system

Abstract

We provide the system. [Solution] A means of summarizing the content from an email, A means of recording the summarized content in a spreadsheet, A means to automatically set a reminder date based on the recorded information, A means of automatically sending notifications based on the reminder date, A system that includes this.

Description

Technical Field

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As digitization of information progresses, many in-house operations are generally managed using email and spreadsheets. However, the task of employees extracting important information from a large amount of emails and manually transcribing it into a spreadsheet not only requires time and effort but also involves the risk of human error. In addition, it is necessary to set reminders for each task and notify relevant parties at appropriate times, but this task is also inefficient if done manually. In such a situation, along with improving the efficiency of operations, it has become an issue to create an environment in which employees can concentrate on their original work.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides the following system. First, it includes means for automatically summarizing the content from emails. Next, it includes means for automatically recording the summarized content in a spreadsheet. Furthermore, it includes means for automatically setting a reminder date based on the recorded content and automatically sending notifications to relevant parties based on that reminder date. By adding functions for filtering emails based on specific conditions and for automatically identifying important tasks from the recorded content, it is possible to further improve the efficiency of operations. In this way, the burden of manual work on employees can be reduced, and the risk of human error can be decreased.

[0006] "Email" refers to electronic messages sent and received using communication networks such as the Internet.

[0007] To "summarize" means to shorten long or complex texts and extract the main points or core essence.

[0008] A "spreadsheet" is software used to manage and display data in a spreadsheet format consisting of rows and columns.

[0009] "To record" means to store information or data in one place.

[0010] A "reminder date" is a specific date set to remind you of the progress of a task or its deadline.

[0011] A "notification" is a message or alert sent to inform relevant parties about a specific event or piece of information.

[0012] "Selecting" means choosing what is necessary from a large number of items based on specific criteria.

[0013] "To identify" means to compare and analyze multiple objects or pieces of information and distinguish between them.

[0014] A "task" refers to the work or activities to be carried out to achieve a specific purpose.

[0015] A "summarized result" refers to the shortened content or points extracted by the summarization operation.

Brief Description of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows 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 Example 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 Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

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 explained.

[0019] In the following embodiments, the numbered 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, the numbered 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 numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[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 is a system that automatically summarizes the content of emails and transfers the summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates for tasks and sending notifications for important tasks.

[0038] System Configuration

[0039] This system has the following main functions:

[0040] 1. Obtaining and summarizing emails

[0041] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[0042] 2. Transfer to a spreadsheet

[0043] The server records the summarized email content in a Google® spreadsheet. The date and time the email was received and the sender are also recorded.

[0044] 3. Automatic setting of reminder dates

[0045] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[0046] 4. Notification of important tasks

[0047] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[0048] Explanation of program processing

[0049] These features are implemented using Google Apps Script.

[0050] Email acquisition and summarization

[0051] The server uses the Gmail service to monitor emails with the specific label "Important." It retrieves the content of the emails and summarizes it using an AI-based summarization engine. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[0052] Transfer to spreadsheet

[0053] The server adds the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Project proposal with client", the email received date and time "October 1, 2023", and the sender "client@example.com" will be newly recorded.

[0054] Automatic reminder date setting

[0055] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[0056] Important task notifications

[0057] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using Gmail. For example, a task due tomorrow, "Schedule a meeting with the client," will be notified to the relevant parties.

[0058] Specific example

[0059] Example 1: Transferring data from Gmail to a task list

[0060] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[0061] The server adds the summarized content as a new row in a Google Spreadsheet. Enter the date and time the email was received in column A, the summarized content in column B, and the sender's email address in column C.

[0062] Example 2: Automatic setting of reminder dates

[0063] The server checks the last added row and calculates the reminder date five days after the task's receipt date. For example, if the receipt date is "October 1, 2023", the reminder date will be "October 6, 2023".

[0064] The server enters the calculated reminder date into the relevant column D.

[0065] Example 3: Notification of important tasks

[0066] The server scans the spreadsheet at a set time each day to detect tasks whose reminder date is the following day. For example, if a task such as "Schedule a meeting with a client" is found, it sets up notifications for the relevant parties.

[0067] Based on the detected task, the server sends an email to the relevant parties requesting them to "confirm the meeting date with the client."

[0068] This system automates email processing, task logging, reminder setting, and important task notifications, significantly improving work efficiency.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] The server uses the Gmail service to search for new emails with the specific label "Important".

[0072] Step 2:

[0073] The server retrieves the latest messages from each thread based on the search results.

[0074] Step 3:

[0075] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[0076] Step 4:

[0077] The server receives the summarization results from the AI ​​summarization engine. For example, an email whose full text is "Proposal for a new project" is summarized as "Project proposal".

[0078] Step 5:

[0079] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[0080] Step 6:

[0081] The server adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[0082] Step 7:

[0083] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later from that date.

[0084] Step 8:

[0085] The server enters the calculated reminder date into the corresponding column in the spreadsheet.

[0086] Step 9:

[0087] The server runs a script at a set time every day, scanning all tasks in the spreadsheet.

[0088] Step 10:

[0089] The server detects tasks from the scanned tasks that have a reminder date for the next day.

[0090] Step 11:

[0091] The server retrieves contact information for those involved based on the detected critical tasks.

[0092] Step 12:

[0093] The server will send a reminder email to the relevant contacts. The email will include a message such as, "Please confirm the meeting date with the client."

[0094] Through these steps, the process of summarizing email content, transferring it to a spreadsheet, setting reminder dates, and notifying important tasks is automated. This not only improves work efficiency but also reduces the risk of human error.

[0095] (Example 1)

[0096] 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."

[0097] Traditional email management systems require users to manually summarize received emails, transfer the content to task management systems or spreadsheets, and set reminder dates. This process is time-consuming and labor-intensive, and its efficiency significantly decreases, especially when handling large volumes of emails. Furthermore, there is a risk of overlooking important tasks or setting incorrect reminders. To address these challenges, a system is needed that centrally and automatically summarizes emails, automatically transfers the content, sets reminder dates, and notifies users of important tasks.

[0098] 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.

[0099] In this invention, the server includes means for summarizing content from emails, means for transferring the summarized content to spreadsheet data, means for automatically setting reminder dates based on the recorded content, means for automatically sending notifications based on the reminder dates, means for monitoring emails with specific identifiers, means for automatically shortening content using a summarization engine, and means for adding data to new rows in a spreadsheet. This automates email management and task management, enabling increased work efficiency and preventing important tasks from being overlooked.

[0100] "Email" refers to messages sent and received electronically.

[0101] "Methods for summarizing content" refer to techniques for shortening the body of an email and extracting important information.

[0102] "Spreadsheet data" refers to software or files used to store data in a format consisting of rows and columns.

[0103] A "reminder date" is the date on which you set a notification or reminder about a particular task or event.

[0104] "Means for automatically sending notifications" refers to a function that automatically sends notification messages based on set conditions.

[0105] A "specific identifier" refers to a tag or label used to identify an email based on specific conditions or attributes.

[0106] A "means of monitoring" refers to a method that has a monitoring function that reacts when specific conditions are met.

[0107] A "summarization engine" is an algorithm or program used to shorten the content of a text and extract only the important parts.

[0108] A "spreadsheet" is a spreadsheet software or file format used to manage data using rows and columns.

[0109] "Method for adding data to a new row" refers to the function of adding new information as a row to a spreadsheet.

[0110] "Recorded content" refers to data that stores the summarized content of emails and related information.

[0111] "Automatic configuration" refers to a function that allows the system to automatically configure settings based on specific conditions.

[0112] An "important task" is a task that has a higher priority than other tasks and requires attention.

[0113] Modes for carrying out the invention

[0114] This invention is a system that automatically summarizes the content of emails and transfers that summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates and sending notifications for important tasks. This system has the following main functions:

[0115] Email acquisition and summarization

[0116] The server uses Google Apps Script to monitor emails via the Gmail API. It identifies emails with the specific identifier "Important" and retrieves their content. The retrieved content is sent to a summarization engine using a generative AI model, which outputs it in a shortened and summarized form.

[0117] Specific example:

[0118] Assume you receive a new email with the specific identifier "Important" and the subject line "Proposal for a New Project with a Client." The content of this email can be summarized as "Project Proposal with a Client."

[0119] Transferring data to a spreadsheet.

[0120] The server transfers the summarized email content to a spreadsheet (e.g., Google Sheets). Specifically, it records the following information in a new row:

[0121] Date and time of receipt

[0122] Summary

[0123] Sender's email address

[0124] Specific example:

[0125] The summarized content, "Project proposal with client," the date received, "October 1, 2023," and the sender, "client@example.com," are recorded in columns A, B, and C, respectively.

[0126] Automatic reminder date setting

[0127] The server automatically calculates the reminder date based on the task's reception date and time recorded in the spreadsheet data, and enters it into the relevant column of the data. The reminder date is set a specified number of days after the reception date.

[0128] Specific example:

[0129] If the received date is "October 1, 2023," the reminder date will be set to "October 6, 2023" and entered in column D of the spreadsheet.

[0130] Important task notifications

[0131] The server scans spreadsheet data at a set time each day to identify tasks with upcoming reminder dates or those of high importance. It then automatically sends notifications to the relevant stakeholders.

[0132] Specific example:

[0133] For the task "Schedule a meeting with the client," which has a reminder date of "October 6, 2023," send a notification email to the relevant parties stating, "Please confirm the meeting date with the client."

[0134] Overall flow

[0135] This system's program is implemented using Google Apps Script. The server works in conjunction with the Gmail API, a summarization engine, and the Google Sheets API to automatically monitor, summarize, transcribe, set reminder dates for, and send notifications for emails. This allows users to efficiently manage their emails and track tasks without any hassle.

[0136] This system is expected to significantly improve work efficiency by automating email processing, task recording, reminder date setting, and important task notifications.

[0137] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0138] Step 1:

[0139] Get email

[0140] The server uses Google Apps Script to initiate a session with the Gmail API. It monitors email folders that have a specific label set to "Important." When a new email is detected, it retrieves its data (sender, received date and time, subject, and body).

[0141] Input: New email with the specific label "Important"

[0142] Output: Email data (sender, received date and time, subject, body)

[0143] Specific actions:

[0144] The server uses the Gmail API to check for new emails labeled "Important." For example, it might find a new email with the subject line "Proposal for a new project with a client."

[0145] Step 2:

[0146] Summary of email content

[0147] The server sends the body of the retrieved email to the AI ​​summarization engine. The summarization engine analyzes the email content, extracts the important parts, and generates a shortened summary.

