminal. The report is provided in PDF or dashboard format and is sent via email or system notification. The input is the report generated in step 9, and the output is the report that arrives on the manager's terminal.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] [Third embodiment]

[0739] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0740] 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.

[0741] 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).

[0742] 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.

[0743] 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.

[0744] 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).

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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."

[0751] The present invention is a system for improving the efficiency of information sharing between a manager and players. Specific embodiments of the present invention will be described below.

[0752] The system includes "means for inputting information," "means for transmitting input information to a server," "means for generating questions based on information received on the server," "means for transmitting the generated questions to players," "means for receiving answers from players," "means for organizing received answers and creating reports," and "means for transmitting the created reports to a manager."

[0753] How to enter information

[0754] The user (manager) inputs the information they want to check into the device (app). Specifically, they can input information such as "task progress" and "current issues" through a project management app or a dedicated interface.

[0755] A means of sending input information to the server

[0756] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[0757] means for generating questions based on the information received on the server;

[0758] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What percentage of your task is complete?" or "What are your current challenges?"

[0759] A means of sending generated questions to players

[0760] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[0761] A means of receiving answers from players

[0762] The user (player) inputs answers to questions displayed on the terminal. Answers can be entered in multiple choice format or free text format, and the terminal sends the answers to the server in real time.

[0763] A means of organizing and reporting on the responses received

[0764] The server analyzes the responses received and generates formatted reports. A database is used to manage the responses, and dashboards and report generation tools allow managers to understand the overall situation at a glance.

[0765] A means of sending the generated report to a manager

[0766] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[0767] Specific examples

[0768] For example, consider a scenario where a manager is checking the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues." The device sends this information to a server, which then uses a generative AI to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[0769] The generated questions are sent to each player's device in chat format. The player then responds with specific answers, such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to a server, which analyzes the received data and creates a standardized report. This report is then sent to the manager's device, allowing them to centrally monitor the project's progress.

[0770] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[0771] The processing flow will be explained below.

[0772] Understood. Below is a detailed explanation of the program processing of this system.

[0773] Step 1:

[0774] The user (manager) enters the progress of the task or questions they want to check into the device (app). For example, they enter "progress of task A" or "current issues of task B" in text format.

[0775] Step 2:

[0776] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[0777] Step 3:

[0778] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (e.g., progress or problem).

[0779] Step 4:

[0780] The server uses a generative AI model to generate questions based on the information it receives, such as "What percentage of task A is complete?" or "What are the current challenges for task B?"

[0781] Step 5:

[0782] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[0783] Step 6:

[0784] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[0785] Step 7:

[0786] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[0787] Step 8:

[0788] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[0789] Step 9:

[0790] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.).

[0791] Step 10:

[0792] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[0793] Step 11:

[0794] The user (manager) can check the progress and a summary of responses from the players through the terminal. Based on the displayed report, they can consider the progress of the project and measures to address issues.

[0795] Through these steps, the system can streamline information sharing between managers and players, improving the speed of the entire operation.

[0796] Example 1

[0797] 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."

[0798] In conventional project management systems, information sharing between managers and players is inefficient, making it difficult to accurately grasp progress and issues. In particular, manual information input and data organization are cumbersome and time-consuming, which can slow down the overall speed of work. Furthermore, because the generated data is not managed centrally, it is difficult to check progress in real time or share issues.

[0799] 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.

[0800] In this invention, the server includes means for analyzing information and generating appropriate questions using a generative AI model, means for sending the generated questions to players' devices in chat format, and means for receiving and analyzing answers sent by players in real time. This makes it possible to streamline information sharing between managers and players and improve the speed of the entire business.

[0801] The "means for inputting information" refers to an interface that allows a manager to use a terminal to input information such as the progress of a task or issues.

[0802] "Structuring as data packets" refers to a technical technique that converts input information into a uniform format so that it can be efficiently transmitted and received.

[0803] A "communications network" is an infrastructure for sending and receiving data, such as the Internet or an internal network.

[0804] A "server" is a computer system that receives requests from clients via a network and processes them appropriately.

[0805] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate appropriate questions and answers based on input data.

[0806] The "means for generating questions" is a function that uses a generative AI model to create questions based on the information received by the server.

[0807] "Chat format" is a user interface format that supports interactive messaging.

[0808] The "means for transmitting to the terminal" is a mechanism for distributing the generated question to the device used by the player via a network.

[0809] A "player" is a user who is in charge of a task in a project and reports on progress and issues.

[0810] The "means for receiving answers" is a process for the server to obtain and analyze the answers entered by the player.

[0811] A "means for analyzing answers" is a system that processes received player answers and converts them into meaningful information.

[0812] A "formatted report" is a report with a set format that is generated based on collected and analyzed data.

[0813] "Electronic format" refers to a document format that can be stored and viewed digitally, such as PDF or Excel.

[0814] The "means for sending to the manager's terminal" is a function for distributing the report created by the server to the manager's device.

[0815] "Real-time" refers to the property that data processing and communication are instantaneous and without delay.

[0816] The present invention is a system for improving the efficiency of information sharing between a manager and players. A specific embodiment of the present invention will be described.

[0817] First, the user (manager) enters information such as the "progress of tasks" or "current issues" they want to check into their device through a project management app or dedicated interface. This information is structured as a data packet and sent to the server via the Internet or an internal network. HTTP or HTTPS is used as the communication network.

[0818] The server receives the information sent from the device, stores it in a database, and prepares it for further analysis, which may include data integrity checks and data validation.

[0819] Based on the received information, the server uses a generative AI model (for example, OpenAI's GPT-4) to generate appropriate questions. This generation uses prompts. A prompt is a sentence that instructs the AI ​​model on what questions it should generate. For example, "What percentage of the task A is complete?" or "Are there any problems with task A right now?"

[0820] The generated questions are sent from the server to the player's device in chat format, allowing players to respond intuitively. This interface is displayed on the mobile and desktop apps.

[0821] Players enter answers to questions displayed on their terminals. Answers can be multiple choice or free description. The terminals send the answers entered by the players to the server in real time. Real-time transmission means that answers are instantly updated on the server.

[0822] The server analyzes the received responses and generates formatted reports, which are then managed using a database and a dashboard and report generation tool, allowing managers to see the overall situation at a glance.

[0823] Finally, the server sends the created report to the manager's terminal in electronic format such as PDF or Excel, and the manager is notified of the report via email or in-system notification.

[0824] Specific examples

[0825] For example, when a manager checks the progress of a new project, they use a device to input the "task progress rate" and "current issues." The device then sends this information to the server, which uses a generative AI model to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[0826] The generated questions are sent to each player's device in chat format, and the player responds with specific answers such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to the server, which analyzes the received data and creates a standardized report.

[0827] Prompt Sentence Examples

[0828] Below are some example prompts to input to a generative AI model:

[0829] "What is the progress percentage of Task A?"

[0830] "Are you currently having any problems with Task A?"

[0831] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[0832] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0833] Step 1:

[0834] The user (manager) enters information such as "task progress" and "current issues" that they want to check into their device through a project management app or a dedicated interface.

[0835] The information entered is specific data such as "Task A's progress rate is 50%" and "Task B's progress rate is 75%."

[0836] This information is input to the terminal, which then structures it into data packets.

[0837] Step 2:

[0838] The terminal sends structured data packets to the server via the Internet or an internal network.

[0839] In this case, HTTP or HTTPS is used as the communication protocol, and the data packets are in JSON or XML format.

[0840] Stable and secure data transfer is achieved.

[0841] Step 3:

[0842] The server receives the data packets sent from the terminal.

[0843] The received information is stored in a database and prepared for analysis.

[0844] Specifically, the received data is checked for consistency and validated, and then formatted into an analyzable format.

[0845] Step 4:

[0846] The server generates prompt sentences for a generative AI model (e.g., OpenAI's GPT-4) based on the received information.

[0847] Prompts are specific questions such as "What percentage of task A is complete?" or "What are your current challenges?"

[0848] This generates appropriate questions based on the information entered.

[0849] Step 5:

[0850] The server sends the generated questions to the player's terminal in chat format.

[0851] Specifically, questions are delivered in real time using WebSocket and API.

[0852] The questions are displayed in a chat box so that players can intuitively enter their answers.

[0853] Step 6:

[0854] The user (player) inputs answers to questions displayed on the terminal.

[0855] The answers are specific data such as "Progress: 80%" or "Challenges: None in particular."

[0856] The terminal transmits this response to the server in real time.

[0857] Step 7:

[0858] The server receives the responses sent by the players.

[0859] The received data is stored in a database and preparations for analysis are underway.

[0860] Specifically, the response data is checked for consistency and formatted.

[0861] Step 8:

[0862] The server analyzes the received responses and generates a formatted report.

[0863] Response data is aggregated from the database and visualized using dashboards and report generation tools.

[0864] View average progress and a list of assignments.

[0865] Step 9:

[0866] The server sends the generated report to the manager's terminal in an electronic format such as PDF or Excel.

