System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a request and recording contents of the request in a database; means for automatically generating and transmitting a primary response to the request; means for searching for an unhandled request and transmitting a remind notification to a person in charge; means for performing a remind notification again when the request is not handled even after a lapse of a certain period; and means for confirming completion of the request and transmitting a completion notification to the requester.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] When there are a wide variety of requests in business, responses can be delayed, or in some cases, the request itself can be forgotten. This can lead to problems such as a loss of trust with the requester and a decrease in the efficiency of the entire business. It is necessary to prevent such missed responses and delays and improve the efficiency and reliability of business operations. [Means for solving the problem]

[0005] A system is provided that includes a means for receiving requests and recording their contents in a database, a means for automatically generating and sending a first response to the request, a means for searching for unprocessed requests and sending a reminder to the person in charge, a means for sending a second reminder if the request has not been processed after a certain period of time has passed, and a means for confirming that the request has been completed and sending a completion notification to the requester.This makes it possible to efficiently manage the entire process from receiving a request to completing it, and to prevent missed or delayed responses.

[0006] A "request" is a request for specific work content or tasks provided by a business requester.

[0007] A "database" is a system for storing and managing request details and their status.

[0008] A "primary reply" is an automatically generated reply message that is sent to the requester immediately upon receiving the request.

[0009] A "reminder notification" is a message sent to a person in charge to alert them when there is an unprocessed request.

[0010] A "second reminder" is a message sent again to remind you if a request is not responded to within a certain period of time after the first reminder.

[0011] A "completion notification" is a report message sent to the requester when the request is completed.

[0012] "Contact Person" refers to the person or department responsible for handling the request. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business. Specific embodiments of this system are described below.

[0035] System Configuration

[0036] The system consists of the following major components:

[0037] 1. Server

[0038] 2. Terminal

[0039] 3. Users

[0040] Program processing overview

[0041] This system accepts requests from users, automatically provides an initial response, and sends reminders for unanswered requests to prevent missed responses.

[0042] Request acceptance

[0043] Step 1.1 - Submit your request

[0044] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0045] Step 1.2 - Receiving the request

[0046] The server receives a request (HTTP POST request) sent by the user.

[0047] The server extracts the request content (e.g., "Request to create monthly report") from the request payload.

[0048] Step 1.3 - Save your request

[0049] The server stores the request in a database and records the request status as "unprocessed."

[0050] First response

[0051] Step 2.1 - Generate a First Response

[0052] The server reads the request details stored in the database and generates a primary response such as "Thank you for your request. We are currently working on it."

[0053] Step 2.2 - Sending a First Response

[0054] The server sends the generated initial response to the user's email address or via a chatbot.

[0055] Reminder function

[0056] Step 3.1 - Search for open requests

[0057] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[0058] Step 3.2 - Generate a Reminder

[0059] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[0060] Step 3.3 - Send Reminder Notifications

[0061] The server sends a reminder notification to the terminal of the person in charge.

[0062] Step 3.4 - Remind me again

[0063] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0064] Final response confirmation

[0065] Step 4.1 - Check for compliance

[0066] The server periodically checks the database to see if the request is "in progress" or "completed."

[0067] Step 4.2 - Generate a Completion Notification

[0068] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[0069] Step 4.3 - Send a Completion Notification

[0070] The server sends the generated completion notification to the user.

[0071] Step 4.4 - Update the Database

[0072] The server records the request status as "completed" in the database and also stores the completion date and time.

[0073] Specific examples

[0074] 1. Request acceptance

[0075] A user submits a "Request to create monthly report."

[0076] The server receives the request and stores the contents in a database.

[0077] 2. First Response

[0078] The server analyzes the "Request to create monthly report" and generates a primary response such as "Thank you for your request. We are currently working on it."

[0079] The server sends this primary response to the user.

[0080] 3. Reminder function

[0081] The server periodically searches the database to detect outstanding requests.

[0082] The server sends a reminder notification to the terminal of the person in charge that "the request to create the monthly report has not yet been completed."

[0083] The server will resend a reminder to respond to the request within 24 hours.

[0084] 4. Final response confirmation

[0085] If the request is fulfilled, the server sends a completion notice to the user stating that "Monthly report creation has been completed."

[0086] The server records the status of the request as "completed" in the database.

[0087] This system prevents requests from being ignored or missed, improving work efficiency and reliability.

[0088] The processing flow will be explained below.

[0089] Request acceptance

[0090] Step 1:

[0091] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0092] Step 2:

[0093] The server receives a request (HTTP POST request) sent by the user.

[0094] Step 3:

[0095] The server extracts the request content from the payload of the request.

[0096] Step 4:

[0097] The server stores the request in a database and records the request status as "unprocessed."

[0098] First response

[0099] Step 5:

[0100] The server reads the request stored in the database.

[0101] Step 6:

[0102] The server generates a primary response such as "Thank you for your request. We are currently working on it."

[0103] Step 7:

[0104] The server sends the generated initial response to the user's email address or via a chatbot.

[0105] Reminder function

[0106] Step 8:

[0107] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[0108] Step 9:

[0109] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[0110] Step 10:

[0111] The server sends a reminder notification to the terminal of the person in charge.

[0112] Step 11:

[0113] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0114] Final response confirmation

[0115] Step 12:

[0116] The server periodically checks the database to see if the request has become "in progress" or "completed."

[0117] Step 13:

[0118] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[0119] Step 14:

[0120] The server sends the generated completion notification to the user.

[0121] Step 15:

[0122] The server records the request status as "completed" in its database along with a timestamp.

[0123] Example 1

[0124] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0125] There is a need to prevent requests from being ignored or overlooked in business operations, and to process and notify requests quickly and reliably. However, with conventional systems, it was difficult to grasp the status of requests, leading to problems such as the person in charge overlooking a request or delaying their response. In addition, there was a lack of appropriate notification and reminder functions for the person in charge and the requester, which led to problems with work efficiency.

[0126] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0127] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request using a generative AI model, means for searching the database for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notification to the requester. This prevents requests from being ignored or missed, and enables quick and reliable processing and notification of requests.

[0128] A "request" refers to a request or request that a user makes to a server as part of a task.

[0129] "Database" refers to a system for systematically storing and managing information such as request details and response status.

[0130] A "primary response" is an initial response generated by a server when it receives a request, and serves to confirm that the request has been accepted.

[0131] A "generative AI model" is an artificial intelligence model trained using large datasets to perform tasks such as natural language generation.

[0132] "Reminder notification" refers to a notification sent by the server to inform the person in charge of an outstanding request.

[0133] A "second reminder notification" refers to a notification that is sent again if a request has not been responded to within a certain period of time since the first reminder notification.

[0134] "Completion notification" refers to a notification sent by the server to inform the requester that the request has been completed.

[0135] This invention is a reply and reminder system that utilizes a generative AI model to prevent requests from being ignored or missed in business. The main components of this system are a server, a terminal, and a user. A specific embodiment of this system is described below.

[0136] System Configuration

[0137] 1. The server is the main processing unit that receives requests and records them in a database. It also generates and sends initial responses, reminders, and completion notifications. It also performs natural language processing using generative AI models.

[0138] 2. The terminal is an input device that allows users to input and send requests, and receives and displays reminder notifications and completion notifications.

[0139] 3. A user is someone who uses the system to enter requests and receive notifications.

[0140] Hardware and software used

[0141] Hardware: Servers (high-performance processing devices, database servers), user devices (PCs, smartphones, tablets)

[0142] Software: Generative AI models (e.g., GPT-3), database management systems (RDBMS, NoSQL databases), web servers (Apache, Nginx), communication protocols (HTTP / HTTPS)

[0143] Process Overview

[0144] 1. Request acceptance

[0145] A user enters a request through a web form and submits it to the server as an HTTP POST request, which the server receives and stores in a database.

[0146] 2. First Response

[0147] The server generates a first response based on the received request. Utilizing a generative AI model, it automatically generates a response such as, "Thank you for your request. We are currently working on it." The first response is sent to the user's email address or via a chat system.

[0148] 3. Reminder function

[0149] After a certain period of time has passed, the server queries the database to find any outstanding requests. A reminder notification is generated for any outstanding requests using a generation AI model. The reminder notification is sent to the employee's device with a message such as, "The request to create a monthly report has not yet been completed." If the request is not responded to within 24 hours of the first reminder, another reminder notification is sent.

[0150] 4. Final response confirmation

[0151] The server periodically checks the database to see if the request is in progress or completed. If the request is completed, it uses the generative AI model to generate a completion notification stating "Monthly report creation completed" and sends it to the user. The request status is recorded as "Completed" in the database.

[0152] Specific examples

[0153] Request acceptance

[0154] The user sends a "Request to create a monthly report." This request is received by the server and recorded in the database.

[0155] First response

[0156] The server reads the request, generates a response saying "Thank you for your request. We are currently working on it," and sends it to the user.

[0157] Reminder function

[0158] The server periodically checks the database to find any outstanding requests, and generates a reminder message such as "The request to create a monthly report has not yet been completed" and sends it to the person in charge's device.

[0159] Final response confirmation

[0160] When the request is completed, a completion notice stating "Monthly report creation completed" is sent to the user, and the server records this information in the database.

[0161] Prompt Sentence Examples

[0162] Please fill out the request form with "Request for monthly report creation" and explain in detail the process required for submission.

[0163] "Please explain how to periodically check the database and send reminders for 'open' requests."

[0164] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0165] Program processing flow (concrete)

[0166] 1. Request acceptance

[0167] Step 1:

[0168] The user enters "Request for monthly report creation" in the request form and clicks the send button.

[0169] Input: Request details (e.g., "Request for monthly report creation")

[0170] Output: HTTP POST request

[0171] Specific behavior:

[0172] The terminal sends the request content entered by the user to the server as an HTTP POST request.

[0173] Step 2:

[0174] The server receives an HTTP POST request sent by the user.

[0175] Input: HTTP POST request payload

[0176] Output: Request details in JSON format

[0177] Specific behavior:

[0178] The server extracts the request content from the request payload and converts it into JSON format.

[0179] Step 3:

[0180] The server stores the extracted request details in a database.

[0181] Input: Request content in JSON format

[0182] Output: A new record in the database

[0183] Specific behavior:

[0184] The server records the request, the status "Not Supported," and a timestamp in the database.

[0185] 2. First Response

[0186] Step 4:

[0187] The server reads the newly saved "open" request from the database.

[0188] Input: A new record in the database

[0189] Output: Request details

[0190] Specific behavior:

[0191] The server executes a query to retrieve the newly added request.

[0192] Step 5:

[0193] The server generates a primary response using a generative AI model.

[0194] Input: Request details

[0195] Output: Response "Thank you for your request. We are currently working on it."

[0196] Specific behavior:

[0197] The server inputs the request as a prompt into the generative AI model and generates an appropriate response.

[0198] Step 6:

[0199] The server generates a primary response and sends it to the user.

[0200] Input: Generated response text, user contact information (email address and chat ID)

[0201] Output: User notification

[0202] Specific behavior:

[0203] The server sends a primary response to the user via a mail server or chat system.

[0204] 3. Reminder function

[0205] Step 7:

[0206] At the end of each day or at set intervals, the server queries the database for requests with a status of "open."

[0207] Input: Entire database

[0208] Output: List of "open" requests

[0209] Specific behavior:

[0210] The server runs a daily query to list requests with a status of "open."

[0211] Step 8:

[0212] The server generates reminder notifications for outstanding requests.

[0213] Input: List of "unhandled" requests

[0214] Output: Reminder message: "Your monthly report request has not yet been completed."

[0215] Specific behavior:

[0216] The server uses the generative AI model to create reminder notifications for outstanding requests.

[0217] Step 9:

[0218] The server sends a reminder notification to the terminal of the person in charge.

[0219] Input: Reminder message, contact information of person in charge

[0220] Output: Notification to the person in charge's terminal

[0221] Specific behavior:

[0222] The server sends a reminder notification to the person in charge via a mail server or chat system.

[0223] Step 10:

[0224] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0225] Input: Time-lapse trigger, open request list

[0226] Output: Remind notification

[0227] Specific behavior:

[0228] The server runs a scheduled job to create and send the next reminder notification.

[0229] 4. Final response confirmation

[0230] Step 11:

[0231] The server periodically checks the database to see if the request is "in progress" or "completed."

[0232] Input: Entire database

[0233] Output: A list of requests with statuses of Open and Completed

[0234] Specific behavior:

[0235] The server runs a scheduled job to check for compliance in the database.

[0236] Step 12:

[0237] The server generates a completion notification when the request is completed.

[0238] Input: Completed request details

[0239] Output: Completion notification message: "Monthly report creation completed."

[0240] Specific behavior:

[0241] The server uses the generative AI model to create a completion notification.

[0242] Step 13:

[0243] The server sends the generated completion notification to the user.

[0244] Input: Completion notification, user contact information

[0245] Output: User notification

[0246] Specific behavior:

[0247] The server sends a completion notification to the user via a mail server or chat system.

[0248] Step 14:

[0249] The server records the request status as "completed" in the database and also stores the completion date and time.

[0250] Input: Completed request details, completion date and time

[0251] Output: Updated records in the database

[0252] Specific behavior:

[0253] The server updates the request record in the database, changing the status to "Completed" and recording the completion date and time.

[0254] (Application example 1)

[0255] 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."

[0256] Because security checklists contain a wide variety of items, it is extremely difficult for personnel to ensure that all items are checked and to keep track of any outstanding items. If some items are missed, security risks increase and serious problems may occur. Therefore, there is a need for a system that automates the management of security checklists and the tracking of outstanding items, ensuring their completion.

[0257] 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.

[0258] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if a request is not processed within a certain period of time after a reminder has been sent, means for confirming that the request has been completed and sending a completion notification to the requester, means for inputting security checklist items, and means for tracking the completion status of each checklist item. This automates the management of the security checklist and the tracking of unprocessed items, making it possible to ensure that all items are completed reliably.

[0259] A "request" is the act of requesting a specific job or task, or the content of that request.

[0260] A "database" is a collection of electronic data that organizes and stores information so that it can be searched and used efficiently.

[0261] A "primary response" is the first response message that is automatically generated after receiving a request.

[0262] A "reminder notification" is a notification sent to alert the person in charge about outstanding requests or tasks.

[0263] A "second reminder notification" is a second reminder notification sent if no action is taken within a specified period after the first reminder notification.

[0264] A "completion notification" is a notification sent to inform the requester that the requested task or work has been completed.

[0265] A "security checklist" is a list of items that should be checked to ensure safety.

[0266] "Completion status" indicates the extent to which a particular task or request has been accomplished.

[0267] This invention is a system for automating security checklist management and tracking of outstanding items. This system mainly includes a server, terminals, and users, each of which has a role to play, ensuring that all checklist items are completed.

[0268] System Program Overview

[0269] 1. Request acceptance and receipt

[0270] The user enters the security checklist items into the request form (smartphone app, tablet app, etc.) and clicks the submit button.

[0271] The server receives the HTTP POST request and parses the request payload to extract the requested content.

[0272] 2. Saving to the database

[0273] The server stores the request in a database (e.g. MongoDB) as raw data.

[0274] 3. Generating and sending a primary response

[0275] The server generates a primary response message saying "Thank you for your confirmation. We will now begin lock confirmation." and sends it to the person in charge. SendGrid or Firebase Cloud Messaging is used as the email notification system.

[0276] 4. Reminder function

[0277] The server periodically checks for unaddressed items, and if they are not addressed within the specified period, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge's device (smartphone / tablet). Re-reminders can also be set in the same way.

[0278] 5. Final response confirmation and notification

[0279] When the request is completed, the server generates and sends a completion notification stating "Door lock confirmation completed" and updates the database record.

[0280] Hardware and software used

[0281] Frontend: Building smartphone / tablet apps using React Native.

[0282] Backend: Uses Node.js to process requests and access the database.

[0283] Database: MongoDB is used to manage the status of requests and checklists.

[0284] Notification system: Use SendGrid or Firebase Cloud Messaging to send emails and push notifications.

[0285] Specific examples

[0286] Example 1: Checking whether the door is locked

[0287] If a security officer were to enter a request into the system to check if the doors in a building are locked, the following prompt text could be used:

[0288] "If you confirm the door is locked, please check the box."

[0289] "Once you've completed the verification, you will be prompted to proceed to the next step."

[0290] In this way, the system receives requests, saves them in a database, sends a first response, sends reminders for outstanding items, and finally confirms that the task has been completed and sends a completion notification, ensuring that all items on the security checklist are carried out and minimizing security risks.

[0291] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0292] Step 1:

[0293] The user enters the security checklist items into the request form and clicks the submit button. The specific input data is a request to "check whether the door is locked." This input data is sent to the server as an HTTP POST request.

[0294] Step 2:

[0295] The server receives the HTTP POST request. The request data, "Check if the door is locked," is extracted from the request payload. The extracted request data is saved in the database and the status is recorded as "Not handled."

[0296] Step 3:

[0297] The server generates a primary response based on the request details stored in the database. It generates a primary response message stating "Thank you for your confirmation. Lock confirmation will begin." and sends it to the person in charge's terminal. The output data is the primary response message.

[0298] Step 4:

[0299] The server periodically checks the database and searches for requests with a status of "open." If there are any open requests, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge. The output data is the reminder notification.

[0300] Step 5:

[0301] When a staff member receives a reminder notification, the notification will be displayed on the terminal. The staff member will check the notification and take action to complete the items on the security checklist. Specifically, the staff member will check that the door is locked and click the Complete button.

[0302] Step 6:

[0303] The server periodically checks the database to confirm the completion of the request. If completion is confirmed, the server generates a completion notification stating "Door lock confirmation completed" and sends it to the requester's terminal. The output data is the completion notification.

[0304] Step 7:

[0305] The requester receives a completion notification, which is displayed on the terminal. The requester can check the completion notification and know that the entire request has been completed. This ensures that the security checklist has been carried out correctly.

[0306] 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.

[0307] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0308] System Configuration

[0309] The system consists of the following major components:

[0310] 1. Server

[0311] 2. Terminal

[0312] 3. Users

[0313] 4. Emotion Engine

[0314] Program processing overview

[0315] This system accepts requests from users, automatically responds to them, and sends reminders for unanswered requests. It also recognizes the user's emotions and responds appropriately to them.

[0316] Acceptance of requests and initial response

[0317] The user enters "Request for monthly report creation" in the request form and clicks the submit button. The server receives the request from the user and saves it in the database. At this time, the status of the request is recorded as "Unprocessed."

[0318] The server reads the received request and analyzes the user's emotions using an emotion engine. For example, if the user expresses dissatisfaction or urgency, the emotion engine recognizes this and adjusts the primary response. It generates an appropriate response such as "Thank you for your request. We will process your request promptly." The generated primary response is then sent from the server to the user.

[0319] Reminder function

[0320] At the end of each day or at set intervals, the server searches the database for outstanding requests and generates a reminder notification, such as "Your monthly report creation request has not yet been completed." The reminder notification is sent to the device of the person in charge.

[0321] When a reminder is sent, the emotion engine again monitors the user's emotions and adjusts the content of the reminder. For example, if the user is frustrated, it can add a follow-up message such as, "Sorry for the wait. It's currently in progress."

[0322] Remind me again

[0323] If the request remains unaddressed 24 hours after the first reminder, the server will send another reminder, and the emotion engine will again check the user's emotions and adjust them if necessary.