[0148] Input: Email body

[0149] Output: Summary

[0150] Specific actions:

[0151] The server sends the email body to the AI ​​summarization engine and receives a summary titled "Project proposal with client."

[0152] Step 3:

[0153] Transfer to spreadsheet

[0154] The server transfers the summarized email content into a spreadsheet. Using the Google Sheets API, the following data is added to a new row: date and time received (column A), summary text (column B), and sender's email address (column C).

[0155] Input: Date and time received, summary, sender's email address

[0156] Output: A new row is added to the spreadsheet.

[0157] Specific actions:

[0158] The server uses the spreadsheet API to add a new row with "October 1, 2023" in column A, "Project Proposal with Client" in column B, and "client@example.com" in column C.

[0159] Step 4:

[0160] Automatic reminder date setting

[0161] The server reads the last row added to the spreadsheet, calculates the reminder date (a specified number of days after the task's receipt date), and enters it into column D of the spreadsheet.

[0162] Input: Received date and time

[0163] Output: Reminder date

[0164] Specific actions:

[0165] The server calculates the reminder date, "October 6, 2023," which is 5 days after the reception date of "October 1, 2023," and enters it in column D.

[0166] Step 5:

[0167] Important task notifications

[0168] The server scans the spreadsheet at a set time every day to identify tasks with upcoming reminder dates or those of high importance. For identified tasks, it uses the Gmail API to send notification emails to the relevant parties.

[0169] Input: Spreadsheet data

[0170] Output: Notification email

[0171] Specific actions:

[0172] The server finds the task "Schedule a meeting with the client" with a reminder date of "October 6, 2023" and sends an email to the relevant parties requesting them to "Confirm the meeting date with the client."

[0173] (Application Example 1)

[0174] 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."

[0175] Logistics centers face the challenge of managing numerous tasks efficiently. Traditional methods require manual recording, management, and reminders, leading to decreased work efficiency. Furthermore, there's a risk of overlooking important tasks. To address these issues, a system is needed that automatically summarizes, records, and reminds tasks, and also notifies managers.

[0176] 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.

[0177] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, and means for notifying the administrator of the task details. This enables the automatic summarization, recording, reminder, and notification of important tasks at the logistics center.

[0178] "Email" refers to digital messages sent and received over the internet.

[0179] A "summary" is a short, concise compilation of long or complex information.

[0180] A "spreadsheet" is a document in spreadsheet software that uses a table format with rows and columns arranged in a regular pattern.

[0181] A "reminder date" is a specific date used to remind someone of a designated task or event.

[0182] A "notification" is an alert or message designed to inform a recipient of specific information or a message.

[0183] A "server" is a computer system that manages, stores, and provides information over a network.

[0184] A "task" is a unit of work or activity that needs to be performed to achieve a specific objective.

[0185] An "administrator" is someone responsible for the operation and supervision of a system or process.

[0186] A "logistics center" is a facility that receives, stores, and ships goods.

[0187] "Automatic configuration" refers to the process by which a system automatically sets values ​​and conditions without user intervention.

[0188] "Management" is the process of planning, organizing, and controlling tasks and resources in order to achieve specific goals.

[0189] This invention is a system aimed at improving the efficiency of task management in logistics centers. This system automatically summarizes task details from emails and transfers them to Google Sheets. It also automatically sets reminder dates and notifies managers when important tasks are approaching, thereby preventing tasks from being overlooked or missed.

[0190] System Configuration

[0191] This system has the following main functions:

[0192] 1. Obtaining and summarizing emails

[0193] The server automatically retrieves emails with specific labels and summarizes their content using an AI model. For example, an email that says "Please pick product A" would be summarized as "Picking product A".

[0194] 2. Transfer to a spreadsheet

[0195] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[0196] 3. Automatic setting of reminder dates

[0197] The server automatically sets a reminder date for tasks recorded in the spreadsheet, a certain number of days later. For example, it can set the reminder date to 5 days after the date of receipt.

[0198] 4. Notification of important tasks

[0199] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[0200] Hardware and software usage

[0201] Hardware: Warehouse management robots (e.g., Amazon Robotics)

[0202] Software: Google Apps Script, Gmail API, Google Sheets API, OpenAI® API

[0203] Explanation of processing details

[0204] Email retrieval and summarization:

[0205] The server uses the Gmail API to monitor emails labeled "Important." It retrieves the email content and summarizes it using a generative AI model (e.g., OpenAI's text-davinci-003). For example, an email that says "Please pick item A" would be summarized as "Pick item A." An example of a specific prompt used is as follows:

[0206] Example of a prompt:

[0207] Email content:

[0208] "Please pick item A."

[0209] Please summarize.

[0210] Transferring to a spreadsheet:

[0211] The server uses the Google Sheets API to add the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Picking task for product A", the email received date and time "October 1, 2023", and the sender "warehouse@example.com" will be newly recorded.

[0212] Automatic reminder date setting:

[0213] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[0214] Important task notifications:

[0215] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using the Gmail API. For example, a task due tomorrow, "Picking Product A," will be notified to relevant parties.

[0216] This enables the logistics center to automatically summarize, record, and remind tasks, as well as notify users of important tasks.

[0217] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0218] Step 1:

[0219] Get email

[0220] The server uses the Gmail API to retrieve emails labeled "Important." The input is unread emails labeled "Important," and the output is the full content of the retrieved emails. The server periodically checks the mailbox and extracts new emails that match the criteria.

[0221] Step 2:

[0222] Summary of email content

[0223] The server uses a generative AI model (e.g., OpenAI's text-davinci-003) to summarize the content of the retrieved emails. The input is the body of the retrieved email, and the output is the summarized text. Specifically, the server prepares a prompt, sends it to the AI ​​model, and receives the summarization result.

[0224] Step 3:

[0225] Transfer to spreadsheet

[0226] The server uses the Google Sheets API to transfer summarized email content to a Google Spreadsheet. The inputs are the summarized email content, the date and time the email was received, and the sender's address; the output is a newly added row in the spreadsheet. The server writes this data to a specific sheet and row and records it as a new task.

[0227] Step 4:

[0228] Automatic reminder date setting

[0229] The server automatically sets reminder dates for tasks recorded in a spreadsheet based on the date and time they were received. The input is the date and time the task was received as recorded in the spreadsheet, and the output is the calculated reminder date. The server calculates the reminder date by adding a certain number of days from the task's received date and enters that date in the relevant column.

[0230] Step 5:

[0231] Important task notifications

[0232] The server scans the spreadsheet at a set time each day to identify tasks with approaching or important reminder dates. The input is task information recorded in the spreadsheet, and the output is notification emails to stakeholders. The server identifies tasks with approaching reminder dates and sends notifications to the email addresses of stakeholders associated with those tasks.

[0233] Examples of specific actions

[0234] The server retrieves emails labeled "important" and sends their content to an AI summarization engine for summarization.

[0235] For example, the action of summarizing an email that says "Please pick product A" to "Pick product A".

[0236] This operation records the summarized content in Google Sheets, with columns A (email received date and time), B (summarized content), and C (sender's email address).

[0237] If the task is received on "October 1, 2023," the system calculates the reminder date and enters it as "October 6, 2023" into the spreadsheet.

[0238] When the reminder date for "Picking Product A" approaches, an email notification is sent to relevant parties stating, "The task deadline is approaching."

[0239] 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.

[0240] This invention is a system that automatically summarizes the content of emails and records the summary in a spreadsheet, and also incorporates an emotion engine that recognizes the user's emotions to adjust notification content and reminder dates. This system improves work efficiency and enables task management that is adapted to the user's emotional state.

[0241] System Configuration

[0242] This system has the following main functions:

[0243] 1. Obtaining and summarizing emails

[0244] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[0245] 2. Transfer to a spreadsheet

[0246] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[0247] 3. Automatic setting of reminder dates

[0248] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[0249] 4. Notification of important tasks

[0250] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[0251] 5. Integration of the Emotional Engine

[0252] The server uses an emotion engine to analyze the user's emotional state.

[0253] 6. Customizing notification content

[0254] The server customizes the content of notifications according to the user's emotional state.

[0255] 7. Adjusting the reminder date

[0256] The server can adjust the reminder date based on the user's emotional state.

[0257] Explanation of program processing

[0258] These features are implemented using Google Apps Script.

[0259] Email acquisition and summarization

[0260] The server automatically retrieves emails with a specific label, "Important," using the Gmail service. It then sends the content of the retrieved emails to an AI-based summarization engine to generate a summary. For example, an email titled "Details about a new project with a client" would be summarized as "New project details."

[0261] Transfer to spreadsheet

[0262] The server adds the summarized content to a new row in a Google Spreadsheet. For example, the summary result "New Project Details," the email received date and time "October 1, 2023," and the sender "client@example.com" will be recorded.

[0263] Automatic reminder date setting

[0264] The server calculates the reminder date based on the task's reception date and time. For example, it might set the reminder date to 5 days after the reception date.

[0265] Important task notifications

[0266] The server scans the spreadsheet and detects tasks with upcoming reminder dates. For detected tasks, it sends email reminder notifications to the relevant parties. For example, if "scheduling a meeting" is due tomorrow, a notification will be sent to the relevant parties.

[0267] Emotional engine integration

[0268] The server analyzes the user's emotions using an emotion engine. For example, the emotion engine detects the emotional state "stress" from the context of the user's emails or the content entered in spreadsheets.

[0269] Customizing notification content

[0270] The server customizes the content of notifications based on the analysis results of the emotion engine. For example, if it detects that the user is stressed, it changes the tone of the notification email to soften it.

[0271] Adjusting the reminder date

[0272] The server can adjust the reminder date based on the user's emotional state. For example, if the server detects that the user is "very busy," it will change the reminder date from two days later to one day later.

[0273] Specific example

[0274] Example 1: Transferring data from Gmail to a task list and integrating sentiment recognition.

[0275] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, content such as "Regarding a new proposal for the project" would be summarized as "New proposal."

[0276] The server adds the summary results to a Google Spreadsheet. Simultaneously, the sentiment engine recognizes from the email context that the user is "focused."

[0277] Example 2: Setting the reminder date and customizing the notification

[0278] The server sets a reminder date five days after the added task. For example, if the email reception date is "October 1, 2023", the reminder date will be "October 6, 2023".

[0279] When the user is in the "concentrating" state, the server customizes and sends a message with a gentle tone such as "This is a notification for reconfirmation" when sending the reminder email.