[0867] Notify managers of the report's existence via email and in-system notifications.

[0868] This allows managers to see the progress of projects from a single location.

[0869] (Application example 1)

[0870] 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."

[0871] In the past, information sharing regarding work progress and issues within factories often relied on manual labor, resulting in frequent delays and miscommunication of information, lower productivity, and delayed problem resolution. It was also difficult to grasp the actual work status of each robot in real time, and it took a long time for managers to grasp the overall situation. As a result, efficient work management and rapid response are required.

[0872] 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.

[0873] In this invention, the server includes a means for inputting information, a means for transmitting the input information to the server via a communication network, a means for generating questions using a generative AI model based on the information received on the server, a means for transmitting the generated questions to players in chat format, a means for receiving answers from players in real time, a means for analyzing the received answers and creating reports on progress and issues, and a means for sending the created reports in PDF or Excel format to a manager. This automates the process from inputting information to generating questions, collecting answers, and creating and sending reports, making it possible to grasp the progress and issues of work in the factory in real time.

[0874] "Means for inputting information" refers to the function that allows the user (manager) to input information such as progress and tasks into the terminal.

[0875] "Means for transmitting input information to a server via a communication network" refers to a communication function for transmitting data from a terminal to a server.

[0876] "Means for generating questions using a generative AI model" refers to a function that automatically generates appropriate questions using generative AI technology based on received information.

[0877] "Means for sending to players in chat format" refers to a function for sending generated questions to players' terminals in chat format.

[0878] "Means for receiving answers from players in real time" refers to a function for instantly transmitting answers from players to a server and receiving them in real time.

[0879] "Means for analyzing the received responses and generating progress and assignment reports" refers to a function that analyzes the responses received by the server and generates reports on progress and current assignments.

[0880] "A means of sending the created report to the manager in PDF or Excel format" refers to the function of converting the created report into PDF or Excel format and sending it to the manager.

[0881] The present invention provides a system that improves the efficiency of information sharing between managers and players and enables real-time understanding of work progress and issues within a factory. Specific embodiments of the present invention will be described below.

[0882] Hardware and software used

[0883] Hardware:

[0884] User device: The smartphone, tablet, or PC used by the manager.

[0885] Player terminal: The control terminal for the robots working in the factory.

[0886] Communication networks: Internal networks, including Wi-Fi and wired LAN, and the Internet.

[0887] software:

[0888] Server: A server on the cloud or a local server in the factory.

[0889] Generative AI models: For example, using generative AI technologies such as OpenAI's GPT series.

[0890] Chat-style interface: Chat applications (e.g. Telegram, Slack, etc.) built into the user and robot devices.

[0891] Data processing and calculation flow

[0892] 1. Enter and submit information:

[0893] Users use smartphones, tablets, or PCs to input information about work progress and current issues. For example, they input specific data such as a task's progress rate of "60%" or a current issue of "missing parts." This information is sent to the server as structured data packets over a communications network.

[0894] 2. Question generation:

[0895] The server analyzes the received data and uses a generative AI model to generate appropriate questions, such as "What is the progress rate of the current task?" or "What is the current challenge?"

[0896] 3. Submitting Questions and Receiving Answers:

[0897] The generated questions are sent to the players' (robots') terminals in chat format. Each robot responds to the questions in real time, and the answers are immediately sent to the server.

[0898] 4. Response analysis and reporting:

[0899] The server analyzes the received responses and creates a progress and task report. This report is converted into a format such as PDF or Excel and sent to the user's device. The report visualizes each robot's progress and tasks so that they can be understood at a glance.

[0900] Specific examples

[0901] For example, consider the case where a manager wants to check the progress of a new project. The manager uses his or her smartphone to input information such as "Progress rate: 60%" and "Current issue: Parts missing." This information is sent to a server, which uses a generative AI model to generate questions such as "What is the progress rate of the current task?" and "What is the current issue?" The generated questions are sent to the chat-style interface of each robot in the factory, and the robot responds in real time with "Progress rate: 75%" and "Issue: New parts have arrived." Based on these responses, the server creates a report and sends it in PDF format to the manager's device.

[0902] Prompt Sentence Examples

[0903] Below are some examples of prompt sentences to input to the generative AI model.

[0904] Problem-solving question generation:

[0905] input:

[0906] Task progress: 60%

[0907] Current issue: Parts shortage

[0908] output:

[0909] What is the current task progress percentage?

[0910] What are your current challenges?

[0911] In this way, the present invention improves the efficiency of information sharing between managers and players, making it possible to grasp the progress and issues of work within the factory in real time.

[0912] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0913] Step 1:

[0914] The user (manager) enters information. Specifically, they log in to the application using a smartphone, tablet, or PC and enter specific data such as "Task progress: 60%" and "Current issue: missing parts." This becomes the input data.

[0915] Step 2:

[0916] The device sends the information entered by the user to the server in real time. The input data is sent as structured data packets using a communication network (Wi-Fi or wired LAN), and the server receives the input data.

[0917] Step 3:

[0918] The server analyzes the received data. Based on the analyzed data, it activates a generative AI model and generates questions. For example, if the prompts "Task progress rate: 60%" and "Current issue: missing parts" are input, the generative AI model will generate questions such as "What is the current task progress rate?" and "What is the current issue?" This becomes the output data.

[0919] Step 4:

[0920] The server sends the generated questions to each robot's terminal in chat format. The robot receives the questions using a chat application and displays the answers. This interface is visual and intuitive.

[0921] Step 5:

[0922] The player (robot) inputs answers to questions sent from the server in real time. For example, they input information such as "Task progress: 75%" or "Current issue: New parts have arrived." This becomes the input data.

[0923] Step 6:

[0924] The terminal transmits the answers entered by the player to the server in real time. The answer data is transmitted to the server as a structured data packet using a communication network, and the server receives the answer data.

[0925] Step 7:

[0926] The server analyzes the received response data. Based on the analyzed data, it generates progress and assignment reports. The reports are created in PDF or Excel format. This report generation process also accesses the database, compares the data with past data, and visualizes it on a dashboard.

[0927] Step 8:

[0928] The server sends the created report to the manager's terminal. The report is communicated via email and in-system notifications, allowing the manager to grasp the project's progress and current issues in real time.

[0929] 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.

[0930] The present invention is a system for improving the efficiency of information sharing between managers and players, and by combining it with an emotion engine that recognizes the emotions of users, it achieves more accurate information gathering and communication. Specific embodiments of the present invention will be described below.

[0931] The system comprises a "means for inputting information," a "means for transmitting input information to a server," a "means for generating questions based on information received on the server," a "means for transmitting the generated questions to the player," a "means for receiving answers from the player," a "means for organizing the received answers and creating a report," a "means for sending the created report to a manager," and an "emotion engine."

[0932] How to enter information

[0933] The user (manager) inputs the information they want to check into the device (app). For example, they can input "progress of task A" or "current issues of task B" through a project management app or a dedicated interface.

[0934] A means of sending input information to the server

[0935] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[0936] means for generating questions based on the information received on the server;

[0937] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?"

[0938] Use of emotion engine

[0939] The emotion engine recognizes emotions from user (player) input and dialogue and analyzes that data. The recognized emotion data is incorporated into the question generation process and used to adjust the tone and specificity of the questions. For example, if the player is feeling stressed, the tone of the questions will be softened or changed to show more interest.

[0940] A means of sending generated questions to players

[0941] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[0942] A means of receiving answers from players

[0943] The user (player) inputs answers to questions displayed on the device. For example, answers can be entered in the form of "Progress: 80%" or "Challenges: None in particular," and the device can send these to the server in real time.

[0944] A means of organizing and reporting on the responses received

[0945] The server analyzes the received response data and creates standardized reports. The database manages responses, and dashboards and report generation tools allow managers to grasp the overall situation at a glance. Analysis of emotional data also allows for the creation of detailed reports that include players' mental states.

[0946] A means of sending the generated report to a manager

[0947] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[0948] Specific examples

[0949] For example, consider a scenario in which a manager checks the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues," and the device sends this information to the server. The server analyzes the received information and uses a generative AI and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player responds with specific answers such as "Progress rate: 80%" and "Issues: None in particular."

[0950] Emotional data is also collected along with the player's responses. For example, if the player is feeling anxious, that emotional data is also sent to the server. The server analyzes the received data and creates a report such as, "The player's progress rate is 80%, there are no particular issues, but he is feeling anxious." Finally, this report is sent to the manager's device, allowing them to understand the player's mental state as well as the project's progress.

[0951] In this way, the present invention is a system that realizes more comprehensive management by streamlining information sharing between managers and players and utilizing emotional data.

[0952] The processing flow will be explained below.

[0953] Understood. The processing of this system will be explained in detail below.

[0954] Step 1:

[0955] The user (manager) inputs the progress of the task or questions they want to check into the device (app). For example, they input information such as "progress of task A" or "current issues of task B."

[0956] Step 2:

[0957] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[0958] Step 3:

[0959] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (progress or problem).

