system

Abstract

We provide the system. [Solution] A means for obtaining the listed data, A method for analyzing acquired data and extracting cases whose occurrence date falls within one month, A means of generating reminders for the extracted cases and saving data including the draft text, A means to automatically register the generated reminder content to the user's calendar, A system that includes means for notifying users.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is important for sales staff to respond promptly and without omission to the occurrence of unreturned damages in maintaining the company's credit and improving customer satisfaction. However, manual response is highly likely to result in omissions and delays, and also places a heavy burden on sales staff. As a result, appropriate responses may not be taken before the deadline, and troubles may occur with customers. To solve such problems, a system that automatically generates reminders based on listed data, registers them in the sales staff's calendar, and notifies them is necessary.

Means for Solving the Problems

[0005] This invention provides a system that includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving the data including the text, means for automatically registering the content of the generated reminders to the user's calendar, and means for notifying the user. This system enables sales representatives to quickly and without fail to address any outstanding unpaid damages. Furthermore, by including means for automatically reflecting detailed case information in the text when generating reminders, the system makes it easy to understand the specific response methods. In addition, by generating a reminder one month before the occurrence date of unpaid damages and automatically registering it to the calendar, the system allows for ample time to respond in advance.

[0006] "Listed data" refers to a collection of information that is organized and categorized according to specific rules and presented in the form of a list.

[0007] "Means of acquisition" refers to the methods and processes for retrieving listed data from databases or other sources and incorporating it into a system.

[0008] "Analysis" is the process of examining acquired data in detail and finding important information and patterns contained within it.

[0009] A "case" refers to a specific event or transaction related to unrefunded damages, which is monitored and managed by the system.

[0010] A "reminder" is a notification or note generated to draw attention to a specific date, time, or event.

[0011] A "draft" is the text displayed as the content of a reminder, and it includes relevant information and instructions.

[0012] "Means of preservation" refers to the methods and processes for recording generated reminders and drafts and storing them so that they can be retrieved when needed.

[0013] "User" refers to the person responsible for managing and handling unrefunded damages using this system.

[0014] "Method for automatically registering to a calendar" refers to a method and process for automatically registering the content of a reminder as an event in the user's electronic calendar service.

[0015] "Means of notification" refers to the methods and processes for informing users of the content of reminders and other important information. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] Displays an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0019] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention relates to a system for automating the response of sales representatives to the occurrence of unrefunded damages. This system has the function of acquiring listed data, generating reminders for cases with an approaching occurrence date, automatically registering them in the user's calendar, and further notifying the user.

[0038] System Overview

[0039] The system of this invention consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unreturned damages.

[0040] Program processing flow and operation

[0041] Data collection

[0042] The server connects to a database containing a list of items and retrieves information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[0043] Create a reminder

[0044] The server generates a reminder one month before the event occurs. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. For example, if unpaid damages are incurred with a specific company A, the server will generate a reminder stating, "Contact company A regarding unpaid damages."

[0045] Calendar registration

[0046] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google® Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0047] notification

[0048] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[0049] Specific example

[0050] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The content of the reminder includes specific wording such as "Notification regarding unrefunded damages for company A." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification saying, "Please check the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[0051] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[0052] The following describes the processing flow.

[0053] Step 1:

[0054] The server connects to the database and retrieves data on unpaid damages that have been listed. Here, the server uses an SQL query to retrieve data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0055] Step 2:

[0056] The server analyzes the acquired data and filters it to extract cases whose occurrence date falls within one month from the current date. The filtering process compares the current date with the occurrence date of each case and lists the relevant cases.

[0057] Step 3:

[0058] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[0059] Step 4:

[0060] The server saves the generated reminders in a data format such as JSON. This saving is done so that the RPA can retrieve and process the reminders in the next step.

[0061] Step 5:

[0062] The server starts the RPA and begins the calendar registration process. At this point, the server provides authentication information for the RPA to access calendar service APIs (for example, Google Calendar API or Microsoft® Graph API).

[0063] Step 6:

[0064] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[0065] Step 7:

[0066] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[0067] Step 8:

[0068] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[0069] Step 9:

[0070] The server sends a notification to the user via the mail server. The user is informed of the reminder content via email or a notification system.

[0071] Step 10:

[0072] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[0073] (Example 1)

[0074] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0075] The procedures and management associated with unrefunded damages are a significant burden for sales representatives. Delays in addressing cases, especially those occurring soon, can damage a company's reputation. Therefore, a system that allows sales representatives to handle these matters quickly and accurately is needed. Traditional manual processes are prone to errors and omissions, making efficiency improvements a challenge.

[0076] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0077] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving the data, including the text, using a generation AI model, means for automatically registering the content of the generated reminders in the user's calendar, and means for notifying the user. This enables sales representatives to respond quickly and efficiently to unrefunded damages.

[0078] "Listed data" refers to a collection of information that is organized in a specified format and managed sequentially as a series of items.

[0079] A "server" refers to a computer system that manages and processes data on a network.

[0080] A "database" refers to an information system designed to efficiently search, manage, and store large amounts of data.

[0081] "Analysis" refers to the process of breaking down data and understanding its meaning and structure.

[0082] A "reminder" refers to a message or alert that notifies the user about a specific time or event, reminding them of an important matter.

[0083] A "generative AI model" refers to a technology that uses artificial intelligence algorithms to automatically generate new data and information.

[0084] "Document draft" refers to a preliminary version or draft of a text created for a specific purpose.

[0085] A "user" refers to an individual or organization that uses a system and receives its functions and services.

[0086] A "schedule" refers to a tool or application used for schedule management, which records a user's plans and appointments.

[0087] "Notification" refers to sending messages or alerts to inform users of information.

[0088] This invention relates to a system for automating the response of sales representatives to the occurrence of unpaid damages. The system consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, which receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unpaid damages.

[0089] Data collection

[0090] The server connects to a database containing a list of items and uses SQL queries to retrieve information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[0091] Create a reminder

[0092] The server generates a reminder one month before the event occurs. A generation AI model is used to generate the message text. For example, the reminder text might include "Company A, please confirm the details regarding the unrefunded damages." The reminder content includes details such as the name of the company, the message text, and specific actions to take.

[0093] Calendar registration

[0094] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0095] notification

[0096] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[0097] Specific example

[0098] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes specific wording such as, "Company A, please check the information regarding the unrefunded damages." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification stating, "Please check the information regarding the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[0099] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[0100] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0101] Step 1: Connect to the database

[0102] The server connects to a listed database. It takes database connection information (host, port, username, password) as input and outputs a database connection object. Specifically, it connects to the database using a database driver (e.g., MySQL® Connector).

[0103] Step 2: Data Acquisition

[0104] The server retrieves information about unreturned damage fees. It uses an SQL query (e.g., SELECT FROM unreturned_damage_fees) as input and obtains records of unreturned damage fees as output. Specifically, it executes the query using a database connection object and retrieves the result set.

[0105] Step 3: Extracting cases with recent occurrence dates

[0106] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month. Using the acquired records of unrefunded damages as input, a filtered list of cases is obtained as output. Specifically, the current date is compared with the occurrence date, and cases that meet the criteria are extracted.

[0107] Step 4: Generate a reminder

[0108] The server generates reminders for the extracted cases. It uses a filtered list of cases as input and obtains a reminder object as output. Specifically, it uses a generation AI model to automatically generate a draft of the reminder text and saves that content to the reminder object.

[0109] Step 5: Register on calendar

[0110] This system automatically registers reminders generated by the server to the user's calendar. It uses a reminder object and the user's calendar authentication information as input, and outputs the result of registering the calendar event. Specifically, it uses the Google Calendar API to create an event and registers the reminder content to the calendar.

[0111] Step 6: Send Notification

[0112] The server notifies the user that the reminder registration was successful. It uses the calendar event registration result as input and obtains the notification sending result as output. Specifically, it uses the email API or in-app notification system to send a notification such as, "Please check the unrefunded damages from Company A."

[0113] This allows users to receive notifications and take appropriate action.

[0114] (Application Example 1)

[0115] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0116] In factory parts management, dealing with parts that are not returned by the deadline is an extremely time-consuming task. Furthermore, missed return deadlines lead to lost opportunities and procedural delays, which are also problematic. Therefore, there is a need for a system that streamlines the management of unreturned parts and reduces the workload of the person in charge of verification.

[0117] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0118] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders to the user's calendar, means for notifying the user, means for acquiring parts management data from the factory and monitoring the return deadlines for unreturned parts, and means for automatically generating reminders for unreturned parts and notifying the person in charge of the relevant parts. This makes it possible to accurately grasp the return deadlines for unreturned parts, automate notifications to the person in charge, and improve the efficiency of parts management.

[0119] "Listed data" refers to a collection of information that is organized and presented in a list format.

[0120] "Means of acquiring data" refers to methods and devices for collecting necessary information from databases or external sources.

[0121] "Means of analysis" refers to methods or devices that analyze acquired data and perform evaluations or classifications based on specific conditions.

[0122] "Means of generating reminders" refers to methods or devices that create notifications or alerts based on certain conditions.

[0123] "Draft" refers to pre-prepared text or content intended for inclusion in notices or reports.

[0124] "Methods for automatically registering to a calendar" refers to methods or devices that automatically add generated events and reminders to the user's calendar system.

[0125] "Means of notification" refers to methods or devices that inform users of events or alerts.

[0126] "Parts management data" refers to a collection of information regarding the inventory, return deadlines, and usage status of parts used within a factory.

[0127] "Means of monitoring return deadlines" refers to methods or devices for checking whether the return date for parts is approaching and taking appropriate action for parts that have passed their deadline.

[0128] "Unreturned parts" refers to factory parts that have not yet been returned by the return deadline.

[0129] "Means of notifying the administrator" refers to methods or devices for conveying necessary information to the person responsible for managing the parts.

[0130] This invention relates to a non-returned parts management system, aiming to automate the management of returned parts within a factory and enable efficient operation. Specifically, it is implemented through the following procedure and configuration.

[0131] Basic System Configuration

[0132] The server is responsible for the main processing of retrieving, analyzing, and generating reminders for the listed data. The terminal is the device used by the user, receiving calendar entries and notifications. The user is the person in charge of parts management.

[0133] Hardware and software to use

[0134] Server: The central device that retrieves necessary information from the database, performs analysis, and generates reminders.

[0135] Terminal: A computing device used by a user, such as a personal computer or smartphone.

[0136] Google Calendar API: An external API for automatically adding reminders to your calendar.

[0137] Notification services: Email sending services and in-app notification functions (e.g., SMTP servers and push notification servers).

[0138] Data processing and calculation procedures

[0139] 1. Data collection: The server connects to the database and retrieves information about unreturned parts.

[0140] 2. Data Analysis: Analyze the acquired information and extract parts whose return deadline is within one month.

[0141] 3. Create Reminders: Generate reminders for the extracted parts. The reminders will include details such as the part ID, return deadline, and how to contact the administrator.

[0142] 4. Calendar Registration: The generated reminders are automatically registered to the user's calendar using the Google Calendar API.

[0143] 5. Notification: Notify the user that the reminder has been successfully registered. Notifications will be sent via email or an in-app notification system.

[0144] Specific example

[0145] As a concrete example, consider a case where the return deadline for part ID "1234" is November 25, 2023. In this case, the server extracts this part information on October 25, 2023, and generates a reminder titled "Confirmation of return of part ID 1234". Next, it uses the Google Calendar API to automatically register this reminder in the user's calendar. Furthermore, the server sends a notification to the administrator stating, "The return deadline for part ID 1234 is approaching. Please contact your administrator." The administrator receives the notification, checks their calendar, and can take appropriate action.

[0146] Example of a prompt

[0147] The following are specific examples of prompt statements for a generative AI model:

[0148] Processing for the unreturned parts management system: If the return deadline for part ID "1234" is November 25, 2023, please write the code for a program that adds an event to the calendar on October 26, 2023, titled "Return Confirmation for Unreturned Part 1234," and notifies the administrator (manager@factory.com).

[0149] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0150] Step 1:

[0151] Data acquisition

[0152] The server connects to the database and retrieves information on unreturned parts. The input is all part information in the database, and the output is data for each part, including its return deadline. The server uses SQL queries to perform the specific actions required to extract the necessary part information.

[0153] Step 2:

[0154] Data analysis

[0155] The server analyzes the acquired parts information and extracts parts whose return deadline is within 30 days. The input is the acquired parts information, and the output is a list of parts whose return deadline is approaching within 30 days. The server then performs the specific processing of comparing the return deadlines of each part and picking out those that meet the criteria.

[0156] Step 3:

[0157] Reminder generation

[0158] The server generates reminders for the extracted parts. The input is a list of extracted parts, and the output is detailed reminder information (part ID, return deadline, and how to contact the administrator). The server performs the specific operations to assemble the content of the corresponding reminder for each extracted part.

[0159] Step 4:

[0160] Add to calendar

[0161] The server automatically registers reminders generated using the Google Calendar API to the user's calendar. The input is the details of the generated reminder, and the output is the event added to the user's calendar. The server calls the Google Calendar API to perform the specific execution process that reflects the reminder content in the calendar.

[0162] Step 5:

[0163] Sending notifications

[0164] The server notifies the user that the reminder has been successfully registered. The inputs for this process are the reminder details and the recipient information, and the output is the notification sent to the user. The server then performs specific notification processing to inform the administrator that the reminder has been added, using email or push notifications.

[0165] Step 6:

[0166] User verification

[0167] The user receives a notification, checks their calendar, and takes appropriate action. The input is the notification from the server, and the output is the user's confirmation and response. The user reads the notification, checks their calendar, and takes specific actions such as contacting or responding as needed.

[0168] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0169] This invention combines a system that automates the response of sales representatives to unpaid damages with an emotion engine. This system acquires listed data, generates reminders for cases with approaching occurrence dates, automatically registers them in the user's calendar, and also has the function of notifying the user. Furthermore, the emotion engine analyzes the user's emotional state and adjusts the content and timing of reminders based on that analysis.

[0170] System Overview

[0171] The system of this invention consists of a server, a terminal, a user, and an emotion engine. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The emotion engine monitors the user's emotions in real time and adjusts the content and timing of reminders based on that information. The user is a sales representative and is responsible for handling cases of unpaid damages.

[0172] Program processing flow and operation

[0173] Data collection

[0174] The server connects to the database and retrieves the data on unpaid damages that have been listed. The server then uses an SQL query to extract data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0175] Create a reminder

[0176] The server generates a reminder one month before the expected date of the incident. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the reminder text accordingly. For example, if the user is feeling stressed, a more concise and intuitive message will be generated.

[0177] Calendar registration

[0178] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0179] notification

[0180] The server notifies the user that the reminder has been successfully registered. This notification is sent via email or the in-app notification system. The emotion engine monitors the user's emotional state in real time to optimize notification timing. For example, it might send notifications during times when the user is less busy. This makes it easier for the user to receive notifications and reduces the risk of delayed responses.

[0181] Specific example

[0182] As a concrete example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes a specific message titled "Notification regarding unrefunded damages for company A." Furthermore, the emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the usual detailed message; if the user is busy, it generates a concise message. Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Finally, the server sends the user a notification saying, "Please check regarding unrefunded damages for company A." This notification is sent at the time when the user is most likely to respond, based on the emotion engine's analysis. The user receives the notification, checks their calendar, and takes the necessary action.