[0280] Example 3: Notification of important tasks and adjustment of reminder date

[0281] The server scans the spreadsheet regularly every day and detects tasks whose reminder date is the next day. For example, if "Meeting schedule adjustment" is applicable, assume that day is tomorrow.

[0282] When the user is detected to be "very busy", the server sends a notification to the relevant user earlier to prompt preparation in advance.

[0283] According to the present invention, not only the content summary of the email, the posting to the spreadsheet, the setting of the reminder date, and the task notification are automated, but also flexible task management considering the emotional state of the user becomes possible. As a result, along with the improvement of work efficiency, stress reduction and productivity improvement of the user can be expected.

[0284] The processing flow will be described below.

[0285] Step 1:

[0286] The server searches for new emails with a specific label "important" using the Gmail service.

[0287] Step 2:

[0288] The server retrieves the latest messages from each thread based on the search results.

[0289] Step 3:

[0290] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[0291] Step 4:

[0292] The server receives the summarization results from the AI ​​summarization engine. For example, an email titled "New Project Proposal" would be summarized as "Project Proposal."

[0293] Step 5:

[0294] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[0295] Step 6:

[0296] The server then adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[0297] Step 7:

[0298] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later.

[0299] Step 8:

[0300] The server simply enters the calculated reminder date into the corresponding column in the spreadsheet.

[0301] Step 9:

[0302] The device sends the emotional state entered by the user into a spreadsheet to the emotion engine.

[0303] Step 10:

[0304] The server receives the analysis result of the emotion engine and recognizes the user's emotional state. For example, it may be recognized as "stress state".

[0305] Step 11:

[0306] The server executes the script regularly every day and scans all tasks in the spreadsheet.

[0307] Step 12:

[0308] The server detects tasks with the reminder date being the next day from the scanned tasks.

[0309] Step 13:

[0310] The server adjusts the content of the notification based on the user's emotional state. For example, if the user is recognized as being in a "stress state", the tone of the notification is softened.

[0311] Step 14:

[0312] The server adjusts the reminder date based on the analysis of the emotion engine. For example, if the user is recognized as "very busy", the reminder date is set one day earlier.

[0313] Step 15:

[0314] The server sends a notification to the relevant parties based on the adjusted reminder date. For example, it sends an email with the content such as "This is a notice for the schedule adjustment of the meeting".

[0315] Through these steps, the content summary of the email, posting to the spreadsheet, setting of the reminder date, and task notification are automated, and a system that flexibly responds according to the user's emotional state is realized. Along with the improvement of business efficiency, stress reduction and productivity improvement of the user can be expected.

[0316] (Example 2)

[0317] 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".

[0318] Traditional systems require manual summarization of email content and manual recording in spreadsheets to manage tasks, which reduces work efficiency. Furthermore, reminder dates and notification content are set uniformly without considering the user's emotional state, potentially negatively impacting user stress and work efficiency. Additionally, the manual selection of important tasks increases the likelihood of oversights and poor prioritization.

[0319] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring emails that meet specific conditions, means for summarizing the contents of acquired emails, means for recording the summarized contents in spreadsheet software, means for automatically setting a reminder date based on the recorded contents, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotional state, means for customizing the content of the notification based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This not only automates email summarization and task management, but also enables flexible task management that adapts to the user's emotional state.

[0320] "Specific conditions" refer to rules or criteria for selecting appropriate emails, such as labels tagged on emails or filter settings.

[0321] "Email" refers to digital messages sent and received via the internet or other computer networks.

[0322] A "summary" is a text that extracts the main points of an email and presents them concisely.

[0323] A "spreadsheet program" is a software application used for organizing, analyzing, and creating graphs from data. It is commonly known as a spreadsheet.

[0324] A "reminder date" is the date on which notifications or alerts are sent in relation to tasks, appointments, etc.

[0325] A "notification" is a message or alert sent to a user based on a specific event or condition.

[0326] "Emotional state" refers to the user's psychological state and emotional tendencies, which are analyzed by the AI ​​engine.

[0327] "Automatic configuration" refers to the process in which the system automatically determines and applies values ​​and conditions without manual intervention.

[0328] "Transmission" refers to the act of delivering digital data to other devices or services via a network or communication channel.

[0329] "Analysis" is the process of thoroughly examining data and information to identify specific patterns and trends.

[0330] "Customization" refers to modifying the operation of a system or service to suit the user's needs or specific conditions.

[0331] "Adjustment" refers to the act of changing settings or values ​​based on specific criteria or conditions.

[0332] This invention relates to a system that retrieves important emails from email addresses based on specific criteria, automatically summarizes their content, and records it in a spreadsheet program. The system also includes a function to recognize the user's emotional state and adjust reminder dates and notification content accordingly. This invention is expected to improve work efficiency and reduce user stress. The system has the following main functions:

[0333] Get email

[0334] The server uses Google's Gmail API to automatically retrieve emails based on specific criteria. These criteria might include emails labeled as "Important," for example. The server periodically retrieves emails that meet these criteria and adds them to a list.

[0335] Summary of email content

[0336] The server sends the content of the retrieved emails to an AI summarization engine, which generates a summary. This AI summarization engine uses natural language processing (NLP) technology to create a concise summary of lengthy email content.

[0337] Specific example

[0338] For example, an email that says, "Let's have a meeting about a new project with the client," can be summarized as, "Meeting about the new project."

[0339] Recording the summary content into a spreadsheet program.

[0340] The server records the summarized content in a Google Spreadsheet. The information recorded includes the summary result, the date and time the email was received, and the sender's information.

[0341] Specific example

[0342] If the summary result is "Meeting for a new project," the email was received on "October 1, 2023," and the sender is "client@example.com," this information will be added to a new row in the spreadsheet.

[0343] Automatic reminder date setting

[0344] The server automatically calculates the reminder date based on the date and time the task was received. For example, it can set the reminder date to 5 days after the email was received.

[0345] Specific example

[0346] If the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[0347] Important task notifications

[0348] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. These notifications are sent via email.

[0349] Specific example

[0350] If the deadline for "scheduling a meeting" is tomorrow, a reminder notification will be sent to all relevant parties.

[0351] User sentiment analysis using an emotion engine

[0352] The server sends the user's emails and spreadsheet contents to the emotion engine to analyze the user's emotional state. The emotional state is expressed in terms such as "stressed" or "concentrated."

[0353] Specific example

[0354] When the emotion engine detects a user's emotional state, such as "busy" or "distracted," that information is recorded.

[0355] Customizing notification content

[0356] The server customizes notification content based on the user's emotional state. If it determines that the user is stressed, it changes the tone of the notification email to soften it.

[0357] Specific example

[0358] A message in a friendly tone is sent, such as, "This is a reminder for confirmation."

[0359] Adjusting the reminder date

[0360] The server adjusts the reminder date based on the user's emotional state. For example, if it detects that the user is extremely busy, it will set the reminder date earlier than originally scheduled.

[0361] Specific example

[0362] If the user is determined to be "extremely busy," the reminder date will be changed from 2 days later to 1 day later.

[0363] Example of a prompt

[0364] The following are examples of prompt statements used for generative AI models.

[0365] "Retrieve emails labeled 'Important' from Gmail, send them to a summarization engine, and have it generate a concise summary."

[0366] Thus, this system streamlines automatic email summarization and task management, and further enables flexible management that takes into account the user's emotional state. The above is a description of embodiments for carrying out the present invention.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The server uses the Gmail API to automatically retrieve emails that meet specific criteria. The input is a specific criterion (e.g., the "Important" label), and the output is a list of unread emails that match that criterion. Specifically, the server runs a scheduled task every hour, issuing a query to search for unread emails with the specified label. It retrieves the IDs of the matching emails and adds them to the list.

[0370] Step 2:

[0371] The server sends the content of the retrieved email to an AI summarization engine to generate a summary. The input is the email body, and the output is the summarized text. Specifically, the server extracts the email body as text, sends this text to the AI ​​summarization engine, and receives the summary result. For example, an email that says "Let's have a meeting about a new project with the client" would be summarized as "Meeting about a new project."

[0372] Step 3:

[0373] The server records the summarized content in a Google Spreadsheet. Inputs include the summary result, the email's received date and time, and sender information; output is this information recorded in the spreadsheet. Specifically, the server adds a new row to the spreadsheet and enters the summary result, received date and time, and sender information into the corresponding cells. For example, the summary result might be "Meeting about a new project," the email received date and time might be "October 1, 2023," and the sender might be "client@example.com."

[0374] Step 4:

[0375] The server automatically calculates the reminder date based on the task's reception date and time. The input is the task's reception date and time, and the output is the calculated reminder date. Specifically, the server calculates a date five days after the task's reception date and enters the reminder date into the corresponding cell in the spreadsheet. For example, if the email was received on "October 1, 2023," the reminder date would be "October 6, 2023."

[0376] Step 5:

[0377] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. The input is task information from the spreadsheet, and the output is the sent reminder notifications. Specifically, the server checks the spreadsheet's reminder dates at a fixed time each day, generates and sends notification emails for tasks with a reminder date the following day. For example, if the task "Schedule a meeting" is due tomorrow, a reminder notification will be sent to the relevant parties.

[0378] Step 6:

[0379] The server sends the user's emails and spreadsheet contents to an emotion engine to analyze the user's emotional state. The input is the user's email text and task information from the spreadsheet, and the output is the analysis of the emotional state. Specifically, the server extracts the user's emails and spreadsheet contents, sends them to the emotion engine, and records the returned emotional state (e.g., "stressed," "focused") in a spreadsheet or database.

[0380] Step 7:

[0381] The server customizes notification content based on the user's emotional state. Input includes the results of the emotion engine's analysis and the standard notification content, while output is the customized notification content. Specifically, if the server determines that the user is "stressed," it softens the wording of the notification email. For example, it might change it to something like, "This is a reminder."

[0382] Step 8:

[0383] The server adjusts the reminder date based on the user's emotional state. The inputs are the results of the emotion engine's analysis and the existing reminder date; the output is the adjusted reminder date. Specifically, if the server detects that the user is "very busy," it will move the reminder date earlier than originally set. For example, it might change the reminder date from two days later to one day later.

[0384] The above outlines the specific flow of the processing steps in this system's program.

[0385] (Application Example 2)

[0386] 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".

[0387] Traditional email summarization systems were useful for summarizing email content and automating reminders, but they failed to consider the user's emotional state and couldn't deliver important notifications at the right time. As a result, important tasks and notifications were often missed, and work efficiency could not be sufficiently improved. Furthermore, it was difficult for users to receive timely notifications when they were stressed or busy.