[0960] Step 4:

[0961] The server starts the emotion engine and collects data to recognize the user's (player's) emotions. The emotion engine analyzes the player's past chat history and current input data to estimate their emotional state.

[0962] Step 5:

[0963] The server uses a generative AI model to generate questions based on the information and emotional data it receives, adjusting the tone of the questions to soften them if the player is feeling stressed, for example.

[0964] Step 6:

[0965] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[0966] Step 7:

[0967] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[0968] Step 8:

[0969] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[0970] Step 9:

[0971] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[0972] Step 10:

[0973] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.). It also analyzes emotional data and reflects the player's mental state in the report.

[0974] Step 11:

[0975] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[0976] Step 12:

[0977] The user (manager) can check the progress and summary of responses from the players through the device. Based on the displayed report, they can consider the progress of the project and measures to address issues. In addition, by understanding the emotional state of the players, they can manage the project more appropriately.

[0978] Through these steps, this system streamlines information sharing between managers and players and utilizes emotional data to achieve more comprehensive management.

[0979] Example 2

[0980] 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."

[0981] Previous information sharing systems lacked efficient communication between managers and players, making it particularly difficult to grasp the players' emotional states. This meant that managers were unable to properly manage players' performance and psychological state. Furthermore, the quality and tone of the questions generated did not match the players' emotional states, resulting in answers that lacked accuracy and candor.

[0982] 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.

[0983] In this invention, the server includes means for generating questions using an emotion engine that recognizes the user's emotions, means for organizing the received answers and emotion data to create a report, and means for sending the created report to the manager. This makes it possible to generate questions that reflect the player's emotional state, increasing the player's response rate and accuracy and enabling the manager to appropriately manage the player's performance and psychological state.

[0984] The "means for inputting information" refers to a means for providing information that the user wants to check through an input device such as a terminal.

[0985] The "means for transmitting input information to a server" refers to a means for structuring information input by a user into a data packet and transmitting it to a server via the Internet or an internal network.

[0986] "Means for generating questions based on information received on the server" refers to means for the server to analyze the information received and automatically create appropriate questions using a generative AI model.

[0987] The "emotion engine that recognizes user emotions" is an engine that recognizes emotions from user input and dialogue data and analyzes that data.

[0988] The "means for sending generated questions to players" refers to a means for the server to send automatically generated questions to the player's terminal, and utilizes a chat-style interface.

[0989] The "means for receiving answers from players" refers to the means by which players answer questions through their terminals and transmit the answers to the server in real time.

[0990] The "means for organizing received answer and emotion data and creating a report" is a means for the server to analyze the answer data and emotion data received from the players and create a standardized report.

[0991] The "means for sending the created report to the manager" is a means for sending the report created by the server to the manager's terminal so that the manager can check the status of the player.

[0992] A "generative AI model" is an artificial intelligence model that automatically generates questions based on received information and data.

[0993] A "prompt" is text that is input into a generative AI model and serves as the basis for the model to generate appropriate questions and responses.

[0994] The present invention is a system that improves the efficiency of information sharing between managers and players and combines it with an emotion engine that recognizes user emotions to achieve more accurate information gathering and communication. This system includes the following components:

[0995] Hardware and software used

[0996] Hardware: Servers, devices (smartphones and PCs)

[0997] Software: Project management apps, emotion engines, generative AI models, database management systems, chat interfaces

[0998] How to enter information

[0999] The user (manager) enters the information they want to check into the device through a project management app or a dedicated interface. Examples of information that a user enters include "progress of task A" and "current issues for task B." For example, they can enter "progress rate: 80%" and "current issues: none" into the text boxes of a project management app.

[1000] A means of sending input information to the server

[1001] The device structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. Protocols such as HTTP and HTTPS are used for communication, and the data is sent while checking its integrity.

[1002] means for generating questions based on the information received on the server;

[1003] The server analyzes the received information and automatically generates appropriate questions using a generative AI model. For example, questions such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?" are generated. The server temporarily stores the generated questions in a database.

[1004] Use of emotion engine

[1005] The server uses an emotion engine to recognize emotions from the user's (player's) input and dialogue, and analyzes the data. For example, if the player is feeling stressed, the server softens the tone of the questions or adjusts the content. The questions generated in this way take into account the player's psychological state.

[1006] A means of sending generated questions to players

[1007] The server then sends the generated questions to the player's device, where they can intuitively answer them using a chat-style interface that is integrated into the mobile and desktop apps.

[1008] A means of receiving answers from players

[1009] The user (player) answers questions displayed on the device. For example, specific answers such as "Progress rate: 80%" or "Challenges: None in particular" are possible. The answers are sent to the server in real time, allowing for efficient data collection.

[1010] A means to organize and report on received response and sentiment data

[1011] The server analyzes the received response data and emotional data and creates a formatted report that details the player's task progress and emotional state, allowing managers to grasp the overall situation at a glance. The report can be output in PDF or Excel format.

[1012] A means of sending the generated report to a manager

[1013] The server then sends the generated report to the manager's device, where it is provided via email and in-system notifications, allowing the manager to check the player's status.

[1014] Specific examples

[1015] For example, if a manager wants to check the progress of a new project, they can use their device to input the "task progress rate" and "current issues." The device sends this information to the server, which analyzes the received information and uses a generative AI model and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player replies with "Progress rate: 80%" and "Issues: None in particular." A report summarizing these answers and emotion data is created by the server and ultimately sent to the manager's device.

[1016] Prompt Sentence Examples

[1017] "Please tell me the progress of your current task."

[1018] "What are your current challenges with the task?"

[1019] The above is an embodiment of the present invention, which makes the information sharing between managers and players more efficient and realizes comprehensive management that utilizes emotion data.

[1020] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1021] Step 1: Enter your information

[1022] The user (manager) uses a project management app or a dedicated interface to input the information they want to check into their device. The information they input includes "progress of task A" and "current issues for task B." Specifically, the user inputs "progress rate: 80%" or "current issues: none" into the app's text boxes. This input data is temporarily stored in the device's internal storage area.

[1023] Step 2: Send the information to the server

[1024] The terminal structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. HTTP or HTTPS is used as the protocol. At this time, the data integrity is checked to ensure reliable communication. The input is the task information entered by the user, and the output is the data packet sent to the server.

[1025] Step 3: Generate questions based on the received information

[1026] The server analyzes the received data packets and extracts the necessary information. Based on this information, the server uses a generative AI model to automatically generate appropriate questions. For example, based on the extracted progress information, questions such as "What is the progress rate for task A?" or "What are the current challenges for task B?" are generated. The input is the task information sent to the server, and the output is the generated question.

[1027] Step 4: Use the emotion engine to tailor your questions

[1028] The server uses an emotion engine to recognize emotions from the user's (player's) past inputs and interactions. For example, if it is determined that the player has been feeling stressed recently, the emotion data is reflected in the question generation process. The tone and content of the question are adjusted to take the player's emotions into consideration. The input is the generated question and emotion data, and the output is the adjusted question.

[1029] Step 5: Send the generated questions to the player

[1030] The server sends the generated question to the player's device through a chat-style interface, through which the player can answer the question. Specifically, the server converts the question into a message format and sends a notification to the player's app. The input is the adjusted question, and the output is the question displayed on the player's device.

[1031] Step 6: Player answers the question

[1032] The user (player) answers questions displayed on the device. For example, they input specific answers such as "Progress: 80%" or "Challenges: None in particular." The input data is sent to the server in real time. The input is the displayed question, and the output is the input answer.

[1033] Step 7: Send the answer to the server

[1034] The terminal structures the answers entered by the user and sends them to the server in real time. The communication is done using HTTP or HTTPS and the data integrity is checked. The input is the answer entered by the user and the output is the answer sent to the server.

[1035] Step 8: Organize response and sentiment data and create reports

[1036] The server analyzes the received response data and emotion data and creates a formatted report. This report details the player's task progress and emotional state. Specifically, the server manages the response data using a database and creates dashboard-style reports using a report generation tool. The input is the response data and emotion data, and the output is the generated report.

[1037] Step 9: Send the generated report to your manager

[1038] The server sends the created report to the manager's terminal. The report is output in PDF or Excel format and provided to the manager via email or internal system notification. The input is the generated report, and the output is the report displayed on the manager's terminal.

[1039] (Application example 2)

[1040] 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."

[1041] In modern factories, information sharing between workers and managers is not yet efficient, and in particular, there is a lack of systems that can take into account the emotional state of workers. As a result, awareness of work progress and problems can be insufficient, leading to reduced work efficiency and safety issues. The present invention aims to solve these problems and provide an efficient information sharing system that takes into account the emotions of workers.

[1042] 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.

[1043] In this invention, the server includes means for inputting information, means for transmitting the input information to the server, means for generating questions based on the information received on the server, means for transmitting the generated questions to players, means for receiving answers from players, means for organizing the received answers and creating a report, means for transmitting the created report to a manager, means for recognizing player emotions using an emotion engine, and means for incorporating recognized emotion data into the question generation process. This improves the efficiency of information sharing between workers and managers and enables management that takes into account the emotional states of workers.