[0324] Final response confirmation and completion notification

[0325] Once the request is complete, the server records the information in the database and generates a completion notification. The server sends a completion notification to the user, such as "Monthly report creation completed." At this point, the emotion engine can again check the user's emotion and add an appropriate follow-up message.

[0326] Specific examples

[0327] 1. Request acceptance

[0328] A user submits a "Request to create monthly report."

[0329] The server receives the request and stores it in a database.

[0330] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[0331] 2. First Response

[0332] Based on the request, the server generates a primary response such as "Thank you for your request. We will process your request promptly."

[0333] The server sends the primary response to the user.

[0334] 3. Reminder function

[0335] The server searches the database at set intervals to detect any outstanding requests.

[0336] The server generates a reminder notification that "Your monthly report creation request has not yet been completed."

[0337] The emotion engine may reanalyze the user's emotions and add a follow-up message to the reminder, such as "Sorry for the wait. It's in progress."

[0338] The server sends a reminder notification to the terminal of the person in charge.

[0339] 4. Remind me again

[0340] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[0341] The emotion engine adjusts the content of the re-reminder depending on the situation.

[0342] 5. Final response confirmation

[0343] When the request is completed, the server records the information in a database.

[0344] The server sends a completion notification to the user stating that "Monthly report creation has been completed."

[0345] The emotion engine checks the user's emotions and adds follow-up messages as needed.

[0346] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[0347] The processing flow will be explained below.

[0348] Acceptance of requests and initial response

[0349] Step 1:

[0350] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0351] Step 2:

[0352] The server receives a request (HTTP POST request) sent by the user.

[0353] Step 3:

[0354] The server extracts the request content from the payload of the request.

[0355] Step 4:

[0356] The server stores the request in a database and records the request status as "unprocessed."

[0357] Step 5:

[0358] The server reads the request and analyzes the user's emotions using an emotion engine.

[0359] Step 6:

[0360] The server adjusts the primary response based on the analysis results from the emotion engine. For example, if the user expresses dissatisfaction, the server generates a response such as "Thank you for your request. We will process your request promptly."

[0361] Step 7:

[0362] The server sends the generated initial response to the user's email address or via a chatbot.

[0363] Reminder function

[0364] Step 8:

[0365] The server searches the database at the end of each day or at set intervals to detect requests with a status of "open."

[0366] Step 9:

[0367] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[0368] Step 10:

[0369] The emotion engine reanalyzes the user's emotions before sending a reminder. For example, if the user shows signs of impatience, it adds a follow-up message such as "We're checking your progress."

[0370] Step 11:

[0371] The server sends a reminder notification to the terminal of the person in charge.

[0372] Step 12:

[0373] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0374] Step 13:

[0375] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content as necessary.

[0376] Final response confirmation and completion notification

[0377] Step 14:

[0378] The server periodically checks the database to see if the request has become "in progress" or "completed."

[0379] Step 15:

[0380] If the request is completed, the server generates a completion notice stating "Monthly report creation completed."

[0381] Step 16:

[0382] The emotion engine performs a final check of the user's emotions and, if necessary, adds a follow-up message such as "Thank you for your cooperation" to the completion notification.

[0383] Step 17:

[0384] The server sends the generated completion notification to the user.

[0385] Step 18:

[0386] The server records the request status as "completed" in the database and also stores the completion date and time.

[0387] Specific examples

[0388] Step 1:

[0389] A user submits a "Request to create monthly report."

[0390] Step 2:

[0391] The server receives the request and stores it in a database.

[0392] Step 3:

[0393] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[0394] Step 4:

[0395] The server generates a primary response based on the request, such as "Thank you for your request. We will process your request promptly," and sends it to the user.

[0396] Step 5:

[0397] At the end of the day, the server searches for open requests and generates a reminder notification saying, "Your request to create a monthly report has not yet been completed."

[0398] Step 6:

[0399] The emotion engine reanalyzes the user's emotions and adds follow-up messages, such as "Sorry for the wait. We're working on it," if necessary.

[0400] Step 7:

[0401] The server sends a reminder notification to the terminal of the person in charge.

[0402] Step 8:

[0403] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[0404] Step 9:

[0405] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content.

[0406] Step 10:

[0407] When the request is completed, the server generates a completion notice stating "Monthly report creation completed" and sends it to the user.

[0408] Step 11:

[0409] The emotion engine provides a final check on the user's emotions and adds follow-up messages if necessary.

[0410] Step 12:

[0411] The request status is recorded as "Completed" in the database, and the completion date and time are also saved.

[0412] This system not only prevents requests from being ignored or missed, but also enables responses that take users' feelings into consideration to a high degree, thereby improving work efficiency and reliability.

[0413] Example 2

[0414] 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."

[0415] Conventional systems often resulted in requests being ignored or left unaddressed, resulting in reduced satisfaction and work efficiency. Furthermore, uniform responses and reminder notifications were sent without considering the requester's feelings, often increasing the requester's dissatisfaction. A prompt and appropriate response is required, especially when the request is urgent or the requester is dissatisfied, but conventional systems had limitations.

[0416] 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.

[0417] In this invention, the server includes means for receiving requests and recording their contents in a database, means for analyzing the requester's emotions using an emotion engine and generating and sending a first response based on the results, means for searching for unaddressed requests, generating a reminder notice, and sending it to the person in charge, means for analyzing the requester's emotions again using the emotion engine when the reminder notice is sent and adding an appropriate follow-up message, means for sending a second reminder notice if the request is not addressed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notice to the requester. This prevents requests from being ignored or overlooked, enables flexible responses that take the requester's emotions into consideration, and realizes business efficiency and improved requester satisfaction.

[0418] A "request" is a user's request to perform a particular task or service.

[0419] A "database" is a collection of data that is structured so that information can be systematically stored, managed, and searched.

[0420] An "emotion engine" refers to software or algorithms that analyze the emotions in text or speech and output the results.

[0421] A "primary reply" is the first response message automatically generated by a server immediately after receiving a request.

[0422] A "reminder notification" is a notification sent to reconfirm an outstanding request.

[0423] A "follow-up message" is a supplementary message that is sent in addition depending on the situation after the reminder notification and the requester's feelings.

[0424] A "second reminder notification" is a notification that is sent again if no action is taken within a certain period of time after the first reminder notification.

[0425] A "completion notice" is a final notice to inform the requester that the request has been successfully completed.

[0426] A "generative AI model" is a type of artificial intelligence that generates text based on a specific prompt.

[0427] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0428] System Configuration

[0429] The system consists of the following major components:

[0430] 1. Server

[0431] 2. Terminal

[0432] 3. Users

[0433] 4. Emotion Engine

[0434] 5. Database

[0435] 6. Generative AI Models

[0436] Hardware and software used

[0437] Server: Responsible for receiving requests, managing the database, generating initial responses and reminder notifications.

[0438] Terminal: A device (PC, smartphone, etc.) used by users and staff to input and confirm request details.

[0439] Database: Use a relational database such as MySQL to store the request details and status.

[0440] Sentiment engine: Analyzes user sentiment using sentiment analysis APIs such as IBM Watson and AWS Comprehend.

[0441] Generative AI models, such as OpenAI's GPT-4, generate responses and follow-up messages based on prompts.

[0442] Specific examples

[0443] 1. Request acceptance

[0444] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0445] The server receives the request from the user via an HTTP request, stores it in a database (e.g., MySQL), and records the request status as "unhandled."

[0446] 2. Sentiment analysis and first response generation

[0447] The server reads the received request and analyzes the user's emotions using an emotion engine (e.g., IBM Watson or AWS Comprehend).

[0448] The emotion engine receives the request text as an API request and returns the emotion analysis result. For example, the emotion engine recognizes if the user is expressing frustration or urgency.

[0449] The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) based on the sentiment analysis results. An example of a prompt is "If the user feels urgent, please create an appropriate response."

[0450] The server sends the generated primary response to the user, for example, "Thank you for your request. We will process your request promptly."

[0451] 3. Generate and send reminder notifications

[0452] The server searches the database for outstanding requests at the end of each day or at set intervals.

[0453] Generate a reminder for outstanding requests stating "Your request to create a monthly report has not yet been completed."

[0454] The server sends a reminder notification to the user's device. At this time, the emotion engine monitors the user's emotions again and adjusts the content of the reminder notification. A follow-up message such as "Sorry for the wait. We are currently working on this." is added.

[0455] 4. Remind notification

[0456] If the request is not acted upon within 24 hours of the first reminder, the server will send another reminder, at which point the emotion engine will reconfirm the user's emotion and adjust the content of the reminder accordingly.

[0457] 5. Final response confirmation and completion notification

[0458] Once the request is completed, the server records the information in a database and generates a completion notification.

[0459] The server sends a completion notification to the user stating, "Monthly report creation completed." At this time, the emotion engine again checks the user's emotion and adds an appropriate follow-up message.

[0460] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[0461] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0462] Step 1: Request acceptance

[0463] Input: The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0464] Processing: The server receives the request from the user as an HTTP request.

[0465] Output: The request is sent to the server.

[0466] Specific operation: The server has an API endpoint that accepts requests and processes them from users. The received request is converted to JSON format and saved in a MySQL database using an "INSERT" query. At this time, the request status is recorded as "Not handled."

[0467] Step 2: Sentiment analysis and generation of first responses

[0468] Input: The server reads the request stored in the database.

[0469] Processing: The server sends the request to an emotion engine (e.g., IBM Watson or AWS Comprehend) to analyze the emotion.

[0470] Output: The emotion engine returns the emotion analysis results to the server.

[0471] Specific operation: The server calls the emotion engine API and sends the text of the request. The emotion engine analyzes the text and returns an emotion label and score. For example, it obtains results such as "Urgent: High" or "Dissatisfied: Medium." Next, the server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "If the user's emotion is urgent, please create an appropriate response," and generates a primary response. The generated primary response might be something like, "Thank you for your request. We will process your request promptly."

[0472] Step 3: Sending a First Response

[0473] Input: Primary response sentence from the generative AI model.

[0474] Processing: The server sends the primary response to the user.

[0475] Output: The primary response is displayed on the user's terminal.

[0476] Specific operation: The server sends the generated primary response as an HTTP response to the user's device. The user's device displays the received primary response on the screen.

[0477] Step 4: Generate and send reminder notifications

[0478] Input: A database that holds open requests.

[0479] Processing: At set intervals, the server searches the database for outstanding requests and generates reminder notifications.

[0480] Output: A reminder notification is sent to the assignee.

[0481] Specific operation: The server runs a regularly scheduled job (e.g., a Cron job) and searches the database with a "SELECT" query. If an open request is found, it generates a reminder message saying, "Your request to create a monthly report has not yet been completed." Next, the server again uses the emotion engine to analyze the user's emotions. Based on the analysis results, it adds a follow-up message (e.g., "Sorry for the wait. It's currently in progress.") to the reminder message. The server then sends a final reminder message to the responsible person's device via an HTTP request.

[0482] Step 5: Send a second reminder

[0483] Input: Requests that remain unaddressed after the first reminder.

[0484] Processing: The server runs a scheduled job for re-reminding, and generates and sends another reminder notification.

[0485] Output: A reminder notification is sent to the assignee.

[0486] Specific operation: The server monitors the timing of re-reminders and re-searches the database as a regular job. If an unprocessed request is found, a re-reminder notification is generated. In this case, the emotion engine is used to analyze the user's emotions and add an appropriate message. The generated re-reminder notification is sent to the person in charge's device.

[0487] Step 6: Final response confirmation and completion notification

[0488] Input: Information that the request has been completed.

[0489] Processing: The server records the completion information in the database and generates a completion notification.

[0490] Output: A completion notification is sent to the user.

[0491] Specific operation: When the person in charge completes the request, they send that information to the server. The server then uses the "UPDATE" query to change the request status in the database to "Completed." The server then generates a completion notification message saying "Monthly report creation completed" and checks the user's emotions using the emotion engine. If necessary, a follow-up message is added, and the final completion notification is sent to the user's device as an HTTP response.

[0492] (Application example 2)

[0493] 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."

[0494] The conventional system had problems with delays in responding to requests and frequent errors. Furthermore, it was unable to respond appropriately while taking into account the requester's feelings, which led to issues with reduced work efficiency and reliability. Furthermore, operators sometimes missed important notifications while working in the factory, which led to a loss of safety and efficiency.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending reminders to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, means for confirming that the request has been completed and sending a completion notification to the requester, and means for analyzing the requester's emotions and optimizing the content of the response and reminder. This prevents delays and errors in request response, enables appropriate responses that take the requester's emotions into consideration, and improves work efficiency and reliability. Furthermore, safety and efficiency are also improved because operators can reliably receive important notifications while working in the factory.

[0496] "Receiving a request" means that the system takes in work instructions or requests from operators or other users.

[0497] "Recording in a database" means saving the received request in data format so that it can be searched or referenced later.

[0498] "Automatic generation of a first response" means automatically creating an initial response to a received request using a predetermined template or algorithm.

[0499] "Send" means forwarding the automatically generated primary response or notification to the requester or contact person.

[0500] "Searching for unprocessed requests" means searching the database for data on requests that have not been processed and are on hold.

[0501] "Send reminder notification" means sending a notification to the person in charge to alert them about a request that is overdue.

[0502] "To send a second reminder" means to send an additional reminder if no response has been made after a certain period of time has passed.

[0503] "Confirming that the request has been completed" means that the system recognizes that the work or response to the request has been completed.

[0504] "Send completion notification" means sending a message to the requester informing them that the request has been successfully completed.

[0505] "Analyzing the client's emotions" means reading and judging the client's emotional state from their input and behavior.

[0506] "Optimizing the content of replies and reminders" means adjusting the content of replies and reminder messages based on the analyzed client's emotions.

[0507] This invention is an AI support system for efficient request management and response in factories and other workplaces. The system consists of the following main components:

[0508] System Configuration

[0509] 1. Server:

[0510] Receives requests and records their contents in a database.

[0511] A first response to the request is automatically generated and sent to the person in charge or the requester.

[0512] Search for outstanding requests and send reminders.

[0513] If no response is received within a certain period of time, a reminder notification will be sent.

[0514] Confirm that the request is completed and send a completion notification.

[0515] Analyze the client's emotions and optimize responses and reminders.

[0516] 2. Terminal:

[0517] Receive and display reminder notifications and completion notifications on the device used by the person in charge.

[0518] To display a message according to a requester's emotions.

[0519] 3. User:

[0520] Send a request and receive a response.

[0521] Hardware and Software Used

[0522] Server: Receives requests, stores them, generates initial responses, and manages reminder notifications.

[0523] Device: Receive reminders and completion notifications on the device used by the person in charge.

[0524] Emotion Engine: Analyzes the emotions of the client and staff and responds appropriately.

[0525] Messaging System: A system that sends notifications and reply messages.

[0526] Operation overview

[0527] The system works as follows:

[0528] 1. Request reception: The user sends a request to the server, and the request is saved in the database. The server uses an emotion engine to analyze the requester's emotions.

[0529] 2. Primary response: Based on the analyzed sentiment, the server automatically generates an appropriate primary response and sends it to the user.

[0530] 3. Reminder notification: If the request is not handled within a certain period of time, the server generates a reminder notification and sends it to the agent's device. The emotion engine analyzes the requester's emotions again and optimizes the notification message.

[0531] 4. Remind: If the request is not acted upon within a certain time period after the first reminder, the server will send a reminder.

[0532] 5. Completion Notification: When the request is completed, the server records the information in the database and sends a completion notification to the requester. The emotion engine checks the emotion again and adds a follow-up message if necessary.

[0533] Specific examples

[0534] In-factory maintenance request management:

[0535] When an operator in a factory requests machine maintenance and inspection, this system can prevent oversight or delay in the request. The server receives the request and provides an appropriate initial response. If the request is not responded to, a reminder notification is sent to the person in charge's device.

[0536] Example prompt sentence:

[0537] "I would like to request a maintenance inspection of my machine."

[0538] This prevents delays and mistakes in responding to requests, enables appropriate responses that take into account the feelings of the requester, and improves work efficiency and reliability. It also ensures that operators receive important notifications while working in the factory, improving safety and efficiency.

[0539] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0540] Step 1: Receiving and recording the request

[0541] The user inputs a request and sends it to the server. The server receives the request and records it in the database. Specifically, if the user inputs a prompt such as "I would like to request maintenance inspection of the machine," the server receives the request and stores it in the database as "unprocessed." The input data is the request and user information, and a record is generated in the appropriate table in the database based on that information.

[0542] Step 2: Sentiment Analysis

[0543] The server passes the received request to the emotion engine, which analyzes the user's emotions. The input data is the request content saved in step 1, and the emotion engine analyzes it. The analysis results output the user's emotional state, such as "I'm in a hurry" or "I'm dissatisfied." This allows subsequent responses to be appropriately optimized.

[0544] Step 3: Generate a first response

[0545] The server generates a primary response message based on the analysis results of the emotion engine. In this step, an appropriate template is selected based on the analyzed emotion and a response message is constructed. For example, if the user is in a hurry, a response such as "Thank you for your request. We will process the maintenance inspection promptly" is generated. The generated message becomes input data and is sent to the user.

[0546] Step 4: Sending a First Response

[0547] The server sends the generated primary response to the user, allowing the user to confirm that the request has been accepted. The primary response message becomes output data and is sent to the user's device, allowing the user to confirm that the request has been accepted.

[0548] Step 5: Search for open requests

[0549] The server periodically searches the database to detect unprocessed requests. The input data is all request records in the database, and by searching these, the processing status is confirmed. If an unprocessed request is found, a reminder is sent based on that information.

[0550] Step 6: Generate and send reminder notifications

[0551] When an unprocessed request is detected, the server generates a reminder notification and sends it to the agent's device. In this step, the emotion engine again analyzes the user's emotions and adjusts the reminder message appropriately. For example, if the user is feeling dissatisfied, it generates a message such as "Sorry for the wait. The request is currently in progress." The generated reminder message becomes input data and is sent to the agent's device.

[0552] Step 7: Generate and send a follow-up reminder notification

[0553] If the request is not addressed within a certain time after the reminder notification, the server generates and sends another reminder notification. At this step, the emotion engine also analyzes the emotion and optimizes the message as necessary. The re-reminding message becomes input data and is sent to the person in charge's device.

[0554] Step 8: Final confirmation and notification

[0555] Once the request is completed, the server records the information in a database and sends a completion notification to the requester. A final confirmation is performed by the emotion engine, and an appropriate follow-up message is added. For example, a message such as "The requested maintenance inspection has been completed. Please check it with confidence" is generated and sent to the user's device. The completion notification message is the input data, and the transmission result is output.

[0556] By using the above processing steps, the present invention can prevent delays and mistakes in responding to requests and can provide appropriate responses that take into account the user's feelings, thereby improving business efficiency and reliability.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] [Second embodiment]

[0561] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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."

[0573] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business. Specific embodiments of this system are described below.

[0574] System Configuration

[0575] The system consists of the following major components:

[0576] 1. Server

[0577] 2. Terminal

[0578] 3. Users

[0579] Program processing overview

[0580] This system accepts requests from users, automatically provides an initial response, and sends reminders for unanswered requests to prevent missed responses.

[0581] Request acceptance

[0582] Step 1.1 - Submit your request

[0583] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0584] Step 1.2 - Receiving the request

[0585] The server receives a request (HTTP POST request) sent by the user.