[0388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0389] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotions using an emotion recognition engine, means for customizing the notification content based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This enables task management and notifications adapted to the user's emotional state, improving work efficiency and reducing user stress.

[0390] "Email" refers to digital messages sent and received over the internet.

[0391] To "summarize" means to extract the essential parts of information and describe them concisely.

[0392] A "spreadsheet" is a digital document in tabular format, consisting of rows and columns, and is primarily used for organizing and calculating data.

[0393] A "reminder date" is a date and time designated for sending a notification about a specific action or task.

[0394] A "notification" is a message or alert used to convey specific information to a recipient.

[0395] An "emotion recognition engine" is a software technology used to analyze and recognize a user's emotional state.

[0396] "To analyze" means to handle data and information and to thoroughly understand its contents.

[0397] "Customizing" means changing the content or functionality according to specific conditions or requirements.

[0398] A "task" is a job or activity performed to achieve a specific objective.

[0399] A "server" is a computer system that provides services over a network.

[0400] This invention is a task management and notification system that takes into account the user's emotional state, and is implemented using the following main hardware and software.

[0401] Hardware and software configuration

[0402] 1. Server

[0403] Google Apps Script: A scripting language for integrating with Google Sheets.

[0404] Gmail API: Retrieving emails and sorting them based on specific labels

[0405] Emotion recognition engine: Software for analyzing a user's emotional state in real time.

[0406] smtplib: A Python library for sending email notifications.

[0407] OpenCV: An open-source library for analyzing emotions from facial expressions and voice tone.

[0408] Program processing flow

[0409] The server will perform the following steps:

[0410] 1. Obtaining and summarizing emails

[0411] The server uses the Gmail API to automatically retrieve emails with specific labels (e.g., Important), and sends their content to an AI-based summarization engine to generate a summary. For example, if the email is titled "Regarding a New Project Proposal," the summary will be "New Proposal."

[0412] 2. Recording in a spreadsheet

[0413] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded. For example, the summary result might be "New Proposal," the email received on "October 1, 2023," and the sender might be "example@domain.com."

[0414] 3. Automatic setting of reminder dates

[0415] The system automatically sets a reminder date based on the recorded information. For example, it can set a reminder date five days after the date of receipt.

[0416] 4. User sentiment analysis using an emotion recognition engine

[0417] The server uses an emotion recognition engine to analyze the user's emotional state. It recognizes emotional states (e.g., stress, concentration) from facial expressions and tone of voice.

[0418] 5. Customizing notification content

[0419] The content of notifications will be customized based on the user's emotional state. For example, if the user is feeling stressed, the tone of the notification email will be softened.

[0420] 6. Adjusting the reminder date

[0421] The reminder date can be adjusted based on the user's emotional state. For example, if the user is detected as "very busy," the reminder date can be changed from two days later to one day later.

[0422] 7. Sending notifications

[0423] Based on the reminder date, notifications will be automatically sent to the relevant parties. For example, if the deadline for "scheduling a meeting" is tomorrow, a notification will be sent to the relevant parties.

[0424] Specific example

[0425] Example 1: Urgent task notification

[0426] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, "New project proposal from your boss" would be summarized as "New project proposal."

[0427] The server adds the summary results to a Google Spreadsheet, and at the same time, the sentiment engine recognizes that the user is "focused."

[0428] Example 2: Setting reminder dates and customizing notifications

[0429] The server will set a reminder date five days later. For example, if the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[0430] If a user is experiencing "stress," the content of the reminder email will be customized to a more gentle tone, such as "This is a reminder for confirmation."

[0431] Example 3: Example of a system response

[0432] Prompt message: "The driver is stressed. Please soften the notification and send an email."

[0433] Result: "Attention! Driver, you are feeling stressed. Take a short break. How about taking some time for a cup of tea?"

[0434] This invention enables task management and notifications that adapt to the user's emotional state, thereby improving work efficiency and reducing user stress.

[0435] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0436] Step 1:

[0437] The server automatically retrieves emails labeled "Important" using the Gmail API. Since these retrieved emails contain detailed information, the retrieved email data is used as input and summarized in the next step.

[0438] Step 2:

[0439] The server sends the retrieved email content to an AI-based summarization engine, which generates a summary. This summarization engine uses natural language processing techniques to extract the essential content of the email. For example, an email titled "Details about a new project with a client" would be summarized as "New project details." This summary is then recorded in a spreadsheet in the next step.

[0440] Step 3:

[0441] The server records the summarized email content in a Google Spreadsheet. Here, relevant data such as the summarized text, the date and time the email was received, and the sender are added to a new row in the spreadsheet. For example, the summarized result "New Project Details", the email received date and time "October 1, 2023", and the sender "example@domain.com" are recorded.

[0442] Step 4:

[0443] The server automatically sets reminder dates for tasks recorded in the spreadsheet. Specifically, it calculates and sets a reminder date a certain number of days (e.g., 5 days) after the date of receipt. This reminder date serves as the basis for sending notifications.

[0444] Step 5:

[0445] The server analyzes the user's emotional state using an emotion recognition engine. This analysis is a process that recognizes the user's emotional state (e.g., stress, concentration) from their facial expressions and tone of voice. The resulting emotional data is then used to customize subsequent notifications.

[0446] Step 6:

[0447] The server customizes notification content based on the user's emotional state. For example, if the user is feeling "stressed," the content of the notification email will be changed to soften the message. This customized notification content can be communicated to the user more effectively.

[0448] Step 7:

[0449] The server takes the user's emotional state into consideration and adjusts the reminder date as needed. For example, if the user is perceived as "extremely busy," the reminder date might be changed from two days later to one day later to ensure the user doesn't miss important tasks.

[0450] Step 8:

[0451] The server automatically sends notifications to relevant parties based on the reminder date. This process uses smtplib to send emails. For example, if the deadline for "scheduling a meeting" is the next day, the server will notify the relevant parties accordingly. This notification allows them to prepare appropriately.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] [Second Embodiment]

[0456] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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.

[0467] 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".

[0468] This invention is a system that automatically summarizes the content of emails and transfers the summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates for tasks and sending notifications for important tasks.

[0469] System Configuration

[0470] This system has the following main functions:

[0471] 1. Obtaining and summarizing emails

[0472] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[0473] 2. Transfer to a spreadsheet

[0474] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[0475] 3. Automatic setting of reminder dates

[0476] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[0477] 4. Notification of important tasks

[0478] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[0479] Explanation of program processing

[0480] These features are implemented using Google Apps Script.

[0481] Email acquisition and summarization

[0482] The server uses the Gmail service to monitor emails with the specific label "Important." It retrieves the content of the emails and summarizes it using an AI-based summarization engine. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[0483] Transfer to spreadsheet

[0484] The server adds the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Project proposal with client", the email received date and time "October 1, 2023", and the sender "client@example.com" will be newly recorded.

[0485] Automatic reminder date setting

[0486] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[0487] Important task notifications

[0488] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using Gmail. For example, a task due tomorrow, "Schedule a meeting with the client," will be notified to the relevant parties.

[0489] Specific example

[0490] Example 1: Transferring data from Gmail to a task list

[0491] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[0492] The server adds the summarized content as a new row in a Google Spreadsheet. Enter the date and time the email was received in column A, the summarized content in column B, and the sender's email address in column C.

[0493] Example 2: Automatic setting of reminder dates

[0494] The server checks the last added row and calculates the reminder date five days after the task's receipt date. For example, if the receipt date is "October 1, 2023", the reminder date will be "October 6, 2023".

[0495] The server enters the calculated reminder date into the relevant column D.

[0496] Example 3: Notification of important tasks

[0497] The server scans the spreadsheet at a set time each day to detect tasks whose reminder date is the following day. For example, if a task such as "Schedule a meeting with a client" is found, it sets up notifications for the relevant parties.

[0498] Based on the detected task, the server sends an email to the relevant parties requesting them to "confirm the meeting date with the client."

[0499] This system automates email processing, task logging, reminder setting, and important task notifications, significantly improving work efficiency.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The server uses the Gmail service to search for new emails with the specific label "Important".

[0503] Step 2:

[0504] The server retrieves the latest messages from each thread based on the search results.

[0505] Step 3:

[0506] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[0507] Step 4:

[0508] The server receives the summarization results from the AI ​​summarization engine. For example, an email whose full text is "Proposal for a new project" is summarized as "Project proposal".

[0509] Step 5:

[0510] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[0511] Step 6:

[0512] The server adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[0513] Step 7:

[0514] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later from that date.

[0515] Step 8:

[0516] The server enters the calculated reminder date into the corresponding column in the spreadsheet.

[0517] Step 9:

[0518] The server runs a script at a set time every day, scanning all tasks in the spreadsheet.

[0519] Step 10:

[0520] The server detects tasks from the scanned tasks that have a reminder date for the next day.

[0521] Step 11:

[0522] The server retrieves contact information for those involved based on the detected critical tasks.

[0523] Step 12:

[0524] The server will send a reminder email to the relevant contacts. The email will include a message such as, "Please confirm the meeting date with the client."

[0525] Through these steps, the process of summarizing email content, transferring it to a spreadsheet, setting reminder dates, and notifying important tasks is automated. This not only improves work efficiency but also reduces the risk of human error.

[0526] (Example 1)

[0527] 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."

[0528] Traditional email management systems require users to manually summarize received emails, transfer the content to task management systems or spreadsheets, and set reminder dates. This process is time-consuming and labor-intensive, and its efficiency significantly decreases, especially when handling large volumes of emails. Furthermore, there is a risk of overlooking important tasks or setting incorrect reminders. To address these challenges, a system is needed that centrally and automatically summarizes emails, automatically transfers the content, sets reminder dates, and notifies users of important tasks.

[0529] 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.

[0530] In this invention, the server includes means for summarizing content from emails, means for transferring the summarized content to spreadsheet data, means for automatically setting reminder dates based on the recorded content, means for automatically sending notifications based on the reminder dates, means for monitoring emails with specific identifiers, means for automatically shortening content using a summarization engine, and means for adding data to new rows in a spreadsheet. This automates email management and task management, enabling increased work efficiency and preventing important tasks from being overlooked.

[0531] "Email" refers to messages sent and received electronically.

[0532] "Methods for summarizing content" refer to techniques for shortening the body of an email and extracting important information.

[0533] "Spreadsheet data" refers to software or files used to store data in a format consisting of rows and columns.

[0534] A "reminder date" is the date on which you set a notification or reminder about a particular task or event.

[0535] "Means for automatically sending notifications" refers to a function that automatically sends notification messages based on set conditions.