[1044] The "means for inputting information" refers to a means for the user to input information that the user wants to check into the terminal.

[1045] The "means for transmitting input information to a server" is a means for structuring information input from a terminal into a data packet and transmitting it to a server.

[1046] The "means for generating a question based on the information received on the server" refers to a means for analyzing the information received by the server and generating an appropriate question based on that information.

[1047] The "means for transmitting the generated question to the player" refers to a means for transmitting the question generated by the server to the player's terminal.

[1048] The "means for receiving answers from the player" refers to the means by which the server receives answers entered by the player using the terminal.

[1049] The "means for organizing received answers and creating reports" refers to the means by which the server stores the player's answers in a database, analyzes them, and creates standard reports.

[1050] The "means for transmitting the created report to the manager" is a means for transmitting the report created by the server to the manager's terminal.

[1051] "Means for recognizing player emotions using an emotion engine" refers to means for recognizing emotions from player inputs and dialogues and analyzing the data.

[1052] The "means for incorporating recognized emotional data into the question generation process" refers to a means for adjusting the tone and specificity of a question based on the emotional data recognized by the emotion engine.

[1053] A "generative AI model" is an artificial intelligence model used to generate questions on the server.

[1054] A "prompt" is a document to be input into a generative AI model, and is a guided sentence to support the generation of appropriate questions.

[1055] The system for implementing this invention is constructed based on a smartphone application, a cloud server, and an emotion engine. The specific configuration and operation of the system are described below.

[1056] First, the user, a worker, uses a smartphone application to input information, such as the "current task progress" or "recognized problems." This input method is usually implemented by text input using a touchscreen or voice input. Furthermore, the application has a built-in emotion engine that also collects emotional data through facial expression and voice analysis of the worker.

[1057] The smartphone application then transmits the input information and emotion data to a cloud server over Wi-Fi or 4G / 5G networks, where the server receives the information and stores it in a database.

[1058] The cloud server uses the generative AI model to generate appropriate questions based on the received information. For example, if the received information is "Current task progress: 60%", the problem is "Machine malfunction", and the emotion is "Anxiety", the server inputs the following prompt sentence into the generative AI model:

[1059] To the question generation AI system:

[1060] Use the information below to generate appropriate questions.

[1061] Work progress: 60%

[1062] Problem: Mechanical malfunction

[1063] Emotion: Anxiety

[1064] Expected output:

[1065] Questions asking for details about the machine malfunction (e.g., What caused the machine malfunction?).

[1066] A generative AI model (e.g., OpenAI GPT-3) uses this prompt to generate an appropriate question, such as "What caused the machine to malfunction?"

[1067] The generated questions are then sent to the smartphone application in chat format, allowing workers to answer questions intuitively, and the response data is sent back to the cloud server in real time.

[1068] The server analyzes the received response data and generates a report that includes progress and issues, as well as worker emotion data. The report can also be used to perform more detailed analysis by referencing information in the database.

[1069] Finally, the cloud server sends the created report to the manager's device, where it is provided in PDF or dashboard format, allowing the manager to comprehensively grasp the progress of the work, any issues, and the emotional state of the workers.

[1070] In this way, the present invention provides a more comprehensive work management system by realizing efficient information sharing between workers and managers and utilizing emotion data.

[1071] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1072] Step 1:

[1073] The user (worker) opens the smartphone application and inputs information such as progress and problems. The emotion engine analyzes the worker's facial expressions and voice to recognize emotional data. At this stage, inputs include "Task progress: 60%", "Problem: machine malfunction", and "Emotion: anxiety". This data is output and temporarily saved within the application.

[1074] Step 2:

[1075] The device structures the input information and emotion data into data packets and sends them to the cloud server. Communication is via Wi-Fi or 4G / 5G networks, and the data is securely encrypted before transmission. The input is the information saved in step 1, and the cloud server receives this data as output.

[1076] Step 3:

[1077] The server parses the received information and stores it in a database. The parsing process involves data format conversion and normalization. The input is the data set sent in step 2, and the output is structured data stored in the server's database.

[1078] Step 4:

[1079] The server generates prompts to be input to the generative AI model based on the stored data. The prompts include progress, problems, and emotional data. As a specific example, the following prompt is generated: "To the question generation AI system: Please generate an appropriate question based on the following information. Work progress: 60%, Problem: Machine malfunction, Emotion: Anxiety." The input is data stored in the database on the server, and the output is the prompt to be sent to the generative AI model.

[1080] Step 5:

[1081] A generative AI model (OpenAI GPT-3) generates an appropriate question based on the prompt. The generated question takes the form, for example, "What is the cause of the machine malfunction?" The input is the prompt generated in step 4, and the output is a specific question.

[1082] Step 6:

[1083] The server sends the generated question to the player's (worker's) smartphone application. This is done in a chat format, allowing the worker to respond intuitively. The input is the question generated in step 5, and the output is the question displayed on the worker's device.

[1084] Step 7:

[1085] The user (worker) enters an answer to the question displayed on the terminal. For example, the answer is "The bolt is loose." The input is the question displayed in step 6, and the output is the answer re-entered into the terminal.

[1086] Step 8:

[1087] The terminal transmits the worker's response to the cloud server in real time. The communication is secure. The input is the response entered in step 7, and the output is the response data received by the cloud server.

[1088] Step 9:

[1089] The server again analyzes the received response data and generates a report, which includes progress, problem details, and sentiment data. This analysis includes structuring and statistical analysis of the data. The input is the response data received in step 8, and the output is the generated report.

[1090] Step 10:

[1091] The server sends the generated report to the manager's terminal. The report is provided in PDF or dashboard format and is sent via email or system notification. The input is the report generated in step 9, and the output is the report that arrives on the manager's terminal.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] [Fourth embodiment]

[1096] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1097] 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.

[1098] 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).

[1099] 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.

[1100] 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.

[1101] 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).

[1102] 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.

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] 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.

[1108] 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."

[1109] The present invention is a system for improving the efficiency of information sharing between a manager and players. Specific embodiments of the present invention will be described below.

[1110] The system includes "means for inputting information," "means for transmitting input information to a server," "means for generating questions based on information received on the server," "means for transmitting the generated questions to players," "means for receiving answers from players," "means for organizing received answers and creating reports," and "means for transmitting the created reports to a manager."

[1111] How to enter information

[1112] The user (manager) inputs the information they want to check into the device (app). Specifically, they can input information such as "task progress" and "current issues" through a project management app or a dedicated interface.

[1113] A means of sending input information to the server

[1114] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[1115] means for generating questions based on the information received on the server;

[1116] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What percentage of your task is complete?" or "What are your current challenges?"

[1117] A means of sending generated questions to players

[1118] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[1119] A means of receiving answers from players

[1120] The user (player) inputs answers to questions displayed on the terminal. Answers can be entered in multiple choice format or free text format, and the terminal sends the answers to the server in real time.

[1121] A means of organizing and reporting on the responses received

[1122] The server analyzes the responses received and generates formatted reports. A database is used to manage the responses, and dashboards and report generation tools allow managers to understand the overall situation at a glance.

[1123] A means of sending the generated report to a manager

[1124] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[1125] Specific examples

[1126] For example, consider a scenario where a manager is checking the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues." The device sends this information to a server, which then uses a generative AI to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[1127] The generated questions are sent to each player's device in chat format. The player then responds with specific answers, such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to a server, which analyzes the received data and creates a standardized report. This report is then sent to the manager's device, allowing them to centrally monitor the project's progress.

[1128] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[1129] The processing flow will be explained below.

[1130] Understood. Below is a detailed explanation of the program processing of this system.

[1131] Step 1:

[1132] The user (manager) enters the progress of the task or questions they want to check into the device (app). For example, they enter "progress of task A" or "current issues of task B" in text format.

[1133] Step 2:

[1134] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[1135] Step 3:

[1136] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (e.g., progress or problem).

[1137] Step 4:

[1138] The server uses a generative AI model to generate questions based on the information it receives, such as "What percentage of task A is complete?" or "What are the current challenges for task B?"

[1139] Step 5:

[1140] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[1141] Step 6:

[1142] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[1143] Step 7:

[1144] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[1145] Step 8:

[1146] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[1147] Step 9:

[1148] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.).

[1149] Step 10:

[1150] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[1151] Step 11:

[1152] The user (manager) can check the progress and a summary of responses from the players through the terminal. Based on the displayed report, they can consider the progress of the project and measures to address issues.

[1153] Through these steps, the system can streamline information sharing between managers and players, improving the speed of the entire operation.

[1154] Example 1

[1155] 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."

[1156] In conventional project management systems, information sharing between managers and players is inefficient, making it difficult to accurately grasp progress and issues. In particular, manual information input and data organization are cumbersome and time-consuming, which can slow down the overall speed of work. Furthermore, because the generated data is not managed centrally, it is difficult to check progress in real time or share issues.