[0183] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently responding to the occurrence of unrefunded damages, and further supports them in responding at the optimal timing and in the optimal manner by taking into account the emotional state of the user.

[0184] The following describes the processing flow.

[0185] Step 1:

[0186] The server connects to the database and retrieves data on unpaid damages that have been listed. The server executes an SQL query to retrieve data including the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0187] Step 2:

[0188] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month from the current date. The server compares the current date with the occurrence date of each case and filters out the relevant cases.

[0189] Step 3:

[0190] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[0191] Step 4:

[0192] The emotion engine analyzes the user's emotions. The user sends data obtained through emotion sensors and input devices on their sales terminal to the emotion engine. The emotion engine analyzes this data to determine the user's stress level and mood.

[0193] Step 5:

[0194] The server adjusts the reminders it generates based on the analysis results of the emotion engine. For example, if the emotion engine determines that the user is stressed, the server changes the wording of the reminder to be more concise and easier to understand.

[0195] Step 6:

[0196] The server saves the adjusted reminders in a data format such as JSON. The saved reminders are then used by the RPA to retrieve and process them in the next step.

[0197] Step 7:

[0198] The server starts the RPA and begins the calendar registration process. At this point, the server provides the RPA with authentication credentials to access the calendar service API (for example, Google Calendar API or Microsoft Graph API).

[0199] Step 8:

[0200] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[0201] Step 9:

[0202] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[0203] Step 10:

[0204] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[0205] Step 11:

[0206] The emotion engine monitors the user's emotional state in real time and optimizes the timing of notifications. For example, it might choose to send notifications during times when the user is less busy.

[0207] Step 12:

[0208] The server sends a notification to the user via the mail server. The user is informed of the reminder content using email or a notification system.

[0209] Step 13:

[0210] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[0211] (Example 2)

[0212] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0213] In existing systems, sales representatives handle unrefunded damages manually, which can lead to delays and ultimately reduce operational efficiency. Furthermore, the system fails to consider the emotional state of sales representatives, increasing stress and burden, and making it difficult to provide optimal responses. These problems require automated and personalized responses.

[0214] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0215] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within a certain period, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders in the user's calendar, means for notifying the user, means for analyzing the user's emotional state, and means for adjusting the text and sending timing of the reminders based on the analysis results. This enables an automated and efficient response to the occurrence of unreturned damages, and further realizes notifications with optimal timing and text that take into account the user's emotional state.

[0216] "Listed data" refers to a collection of information that has been organized based on specific criteria and stored in a format that allows it to be displayed as a list.

[0217] "Means of acquisition" refers to the functions or methods by which a server or related device retrieves necessary information from a database or external system.

[0218] "Methods of analysis" refer to the process of evaluating acquired data and extracting necessary information based on specific conditions or patterns.

[0219] "Events that will occur within a certain period" refers to events or tasks that are scheduled to occur within a specific period (e.g., within one month).

[0220] A "reminder" is a message or alert that notifies a user of an action or response that should be taken based on a specific date, time, or condition.

[0221] A "draft" refers to the specific text or content created to be presented to users, such as in reminders.

[0222] A "schedule" refers to a calendar or schedule management system that a user uses on a daily basis.

[0223] "Means of notification" refer to functions and methods for communicating reminders and important information to users.

[0224] "Methods for analyzing emotional states" refer to technologies that evaluate and analyze emotions and mental states based on user behavior, usage data, voice, etc.

[0225] "Means for adjusting the wording and sending timing of reminders based on analysis results" refers to a function that optimizes the content and timing of reminders according to the results of sentiment analysis.

[0226] This invention combines a system that automates sales representatives' responses to unpaid damages with an emotion engine. This system consists of a server, terminals, users, and an emotion engine, and each element works in coordination to achieve efficient business support.

[0227] Hardware and software to be used

[0228] Server: Performs various processes such as database connection, data analysis, reminder generation, calendar registration, and notification sending. Specific examples include web server software such as Apache® and Nginx, and database management using MySQL or PostgreSQL.

[0229] Terminal: A device used by a user, such as a PC or smartphone, that can run applications for receiving notifications and displaying calendars.

[0230] Emotion Engine: Dedicated software for analyzing a user's emotional state. Emotion analysis uses an emotion recognition API (e.g., Microsoft Azure's Emotion API).

[0231] Program processing

[0232] Data acquisition

[0233] The server connects to the database and retrieves data on unpaid damages. The database contains a table called "Unpaid Damages," from which the date the damage occurred, the name of the company involved, and terminal information are retrieved. This is done using an SQL query, for example, in the format "SELECT FROM Unpaid Damages WHERE Date >= CURRENT_DATE".

[0234] Reminder generation

[0235] The server generates a reminder one month before the expected date of the incident. The reminder includes the name of the company involved, a draft message, and specific instructions on how to respond. The emotion engine analyzes the user's emotional state and adjusts the message accordingly. For example, if the user is feeling stressed, the server generates a more concise message based on the emotion engine's analysis data.

[0236] Calendar registration

[0237] The server automatically registers the generated reminder to the user's calendar using the Google Calendar API. It accesses the API using an authentication token and writes a new event. Specifically, it sends JSON data containing the reminder details to the API to add the event to the calendar.

[0238] Send notification

[0239] The server notifies the user that the reminder registration was successful. The notification system uses email sending via an SMTP server or a push notification server. The emotion engine monitors the user's emotional state in real time and sends notifications at the optimal time.

[0240] Specific example

[0241] For example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server retrieves this case from the database and generates a reminder. The reminder includes the phrase "Notification regarding unrefunded damages to company A" and specific wording. The emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the detailed wording.

[0242] Next, the server uses the Google Calendar API to automatically register reminders in the user's calendar. This can be achieved, for example, by inputting the following prompt into the AI ​​model:

[0243] For a case involving Company A, where the date of accrual of unrefunded damages is November 30, 2023, please describe the process of creating a reminder one month prior to the accrual date (October 30, 2023), registering it in Google Calendar, and sending a notification. Include adjustments to the reminder text based on the user's emotional state using an emotion engine, and optimization of the notification sending timing.

[0244] Furthermore, the server sends a notification to the user stating, "Please check the outstanding damages from Company A." This notification is sent at the optimal time based on the analysis results of the emotion engine. Upon receiving the notification, the user can check their calendar and take the necessary action quickly.

[0245] In this way, this system automates the sales representative's response to unpaid damages, providing efficient business support and enabling optimal responses tailored to the user's emotional state.

[0246] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0247] Step 1: Data Collection

[0248] The server connects to the database and retrieves data on unpaid damages. Specifically, the server loads a driver to connect to the database. Next, the server executes an SQL query containing the necessary information (date the unpaid damages occurred, name of the company involved, terminal information, etc.).

[0249] Input: The server enters the database connection information and queries.

[0250] Data processing: Execute SQL queries, format the results, and create a list.

[0251] Output: Data on unrefunded damages is returned to the server in a list format.

[0252] Specific operation: The server executes the query "SELECT Occurrence Date, Company Name, Terminal Information FROM Unreturned Damages WHERE Occurrence Date >= CURRENT_DATE".

[0253] Step 2: Case Extraction

[0254] The server analyzes the acquired data and extracts cases whose occurrence date falls within a certain period.

[0255] Input: Enter the unreturned damage data listed on the server.

[0256] Data processing: Filter cases to include those that occurred within one month of the current date.

[0257] Output: A list of cases whose occurrence date falls within the specified period will be retrieved.

[0258] Specific operation: The server applies a filter condition (occurrence date <= current date + 30 days) and extracts the relevant cases.

[0259] Step 3: Generate a reminder

[0260] The server generates reminders for the extracted cases. The reminders include the name of the company in question and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the text accordingly.

[0261] Input: The extracted list of cases and user sentiment data are entered.

[0262] Data processing: Generate a basic reminder text based on case data, and then adjust the text to reflect the results of the emotion engine's analysis.

[0263] Output: A customized reminder is generated.

[0264] Specific operation: The server calls the emotion engine API and modifies the reminder content based on the user's emotional state. For example, if the user is feeling stressed, the message might read, "Notification of unrefunded damages to Company A. Please check the next step for specific details."

[0265] Step 4: Register on calendar

[0266] The server automatically registers the generated reminders to the user's calendar.

[0267] Input: The generated reminder and the Google Calendar API authentication token will be entered.

[0268] Data processing: Convert the reminder content into a format suitable for sending to the Google Calendar API.

[0269] Output: A new event is added to the calendar.

[0270] Specific operation: The server connects to the Google Calendar API, sends event data including reminder details, and registers it in the user's calendar.

[0271] Step 5: Send Notification

[0272] The server notifies the user that the reminder has been successfully registered.

[0273] Input: Calendar registration results and notification messages are entered.

[0274] Data processing: Optimize the timing of notification sending based on the analysis results of the emotion engine.

[0275] Output: A notification is sent to the user at the appropriate time.

[0276] Specific operation: The server generates a notification message stating, "Please check the outstanding damages for Company A," and sends it via email through the SMTP server, or sends a notification to the user's device using a push notification system.

[0277] These steps automate the sales representative's response to unreturned damages and enable the system to provide the most appropriate response based on the user's emotional state.

[0278] (Application Example 2)

[0279] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0280] In traditional systems, setting reminders and notifications for deadlines for specific projects or tasks is often done manually, making efficient management difficult. Furthermore, it's impossible to respond at the optimal time considering the user's emotional state, posing challenges to improving operational efficiency and customer satisfaction. Especially in retail settings, quick and accurate action is required for inventory management and customer service, but there is a lack of appropriate systems to support this.

[0281] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence dates will arrive within one month, means for generating a reminder for the extracted cases and storing data including text, means for automatically registering the content of the generated reminder in the user's calendar, means for notifying the user, means for analyzing the user's emotional state, means for adjusting the content and transmission timing of the reminder and notification based on the analysis result, means for generating a reminder when the inventory is low or the return deadline is approaching in store inventory management and customer response, means for adjusting the generation timing of the reminder according to the user's emotional state, means for registering the reminder content in the user's calendar, and means for sending a notification to the user. Thereby, it becomes possible to improve work efficiency and provide an optimal response considering the user's emotional state.

[0282] The "listed data" is a collection of information that has been pre-organized and stored in a list format.

[0283] The "means for acquiring data" refers to a method or device for collecting necessary information from a database or an external source.

[0284] The "means for analyzing data" refers to a method or device for analyzing the collected data and extracting useful information.

[0285] A "case" refers to an event or information that is the object of a specific business or process.

[0286] A "reminder" refers to a message or notification for唤起 attention about a specific event or date and time.

[0287] The "text" refers to the design and content of the text included in the reminder or notification.

[0288] A "calendar" refers to a tool or application that users use to manage their schedules and reminders.

[0289] "Means of notification" refers to methods or devices for sending messages or alerts to users.

[0290] "Means for analyzing a user's emotional state" refers to methods or devices for analyzing a user's current emotions and psychological state.

[0291] "Means of adjustment based on analysis results" refers to methods or devices for optimizing the content and timing of reminders and notifications based on the obtained analysis results.

[0292] "Inventory management" refers to activities aimed at monitoring the quantity and condition of goods in stores and warehouses and maintaining optimal levels.

[0293] "Customer service" refers to providing appropriate services and support in response to customer inquiries and requests.

[0294] "Return deadline" refers to the date and time when borrowed items should be returned.

[0295] A system for carrying out this invention is constructed using the following main components and software.

[0296] Hardware and software

[0297] hardware

[0298] 1. Server: Plays a central role in collecting, analyzing, and storing data, and generating reminders.

[0299] 2. Terminal: The device used by the user. This may be a smartphone, tablet, desktop PC, or interactive robot (e.g., Pepper).

[0300] Software

[0301] 1. SQL database (e.g., MySQL): Used to store the listed data and reminder information.

[0302] 2. Google Calendar API: Used to automatically register reminders to the user's calendar.

[0303] 3. Apache Emotion API: Used to analyze the user's emotional state and adjust the appropriate notification timing and reminder content.

[0304] 4. Firebase Cloud Messaging: A service for sending notifications to users.

[0305] System Operation

[0306] Data Collection

[0307] The server connects to the SQL database regularly to obtain the listed data. For example, it collects inventory information and product return deadline data.

[0308] Data Analysis

[0309] The server analyzes the obtained data and extracts cases that meet specific conditions (for example, the inventory is below a certain level or the return deadline is approaching).

[0310] Reminder Generation

[0311] Generate reminders for the extracted cases. First, analyze the user's emotional state using the Apache Emotion API and adjust the reminder content based on the results. For example, if the user is judged to be busy, simplify the notification content.

[0312] Calendar Registration

[0313] The generated reminders are automatically added to the user's calendar using the Google Calendar API. This process assumes that authentication information has been provided in advance.

[0314] Sending notifications

[0315] Notifications are sent to users at the optimal time via Firebase Cloud Messaging. This timing is adjusted based on the user's emotional state, which is monitored in real time.

[0316] Specific example

[0317] For example, if a physical store has low stock of product A, the server retrieves this information and generates a reminder. This reminder will contain detailed instructions if the user is not stressed, and a concise message if they are stressed. Next, the reminder is automatically added to the user's calendar using the Google Calendar API as "Inventory Management Alert: Product A". Then, Firebase Cloud Messaging sends a notification to the user saying, "Product A is running low on stock, please take immediate action."

[0318] Example of a prompt

[0319] "We are running low on stock of product A; please take immediate action."

[0320] "Customer B's return deadline is approaching. Please take action."

[0321] This allows physical stores to manage inventory and customer service efficiently and accurately. Furthermore, by considering emotional states, the workload can be reduced.

[0322] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0323] Step 1:

[0324] The server periodically connects to the SQL database to retrieve listed inventory information and product return deadline data. Specifically, the server sends SQL queries to fetch records containing inventory levels and return deadlines. This allows the server to obtain information about the current inventory status and return deadlines. The input is the SQL database, and the output is the fetched data.

[0325] Step 2:

[0326] The server analyzes the retrieved data and extracts items whose inventory levels are below a specified threshold or whose return deadline is within one week. Using data retrieved from the database, it filters items with low inventory levels or approaching return deadlines. The input is the fetched data, and the output is a list of extracted items.

[0327] Step 3:

[0328] The server generates reminders for the extracted cases. During this process, it uses the Apache Emotion API to analyze the user's emotional state and adjusts the reminder content based on the analysis results. For example, if the user is stressed, the notification content will be simplified. The input is a list of extracted cases and the results of the emotional analysis; the output is a list of the adjusted reminders.

[0329] Step 4:

[0330] The server automatically adds the generated reminders to the user's calendar using the Google Calendar API. The reminders are added to the calendar in the appropriate format, including the event title and description. The input is a list of formatted reminders, and the output is the event added to the user's calendar.

[0331] Step 5:

[0332] The server uses Firebase Cloud Messaging to send notifications to the user at the optimal time. This timing is adjusted based on the user's emotional state, which is monitored in real time. Specifically, it selects times when the user is not busy or stressed to send notifications. The inputs are events registered in the user's calendar and the results of the emotional analysis, and the output is the notification sent to the user.

[0333] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0334] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0335] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0336] [Second Embodiment]

[0337] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0338] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0339] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0340] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0341] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0342] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0343] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0344] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0345] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0346] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0347] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0348] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0349] This invention relates to a system for automating the response of sales representatives to the occurrence of unrefunded damages. This system has the function of acquiring listed data, generating reminders for cases with an approaching occurrence date, automatically registering them in the user's calendar, and further notifying the user.