[0536] A "specific identifier" refers to a tag or label used to identify an email based on specific conditions or attributes.

[0537] A "means of monitoring" refers to a method that has a monitoring function that reacts when specific conditions are met.

[0538] A "summarization engine" is an algorithm or program used to shorten the content of a text and extract only the important parts.

[0539] A "spreadsheet" is a spreadsheet software or file format used to manage data using rows and columns.

[0540] "Method for adding data to a new row" refers to the function of adding new information as a row to a spreadsheet.

[0541] "Recorded content" refers to data that stores the summarized content of emails and related information.

[0542] "Automatic configuration" refers to a function that allows the system to automatically configure settings based on specific conditions.

[0543] An "important task" is a task that has a higher priority than other tasks and requires attention.

[0544] Modes for carrying out the invention

[0545] This invention is a system that automatically summarizes the content of emails and transfers that summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates and sending notifications for important tasks. This system has the following main functions:

[0546] Email acquisition and summarization

[0547] The server uses Google Apps Script to monitor emails via the Gmail API. It identifies emails with the specific identifier "Important" and retrieves their content. The retrieved content is sent to a summarization engine using a generative AI model, which outputs it in a shortened and summarized form.

[0548] Specific example:

[0549] Assume you receive a new email with the specific identifier "Important" and the subject line "Proposal for a New Project with a Client." The content of this email can be summarized as "Project Proposal with a Client."

[0550] Transferring data to a spreadsheet.

[0551] The server transfers the summarized email content to a spreadsheet (e.g., Google Sheets). Specifically, it records the following information in a new row:

[0552] Date and time of receipt

[0553] Summary

[0554] Sender's email address

[0555] Specific example:

[0556] The summarized content, "Project proposal with client," the date received, "October 1, 2023," and the sender, "client@example.com," are recorded in columns A, B, and C, respectively.

[0557] Automatic reminder date setting

[0558] The server automatically calculates the reminder date based on the task's reception date and time recorded in the spreadsheet data, and enters it into the relevant column of the data. The reminder date is set a specified number of days after the reception date.

[0559] Specific example:

[0560] If the received date is "October 1, 2023," the reminder date will be set to "October 6, 2023" and entered in column D of the spreadsheet.

[0561] Important task notifications

[0562] The server scans spreadsheet data at a set time each day to identify tasks with upcoming reminder dates or those of high importance. It then automatically sends notifications to the relevant stakeholders.

[0563] Specific example:

[0564] For the task "Schedule a meeting with the client," which has a reminder date of "October 6, 2023," send a notification email to the relevant parties stating, "Please confirm the meeting date with the client."

[0565] Overall flow

[0566] This system's program is implemented using Google Apps Script. The server works in conjunction with the Gmail API, a summarization engine, and the Google Sheets API to automatically monitor, summarize, transcribe, set reminder dates for, and send notifications for emails. This allows users to efficiently manage their emails and track tasks without any hassle.

[0567] This system is expected to significantly improve work efficiency by automating email processing, task recording, reminder date setting, and important task notifications.

[0568] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0569] Step 1:

[0570] Get email

[0571] The server uses Google Apps Script to initiate a session with the Gmail API. It monitors email folders that have a specific label set to "Important." When a new email is detected, it retrieves its data (sender, received date and time, subject, and body).

[0572] Input: New email with the specific label "Important"

[0573] Output: Email data (sender, received date and time, subject, body)

[0574] Specific actions:

[0575] The server uses the Gmail API to check for new emails labeled "Important." For example, it might find a new email with the subject line "Proposal for a new project with a client."

[0576] Step 2:

[0577] Summary of email content

[0578] The server sends the body of the retrieved email to the AI ​​summarization engine. The summarization engine analyzes the email content, extracts the important parts, and generates a shortened summary.

[0579] Input: Email body

[0580] Output: Summary

[0581] Specific actions:

[0582] The server sends the email body to the AI ​​summarization engine and receives a summary titled "Project proposal with client."

[0583] Step 3:

[0584] Transfer to spreadsheet

[0585] The server transfers the summarized email content into a spreadsheet. Using the Google Sheets API, the following data is added to a new row: date and time received (column A), summary text (column B), and sender's email address (column C).

[0586] Input: Date and time received, summary, sender's email address

[0587] Output: A new row is added to the spreadsheet.

[0588] Specific actions:

[0589] The server uses the spreadsheet API to add a new row with "October 1, 2023" in column A, "Project Proposal with Client" in column B, and "client@example.com" in column C.

[0590] Step 4:

[0591] Automatic reminder date setting

[0592] The server reads the last row added to the spreadsheet, calculates the reminder date (a specified number of days after the task's receipt date), and enters it into column D of the spreadsheet.

[0593] Input: Received date and time

[0594] Output: Reminder date

[0595] Specific actions:

[0596] The server calculates the reminder date, "October 6, 2023," which is 5 days after the reception date of "October 1, 2023," and enters it in column D.

[0597] Step 5:

[0598] Important task notifications

[0599] The server scans the spreadsheet at a set time every day to identify tasks with upcoming reminder dates or those of high importance. For identified tasks, it uses the Gmail API to send notification emails to the relevant parties.

[0600] Input: Spreadsheet data

[0601] Output: Notification email

[0602] Specific actions:

[0603] The server finds the task "Schedule a meeting with the client" with a reminder date of "October 6, 2023" and sends an email to the relevant parties requesting them to "Confirm the meeting date with the client."

[0604] (Application Example 1)

[0605] 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."

[0606] Logistics centers face the challenge of managing numerous tasks efficiently. Traditional methods require manual recording, management, and reminders, leading to decreased work efficiency. Furthermore, there's a risk of overlooking important tasks. To address these issues, a system is needed that automatically summarizes, records, and reminds tasks, and also notifies managers.

[0607] 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.

[0608] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, and means for notifying the administrator of the task details. This enables the automatic summarization, recording, reminder, and notification of important tasks at the logistics center.

[0609] "Email" refers to digital messages sent and received over the internet.

[0610] A "summary" is a short, concise compilation of long or complex information.

[0611] A "spreadsheet" is a document in spreadsheet software that uses a table format with rows and columns arranged in a regular pattern.

[0612] A "reminder date" is a specific date used to remind someone of a designated task or event.

[0613] A "notification" is an alert or message designed to inform a recipient of specific information or a message.

[0614] A "server" is a computer system that manages, stores, and provides information over a network.

[0615] A "task" is a unit of work or activity that needs to be performed to achieve a specific objective.

[0616] An "administrator" is someone responsible for the operation and supervision of a system or process.

[0617] A "logistics center" is a facility that receives, stores, and ships goods.

[0618] "Automatic configuration" refers to the process by which a system automatically sets values ​​and conditions without user intervention.

[0619] "Management" is the process of planning, organizing, and controlling tasks and resources in order to achieve specific goals.

[0620] This invention is a system aimed at improving the efficiency of task management in logistics centers. This system automatically summarizes task details from emails and transfers them to Google Sheets. It also automatically sets reminder dates and notifies managers when important tasks are approaching, thereby preventing tasks from being overlooked or missed.

[0621] System Configuration

[0622] This system has the following main functions:

[0623] 1. Obtaining and summarizing emails

[0624] The server automatically retrieves emails with specific labels and summarizes their content using an AI model. For example, an email that says "Please pick product A" would be summarized as "Picking product A".

[0625] 2. Transfer to a spreadsheet

[0626] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[0627] 3. Automatic setting of reminder dates

[0628] The server automatically sets a reminder date for tasks recorded in the spreadsheet, a certain number of days later. For example, it can set the reminder date to 5 days after the date of receipt.

[0629] 4. Notification of important tasks

[0630] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[0631] Hardware and software usage

[0632] Hardware: Warehouse management robots (e.g., Amazon Robotics)

[0633] Software: Google Apps Script, Gmail API, Google Sheets API, OpenAI API

[0634] Explanation of processing details

[0635] Email retrieval and summarization:

[0636] The server uses the Gmail API to monitor emails labeled "Important." It retrieves the email content and summarizes it using a generative AI model (e.g., OpenAI's text-davinci-003). For example, an email that says "Please pick item A" would be summarized as "Pick item A." An example of a specific prompt used is as follows:

[0637] Example of a prompt:

[0638] Email content:

[0639] "Please pick item A."

[0640] Please summarize.

[0641] Transferring to a spreadsheet:

[0642] The server uses the Google Sheets API to add the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Picking task for product A", the email received date and time "October 1, 2023", and the sender "warehouse@example.com" will be newly recorded.

[0643] Automatic reminder date setting:

[0644] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[0645] Important task notifications:

[0646] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using the Gmail API. For example, a task due tomorrow, "Picking Product A," will be notified to relevant parties.

[0647] This enables the logistics center to automatically summarize, record, and remind tasks, as well as notify users of important tasks.

[0648] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0649] Step 1:

[0650] Get email

[0651] The server uses the Gmail API to retrieve emails labeled "Important." The input is unread emails labeled "Important," and the output is the full content of the retrieved emails. The server periodically checks the mailbox and extracts new emails that match the criteria.

[0652] Step 2:

[0653] Summary of email content

[0654] The server uses a generative AI model (e.g., OpenAI's text-davinci-003) to summarize the content of the retrieved emails. The input is the body of the retrieved email, and the output is the summarized text. Specifically, the server prepares a prompt, sends it to the AI ​​model, and receives the summarization result.

[0655] Step 3:

[0656] Transfer to spreadsheet

[0657] The server uses the Google Sheets API to transfer summarized email content to a Google Spreadsheet. The inputs are the summarized email content, the date and time the email was received, and the sender's address; the output is a newly added row in the spreadsheet. The server writes this data to a specific sheet and row and records it as a new task.

[0658] Step 4:

[0659] Automatic reminder date setting

[0660] The server automatically sets reminder dates for tasks recorded in a spreadsheet based on the date and time they were received. The input is the date and time the task was received as recorded in the spreadsheet, and the output is the calculated reminder date. The server calculates the reminder date by adding a certain number of days from the task's received date and enters that date in the relevant column.

[0661] Step 5:

[0662] Important task notifications

[0663] The server scans the spreadsheet at a set time each day to identify tasks with approaching or important reminder dates. The input is task information recorded in the spreadsheet, and the output is notification emails to stakeholders. The server identifies tasks with approaching reminder dates and sends notifications to the email addresses of stakeholders associated with those tasks.

[0664] Examples of specific actions

[0665] The server retrieves emails labeled "important" and sends their content to an AI summarization engine for summarization.