[0586] The server extracts the request content (e.g., "Request to create monthly report") from the request payload.

[0587] Step 1.3 - Save your request

[0588] The server stores the request in a database and records the request status as "unprocessed."

[0589] First response

[0590] Step 2.1 - Generate a First Response

[0591] The server reads the request details stored in the database and generates a primary response such as "Thank you for your request. We are currently working on it."

[0592] Step 2.2 - Sending a First Response

[0593] The server sends the generated initial response to the user's email address or via a chatbot.

[0594] Reminder function

[0595] Step 3.1 - Search for open requests

[0596] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[0597] Step 3.2 - Generate a Reminder

[0598] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[0599] Step 3.3 - Send Reminder Notifications

[0600] The server sends a reminder notification to the terminal of the person in charge.

[0601] Step 3.4 - Remind me again

[0602] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0603] Final response confirmation

[0604] Step 4.1 - Check for compliance

[0605] The server periodically checks the database to see if the request is "in progress" or "completed."

[0606] Step 4.2 - Generate a Completion Notification

[0607] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[0608] Step 4.3 - Send a Completion Notification

[0609] The server sends the generated completion notification to the user.

[0610] Step 4.4 - Update the Database

[0611] The server records the request status as "completed" in the database and also stores the completion date and time.

[0612] Specific examples

[0613] 1. Request acceptance

[0614] A user submits a "Request to create monthly report."

[0615] The server receives the request and stores the contents in a database.

[0616] 2. First Response

[0617] The server analyzes the "Request to create monthly report" and generates a primary response such as "Thank you for your request. We are currently working on it."

[0618] The server sends this primary response to the user.

[0619] 3. Reminder function

[0620] The server periodically searches the database to detect outstanding requests.

[0621] The server sends a reminder notification to the terminal of the person in charge that "the request to create the monthly report has not yet been completed."

[0622] The server will resend a reminder to respond to the request within 24 hours.

[0623] 4. Final response confirmation

[0624] If the request is fulfilled, the server sends a completion notice to the user stating that "Monthly report creation has been completed."

[0625] The server records the status of the request as "completed" in the database.

[0626] This system prevents requests from being ignored or missed, improving work efficiency and reliability.

[0627] The processing flow will be explained below.

[0628] Request acceptance

[0629] Step 1:

[0630] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0631] Step 2:

[0632] The server receives a request (HTTP POST request) sent by the user.

[0633] Step 3:

[0634] The server extracts the request content from the payload of the request.

[0635] Step 4:

[0636] The server stores the request in a database and records the request status as "unprocessed."

[0637] First response

[0638] Step 5:

[0639] The server reads the request stored in the database.

[0640] Step 6:

[0641] The server generates a primary response such as "Thank you for your request. We are currently working on it."

[0642] Step 7:

[0643] The server sends the generated initial response to the user's email address or via a chatbot.

[0644] Reminder function

[0645] Step 8:

[0646] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[0647] Step 9:

[0648] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[0649] Step 10:

[0650] The server sends a reminder notification to the terminal of the person in charge.

[0651] Step 11:

[0652] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0653] Final response confirmation

[0654] Step 12:

[0655] The server periodically checks the database to see if the request has become "in progress" or "completed."

[0656] Step 13:

[0657] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[0658] Step 14:

[0659] The server sends the generated completion notification to the user.

[0660] Step 15:

[0661] The server records the request status as "completed" in its database along with a timestamp.

[0662] Example 1

[0663] 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."

[0664] There is a need to prevent requests from being ignored or overlooked in business operations, and to process and notify requests quickly and reliably. However, with conventional systems, it was difficult to grasp the status of requests, leading to problems such as the person in charge overlooking a request or delaying their response. In addition, there was a lack of appropriate notification and reminder functions for the person in charge and the requester, which led to problems with work efficiency.

[0665] 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.

[0666] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request using a generative AI model, means for searching the database for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notification to the requester. This prevents requests from being ignored or missed, and enables quick and reliable processing and notification of requests.

[0667] A "request" refers to a request or request that a user makes to a server as part of a task.

[0668] "Database" refers to a system for systematically storing and managing information such as request details and response status.

[0669] A "primary response" is an initial response generated by a server when it receives a request, and serves to confirm that the request has been accepted.

[0670] A "generative AI model" is an artificial intelligence model trained using large datasets to perform tasks such as natural language generation.

[0671] "Reminder notification" refers to a notification sent by the server to inform the person in charge of an outstanding request.

[0672] A "second reminder notification" refers to a notification that is sent again if a request has not been responded to within a certain period of time since the first reminder notification.

[0673] "Completion notification" refers to a notification sent by the server to inform the requester that the request has been completed.

[0674] This invention is a reply and reminder system that utilizes a generative AI model to prevent requests from being ignored or missed in business. The main components of this system are a server, a terminal, and a user. A specific embodiment of this system is described below.

[0675] System Configuration

[0676] 1. The server is the main processing unit that receives requests and records them in a database. It also generates and sends initial responses, reminders, and completion notifications. It also performs natural language processing using generative AI models.

[0677] 2. The terminal is an input device that allows users to input and send requests, and receives and displays reminder notifications and completion notifications.

[0678] 3. A user is someone who uses the system to enter requests and receive notifications.

[0679] Hardware and software used

[0680] Hardware: Servers (high-performance processing devices, database servers), user devices (PCs, smartphones, tablets)

[0681] Software: Generative AI models (e.g., GPT-3), database management systems (RDBMS, NoSQL databases), web servers (Apache, Nginx), communication protocols (HTTP / HTTPS)

[0682] Process Overview

[0683] 1. Request acceptance

[0684] A user enters a request through a web form and submits it to the server as an HTTP POST request, which the server receives and stores in a database.

[0685] 2. First Response

[0686] The server generates a first response based on the received request. Utilizing a generative AI model, it automatically generates a response such as, "Thank you for your request. We are currently working on it." The first response is sent to the user's email address or via a chat system.

[0687] 3. Reminder function

[0688] After a certain period of time has passed, the server queries the database to find any outstanding requests. A reminder notification is generated for any outstanding requests using a generation AI model. The reminder notification is sent to the employee's device with a message such as, "The request to create a monthly report has not yet been completed." If the request is not responded to within 24 hours of the first reminder, another reminder notification is sent.

[0689] 4. Final response confirmation

[0690] The server periodically checks the database to see if the request is in progress or completed. If the request is completed, it uses the generative AI model to generate a completion notification stating "Monthly report creation completed" and sends it to the user. The request status is recorded as "Completed" in the database.

[0691] Specific examples

[0692] Request acceptance

[0693] The user sends a "Request to create a monthly report." This request is received by the server and recorded in the database.

[0694] First response

[0695] The server reads the request, generates a response saying "Thank you for your request. We are currently working on it," and sends it to the user.

[0696] Reminder function

[0697] The server periodically checks the database to find any outstanding requests, and generates a reminder message such as "The request to create a monthly report has not yet been completed" and sends it to the person in charge's device.

[0698] Final response confirmation

[0699] When the request is completed, a completion notice stating "Monthly report creation completed" is sent to the user, and the server records this information in the database.

[0700] Prompt Sentence Examples

[0701] Please fill out the request form with "Request for monthly report creation" and explain in detail the process required for submission.

[0702] "Please explain how to periodically check the database and send reminders for 'open' requests."

[0703] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0704] Program processing flow (concrete)

[0705] 1. Request acceptance

[0706] Step 1:

[0707] The user enters "Request for monthly report creation" in the request form and clicks the send button.

[0708] Input: Request details (e.g., "Request for monthly report creation")

[0709] Output: HTTP POST request

[0710] Specific behavior:

[0711] The terminal sends the request content entered by the user to the server as an HTTP POST request.

[0712] Step 2:

[0713] The server receives an HTTP POST request sent by the user.

[0714] Input: HTTP POST request payload

[0715] Output: Request details in JSON format

[0716] Specific behavior:

[0717] The server extracts the request content from the request payload and converts it into JSON format.

[0718] Step 3:

[0719] The server stores the extracted request details in a database.

[0720] Input: Request content in JSON format

[0721] Output: A new record in the database

[0722] Specific behavior:

[0723] The server records the request, the status "Not Supported," and a timestamp in the database.

[0724] 2. First Response

[0725] Step 4:

[0726] The server reads the newly saved "open" request from the database.

[0727] Input: A new record in the database

[0728] Output: Request details

[0729] Specific behavior:

[0730] The server executes a query to retrieve the newly added request.

[0731] Step 5:

[0732] The server generates a primary response using a generative AI model.

[0733] Input: Request details

[0734] Output: Response "Thank you for your request. We are currently working on it."

[0735] Specific behavior:

[0736] The server inputs the request as a prompt into the generative AI model and generates an appropriate response.

[0737] Step 6:

[0738] The server generates a primary response and sends it to the user.

[0739] Input: Generated response text, user contact information (email address and chat ID)

[0740] Output: User notification

[0741] Specific behavior:

[0742] The server sends a primary response to the user via a mail server or chat system.

[0743] 3. Reminder function

[0744] Step 7:

[0745] At the end of each day or at set intervals, the server queries the database for requests with a status of "open."

[0746] Input: Entire database

[0747] Output: List of "open" requests

[0748] Specific behavior:

[0749] The server runs a daily query to list requests with a status of "open."

[0750] Step 8:

[0751] The server generates reminder notifications for outstanding requests.

[0752] Input: List of "unhandled" requests

[0753] Output: Reminder message: "Your monthly report request has not yet been completed."

[0754] Specific behavior:

[0755] The server uses the generative AI model to create reminder notifications for outstanding requests.

[0756] Step 9:

[0757] The server sends a reminder notification to the terminal of the person in charge.

[0758] Input: Reminder message, contact information of person in charge

[0759] Output: Notification to the person in charge's terminal

[0760] Specific behavior:

[0761] The server sends a reminder notification to the person in charge via a mail server or chat system.

[0762] Step 10:

[0763] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0764] Input: Time-lapse trigger, open request list

[0765] Output: Remind notification

[0766] Specific behavior:

[0767] The server runs a scheduled job to create and send the next reminder notification.

[0768] 4. Final response confirmation

[0769] Step 11:

[0770] The server periodically checks the database to see if the request is "in progress" or "completed."

[0771] Input: Entire database

[0772] Output: A list of requests with statuses of Open and Completed

[0773] Specific behavior:

[0774] The server runs a scheduled job to check for compliance in the database.

[0775] Step 12:

[0776] The server generates a completion notification when the request is completed.

[0777] Input: Completed request details

[0778] Output: Completion notification message: "Monthly report creation completed."

[0779] Specific behavior:

[0780] The server uses the generative AI model to create a completion notification.

[0781] Step 13:

[0782] The server sends the generated completion notification to the user.

[0783] Input: Completion notification, user contact information

[0784] Output: User notification

[0785] Specific behavior:

[0786] The server sends a completion notification to the user via a mail server or chat system.

[0787] Step 14:

[0788] The server records the request status as "completed" in the database and also stores the completion date and time.

[0789] Input: Completed request details, completion date and time

[0790] Output: Updated records in the database

[0791] Specific behavior:

[0792] The server updates the request record in the database, changing the status to "Completed" and recording the completion date and time.

[0793] (Application example 1)

[0794] 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."

[0795] Because security checklists contain a wide variety of items, it is extremely difficult for personnel to ensure that all items are checked and to keep track of any outstanding items. If some items are missed, security risks increase and serious problems may occur. Therefore, there is a need for a system that automates the management of security checklists and the tracking of outstanding items, ensuring their completion.

[0796] 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.

[0797] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if a request is not processed within a certain period of time after a reminder has been sent, means for confirming that the request has been completed and sending a completion notification to the requester, means for inputting security checklist items, and means for tracking the completion status of each checklist item. This automates the management of the security checklist and the tracking of unprocessed items, making it possible to ensure that all items are completed reliably.

[0798] A "request" is the act of requesting a specific job or task, or the content of that request.

[0799] A "database" is a collection of electronic data that organizes and stores information so that it can be searched and used efficiently.

[0800] A "primary response" is the first response message that is automatically generated after receiving a request.

[0801] A "reminder notification" is a notification sent to alert the person in charge about outstanding requests or tasks.

[0802] A "second reminder notification" is a second reminder notification sent if no action is taken within a specified period after the first reminder notification.

[0803] A "completion notification" is a notification sent to inform the requester that the requested task or work has been completed.

[0804] A "security checklist" is a list of items that should be checked to ensure safety.

[0805] "Completion status" indicates the extent to which a particular task or request has been accomplished.

[0806] This invention is a system for automating security checklist management and tracking of outstanding items. This system mainly includes a server, terminals, and users, each of which has a role to play, ensuring that all checklist items are completed.

[0807] System Program Overview

[0808] 1. Request acceptance and receipt

[0809] The user enters the security checklist items into the request form (smartphone app, tablet app, etc.) and clicks the submit button.

[0810] The server receives the HTTP POST request and parses the request payload to extract the requested content.

[0811] 2. Saving to the database

[0812] The server stores the request in a database (e.g. MongoDB) as raw data.

[0813] 3. Generating and sending a primary response

[0814] The server generates a primary response message saying "Thank you for your confirmation. We will now begin lock confirmation." and sends it to the person in charge. SendGrid or Firebase Cloud Messaging is used as the email notification system.

[0815] 4. Reminder function

[0816] The server periodically checks for unaddressed items, and if they are not addressed within the specified period, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge's device (smartphone / tablet). Re-reminders can also be set in the same way.

[0817] 5. Final response confirmation and notification

[0818] When the request is completed, the server generates and sends a completion notification stating "Door lock confirmation completed" and updates the database record.

[0819] Hardware and software used

[0820] Frontend: Building smartphone / tablet apps using React Native.

[0821] Backend: Uses Node.js to process requests and access the database.

[0822] Database: MongoDB is used to manage the status of requests and checklists.

[0823] Notification system: Use SendGrid or Firebase Cloud Messaging to send emails and push notifications.

[0824] Specific examples

[0825] Example 1: Checking whether the door is locked

[0826] If a security officer were to enter a request into the system to check if the doors in a building are locked, the following prompt text could be used:

[0827] "If you confirm the door is locked, please check the box."

[0828] "Once you've completed the verification, you will be prompted to proceed to the next step."

[0829] In this way, the system receives requests, saves them in a database, sends a first response, sends reminders for outstanding items, and finally confirms that the task has been completed and sends a completion notification, ensuring that all items on the security checklist are carried out and minimizing security risks.

[0830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0831] Step 1:

[0832] The user enters the security checklist items into the request form and clicks the submit button. The specific input data is a request to "check whether the door is locked." This input data is sent to the server as an HTTP POST request.

[0833] Step 2:

[0834] The server receives the HTTP POST request. The request data, "Check if the door is locked," is extracted from the request payload. The extracted request data is saved in the database and the status is recorded as "Not handled."

[0835] Step 3:

[0836] The server generates a primary response based on the request details stored in the database. It generates a primary response message stating "Thank you for your confirmation. Lock confirmation will begin." and sends it to the person in charge's terminal. The output data is the primary response message.

[0837] Step 4:

[0838] The server periodically checks the database and searches for requests with a status of "open." If there are any open requests, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge. The output data is the reminder notification.

[0839] Step 5:

[0840] When a staff member receives a reminder notification, the notification will be displayed on the terminal. The staff member will check the notification and take action to complete the items on the security checklist. Specifically, the staff member will check that the door is locked and click the Complete button.

[0841] Step 6:

[0842] The server periodically checks the database to confirm the completion of the request. If completion is confirmed, the server generates a completion notification stating "Door lock confirmation completed" and sends it to the requester's terminal. The output data is the completion notification.

[0843] Step 7:

[0844] The requester receives a completion notification, which is displayed on the terminal. The requester can check the completion notification and know that the entire request has been completed. This ensures that the security checklist has been carried out correctly.

[0845] 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.

[0846] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0847] System Configuration

[0848] The system consists of the following major components:

[0849] 1. Server

[0850] 2. Terminal

[0851] 3. Users

[0852] 4. Emotion Engine

[0853] Program processing overview

[0854] This system accepts requests from users, automatically responds to them, and sends reminders for unanswered requests. It also recognizes the user's emotions and responds appropriately to them.

[0855] Acceptance of requests and initial response

[0856] The user enters "Request for monthly report creation" in the request form and clicks the submit button. The server receives the request from the user and saves it in the database. At this time, the status of the request is recorded as "Unprocessed."

[0857] The server reads the received request and analyzes the user's emotions using an emotion engine. For example, if the user expresses dissatisfaction or urgency, the emotion engine recognizes this and adjusts the primary response. It generates an appropriate response such as "Thank you for your request. We will process your request promptly." The generated primary response is then sent from the server to the user.

[0858] Reminder function

[0859] At the end of each day or at set intervals, the server searches the database for outstanding requests and generates a reminder notification, such as "Your monthly report creation request has not yet been completed." The reminder notification is sent to the device of the person in charge.

[0860] When a reminder is sent, the emotion engine again monitors the user's emotions and adjusts the content of the reminder. For example, if the user is frustrated, it can add a follow-up message such as, "Sorry for the wait. It's currently in progress."

[0861] Remind me again

[0862] If the request remains unaddressed 24 hours after the first reminder, the server will send another reminder, and the emotion engine will again check the user's emotions and adjust them if necessary.

[0863] Final response confirmation and completion notification

[0864] Once the request is complete, the server records the information in the database and generates a completion notification. The server sends a completion notification to the user, such as "Monthly report creation completed." At this point, the emotion engine can again check the user's emotion and add an appropriate follow-up message.

[0865] Specific examples

[0866] 1. Request acceptance

[0867] A user submits a "Request to create monthly report."

[0868] The server receives the request and stores it in a database.

[0869] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[0870] 2. First Response

[0871] Based on the request, the server generates a primary response such as "Thank you for your request. We will process your request promptly."

[0872] The server sends the primary response to the user.

[0873] 3. Reminder function

[0874] The server searches the database at set intervals to detect any outstanding requests.

[0875] The server generates a reminder notification that "Your monthly report creation request has not yet been completed."

[0876] The emotion engine may reanalyze the user's emotions and add a follow-up message to the reminder, such as "Sorry for the wait. It's in progress."

[0877] The server sends a reminder notification to the terminal of the person in charge.

[0878] 4. Remind me again

[0879] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[0880] The emotion engine adjusts the content of the re-reminder depending on the situation.

[0881] 5. Final response confirmation

[0882] When the request is completed, the server records the information in a database.

[0883] The server sends a completion notification to the user stating that "Monthly report creation has been completed."

[0884] The emotion engine checks the user's emotions and adds follow-up messages as needed.

[0885] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[0886] The processing flow will be explained below.

[0887] Acceptance of requests and initial response

[0888] Step 1:

[0889] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0890] Step 2:

[0891] The server receives a request (HTTP POST request) sent by the user.

[0892] Step 3:

[0893] The server extracts the request content from the payload of the request.

[0894] Step 4:

[0895] The server stores the request in a database and records the request status as "unprocessed."

[0896] Step 5:

[0897] The server reads the request and analyzes the user's emotions using an emotion engine.

[0898] Step 6:

[0899] The server adjusts the primary response based on the analysis results from the emotion engine. For example, if the user expresses dissatisfaction, the server generates a response such as "Thank you for your request. We will process your request promptly."