[0350] System Overview

[0351] The system of this invention consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unreturned damages.

[0352] Program processing flow and operation

[0353] Data collection

[0354] The server connects to a database containing a list of items and retrieves information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[0355] Create a reminder

[0356] The server generates a reminder one month before the event occurs. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. For example, if unpaid damages are incurred with a specific company A, the server will generate a reminder stating, "Contact company A regarding unpaid damages."

[0357] Calendar registration

[0358] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0359] notification

[0360] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[0361] Specific example

[0362] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The content of the reminder includes specific wording such as "Notification regarding unrefunded damages for company A." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification saying, "Please check the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[0363] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[0364] The following describes the processing flow.

[0365] Step 1:

[0366] The server connects to the database and retrieves data on unpaid damages that have been listed. Here, the server uses an SQL query to retrieve data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0367] Step 2:

[0368] The server analyzes the acquired data and filters it to extract cases whose occurrence date falls within one month from the current date. The filtering process compares the current date with the occurrence date of each case and lists the relevant cases.

[0369] Step 3:

[0370] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[0371] Step 4:

[0372] The server saves the generated reminders in a data format such as JSON. This saving is done so that the RPA can retrieve and process the reminders in the next step.

[0373] Step 5:

[0374] The server starts the RPA and begins the calendar registration process. At this point, the server provides the RPA with authentication credentials to access the calendar service API (for example, Google Calendar API or Microsoft Graph API).

[0375] Step 6:

[0376] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[0377] Step 7:

[0378] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[0379] Step 8:

[0380] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[0381] Step 9:

[0382] The server sends a notification to the user via the mail server. The user is informed of the reminder content via email or a notification system.

[0383] Step 10:

[0384] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[0385] (Example 1)

[0386] Next, we will describe Example 1. 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."

[0387] The procedures and management associated with unrefunded damages are a significant burden for sales representatives. Delays in addressing cases, especially those occurring soon, can damage a company's reputation. Therefore, a system that allows sales representatives to handle these matters quickly and accurately is needed. Traditional manual processes are prone to errors and omissions, making efficiency improvements a challenge.

[0388] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0389] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving the data, including the text, using a generation AI model, means for automatically registering the content of the generated reminders in the user's calendar, and means for notifying the user. This enables sales representatives to respond quickly and efficiently to unrefunded damages.

[0390] "Listed data" refers to a collection of information that is organized in a specified format and managed sequentially as a series of items.

[0391] A "server" refers to a computer system that manages and processes data on a network.

[0392] A "database" refers to an information system designed to efficiently search, manage, and store large amounts of data.

[0393] "Analysis" refers to the process of breaking down data and understanding its meaning and structure.

[0394] A "reminder" refers to a message or alert that notifies the user about a specific time or event, reminding them of an important matter.

[0395] A "generative AI model" refers to a technology that uses artificial intelligence algorithms to automatically generate new data and information.

[0396] "Document draft" refers to a preliminary version or draft of a text created for a specific purpose.

[0397] A "user" refers to an individual or organization that uses a system and receives its functions and services.

[0398] A "schedule" refers to a tool or application used for schedule management, which records a user's plans and appointments.

[0399] "Notification" refers to sending messages or alerts to inform users of information.

[0400] This invention relates to a system for automating the response of sales representatives to the occurrence of unpaid damages. The system consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, which receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unpaid damages.

[0401] Data collection

[0402] The server connects to a database containing a list of items and uses SQL queries to retrieve information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[0403] Create a reminder

[0404] The server generates a reminder one month before the event occurs. A generation AI model is used to generate the message text. For example, the reminder text might include "Company A, please confirm the details regarding the unrefunded damages." The reminder content includes details such as the name of the company, the message text, and specific actions to take.

[0405] Calendar registration

[0406] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0407] notification

[0408] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[0409] Specific example

[0410] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes specific wording such as, "Company A, please check the information regarding the unrefunded damages." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification stating, "Please check the information regarding the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[0411] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[0412] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0413] Step 1: Connect to the database

[0414] The server connects to a listed database. It takes database connection information (host, port, username, password) as input and outputs a database connection object. Specifically, it connects to the database using a database driver (e.g., MySQL Connector).

[0415] Step 2: Data Acquisition

[0416] The server retrieves information about unreturned damage fees. It uses an SQL query (e.g., SELECT FROM unreturned_damage_fees) as input and obtains records of unreturned damage fees as output. Specifically, it executes the query using a database connection object and retrieves the result set.

[0417] Step 3: Extracting cases with recent occurrence dates

[0418] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month. Using the acquired records of unrefunded damages as input, a filtered list of cases is obtained as output. Specifically, the current date is compared with the occurrence date, and cases that meet the criteria are extracted.

[0419] Step 4: Generate a reminder

[0420] The server generates reminders for the extracted cases. It uses a filtered list of cases as input and obtains a reminder object as output. Specifically, it uses a generation AI model to automatically generate a draft of the reminder text and saves that content to the reminder object.

[0421] Step 5: Register on calendar

[0422] This system automatically registers reminders generated by the server to the user's calendar. It uses a reminder object and the user's calendar authentication information as input, and outputs the result of registering the calendar event. Specifically, it uses the Google Calendar API to create an event and registers the reminder content to the calendar.

[0423] Step 6: Send Notification

[0424] The server notifies the user that the reminder registration was successful. It uses the calendar event registration result as input and obtains the notification sending result as output. Specifically, it uses the email API or in-app notification system to send a notification such as, "Please check the unrefunded damages from Company A."

[0425] This allows users to receive notifications and take appropriate action.

[0426] (Application Example 1)

[0427] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0428] In factory parts management, dealing with parts that are not returned by the deadline is an extremely time-consuming task. Furthermore, missed return deadlines lead to lost opportunities and procedural delays, which are also problematic. Therefore, there is a need for a system that streamlines the management of unreturned parts and reduces the workload of the person in charge of verification.

[0429] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0430] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders to the user's calendar, means for notifying the user, means for acquiring parts management data from the factory and monitoring the return deadlines for unreturned parts, and means for automatically generating reminders for unreturned parts and notifying the person in charge of the relevant parts. This makes it possible to accurately grasp the return deadlines for unreturned parts, automate notifications to the person in charge, and improve the efficiency of parts management.

[0431] "Listed data" refers to a collection of information that is organized and presented in a list format.

[0432] "Means of acquiring data" refers to methods and devices for collecting necessary information from databases or external sources.

[0433] "Means of analysis" refers to methods or devices that analyze acquired data and perform evaluations or classifications based on specific conditions.

[0434] "Means of generating reminders" refers to methods or devices that create notifications or alerts based on certain conditions.

[0435] "Draft" refers to pre-prepared text or content intended for inclusion in notices or reports.

[0436] "Methods for automatically registering to a calendar" refers to methods or devices that automatically add generated events and reminders to the user's calendar system.

[0437] "Means of notification" refers to methods or devices that inform users of events or alerts.

[0438] "Parts management data" refers to a collection of information regarding the inventory, return deadlines, and usage status of parts used within a factory.

[0439] "Means of monitoring return deadlines" refers to methods or devices for checking whether the return date for parts is approaching and taking appropriate action for parts that have passed their deadline.

[0440] "Unreturned parts" refers to factory parts that have not yet been returned by the return deadline.

[0441] "Means of notifying the administrator" refers to methods or devices for conveying necessary information to the person responsible for managing the parts.

[0442] This invention relates to a non-returned parts management system, aiming to automate the management of returned parts within a factory and enable efficient operation. Specifically, it is implemented through the following procedure and configuration.

[0443] Basic System Configuration

[0444] The server is responsible for the main processing of retrieving, analyzing, and generating reminders for the listed data. The terminal is the device used by the user, receiving calendar entries and notifications. The user is the person in charge of parts management.

[0445] Hardware and software to use

[0446] Server: The central device that retrieves necessary information from the database, performs analysis, and generates reminders.

[0447] Terminal: A computing device used by a user, such as a personal computer or smartphone.

[0448] Google Calendar API: An external API for automatically adding reminders to your calendar.

[0449] Notification services: Email sending services and in-app notification functions (e.g., SMTP servers and push notification servers).

[0450] Data processing and calculation procedures

[0451] 1. Data collection: The server connects to the database and retrieves information about unreturned parts.

[0452] 2. Data Analysis: Analyze the acquired information and extract parts whose return deadline is within one month.

[0453] 3. Create Reminders: Generate reminders for the extracted parts. The reminders will include details such as the part ID, return deadline, and how to contact the administrator.

[0454] 4. Calendar Registration: The generated reminders are automatically registered to the user's calendar using the Google Calendar API.

[0455] 5. Notification: Notify the user that the reminder has been successfully registered. Notifications will be sent via email or an in-app notification system.

[0456] Specific example

[0457] As a concrete example, consider a case where the return deadline for part ID "1234" is November 25, 2023. In this case, the server extracts this part information on October 25, 2023, and generates a reminder titled "Confirmation of return of part ID 1234". Next, it uses the Google Calendar API to automatically register this reminder in the user's calendar. Furthermore, the server sends a notification to the administrator stating, "The return deadline for part ID 1234 is approaching. Please contact your administrator." The administrator receives the notification, checks their calendar, and can take appropriate action.

[0458] Example of a prompt

[0459] The following are specific examples of prompt statements for a generative AI model:

[0460] Processing for the unreturned parts management system: If the return deadline for part ID "1234" is November 25, 2023, please write the code for a program that adds an event to the calendar on October 26, 2023, titled "Return Confirmation for Unreturned Part 1234," and notifies the administrator (manager@factory.com).

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] Data acquisition

[0464] The server connects to the database and retrieves information on unreturned parts. The input is all part information in the database, and the output is data for each part, including its return deadline. The server uses SQL queries to perform the specific actions required to extract the necessary part information.

[0465] Step 2:

[0466] Data analysis

[0467] The server analyzes the acquired parts information and extracts parts whose return deadline is within 30 days. The input is the acquired parts information, and the output is a list of parts whose return deadline is approaching within 30 days. The server then performs the specific processing of comparing the return deadlines of each part and picking out those that meet the criteria.

[0468] Step 3:

[0469] Reminder generation

[0470] The server generates reminders for the extracted parts. The input is a list of extracted parts, and the output is detailed reminder information (part ID, return deadline, and how to contact the administrator). The server performs the specific operations to assemble the content of the corresponding reminder for each extracted part.

[0471] Step 4:

[0472] Add to calendar

[0473] The server automatically registers reminders generated using the Google Calendar API to the user's calendar. The input is the details of the generated reminder, and the output is the event added to the user's calendar. The server calls the Google Calendar API to perform the specific execution process that reflects the reminder content in the calendar.

[0474] Step 5:

[0475] Sending notifications

[0476] The server notifies the user that the reminder has been successfully registered. The inputs for this process are the reminder details and the recipient information, and the output is the notification sent to the user. The server then performs specific notification processing to inform the administrator that the reminder has been added, using email or push notifications.

[0477] Step 6:

[0478] User verification

[0479] The user receives a notification, checks their calendar, and takes appropriate action. The input is the notification from the server, and the output is the user's confirmation and response. The user reads the notification, checks their calendar, and takes specific actions such as contacting or responding as needed.

[0480] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0481] This invention combines a system that automates the response of sales representatives to unpaid damages with an emotion engine. This system acquires listed data, generates reminders for cases with approaching occurrence dates, automatically registers them in the user's calendar, and also has the function of notifying the user. Furthermore, the emotion engine analyzes the user's emotional state and adjusts the content and timing of reminders based on that analysis.

[0482] System Overview

[0483] The system of this invention consists of a server, a terminal, a user, and an emotion engine. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The emotion engine monitors the user's emotions in real time and adjusts the content and timing of reminders based on that information. The user is a sales representative and is responsible for handling cases of unpaid damages.

[0484] Program processing flow and operation

[0485] Data collection

[0486] The server connects to the database and retrieves the data on unpaid damages that have been listed. The server then uses an SQL query to extract data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0487] Create a reminder

[0488] The server generates a reminder one month before the expected date of the incident. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the reminder text accordingly. For example, if the user is feeling stressed, a more concise and intuitive message will be generated.

[0489] Calendar registration

[0490] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0491] notification

[0492] The server notifies the user that the reminder has been successfully registered. This notification is sent via email or the in-app notification system. The emotion engine monitors the user's emotional state in real time to optimize notification timing. For example, it might send notifications during times when the user is less busy. This makes it easier for the user to receive notifications and reduces the risk of delayed responses.

[0493] Specific example

[0494] As a concrete example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes a specific message titled "Notification regarding unrefunded damages for company A." Furthermore, the emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the usual detailed message; if the user is busy, it generates a concise message. Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Finally, the server sends the user a notification saying, "Please check regarding unrefunded damages for company A." This notification is sent at the time when the user is most likely to respond, based on the emotion engine's analysis. The user receives the notification, checks their calendar, and takes the necessary action.

[0495] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently responding to the occurrence of unrefunded damages, and further supports them in responding at the optimal timing and in the optimal manner by taking into account the emotional state of the user.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The server connects to the database and retrieves data on unpaid damages that have been listed. The server executes an SQL query to retrieve data including the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0499] Step 2:

[0500] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month from the current date. The server compares the current date with the occurrence date of each case and filters out the relevant cases.

[0501] Step 3:

[0502] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[0503] Step 4:

[0504] The emotion engine analyzes the user's emotions. The user sends data obtained through emotion sensors and input devices on their sales terminal to the emotion engine. The emotion engine analyzes this data to determine the user's stress level and mood.

[0505] Step 5:

[0506] The server adjusts the reminders it generates based on the analysis results of the emotion engine. For example, if the emotion engine determines that the user is stressed, the server changes the wording of the reminder to be more concise and easier to understand.

[0507] Step 6:

[0508] The server saves the adjusted reminders in a data format such as JSON. The saved reminders are then used by the RPA to retrieve and process them in the next step.

[0509] Step 7:

[0510] The server starts the RPA and begins the calendar registration process. At this point, the server provides the RPA with authentication credentials to access the calendar service API (for example, Google Calendar API or Microsoft Graph API).

[0511] Step 8:

[0512] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[0513] Step 9:

[0514] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[0515] Step 10:

[0516] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[0517] Step 11:

[0518] The emotion engine monitors the user's emotional state in real time and optimizes the timing of notifications. For example, it might choose to send notifications during times when the user is less busy.

[0519] Step 12:

[0520] The server sends a notification to the user via the mail server. The user is informed of the reminder content using email or a notification system.

[0521] Step 13:

[0522] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[0523] (Example 2)

[0524] Next, we will describe Example 2. 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".

[0525] In existing systems, sales representatives handle unrefunded damages manually, which can lead to delays and ultimately reduce operational efficiency. Furthermore, the system fails to consider the emotional state of sales representatives, increasing stress and burden, and making it difficult to provide optimal responses. These problems require automated and personalized responses.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0527] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within a certain period, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders in the user's calendar, means for notifying the user, means for analyzing the user's emotional state, and means for adjusting the text and sending timing of the reminders based on the analysis results. This enables an automated and efficient response to the occurrence of unreturned damages, and further realizes notifications with optimal timing and text that take into account the user's emotional state.