[0666] For example, the action of summarizing an email that says "Please pick product A" to "Pick product A".

[0667] This operation records the summarized content in Google Sheets, with columns A (email received date and time), B (summarized content), and C (sender's email address).

[0668] If the task is received on "October 1, 2023," the system calculates the reminder date and enters it as "October 6, 2023" into the spreadsheet.

[0669] When the reminder date for "Picking Product A" approaches, an email notification is sent to relevant parties stating, "The task deadline is approaching."

[0670] 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.

[0671] This invention is a system that automatically summarizes the content of emails and records the summary in a spreadsheet, and also incorporates an emotion engine that recognizes the user's emotions to adjust notification content and reminder dates. This system improves work efficiency and enables task management that is adapted to the user's emotional state.

[0672] System Configuration

[0673] This system has the following main functions:

[0674] 1. Obtaining and summarizing emails

[0675] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[0676] 2. Transfer to a spreadsheet

[0677] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[0678] 3. Automatic setting of reminder dates

[0679] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[0680] 4. Notification of important tasks

[0681] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[0682] 5. Integration of the Emotional Engine

[0683] The server uses an emotion engine to analyze the user's emotional state.

[0684] 6. Customizing notification content

[0685] The server customizes the content of notifications according to the user's emotional state.

[0686] 7. Adjusting the reminder date

[0687] The server can adjust the reminder date based on the user's emotional state.

[0688] Explanation of program processing

[0689] These features are implemented using Google Apps Script.

[0690] Email acquisition and summarization

[0691] The server automatically retrieves emails with a specific label, "Important," using the Gmail service. It then sends the content of the retrieved emails to an AI-based summarization engine to generate a summary. For example, an email titled "Details about a new project with a client" would be summarized as "New project details."

[0692] Transfer to spreadsheet

[0693] The server adds the summarized content to a new row in a Google Spreadsheet. For example, the summary result "New Project Details," the email received date and time "October 1, 2023," and the sender "client@example.com" will be recorded.

[0694] Automatic reminder date setting

[0695] The server calculates the reminder date based on the task's reception date and time. For example, it might set the reminder date to 5 days after the reception date.

[0696] Important task notifications

[0697] The server scans the spreadsheet and detects tasks with upcoming reminder dates. For detected tasks, it sends email reminder notifications to the relevant parties. For example, if "scheduling a meeting" is due tomorrow, a notification will be sent to the relevant parties.

[0698] Emotional engine integration

[0699] The server analyzes the user's emotions using an emotion engine. For example, the emotion engine detects the emotional state "stress" from the context of the user's emails or the content entered in spreadsheets.

[0700] Customizing notification content

[0701] The server customizes the content of notifications based on the analysis results of the emotion engine. For example, if it detects that the user is stressed, it changes the tone of the notification email to soften it.

[0702] Adjusting the reminder date

[0703] The server can adjust the reminder date based on the user's emotional state. For example, if the server detects that the user is "very busy," it will change the reminder date from two days later to one day later.

[0704] Specific example

[0705] Example 1: Transferring data from Gmail to a task list and integrating sentiment recognition.

[0706] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, content such as "Regarding a new proposal for the project" would be summarized as "New proposal."

[0707] The server adds the summary results to a Google Spreadsheet. Simultaneously, the sentiment engine recognizes from the email context that the user is "focused."

[0708] Example 2: Setting reminder dates and customizing notifications

[0709] The server sets a reminder date of 5 days later for added tasks. For example, if the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[0710] When a user is in a "high-concentration" state, the server will customize the message sent as a reminder email to have a more pleasant tone, such as "This is a reminder for confirmation."

[0711] Example 3: Notification and reminder scheduling for important tasks

[0712] The server scans the spreadsheet at a set time each day to detect tasks whose reminder date is the next day. For example, if "schedule a meeting" is one of the tasks, that day would be tomorrow.

[0713] If the server detects that a user is "extremely busy," it will prompt the relevant user to prepare in advance by sending an early notification.

[0714] This invention not only automates email content summarization, transfer to spreadsheets, setting reminder dates, and task notifications, but also enables flexible task management that takes into account the user's emotional state. As a result, it is expected to improve work efficiency, reduce user stress, and increase productivity.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The server uses the Gmail service to search for new emails with the specific label "Important".

[0718] Step 2:

[0719] The server retrieves the latest messages from each thread based on the search results.

[0720] Step 3:

[0721] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[0722] Step 4:

[0723] The server receives the summarization results from the AI ​​summarization engine. For example, an email titled "New Project Proposal" would be summarized as "Project Proposal."

[0724] Step 5:

[0725] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[0726] Step 6:

[0727] The server then adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[0728] Step 7:

[0729] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later.

[0730] Step 8:

[0731] The server simply enters the calculated reminder date into the corresponding column in the spreadsheet.

[0732] Step 9:

[0733] The device sends the emotional state entered by the user into a spreadsheet to the emotion engine.

[0734] Step 10:

[0735] The server receives the analysis results from the emotion engine and recognizes the user's emotional state. For example, it might recognize the user as being in a "stressed state."

[0736] Step 11:

[0737] The server runs a script at a set time every day, scanning all tasks in the spreadsheet.

[0738] Step 12:

[0739] The server then detects tasks from the scanned tasks that have a reminder date for the following day.

[0740] Step 13:

[0741] The server adjusts the content of notifications based on the user's emotional state. For example, if the server detects that the user is in a "stressed state," it will soften the tone of the notification.

[0742] Step 14:

[0743] The server adjusts the reminder date based on the emotion engine's analysis. For example, if the server recognizes the user as "very busy," it will set the reminder date to one day earlier.

[0744] Step 15:

[0745] The server will send notifications to relevant parties based on the adjusted reminder date. For example, it might send an email with a message like, "This is a reminder to reschedule the meeting."

[0746] Through these steps, summarizing email content, transferring it to a spreadsheet, setting reminder dates, and sending task notifications are automated, resulting in a system that flexibly responds to the user's emotional state. This is expected to improve work efficiency, reduce user stress, and increase productivity.

[0747] (Example 2)

[0748] 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".

[0749] Traditional systems require manual summarization of email content and manual recording in spreadsheets to manage tasks, which reduces work efficiency. Furthermore, reminder dates and notification content are set uniformly without considering the user's emotional state, potentially negatively impacting user stress and work efficiency. Additionally, the manual selection of important tasks increases the likelihood of oversights and poor prioritization.

[0750] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring emails that meet specific conditions, means for summarizing the contents of acquired emails, means for recording the summarized contents in spreadsheet software, means for automatically setting a reminder date based on the recorded contents, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotional state, means for customizing the content of the notification based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This not only automates email summarization and task management, but also enables flexible task management that adapts to the user's emotional state.

[0751] "Specific conditions" refer to rules or criteria for selecting appropriate emails, such as labels tagged on emails or filter settings.

[0752] "Email" refers to digital messages sent and received via the internet or other computer networks.

[0753] A "summary" is a text that extracts the main points of an email and presents them concisely.

[0754] A "spreadsheet program" is a software application used for organizing, analyzing, and creating graphs from data. It is commonly known as a spreadsheet.

[0755] A "reminder date" is the date on which notifications or alerts are sent in relation to tasks, appointments, etc.

[0756] A "notification" is a message or alert sent to a user based on a specific event or condition.

[0757] "Emotional state" refers to the user's psychological state and emotional tendencies, which are analyzed by the AI ​​engine.

[0758] "Automatic configuration" refers to the process in which the system automatically determines and applies values ​​and conditions without manual intervention.

[0759] "Transmission" refers to the act of delivering digital data to other devices or services via a network or communication channel.

[0760] "Analysis" is the process of thoroughly examining data and information to identify specific patterns and trends.

[0761] "Customization" refers to modifying the operation of a system or service to suit the user's needs or specific conditions.

[0762] "Adjustment" refers to the act of changing settings or values ​​based on specific criteria or conditions.

[0763] This invention relates to a system that retrieves important emails from email addresses based on specific criteria, automatically summarizes their content, and records it in a spreadsheet program. The system also includes a function to recognize the user's emotional state and adjust reminder dates and notification content accordingly. This invention is expected to improve work efficiency and reduce user stress. The system has the following main functions:

[0764] Get email

[0765] The server uses Google's Gmail API to automatically retrieve emails based on specific criteria. These criteria might include emails labeled as "Important," for example. The server periodically retrieves emails that meet these criteria and adds them to a list.

[0766] Summary of email content

[0767] The server sends the content of the retrieved emails to an AI summarization engine, which generates a summary. This AI summarization engine uses natural language processing (NLP) technology to create a concise summary of lengthy email content.

[0768] Specific example

[0769] For example, an email that says, "Let's have a meeting about a new project with the client," can be summarized as, "Meeting about the new project."

[0770] Recording the summary content into a spreadsheet program.

[0771] The server records the summarized content in a Google Spreadsheet. The information recorded includes the summary result, the date and time the email was received, and the sender's information.

[0772] Specific example

[0773] If the summary result is "Meeting for a new project," the email was received on "October 1, 2023," and the sender is "client@example.com," this information will be added to a new row in the spreadsheet.

[0774] Automatic reminder date setting

[0775] The server automatically calculates the reminder date based on the date and time the task was received. For example, it can set the reminder date to 5 days after the email was received.

[0776] Specific example

[0777] If the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[0778] Important task notifications

[0779] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. These notifications are sent via email.

[0780] Specific example

[0781] If the deadline for "scheduling a meeting" is tomorrow, a reminder notification will be sent to all relevant parties.

[0782] User sentiment analysis using an emotion engine

[0783] The server sends the user's emails and spreadsheet contents to the emotion engine to analyze the user's emotional state. The emotional state is expressed in terms such as "stressed" or "concentrated."

[0784] Specific example

[0785] When the emotion engine detects a user's emotional state, such as "busy" or "distracted," that information is recorded.

[0786] Customizing notification content

[0787] The server customizes notification content based on the user's emotional state. If it determines that the user is stressed, it changes the tone of the notification email to soften it.

[0788] Specific example

[0789] A message in a friendly tone is sent, such as, "This is a reminder for confirmation."

[0790] Adjusting the reminder date

[0791] The server adjusts the reminder date based on the user's emotional state. For example, if it detects that the user is extremely busy, it will set the reminder date earlier than originally scheduled.

[0792] Specific example

[0793] If the user is determined to be "extremely busy," the reminder date will be changed from 2 days later to 1 day later.

[0794] Example of a prompt

[0795] The following are examples of prompt statements used for generative AI models.