[0900] Step 7:

[0901] The server sends the generated initial response to the user's email address or via a chatbot.

[0902] Reminder function

[0903] Step 8:

[0904] The server searches the database at the end of each day or at set intervals to detect requests with a status of "open."

[0905] Step 9:

[0906] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[0907] Step 10:

[0908] The emotion engine reanalyzes the user's emotions before sending a reminder. For example, if the user shows signs of impatience, it adds a follow-up message such as "We're checking your progress."

[0909] Step 11:

[0910] The server sends a reminder notification to the terminal of the person in charge.

[0911] Step 12:

[0912] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[0913] Step 13:

[0914] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content as necessary.

[0915] Final response confirmation and completion notification

[0916] Step 14:

[0917] The server periodically checks the database to see if the request has become "in progress" or "completed."

[0918] Step 15:

[0919] If the request is completed, the server generates a completion notice stating "Monthly report creation completed."

[0920] Step 16:

[0921] The emotion engine performs a final check of the user's emotions and, if necessary, adds a follow-up message such as "Thank you for your cooperation" to the completion notification.

[0922] Step 17:

[0923] The server sends the generated completion notification to the user.

[0924] Step 18:

[0925] The server records the request status as "completed" in the database and also stores the completion date and time.

[0926] Specific examples

[0927] Step 1:

[0928] A user submits a "Request to create monthly report."

[0929] Step 2:

[0930] The server receives the request and stores it in a database.

[0931] Step 3:

[0932] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[0933] Step 4:

[0934] The server generates a primary response based on the request, such as "Thank you for your request. We will process your request promptly," and sends it to the user.

[0935] Step 5:

[0936] At the end of the day, the server searches for open requests and generates a reminder notification saying, "Your request to create a monthly report has not yet been completed."

[0937] Step 6:

[0938] The emotion engine reanalyzes the user's emotions and adds follow-up messages, such as "Sorry for the wait. We're working on it," if necessary.

[0939] Step 7:

[0940] The server sends a reminder notification to the terminal of the person in charge.

[0941] Step 8:

[0942] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[0943] Step 9:

[0944] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content.

[0945] Step 10:

[0946] When the request is completed, the server generates a completion notice stating "Monthly report creation completed" and sends it to the user.

[0947] Step 11:

[0948] The emotion engine provides a final check on the user's emotions and adds follow-up messages if necessary.

[0949] Step 12:

[0950] The request status is recorded as "Completed" in the database, and the completion date and time are also saved.

[0951] This system not only prevents requests from being ignored or missed, but also enables responses that take users' feelings into consideration to a high degree, thereby improving work efficiency and reliability.

[0952] Example 2

[0953] 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."

[0954] Conventional systems often resulted in requests being ignored or left unaddressed, resulting in reduced satisfaction and work efficiency. Furthermore, uniform responses and reminder notifications were sent without considering the requester's feelings, often increasing the requester's dissatisfaction. A prompt and appropriate response is required, especially when the request is urgent or the requester is dissatisfied, but conventional systems had limitations.

[0955] 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.

[0956] In this invention, the server includes means for receiving requests and recording their contents in a database, means for analyzing the requester's emotions using an emotion engine and generating and sending a first response based on the results, means for searching for unaddressed requests, generating a reminder notice, and sending it to the person in charge, means for analyzing the requester's emotions again using the emotion engine when the reminder notice is sent and adding an appropriate follow-up message, means for sending a second reminder notice if the request is not addressed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notice to the requester. This prevents requests from being ignored or overlooked, enables flexible responses that take the requester's emotions into consideration, and realizes business efficiency and improved requester satisfaction.

[0957] A "request" is a user's request to perform a particular task or service.

[0958] A "database" is a collection of data that is structured so that information can be systematically stored, managed, and searched.

[0959] An "emotion engine" refers to software or algorithms that analyze the emotions in text or speech and output the results.

[0960] A "primary reply" is the first response message automatically generated by a server immediately after receiving a request.

[0961] A "reminder notification" is a notification sent to reconfirm an outstanding request.

[0962] A "follow-up message" is a supplementary message that is sent in addition depending on the situation after the reminder notification and the requester's feelings.

[0963] A "second reminder notification" is a notification that is sent again if no action is taken within a certain period of time after the first reminder notification.

[0964] A "completion notice" is a final notice to inform the requester that the request has been successfully completed.

[0965] A "generative AI model" is a type of artificial intelligence that generates text based on a specific prompt.

[0966] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0967] System Configuration

[0968] The system consists of the following major components:

[0969] 1. Server

[0970] 2. Terminal

[0971] 3. Users

[0972] 4. Emotion Engine

[0973] 5. Database

[0974] 6. Generative AI Models

[0975] Hardware and software used

[0976] Server: Responsible for receiving requests, managing the database, generating initial responses and reminder notifications.

[0977] Terminal: A device (PC, smartphone, etc.) used by users and staff to input and confirm request details.

[0978] Database: Use a relational database such as MySQL to store the request details and status.

[0979] Sentiment engine: Analyzes user sentiment using sentiment analysis APIs such as IBM Watson and AWS Comprehend.

[0980] Generative AI models, such as OpenAI's GPT-4, generate responses and follow-up messages based on prompts.

[0981] Specific examples

[0982] 1. Request acceptance

[0983] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[0984] The server receives the request from the user via an HTTP request, stores it in a database (e.g., MySQL), and records the request status as "unhandled."

[0985] 2. Sentiment analysis and first response generation

[0986] The server reads the received request and analyzes the user's emotions using an emotion engine (e.g., IBM Watson or AWS Comprehend).

[0987] The emotion engine receives the request text as an API request and returns the emotion analysis result. For example, the emotion engine recognizes if the user is expressing frustration or urgency.

[0988] The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) based on the sentiment analysis results. An example of a prompt is "If the user feels urgent, please create an appropriate response."

[0989] The server sends the generated primary response to the user, for example, "Thank you for your request. We will process your request promptly."

[0990] 3. Generate and send reminder notifications

[0991] The server searches the database for outstanding requests at the end of each day or at set intervals.

[0992] Generate a reminder for outstanding requests stating "Your request to create a monthly report has not yet been completed."

[0993] The server sends a reminder notification to the user's device. At this time, the emotion engine monitors the user's emotions again and adjusts the content of the reminder notification. A follow-up message such as "Sorry for the wait. We are currently working on this." is added.

[0994] 4. Remind notification

[0995] If the request is not acted upon within 24 hours of the first reminder, the server will send another reminder, at which point the emotion engine will reconfirm the user's emotion and adjust the content of the reminder accordingly.

[0996] 5. Final response confirmation and completion notification

[0997] Once the request is completed, the server records the information in a database and generates a completion notification.

[0998] The server sends a completion notification to the user stating, "Monthly report creation completed." At this time, the emotion engine again checks the user's emotion and adds an appropriate follow-up message.

[0999] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[1000] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1001] Step 1: Request acceptance

[1002] Input: The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1003] Processing: The server receives the request from the user as an HTTP request.

[1004] Output: The request is sent to the server.

[1005] Specific operation: The server has an API endpoint that accepts requests and processes them from users. The received request is converted to JSON format and saved in a MySQL database using an "INSERT" query. At this time, the request status is recorded as "Not handled."

[1006] Step 2: Sentiment analysis and generation of first responses

[1007] Input: The server reads the request stored in the database.

[1008] Processing: The server sends the request to an emotion engine (e.g., IBM Watson or AWS Comprehend) to analyze the emotion.

[1009] Output: The emotion engine returns the emotion analysis results to the server.

[1010] Specific operation: The server calls the emotion engine API and sends the text of the request. The emotion engine analyzes the text and returns an emotion label and score. For example, it obtains results such as "Urgent: High" or "Dissatisfied: Medium." Next, the server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "If the user's emotion is urgent, please create an appropriate response," and generates a primary response. The generated primary response might be something like, "Thank you for your request. We will process your request promptly."

[1011] Step 3: Sending a First Response

[1012] Input: Primary response sentence from the generative AI model.

[1013] Processing: The server sends the primary response to the user.

[1014] Output: The primary response is displayed on the user's terminal.

[1015] Specific operation: The server sends the generated primary response as an HTTP response to the user's device. The user's device displays the received primary response on the screen.

[1016] Step 4: Generate and send reminder notifications

[1017] Input: A database that holds open requests.

[1018] Processing: At set intervals, the server searches the database for outstanding requests and generates reminder notifications.

[1019] Output: A reminder notification is sent to the assignee.

[1020] Specific operation: The server runs a regularly scheduled job (e.g., a Cron job) and searches the database with a "SELECT" query. If an open request is found, it generates a reminder message saying, "Your request to create a monthly report has not yet been completed." Next, the server again uses the emotion engine to analyze the user's emotions. Based on the analysis results, it adds a follow-up message (e.g., "Sorry for the wait. It's currently in progress.") to the reminder message. The server then sends a final reminder message to the responsible person's device via an HTTP request.

[1021] Step 5: Send a second reminder

[1022] Input: Requests that remain unaddressed after the first reminder.

[1023] Processing: The server runs a scheduled job for re-reminding, and generates and sends another reminder notification.

[1024] Output: A reminder notification is sent to the assignee.

[1025] Specific operation: The server monitors the timing of re-reminders and re-searches the database as a regular job. If an unprocessed request is found, a re-reminder notification is generated. In this case, the emotion engine is used to analyze the user's emotions and add an appropriate message. The generated re-reminder notification is sent to the person in charge's device.

[1026] Step 6: Final response confirmation and completion notification

[1027] Input: Information that the request has been completed.

[1028] Processing: The server records the completion information in the database and generates a completion notification.

[1029] Output: A completion notification is sent to the user.

[1030] Specific operation: When the person in charge completes the request, they send that information to the server. The server then uses the "UPDATE" query to change the request status in the database to "Completed." The server then generates a completion notification message saying "Monthly report creation completed" and checks the user's emotions using the emotion engine. If necessary, a follow-up message is added, and the final completion notification is sent to the user's device as an HTTP response.

[1031] (Application example 2)

[1032] 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."

[1033] The conventional system had problems with delays in responding to requests and frequent errors. Furthermore, it was unable to respond appropriately while taking into account the requester's feelings, which led to issues with reduced work efficiency and reliability. Furthermore, operators sometimes missed important notifications while working in the factory, which led to a loss of safety and efficiency.

[1034] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending reminders to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, means for confirming that the request has been completed and sending a completion notification to the requester, and means for analyzing the requester's emotions and optimizing the content of the response and reminder. This prevents delays and errors in request response, enables appropriate responses that take the requester's emotions into consideration, and improves work efficiency and reliability. Furthermore, safety and efficiency are also improved because operators can reliably receive important notifications while working in the factory.

[1035] "Receiving a request" means that the system takes in work instructions or requests from operators or other users.

[1036] "Recording in a database" means saving the received request in data format so that it can be searched or referenced later.

[1037] "Automatic generation of a first response" means automatically creating an initial response to a received request using a predetermined template or algorithm.

[1038] "Send" means forwarding the automatically generated primary response or notification to the requester or contact person.

[1039] "Searching for unprocessed requests" means searching the database for data on requests that have not been processed and are on hold.

[1040] "Send reminder notification" means sending a notification to the person in charge to alert them about a request that is overdue.

[1041] "To send a second reminder" means to send an additional reminder if no response has been made after a certain period of time has passed.

[1042] "Confirming that the request has been completed" means that the system recognizes that the work or response to the request has been completed.

[1043] "Send completion notification" means sending a message to the requester informing them that the request has been successfully completed.

[1044] "Analyzing the client's emotions" means reading and judging the client's emotional state from their input and behavior.

[1045] "Optimizing the content of replies and reminders" means adjusting the content of replies and reminder messages based on the analyzed client's emotions.

[1046] This invention is an AI support system for efficient request management and response in factories and other workplaces. The system consists of the following main components:

[1047] System Configuration

[1048] 1. Server:

[1049] Receives requests and records their contents in a database.

[1050] A first response to the request is automatically generated and sent to the person in charge or the requester.

[1051] Search for outstanding requests and send reminders.

[1052] If no response is received within a certain period of time, a reminder notification will be sent.

[1053] Confirm that the request is completed and send a completion notification.

[1054] Analyze the client's emotions and optimize responses and reminders.

[1055] 2. Terminal:

[1056] Receive and display reminder notifications and completion notifications on the device used by the person in charge.

[1057] To display a message according to a requester's emotions.

[1058] 3. User:

[1059] Send a request and receive a response.

[1060] Hardware and Software Used

[1061] Server: Receives requests, stores them, generates initial responses, and manages reminder notifications.

[1062] Device: Receive reminders and completion notifications on the device used by the person in charge.

[1063] Emotion Engine: Analyzes the emotions of the client and staff and responds appropriately.

[1064] Messaging System: A system that sends notifications and reply messages.

[1065] Operation overview

[1066] The system works as follows:

[1067] 1. Request reception: The user sends a request to the server, and the request is saved in the database. The server uses an emotion engine to analyze the requester's emotions.

[1068] 2. Primary response: Based on the analyzed sentiment, the server automatically generates an appropriate primary response and sends it to the user.

[1069] 3. Reminder notification: If the request is not handled within a certain period of time, the server generates a reminder notification and sends it to the agent's device. The emotion engine analyzes the requester's emotions again and optimizes the notification message.

[1070] 4. Remind: If the request is not acted upon within a certain time period after the first reminder, the server will send a reminder.

[1071] 5. Completion Notification: When the request is completed, the server records the information in the database and sends a completion notification to the requester. The emotion engine checks the emotion again and adds a follow-up message if necessary.

[1072] Specific examples

[1073] In-factory maintenance request management:

[1074] When an operator in a factory requests machine maintenance and inspection, this system can prevent oversight or delay in the request. The server receives the request and provides an appropriate initial response. If the request is not responded to, a reminder notification is sent to the person in charge's device.

[1075] Example prompt sentence:

[1076] "I would like to request a maintenance inspection of my machine."

[1077] This prevents delays and mistakes in responding to requests, enables appropriate responses that take into account the feelings of the requester, and improves work efficiency and reliability. It also ensures that operators receive important notifications while working in the factory, improving safety and efficiency.

[1078] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1079] Step 1: Receiving and recording the request

[1080] The user inputs a request and sends it to the server. The server receives the request and records it in the database. Specifically, if the user inputs a prompt such as "I would like to request maintenance inspection of the machine," the server receives the request and stores it in the database as "unprocessed." The input data is the request and user information, and a record is generated in the appropriate table in the database based on that information.

[1081] Step 2: Sentiment Analysis

[1082] The server passes the received request to the emotion engine, which analyzes the user's emotions. The input data is the request content saved in step 1, and the emotion engine analyzes it. The analysis results output the user's emotional state, such as "I'm in a hurry" or "I'm dissatisfied." This allows subsequent responses to be appropriately optimized.

[1083] Step 3: Generate a first response

[1084] The server generates a primary response message based on the analysis results of the emotion engine. In this step, an appropriate template is selected based on the analyzed emotion and a response message is constructed. For example, if the user is in a hurry, a response such as "Thank you for your request. We will process the maintenance inspection promptly" is generated. The generated message becomes input data and is sent to the user.

[1085] Step 4: Sending a First Response

[1086] The server sends the generated primary response to the user, allowing the user to confirm that the request has been accepted. The primary response message becomes output data and is sent to the user's device, allowing the user to confirm that the request has been accepted.

[1087] Step 5: Search for open requests

[1088] The server periodically searches the database to detect unprocessed requests. The input data is all request records in the database, and by searching these, the processing status is confirmed. If an unprocessed request is found, a reminder is sent based on that information.

[1089] Step 6: Generate and send reminder notifications

[1090] When an unprocessed request is detected, the server generates a reminder notification and sends it to the agent's device. In this step, the emotion engine again analyzes the user's emotions and adjusts the reminder message appropriately. For example, if the user is feeling dissatisfied, it generates a message such as "Sorry for the wait. The request is currently in progress." The generated reminder message becomes input data and is sent to the agent's device.

[1091] Step 7: Generate and send a follow-up reminder notification

[1092] If the request is not addressed within a certain time after the reminder notification, the server generates and sends another reminder notification. At this step, the emotion engine also analyzes the emotion and optimizes the message as necessary. The re-reminding message becomes input data and is sent to the person in charge's device.

[1093] Step 8: Final confirmation and notification

[1094] Once the request is completed, the server records the information in a database and sends a completion notification to the requester. A final confirmation is performed by the emotion engine, and an appropriate follow-up message is added. For example, a message such as "The requested maintenance inspection has been completed. Please check it with confidence" is generated and sent to the user's device. The completion notification message is the input data, and the transmission result is output.

[1095] By using the above processing steps, the present invention can prevent delays and mistakes in responding to requests and can provide appropriate responses that take into account the user's feelings, thereby improving business efficiency and reliability.

[1096] 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.

[1097] 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.

[1098] 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.

[1099] [Third embodiment]

[1100] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1101] 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.

[1102] 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).

[1103] 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.

[1104] 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.

[1105] 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).

[1106] 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.

[1107] 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.

[1108] 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.

[1109] 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.

[1110] 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.

[1111] 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."

[1112] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business. Specific embodiments of this system are described below.

[1113] System Configuration

[1114] The system consists of the following major components:

[1115] 1. Server

[1116] 2. Terminal

[1117] 3. Users

[1118] Program processing overview

[1119] This system accepts requests from users, automatically provides an initial response, and sends reminders for unanswered requests to prevent missed responses.

[1120] Request acceptance

[1121] Step 1.1 - Submit your request

[1122] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1123] Step 1.2 - Receiving the request

[1124] The server receives a request (HTTP POST request) sent by the user.

[1125] The server extracts the request content (e.g., "Request to create monthly report") from the request payload.

[1126] Step 1.3 - Save your request

[1127] The server stores the request in a database and records the request status as "unprocessed."

[1128] First response

[1129] Step 2.1 - Generate a First Response

[1130] The server reads the request details stored in the database and generates a primary response such as "Thank you for your request. We are currently working on it."

[1131] Step 2.2 - Sending a First Response

[1132] The server sends the generated initial response to the user's email address or via a chatbot.

[1133] Reminder function

[1134] Step 3.1 - Search for open requests

[1135] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[1136] Step 3.2 - Generate a Reminder

[1137] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[1138] Step 3.3 - Send Reminder Notifications

[1139] The server sends a reminder notification to the terminal of the person in charge.

[1140] Step 3.4 - Remind me again

[1141] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1142] Final response confirmation

[1143] Step 4.1 - Check for compliance

[1144] The server periodically checks the database to see if the request is "in progress" or "completed."

[1145] Step 4.2 - Generate a Completion Notification

[1146] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[1147] Step 4.3 - Send a Completion Notification

[1148] The server sends the generated completion notification to the user.

[1149] Step 4.4 - Update the Database

[1150] The server records the request status as "completed" in the database and also stores the completion date and time.

[1151] Specific examples

[1152] 1. Request acceptance

[1153] A user submits a "Request to create monthly report."

[1154] The server receives the request and stores the contents in a database.

[1155] 2. First Response

[1156] The server analyzes the "Request to create monthly report" and generates a primary response such as "Thank you for your request. We are currently working on it."

[1157] The server sends this primary response to the user.

[1158] 3. Reminder function

[1159] The server periodically searches the database to detect outstanding requests.

[1160] The server sends a reminder notification to the terminal of the person in charge that "the request to create the monthly report has not yet been completed."