[1157] 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.

[1158] In this invention, the server includes means for analyzing information and generating appropriate questions using a generative AI model, means for sending the generated questions to players' devices in chat format, and means for receiving and analyzing answers sent by players in real time. This makes it possible to streamline information sharing between managers and players and improve the speed of the entire business.

[1159] The "means for inputting information" refers to an interface that allows a manager to use a terminal to input information such as the progress of a task or issues.

[1160] "Structuring as data packets" refers to a technical technique that converts input information into a uniform format so that it can be efficiently transmitted and received.

[1161] A "communications network" is an infrastructure for sending and receiving data, such as the Internet or an internal network.

[1162] A "server" is a computer system that receives requests from clients via a network and processes them appropriately.

[1163] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate appropriate questions and answers based on input data.

[1164] The "means for generating questions" is a function that uses a generative AI model to create questions based on the information received by the server.

[1165] "Chat format" is a user interface format that supports interactive messaging.

[1166] The "means for transmitting to the terminal" is a mechanism for distributing the generated question to the device used by the player via a network.

[1167] A "player" is a user who is in charge of a task in a project and reports on progress and issues.

[1168] The "means for receiving answers" is a process for the server to obtain and analyze the answers entered by the player.

[1169] A "means for analyzing answers" is a system that processes received player answers and converts them into meaningful information.

[1170] A "formatted report" is a report with a set format that is generated based on collected and analyzed data.

[1171] "Electronic format" refers to a document format that can be stored and viewed digitally, such as PDF or Excel.

[1172] The "means for sending to the manager's terminal" is a function for distributing the report created by the server to the manager's device.

[1173] "Real-time" refers to the property that data processing and communication are instantaneous and without delay.

[1174] The present invention is a system for improving the efficiency of information sharing between a manager and players. A specific embodiment of the present invention will be described.

[1175] First, the user (manager) enters information such as the "progress of tasks" or "current issues" they want to check into their device through a project management app or dedicated interface. This information is structured as a data packet and sent to the server via the Internet or an internal network. HTTP or HTTPS is used as the communication network.

[1176] The server receives the information sent from the device, stores it in a database, and prepares it for further analysis, which may include data integrity checks and data validation.

[1177] Based on the received information, the server uses a generative AI model (for example, OpenAI's GPT-4) to generate appropriate questions. This generation uses prompts. A prompt is a sentence that instructs the AI ​​model on what questions it should generate. For example, "What percentage of the task A is complete?" or "Are there any problems with task A right now?"

[1178] The generated questions are sent from the server to the player's device in chat format, allowing players to respond intuitively. This interface is displayed on the mobile and desktop apps.

[1179] Players enter answers to questions displayed on their terminals. Answers can be multiple choice or free description. The terminals send the answers entered by the players to the server in real time. Real-time transmission means that answers are instantly updated on the server.

[1180] The server analyzes the received responses and generates formatted reports, which are then managed using a database and a dashboard and report generation tool, allowing managers to see the overall situation at a glance.

[1181] Finally, the server sends the created report to the manager's terminal in electronic format such as PDF or Excel, and the manager is notified of the report via email or in-system notification.

[1182] Specific examples

[1183] For example, when a manager checks the progress of a new project, they use a device to input the "task progress rate" and "current issues." The device then sends this information to the server, which uses a generative AI model to generate questions such as "What is the current progress rate?" and "What are the current issues?"

[1184] The generated questions are sent to each player's device in chat format, and the player responds with specific answers such as "Progress: 80%" or "No particular issues." The answers are sent in real time via the device to the server, which analyzes the received data and creates a standardized report.

[1185] Prompt Sentence Examples

[1186] Below are some example prompts to input to a generative AI model:

[1187] "What is the progress percentage of Task A?"

[1188] "Are you currently having any problems with Task A?"

[1189] In this way, the present invention is a system that can streamline information sharing between managers and players and improve the speed of the entire business.

[1190] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] The user (manager) enters information such as "task progress" and "current issues" that they want to check into their device through a project management app or a dedicated interface.

[1193] The information entered is specific data such as "Task A's progress rate is 50%" and "Task B's progress rate is 75%."

[1194] This information is input to the terminal, which then structures it into data packets.

[1195] Step 2:

[1196] The terminal sends structured data packets to the server via the Internet or an internal network.

[1197] In this case, HTTP or HTTPS is used as the communication protocol, and the data packets are in JSON or XML format.

[1198] Stable and secure data transfer is achieved.

[1199] Step 3:

[1200] The server receives the data packets sent from the terminal.

[1201] The received information is stored in a database and prepared for analysis.

[1202] Specifically, the received data is checked for consistency and validated, and then formatted into an analyzable format.

[1203] Step 4:

[1204] The server generates prompt sentences for a generative AI model (e.g., OpenAI's GPT-4) based on the received information.

[1205] Prompts are specific questions such as "What percentage of task A is complete?" or "What are your current challenges?"

[1206] This generates appropriate questions based on the information entered.

[1207] Step 5:

[1208] The server sends the generated questions to the player's terminal in chat format.

[1209] Specifically, questions are delivered in real time using WebSocket and API.

[1210] The questions are displayed in a chat box so that players can intuitively enter their answers.

[1211] Step 6:

[1212] The user (player) inputs answers to questions displayed on the terminal.

[1213] The answers are specific data such as "Progress: 80%" or "Challenges: None in particular."

[1214] The terminal transmits this response to the server in real time.

[1215] Step 7:

[1216] The server receives the responses sent by the players.

[1217] The received data is stored in a database and preparations for analysis are underway.

[1218] Specifically, the response data is checked for consistency and formatted.

[1219] Step 8:

[1220] The server analyzes the received responses and generates a formatted report.

[1221] Response data is aggregated from the database and visualized using dashboards and report generation tools.

[1222] View average progress and a list of assignments.

[1223] Step 9:

[1224] The server sends the generated report to the manager's terminal in an electronic format such as PDF or Excel.

[1225] Notify managers of the report's existence via email and in-system notifications.

[1226] This allows managers to see the progress of projects from a single location.

[1227] (Application example 1)

[1228] 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."

[1229] In the past, information sharing regarding work progress and issues within factories often relied on manual labor, resulting in frequent delays and miscommunication of information, lower productivity, and delayed problem resolution. It was also difficult to grasp the actual work status of each robot in real time, and it took a long time for managers to grasp the overall situation. As a result, efficient work management and rapid response are required.

[1230] 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.

[1231] In this invention, the server includes a means for inputting information, a means for transmitting the input information to the server via a communication network, a means for generating questions using a generative AI model based on the information received on the server, a means for transmitting the generated questions to players in chat format, a means for receiving answers from players in real time, a means for analyzing the received answers and creating reports on progress and issues, and a means for sending the created reports in PDF or Excel format to a manager. This automates the process from inputting information to generating questions, collecting answers, and creating and sending reports, making it possible to grasp the progress and issues of work in the factory in real time.

[1232] "Means for inputting information" refers to the function that allows the user (manager) to input information such as progress and tasks into the terminal.

[1233] "Means for transmitting input information to a server via a communication network" refers to a communication function for transmitting data from a terminal to a server.

[1234] "Means for generating questions using a generative AI model" refers to a function that automatically generates appropriate questions using generative AI technology based on received information.

[1235] "Means for sending to players in chat format" refers to a function for sending generated questions to players' terminals in chat format.

[1236] "Means for receiving answers from players in real time" refers to a function for instantly transmitting answers from players to a server and receiving them in real time.

[1237] "Means for analyzing the received responses and generating progress and assignment reports" refers to a function that analyzes the responses received by the server and generates reports on progress and current assignments.

[1238] "A means of sending the created report to the manager in PDF or Excel format" refers to the function of converting the created report into PDF or Excel format and sending it to the manager.

[1239] The present invention provides a system that improves the efficiency of information sharing between managers and players and enables real-time understanding of work progress and issues within a factory. Specific embodiments of the present invention will be described below.

[1240] Hardware and software used

[1241] Hardware:

[1242] User device: The smartphone, tablet, or PC used by the manager.

[1243] Player terminal: The control terminal for the robots working in the factory.

[1244] Communication networks: Internal networks, including Wi-Fi and wired LAN, and the Internet.

[1245] software:

[1246] Server: A server on the cloud or a local server in the factory.

[1247] Generative AI models: For example, using generative AI technologies such as OpenAI's GPT series.

[1248] Chat-style interface: Chat applications (e.g. Telegram, Slack, etc.) built into the user and robot devices.

[1249] Data processing and calculation flow

[1250] 1. Enter and submit information:

[1251] Users use smartphones, tablets, or PCs to input information about work progress and current issues. For example, they input specific data such as a task's progress rate of "60%" or a current issue of "missing parts." This information is sent to the server as structured data packets over a communications network.

[1252] 2. Question generation:

[1253] The server analyzes the received data and uses a generative AI model to generate appropriate questions, such as "What is the progress rate of the current task?" or "What is the current challenge?"