[0528] "Listed data" refers to a collection of information that has been organized based on specific criteria and stored in a format that allows it to be displayed as a list.

[0529] "Means of acquisition" refers to the functions or methods by which a server or related device retrieves necessary information from a database or external system.

[0530] "Methods of analysis" refer to the process of evaluating acquired data and extracting necessary information based on specific conditions or patterns.

[0531] "Events that will occur within a certain period" refers to events or tasks that are scheduled to occur within a specific period (e.g., within one month).

[0532] A "reminder" is a message or alert that notifies a user of an action or response that should be taken based on a specific date, time, or condition.

[0533] A "draft" refers to the specific text or content created to be presented to users, such as in reminders.

[0534] A "schedule" refers to a calendar or schedule management system that a user uses on a daily basis.

[0535] "Means of notification" refer to functions and methods for communicating reminders and important information to users.

[0536] "Methods for analyzing emotional states" refer to technologies that evaluate and analyze emotions and mental states based on user behavior, usage data, voice, etc.

[0537] "Means for adjusting the wording and sending timing of reminders based on analysis results" refers to a function that optimizes the content and timing of reminders according to the results of sentiment analysis.

[0538] This invention combines a system that automates sales representatives' responses to unpaid damages with an emotion engine. This system consists of a server, terminals, users, and an emotion engine, and each element works in coordination to achieve efficient business support.

[0539] Hardware and software to be used

[0540] Server: Performs various processes such as database connection, data analysis, reminder generation, calendar registration, and notification sending. Specific examples include web server software such as Apache and Nginx, and database management using MySQL or PostgreSQL.

[0541] Terminal: A device used by a user, such as a PC or smartphone, that can run applications for receiving notifications and displaying calendars.

[0542] Emotion Engine: Dedicated software for analyzing a user's emotional state. Emotion recognition APIs (e.g., Microsoft Azure's Emotion API) are used for emotion analysis.

[0543] Program processing

[0544] Data acquisition

[0545] The server connects to the database and retrieves data on unpaid damages. The database contains a table called "Unpaid Damages," from which the date the damage occurred, the name of the company involved, and terminal information are retrieved. This is done using an SQL query, for example, in the format "SELECT FROM Unpaid Damages WHERE Date >= CURRENT_DATE".

[0546] Reminder generation

[0547] The server generates a reminder one month before the expected date of the incident. The reminder includes the name of the company involved, a draft message, and specific instructions on how to respond. The emotion engine analyzes the user's emotional state and adjusts the message accordingly. For example, if the user is feeling stressed, the server generates a more concise message based on the emotion engine's analysis data.

[0548] Calendar registration

[0549] The server automatically registers the generated reminder to the user's calendar using the Google Calendar API. It accesses the API using an authentication token and writes a new event. Specifically, it sends JSON data containing the reminder details to the API to add the event to the calendar.

[0550] Send notification

[0551] The server notifies the user that the reminder registration was successful. The notification system uses email sending via an SMTP server or a push notification server. The emotion engine monitors the user's emotional state in real time and sends notifications at the optimal time.

[0552] Specific example

[0553] For example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server retrieves this case from the database and generates a reminder. The reminder includes the phrase "Notification regarding unrefunded damages to company A" and specific wording. The emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the detailed wording.

[0554] Next, the server uses the Google Calendar API to automatically register reminders in the user's calendar. This can be achieved, for example, by inputting the following prompt into the AI ​​model:

[0555] For a case involving Company A, where the date of accrual of unrefunded damages is November 30, 2023, please describe the process of creating a reminder one month prior to the accrual date (October 30, 2023), registering it in Google Calendar, and sending a notification. Include adjustments to the reminder text based on the user's emotional state using an emotion engine, and optimization of the notification sending timing.

[0556] Furthermore, the server sends a notification to the user stating, "Please check the outstanding damages from Company A." This notification is sent at the optimal time based on the analysis results of the emotion engine. Upon receiving the notification, the user can check their calendar and take the necessary action quickly.

[0557] In this way, this system automates the sales representative's response to unpaid damages, providing efficient business support and enabling optimal responses tailored to the user's emotional state.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1: Data Collection

[0560] The server connects to the database and retrieves data on unpaid damages. Specifically, the server loads a driver to connect to the database. Next, the server executes an SQL query containing the necessary information (date the unpaid damages occurred, name of the company involved, terminal information, etc.).

[0561] Input: The server enters the database connection information and queries.

[0562] Data processing: Execute SQL queries, format the results, and create a list.

[0563] Output: Data on unrefunded damages is returned to the server in a list format.

[0564] Specific operation: The server executes the query "SELECT Occurrence Date, Company Name, Terminal Information FROM Unreturned Damages WHERE Occurrence Date >= CURRENT_DATE".

[0565] Step 2: Case Extraction

[0566] The server analyzes the acquired data and extracts cases whose occurrence date falls within a certain period.

[0567] Input: Enter the unreturned damage data listed on the server.

[0568] Data processing: Filter cases to include those that occurred within one month of the current date.

[0569] Output: A list of cases whose occurrence date falls within the specified period will be retrieved.

[0570] Specific operation: The server applies a filter condition (occurrence date <= current date + 30 days) and extracts the relevant cases.

[0571] Step 3: Generate a reminder

[0572] The server generates reminders for the extracted cases. The reminders include the name of the company in question and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the text accordingly.

[0573] Input: The extracted list of cases and user sentiment data are entered.

[0574] Data processing: Generate a basic reminder text based on case data, and then adjust the text to reflect the results of the emotion engine's analysis.

[0575] Output: A customized reminder is generated.

[0576] Specific operation: The server calls the emotion engine API and modifies the reminder content based on the user's emotional state. For example, if the user is feeling stressed, the message might read, "Notification of unrefunded damages to Company A. Please check the next step for specific details."

[0577] Step 4: Register on calendar

[0578] The server automatically registers the generated reminders to the user's calendar.

[0579] Input: The generated reminder and the Google Calendar API authentication token will be entered.

[0580] Data processing: Convert the reminder content into a format suitable for sending to the Google Calendar API.

[0581] Output: A new event is added to the calendar.

[0582] Specific operation: The server connects to the Google Calendar API, sends event data including reminder details, and registers it in the user's calendar.

[0583] Step 5: Send Notification

[0584] The server notifies the user that the reminder has been successfully registered.

[0585] Input: Calendar registration results and notification messages are entered.

[0586] Data processing: Optimize the timing of notification sending based on the analysis results of the emotion engine.

[0587] Output: A notification is sent to the user at the appropriate time.

[0588] Specific operation: The server generates a notification message stating, "Please check the outstanding damages for Company A," and sends it via email through the SMTP server, or sends a notification to the user's device using a push notification system.

[0589] These steps automate the sales representative's response to unreturned damages and enable the system to provide the most appropriate response based on the user's emotional state.

[0590] (Application Example 2)

[0591] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0592] In traditional systems, setting reminders and notifications for deadlines for specific projects or tasks is often done manually, making efficient management difficult. Furthermore, it's impossible to respond at the optimal time considering the user's emotional state, posing challenges to improving operational efficiency and customer satisfaction. Especially in retail settings, quick and accurate action is required for inventory management and customer service, but there is a lack of appropriate systems to support this.

[0593] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders to the user's calendar, means for notifying the user, means for analyzing the user's emotional state, means for adjusting the content and sending timing of reminders and notifications based on the analysis results, means for generating reminders in store inventory management and customer service when inventory is low or the return deadline is approaching, means for adjusting the timing of the reminder generation according to the user's emotional state, means for registering the reminder content to the user's calendar, and means for sending notifications to the user. This enables improved operational efficiency and optimal responses that take into account the user's emotional state.

[0594] "Listed data" refers to a collection of information that has been pre-organized and stored in a list format.

[0595] "Means of acquiring data" refers to methods and devices for collecting necessary information from databases or external sources.

[0596] "Means of data analysis" refers to methods and devices for analyzing collected data and extracting useful information.

[0597] A "case" refers to an event or piece of information that is the subject of a specific task or process.

[0598] A "reminder" refers to a message or notification used to draw attention to a specific event or date.

[0599] "Draft text" refers to the design and content of the text included in reminders and notifications.

[0600] A "calendar" refers to a tool or application that users use to manage their schedules and reminders.

[0601] "Means of notification" refers to methods or devices for sending messages or alerts to users.

[0602] "Means for analyzing a user's emotional state" refers to methods or devices for analyzing a user's current emotions and psychological state.

[0603] "Means of adjustment based on analysis results" refers to methods or devices for optimizing the content and timing of reminders and notifications based on the obtained analysis results.

[0604] "Inventory management" refers to activities aimed at monitoring the quantity and condition of goods in stores and warehouses and maintaining optimal levels.

[0605] "Customer service" refers to providing appropriate services and support in response to customer inquiries and requests.

[0606] "Return deadline" refers to the date and time when borrowed items should be returned.

[0607] A system for carrying out this invention is constructed using the following main components and software.

[0608] Hardware and software

[0609] hardware

[0610] 1. Server: Plays a central role in collecting, analyzing, and storing data, and generating reminders.

[0611] 2. Terminal: The device used by the user. This may be a smartphone, tablet, desktop PC, or interactive robot (e.g., Pepper).

[0612] software

[0613] 1. SQL database (e.g., MySQL): Used to store listed data and reminder information.

[0614] 2. Google Calendar API: Used to automatically add reminders to the user's calendar.

[0615] 3. Apache Emotion API: Used to analyze the user's emotional state and adjust the appropriate notification timing and reminder content.

[0616] 4. Firebase Cloud Messaging: This is a service for sending notifications to users.

[0617] System operation

[0618] Data collection

[0619] The server periodically connects to the SQL database to retrieve listed data. For example, it collects inventory information and data on product return deadlines.

[0620] Data Analysis

[0621] The server analyzes the acquired data and extracts cases that meet specific criteria (for example, inventory levels below a certain level, or the return deadline is approaching).

[0622] Reminder generation

[0623] Reminders are generated for the extracted cases. First, the user's emotional state is analyzed using the Apache Emotion API, and the content of the reminder is adjusted based on the results. For example, if the user is determined to be busy, the notification content is made concise.

[0624] Calendar registration

[0625] The generated reminders are automatically added to the user's calendar using the Google Calendar API. This process assumes that authentication information has been provided in advance.

[0626] Sending notifications

[0627] Notifications are sent to users at the optimal time via Firebase Cloud Messaging. This timing is adjusted based on the user's emotional state, which is monitored in real time.

[0628] Specific example

[0629] For example, if a physical store has low stock of product A, the server retrieves this information and generates a reminder. This reminder will contain detailed instructions if the user is not stressed, and a concise message if they are stressed. Next, the reminder is automatically added to the user's calendar using the Google Calendar API as "Inventory Management Alert: Product A". Then, Firebase Cloud Messaging sends a notification to the user saying, "Product A is running low on stock, please take immediate action."

[0630] Example of a prompt

[0631] "We are running low on stock of product A; please take immediate action."

[0632] "Customer B's return deadline is approaching. Please take action."

[0633] This allows physical stores to manage inventory and customer service efficiently and accurately. Furthermore, by considering emotional states, the workload can be reduced.

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The server periodically connects to the SQL database to retrieve listed inventory information and product return deadline data. Specifically, the server sends SQL queries to fetch records containing inventory levels and return deadlines. This allows the server to obtain information about the current inventory status and return deadlines. The input is the SQL database, and the output is the fetched data.

[0637] Step 2:

[0638] The server analyzes the retrieved data and extracts items whose inventory levels are below a specified threshold or whose return deadline is within one week. Using data retrieved from the database, it filters items with low inventory levels or approaching return deadlines. The input is the fetched data, and the output is a list of extracted items.

[0639] Step 3:

[0640] The server generates reminders for the extracted cases. During this process, it uses the Apache Emotion API to analyze the user's emotional state and adjusts the reminder content based on the analysis results. For example, if the user is stressed, the notification content will be simplified. The input is a list of extracted cases and the results of the emotional analysis; the output is a list of the adjusted reminders.

[0641] Step 4:

[0642] The server automatically adds the generated reminders to the user's calendar using the Google Calendar API. The reminders are added to the calendar in the appropriate format, including the event title and description. The input is a list of formatted reminders, and the output is the event added to the user's calendar.

[0643] Step 5:

[0644] The server uses Firebase Cloud Messaging to send notifications to the user at the optimal time. This timing is adjusted based on the user's emotional state, which is monitored in real time. Specifically, it selects times when the user is not busy or stressed to send notifications. The inputs are events registered in the user's calendar and the results of the emotional analysis, and the output is the notification sent to the user.

[0645] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0646] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0647] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0648] [Third Embodiment]

[0649] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0650] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0651] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0652] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0653] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0654] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0655] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0656] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0657] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0658] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0659] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0660] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0661] This invention relates to a system for automating the response of sales representatives to the occurrence of unrefunded damages. This system has the function of acquiring listed data, generating reminders for cases with an approaching occurrence date, automatically registering them in the user's calendar, and further notifying the user.

[0662] System Overview

[0663] The system of this invention consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unreturned damages.

[0664] Program processing flow and operation

[0665] Data collection

[0666] The server connects to a database containing a list of items and retrieves information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[0667] Create a reminder

[0668] The server generates a reminder one month before the event occurs. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. For example, if unpaid damages are incurred with a specific company A, the server will generate a reminder stating, "Contact company A regarding unpaid damages."

[0669] Calendar registration

[0670] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0671] notification

[0672] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[0673] Specific example

[0674] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The content of the reminder includes specific wording such as "Notification regarding unrefunded damages for company A." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification saying, "Please check the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[0675] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] The server connects to the database and retrieves data on unpaid damages that have been listed. Here, the server uses an SQL query to retrieve data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0679] Step 2:

[0680] The server analyzes the acquired data and filters it to extract cases whose occurrence date falls within one month from the current date. The filtering process compares the current date with the occurrence date of each case and lists the relevant cases.

[0681] Step 3:

[0682] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[0683] Step 4:

[0684] The server saves the generated reminders in a data format such as JSON. This saving is done so that the RPA can retrieve and process the reminders in the next step.

[0685] Step 5:

[0686] The server starts the RPA and begins the calendar registration process. At this point, the server provides the RPA with authentication credentials to access the calendar service API (for example, Google Calendar API or Microsoft Graph API).

[0687] Step 6:

[0688] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[0689] Step 7:

[0690] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[0691] Step 8:

[0692] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[0693] Step 9:

[0694] The server sends a notification to the user via the mail server. The user is informed of the reminder content via email or a notification system.

[0695] Step 10:

[0696] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[0697] (Example 1)

[0698] Next, we will describe Example 1. 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."

[0699] The procedures and management associated with unrefunded damages are a significant burden for sales representatives. Delays in addressing cases, especially those occurring soon, can damage a company's reputation. Therefore, a system that allows sales representatives to handle these matters quickly and accurately is needed. Traditional manual processes are prone to errors and omissions, making efficiency improvements a challenge.

[0700] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0701] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving the data, including the text, using a generation AI model, means for automatically registering the content of the generated reminders in the user's calendar, and means for notifying the user. This enables sales representatives to respond quickly and efficiently to unrefunded damages.

[0702] "Listed data" refers to a collection of information that is organized in a specified format and managed sequentially as a series of items.

[0703] A "server" refers to a computer system that manages and processes data on a network.