[1161] The server will resend a reminder to respond to the request within 24 hours.

[1162] 4. Final response confirmation

[1163] If the request is fulfilled, the server sends a completion notice to the user stating that "Monthly report creation has been completed."

[1164] The server records the status of the request as "completed" in the database.

[1165] This system prevents requests from being ignored or missed, improving work efficiency and reliability.

[1166] The processing flow will be explained below.

[1167] Request acceptance

[1168] Step 1:

[1169] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1170] Step 2:

[1171] The server receives a request (HTTP POST request) sent by the user.

[1172] Step 3:

[1173] The server extracts the request content from the payload of the request.

[1174] Step 4:

[1175] The server stores the request in a database and records the request status as "unprocessed."

[1176] First response

[1177] Step 5:

[1178] The server reads the request stored in the database.

[1179] Step 6:

[1180] The server generates a primary response such as "Thank you for your request. We are currently working on it."

[1181] Step 7:

[1182] The server sends the generated initial response to the user's email address or via a chatbot.

[1183] Reminder function

[1184] Step 8:

[1185] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[1186] Step 9:

[1187] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[1188] Step 10:

[1189] The server sends a reminder notification to the terminal of the person in charge.

[1190] Step 11:

[1191] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1192] Final response confirmation

[1193] Step 12:

[1194] The server periodically checks the database to see if the request has become "in progress" or "completed."

[1195] Step 13:

[1196] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[1197] Step 14:

[1198] The server sends the generated completion notification to the user.

[1199] Step 15:

[1200] The server records the request status as "completed" in its database along with a timestamp.

[1201] Example 1

[1202] 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."

[1203] There is a need to prevent requests from being ignored or overlooked in business operations, and to process and notify requests quickly and reliably. However, with conventional systems, it was difficult to grasp the status of requests, leading to problems such as the person in charge overlooking a request or delaying their response. In addition, there was a lack of appropriate notification and reminder functions for the person in charge and the requester, which led to problems with work efficiency.

[1204] 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.

[1205] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request using a generative AI model, means for searching the database for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notification to the requester. This prevents requests from being ignored or missed, and enables quick and reliable processing and notification of requests.

[1206] A "request" refers to a request or request that a user makes to a server as part of a task.

[1207] "Database" refers to a system for systematically storing and managing information such as request details and response status.

[1208] A "primary response" is an initial response generated by a server when it receives a request, and serves to confirm that the request has been accepted.

[1209] A "generative AI model" is an artificial intelligence model trained using large datasets to perform tasks such as natural language generation.

[1210] "Reminder notification" refers to a notification sent by the server to inform the person in charge of an outstanding request.

[1211] A "second reminder notification" refers to a notification that is sent again if a request has not been responded to within a certain period of time since the first reminder notification.

[1212] "Completion notification" refers to a notification sent by the server to inform the requester that the request has been completed.

[1213] This invention is a reply and reminder system that utilizes a generative AI model to prevent requests from being ignored or missed in business. The main components of this system are a server, a terminal, and a user. A specific embodiment of this system is described below.

[1214] System Configuration

[1215] 1. The server is the main processing unit that receives requests and records them in a database. It also generates and sends initial responses, reminders, and completion notifications. It also performs natural language processing using generative AI models.

[1216] 2. The terminal is an input device that allows users to input and send requests, and receives and displays reminder notifications and completion notifications.

[1217] 3. A user is someone who uses the system to enter requests and receive notifications.

[1218] Hardware and software used

[1219] Hardware: Servers (high-performance processing devices, database servers), user devices (PCs, smartphones, tablets)

[1220] Software: Generative AI models (e.g., GPT-3), database management systems (RDBMS, NoSQL databases), web servers (Apache, Nginx), communication protocols (HTTP / HTTPS)

[1221] Process Overview

[1222] 1. Request acceptance

[1223] A user enters a request through a web form and submits it to the server as an HTTP POST request, which the server receives and stores in a database.

[1224] 2. First Response

[1225] The server generates a first response based on the received request. Utilizing a generative AI model, it automatically generates a response such as, "Thank you for your request. We are currently working on it." The first response is sent to the user's email address or via a chat system.

[1226] 3. Reminder function

[1227] After a certain period of time has passed, the server queries the database to find any outstanding requests. A reminder notification is generated for any outstanding requests using a generation AI model. The reminder notification is sent to the employee's device with a message such as, "The request to create a monthly report has not yet been completed." If the request is not responded to within 24 hours of the first reminder, another reminder notification is sent.

[1228] 4. Final response confirmation

[1229] The server periodically checks the database to see if the request is in progress or completed. If the request is completed, it uses the generative AI model to generate a completion notification stating "Monthly report creation completed" and sends it to the user. The request status is recorded as "Completed" in the database.

[1230] Specific examples

[1231] Request acceptance

[1232] The user sends a "Request to create a monthly report." This request is received by the server and recorded in the database.

[1233] First response

[1234] The server reads the request, generates a response saying "Thank you for your request. We are currently working on it," and sends it to the user.

[1235] Reminder function

[1236] The server periodically checks the database to find any outstanding requests, and generates a reminder message such as "The request to create a monthly report has not yet been completed" and sends it to the person in charge's device.

[1237] Final response confirmation

[1238] When the request is completed, a completion notice stating "Monthly report creation completed" is sent to the user, and the server records this information in the database.

[1239] Prompt Sentence Examples

[1240] Please fill out the request form with "Request for monthly report creation" and explain in detail the process required for submission.

[1241] "Please explain how to periodically check the database and send reminders for 'open' requests."

[1242] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1243] Program processing flow (concrete)

[1244] 1. Request acceptance

[1245] Step 1:

[1246] The user enters "Request for monthly report creation" in the request form and clicks the send button.

[1247] Input: Request details (e.g., "Request for monthly report creation")

[1248] Output: HTTP POST request

[1249] Specific behavior:

[1250] The terminal sends the request content entered by the user to the server as an HTTP POST request.

[1251] Step 2:

[1252] The server receives an HTTP POST request sent by the user.

[1253] Input: HTTP POST request payload

[1254] Output: Request details in JSON format

[1255] Specific behavior:

[1256] The server extracts the request content from the request payload and converts it into JSON format.

[1257] Step 3:

[1258] The server stores the extracted request details in a database.

[1259] Input: Request content in JSON format

[1260] Output: A new record in the database

[1261] Specific behavior:

[1262] The server records the request, the status "Not Supported," and a timestamp in the database.

[1263] 2. First Response

[1264] Step 4:

[1265] The server reads the newly saved "open" request from the database.

[1266] Input: A new record in the database

[1267] Output: Request details

[1268] Specific behavior:

[1269] The server executes a query to retrieve the newly added request.

[1270] Step 5:

[1271] The server generates a primary response using a generative AI model.

[1272] Input: Request details

[1273] Output: Response "Thank you for your request. We are currently working on it."

[1274] Specific behavior:

[1275] The server inputs the request as a prompt into the generative AI model and generates an appropriate response.

[1276] Step 6:

[1277] The server generates a primary response and sends it to the user.

[1278] Input: Generated response text, user contact information (email address and chat ID)

[1279] Output: User notification

[1280] Specific behavior:

[1281] The server sends a primary response to the user via a mail server or chat system.

[1282] 3. Reminder function

[1283] Step 7:

[1284] At the end of each day or at set intervals, the server queries the database for requests with a status of "open."

[1285] Input: Entire database

[1286] Output: List of "open" requests

[1287] Specific behavior:

[1288] The server runs a daily query to list requests with a status of "open."

[1289] Step 8:

[1290] The server generates reminder notifications for outstanding requests.

[1291] Input: List of "unhandled" requests

[1292] Output: Reminder message: "Your monthly report request has not yet been completed."

[1293] Specific behavior:

[1294] The server uses the generative AI model to create reminder notifications for outstanding requests.

[1295] Step 9:

[1296] The server sends a reminder notification to the terminal of the person in charge.

[1297] Input: Reminder message, contact information of person in charge

[1298] Output: Notification to the person in charge's terminal

[1299] Specific behavior:

[1300] The server sends a reminder notification to the person in charge via a mail server or chat system.

[1301] Step 10:

[1302] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1303] Input: Time-lapse trigger, open request list

[1304] Output: Remind notification

[1305] Specific behavior:

[1306] The server runs a scheduled job to create and send the next reminder notification.

[1307] 4. Final response confirmation

[1308] Step 11:

[1309] The server periodically checks the database to see if the request is "in progress" or "completed."

[1310] Input: Entire database

[1311] Output: A list of requests with statuses of Open and Completed

[1312] Specific behavior:

[1313] The server runs a scheduled job to check for compliance in the database.

[1314] Step 12:

[1315] The server generates a completion notification when the request is completed.

[1316] Input: Completed request details

[1317] Output: Completion notification message: "Monthly report creation completed."

[1318] Specific behavior:

[1319] The server uses the generative AI model to create a completion notification.

[1320] Step 13:

[1321] The server sends the generated completion notification to the user.

[1322] Input: Completion notification, user contact information

[1323] Output: User notification

[1324] Specific behavior:

[1325] The server sends a completion notification to the user via a mail server or chat system.

[1326] Step 14:

[1327] The server records the request status as "completed" in the database and also stores the completion date and time.

[1328] Input: Completed request details, completion date and time

[1329] Output: Updated records in the database

[1330] Specific behavior:

[1331] The server updates the request record in the database, changing the status to "Completed" and recording the completion date and time.

[1332] (Application example 1)

[1333] 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."

[1334] Because security checklists contain a wide variety of items, it is extremely difficult for personnel to ensure that all items are checked and to keep track of any outstanding items. If some items are missed, security risks increase and serious problems may occur. Therefore, there is a need for a system that automates the management of security checklists and the tracking of outstanding items, ensuring their completion.

[1335] 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.

[1336] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if a request is not processed within a certain period of time after a reminder has been sent, means for confirming that the request has been completed and sending a completion notification to the requester, means for inputting security checklist items, and means for tracking the completion status of each checklist item. This automates the management of the security checklist and the tracking of unprocessed items, making it possible to ensure that all items are completed reliably.

[1337] A "request" is the act of requesting a specific job or task, or the content of that request.

[1338] A "database" is a collection of electronic data that organizes and stores information so that it can be searched and used efficiently.

[1339] A "primary response" is the first response message that is automatically generated after receiving a request.

[1340] A "reminder notification" is a notification sent to alert the person in charge about outstanding requests or tasks.

[1341] A "second reminder notification" is a second reminder notification sent if no action is taken within a specified period after the first reminder notification.

[1342] A "completion notification" is a notification sent to inform the requester that the requested task or work has been completed.

[1343] A "security checklist" is a list of items that should be checked to ensure safety.

[1344] "Completion status" indicates the extent to which a particular task or request has been accomplished.

[1345] This invention is a system for automating security checklist management and tracking of outstanding items. This system mainly includes a server, terminals, and users, each of which has a role to play, ensuring that all checklist items are completed.

[1346] System Program Overview

[1347] 1. Request acceptance and receipt

[1348] The user enters the security checklist items into the request form (smartphone app, tablet app, etc.) and clicks the submit button.

[1349] The server receives the HTTP POST request and parses the request payload to extract the requested content.

[1350] 2. Saving to the database

[1351] The server stores the request in a database (e.g. MongoDB) as raw data.

[1352] 3. Generating and sending a primary response

[1353] The server generates a primary response message saying "Thank you for your confirmation. We will now begin lock confirmation." and sends it to the person in charge. SendGrid or Firebase Cloud Messaging is used as the email notification system.

[1354] 4. Reminder function

[1355] The server periodically checks for unaddressed items, and if they are not addressed within the specified period, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge's device (smartphone / tablet). Re-reminders can also be set in the same way.

[1356] 5. Final response confirmation and notification

[1357] When the request is completed, the server generates and sends a completion notification stating "Door lock confirmation completed" and updates the database record.

[1358] Hardware and software used

[1359] Frontend: Building smartphone / tablet apps using React Native.

[1360] Backend: Uses Node.js to process requests and access the database.

[1361] Database: MongoDB is used to manage the status of requests and checklists.

[1362] Notification system: Use SendGrid or Firebase Cloud Messaging to send emails and push notifications.

[1363] Specific examples

[1364] Example 1: Checking whether the door is locked

[1365] If a security officer were to enter a request into the system to check if the doors in a building are locked, the following prompt text could be used:

[1366] "If you confirm the door is locked, please check the box."

[1367] "Once you've completed the verification, you will be prompted to proceed to the next step."

[1368] In this way, the system receives requests, saves them in a database, sends a first response, sends reminders for outstanding items, and finally confirms that the task has been completed and sends a completion notification, ensuring that all items on the security checklist are carried out and minimizing security risks.

[1369] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1370] Step 1:

[1371] The user enters the security checklist items into the request form and clicks the submit button. The specific input data is a request to "check whether the door is locked." This input data is sent to the server as an HTTP POST request.

[1372] Step 2:

[1373] The server receives the HTTP POST request. The request data, "Check if the door is locked," is extracted from the request payload. The extracted request data is saved in the database and the status is recorded as "Not handled."

[1374] Step 3:

[1375] The server generates a primary response based on the request details stored in the database. It generates a primary response message stating "Thank you for your confirmation. Lock confirmation will begin." and sends it to the person in charge's terminal. The output data is the primary response message.

[1376] Step 4:

[1377] The server periodically checks the database and searches for requests with a status of "open." If there are any open requests, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge. The output data is the reminder notification.

[1378] Step 5:

[1379] When a staff member receives a reminder notification, the notification will be displayed on the terminal. The staff member will check the notification and take action to complete the items on the security checklist. Specifically, the staff member will check that the door is locked and click the Complete button.

[1380] Step 6:

[1381] The server periodically checks the database to confirm the completion of the request. If completion is confirmed, the server generates a completion notification stating "Door lock confirmation completed" and sends it to the requester's terminal. The output data is the completion notification.

[1382] Step 7:

[1383] The requester receives a completion notification, which is displayed on the terminal. The requester can check the completion notification and know that the entire request has been completed. This ensures that the security checklist has been carried out correctly.

[1384] 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.

[1385] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1386] System Configuration

[1387] The system consists of the following major components:

[1388] 1. Server

[1389] 2. Terminal

[1390] 3. Users

[1391] 4. Emotion Engine

[1392] Program processing overview

[1393] This system accepts requests from users, automatically responds to them, and sends reminders for unanswered requests. It also recognizes the user's emotions and responds appropriately to them.

[1394] Acceptance of requests and initial response

[1395] The user enters "Request for monthly report creation" in the request form and clicks the submit button. The server receives the request from the user and saves it in the database. At this time, the status of the request is recorded as "Unprocessed."

[1396] The server reads the received request and analyzes the user's emotions using an emotion engine. For example, if the user expresses dissatisfaction or urgency, the emotion engine recognizes this and adjusts the primary response. It generates an appropriate response such as "Thank you for your request. We will process your request promptly." The generated primary response is then sent from the server to the user.

[1397] Reminder function

[1398] At the end of each day or at set intervals, the server searches the database for outstanding requests and generates a reminder notification, such as "Your monthly report creation request has not yet been completed." The reminder notification is sent to the device of the person in charge.

[1399] When a reminder is sent, the emotion engine again monitors the user's emotions and adjusts the content of the reminder. For example, if the user is frustrated, it can add a follow-up message such as, "Sorry for the wait. It's currently in progress."

[1400] Remind me again

[1401] If the request remains unaddressed 24 hours after the first reminder, the server will send another reminder, and the emotion engine will again check the user's emotions and adjust them if necessary.

[1402] Final response confirmation and completion notification

[1403] Once the request is complete, the server records the information in the database and generates a completion notification. The server sends a completion notification to the user, such as "Monthly report creation completed." At this point, the emotion engine can again check the user's emotion and add an appropriate follow-up message.

[1404] Specific examples

[1405] 1. Request acceptance

[1406] A user submits a "Request to create monthly report."

[1407] The server receives the request and stores it in a database.

[1408] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[1409] 2. First Response

[1410] Based on the request, the server generates a primary response such as "Thank you for your request. We will process your request promptly."

[1411] The server sends the primary response to the user.

[1412] 3. Reminder function

[1413] The server searches the database at set intervals to detect any outstanding requests.

[1414] The server generates a reminder notification that "Your monthly report creation request has not yet been completed."

[1415] The emotion engine may reanalyze the user's emotions and add a follow-up message to the reminder, such as "Sorry for the wait. It's in progress."

[1416] The server sends a reminder notification to the terminal of the person in charge.

[1417] 4. Remind me again

[1418] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[1419] The emotion engine adjusts the content of the re-reminder depending on the situation.

[1420] 5. Final response confirmation

[1421] When the request is completed, the server records the information in a database.

[1422] The server sends a completion notification to the user stating that "Monthly report creation has been completed."

[1423] The emotion engine checks the user's emotions and adds follow-up messages as needed.

[1424] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[1425] The processing flow will be explained below.

[1426] Acceptance of requests and initial response

[1427] Step 1:

[1428] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1429] Step 2:

[1430] The server receives a request (HTTP POST request) sent by the user.

[1431] Step 3:

[1432] The server extracts the request content from the payload of the request.

[1433] Step 4:

[1434] The server stores the request in a database and records the request status as "unprocessed."

[1435] Step 5:

[1436] The server reads the request and analyzes the user's emotions using an emotion engine.

[1437] Step 6:

[1438] The server adjusts the primary response based on the analysis results from the emotion engine. For example, if the user expresses dissatisfaction, the server generates a response such as "Thank you for your request. We will process your request promptly."

[1439] Step 7:

[1440] The server sends the generated initial response to the user's email address or via a chatbot.

[1441] Reminder function

[1442] Step 8:

[1443] The server searches the database at the end of each day or at set intervals to detect requests with a status of "open."

[1444] Step 9:

[1445] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[1446] Step 10:

[1447] The emotion engine reanalyzes the user's emotions before sending a reminder. For example, if the user shows signs of impatience, it adds a follow-up message such as "We're checking your progress."

[1448] Step 11:

[1449] The server sends a reminder notification to the terminal of the person in charge.

[1450] Step 12:

[1451] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1452] Step 13:

[1453] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content as necessary.

[1454] Final response confirmation and completion notification

[1455] Step 14:

[1456] The server periodically checks the database to see if the request has become "in progress" or "completed."

[1457] Step 15:

[1458] If the request is completed, the server generates a completion notice stating "Monthly report creation completed."

[1459] Step 16:

[1460] The emotion engine performs a final check of the user's emotions and, if necessary, adds a follow-up message such as "Thank you for your cooperation" to the completion notification.

[1461] Step 17:

[1462] The server sends the generated completion notification to the user.

[1463] Step 18:

[1464] The server records the request status as "completed" in the database and also stores the completion date and time.

[1465] Specific examples

[1466] Step 1:

[1467] A user submits a "Request to create monthly report."

[1468] Step 2:

[1469] The server receives the request and stores it in a database.

[1470] Step 3:

[1471] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[1472] Step 4:

[1473] The server generates a primary response based on the request, such as "Thank you for your request. We will process your request promptly," and sends it to the user.

[1474] Step 5:

[1475] At the end of the day, the server searches for open requests and generates a reminder notification saying, "Your request to create a monthly report has not yet been completed."

[1476] Step 6:

[1477] The emotion engine reanalyzes the user's emotions and adds follow-up messages, such as "Sorry for the wait. We're working on it," if necessary.