[0796] "Retrieve emails labeled 'Important' from Gmail, send them to a summarization engine, and have it generate a concise summary."

[0797] Thus, this system streamlines automatic email summarization and task management, and further enables flexible management that takes into account the user's emotional state. The above is a description of embodiments for carrying out the present invention.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The server uses the Gmail API to automatically retrieve emails that meet specific criteria. The input is a specific criterion (e.g., the "Important" label), and the output is a list of unread emails that match that criterion. Specifically, the server runs a scheduled task every hour, issuing a query to search for unread emails with the specified label. It retrieves the IDs of the matching emails and adds them to the list.

[0801] Step 2:

[0802] The server sends the content of the retrieved email to an AI summarization engine to generate a summary. The input is the email body, and the output is the summarized text. Specifically, the server extracts the email body as text, sends this text to the AI ​​summarization engine, and receives the summary result. For example, an email that says "Let's have a meeting about a new project with the client" would be summarized as "Meeting about a new project."

[0803] Step 3:

[0804] The server records the summarized content in a Google Spreadsheet. Inputs include the summary result, the email's received date and time, and sender information; output is this information recorded in the spreadsheet. Specifically, the server adds a new row to the spreadsheet and enters the summary result, received date and time, and sender information into the corresponding cells. For example, the summary result might be "Meeting about a new project," the email received date and time might be "October 1, 2023," and the sender might be "client@example.com."

[0805] Step 4:

[0806] The server automatically calculates the reminder date based on the task's reception date and time. The input is the task's reception date and time, and the output is the calculated reminder date. Specifically, the server calculates a date five days after the task's reception date and enters the reminder date into the corresponding cell in the spreadsheet. For example, if the email was received on "October 1, 2023," the reminder date would be "October 6, 2023."

[0807] Step 5:

[0808] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. The input is task information from the spreadsheet, and the output is the sent reminder notifications. Specifically, the server checks the spreadsheet's reminder dates at a fixed time each day, generates and sends notification emails for tasks with a reminder date the following day. For example, if the task "Schedule a meeting" is due tomorrow, a reminder notification will be sent to the relevant parties.

[0809] Step 6:

[0810] The server sends the user's emails and spreadsheet contents to an emotion engine to analyze the user's emotional state. The input is the user's email text and task information from the spreadsheet, and the output is the analysis of the emotional state. Specifically, the server extracts the user's emails and spreadsheet contents, sends them to the emotion engine, and records the returned emotional state (e.g., "stressed," "focused") in a spreadsheet or database.

[0811] Step 7:

[0812] The server customizes notification content based on the user's emotional state. Input includes the results of the emotion engine's analysis and the standard notification content, while output is the customized notification content. Specifically, if the server determines that the user is "stressed," it softens the wording of the notification email. For example, it might change it to something like, "This is a reminder."

[0813] Step 8:

[0814] The server adjusts the reminder date based on the user's emotional state. The inputs are the results of the emotion engine's analysis and the existing reminder date; the output is the adjusted reminder date. Specifically, if the server detects that the user is "very busy," it will move the reminder date earlier than originally set. For example, it might change the reminder date from two days later to one day later.

[0815] The above outlines the specific flow of the processing steps in this system's program.

[0816] (Application Example 2)

[0817] 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."

[0818] Traditional email summarization systems were useful for summarizing email content and automating reminders, but they failed to consider the user's emotional state and couldn't deliver important notifications at the right time. As a result, important tasks and notifications were often missed, and work efficiency could not be sufficiently improved. Furthermore, it was difficult for users to receive timely notifications when they were stressed or busy.

[0819] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0820] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotions using an emotion recognition engine, means for customizing the notification content based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This enables task management and notifications adapted to the user's emotional state, improving work efficiency and reducing user stress.

[0821] "Email" refers to digital messages sent and received over the internet.

[0822] To "summarize" means to extract the essential parts of information and describe them concisely.

[0823] A "spreadsheet" is a digital document in tabular format, consisting of rows and columns, and is primarily used for organizing and calculating data.

[0824] A "reminder date" is a date and time designated for sending a notification about a specific action or task.

[0825] A "notification" is a message or alert used to convey specific information to a recipient.

[0826] An "emotion recognition engine" is a software technology used to analyze and recognize a user's emotional state.

[0827] "To analyze" means to handle data and information and to thoroughly understand its contents.

[0828] "Customizing" means changing the content or functionality according to specific conditions or requirements.

[0829] A "task" is a job or activity performed to achieve a specific objective.

[0830] A "server" is a computer system that provides services over a network.

[0831] This invention is a task management and notification system that takes into account the user's emotional state, and is implemented using the following main hardware and software.

[0832] Hardware and software configuration

[0833] 1. Server

[0834] Google Apps Script: A scripting language for integrating with Google Sheets.

[0835] Gmail API: Retrieving emails and sorting them based on specific labels

[0836] Emotion recognition engine: Software for analyzing a user's emotional state in real time.

[0837] smtplib: A Python library for sending email notifications.

[0838] OpenCV: An open-source library for analyzing emotions from facial expressions and voice tone.

[0839] Program processing flow

[0840] The server will perform the following steps:

[0841] 1. Obtaining and summarizing emails

[0842] The server uses the Gmail API to automatically retrieve emails with specific labels (e.g., Important), and sends their content to an AI-based summarization engine to generate a summary. For example, if the email is titled "Regarding a New Project Proposal," the summary will be "New Proposal."

[0843] 2. Recording in a spreadsheet

[0844] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded. For example, the summary result might be "New Proposal," the email received on "October 1, 2023," and the sender might be "example@domain.com."

[0845] 3. Automatic setting of reminder dates

[0846] The system automatically sets a reminder date based on the recorded information. For example, it can set a reminder date five days after the date of receipt.

[0847] 4. User sentiment analysis using an emotion recognition engine

[0848] The server uses an emotion recognition engine to analyze the user's emotional state. It recognizes emotional states (e.g., stress, concentration) from facial expressions and tone of voice.

[0849] 5. Customizing notification content

[0850] The content of notifications will be customized based on the user's emotional state. For example, if the user is feeling stressed, the tone of the notification email will be softened.

[0851] 6. Adjusting the reminder date

[0852] The reminder date can be adjusted based on the user's emotional state. For example, if the user is detected as "very busy," the reminder date can be changed from two days later to one day later.

[0853] 7. Sending notifications

[0854] Based on the reminder date, notifications will be automatically sent to the relevant parties. For example, if the deadline for "scheduling a meeting" is tomorrow, a notification will be sent to the relevant parties.

[0855] Specific example

[0856] Example 1: Urgent task notification

[0857] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, "New project proposal from your boss" would be summarized as "New project proposal."

[0858] The server adds the summary results to a Google Spreadsheet, and at the same time, the sentiment engine recognizes that the user is "focused."

[0859] Example 2: Setting reminder dates and customizing notifications

[0860] The server will set a reminder date five days later. For example, if the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[0861] If a user is experiencing "stress," the content of the reminder email will be customized to a more gentle tone, such as "This is a reminder for confirmation."

[0862] Example 3: Example of a system response

[0863] Prompt message: "The driver is stressed. Please soften the notification and send an email."

[0864] Result: "Attention! Driver, you are feeling stressed. Take a short break. How about taking some time for a cup of tea?"

[0865] This invention enables task management and notifications that adapt to the user's emotional state, thereby improving work efficiency and reducing user stress.

[0866] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0867] Step 1:

[0868] The server automatically retrieves emails labeled "Important" using the Gmail API. Since these retrieved emails contain detailed information, the retrieved email data is used as input and summarized in the next step.

[0869] Step 2:

[0870] The server sends the retrieved email content to an AI-based summarization engine, which generates a summary. This summarization engine uses natural language processing techniques to extract the essential content of the email. For example, an email titled "Details about a new project with a client" would be summarized as "New project details." This summary is then recorded in a spreadsheet in the next step.

[0871] Step 3:

[0872] The server records the summarized email content in a Google Spreadsheet. Here, relevant data such as the summarized text, the date and time the email was received, and the sender are added to a new row in the spreadsheet. For example, the summarized result "New Project Details", the email received date and time "October 1, 2023", and the sender "example@domain.com" are recorded.

[0873] Step 4:

[0874] The server automatically sets reminder dates for tasks recorded in the spreadsheet. Specifically, it calculates and sets a reminder date a certain number of days (e.g., 5 days) after the date of receipt. This reminder date serves as the basis for sending notifications.

[0875] Step 5:

[0876] The server analyzes the user's emotional state using an emotion recognition engine. This analysis is a process that recognizes the user's emotional state (e.g., stress, concentration) from their facial expressions and tone of voice. The resulting emotional data is then used to customize subsequent notifications.

[0877] Step 6:

[0878] The server customizes notification content based on the user's emotional state. For example, if the user is feeling "stressed," the content of the notification email will be changed to soften the message. This customized notification content can be communicated to the user more effectively.

[0879] Step 7:

[0880] The server takes the user's emotional state into consideration and adjusts the reminder date as needed. For example, if the user is perceived as "extremely busy," the reminder date might be changed from two days later to one day later to ensure the user doesn't miss important tasks.

[0881] Step 8:

[0882] The server automatically sends notifications to relevant parties based on the reminder date. This process uses smtplib to send emails. For example, if the deadline for "scheduling a meeting" is the next day, the server will notify the relevant parties accordingly. This notification allows them to prepare appropriately.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] [Third Embodiment]

[0887] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0888] 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.

[0889] 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).

[0890] 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.

[0891] 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.

[0892] 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).

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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".

[0899] This invention is a system that automatically summarizes the content of emails and transfers the summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates for tasks and sending notifications for important tasks.

[0900] System Configuration

[0901] This system has the following main functions:

[0902] 1. Obtaining and summarizing emails

[0903] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[0904] 2. Transfer to a spreadsheet

[0905] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[0906] 3. Automatic setting of reminder dates

[0907] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[0908] 4. Notification of important tasks

[0909] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[0910] Explanation of program processing

[0911] These features are implemented using Google Apps Script.

[0912] Email acquisition and summarization

[0913] The server uses the Gmail service to monitor emails with the specific label "Important." It retrieves the content of the emails and summarizes it using an AI-based summarization engine. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[0914] Transfer to spreadsheet

[0915] The server adds the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Project proposal with client", the email received date and time "October 1, 2023", and the sender "client@example.com" will be newly recorded.

[0916] Automatic reminder date setting

[0917] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[0918] Important task notifications

[0919] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using Gmail. For example, a task due tomorrow, "Schedule a meeting with the client," will be notified to the relevant parties.