[1254] 3. Submitting Questions and Receiving Answers:

[1255] The generated questions are sent to the players' (robots') terminals in chat format. Each robot responds to the questions in real time, and the answers are immediately sent to the server.

[1256] 4. Response analysis and reporting:

[1257] The server analyzes the received responses and creates a progress and task report. This report is converted into a format such as PDF or Excel and sent to the user's device. The report visualizes each robot's progress and tasks so that they can be understood at a glance.

[1258] Specific examples

[1259] For example, consider the case where a manager wants to check the progress of a new project. The manager uses his or her smartphone to input information such as "Progress rate: 60%" and "Current issue: Parts missing." This information is sent to a server, which uses a generative AI model to generate questions such as "What is the progress rate of the current task?" and "What is the current issue?" The generated questions are sent to the chat-style interface of each robot in the factory, and the robot responds in real time with "Progress rate: 75%" and "Issue: New parts have arrived." Based on these responses, the server creates a report and sends it in PDF format to the manager's device.

[1260] Prompt Sentence Examples

[1261] Below are some examples of prompt sentences to input to the generative AI model.

[1262] Problem-solving question generation:

[1263] input:

[1264] Task progress: 60%

[1265] Current issue: Parts shortage

[1266] output:

[1267] What is the current task progress percentage?

[1268] What are your current challenges?

[1269] In this way, the present invention improves the efficiency of information sharing between managers and players, making it possible to grasp the progress and issues of work within the factory in real time.

[1270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1271] Step 1:

[1272] The user (manager) enters information. Specifically, they log in to the application using a smartphone, tablet, or PC and enter specific data such as "Task progress: 60%" and "Current issue: missing parts." This becomes the input data.

[1273] Step 2:

[1274] The device sends the information entered by the user to the server in real time. The input data is sent as structured data packets using a communication network (Wi-Fi or wired LAN), and the server receives the input data.

[1275] Step 3:

[1276] The server analyzes the received data. Based on the analyzed data, it activates a generative AI model and generates questions. For example, if the prompts "Task progress rate: 60%" and "Current issue: missing parts" are input, the generative AI model will generate questions such as "What is the current task progress rate?" and "What is the current issue?" This becomes the output data.

[1277] Step 4:

[1278] The server sends the generated questions to each robot's terminal in chat format. The robot receives the questions using a chat application and displays the answers. This interface is visual and intuitive.

[1279] Step 5:

[1280] The player (robot) inputs answers to questions sent from the server in real time. For example, they input information such as "Task progress: 75%" or "Current issue: New parts have arrived." This becomes the input data.

[1281] Step 6:

[1282] The terminal transmits the answers entered by the player to the server in real time. The answer data is transmitted to the server as a structured data packet using a communication network, and the server receives the answer data.

[1283] Step 7:

[1284] The server analyzes the received response data. Based on the analyzed data, it generates progress and assignment reports. The reports are created in PDF or Excel format. This report generation process also accesses the database, compares the data with past data, and visualizes it on a dashboard.

[1285] Step 8:

[1286] The server sends the created report to the manager's terminal. The report is communicated via email and in-system notifications, allowing the manager to grasp the project's progress and current issues in real time.

[1287] 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.

[1288] The present invention is a system for improving the efficiency of information sharing between managers and players, and by combining it with an emotion engine that recognizes the emotions of users, it achieves more accurate information gathering and communication. Specific embodiments of the present invention will be described below.

[1289] The system comprises a "means for inputting information," a "means for transmitting input information to a server," a "means for generating questions based on information received on the server," a "means for transmitting the generated questions to the player," a "means for receiving answers from the player," a "means for organizing the received answers and creating a report," a "means for sending the created report to a manager," and an "emotion engine."

[1290] How to enter information

[1291] The user (manager) inputs the information they want to check into the device (app). For example, they can input "progress of task A" or "current issues of task B" through a project management app or a dedicated interface.

[1292] A means of sending input information to the server

[1293] The terminal structures the information input from the manager as a data packet and sends it to the server. Communication is via the Internet or an internal network, and the system is designed to ensure stable data transfer.

[1294] means for generating questions based on the information received on the server;

[1295] The server analyzes the received information and uses generative AI to automatically generate appropriate questions, such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?"

[1296] Use of emotion engine

[1297] The emotion engine recognizes emotions from user (player) input and dialogue and analyzes that data. The recognized emotion data is incorporated into the question generation process and used to adjust the tone and specificity of the questions. For example, if the player is feeling stressed, the tone of the questions will be softened or changed to show more interest.

[1298] A means of sending generated questions to players

[1299] The server generates questions and sends them to each player's device, using a chat-style interface that allows players to respond intuitively. The chat-style interface is integrated into the mobile and desktop apps.

[1300] A means of receiving answers from players

[1301] The user (player) inputs answers to questions displayed on the device. For example, answers can be entered in the form of "Progress: 80%" or "Challenges: None in particular," and the device can send these to the server in real time.

[1302] A means of organizing and reporting on the responses received

[1303] The server analyzes the received response data and creates standardized reports. The database manages responses, and dashboards and report generation tools allow managers to grasp the overall situation at a glance. Analysis of emotional data also allows for the creation of detailed reports that include players' mental states.

[1304] A means of sending the generated report to a manager

[1305] The server sends the created report to the manager's terminal, where it is provided in formats such as PDF and Excel, and is notified to the manager via email or in-system notifications.

[1306] Specific examples

[1307] For example, consider a scenario in which a manager checks the progress of a new project. The manager uses a device to input the "task progress rate" and "current issues," and the device sends this information to the server. The server analyzes the received information and uses a generative AI and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player responds with specific answers such as "Progress rate: 80%" and "Issues: None in particular."

[1308] Emotional data is also collected along with the player's responses. For example, if the player is feeling anxious, that emotional data is also sent to the server. The server analyzes the received data and creates a report such as, "The player's progress rate is 80%, there are no particular issues, but he is feeling anxious." Finally, this report is sent to the manager's device, allowing them to understand the player's mental state as well as the project's progress.

[1309] In this way, the present invention is a system that realizes more comprehensive management by streamlining information sharing between managers and players and utilizing emotional data.

[1310] The processing flow will be explained below.

[1311] Understood. The processing of this system will be explained in detail below.

[1312] Step 1:

[1313] The user (manager) inputs the progress of the task or questions they want to check into the device (app). For example, they input information such as "progress of task A" or "current issues of task B."

[1314] Step 2:

[1315] The device sends the input information to the server. Specifically, it converts the input information into a data format such as JSON and sends a request to the server via the API.

[1316] Step 3:

[1317] The server analyzes the received information. The analysis program tokenizes the input information and processes the data to identify the type of information (progress or problem).

[1318] Step 4:

[1319] The server starts the emotion engine and collects data to recognize the user's (player's) emotions. The emotion engine analyzes the player's past chat history and current input data to estimate their emotional state.

[1320] Step 5:

[1321] The server uses a generative AI model to generate questions based on the information and emotional data it receives, adjusting the tone of the questions to soften them if the player is feeling stressed, for example.

[1322] Step 6:

[1323] The server sends the generated questions to each player's device, constructing personalized messages based on the player's ID and contact information and sending them through a chat-style interface.

[1324] Step 7:

[1325] The user (player) answers questions displayed on the device in a chat format, for example, by entering answers in the form of "Progress rate: 80%" or "Challenges: None in particular."

[1326] Step 8:

[1327] The terminal transmits the player's answers to the server in real time, structuring the player's answers into data packets and rapidly transferring them to the server.

[1328] Step 9:

[1329] The server analyzes the received response data, stores the responses in a database, and classifies and organizes the responses by category.

[1330] Step 10:

[1331] The server creates a report based on the organized response data. The report creation program compiles information about progress and tasks in the specified format (e.g., PDF, Excel, etc.). It also analyzes emotional data and reflects the player's mental state in the report.

[1332] Step 11:

[1333] The server sends the created report to the manager's terminal. The report data is sent to the terminal via API, and the notification function notifies the manager that the report is available for viewing.

[1334] Step 12:

[1335] The user (manager) can check the progress and summary of responses from the players through the device. Based on the displayed report, they can consider the progress of the project and measures to address issues. In addition, by understanding the emotional state of the players, they can manage the project more appropriately.

[1336] Through these steps, this system streamlines information sharing between managers and players and utilizes emotional data to achieve more comprehensive management.

[1337] Example 2

[1338] 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."

[1339] Previous information sharing systems lacked efficient communication between managers and players, making it particularly difficult to grasp the players' emotional states. This meant that managers were unable to properly manage players' performance and psychological state. Furthermore, the quality and tone of the questions generated did not match the players' emotional states, resulting in answers that lacked accuracy and candor.

[1340] 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.

[1341] In this invention, the server includes means for generating questions using an emotion engine that recognizes the user's emotions, means for organizing the received answers and emotion data to create a report, and means for sending the created report to the manager. This makes it possible to generate questions that reflect the player's emotional state, increasing the player's response rate and accuracy and enabling the manager to appropriately manage the player's performance and psychological state.