[0704] A "database" refers to an information system designed to efficiently search, manage, and store large amounts of data.

[0705] "Analysis" refers to the process of breaking down data and understanding its meaning and structure.

[0706] A "reminder" refers to a message or alert that notifies the user about a specific time or event, reminding them of an important matter.

[0707] A "generative AI model" refers to a technology that uses artificial intelligence algorithms to automatically generate new data and information.

[0708] "Document draft" refers to a preliminary version or draft of a text created for a specific purpose.

[0709] A "user" refers to an individual or organization that uses a system and receives its functions and services.

[0710] A "schedule" refers to a tool or application used for schedule management, which records a user's plans and appointments.

[0711] "Notification" refers to sending messages or alerts to inform users of information.

[0712] This invention relates to a system for automating the response of sales representatives to the occurrence of unpaid damages. The system consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, which receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unpaid damages.

[0713] Data collection

[0714] The server connects to a database containing a list of items and uses SQL queries to retrieve information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[0715] Create a reminder

[0716] The server generates a reminder one month before the event occurs. A generation AI model is used to generate the message text. For example, the reminder text might include "Company A, please confirm the details regarding the unrefunded damages." The reminder content includes details such as the name of the company, the message text, and specific actions to take.

[0717] Calendar registration

[0718] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0719] notification

[0720] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[0721] Specific example

[0722] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes specific wording such as, "Company A, please check the information regarding the unrefunded damages." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification stating, "Please check the information regarding the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[0723] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[0724] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0725] Step 1: Connect to the database

[0726] The server connects to a listed database. It takes database connection information (host, port, username, password) as input and outputs a database connection object. Specifically, it connects to the database using a database driver (e.g., MySQL Connector).

[0727] Step 2: Data Acquisition

[0728] The server retrieves information about unreturned damage fees. It uses an SQL query (e.g., SELECT FROM unreturned_damage_fees) as input and obtains records of unreturned damage fees as output. Specifically, it executes the query using a database connection object and retrieves the result set.

[0729] Step 3: Extracting cases with recent occurrence dates

[0730] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month. Using the acquired records of unrefunded damages as input, a filtered list of cases is obtained as output. Specifically, the current date is compared with the occurrence date, and cases that meet the criteria are extracted.

[0731] Step 4: Generate a reminder

[0732] The server generates reminders for the extracted cases. It uses a filtered list of cases as input and obtains a reminder object as output. Specifically, it uses a generation AI model to automatically generate a draft of the reminder text and saves that content to the reminder object.

[0733] Step 5: Register on calendar

[0734] This system automatically registers reminders generated by the server to the user's calendar. It uses a reminder object and the user's calendar authentication information as input, and outputs the result of registering the calendar event. Specifically, it uses the Google Calendar API to create an event and registers the reminder content to the calendar.

[0735] Step 6: Send Notification

[0736] The server notifies the user that the reminder registration was successful. It uses the calendar event registration result as input and obtains the notification sending result as output. Specifically, it uses the email API or in-app notification system to send a notification such as, "Please check the unrefunded damages from Company A."

[0737] This allows users to receive notifications and take appropriate action.

[0738] (Application Example 1)

[0739] Next, we will explain Application Example 1. In the following explanation, 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."

[0740] In factory parts management, dealing with parts that are not returned by the deadline is an extremely time-consuming task. Furthermore, missed return deadlines lead to lost opportunities and procedural delays, which are also problematic. Therefore, there is a need for a system that streamlines the management of unreturned parts and reduces the workload of the person in charge of verification.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0742] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders to the user's calendar, means for notifying the user, means for acquiring parts management data from the factory and monitoring the return deadlines for unreturned parts, and means for automatically generating reminders for unreturned parts and notifying the person in charge of the relevant parts. This makes it possible to accurately grasp the return deadlines for unreturned parts, automate notifications to the person in charge, and improve the efficiency of parts management.

[0743] "Listed data" refers to a collection of information that is organized and presented in a list format.

[0744] "Means of acquiring data" refers to methods and devices for collecting necessary information from databases or external sources.

[0745] "Means of analysis" refers to methods or devices that analyze acquired data and perform evaluations or classifications based on specific conditions.

[0746] "Means of generating reminders" refers to methods or devices that create notifications or alerts based on certain conditions.

[0747] "Draft" refers to pre-prepared text or content intended for inclusion in notices or reports.

[0748] "Methods for automatically registering to a calendar" refers to methods or devices that automatically add generated events and reminders to the user's calendar system.

[0749] "Means of notification" refers to methods or devices that inform users of events or alerts.

[0750] "Parts management data" refers to a collection of information regarding the inventory, return deadlines, and usage status of parts used within a factory.

[0751] "Means of monitoring return deadlines" refers to methods or devices for checking whether the return date for parts is approaching and taking appropriate action for parts that have passed their deadline.

[0752] "Unreturned parts" refers to factory parts that have not yet been returned by the return deadline.

[0753] "Means of notifying the administrator" refers to methods or devices for conveying necessary information to the person responsible for managing the parts.

[0754] This invention relates to a non-returned parts management system, aiming to automate the management of returned parts within a factory and enable efficient operation. Specifically, it is implemented through the following procedure and configuration.

[0755] Basic System Configuration

[0756] The server is responsible for the main processing of retrieving, analyzing, and generating reminders for the listed data. The terminal is the device used by the user, receiving calendar entries and notifications. The user is the person in charge of parts management.

[0757] Hardware and software to use

[0758] Server: The central device that retrieves necessary information from the database, performs analysis, and generates reminders.

[0759] Terminal: A computing device used by a user, such as a personal computer or smartphone.

[0760] Google Calendar API: An external API for automatically adding reminders to your calendar.

[0761] Notification services: Email sending services and in-app notification functions (e.g., SMTP servers and push notification servers).

[0762] Data processing and calculation procedures

[0763] 1. Data collection: The server connects to the database and retrieves information about unreturned parts.

[0764] 2. Data Analysis: Analyze the acquired information and extract parts whose return deadline is within one month.

[0765] 3. Create Reminders: Generate reminders for the extracted parts. The reminders will include details such as the part ID, return deadline, and how to contact the administrator.

[0766] 4. Calendar Registration: The generated reminders are automatically registered to the user's calendar using the Google Calendar API.

[0767] 5. Notification: Notify the user that the reminder has been successfully registered. Notifications will be sent via email or an in-app notification system.

[0768] Specific example

[0769] As a concrete example, consider a case where the return deadline for part ID "1234" is November 25, 2023. In this case, the server extracts this part information on October 25, 2023, and generates a reminder titled "Confirmation of return of part ID 1234". Next, it uses the Google Calendar API to automatically register this reminder in the user's calendar. Furthermore, the server sends a notification to the administrator stating, "The return deadline for part ID 1234 is approaching. Please contact your administrator." The administrator receives the notification, checks their calendar, and can take appropriate action.

[0770] Example of a prompt

[0771] The following are specific examples of prompt statements for a generative AI model:

[0772] Processing for the unreturned parts management system: If the return deadline for part ID "1234" is November 25, 2023, please write the code for a program that adds an event to the calendar on October 26, 2023, titled "Return Confirmation for Unreturned Part 1234," and notifies the administrator (manager@factory.com).

[0773] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0774] Step 1:

[0775] Data acquisition

[0776] The server connects to the database and retrieves information on unreturned parts. The input is all part information in the database, and the output is data for each part, including its return deadline. The server uses SQL queries to perform the specific actions required to extract the necessary part information.

[0777] Step 2:

[0778] Data analysis

[0779] The server analyzes the acquired parts information and extracts parts whose return deadline is within 30 days. The input is the acquired parts information, and the output is a list of parts whose return deadline is approaching within 30 days. The server then performs the specific processing of comparing the return deadlines of each part and picking out those that meet the criteria.

[0780] Step 3:

[0781] Reminder generation

[0782] The server generates reminders for the extracted parts. The input is a list of extracted parts, and the output is detailed reminder information (part ID, return deadline, and how to contact the administrator). The server performs the specific operations to assemble the content of the corresponding reminder for each extracted part.

[0783] Step 4:

[0784] Add to calendar

[0785] The server automatically registers reminders generated using the Google Calendar API to the user's calendar. The input is the details of the generated reminder, and the output is the event added to the user's calendar. The server calls the Google Calendar API to perform the specific execution process that reflects the reminder content in the calendar.

[0786] Step 5:

[0787] Sending notifications

[0788] The server notifies the user that the reminder has been successfully registered. The inputs for this process are the reminder details and the recipient information, and the output is the notification sent to the user. The server then performs specific notification processing to inform the administrator that the reminder has been added, using email or push notifications.

[0789] Step 6:

[0790] User verification

[0791] The user receives a notification, checks their calendar, and takes appropriate action. The input is the notification from the server, and the output is the user's confirmation and response. The user reads the notification, checks their calendar, and takes specific actions such as contacting or responding as needed.

[0792] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0793] This invention combines a system that automates the response of sales representatives to unpaid damages with an emotion engine. This system acquires listed data, generates reminders for cases with approaching occurrence dates, automatically registers them in the user's calendar, and also has the function of notifying the user. Furthermore, the emotion engine analyzes the user's emotional state and adjusts the content and timing of reminders based on that analysis.

[0794] System Overview

[0795] The system of this invention consists of a server, a terminal, a user, and an emotion engine. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The emotion engine monitors the user's emotions in real time and adjusts the content and timing of reminders based on that information. The user is a sales representative and is responsible for handling cases of unpaid damages.

[0796] Program processing flow and operation

[0797] Data collection

[0798] The server connects to the database and retrieves the data on unpaid damages that have been listed. The server then uses an SQL query to extract data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0799] Create a reminder

[0800] The server generates a reminder one month before the expected date of the incident. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the reminder text accordingly. For example, if the user is feeling stressed, a more concise and intuitive message will be generated.

[0801] Calendar registration

[0802] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0803] notification

[0804] The server notifies the user that the reminder has been successfully registered. This notification is sent via email or the in-app notification system. The emotion engine monitors the user's emotional state in real time to optimize notification timing. For example, it might send notifications during times when the user is less busy. This makes it easier for the user to receive notifications and reduces the risk of delayed responses.

[0805] Specific example

[0806] As a concrete example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes a specific message titled "Notification regarding unrefunded damages for company A." Furthermore, the emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the usual detailed message; if the user is busy, it generates a concise message. Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Finally, the server sends the user a notification saying, "Please check regarding unrefunded damages for company A." This notification is sent at the time when the user is most likely to respond, based on the emotion engine's analysis. The user receives the notification, checks their calendar, and takes the necessary action.

[0807] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently responding to the occurrence of unrefunded damages, and further supports them in responding at the optimal timing and in the optimal manner by taking into account the emotional state of the user.

[0808] The following describes the processing flow.

[0809] Step 1:

[0810] The server connects to the database and retrieves data on unpaid damages that have been listed. The server executes an SQL query to retrieve data including the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0811] Step 2:

[0812] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month from the current date. The server compares the current date with the occurrence date of each case and filters out the relevant cases.

[0813] Step 3:

[0814] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[0815] Step 4:

[0816] The emotion engine analyzes the user's emotions. The user sends data obtained through emotion sensors and input devices on their sales terminal to the emotion engine. The emotion engine analyzes this data to determine the user's stress level and mood.

[0817] Step 5:

[0818] The server adjusts the reminders it generates based on the analysis results of the emotion engine. For example, if the emotion engine determines that the user is stressed, the server changes the wording of the reminder to be more concise and easier to understand.

[0819] Step 6:

[0820] The server saves the adjusted reminders in a data format such as JSON. The saved reminders are then used by the RPA to retrieve and process them in the next step.

[0821] Step 7:

[0822] The server starts the RPA and begins the calendar registration process. At this point, the server provides the RPA with authentication credentials to access the calendar service API (for example, Google Calendar API or Microsoft Graph API).

[0823] Step 8:

[0824] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[0825] Step 9:

[0826] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[0827] Step 10:

[0828] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[0829] Step 11:

[0830] The emotion engine monitors the user's emotional state in real time and optimizes the timing of notifications. For example, it might choose to send notifications during times when the user is less busy.

[0831] Step 12:

[0832] The server sends a notification to the user via the mail server. The user is informed of the reminder content using email or a notification system.

[0833] Step 13:

[0834] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[0835] (Example 2)

[0836] Next, we will describe Example 2. 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."

[0837] In existing systems, sales representatives handle unrefunded damages manually, which can lead to delays and ultimately reduce operational efficiency. Furthermore, the system fails to consider the emotional state of sales representatives, increasing stress and burden, and making it difficult to provide optimal responses. These problems require automated and personalized responses.

[0838] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0839] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within a certain period, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders in the user's calendar, means for notifying the user, means for analyzing the user's emotional state, and means for adjusting the text and sending timing of the reminders based on the analysis results. This enables an automated and efficient response to the occurrence of unreturned damages, and further realizes notifications with optimal timing and text that take into account the user's emotional state.

[0840] "Listed data" refers to a collection of information that has been organized based on specific criteria and stored in a format that allows it to be displayed as a list.

[0841] "Means of acquisition" refers to the functions or methods by which a server or related device retrieves necessary information from a database or external system.

[0842] "Methods of analysis" refer to the process of evaluating acquired data and extracting necessary information based on specific conditions or patterns.

[0843] "Events that will occur within a certain period" refers to events or tasks that are scheduled to occur within a specific period (e.g., within one month).

[0844] A "reminder" is a message or alert that notifies a user of an action or response that should be taken based on a specific date, time, or condition.

[0845] A "draft" refers to the specific text or content created to be presented to users, such as in reminders.

[0846] A "schedule" refers to a calendar or schedule management system that a user uses on a daily basis.

[0847] "Means of notification" refer to functions and methods for communicating reminders and important information to users.

[0848] "Methods for analyzing emotional states" refer to technologies that evaluate and analyze emotions and mental states based on user behavior, usage data, voice, etc.

[0849] "Means for adjusting the wording and sending timing of reminders based on analysis results" refers to a function that optimizes the content and timing of reminders according to the results of sentiment analysis.

[0850] This invention combines a system that automates sales representatives' responses to unpaid damages with an emotion engine. This system consists of a server, terminals, users, and an emotion engine, and each element works in coordination to achieve efficient business support.

[0851] Hardware and software to be used

[0852] Server: Performs various processes such as database connection, data analysis, reminder generation, calendar registration, and notification sending. Specific examples include web server software such as Apache and Nginx, and database management using MySQL or PostgreSQL.

[0853] Terminal: A device used by a user, such as a PC or smartphone, that can run applications for receiving notifications and displaying calendars.

[0854] Emotion Engine: Dedicated software for analyzing a user's emotional state. Emotion recognition APIs (e.g., Microsoft Azure's Emotion API) are used for emotion analysis.

[0855] Program processing

[0856] Data acquisition

[0857] The server connects to the database and retrieves data on unpaid damages. The database contains a table called "Unpaid Damages," from which the date the damage occurred, the name of the company involved, and terminal information are retrieved. This is done using an SQL query, for example, in the format "SELECT FROM Unpaid Damages WHERE Date >= CURRENT_DATE".

[0858] Reminder generation

[0859] The server generates a reminder one month before the expected date of the incident. The reminder includes the name of the company involved, a draft message, and specific instructions on how to respond. The emotion engine analyzes the user's emotional state and adjusts the message accordingly. For example, if the user is feeling stressed, the server generates a more concise message based on the emotion engine's analysis data.