[1478] Step 7:

[1479] The server sends a reminder notification to the terminal of the person in charge.

[1480] Step 8:

[1481] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[1482] Step 9:

[1483] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content.

[1484] Step 10:

[1485] When the request is completed, the server generates a completion notice stating "Monthly report creation completed" and sends it to the user.

[1486] Step 11:

[1487] The emotion engine provides a final check on the user's emotions and adds follow-up messages if necessary.

[1488] Step 12:

[1489] The request status is recorded as "Completed" in the database, and the completion date and time are also saved.

[1490] This system not only prevents requests from being ignored or missed, but also enables responses that take users' feelings into consideration to a high degree, thereby improving work efficiency and reliability.

[1491] Example 2

[1492] 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."

[1493] Conventional systems often resulted in requests being ignored or left unaddressed, resulting in reduced satisfaction and work efficiency. Furthermore, uniform responses and reminder notifications were sent without considering the requester's feelings, often increasing the requester's dissatisfaction. A prompt and appropriate response is required, especially when the request is urgent or the requester is dissatisfied, but conventional systems had limitations.

[1494] 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.

[1495] In this invention, the server includes means for receiving requests and recording their contents in a database, means for analyzing the requester's emotions using an emotion engine and generating and sending a first response based on the results, means for searching for unaddressed requests, generating a reminder notice, and sending it to the person in charge, means for analyzing the requester's emotions again using the emotion engine when the reminder notice is sent and adding an appropriate follow-up message, means for sending a second reminder notice if the request is not addressed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notice to the requester. This prevents requests from being ignored or overlooked, enables flexible responses that take the requester's emotions into consideration, and realizes business efficiency and improved requester satisfaction.

[1496] A "request" is a user's request to perform a particular task or service.

[1497] A "database" is a collection of data that is structured so that information can be systematically stored, managed, and searched.

[1498] An "emotion engine" refers to software or algorithms that analyze the emotions in text or speech and output the results.

[1499] A "primary reply" is the first response message automatically generated by a server immediately after receiving a request.

[1500] A "reminder notification" is a notification sent to reconfirm an outstanding request.

[1501] A "follow-up message" is a supplementary message that is sent in addition depending on the situation after the reminder notification and the requester's feelings.

[1502] A "second reminder notification" is a notification that is sent again if no action is taken within a certain period of time after the first reminder notification.

[1503] A "completion notice" is a final notice to inform the requester that the request has been successfully completed.

[1504] A "generative AI model" is a type of artificial intelligence that generates text based on a specific prompt.

[1505] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1506] System Configuration

[1507] The system consists of the following major components:

[1508] 1. Server

[1509] 2. Terminal

[1510] 3. Users

[1511] 4. Emotion Engine

[1512] 5. Database

[1513] 6. Generative AI Models

[1514] Hardware and software used

[1515] Server: Responsible for receiving requests, managing the database, generating initial responses and reminder notifications.

[1516] Terminal: A device (PC, smartphone, etc.) used by users and staff to input and confirm request details.

[1517] Database: Use a relational database such as MySQL to store the request details and status.

[1518] Sentiment engine: Analyzes user sentiment using sentiment analysis APIs such as IBM Watson and AWS Comprehend.

[1519] Generative AI models, such as OpenAI's GPT-4, generate responses and follow-up messages based on prompts.

[1520] Specific examples

[1521] 1. Request acceptance

[1522] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1523] The server receives the request from the user via an HTTP request, stores it in a database (e.g., MySQL), and records the request status as "unhandled."

[1524] 2. Sentiment analysis and first response generation

[1525] The server reads the received request and analyzes the user's emotions using an emotion engine (e.g., IBM Watson or AWS Comprehend).

[1526] The emotion engine receives the request text as an API request and returns the emotion analysis result. For example, the emotion engine recognizes if the user is expressing frustration or urgency.

[1527] The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) based on the sentiment analysis results. An example of a prompt is "If the user feels urgent, please create an appropriate response."

[1528] The server sends the generated primary response to the user, for example, "Thank you for your request. We will process your request promptly."

[1529] 3. Generate and send reminder notifications

[1530] The server searches the database for outstanding requests at the end of each day or at set intervals.

[1531] Generate a reminder for outstanding requests stating "Your request to create a monthly report has not yet been completed."

[1532] The server sends a reminder notification to the user's device. At this time, the emotion engine monitors the user's emotions again and adjusts the content of the reminder notification. A follow-up message such as "Sorry for the wait. We are currently working on this." is added.

[1533] 4. Remind notification

[1534] If the request is not acted upon within 24 hours of the first reminder, the server will send another reminder, at which point the emotion engine will reconfirm the user's emotion and adjust the content of the reminder accordingly.

[1535] 5. Final response confirmation and completion notification

[1536] Once the request is completed, the server records the information in a database and generates a completion notification.

[1537] The server sends a completion notification to the user stating, "Monthly report creation completed." At this time, the emotion engine again checks the user's emotion and adds an appropriate follow-up message.

[1538] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[1539] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1540] Step 1: Request acceptance

[1541] Input: The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1542] Processing: The server receives the request from the user as an HTTP request.

[1543] Output: The request is sent to the server.

[1544] Specific operation: The server has an API endpoint that accepts requests and processes them from users. The received request is converted to JSON format and saved in a MySQL database using an "INSERT" query. At this time, the request status is recorded as "Not handled."

[1545] Step 2: Sentiment analysis and generation of first responses

[1546] Input: The server reads the request stored in the database.

[1547] Processing: The server sends the request to an emotion engine (e.g., IBM Watson or AWS Comprehend) to analyze the emotion.

[1548] Output: The emotion engine returns the emotion analysis results to the server.

[1549] Specific operation: The server calls the emotion engine API and sends the text of the request. The emotion engine analyzes the text and returns an emotion label and score. For example, it obtains results such as "Urgent: High" or "Dissatisfied: Medium." Next, the server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "If the user's emotion is urgent, please create an appropriate response," and generates a primary response. The generated primary response might be something like, "Thank you for your request. We will process your request promptly."

[1550] Step 3: Sending a First Response

[1551] Input: Primary response sentence from the generative AI model.

[1552] Processing: The server sends the primary response to the user.

[1553] Output: The primary response is displayed on the user's terminal.

[1554] Specific operation: The server sends the generated primary response as an HTTP response to the user's device. The user's device displays the received primary response on the screen.

[1555] Step 4: Generate and send reminder notifications

[1556] Input: A database that holds open requests.

[1557] Processing: At set intervals, the server searches the database for outstanding requests and generates reminder notifications.

[1558] Output: A reminder notification is sent to the assignee.

[1559] Specific operation: The server runs a regularly scheduled job (e.g., a Cron job) and searches the database with a "SELECT" query. If an open request is found, it generates a reminder message saying, "Your request to create a monthly report has not yet been completed." Next, the server again uses the emotion engine to analyze the user's emotions. Based on the analysis results, it adds a follow-up message (e.g., "Sorry for the wait. It's currently in progress.") to the reminder message. The server then sends a final reminder message to the responsible person's device via an HTTP request.

[1560] Step 5: Send a second reminder

[1561] Input: Requests that remain unaddressed after the first reminder.

[1562] Processing: The server runs a scheduled job for re-reminding, and generates and sends another reminder notification.

[1563] Output: A reminder notification is sent to the assignee.

[1564] Specific operation: The server monitors the timing of re-reminders and re-searches the database as a regular job. If an unprocessed request is found, a re-reminder notification is generated. In this case, the emotion engine is used to analyze the user's emotions and add an appropriate message. The generated re-reminder notification is sent to the person in charge's device.

[1565] Step 6: Final response confirmation and completion notification

[1566] Input: Information that the request has been completed.

[1567] Processing: The server records the completion information in the database and generates a completion notification.

[1568] Output: A completion notification is sent to the user.

[1569] Specific operation: When the person in charge completes the request, they send that information to the server. The server then uses the "UPDATE" query to change the request status in the database to "Completed." The server then generates a completion notification message saying "Monthly report creation completed" and checks the user's emotions using the emotion engine. If necessary, a follow-up message is added, and the final completion notification is sent to the user's device as an HTTP response.

[1570] (Application example 2)

[1571] 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."

[1572] The conventional system had problems with delays in responding to requests and frequent errors. Furthermore, it was unable to respond appropriately while taking into account the requester's feelings, which led to issues with reduced work efficiency and reliability. Furthermore, operators sometimes missed important notifications while working in the factory, which led to a loss of safety and efficiency.

[1573] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending reminders to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, means for confirming that the request has been completed and sending a completion notification to the requester, and means for analyzing the requester's emotions and optimizing the content of the response and reminder. This prevents delays and errors in request response, enables appropriate responses that take the requester's emotions into consideration, and improves work efficiency and reliability. Furthermore, safety and efficiency are also improved because operators can reliably receive important notifications while working in the factory.

[1574] "Receiving a request" means that the system takes in work instructions or requests from operators or other users.

[1575] "Recording in a database" means saving the received request in data format so that it can be searched or referenced later.

[1576] "Automatic generation of a first response" means automatically creating an initial response to a received request using a predetermined template or algorithm.

[1577] "Send" means forwarding the automatically generated primary response or notification to the requester or contact person.

[1578] "Searching for unprocessed requests" means searching the database for data on requests that have not been processed and are on hold.

[1579] "Send reminder notification" means sending a notification to the person in charge to alert them about a request that is overdue.

[1580] "To send a second reminder" means to send an additional reminder if no response has been made after a certain period of time has passed.

[1581] "Confirming that the request has been completed" means that the system recognizes that the work or response to the request has been completed.

[1582] "Send completion notification" means sending a message to the requester informing them that the request has been successfully completed.

[1583] "Analyzing the client's emotions" means reading and judging the client's emotional state from their input and behavior.

[1584] "Optimizing the content of replies and reminders" means adjusting the content of replies and reminder messages based on the analyzed client's emotions.

[1585] This invention is an AI support system for efficient request management and response in factories and other workplaces. The system consists of the following main components:

[1586] System Configuration

[1587] 1. Server:

[1588] Receives requests and records their contents in a database.

[1589] A first response to the request is automatically generated and sent to the person in charge or the requester.

[1590] Search for outstanding requests and send reminders.

[1591] If no response is received within a certain period of time, a reminder notification will be sent.

[1592] Confirm that the request is completed and send a completion notification.

[1593] Analyze the client's emotions and optimize responses and reminders.

[1594] 2. Terminal:

[1595] Receive and display reminder notifications and completion notifications on the device used by the person in charge.

[1596] To display a message according to a requester's emotions.

[1597] 3. User:

[1598] Send a request and receive a response.

[1599] Hardware and Software Used

[1600] Server: Receives requests, stores them, generates initial responses, and manages reminder notifications.

[1601] Device: Receive reminders and completion notifications on the device used by the person in charge.

[1602] Emotion Engine: Analyzes the emotions of the client and staff and responds appropriately.

[1603] Messaging System: A system that sends notifications and reply messages.

[1604] Operation overview

[1605] The system works as follows:

[1606] 1. Request reception: The user sends a request to the server, and the request is saved in the database. The server uses an emotion engine to analyze the requester's emotions.

[1607] 2. Primary response: Based on the analyzed sentiment, the server automatically generates an appropriate primary response and sends it to the user.

[1608] 3. Reminder notification: If the request is not handled within a certain period of time, the server generates a reminder notification and sends it to the agent's device. The emotion engine analyzes the requester's emotions again and optimizes the notification message.

[1609] 4. Remind: If the request is not acted upon within a certain time period after the first reminder, the server will send a reminder.

[1610] 5. Completion Notification: When the request is completed, the server records the information in the database and sends a completion notification to the requester. The emotion engine checks the emotion again and adds a follow-up message if necessary.

[1611] Specific examples

[1612] In-factory maintenance request management:

[1613] When an operator in a factory requests machine maintenance and inspection, this system can prevent oversight or delay in the request. The server receives the request and provides an appropriate initial response. If the request is not responded to, a reminder notification is sent to the person in charge's device.

[1614] Example prompt sentence:

[1615] "I would like to request a maintenance inspection of my machine."

[1616] This prevents delays and mistakes in responding to requests, enables appropriate responses that take into account the feelings of the requester, and improves work efficiency and reliability. It also ensures that operators receive important notifications while working in the factory, improving safety and efficiency.

[1617] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1618] Step 1: Receiving and recording the request

[1619] The user inputs a request and sends it to the server. The server receives the request and records it in the database. Specifically, if the user inputs a prompt such as "I would like to request maintenance inspection of the machine," the server receives the request and stores it in the database as "unprocessed." The input data is the request and user information, and a record is generated in the appropriate table in the database based on that information.

[1620] Step 2: Sentiment Analysis

[1621] The server passes the received request to the emotion engine, which analyzes the user's emotions. The input data is the request content saved in step 1, and the emotion engine analyzes it. The analysis results output the user's emotional state, such as "I'm in a hurry" or "I'm dissatisfied." This allows subsequent responses to be appropriately optimized.

[1622] Step 3: Generate a first response

[1623] The server generates a primary response message based on the analysis results of the emotion engine. In this step, an appropriate template is selected based on the analyzed emotion and a response message is constructed. For example, if the user is in a hurry, a response such as "Thank you for your request. We will process the maintenance inspection promptly" is generated. The generated message becomes input data and is sent to the user.

[1624] Step 4: Sending a First Response

[1625] The server sends the generated primary response to the user, allowing the user to confirm that the request has been accepted. The primary response message becomes output data and is sent to the user's device, allowing the user to confirm that the request has been accepted.

[1626] Step 5: Search for open requests

[1627] The server periodically searches the database to detect unprocessed requests. The input data is all request records in the database, and by searching these, the processing status is confirmed. If an unprocessed request is found, a reminder is sent based on that information.

[1628] Step 6: Generate and send reminder notifications

[1629] When an unprocessed request is detected, the server generates a reminder notification and sends it to the agent's device. In this step, the emotion engine again analyzes the user's emotions and adjusts the reminder message appropriately. For example, if the user is feeling dissatisfied, it generates a message such as "Sorry for the wait. The request is currently in progress." The generated reminder message becomes input data and is sent to the agent's device.

[1630] Step 7: Generate and send a follow-up reminder notification

[1631] If the request is not addressed within a certain time after the reminder notification, the server generates and sends another reminder notification. At this step, the emotion engine also analyzes the emotion and optimizes the message as necessary. The re-reminding message becomes input data and is sent to the person in charge's device.

[1632] Step 8: Final confirmation and notification

[1633] Once the request is completed, the server records the information in a database and sends a completion notification to the requester. A final confirmation is performed by the emotion engine, and an appropriate follow-up message is added. For example, a message such as "The requested maintenance inspection has been completed. Please check it with confidence" is generated and sent to the user's device. The completion notification message is the input data, and the transmission result is output.

[1634] By using the above processing steps, the present invention can prevent delays and mistakes in responding to requests and can provide appropriate responses that take into account the user's feelings, thereby improving business efficiency and reliability.

[1635] 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.

[1636] 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.

[1637] 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.

[1638] [Fourth embodiment]

[1639] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1640] 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.

[1641] 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).

[1642] 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.

[1643] 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.

[1644] 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).

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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.

[1651] 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."

[1652] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business. Specific embodiments of this system are described below.

[1653] System Configuration

[1654] The system consists of the following major components:

[1655] 1. Server

[1656] 2. Terminal

[1657] 3. Users

[1658] Program processing overview

[1659] This system accepts requests from users, automatically provides an initial response, and sends reminders for unanswered requests to prevent missed responses.

[1660] Request acceptance

[1661] Step 1.1 - Submit your request

[1662] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1663] Step 1.2 - Receiving the request

[1664] The server receives a request (HTTP POST request) sent by the user.

[1665] The server extracts the request content (e.g., "Request to create monthly report") from the request payload.

[1666] Step 1.3 - Save your request

[1667] The server stores the request in a database and records the request status as "unprocessed."

[1668] First response

[1669] Step 2.1 - Generate a First Response

[1670] The server reads the request details stored in the database and generates a primary response such as "Thank you for your request. We are currently working on it."

[1671] Step 2.2 - Sending a First Response

[1672] The server sends the generated initial response to the user's email address or via a chatbot.

[1673] Reminder function

[1674] Step 3.1 - Search for open requests

[1675] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[1676] Step 3.2 - Generate a Reminder

[1677] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[1678] Step 3.3 - Send Reminder Notifications

[1679] The server sends a reminder notification to the terminal of the person in charge.

[1680] Step 3.4 - Remind me again

[1681] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1682] Final response confirmation

[1683] Step 4.1 - Check for compliance

[1684] The server periodically checks the database to see if the request is "in progress" or "completed."

[1685] Step 4.2 - Generate a Completion Notification

[1686] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[1687] Step 4.3 - Send a Completion Notification

[1688] The server sends the generated completion notification to the user.

[1689] Step 4.4 - Update the Database

[1690] The server records the request status as "completed" in the database and also stores the completion date and time.

[1691] Specific examples

[1692] 1. Request acceptance

[1693] A user submits a "Request to create monthly report."

[1694] The server receives the request and stores the contents in a database.

[1695] 2. First Response

[1696] The server analyzes the "Request to create monthly report" and generates a primary response such as "Thank you for your request. We are currently working on it."

[1697] The server sends this primary response to the user.

[1698] 3. Reminder function

[1699] The server periodically searches the database to detect outstanding requests.

[1700] The server sends a reminder notification to the terminal of the person in charge that "the request to create the monthly report has not yet been completed."

[1701] The server will resend a reminder to respond to the request within 24 hours.

[1702] 4. Final response confirmation

[1703] If the request is fulfilled, the server sends a completion notice to the user stating that "Monthly report creation has been completed."

[1704] The server records the status of the request as "completed" in the database.

[1705] This system prevents requests from being ignored or missed, improving work efficiency and reliability.

[1706] The processing flow will be explained below.

[1707] Request acceptance

[1708] Step 1:

[1709] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1710] Step 2:

[1711] The server receives a request (HTTP POST request) sent by the user.

[1712] Step 3:

[1713] The server extracts the request content from the payload of the request.

[1714] Step 4:

[1715] The server stores the request in a database and records the request status as "unprocessed."

[1716] First response

[1717] Step 5:

[1718] The server reads the request stored in the database.

[1719] Step 6:

[1720] The server generates a primary response such as "Thank you for your request. We are currently working on it."

[1721] Step 7:

[1722] The server sends the generated initial response to the user's email address or via a chatbot.

[1723] Reminder function

[1724] Step 8:

[1725] The server queries the database at the end of each day or at specified intervals to find requests with a status of "open."

[1726] Step 9:

[1727] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[1728] Step 10:

[1729] The server sends a reminder notification to the terminal of the person in charge.

[1730] Step 11:

[1731] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1732] Final response confirmation

[1733] Step 12:

[1734] The server periodically checks the database to see if the request has become "in progress" or "completed."

[1735] Step 13:

[1736] If the request is "completed", the server generates a completion notice stating "Monthly report creation completed."

[1737] Step 14:

[1738] The server sends the generated completion notification to the user.

[1739] Step 15:

[1740] The server records the request status as "completed" in its database along with a timestamp.

[1741] Example 1

[1742] 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."