[1342] The "means for inputting information" refers to a means for providing information that the user wants to check through an input device such as a terminal.

[1343] The "means for transmitting input information to a server" refers to a means for structuring information input by a user into a data packet and transmitting it to a server via the Internet or an internal network.

[1344] "Means for generating questions based on information received on the server" refers to means for the server to analyze the information received and automatically create appropriate questions using a generative AI model.

[1345] The "emotion engine that recognizes user emotions" is an engine that recognizes emotions from user input and dialogue data and analyzes that data.

[1346] The "means for sending generated questions to players" refers to a means for the server to send automatically generated questions to the player's terminal, and utilizes a chat-style interface.

[1347] The "means for receiving answers from players" refers to the means by which players answer questions through their terminals and transmit the answers to the server in real time.

[1348] The "means for organizing received answer and emotion data and creating a report" is a means for the server to analyze the answer data and emotion data received from the players and create a standardized report.

[1349] The "means for sending the created report to the manager" is a means for sending the report created by the server to the manager's terminal so that the manager can check the status of the player.

[1350] A "generative AI model" is an artificial intelligence model that automatically generates questions based on received information and data.

[1351] A "prompt" is text that is input into a generative AI model and serves as the basis for the model to generate appropriate questions and responses.

[1352] The present invention is a system that improves the efficiency of information sharing between managers and players and combines it with an emotion engine that recognizes user emotions to achieve more accurate information gathering and communication. This system includes the following components:

[1353] Hardware and software used

[1354] Hardware: Servers, devices (smartphones and PCs)

[1355] Software: Project management apps, emotion engines, generative AI models, database management systems, chat interfaces

[1356] How to enter information

[1357] The user (manager) enters the information they want to check into the device through a project management app or a dedicated interface. Examples of information that a user enters include "progress of task A" and "current issues for task B." For example, they can enter "progress rate: 80%" and "current issues: none" into the text boxes of a project management app.

[1358] A means of sending input information to the server

[1359] The device structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. Protocols such as HTTP and HTTPS are used for communication, and the data is sent while checking its integrity.

[1360] means for generating questions based on the information received on the server;

[1361] The server analyzes the received information and automatically generates appropriate questions using a generative AI model. For example, questions such as "What is the progress rate for Task A?" or "What are the current challenges for Task B?" are generated. The server temporarily stores the generated questions in a database.

[1362] Use of emotion engine

[1363] The server uses an emotion engine to recognize emotions from the user's (player's) input and dialogue, and analyzes the data. For example, if the player is feeling stressed, the server softens the tone of the questions or adjusts the content. The questions generated in this way take into account the player's psychological state.

[1364] A means of sending generated questions to players

[1365] The server then sends the generated questions to the player's device, where they can intuitively answer them using a chat-style interface that is integrated into the mobile and desktop apps.

[1366] A means of receiving answers from players

[1367] The user (player) answers questions displayed on the device. For example, specific answers such as "Progress rate: 80%" or "Challenges: None in particular" are possible. The answers are sent to the server in real time, allowing for efficient data collection.

[1368] A means to organize and report on received response and sentiment data

[1369] The server analyzes the received response data and emotional data and creates a formatted report that details the player's task progress and emotional state, allowing managers to grasp the overall situation at a glance. The report can be output in PDF or Excel format.

[1370] A means of sending the generated report to a manager

[1371] The server then sends the generated report to the manager's device, where it is provided via email and in-system notifications, allowing the manager to check the player's status.

[1372] Specific examples

[1373] For example, if a manager wants to check the progress of a new project, they can use their device to input the "task progress rate" and "current issues." The device sends this information to the server, which analyzes the received information and uses a generative AI model and emotion engine to generate questions such as "What is the current progress rate?" and "What are the current issues?" These questions are sent to the player's device in chat format, and the player replies with "Progress rate: 80%" and "Issues: None in particular." A report summarizing these answers and emotion data is created by the server and ultimately sent to the manager's device.

[1374] Prompt Sentence Examples

[1375] "Please tell me the progress of your current task."

[1376] "What are your current challenges with the task?"

[1377] The above is an embodiment of the present invention, which makes the information sharing between managers and players more efficient and realizes comprehensive management that utilizes emotion data.

[1378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1379] Step 1: Enter your information

[1380] The user (manager) uses a project management app or a dedicated interface to input the information they want to check into their device. The information they input includes "progress of task A" and "current issues for task B." Specifically, the user inputs "progress rate: 80%" or "current issues: none" into the app's text boxes. This input data is temporarily stored in the device's internal storage area.

[1381] Step 2: Send the information to the server

[1382] The terminal structures the information entered by the user as a data packet and sends it to the server via the Internet or an internal network. HTTP or HTTPS is used as the protocol. At this time, the data integrity is checked to ensure reliable communication. The input is the task information entered by the user, and the output is the data packet sent to the server.

[1383] Step 3: Generate questions based on the received information

[1384] The server analyzes the received data packets and extracts the necessary information. Based on this information, the server uses a generative AI model to automatically generate appropriate questions. For example, based on the extracted progress information, questions such as "What is the progress rate for task A?" or "What are the current challenges for task B?" are generated. The input is the task information sent to the server, and the output is the generated question.

[1385] Step 4: Use the emotion engine to tailor your questions

[1386] The server uses an emotion engine to recognize emotions from the user's (player's) past inputs and interactions. For example, if it is determined that the player has been feeling stressed recently, the emotion data is reflected in the question generation process. The tone and content of the question are adjusted to take the player's emotions into consideration. The input is the generated question and emotion data, and the output is the adjusted question.

[1387] Step 5: Send the generated questions to the player

[1388] The server sends the generated question to the player's device through a chat-style interface, through which the player can answer the question. Specifically, the server converts the question into a message format and sends a notification to the player's app. The input is the adjusted question, and the output is the question displayed on the player's device.

[1389] Step 6: Player answers the question

[1390] The user (player) answers questions displayed on the device. For example, they input specific answers such as "Progress: 80%" or "Challenges: None in particular." The input data is sent to the server in real time. The input is the displayed question, and the output is the input answer.

[1391] Step 7: Send the answer to the server

[1392] The terminal structures the answers entered by the user and sends them to the server in real time. The communication is done using HTTP or HTTPS and the data integrity is checked. The input is the answer entered by the user and the output is the answer sent to the server.

[1393] Step 8: Organize response and sentiment data and create reports

[1394] The server analyzes the received response data and emotion data and creates a formatted report. This report details the player's task progress and emotional state. Specifically, the server manages the response data using a database and creates dashboard-style reports using a report generation tool. The input is the response data and emotion data, and the output is the generated report.

[1395] Step 9: Send the generated report to your manager

[1396] The server sends the created report to the manager's terminal. The report is output in PDF or Excel format and provided to the manager via email or internal system notification. The input is the generated report, and the output is the report displayed on the manager's terminal.

[1397] (Application example 2)

[1398] 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."

[1399] In modern factories, information sharing between workers and managers is not yet efficient, and in particular, there is a lack of systems that can take into account the emotional state of workers. As a result, awareness of work progress and problems can be insufficient, leading to reduced work efficiency and safety issues. The present invention aims to solve these problems and provide an efficient information sharing system that takes into account the emotions of workers.

[1400] 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.

[1401] In this invention, the server includes means for inputting information, means for transmitting the input information to the server, means for generating questions based on the information received on the server, means for transmitting the generated questions to players, means for receiving answers from players, means for organizing the received answers and creating a report, means for transmitting the created report to a manager, means for recognizing player emotions using an emotion engine, and means for incorporating recognized emotion data into the question generation process. This improves the efficiency of information sharing between workers and managers and enables management that takes into account the emotional states of workers.

[1402] The "means for inputting information" refers to a means for the user to input information that the user wants to check into the terminal.

[1403] The "means for transmitting input information to a server" is a means for structuring information input from a terminal into a data packet and transmitting it to a server.

[1404] The "means for generating a question based on the information received on the server" refers to a means for analyzing the information received by the server and generating an appropriate question based on that information.

[1405] The "means for transmitting the generated question to the player" refers to a means for transmitting the question generated by the server to the player's terminal.

[1406] The "means for receiving answers from the player" refers to the means by which the server receives answers entered by the player using the terminal.

[1407] The "means for organizing received answers and creating reports" refers to the means by which the server stores the player's answers in a database, analyzes them, and creates standard reports.

[1408] The "means for transmitting the created report to the manager" is a means for transmitting the report created by the server to the manager's terminal.

[1409] "Means for recognizing player emotions using an emotion engine" refers to means for recognizing emotions from player inputs and dialogues and analyzing the data.

[1410] The "means for incorporating recognized emotional data into the question generation process" refers to a means for adjusting the tone and specificity of a question based on the emotional data recognized by the emotion engine.

[1411] A "generative AI model" is an artificial intelligence model used to generate questions on the server.

[1412] A "prompt" is a document to be input into a generative AI model, and is a guided sentence to support the generation of appropriate questions.