[0860] Calendar registration

[0861] The server automatically registers the generated reminder to the user's calendar using the Google Calendar API. It accesses the API using an authentication token and writes a new event. Specifically, it sends JSON data containing the reminder details to the API to add the event to the calendar.

[0862] Send notification

[0863] The server notifies the user that the reminder registration was successful. The notification system uses email sending via an SMTP server or a push notification server. The emotion engine monitors the user's emotional state in real time and sends notifications at the optimal time.

[0864] Specific example

[0865] For example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server retrieves this case from the database and generates a reminder. The reminder includes the phrase "Notification regarding unrefunded damages to company A" and specific wording. The emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the detailed wording.

[0866] Next, the server uses the Google Calendar API to automatically register reminders in the user's calendar. This can be achieved, for example, by inputting the following prompt into the AI ​​model:

[0867] For a case involving Company A, where the date of accrual of unrefunded damages is November 30, 2023, please describe the process of creating a reminder one month prior to the accrual date (October 30, 2023), registering it in Google Calendar, and sending a notification. Include adjustments to the reminder text based on the user's emotional state using an emotion engine, and optimization of the notification sending timing.

[0868] Furthermore, the server sends a notification to the user stating, "Please check the outstanding damages from Company A." This notification is sent at the optimal time based on the analysis results of the emotion engine. Upon receiving the notification, the user can check their calendar and take the necessary action quickly.

[0869] In this way, this system automates the sales representative's response to unpaid damages, providing efficient business support and enabling optimal responses tailored to the user's emotional state.

[0870] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0871] Step 1: Data Collection

[0872] The server connects to the database and retrieves data on unpaid damages. Specifically, the server loads a driver to connect to the database. Next, the server executes an SQL query containing the necessary information (date the unpaid damages occurred, name of the company involved, terminal information, etc.).

[0873] Input: The server enters the database connection information and queries.

[0874] Data processing: Execute SQL queries, format the results, and create a list.

[0875] Output: Data on unrefunded damages is returned to the server in a list format.

[0876] Specific operation: The server executes the query "SELECT Occurrence Date, Company Name, Terminal Information FROM Unreturned Damages WHERE Occurrence Date >= CURRENT_DATE".

[0877] Step 2: Case Extraction

[0878] The server analyzes the acquired data and extracts cases whose occurrence date falls within a certain period.

[0879] Input: Enter the unreturned damage data listed on the server.

[0880] Data processing: Filter cases to include those that occurred within one month of the current date.

[0881] Output: A list of cases whose occurrence date falls within the specified period will be retrieved.

[0882] Specific operation: The server applies a filter condition (occurrence date <= current date + 30 days) and extracts the relevant cases.

[0883] Step 3: Generate a reminder

[0884] The server generates reminders for the extracted cases. The reminders include the name of the company in question and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the text accordingly.

[0885] Input: The extracted list of cases and user sentiment data are entered.

[0886] Data processing: Generate a basic reminder text based on case data, and then adjust the text to reflect the results of the emotion engine's analysis.

[0887] Output: A customized reminder is generated.

[0888] Specific operation: The server calls the emotion engine API and modifies the reminder content based on the user's emotional state. For example, if the user is feeling stressed, the message might read, "Notification of unrefunded damages to Company A. Please check the next step for specific details."

[0889] Step 4: Register on calendar

[0890] The server automatically registers the generated reminders to the user's calendar.

[0891] Input: The generated reminder and the Google Calendar API authentication token will be entered.

[0892] Data processing: Convert the reminder content into a format suitable for sending to the Google Calendar API.

[0893] Output: A new event is added to the calendar.

[0894] Specific operation: The server connects to the Google Calendar API, sends event data including reminder details, and registers it in the user's calendar.

[0895] Step 5: Send Notification

[0896] The server notifies the user that the reminder has been successfully registered.

[0897] Input: Calendar registration results and notification messages are entered.

[0898] Data processing: Optimize the timing of notification sending based on the analysis results of the emotion engine.

[0899] Output: A notification is sent to the user at the appropriate time.

[0900] Specific operation: The server generates a notification message stating, "Please check the outstanding damages for Company A," and sends it via email through the SMTP server, or sends a notification to the user's device using a push notification system.

[0901] These steps automate the sales representative's response to unreturned damages and enable the system to provide the most appropriate response based on the user's emotional state.

[0902] (Application Example 2)

[0903] Next, we will explain application example 2. In the following explanation, 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."

[0904] In traditional systems, setting reminders and notifications for deadlines for specific projects or tasks is often done manually, making efficient management difficult. Furthermore, it's impossible to respond at the optimal time considering the user's emotional state, posing challenges to improving operational efficiency and customer satisfaction. Especially in retail settings, quick and accurate action is required for inventory management and customer service, but there is a lack of appropriate systems to support this.

[0905] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders to the user's calendar, means for notifying the user, means for analyzing the user's emotional state, means for adjusting the content and sending timing of reminders and notifications based on the analysis results, means for generating reminders in store inventory management and customer service when inventory is low or the return deadline is approaching, means for adjusting the timing of the reminder generation according to the user's emotional state, means for registering the reminder content to the user's calendar, and means for sending notifications to the user. This enables improved operational efficiency and optimal responses that take into account the user's emotional state.

[0906] "Listed data" refers to a collection of information that has been pre-organized and stored in a list format.

[0907] "Means of acquiring data" refers to methods and devices for collecting necessary information from databases or external sources.

[0908] "Means of data analysis" refers to methods and devices for analyzing collected data and extracting useful information.

[0909] A "case" refers to an event or piece of information that is the subject of a specific task or process.

[0910] A "reminder" refers to a message or notification used to draw attention to a specific event or date.

[0911] "Draft text" refers to the design and content of the text included in reminders and notifications.

[0912] A "calendar" refers to a tool or application that users use to manage their schedules and reminders.

[0913] "Means of notification" refers to methods or devices for sending messages or alerts to users.

[0914] "Means for analyzing a user's emotional state" refers to methods or devices for analyzing a user's current emotions and psychological state.

[0915] "Means of adjustment based on analysis results" refers to methods or devices for optimizing the content and timing of reminders and notifications based on the obtained analysis results.

[0916] "Inventory management" refers to activities aimed at monitoring the quantity and condition of goods in stores and warehouses and maintaining optimal levels.

[0917] "Customer service" refers to providing appropriate services and support in response to customer inquiries and requests.

[0918] "Return deadline" refers to the date and time when borrowed items should be returned.

[0919] A system for carrying out this invention is constructed using the following main components and software.

[0920] Hardware and software

[0921] hardware

[0922] 1. Server: Plays a central role in collecting, analyzing, and storing data, and generating reminders.

[0923] 2. Terminal: The device used by the user. This may be a smartphone, tablet, desktop PC, or interactive robot (e.g., Pepper).

[0924] software

[0925] 1. SQL database (e.g., MySQL): Used to store listed data and reminder information.

[0926] 2. Google Calendar API: Used to automatically add reminders to the user's calendar.

[0927] 3. Apache Emotion API: Used to analyze the user's emotional state and adjust the appropriate notification timing and reminder content.

[0928] 4. Firebase Cloud Messaging: This is a service for sending notifications to users.

[0929] System operation

[0930] Data collection

[0931] The server periodically connects to the SQL database to retrieve listed data. For example, it collects inventory information and data on product return deadlines.

[0932] Data Analysis

[0933] The server analyzes the acquired data and extracts cases that meet specific criteria (for example, inventory levels below a certain level, or the return deadline is approaching).

[0934] Reminder generation

[0935] Reminders are generated for the extracted cases. First, the user's emotional state is analyzed using the Apache Emotion API, and the content of the reminder is adjusted based on the results. For example, if the user is determined to be busy, the notification content is made concise.

[0936] Calendar registration

[0937] The generated reminders are automatically added to the user's calendar using the Google Calendar API. This process assumes that authentication information has been provided in advance.

[0938] Sending notifications

[0939] Notifications are sent to users at the optimal time via Firebase Cloud Messaging. This timing is adjusted based on the user's emotional state, which is monitored in real time.

[0940] Specific example

[0941] For example, if a physical store has low stock of product A, the server retrieves this information and generates a reminder. This reminder will contain detailed instructions if the user is not stressed, and a concise message if they are stressed. Next, the reminder is automatically added to the user's calendar using the Google Calendar API as "Inventory Management Alert: Product A". Then, Firebase Cloud Messaging sends a notification to the user saying, "Product A is running low on stock, please take immediate action."

[0942] Example of a prompt

[0943] "We are running low on stock of product A; please take immediate action."

[0944] "Customer B's return deadline is approaching. Please take action."

[0945] This allows physical stores to manage inventory and customer service efficiently and accurately. Furthermore, by considering emotional states, the workload can be reduced.

[0946] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0947] Step 1:

[0948] The server periodically connects to the SQL database to retrieve listed inventory information and product return deadline data. Specifically, the server sends SQL queries to fetch records containing inventory levels and return deadlines. This allows the server to obtain information about the current inventory status and return deadlines. The input is the SQL database, and the output is the fetched data.

[0949] Step 2:

[0950] The server analyzes the retrieved data and extracts items whose inventory levels are below a specified threshold or whose return deadline is within one week. Using data retrieved from the database, it filters items with low inventory levels or approaching return deadlines. The input is the fetched data, and the output is a list of extracted items.

[0951] Step 3:

[0952] The server generates reminders for the extracted cases. During this process, it uses the Apache Emotion API to analyze the user's emotional state and adjusts the reminder content based on the analysis results. For example, if the user is stressed, the notification content will be simplified. The input is a list of extracted cases and the results of the emotional analysis; the output is a list of the adjusted reminders.

[0953] Step 4:

[0954] The server automatically adds the generated reminders to the user's calendar using the Google Calendar API. The reminders are added to the calendar in the appropriate format, including the event title and description. The input is a list of formatted reminders, and the output is the event added to the user's calendar.

[0955] Step 5:

[0956] The server uses Firebase Cloud Messaging to send notifications to the user at the optimal time. This timing is adjusted based on the user's emotional state, which is monitored in real time. Specifically, it selects times when the user is not busy or stressed to send notifications. The inputs are events registered in the user's calendar and the results of the emotional analysis, and the output is the notification sent to the user.

[0957] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0958] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0959] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0960] [Fourth Embodiment]

[0961] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0962] As shown in Figure 7, the 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.

[0963] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0964] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0965] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0966] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0967] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0968] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0969] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0970] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0971] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0972] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0973] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0974] This invention relates to a system for automating the response of sales representatives to the occurrence of unrefunded damages. This system has the function of acquiring listed data, generating reminders for cases with an approaching occurrence date, automatically registering them in the user's calendar, and further notifying the user.

[0975] System Overview

[0976] The system of this invention consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unreturned damages.

[0977] Program processing flow and operation

[0978] Data collection

[0979] The server connects to a database containing a list of items and retrieves information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[0980] Create a reminder

[0981] The server generates a reminder one month before the event occurs. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. For example, if unpaid damages are incurred with a specific company A, the server will generate a reminder stating, "Contact company A regarding unpaid damages."

[0982] Calendar registration

[0983] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[0984] notification

[0985] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[0986] Specific example

[0987] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The content of the reminder includes specific wording such as "Notification regarding unrefunded damages for company A." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification saying, "Please check the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[0988] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[0989] The following describes the processing flow.

[0990] Step 1:

[0991] The server connects to the database and retrieves data on unpaid damages that have been listed. Here, the server uses an SQL query to retrieve data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[0992] Step 2:

[0993] The server analyzes the acquired data and filters it to extract cases whose occurrence date falls within one month from the current date. The filtering process compares the current date with the occurrence date of each case and lists the relevant cases.

[0994] Step 3:

[0995] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[0996] Step 4:

[0997] The server saves the generated reminders in a data format such as JSON. This saving is done so that the RPA can retrieve and process the reminders in the next step.

[0998] Step 5:

[0999] The server starts the RPA and begins the calendar registration process. At this point, the server provides the RPA with authentication credentials to access the calendar service API (for example, Google Calendar API or Microsoft Graph API).

[1000] Step 6:

[1001] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[1002] Step 7:

[1003] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[1004] Step 8:

[1005] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[1006] Step 9:

[1007] The server sends a notification to the user via the mail server. The user is informed of the reminder content via email or a notification system.

[1008] Step 10:

[1009] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[1010] (Example 1)

[1011] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1012] The procedures and management associated with unrefunded damages are a significant burden for sales representatives. Delays in addressing cases, especially those occurring soon, can damage a company's reputation. Therefore, a system that allows sales representatives to handle these matters quickly and accurately is needed. Traditional manual processes are prone to errors and omissions, making efficiency improvements a challenge.

[1013] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1014] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving the data, including the text, using a generation AI model, means for automatically registering the content of the generated reminders in the user's calendar, and means for notifying the user. This enables sales representatives to respond quickly and efficiently to unrefunded damages.

[1015] "Listed data" refers to a collection of information that is organized in a specified format and managed sequentially as a series of items.

[1016] A "server" refers to a computer system that manages and processes data on a network.

[1017] A "database" refers to an information system designed to efficiently search, manage, and store large amounts of data.

[1018] "Analysis" refers to the process of breaking down data and understanding its meaning and structure.

[1019] A "reminder" refers to a message or alert that notifies the user about a specific time or event, reminding them of an important matter.

[1020] A "generative AI model" refers to a technology that uses artificial intelligence algorithms to automatically generate new data and information.

[1021] "Document draft" refers to a preliminary version or draft of a text created for a specific purpose.

[1022] A "user" refers to an individual or organization that uses a system and receives its functions and services.

[1023] A "schedule" refers to a tool or application used for schedule management, which records a user's plans and appointments.

[1024] "Notification" refers to sending messages or alerts to inform users of information.

[1025] This invention relates to a system for automating the response of sales representatives to the occurrence of unpaid damages. The system consists of a server, a terminal, and a user. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, which receives calendar entries and notifications. The user is a sales representative and is responsible for handling cases of unpaid damages.

[1026] Data collection

[1027] The server connects to a database containing a list of items and uses SQL queries to retrieve information about unpaid penalties. The database stores terminal information, the date the unpaid penalties occurred, the name of the company involved, etc. The server periodically checks this data and extracts cases with recent occurrence dates.

[1028] Create a reminder

[1029] The server generates a reminder one month before the event occurs. A generation AI model is used to generate the message text. For example, the reminder text might include "Company A, please confirm the details regarding the unrefunded damages." The reminder content includes details such as the name of the company, the message text, and specific actions to take.

[1030] Calendar registration

[1031] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[1032] notification

[1033] The server notifies the user that the reminder has been successfully registered. The notification is sent via email or an in-app notification system. The user might receive a notification such as, "You need to contact Company A regarding unrefunded damages." This notification allows the user to check the reminder and take appropriate action.

[1034] Specific example

[1035] As a concrete example, consider a case involving company A where the date of the unrefunded damages is November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes specific wording such as, "Company A, please check the information regarding the unrefunded damages." Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Furthermore, the server sends the user a notification stating, "Please check the information regarding the unrefunded damages for company A." The user receives the notification, checks their calendar, and can take the necessary action.

[1036] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently dealing with the occurrence of unrefunded damages.