[1743] There is a need to prevent requests from being ignored or overlooked in business operations, and to process and notify requests quickly and reliably. However, with conventional systems, it was difficult to grasp the status of requests, leading to problems such as the person in charge overlooking a request or delaying their response. In addition, there was a lack of appropriate notification and reminder functions for the person in charge and the requester, which led to problems with work efficiency.

[1744] 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.

[1745] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request using a generative AI model, means for searching the database for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notification to the requester. This prevents requests from being ignored or missed, and enables quick and reliable processing and notification of requests.

[1746] A "request" refers to a request or request that a user makes to a server as part of a task.

[1747] "Database" refers to a system for systematically storing and managing information such as request details and response status.

[1748] A "primary response" is an initial response generated by a server when it receives a request, and serves to confirm that the request has been accepted.

[1749] A "generative AI model" is an artificial intelligence model trained using large datasets to perform tasks such as natural language generation.

[1750] "Reminder notification" refers to a notification sent by the server to inform the person in charge of an outstanding request.

[1751] A "second reminder notification" refers to a notification that is sent again if a request has not been responded to within a certain period of time since the first reminder notification.

[1752] "Completion notification" refers to a notification sent by the server to inform the requester that the request has been completed.

[1753] This invention is a reply and reminder system that utilizes a generative AI model to prevent requests from being ignored or missed in business. The main components of this system are a server, a terminal, and a user. A specific embodiment of this system is described below.

[1754] System Configuration

[1755] 1. The server is the main processing unit that receives requests and records them in a database. It also generates and sends initial responses, reminders, and completion notifications. It also performs natural language processing using generative AI models.

[1756] 2. The terminal is an input device that allows users to input and send requests, and receives and displays reminder notifications and completion notifications.

[1757] 3. A user is someone who uses the system to enter requests and receive notifications.

[1758] Hardware and software used

[1759] Hardware: Servers (high-performance processing devices, database servers), user devices (PCs, smartphones, tablets)

[1760] Software: Generative AI models (e.g., GPT-3), database management systems (RDBMS, NoSQL databases), web servers (Apache, Nginx), communication protocols (HTTP / HTTPS)

[1761] Process Overview

[1762] 1. Request acceptance

[1763] A user enters a request through a web form and submits it to the server as an HTTP POST request, which the server receives and stores in a database.

[1764] 2. First Response

[1765] The server generates a first response based on the received request. Utilizing a generative AI model, it automatically generates a response such as, "Thank you for your request. We are currently working on it." The first response is sent to the user's email address or via a chat system.

[1766] 3. Reminder function

[1767] After a certain period of time has passed, the server queries the database to find any outstanding requests. A reminder notification is generated for any outstanding requests using a generation AI model. The reminder notification is sent to the employee's device with a message such as, "The request to create a monthly report has not yet been completed." If the request is not responded to within 24 hours of the first reminder, another reminder notification is sent.

[1768] 4. Final response confirmation

[1769] The server periodically checks the database to see if the request is in progress or completed. If the request is completed, it uses the generative AI model to generate a completion notification stating "Monthly report creation completed" and sends it to the user. The request status is recorded as "Completed" in the database.

[1770] Specific examples

[1771] Request acceptance

[1772] The user sends a "Request to create a monthly report." This request is received by the server and recorded in the database.

[1773] First response

[1774] The server reads the request, generates a response saying "Thank you for your request. We are currently working on it," and sends it to the user.

[1775] Reminder function

[1776] The server periodically checks the database to find any outstanding requests, and generates a reminder message such as "The request to create a monthly report has not yet been completed" and sends it to the person in charge's device.

[1777] Final response confirmation

[1778] When the request is completed, a completion notice stating "Monthly report creation completed" is sent to the user, and the server records this information in the database.

[1779] Prompt Sentence Examples

[1780] Please fill out the request form with "Request for monthly report creation" and explain in detail the process required for submission.

[1781] "Please explain how to periodically check the database and send reminders for 'open' requests."

[1782] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1783] Program processing flow (concrete)

[1784] 1. Request acceptance

[1785] Step 1:

[1786] The user enters "Request for monthly report creation" in the request form and clicks the send button.

[1787] Input: Request details (e.g., "Request for monthly report creation")

[1788] Output: HTTP POST request

[1789] Specific behavior:

[1790] The terminal sends the request content entered by the user to the server as an HTTP POST request.

[1791] Step 2:

[1792] The server receives an HTTP POST request sent by the user.

[1793] Input: HTTP POST request payload

[1794] Output: Request details in JSON format

[1795] Specific behavior:

[1796] The server extracts the request content from the request payload and converts it into JSON format.

[1797] Step 3:

[1798] The server stores the extracted request details in a database.

[1799] Input: Request content in JSON format

[1800] Output: A new record in the database

[1801] Specific behavior:

[1802] The server records the request, the status "Not Supported," and a timestamp in the database.

[1803] 2. First Response

[1804] Step 4:

[1805] The server reads the newly saved "open" request from the database.

[1806] Input: A new record in the database

[1807] Output: Request details

[1808] Specific behavior:

[1809] The server executes a query to retrieve the newly added request.

[1810] Step 5:

[1811] The server generates a primary response using a generative AI model.

[1812] Input: Request details

[1813] Output: Response "Thank you for your request. We are currently working on it."

[1814] Specific behavior:

[1815] The server inputs the request as a prompt into the generative AI model and generates an appropriate response.

[1816] Step 6:

[1817] The server generates a primary response and sends it to the user.

[1818] Input: Generated response text, user contact information (email address and chat ID)

[1819] Output: User notification

[1820] Specific behavior:

[1821] The server sends a primary response to the user via a mail server or chat system.

[1822] 3. Reminder function

[1823] Step 7:

[1824] At the end of each day or at set intervals, the server queries the database for requests with a status of "open."

[1825] Input: Entire database

[1826] Output: List of "open" requests

[1827] Specific behavior:

[1828] The server runs a daily query to list requests with a status of "open."

[1829] Step 8:

[1830] The server generates reminder notifications for outstanding requests.

[1831] Input: List of "unhandled" requests

[1832] Output: Reminder message: "Your monthly report request has not yet been completed."

[1833] Specific behavior:

[1834] The server uses the generative AI model to create reminder notifications for outstanding requests.

[1835] Step 9:

[1836] The server sends a reminder notification to the terminal of the person in charge.

[1837] Input: Reminder message, contact information of person in charge

[1838] Output: Notification to the person in charge's terminal

[1839] Specific behavior:

[1840] The server sends a reminder notification to the person in charge via a mail server or chat system.

[1841] Step 10:

[1842] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1843] Input: Time-lapse trigger, open request list

[1844] Output: Remind notification

[1845] Specific behavior:

[1846] The server runs a scheduled job to create and send the next reminder notification.

[1847] 4. Final response confirmation

[1848] Step 11:

[1849] The server periodically checks the database to see if the request is "in progress" or "completed."

[1850] Input: Entire database

[1851] Output: A list of requests with statuses of Open and Completed

[1852] Specific behavior:

[1853] The server runs a scheduled job to check for compliance in the database.

[1854] Step 12:

[1855] The server generates a completion notification when the request is completed.

[1856] Input: Completed request details

[1857] Output: Completion notification message: "Monthly report creation completed."

[1858] Specific behavior:

[1859] The server uses the generative AI model to create a completion notification.

[1860] Step 13:

[1861] The server sends the generated completion notification to the user.

[1862] Input: Completion notification, user contact information

[1863] Output: User notification

[1864] Specific behavior:

[1865] The server sends a completion notification to the user via a mail server or chat system.

[1866] Step 14:

[1867] The server records the request status as "completed" in the database and also stores the completion date and time.

[1868] Input: Completed request details, completion date and time

[1869] Output: Updated records in the database

[1870] Specific behavior:

[1871] The server updates the request record in the database, changing the status to "Completed" and recording the completion date and time.

[1872] (Application example 1)

[1873] 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."

[1874] Because security checklists contain a wide variety of items, it is extremely difficult for personnel to ensure that all items are checked and to keep track of any outstanding items. If some items are missed, security risks increase and serious problems may occur. Therefore, there is a need for a system that automates the management of security checklists and the tracking of outstanding items, ensuring their completion.

[1875] 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.

[1876] In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending a reminder to the person in charge, means for sending a second reminder if a request is not processed within a certain period of time after a reminder has been sent, means for confirming that the request has been completed and sending a completion notification to the requester, means for inputting security checklist items, and means for tracking the completion status of each checklist item. This automates the management of the security checklist and the tracking of unprocessed items, making it possible to ensure that all items are completed reliably.

[1877] A "request" is the act of requesting a specific job or task, or the content of that request.

[1878] A "database" is a collection of electronic data that organizes and stores information so that it can be searched and used efficiently.

[1879] A "primary response" is the first response message that is automatically generated after receiving a request.

[1880] A "reminder notification" is a notification sent to alert the person in charge about outstanding requests or tasks.

[1881] A "second reminder notification" is a second reminder notification sent if no action is taken within a specified period after the first reminder notification.

[1882] A "completion notification" is a notification sent to inform the requester that the requested task or work has been completed.

[1883] A "security checklist" is a list of items that should be checked to ensure safety.

[1884] "Completion status" indicates the extent to which a particular task or request has been accomplished.

[1885] This invention is a system for automating security checklist management and tracking of outstanding items. This system mainly includes a server, terminals, and users, each of which has a role to play, ensuring that all checklist items are completed.

[1886] System Program Overview

[1887] 1. Request acceptance and receipt

[1888] The user enters the security checklist items into the request form (smartphone app, tablet app, etc.) and clicks the submit button.

[1889] The server receives the HTTP POST request and parses the request payload to extract the requested content.

[1890] 2. Saving to the database

[1891] The server stores the request in a database (e.g. MongoDB) as raw data.

[1892] 3. Generating and sending a primary response

[1893] The server generates a primary response message saying "Thank you for your confirmation. We will now begin lock confirmation." and sends it to the person in charge. SendGrid or Firebase Cloud Messaging is used as the email notification system.

[1894] 4. Reminder function

[1895] The server periodically checks for unaddressed items, and if they are not addressed within the specified period, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge's device (smartphone / tablet). Re-reminders can also be set in the same way.

[1896] 5. Final response confirmation and notification

[1897] When the request is completed, the server generates and sends a completion notification stating "Door lock confirmation completed" and updates the database record.

[1898] Hardware and software used

[1899] Frontend: Building smartphone / tablet apps using React Native.

[1900] Backend: Uses Node.js to process requests and access the database.

[1901] Database: MongoDB is used to manage the status of requests and checklists.

[1902] Notification system: Use SendGrid or Firebase Cloud Messaging to send emails and push notifications.

[1903] Specific examples

[1904] Example 1: Checking whether the door is locked

[1905] If a security officer were to enter a request into the system to check if the doors in a building are locked, the following prompt text could be used:

[1906] "If you confirm the door is locked, please check the box."

[1907] "Once you've completed the verification, you will be prompted to proceed to the next step."

[1908] In this way, the system receives requests, saves them in a database, sends a first response, sends reminders for outstanding items, and finally confirms that the task has been completed and sends a completion notification, ensuring that all items on the security checklist are carried out and minimizing security risks.

[1909] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1910] Step 1:

[1911] The user enters the security checklist items into the request form and clicks the submit button. The specific input data is a request to "check whether the door is locked." This input data is sent to the server as an HTTP POST request.

[1912] Step 2:

[1913] The server receives the HTTP POST request. The request data, "Check if the door is locked," is extracted from the request payload. The extracted request data is saved in the database and the status is recorded as "Not handled."

[1914] Step 3:

[1915] The server generates a primary response based on the request details stored in the database. It generates a primary response message stating "Thank you for your confirmation. Lock confirmation will begin." and sends it to the person in charge's terminal. The output data is the primary response message.

[1916] Step 4:

[1917] The server periodically checks the database and searches for requests with a status of "open." If there are any open requests, it generates a reminder notification stating "Door lock confirmation has not yet been completed" and sends it to the person in charge. The output data is the reminder notification.

[1918] Step 5:

[1919] When a staff member receives a reminder notification, the notification will be displayed on the terminal. The staff member will check the notification and take action to complete the items on the security checklist. Specifically, the staff member will check that the door is locked and click the Complete button.

[1920] Step 6:

[1921] The server periodically checks the database to confirm the completion of the request. If completion is confirmed, the server generates a completion notification stating "Door lock confirmation completed" and sends it to the requester's terminal. The output data is the completion notification.

[1922] Step 7:

[1923] The requester receives a completion notification, which is displayed on the terminal. The requester can check the completion notification and know that the entire request has been completed. This ensures that the security checklist has been carried out correctly.

[1924] 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.

[1925] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1926] System Configuration

[1927] The system consists of the following major components:

[1928] 1. Server

[1929] 2. Terminal

[1930] 3. Users

[1931] 4. Emotion Engine

[1932] Program processing overview

[1933] This system accepts requests from users, automatically responds to them, and sends reminders for unanswered requests. It also recognizes the user's emotions and responds appropriately to them.

[1934] Acceptance of requests and initial response

[1935] The user enters "Request for monthly report creation" in the request form and clicks the submit button. The server receives the request from the user and saves it in the database. At this time, the status of the request is recorded as "Unprocessed."

[1936] The server reads the received request and analyzes the user's emotions using an emotion engine. For example, if the user expresses dissatisfaction or urgency, the emotion engine recognizes this and adjusts the primary response. It generates an appropriate response such as "Thank you for your request. We will process your request promptly." The generated primary response is then sent from the server to the user.

[1937] Reminder function

[1938] At the end of each day or at set intervals, the server searches the database for outstanding requests and generates a reminder notification, such as "Your monthly report creation request has not yet been completed." The reminder notification is sent to the device of the person in charge.

[1939] When a reminder is sent, the emotion engine again monitors the user's emotions and adjusts the content of the reminder. For example, if the user is frustrated, it can add a follow-up message such as, "Sorry for the wait. It's currently in progress."

[1940] Remind me again

[1941] If the request remains unaddressed 24 hours after the first reminder, the server will send another reminder, and the emotion engine will again check the user's emotions and adjust them if necessary.

[1942] Final response confirmation and completion notification

[1943] Once the request is complete, the server records the information in the database and generates a completion notification. The server sends a completion notification to the user, such as "Monthly report creation completed." At this point, the emotion engine can again check the user's emotion and add an appropriate follow-up message.

[1944] Specific examples

[1945] 1. Request acceptance

[1946] A user submits a "Request to create monthly report."

[1947] The server receives the request and stores it in a database.

[1948] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[1949] 2. First Response

[1950] Based on the request, the server generates a primary response such as "Thank you for your request. We will process your request promptly."

[1951] The server sends the primary response to the user.

[1952] 3. Reminder function

[1953] The server searches the database at set intervals to detect any outstanding requests.

[1954] The server generates a reminder notification that "Your monthly report creation request has not yet been completed."

[1955] The emotion engine may reanalyze the user's emotions and add a follow-up message to the reminder, such as "Sorry for the wait. It's in progress."

[1956] The server sends a reminder notification to the terminal of the person in charge.

[1957] 4. Remind me again

[1958] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[1959] The emotion engine adjusts the content of the re-reminder depending on the situation.

[1960] 5. Final response confirmation

[1961] When the request is completed, the server records the information in a database.

[1962] The server sends a completion notification to the user stating that "Monthly report creation has been completed."

[1963] The emotion engine checks the user's emotions and adds follow-up messages as needed.

[1964] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[1965] The processing flow will be explained below.

[1966] Acceptance of requests and initial response

[1967] Step 1:

[1968] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[1969] Step 2:

[1970] The server receives a request (HTTP POST request) sent by the user.

[1971] Step 3:

[1972] The server extracts the request content from the payload of the request.

[1973] Step 4:

[1974] The server stores the request in a database and records the request status as "unprocessed."

[1975] Step 5:

[1976] The server reads the request and analyzes the user's emotions using an emotion engine.

[1977] Step 6:

[1978] The server adjusts the primary response based on the analysis results from the emotion engine. For example, if the user expresses dissatisfaction, the server generates a response such as "Thank you for your request. We will process your request promptly."

[1979] Step 7:

[1980] The server sends the generated initial response to the user's email address or via a chatbot.

[1981] Reminder function

[1982] Step 8:

[1983] The server searches the database at the end of each day or at set intervals to detect requests with a status of "open."

[1984] Step 9:

[1985] The server generates a reminder for the outstanding request saying "Your request to create monthly report is not yet completed."

[1986] Step 10:

[1987] The emotion engine reanalyzes the user's emotions before sending a reminder. For example, if the user shows signs of impatience, it adds a follow-up message such as "We're checking your progress."

[1988] Step 11:

[1989] The server sends a reminder notification to the terminal of the person in charge.

[1990] Step 12:

[1991] The server will send another reminder if the request is not acted upon within 24 hours of the first reminder.

[1992] Step 13:

[1993] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content as necessary.

[1994] Final response confirmation and completion notification

[1995] Step 14:

[1996] The server periodically checks the database to see if the request has become "in progress" or "completed."

[1997] Step 15:

[1998] If the request is completed, the server generates a completion notice stating "Monthly report creation completed."

[1999] Step 16:

[2000] The emotion engine performs a final check of the user's emotions and, if necessary, adds a follow-up message such as "Thank you for your cooperation" to the completion notification.

[2001] Step 17:

[2002] The server sends the generated completion notification to the user.

[2003] Step 18:

[2004] The server records the request status as "completed" in the database and also stores the completion date and time.

[2005] Specific examples

[2006] Step 1:

[2007] A user submits a "Request to create monthly report."

[2008] Step 2:

[2009] The server receives the request and stores it in a database.

[2010] Step 3:

[2011] The emotion engine analyzes the user's emotions when sending and detects feelings of dissatisfaction or urgency.

[2012] Step 4:

[2013] The server generates a primary response based on the request, such as "Thank you for your request. We will process your request promptly," and sends it to the user.

[2014] Step 5:

[2015] At the end of the day, the server searches for open requests and generates a reminder notification saying, "Your request to create a monthly report has not yet been completed."

[2016] Step 6:

[2017] The emotion engine reanalyzes the user's emotions and adds follow-up messages, such as "Sorry for the wait. We're working on it," if necessary.

[2018] Step 7:

[2019] The server sends a reminder notification to the terminal of the person in charge.

[2020] Step 8:

[2021] If no action is taken within 24 hours of the first reminder, the server will send another reminder.

[2022] Step 9:

[2023] The emotion engine reanalyzes the user's emotions before sending a re-reminding notification and adjusts the notification content.

[2024] Step 10:

[2025] When the request is completed, the server generates a completion notice stating "Monthly report creation completed" and sends it to the user.

[2026] Step 11:

[2027] The emotion engine provides a final check on the user's emotions and adds follow-up messages if necessary.

[2028] Step 12:

[2029] The request status is recorded as "Completed" in the database, and the completion date and time are also saved.

[2030] This system not only prevents requests from being ignored or missed, but also enables responses that take users' feelings into consideration to a high degree, thereby improving work efficiency and reliability.

[2031] Example 2

[2032] 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."

[2033] Conventional systems often resulted in requests being ignored or left unaddressed, resulting in reduced satisfaction and work efficiency. Furthermore, uniform responses and reminder notifications were sent without considering the requester's feelings, often increasing the requester's dissatisfaction. A prompt and appropriate response is required, especially when the request is urgent or the requester is dissatisfied, but conventional systems had limitations.

[2034] 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.