[1413] The system for implementing this invention is constructed based on a smartphone application, a cloud server, and an emotion engine. The specific configuration and operation of the system are described below.

[1414] First, the user, a worker, uses a smartphone application to input information, such as the "current task progress" or "recognized problems." This input method is usually implemented by text input using a touchscreen or voice input. Furthermore, the application has a built-in emotion engine that also collects emotional data through facial expression and voice analysis of the worker.

[1415] The smartphone application then transmits the input information and emotion data to a cloud server over Wi-Fi or 4G / 5G networks, where the server receives the information and stores it in a database.

[1416] The cloud server uses the generative AI model to generate appropriate questions based on the received information. For example, if the received information is "Current task progress: 60%", the problem is "Machine malfunction", and the emotion is "Anxiety", the server inputs the following prompt sentence into the generative AI model:

[1417] To the question generation AI system:

[1418] Use the information below to generate appropriate questions.

[1419] Work progress: 60%

[1420] Problem: Mechanical malfunction

[1421] Emotion: Anxiety

[1422] Expected output:

[1423] Questions asking for details about the machine malfunction (e.g., What caused the machine malfunction?).

[1424] A generative AI model (e.g., OpenAI GPT-3) uses this prompt to generate an appropriate question, such as "What caused the machine to malfunction?"

[1425] The generated questions are then sent to the smartphone application in chat format, allowing workers to answer questions intuitively, and the response data is sent back to the cloud server in real time.

[1426] The server analyzes the received response data and generates a report that includes progress and issues, as well as worker emotion data. The report can also be used to perform more detailed analysis by referencing information in the database.

[1427] Finally, the cloud server sends the created report to the manager's device, where it is provided in PDF or dashboard format, allowing the manager to comprehensively grasp the progress of the work, any issues, and the emotional state of the workers.

[1428] In this way, the present invention provides a more comprehensive work management system by realizing efficient information sharing between workers and managers and utilizing emotion data.

[1429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1430] Step 1:

[1431] The user (worker) opens the smartphone application and inputs information such as progress and problems. The emotion engine analyzes the worker's facial expressions and voice to recognize emotional data. At this stage, inputs include "Task progress: 60%", "Problem: machine malfunction", and "Emotion: anxiety". This data is output and temporarily saved within the application.

[1432] Step 2:

[1433] The device structures the input information and emotion data into data packets and sends them to the cloud server. Communication is via Wi-Fi or 4G / 5G networks, and the data is securely encrypted before transmission. The input is the information saved in step 1, and the cloud server receives this data as output.

[1434] Step 3:

[1435] The server parses the received information and stores it in a database. The parsing process involves data format conversion and normalization. The input is the data set sent in step 2, and the output is structured data stored in the server's database.

[1436] Step 4:

[1437] The server generates prompts to be input to the generative AI model based on the stored data. The prompts include progress, problems, and emotional data. As a specific example, the following prompt is generated: "To the question generation AI system: Please generate an appropriate question based on the following information. Work progress: 60%, Problem: Machine malfunction, Emotion: Anxiety." The input is data stored in the database on the server, and the output is the prompt to be sent to the generative AI model.

[1438] Step 5:

[1439] A generative AI model (OpenAI GPT-3) generates an appropriate question based on the prompt. The generated question takes the form, for example, "What is the cause of the machine malfunction?" The input is the prompt generated in step 4, and the output is a specific question.

[1440] Step 6:

[1441] The server sends the generated question to the player's (worker's) smartphone application. This is done in a chat format, allowing the worker to respond intuitively. The input is the question generated in step 5, and the output is the question displayed on the worker's device.

[1442] Step 7:

[1443] The user (worker) enters an answer to the question displayed on the terminal. For example, the answer is "The bolt is loose." The input is the question displayed in step 6, and the output is the answer re-entered into the terminal.

[1444] Step 8:

[1445] The terminal transmits the worker's response to the cloud server in real time. The communication is secure. The input is the response entered in step 7, and the output is the response data received by the cloud server.

[1446] Step 9:

[1447] The server again analyzes the received response data and generates a report, which includes progress, problem details, and sentiment data. This analysis includes structuring and statistical analysis of the data. The input is the response data received in step 8, and the output is the generated report.

[1448] Step 10:

[1449] The server sends the generated report to the manager's terminal. The report is provided in PDF or dashboard format and is sent via email or system notification. The input is the report generated in step 9, and the output is the report that arrives on the manager's terminal.

[1450] 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.

[1451] 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.

[1452] 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.

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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).

[1457] 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.

[1458] 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."

[1459] 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.

[1460] 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).

[1461] 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.

[1462] 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.

[1463] 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.

[1464] 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.

[1465] 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.

[1466] 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.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] The following is further disclosed regarding the above embodiment.

[1472] (Claim 1)

[1473] a means for inputting information;

[1474] means for transmitting the input information to a server;

[1475] means for generating a query based on the received information on the server;

[1476] means for transmitting the generated questions to the player;

[1477] means for receiving answers from players;

[1478] a means of organizing and reporting on the responses received;

[1479] A means for sending the generated report to a manager;

[1480] A system including:

[1481] (Claim 2)

[1482] 10. The system of claim 1, further comprising means for using a generation AI in generating questions on the server.

[1483] (Claim 3)

[1484] 2. The system according to claim 1, further comprising means for sending questions to players in a chat format.

[1485] "Example 1"

[1486] (Claim 1)

[1487] a means for inputting information;

[1488] means for structuring the input information into data packets and transmitting the packets to a server via a communications network;

[1489] A means for analyzing the received information on the server and generating appropriate questions using a generative AI model;

[1490] A means for sending the generated questions to the player's terminal in a chat format;

[1491] means for receiving answers submitted by the players;

[1492] a means of analyzing the responses received and generating formatted reports;

[1493] means for transmitting the generated report in electronic format to a manager's terminal;

[1494] A system including:

[1495] (Claim 2)

[1496] 2. The system according to claim 1, further comprising means for displaying the generated questions in a chat format on the player's terminal, allowing the user to intuitively answer the questions.

[1497] (Claim 3)

[1498] 10. The system of claim 1, further comprising means for receiving and analyzing answers submitted by players in real time.

[1499] "Application Example 1"

[1500] (Claim 1)

[1501] a means for inputting information;

[1502] means for transmitting the input information to a server via a communication network;

[1503] a means for generating questions using a generative AI model based on the received information on a server;

[1504] a means for sending the generated questions to the player in a chat format;

[1505] means for receiving answers from players in real time;

[1506] A means to analyze the responses received and generate progress and assignment reports;

[1507] A way to send the generated reports to your manager in PDF or Excel format,

[1508] A system including:

[1509] (Claim 2)

[1510] 10. The system of claim 1, further comprising means for using the generative AI model in generating questions on the server.

[1511] (Claim 3)

[1512] 2. The system according to claim 1, further comprising means for sending questions to players in a chat format and receiving answers from players in real time.

[1513] "Example 2: Combining Emotion Engines"

[1514] (Claim 1)

[1515] a means for inputting information;

[1516] means for transmitting the input information to a server;

[1517] means for generating a query based on the received information on the server;

[1518] a means for generating questions using an emotion engine that recognizes the emotions of a user;

[1519] means for transmitting the generated questions to the player;

[1520] means for receiving answers from players;

[1521] a means of organizing and reporting on received response and sentiment data;

[1522] A means for sending the generated report to a manager;

[1523] A system including:

[1524] (Claim 2)

[1525] 10. The system of claim 1, further comprising means for using the generative AI model in generating questions on the server.

[1526] (Claim 3)

[1527] 2. The system according to claim 1, further comprising means for sending questions to players in a chat format.

[1528] "Application example 2 when combining emotion engines"

[1529] (Claim 1)

[1530] a means for inputting information;

[1531] means for transmitting the input information to a server;

[1532] means for generating a query based on the received information on the server;

[1533] means for transmitting the generated questions to the player;

[1534] means for receiving answers from players;

[1535] a means of organizing and reporting on the responses received;

[1536] A means for sending the generated report to a manager;

[1537] a means for recognizing a player's emotion using an emotion engine;

[1538] a means for incorporating the recognized emotion data into the question generation process;

[1539] A system including:

[1540] (Claim 2)

[1541] 2. The system of claim 1, further comprising: means for using the generative AI model when generating questions on the server; and means for generating prompt sentences to be input to the generative AI model.

[1542] (Claim 3)

[1543] 2. The system according to claim 1, further comprising means for sending questions to players in a chat format. [Explanation of symbols]

[1544] 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 inputting information; means for transmitting the input information to a server; means for generating a query based on the received information on the server; means for transmitting the generated questions to the player; means for receiving answers from players; a means of organizing and reporting on the responses received; A means for sending the generated report to a manager; A system including:

2. 10. The system of claim 1, further comprising means for using a generation AI in generating questions on the server.

3. 2. The system according to claim 1, further comprising means for sending questions to players in a chat format.

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