[1037] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1038] Step 1: Connect to the database

[1039] The server connects to a listed database. It takes database connection information (host, port, username, password) as input and outputs a database connection object. Specifically, it connects to the database using a database driver (e.g., MySQL Connector).

[1040] Step 2: Data Acquisition

[1041] The server retrieves information about unreturned damage fees. It uses an SQL query (e.g., SELECT FROM unreturned_damage_fees) as input and obtains records of unreturned damage fees as output. Specifically, it executes the query using a database connection object and retrieves the result set.

[1042] Step 3: Extracting cases with recent occurrence dates

[1043] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month. Using the acquired records of unrefunded damages as input, a filtered list of cases is obtained as output. Specifically, the current date is compared with the occurrence date, and cases that meet the criteria are extracted.

[1044] Step 4: Generate a reminder

[1045] The server generates reminders for the extracted cases. It uses a filtered list of cases as input and obtains a reminder object as output. Specifically, it uses a generation AI model to automatically generate a draft of the reminder text and saves that content to the reminder object.

[1046] Step 5: Register on calendar

[1047] This system automatically registers reminders generated by the server to the user's calendar. It uses a reminder object and the user's calendar authentication information as input, and outputs the result of registering the calendar event. Specifically, it uses the Google Calendar API to create an event and registers the reminder content to the calendar.

[1048] Step 6: Send Notification

[1049] The server notifies the user that the reminder registration was successful. It uses the calendar event registration result as input and obtains the notification sending result as output. Specifically, it uses the email API or in-app notification system to send a notification such as, "Please check the unrefunded damages from Company A."

[1050] This allows users to receive notifications and take appropriate action.

[1051] (Application Example 1)

[1052] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1053] In factory parts management, dealing with parts that are not returned by the deadline is an extremely time-consuming task. Furthermore, missed return deadlines lead to lost opportunities and procedural delays, which are also problematic. Therefore, there is a need for a system that streamlines the management of unreturned parts and reduces the workload of the person in charge of verification.

[1054] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1055] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders to the user's calendar, means for notifying the user, means for acquiring parts management data from the factory and monitoring the return deadlines for unreturned parts, and means for automatically generating reminders for unreturned parts and notifying the person in charge of the relevant parts. This makes it possible to accurately grasp the return deadlines for unreturned parts, automate notifications to the person in charge, and improve the efficiency of parts management.

[1056] "Listed data" refers to a collection of information that is organized and presented in a list format.

[1057] "Means of acquiring data" refers to methods and devices for collecting necessary information from databases or external sources.

[1058] "Means of analysis" refers to methods or devices that analyze acquired data and perform evaluations or classifications based on specific conditions.

[1059] "Means of generating reminders" refers to methods or devices that create notifications or alerts based on certain conditions.

[1060] "Draft" refers to pre-prepared text or content intended for inclusion in notices or reports.

[1061] "Methods for automatically registering to a calendar" refers to methods or devices that automatically add generated events and reminders to the user's calendar system.

[1062] "Means of notification" refers to methods or devices that inform users of events or alerts.

[1063] "Parts management data" refers to a collection of information regarding the inventory, return deadlines, and usage status of parts used within a factory.

[1064] "Means of monitoring return deadlines" refers to methods or devices for checking whether the return date for parts is approaching and taking appropriate action for parts that have passed their deadline.

[1065] "Unreturned parts" refers to factory parts that have not yet been returned by the return deadline.

[1066] "Means of notifying the administrator" refers to methods or devices for conveying necessary information to the person responsible for managing the parts.

[1067] This invention relates to a non-returned parts management system, aiming to automate the management of returned parts within a factory and enable efficient operation. Specifically, it is implemented through the following procedure and configuration.

[1068] Basic System Configuration

[1069] The server is responsible for the main processing of retrieving, analyzing, and generating reminders for the listed data. The terminal is the device used by the user, receiving calendar entries and notifications. The user is the person in charge of parts management.

[1070] Hardware and software to use

[1071] Server: The central device that retrieves necessary information from the database, performs analysis, and generates reminders.

[1072] Terminal: A computing device used by a user, such as a personal computer or smartphone.

[1073] Google Calendar API: An external API for automatically adding reminders to your calendar.

[1074] Notification services: Email sending services and in-app notification functions (e.g., SMTP servers and push notification servers).

[1075] Data processing and calculation procedures

[1076] 1. Data collection: The server connects to the database and retrieves information about unreturned parts.

[1077] 2. Data Analysis: Analyze the acquired information and extract parts whose return deadline is within one month.

[1078] 3. Create Reminders: Generate reminders for the extracted parts. The reminders will include details such as the part ID, return deadline, and how to contact the administrator.

[1079] 4. Calendar Registration: The generated reminders are automatically registered to the user's calendar using the Google Calendar API.

[1080] 5. Notification: Notify the user that the reminder has been successfully registered. Notifications will be sent via email or an in-app notification system.

[1081] Specific example

[1082] As a concrete example, consider a case where the return deadline for part ID "1234" is November 25, 2023. In this case, the server extracts this part information on October 25, 2023, and generates a reminder titled "Confirmation of return of part ID 1234". Next, it uses the Google Calendar API to automatically register this reminder in the user's calendar. Furthermore, the server sends a notification to the administrator stating, "The return deadline for part ID 1234 is approaching. Please contact your administrator." The administrator receives the notification, checks their calendar, and can take appropriate action.

[1083] Example of a prompt

[1084] The following are specific examples of prompt statements for a generative AI model:

[1085] Processing for the unreturned parts management system: If the return deadline for part ID "1234" is November 25, 2023, please write the code for a program that adds an event to the calendar on October 26, 2023, titled "Return Confirmation for Unreturned Part 1234," and notifies the administrator (manager@factory.com).

[1086] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1087] Step 1:

[1088] Data acquisition

[1089] The server connects to the database and retrieves information on unreturned parts. The input is all part information in the database, and the output is data for each part, including its return deadline. The server uses SQL queries to perform the specific actions required to extract the necessary part information.

[1090] Step 2:

[1091] Data analysis

[1092] The server analyzes the acquired parts information and extracts parts whose return deadline is within 30 days. The input is the acquired parts information, and the output is a list of parts whose return deadline is approaching within 30 days. The server then performs the specific processing of comparing the return deadlines of each part and picking out those that meet the criteria.

[1093] Step 3:

[1094] Reminder generation

[1095] The server generates reminders for the extracted parts. The input is a list of extracted parts, and the output is detailed reminder information (part ID, return deadline, and how to contact the administrator). The server performs the specific operations to assemble the content of the corresponding reminder for each extracted part.

[1096] Step 4:

[1097] Add to calendar

[1098] The server automatically registers reminders generated using the Google Calendar API to the user's calendar. The input is the details of the generated reminder, and the output is the event added to the user's calendar. The server calls the Google Calendar API to perform the specific execution process that reflects the reminder content in the calendar.

[1099] Step 5:

[1100] Sending notifications

[1101] The server notifies the user that the reminder has been successfully registered. The inputs for this process are the reminder details and the recipient information, and the output is the notification sent to the user. The server then performs specific notification processing to inform the administrator that the reminder has been added, using email or push notifications.

[1102] Step 6:

[1103] User verification

[1104] The user receives a notification, checks their calendar, and takes appropriate action. The input is the notification from the server, and the output is the user's confirmation and response. The user reads the notification, checks their calendar, and takes specific actions such as contacting or responding as needed.

[1105] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1106] This invention combines a system that automates the response of sales representatives to unpaid damages with an emotion engine. This system acquires listed data, generates reminders for cases with approaching occurrence dates, automatically registers them in the user's calendar, and also has the function of notifying the user. Furthermore, the emotion engine analyzes the user's emotional state and adjusts the content and timing of reminders based on that analysis.

[1107] System Overview

[1108] The system of this invention consists of a server, a terminal, a user, and an emotion engine. The server acquires and analyzes data, generates and stores reminders. The terminal is a device used by the user, and receives calendar entries and notifications. The emotion engine monitors the user's emotions in real time and adjusts the content and timing of reminders based on that information. The user is a sales representative and is responsible for handling cases of unpaid damages.

[1109] Program processing flow and operation

[1110] Data collection

[1111] The server connects to the database and retrieves the data on unpaid damages that have been listed. The server then uses an SQL query to extract data that includes the date the unpaid damages occurred, the name of the company involved, and terminal information.

[1112] Create a reminder

[1113] The server generates a reminder one month before the expected date of the incident. The reminder includes details such as the name of the company involved, a draft of the message, and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the reminder text accordingly. For example, if the user is feeling stressed, a more concise and intuitive message will be generated.

[1114] Calendar registration

[1115] The server generates reminders, which are then automatically registered in the user's calendar using an API. For example, the Google Calendar API is used to set up an event with the title "Incurrence of Unpaid Damages" and the description "Contact Company A." This process assumes that the user has previously provided authentication information to the system.

[1116] notification

[1117] The server notifies the user that the reminder has been successfully registered. This notification is sent via email or the in-app notification system. The emotion engine monitors the user's emotional state in real time to optimize notification timing. For example, it might send notifications during times when the user is less busy. This makes it easier for the user to receive notifications and reduces the risk of delayed responses.

[1118] Specific example

[1119] As a concrete example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server extracts this case and generates a reminder. The reminder includes a specific message titled "Notification regarding unrefunded damages for company A." Furthermore, the emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the usual detailed message; if the user is busy, it generates a concise message. Next, the server uses the Google Calendar API to automatically register the reminder in the user's calendar. Finally, the server sends the user a notification saying, "Please check regarding unrefunded damages for company A." This notification is sent at the time when the user is most likely to respond, based on the emotion engine's analysis. The user receives the notification, checks their calendar, and takes the necessary action.

[1120] Thus, the system of the present invention is a system that supports sales representatives in automatically and efficiently responding to the occurrence of unrefunded damages, and further supports them in responding at the optimal timing and in the optimal manner by taking into account the emotional state of the user.

[1121] The following describes the processing flow.

[1122] Step 1:

[1123] The server connects to the database and retrieves data on unpaid damages that have been listed. The server executes an SQL query to retrieve data including the date the unpaid damages occurred, the name of the company involved, and terminal information.

[1124] Step 2:

[1125] The server analyzes the acquired data and extracts cases whose occurrence date falls within one month from the current date. The server compares the current date with the occurrence date of each case and filters out the relevant cases.

[1126] Step 3:

[1127] The server generates a reminder for each case it extracts. The reminder will have a timestamp set to the date and time one month prior to the date the incident occurred, and will automatically include detailed information about the case (details of the unrefunded damages, the name of the company involved, and a draft of the contact message).

[1128] Step 4:

[1129] The emotion engine analyzes the user's emotions. The user sends data obtained through emotion sensors and input devices on their sales terminal to the emotion engine. The emotion engine analyzes this data to determine the user's stress level and mood.

[1130] Step 5:

[1131] The server adjusts the reminders it generates based on the analysis results of the emotion engine. For example, if the emotion engine determines that the user is stressed, the server changes the wording of the reminder to be more concise and easier to understand.

[1132] Step 6:

[1133] The server saves the adjusted reminders in a data format such as JSON. The saved reminders are then used by the RPA to retrieve and process them in the next step.

[1134] Step 7:

[1135] The server starts the RPA and begins the calendar registration process. At this point, the server provides the RPA with authentication credentials to access the calendar service API (for example, Google Calendar API or Microsoft Graph API).

[1136] Step 8:

[1137] The RPA retrieves reminder data from the server and creates an event in the user's calendar. The RPA registers the calendar event using the content of the reminder (title "Incurrence of Unpaid Damages", reminder timestamp, and description field containing text and the name of the target company).

[1138] Step 9:

[1139] The server checks the success or failure of calendar registration from the API response and logs the result. If successful, it records the details of the registered event; if it fails, it records an error message.

[1140] Step 10:

[1141] The server prepares to create and send a notification to the user. The notification will include a reminder message ("Please check on the unrefunded damages for Company A").

[1142] Step 11:

[1143] The emotion engine monitors the user's emotional state in real time and optimizes the timing of notifications. For example, it might choose to send notifications during times when the user is less busy.

[1144] Step 12:

[1145] The server sends a notification to the user via the mail server. The user is informed of the reminder content using email or a notification system.

[1146] Step 13:

[1147] The server checks the notification transmission results and performs retransmission or error handling if necessary. It verifies whether the notification was sent successfully and, if it fails, attempts to retransmit or logs an error.

[1148] (Example 2)

[1149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1150] In existing systems, sales representatives handle unrefunded damages manually, which can lead to delays and ultimately reduce operational efficiency. Furthermore, the system fails to consider the emotional state of sales representatives, increasing stress and burden, and making it difficult to provide optimal responses. These problems require automated and personalized responses.

[1151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1152] In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within a certain period, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders in the user's calendar, means for notifying the user, means for analyzing the user's emotional state, and means for adjusting the text and sending timing of the reminders based on the analysis results. This enables an automated and efficient response to the occurrence of unreturned damages, and further realizes notifications with optimal timing and text that take into account the user's emotional state.

[1153] "Listed data" refers to a collection of information that has been organized based on specific criteria and stored in a format that allows it to be displayed as a list.

[1154] "Means of acquisition" refers to the functions or methods by which a server or related device retrieves necessary information from a database or external system.

[1155] "Methods of analysis" refer to the process of evaluating acquired data and extracting necessary information based on specific conditions or patterns.

[1156] "Events that will occur within a certain period" refers to events or tasks that are scheduled to occur within a specific period (e.g., within one month).

[1157] A "reminder" is a message or alert that notifies a user of an action or response that should be taken based on a specific date, time, or condition.

[1158] A "draft" refers to the specific text or content created to be presented to users, such as in reminders.

[1159] A "schedule" refers to a calendar or schedule management system that a user uses on a daily basis.

[1160] "Means of notification" refer to functions and methods for communicating reminders and important information to users.

[1161] "Methods for analyzing emotional states" refer to technologies that evaluate and analyze emotions and mental states based on user behavior, usage data, voice, etc.

[1162] "Means for adjusting the wording and sending timing of reminders based on analysis results" refers to a function that optimizes the content and timing of reminders according to the results of sentiment analysis.

[1163] This invention combines a system that automates sales representatives' responses to unpaid damages with an emotion engine. This system consists of a server, terminals, users, and an emotion engine, and each element works in coordination to achieve efficient business support.

[1164] Hardware and software to be used

[1165] Server: Performs various processes such as database connection, data analysis, reminder generation, calendar registration, and notification sending. Specific examples include web server software such as Apache and Nginx, and database management using MySQL or PostgreSQL.

[1166] Terminal: A device used by a user, such as a PC or smartphone, that can run applications for receiving notifications and displaying calendars.

[1167] Emotion Engine: Dedicated software for analyzing a user's emotional state. Emotion recognition APIs (e.g., Microsoft Azure's Emotion API) are used for emotion analysis.

[1168] Program processing

[1169] Data acquisition

[1170] The server connects to the database and retrieves data on unpaid damages. The database contains a table called "Unpaid Damages," from which the date the damage occurred, the name of the company involved, and terminal information are retrieved. This is done using an SQL query, for example, in the format "SELECT FROM Unpaid Damages WHERE Date >= CURRENT_DATE".

[1171] Reminder generation

[1172] The server generates a reminder one month before the expected date of the incident. The reminder includes the name of the company involved, a draft message, and specific instructions on how to respond. The emotion engine analyzes the user's emotional state and adjusts the message accordingly. For example, if the user is feeling stressed, the server generates a more concise message based on the emotion engine's analysis data.