[2035] In this invention, the server includes means for receiving requests and recording their contents in a database, means for analyzing the requester's emotions using an emotion engine and generating and sending a first response based on the results, means for searching for unaddressed requests, generating a reminder notice, and sending it to the person in charge, means for analyzing the requester's emotions again using the emotion engine when the reminder notice is sent and adding an appropriate follow-up message, means for sending a second reminder notice if the request is not addressed after a certain period of time has passed, and means for confirming that the request has been completed and sending a completion notice to the requester. This prevents requests from being ignored or overlooked, enables flexible responses that take the requester's emotions into consideration, and realizes business efficiency and improved requester satisfaction.

[2036] A "request" is a user's request to perform a particular task or service.

[2037] A "database" is a collection of data that is structured so that information can be systematically stored, managed, and searched.

[2038] An "emotion engine" refers to software or algorithms that analyze the emotions in text or speech and output the results.

[2039] A "primary reply" is the first response message automatically generated by a server immediately after receiving a request.

[2040] A "reminder notification" is a notification sent to reconfirm an outstanding request.

[2041] A "follow-up message" is a supplementary message that is sent in addition depending on the situation after the reminder notification and the requester's feelings.

[2042] A "second reminder notification" is a notification that is sent again if no action is taken within a certain period of time after the first reminder notification.

[2043] A "completion notice" is a final notice to inform the requester that the request has been successfully completed.

[2044] A "generative AI model" is a type of artificial intelligence that generates text based on a specific prompt.

[2045] This invention is a reply and reminder system that utilizes AI to prevent requests from being ignored or overlooked in business, and also combines it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[2046] System Configuration

[2047] The system consists of the following major components:

[2048] 1. Server

[2049] 2. Terminal

[2050] 3. Users

[2051] 4. Emotion Engine

[2052] 5. Database

[2053] 6. Generative AI Models

[2054] Hardware and software used

[2055] Server: Responsible for receiving requests, managing the database, generating initial responses and reminder notifications.

[2056] Terminal: A device (PC, smartphone, etc.) used by users and staff to input and confirm request details.

[2057] Database: Use a relational database such as MySQL to store the request details and status.

[2058] Sentiment engine: Analyzes user sentiment using sentiment analysis APIs such as IBM Watson and AWS Comprehend.

[2059] Generative AI models, such as OpenAI's GPT-4, generate responses and follow-up messages based on prompts.

[2060] Specific examples

[2061] 1. Request acceptance

[2062] The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[2063] The server receives the request from the user via an HTTP request, stores it in a database (e.g., MySQL), and records the request status as "unhandled."

[2064] 2. Sentiment analysis and first response generation

[2065] The server reads the received request and analyzes the user's emotions using an emotion engine (e.g., IBM Watson or AWS Comprehend).

[2066] The emotion engine receives the request text as an API request and returns the emotion analysis result. For example, the emotion engine recognizes if the user is expressing frustration or urgency.

[2067] The server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) based on the sentiment analysis results. An example of a prompt is "If the user feels urgent, please create an appropriate response."

[2068] The server sends the generated primary response to the user, for example, "Thank you for your request. We will process your request promptly."

[2069] 3. Generate and send reminder notifications

[2070] The server searches the database for outstanding requests at the end of each day or at set intervals.

[2071] Generate a reminder for outstanding requests stating "Your request to create a monthly report has not yet been completed."

[2072] The server sends a reminder notification to the user's device. At this time, the emotion engine monitors the user's emotions again and adjusts the content of the reminder notification. A follow-up message such as "Sorry for the wait. We are currently working on this." is added.

[2073] 4. Remind notification

[2074] If the request is not acted upon within 24 hours of the first reminder, the server will send another reminder, at which point the emotion engine will reconfirm the user's emotion and adjust the content of the reminder accordingly.

[2075] 5. Final response confirmation and completion notification

[2076] Once the request is completed, the server records the information in a database and generates a completion notification.

[2077] The server sends a completion notification to the user stating, "Monthly report creation completed." At this time, the emotion engine again checks the user's emotion and adds an appropriate follow-up message.

[2078] This system not only prevents requests from being ignored or missed, but also enables responses that take into account the user's feelings, thereby improving work efficiency and reliability.

[2079] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2080] Step 1: Request acceptance

[2081] Input: The user enters "Request for monthly report creation" in the request form and clicks the submit button.

[2082] Processing: The server receives the request from the user as an HTTP request.

[2083] Output: The request is sent to the server.

[2084] Specific operation: The server has an API endpoint that accepts requests and processes them from users. The received request is converted to JSON format and saved in a MySQL database using an "INSERT" query. At this time, the request status is recorded as "Not handled."

[2085] Step 2: Sentiment analysis and generation of first responses

[2086] Input: The server reads the request stored in the database.

[2087] Processing: The server sends the request to an emotion engine (e.g., IBM Watson or AWS Comprehend) to analyze the emotion.

[2088] Output: The emotion engine returns the emotion analysis results to the server.

[2089] Specific operation: The server calls the emotion engine API and sends the text of the request. The emotion engine analyzes the text and returns an emotion label and score. For example, it obtains results such as "Urgent: High" or "Dissatisfied: Medium." Next, the server sends a prompt to the generative AI model (e.g., OpenAI's GPT-4) saying, "If the user's emotion is urgent, please create an appropriate response," and generates a primary response. The generated primary response might be something like, "Thank you for your request. We will process your request promptly."

[2090] Step 3: Sending a First Response

[2091] Input: Primary response sentence from the generative AI model.

[2092] Processing: The server sends the primary response to the user.

[2093] Output: The primary response is displayed on the user's terminal.

[2094] Specific operation: The server sends the generated primary response as an HTTP response to the user's device. The user's device displays the received primary response on the screen.

[2095] Step 4: Generate and send reminder notifications

[2096] Input: A database that holds open requests.

[2097] Processing: At set intervals, the server searches the database for outstanding requests and generates reminder notifications.

[2098] Output: A reminder notification is sent to the assignee.

[2099] Specific operation: The server runs a regularly scheduled job (e.g., a Cron job) and searches the database with a "SELECT" query. If an open request is found, it generates a reminder message saying, "Your request to create a monthly report has not yet been completed." Next, the server again uses the emotion engine to analyze the user's emotions. Based on the analysis results, it adds a follow-up message (e.g., "Sorry for the wait. It's currently in progress.") to the reminder message. The server then sends a final reminder message to the responsible person's device via an HTTP request.

[2100] Step 5: Send a second reminder

[2101] Input: Requests that remain unaddressed after the first reminder.

[2102] Processing: The server runs a scheduled job for re-reminding, and generates and sends another reminder notification.

[2103] Output: A reminder notification is sent to the assignee.

[2104] Specific operation: The server monitors the timing of re-reminders and re-searches the database as a regular job. If an unprocessed request is found, a re-reminder notification is generated. In this case, the emotion engine is used to analyze the user's emotions and add an appropriate message. The generated re-reminder notification is sent to the person in charge's device.

[2105] Step 6: Final response confirmation and completion notification

[2106] Input: Information that the request has been completed.

[2107] Processing: The server records the completion information in the database and generates a completion notification.

[2108] Output: A completion notification is sent to the user.

[2109] Specific operation: When the person in charge completes the request, they send that information to the server. The server then uses the "UPDATE" query to change the request status in the database to "Completed." The server then generates a completion notification message saying "Monthly report creation completed" and checks the user's emotions using the emotion engine. If necessary, a follow-up message is added, and the final completion notification is sent to the user's device as an HTTP response.

[2110] (Application example 2)

[2111] 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."

[2112] The conventional system had problems with delays in responding to requests and frequent errors. Furthermore, it was unable to respond appropriately while taking into account the requester's feelings, which led to issues with reduced work efficiency and reliability. Furthermore, operators sometimes missed important notifications while working in the factory, which led to a loss of safety and efficiency.

[2113] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving requests and recording their contents in a database, means for automatically generating and sending a first response to the request, means for searching for unprocessed requests and sending reminders to the person in charge, means for sending a second reminder if the request is not processed after a certain period of time has passed, means for confirming that the request has been completed and sending a completion notification to the requester, and means for analyzing the requester's emotions and optimizing the content of the response and reminder. This prevents delays and errors in request response, enables appropriate responses that take the requester's emotions into consideration, and improves work efficiency and reliability. Furthermore, safety and efficiency are also improved because operators can reliably receive important notifications while working in the factory.

[2114] "Receiving a request" means that the system takes in work instructions or requests from operators or other users.

[2115] "Recording in a database" means saving the received request in data format so that it can be searched or referenced later.

[2116] "Automatic generation of a first response" means automatically creating an initial response to a received request using a predetermined template or algorithm.

[2117] "Send" means forwarding the automatically generated primary response or notification to the requester or contact person.

[2118] "Searching for unprocessed requests" means searching the database for data on requests that have not been processed and are on hold.

[2119] "Send reminder notification" means sending a notification to the person in charge to alert them about a request that is overdue.

[2120] "To send a second reminder" means to send an additional reminder if no response has been made after a certain period of time has passed.

[2121] "Confirming that the request has been completed" means that the system recognizes that the work or response to the request has been completed.

[2122] "Send completion notification" means sending a message to the requester informing them that the request has been successfully completed.

[2123] "Analyzing the client's emotions" means reading and judging the client's emotional state from their input and behavior.

[2124] "Optimizing the content of replies and reminders" means adjusting the content of replies and reminder messages based on the analyzed client's emotions.

[2125] This invention is an AI support system for efficient request management and response in factories and other workplaces. The system consists of the following main components:

[2126] System Configuration

[2127] 1. Server:

[2128] Receives requests and records their contents in a database.

[2129] A first response to the request is automatically generated and sent to the person in charge or the requester.

[2130] Search for outstanding requests and send reminders.

[2131] If no response is received within a certain period of time, a reminder notification will be sent.

[2132] Confirm that the request is completed and send a completion notification.

[2133] Analyze the client's emotions and optimize responses and reminders.

[2134] 2. Terminal:

[2135] Receive and display reminder notifications and completion notifications on the device used by the person in charge.

[2136] To display a message according to a requester's emotions.

[2137] 3. User:

[2138] Send a request and receive a response.

[2139] Hardware and Software Used

[2140] Server: Receives requests, stores them, generates initial responses, and manages reminder notifications.

[2141] Device: Receive reminders and completion notifications on the device used by the person in charge.

[2142] Emotion Engine: Analyzes the emotions of the client and staff and responds appropriately.

[2143] Messaging System: A system that sends notifications and reply messages.

[2144] Operation overview

[2145] The system works as follows:

[2146] 1. Request reception: The user sends a request to the server, and the request is saved in the database. The server uses an emotion engine to analyze the requester's emotions.

[2147] 2. Primary response: Based on the analyzed sentiment, the server automatically generates an appropriate primary response and sends it to the user.

[2148] 3. Reminder notification: If the request is not handled within a certain period of time, the server generates a reminder notification and sends it to the agent's device. The emotion engine analyzes the requester's emotions again and optimizes the notification message.

[2149] 4. Remind: If the request is not acted upon within a certain time period after the first reminder, the server will send a reminder.

[2150] 5. Completion Notification: When the request is completed, the server records the information in the database and sends a completion notification to the requester. The emotion engine checks the emotion again and adds a follow-up message if necessary.

[2151] Specific examples

[2152] In-factory maintenance request management:

[2153] When an operator in a factory requests machine maintenance and inspection, this system can prevent oversight or delay in the request. The server receives the request and provides an appropriate initial response. If the request is not responded to, a reminder notification is sent to the person in charge's device.

[2154] Example prompt sentence:

[2155] "I would like to request a maintenance inspection of my machine."

[2156] This prevents delays and mistakes in responding to requests, enables appropriate responses that take into account the feelings of the requester, and improves work efficiency and reliability. It also ensures that operators receive important notifications while working in the factory, improving safety and efficiency.

[2157] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2158] Step 1: Receiving and recording the request

[2159] The user inputs a request and sends it to the server. The server receives the request and records it in the database. Specifically, if the user inputs a prompt such as "I would like to request maintenance inspection of the machine," the server receives the request and stores it in the database as "unprocessed." The input data is the request and user information, and a record is generated in the appropriate table in the database based on that information.

[2160] Step 2: Sentiment Analysis

[2161] The server passes the received request to the emotion engine, which analyzes the user's emotions. The input data is the request content saved in step 1, and the emotion engine analyzes it. The analysis results output the user's emotional state, such as "I'm in a hurry" or "I'm dissatisfied." This allows subsequent responses to be appropriately optimized.

[2162] Step 3: Generate a first response

[2163] The server generates a primary response message based on the analysis results of the emotion engine. In this step, an appropriate template is selected based on the analyzed emotion and a response message is constructed. For example, if the user is in a hurry, a response such as "Thank you for your request. We will process the maintenance inspection promptly" is generated. The generated message becomes input data and is sent to the user.

[2164] Step 4: Sending a First Response

[2165] The server sends the generated primary response to the user, allowing the user to confirm that the request has been accepted. The primary response message becomes output data and is sent to the user's device, allowing the user to confirm that the request has been accepted.

[2166] Step 5: Search for open requests

[2167] The server periodically searches the database to detect unprocessed requests. The input data is all request records in the database, and by searching these, the processing status is confirmed. If an unprocessed request is found, a reminder is sent based on that information.

[2168] Step 6: Generate and send reminder notifications

[2169] When an unprocessed request is detected, the server generates a reminder notification and sends it to the agent's device. In this step, the emotion engine again analyzes the user's emotions and adjusts the reminder message appropriately. For example, if the user is feeling dissatisfied, it generates a message such as "Sorry for the wait. The request is currently in progress." The generated reminder message becomes input data and is sent to the agent's device.

[2170] Step 7: Generate and send a follow-up reminder notification

[2171] If the request is not addressed within a certain time after the reminder notification, the server generates and sends another reminder notification. At this step, the emotion engine also analyzes the emotion and optimizes the message as necessary. The re-reminding message becomes input data and is sent to the person in charge's device.

[2172] Step 8: Final confirmation and notification

[2173] Once the request is completed, the server records the information in a database and sends a completion notification to the requester. A final confirmation is performed by the emotion engine, and an appropriate follow-up message is added. For example, a message such as "The requested maintenance inspection has been completed. Please check it with confidence" is generated and sent to the user's device. The completion notification message is the input data, and the transmission result is output.

[2174] By using the above processing steps, the present invention can prevent delays and mistakes in responding to requests and can provide appropriate responses that take into account the user's feelings, thereby improving business efficiency and reliability.

[2175] 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.

[2176] 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.

[2177] 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.

[2178] 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.

[2179] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2180] 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.

[2181] 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).

[2182] 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.

[2183] 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."

[2184] 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.

[2185] 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).

[2186] 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.

[2187] 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.

[2188] 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.

[2189] 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.

[2190] 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.

[2191] 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.

[2192] 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.

[2193] 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.

[2194] 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.

[2195] 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.

[2196] The following is further disclosed regarding the above embodiment.

[2197] (Claim 1)

[2198] means for receiving the request and recording the request in a database;

[2199] means for automatically generating and transmitting a first response to the request;

[2200] A way to search for pending requests and send reminders to the person in charge,

[2201] A means for sending a reminder notification if no response is received within a certain period of time;

[2202] a means for confirming that the request has been completed and sending a completion notification to the requester;

[2203] A system including:

[2204] (Claim 2)

[2205] 10. The system of claim 1, wherein in generating the first reply, an appropriate reply template is selected based on the request content.

[2206] (Claim 3)

[2207] 2. The system according to claim 1, wherein when a reminder notification is sent, the notification is received and displayed on the terminal of the person in charge.

[2208] "Example 1"

[2209] (Claim 1)

[2210] means for receiving the request and recording the request in a database;

[2211] A means for automatically generating and transmitting a first response to the request using a generative AI model;

[2212] A means to search the database for unprocessed requests and send reminder notifications to the person in charge,

[2213] A means for sending a reminder notification if no response is received within a certain period of time;

[2214] a means for confirming that the request has been completed and sending a completion notification to the requester;

[2215] A system including:

[2216] (Claim 2)

[2217] The system according to claim 1, wherein in generating a first response, a response template is automatically generated based on the request content using a generative AI model.

[2218] (Claim 3)

[2219] 2. The system according to claim 1, wherein when a reminder notification is sent, the notification is received and displayed on the terminal of the person in charge.

[2220] "Application Example 1"

[2221] (Claim 1)

[2222] means for receiving the request and recording the request in a database;

[2223] means for automatically generating and transmitting a first response to the request;

[2224] A way to search for pending requests and send reminders to the person in charge,

[2225] A means for sending a reminder notification if no response is received within a certain period of time;

[2226] a means for confirming that the request has been completed and sending a completion notification to the requester;

[2227] a means for inputting security checklist items;

[2228] A means to track completion of each checklist item;

[2229] A system including:

[2230] (Claim 2)

[2231] 10. The system of claim 1, further comprising means for selecting an appropriate reply template based on the request content in generating the initial reply.

[2232] (Claim 3)

[2233] 2. The system according to claim 1, further comprising means for receiving and displaying a notification on a terminal of the person in charge when the reminder notification is sent.

[2234] "Example 2: Combining Emotion Engines"

[2235] (Claim 1)

[2236] means for receiving the request and recording the request in a database;

[2237] means for analyzing the requester's emotions using an emotion engine and generating and sending a first response based on the results of the analysis;

[2238] A means to search for open requests, generate reminders, and send them to the appropriate person;

[2239] When a reminder is sent, the emotion engine will analyze the client's emotions again and add an appropriate follow-up message.

[2240] A means for sending a reminder notification if no response is received within a certain period of time;

[2241] a means for confirming that the request has been completed and sending a completion notification to the requester;

[2242] A system including:

[2243] (Claim 2)

[2244] 10. The system of claim 1, further comprising means for generating an appropriate response or follow-up message using a generative AI model in generating the initial response or reminder.

[2245] (Claim 3)

[2246] 2. The system according to claim 1, wherein when a reminder notification is sent, the notification is received and displayed on the terminal of the person in charge.

[2247] "Application example 2 when combining emotion engines"

[2248] (Claim 1)

[2249] means for receiving the request and recording the request in a database;

[2250] means for automatically generating and transmitting a first response to the request;

[2251] A way to search for pending requests and send reminders to the person in charge,

[2252] A means for sending a reminder notification if no response is received within a certain period of time;

[2253] a means for confirming that the request has been completed and sending a completion notification to the requester;

[2254] A means for analyzing the client's emotions and optimizing the content of replies and reminders;

[2255] A system including:

[2256] (Claim 2)

[2257] The system according to claim 1, wherein an appropriate reply template is selected based on the request content and the requester's feelings.

[2258] (Claim 3)

[2259] The system according to claim 1, wherein when a reminder notification is sent, the notification is received on the terminal of the person in charge and a message according to the client's emotions is displayed. [Explanation of symbols]

[2260] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving the request and recording the request in a database; means for automatically generating and transmitting a first response to the request; A way to search for pending requests and send reminders to the person in charge, A means for sending a reminder notification if no response is received within a certain period of time; a means for confirming that the request has been completed and sending a completion notification to the requester; A system including:

2. 2. The system of claim 1, wherein an appropriate reply template is selected based on the content of the request in generating the primary reply.

3. 2. The system according to claim 1, wherein when a reminder notice is sent, the notice is received and displayed on a terminal of the person in charge.

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