[1173] Calendar registration

[1174] The server automatically registers the generated reminder to the user's calendar using the Google Calendar API. It accesses the API using an authentication token and writes a new event. Specifically, it sends JSON data containing the reminder details to the API to add the event to the calendar.

[1175] Send notification

[1176] The server notifies the user that the reminder registration was successful. The notification system uses email sending via an SMTP server or a push notification server. The emotion engine monitors the user's emotional state in real time and sends notifications at the optimal time.

[1177] Specific example

[1178] For example, consider a case involving company A where unrefunded damages incurred on November 30, 2023. On October 30, 2023, the server retrieves this case from the database and generates a reminder. The reminder includes the phrase "Notification regarding unrefunded damages to company A" and specific wording. The emotion engine analyzes the user's emotions and, if it determines the user is not stressed, uses the detailed wording.

[1179] Next, the server uses the Google Calendar API to automatically register reminders in the user's calendar. This can be achieved, for example, by inputting the following prompt into the AI ​​model:

[1180] For a case involving Company A, where the date of accrual of unrefunded damages is November 30, 2023, please describe the process of creating a reminder one month prior to the accrual date (October 30, 2023), registering it in Google Calendar, and sending a notification. Include adjustments to the reminder text based on the user's emotional state using an emotion engine, and optimization of the notification sending timing.

[1181] Furthermore, the server sends a notification to the user stating, "Please check the outstanding damages from Company A." This notification is sent at the optimal time based on the analysis results of the emotion engine. Upon receiving the notification, the user can check their calendar and take the necessary action quickly.

[1182] In this way, this system automates the sales representative's response to unpaid damages, providing efficient business support and enabling optimal responses tailored to the user's emotional state.

[1183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1184] Step 1: Data Collection

[1185] The server connects to the database and retrieves data on unpaid damages. Specifically, the server loads a driver to connect to the database. Next, the server executes an SQL query containing the necessary information (date the unpaid damages occurred, name of the company involved, terminal information, etc.).

[1186] Input: The server enters the database connection information and queries.

[1187] Data processing: Execute SQL queries, format the results, and create a list.

[1188] Output: Data on unrefunded damages is returned to the server in a list format.

[1189] Specific operation: The server executes the query "SELECT Occurrence Date, Company Name, Terminal Information FROM Unreturned Damages WHERE Occurrence Date >= CURRENT_DATE".

[1190] Step 2: Case Extraction

[1191] The server analyzes the acquired data and extracts cases whose occurrence date falls within a certain period.

[1192] Input: Enter the unreturned damage data listed on the server.

[1193] Data processing: Filter cases to include those that occurred within one month of the current date.

[1194] Output: A list of cases whose occurrence date falls within the specified period will be retrieved.

[1195] Specific operation: The server applies a filter condition (occurrence date <= current date + 30 days) and extracts the relevant cases.

[1196] Step 3: Generate a reminder

[1197] The server generates reminders for the extracted cases. The reminders include the name of the company in question and specific actions to take. The emotion engine analyzes the user's emotional state and adjusts the text accordingly.

[1198] Input: The extracted list of cases and user sentiment data are entered.

[1199] Data processing: Generate a basic reminder text based on case data, and then adjust the text to reflect the results of the emotion engine's analysis.

[1200] Output: A customized reminder is generated.

[1201] Specific operation: The server calls the emotion engine API and modifies the reminder content based on the user's emotional state. For example, if the user is feeling stressed, the message might read, "Notification of unrefunded damages to Company A. Please check the next step for specific details."

[1202] Step 4: Register on calendar

[1203] The server automatically registers the generated reminders to the user's calendar.

[1204] Input: The generated reminder and the Google Calendar API authentication token will be entered.

[1205] Data processing: Convert the reminder content into a format suitable for sending to the Google Calendar API.

[1206] Output: A new event is added to the calendar.

[1207] Specific operation: The server connects to the Google Calendar API, sends event data including reminder details, and registers it in the user's calendar.

[1208] Step 5: Send Notification

[1209] The server notifies the user that the reminder has been successfully registered.

[1210] Input: Calendar registration results and notification messages are entered.

[1211] Data processing: Optimize the timing of notification sending based on the analysis results of the emotion engine.

[1212] Output: A notification is sent to the user at the appropriate time.

[1213] Specific operation: The server generates a notification message stating, "Please check the outstanding damages for Company A," and sends it via email through the SMTP server, or sends a notification to the user's device using a push notification system.

[1214] These steps automate the sales representative's response to unreturned damages and enable the system to provide the most appropriate response based on the user's emotional state.

[1215] (Application Example 2)

[1216] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1217] In traditional systems, setting reminders and notifications for deadlines for specific projects or tasks is often done manually, making efficient management difficult. Furthermore, it's impossible to respond at the optimal time considering the user's emotional state, posing challenges to improving operational efficiency and customer satisfaction. Especially in retail settings, quick and accurate action is required for inventory management and customer service, but there is a lack of appropriate systems to support this.

[1218] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for acquiring listed data, means for analyzing the acquired data and extracting cases whose occurrence date falls within one month, means for generating reminders for the extracted cases and saving data including the text, means for automatically registering the content of the generated reminders to the user's calendar, means for notifying the user, means for analyzing the user's emotional state, means for adjusting the content and sending timing of reminders and notifications based on the analysis results, means for generating reminders in store inventory management and customer service when inventory is low or the return deadline is approaching, means for adjusting the timing of the reminder generation according to the user's emotional state, means for registering the reminder content to the user's calendar, and means for sending notifications to the user. This enables improved operational efficiency and optimal responses that take into account the user's emotional state.

[1219] "Listed data" refers to a collection of information that has been pre-organized and stored in a list format.

[1220] "Means of acquiring data" refers to methods and devices for collecting necessary information from databases or external sources.

[1221] "Means of data analysis" refers to methods and devices for analyzing collected data and extracting useful information.

[1222] A "case" refers to an event or piece of information that is the subject of a specific task or process.

[1223] A "reminder" refers to a message or notification used to draw attention to a specific event or date.

[1224] "Draft text" refers to the design and content of the text included in reminders and notifications.

[1225] A "calendar" refers to a tool or application that users use to manage their schedules and reminders.

[1226] "Means of notification" refers to methods or devices for sending messages or alerts to users.

[1227] "Means for analyzing a user's emotional state" refers to methods or devices for analyzing a user's current emotions and psychological state.

[1228] "Means of adjustment based on analysis results" refers to methods or devices for optimizing the content and timing of reminders and notifications based on the obtained analysis results.

[1229] "Inventory management" refers to activities aimed at monitoring the quantity and condition of goods in stores and warehouses and maintaining optimal levels.

[1230] "Customer service" refers to providing appropriate services and support in response to customer inquiries and requests.

[1231] "Return deadline" refers to the date and time when borrowed items should be returned.

[1232] A system for carrying out this invention is constructed using the following main components and software.

[1233] Hardware and software

[1234] hardware

[1235] 1. Server: Plays a central role in collecting, analyzing, and storing data, and generating reminders.

[1236] 2. Terminal: The device used by the user. This may be a smartphone, tablet, desktop PC, or interactive robot (e.g., Pepper).

[1237] software

[1238] 1. SQL database (e.g., MySQL): Used to store listed data and reminder information.

[1239] 2. Google Calendar API: Used to automatically add reminders to the user's calendar.

[1240] 3. Apache Emotion API: Used to analyze the user's emotional state and adjust the appropriate notification timing and reminder content.

[1241] 4. Firebase Cloud Messaging: This is a service for sending notifications to users.

[1242] System operation

[1243] Data collection

[1244] The server periodically connects to the SQL database to retrieve listed data. For example, it collects inventory information and data on product return deadlines.

[1245] Data Analysis

[1246] The server analyzes the acquired data and extracts cases that meet specific criteria (for example, inventory levels below a certain level, or the return deadline is approaching).

[1247] Reminder generation

[1248] Reminders are generated for the extracted cases. First, the user's emotional state is analyzed using the Apache Emotion API, and the content of the reminder is adjusted based on the results. For example, if the user is determined to be busy, the notification content is made concise.

[1249] Calendar registration

[1250] The generated reminders are automatically added to the user's calendar using the Google Calendar API. This process assumes that authentication information has been provided in advance.

[1251] Sending notifications

[1252] Notifications are sent to users at the optimal time via Firebase Cloud Messaging. This timing is adjusted based on the user's emotional state, which is monitored in real time.

[1253] Specific example

[1254] For example, if a physical store has low stock of product A, the server retrieves this information and generates a reminder. This reminder will contain detailed instructions if the user is not stressed, and a concise message if they are stressed. Next, the reminder is automatically added to the user's calendar using the Google Calendar API as "Inventory Management Alert: Product A". Then, Firebase Cloud Messaging sends a notification to the user saying, "Product A is running low on stock, please take immediate action."

[1255] Example of a prompt

[1256] "We are running low on stock of product A; please take immediate action."

[1257] "Customer B's return deadline is approaching. Please take action."

[1258] This allows physical stores to manage inventory and customer service efficiently and accurately. Furthermore, by considering emotional states, the workload can be reduced.

[1259] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1260] Step 1:

[1261] The server periodically connects to the SQL database to retrieve listed inventory information and product return deadline data. Specifically, the server sends SQL queries to fetch records containing inventory levels and return deadlines. This allows the server to obtain information about the current inventory status and return deadlines. The input is the SQL database, and the output is the fetched data.

[1262] Step 2:

[1263] The server analyzes the retrieved data and extracts items whose inventory levels are below a specified threshold or whose return deadline is within one week. Using data retrieved from the database, it filters items with low inventory levels or approaching return deadlines. The input is the fetched data, and the output is a list of extracted items.

[1264] Step 3:

[1265] The server generates reminders for the extracted cases. During this process, it uses the Apache Emotion API to analyze the user's emotional state and adjusts the reminder content based on the analysis results. For example, if the user is stressed, the notification content will be simplified. The input is a list of extracted cases and the results of the emotional analysis; the output is a list of the adjusted reminders.

[1266] Step 4:

[1267] The server automatically adds the generated reminders to the user's calendar using the Google Calendar API. The reminders are added to the calendar in the appropriate format, including the event title and description. The input is a list of formatted reminders, and the output is the event added to the user's calendar.

[1268] Step 5:

[1269] The server uses Firebase Cloud Messaging to send notifications to the user at the optimal time. This timing is adjusted based on the user's emotional state, which is monitored in real time. Specifically, it selects times when the user is not busy or stressed to send notifications. The inputs are events registered in the user's calendar and the results of the emotional analysis, and the output is the notification sent to the user.

[1270] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1271] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1272] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1273] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1274] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1275] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1276] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1277] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1278] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1279] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1280] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1281] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1282] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1283] 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.

[1284] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1285] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1286] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1287] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1288] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1289] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1290] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1291] The following is further disclosed regarding the embodiments described above.

[1292] (Claim 1)

[1293] Means of obtaining listed data,

[1294] A method for analyzing acquired data and extracting cases whose occurrence date falls within one month,

[1295] A means of generating reminders for the extracted cases and saving data including the draft text,

[1296] A means to automatically register the generated reminder content to the user's calendar,

[1297] A system that includes means for notifying users.

[1298] (Claim 2)

[1299] The system according to claim 1, further comprising means for automatically reflecting the details of a case in the text when generating a reminder.

[1300] (Claim 3)

[1301] The system according to claim 1, comprising means for generating a reminder one month before the date on which the unreturned damages will be incurred and automatically registering it in a calendar.

[1302] "Example 1"

[1303] (Claim 1)

[1304] Means of obtaining listed data,

[1305] A method for analyzing acquired data and extracting cases whose occurrence date falls within one month,

[1306] A means of generating reminders for extracted cases and saving data including drafts using a generation AI model,

[1307] A means to automatically register the content of the generated reminder to the user's calendar,

[1308] A system that includes means for notifying users.

[1309] (Claim 2)

[1310] The system according to claim 1, further comprising means for automatically reflecting the details of a case in the text when generating a reminder.

[1311] (Claim 3)

[1312] The system according to claim 1, comprising means for generating a reminder one month before the date on which the unreturned damages will be incurred and automatically registering it in a calendar.

[1313] "Application Example 1"

[1314] (Claim 1)

[1315] Means of obtaining listed data,

[1316] A method for analyzing acquired data and extracting cases whose occurrence date falls within one month,

[1317] A means of generating reminders for the extracted cases and saving data including the draft text,

[1318] A means to automatically register the generated reminder content to the user's calendar,

[1319] Means of notifying users,

[1320] A means of acquiring parts management data at the factory and monitoring the return deadline for unreturned parts,

[1321] A means to automatically generate reminders for unreturned parts and notify the person in charge of the relevant part,

[1322] A system that includes this.

[1323] (Claim 2)

[1324] The system according to claim 1, further comprising means for automatically reflecting the details of a case in the text when generating a reminder.

[1325] (Claim 3)

[1326] The system according to claim 1, comprising means for generating a reminder one month before the return deadline for unreturned parts and automatically registering it in a calendar.

[1327] "Example 2 of combining an emotion engine"

[1328] (Claim 1)

[1329] Means of obtaining listed data,

[1330] A method for analyzing acquired data and extracting cases whose occurrence date falls within a certain period,

[1331] A means of generating reminders for the extracted cases and saving data including the draft text,

[1332] A means to automatically register the content of the generated reminder to the user's calendar,

[1333] Means of notifying users,

[1334] A means of analyzing the user's emotional state,

[1335] A means of adjusting the text and sending timing of reminders based on the analysis results,

[1336] A system that includes this.

[1337] (Claim 2)

[1338] The system according to claim 1, further comprising means for automatically reflecting the details of a case in the text when generating a reminder.

[1339] (Claim 3)

[1340] The system according to claim 1, comprising means for generating a reminder a certain period in advance and automatically registering it in a schedule.

[1341] "Application example 2 when combining with an emotional engine"

[1342] (Claim 1)

[1343] Means of obtaining listed data,

[1344] A method for analyzing acquired data and extracting cases whose occurrence date falls within one month,

[1345] A means of generating reminders for the extracted cases and saving data including the draft text,

[1346] A means to automatically register the generated reminder content to the user's calendar,

[1347] Means of notifying users,

[1348] A means of analyzing the user's emotional state,

[1349] A means of adjusting the content and timing of reminders and notifications based on the analysis results,

[1350] A method for generating reminders in store inventory management and customer service when inventory levels are low or when return deadlines are approaching.

[1351] A system that includes this.

[1352] (Claim 2)

[1353] The system according to claim 1, further comprising means for automatically reflecting the details of a case in the text when generating a reminder.

[1354] (Claim 3)

[1355] The system according to claim 1, comprising means for generating a reminder one month before the date on which the unreturned damages will be incurred and automatically registering it in a calendar. [Explanation of symbols]

[1356] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining listed data, A method for analyzing acquired data and extracting cases whose occurrence date falls within one month, A means of generating reminders for the extracted cases and saving data including the draft text, A means to automatically register the generated reminder content to the user's calendar, A system that includes means for notifying users.

2. The system according to claim 1, further comprising means for automatically reflecting detailed information of a case in the text when generating a reminder.

3. The system according to claim 1, comprising means for generating a reminder one month before the date on which non-return damages will be incurred and automatically registering it in a calendar.

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