[0920] Specific example

[0921] Example 1: Transferring data from Gmail to a task list

[0922] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[0923] The server adds the summarized content as a new row in a Google Spreadsheet. Enter the date and time the email was received in column A, the summarized content in column B, and the sender's email address in column C.

[0924] Example 2: Automatic setting of reminder dates

[0925] The server checks the last added row and calculates the reminder date five days after the task's receipt date. For example, if the receipt date is "October 1, 2023", the reminder date will be "October 6, 2023".

[0926] The server enters the calculated reminder date into the relevant column D.

[0927] Example 3: Notification of important tasks

[0928] The server scans the spreadsheet at a set time each day to detect tasks whose reminder date is the following day. For example, if a task such as "Schedule a meeting with a client" is found, it sets up notifications for the relevant parties.

[0929] Based on the detected task, the server sends an email to the relevant parties requesting them to "confirm the meeting date with the client."

[0930] This system automates email processing, task logging, reminder setting, and important task notifications, significantly improving work efficiency.

[0931] The following describes the processing flow.

[0932] Step 1:

[0933] The server uses the Gmail service to search for new emails with the specific label "Important".

[0934] Step 2:

[0935] The server retrieves the latest messages from each thread based on the search results.

[0936] Step 3:

[0937] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[0938] Step 4:

[0939] The server receives the summarization results from the AI ​​summarization engine. For example, an email whose full text is "Proposal for a new project" is summarized as "Project proposal".

[0940] Step 5:

[0941] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[0942] Step 6:

[0943] The server adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[0944] Step 7:

[0945] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later from that date.

[0946] Step 8:

[0947] The server enters the calculated reminder date into the corresponding column in the spreadsheet.

[0948] Step 9:

[0949] The server runs a script at a set time every day, scanning all tasks in the spreadsheet.

[0950] Step 10:

[0951] The server detects tasks from the scanned tasks that have a reminder date for the next day.

[0952] Step 11:

[0953] The server retrieves contact information for those involved based on the detected critical tasks.

[0954] Step 12:

[0955] The server will send a reminder email to the relevant contacts. The email will include a message such as, "Please confirm the meeting date with the client."

[0956] Through these steps, the process of summarizing email content, transferring it to a spreadsheet, setting reminder dates, and notifying important tasks is automated. This not only improves work efficiency but also reduces the risk of human error.

[0957] (Example 1)

[0958] 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."

[0959] Traditional email management systems require users to manually summarize received emails, transfer the content to task management systems or spreadsheets, and set reminder dates. This process is time-consuming and labor-intensive, and its efficiency significantly decreases, especially when handling large volumes of emails. Furthermore, there is a risk of overlooking important tasks or setting incorrect reminders. To address these challenges, a system is needed that centrally and automatically summarizes emails, automatically transfers the content, sets reminder dates, and notifies users of important tasks.

[0960] 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.

[0961] In this invention, the server includes means for summarizing content from emails, means for transferring the summarized content to spreadsheet data, means for automatically setting reminder dates based on the recorded content, means for automatically sending notifications based on the reminder dates, means for monitoring emails with specific identifiers, means for automatically shortening content using a summarization engine, and means for adding data to new rows in a spreadsheet. This automates email management and task management, enabling increased work efficiency and preventing important tasks from being overlooked.

[0962] "Email" refers to messages sent and received electronically.

[0963] "Methods for summarizing content" refer to techniques for shortening the body of an email and extracting important information.

[0964] "Spreadsheet data" refers to software or files used to store data in a format consisting of rows and columns.

[0965] A "reminder date" is the date on which you set a notification or reminder about a particular task or event.

[0966] "Means for automatically sending notifications" refers to a function that automatically sends notification messages based on set conditions.

[0967] A "specific identifier" refers to a tag or label used to identify an email based on specific conditions or attributes.

[0968] A "means of monitoring" refers to a method that has a monitoring function that reacts when specific conditions are met.

[0969] A "summarization engine" is an algorithm or program used to shorten the content of a text and extract only the important parts.

[0970] A "spreadsheet" is a spreadsheet software or file format used to manage data using rows and columns.

[0971] "Method for adding data to a new row" refers to the function of adding new information as a row to a spreadsheet.

[0972] "Recorded content" refers to data that stores the summarized content of emails and related information.

[0973] "Automatic configuration" refers to a function that allows the system to automatically configure settings based on specific conditions.

[0974] An "important task" is a task that has a higher priority than other tasks and requires attention.

[0975] Modes for carrying out the invention

[0976] This invention is a system that automatically summarizes the content of emails and transfers that summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates and sending notifications for important tasks. This system has the following main functions:

[0977] Email acquisition and summarization

[0978] The server uses Google Apps Script to monitor emails via the Gmail API. It identifies emails with the specific identifier "Important" and retrieves their content. The retrieved content is sent to a summarization engine using a generative AI model, which outputs it in a shortened and summarized form.

[0979] Specific example:

[0980] Assume you receive a new email with the specific identifier "Important" and the subject line "Proposal for a New Project with a Client." The content of this email can be summarized as "Project Proposal with a Client."

[0981] Transferring data to a spreadsheet.

[0982] The server transfers the summarized email content to a spreadsheet (e.g., Google Sheets). Specifically, it records the following information in a new row:

[0983] Date and time of receipt

[0984] Summary

[0985] Sender's email address

[0986] Specific example:

[0987] The summarized content, "Project proposal with client," the date received, "October 1, 2023," and the sender, "client@example.com," are recorded in columns A, B, and C, respectively.

[0988] Automatic reminder date setting

[0989] The server automatically calculates the reminder date based on the task's reception date and time recorded in the spreadsheet data, and enters it into the relevant column of the data. The reminder date is set a specified number of days after the reception date.

[0990] Specific example:

[0991] If the received date is "October 1, 2023," the reminder date will be set to "October 6, 2023" and entered in column D of the spreadsheet.

[0992] Important task notifications

[0993] The server scans spreadsheet data at a set time each day to identify tasks with upcoming reminder dates or those of high importance. It then automatically sends notifications to the relevant stakeholders.

[0994] Specific example:

[0995] For the task "Schedule a meeting with the client," which has a reminder date of "October 6, 2023," send a notification email to the relevant parties stating, "Please confirm the meeting date with the client."

[0996] Overall flow

[0997] This system's program is implemented using Google Apps Script. The server works in conjunction with the Gmail API, a summarization engine, and the Google Sheets API to automatically monitor, summarize, transcribe, set reminder dates for, and send notifications for emails. This allows users to efficiently manage their emails and track tasks without any hassle.

[0998] This system is expected to significantly improve work efficiency by automating email processing, task recording, reminder date setting, and important task notifications.

[0999] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1000] Step 1:

[1001] Get email

[1002] The server uses Google Apps Script to initiate a session with the Gmail API. It monitors email folders that have a specific label set to "Important." When a new email is detected, it retrieves its data (sender, received date and time, subject, and body).

[1003] Input: New email with the specific label "Important"

[1004] Output: Email data (sender, received date and time, subject, body)

[1005] Specific actions:

[1006] The server uses the Gmail API to check for new emails labeled "Important." For example, it might find a new email with the subject line "Proposal for a new project with a client."

[1007] Step 2:

[1008] Summary of email content

[1009] The server sends the body of the retrieved email to the AI ​​summarization engine. The summarization engine analyzes the email content, extracts the important parts, and generates a shortened summary.

[1010] Input: Email body

[1011] Output: Summary

[1012] Specific actions:

[1013] The server sends the email body to the AI ​​summarization engine and receives a summary titled "Project proposal with client."

[1014] Step 3:

[1015] Transfer to spreadsheet

[1016] The server transfers the summarized email content into a spreadsheet. Using the Google Sheets API, the following data is added to a new row: date and time received (column A), summary text (column B), and sender's email address (column C).

[1017] Input: Date and time received, summary, sender's email address

[1018] Output: A new row is added to the spreadsheet.

[1019] Specific actions:

[1020] The server uses the spreadsheet API to add a new row with "October 1, 2023" in column A, "Project Proposal with Client" in column B, and "client@example.com" in column C.

[1021] Step 4:

[1022] Automatic reminder date setting

[1023] The server reads the last row added to the spreadsheet, calculates the reminder date (a specified number of days after the task's receipt date), and enters it into column D of the spreadsheet.

[1024] Input: Received date and time

[1025] Output: Reminder date

[1026] Specific actions:

[1027] The server calculates the reminder date, "October 6, 2023," which is 5 days after the reception date of "October 1, 2023," and enters it in column D.

[1028] Step 5:

[1029] Important task notifications

[1030] The server scans the spreadsheet at a set time every day to identify tasks with upcoming reminder dates or those of high importance. For identified tasks, it uses the Gmail API to send notification emails to the relevant parties.

[1031] Input: Spreadsheet data

[1032] Output: Notification email

[1033] Specific actions:

[1034] The server finds the task "Schedule a meeting with the client" with a reminder date of "October 6, 2023" and sends an email to the relevant parties requesting them to "Confirm the meeting date with the client."

[1035] (Application Example 1)

[1036] 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."

[1037] Logistics centers face the challenge of managing numerous tasks efficiently. Traditional methods require manual recording, management, and reminders, leading to decreased work efficiency. Furthermore, there's a risk of overlooking important tasks. To address these issues, a system is needed that automatically summarizes, records, and reminds tasks, and also notifies managers.

[1038] 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.

[1039] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, and means for notifying the administrator of the task details. This enables the automatic summarization, recording, reminder, and notification of important tasks at the logistics center.

[1040] "Email" refers to digital messages sent and received over the internet.

[1041] A "summary" is a short, concise compilation of long or complex information.

[1042] A "spreadsheet" is a document in spreadsheet software that uses a table format with rows and columns arranged in a regular pattern.

[1043] A "reminder date" is a specific date used to remind someone of a designated task or event.

[1044] A "notification" is an alert or message designed to inform a recipient of specific information or a message.

[1045] A "server" is a computer system that manages, stores, and provides information over a network.

[1046] A "task" is a unit of work or activity that needs to be performed to achieve a specific objective.

[1047] An "administrator" is someone responsible for the operation and supervision of a system or process.

[1048] A "logistics center" is a facility that receives, stores, and ships goods.

[1049] "Automatic configuration" refers to the process by which a system automatically sets values ​​and conditions without user intervention.

[1050] "Management" is the process of planning, organizing, and controlling tasks and resources in order to achieve specific goals.

[1051] This invention is a system aimed at improving the efficiency of task management in logistics centers. This system automatically summarizes task details from emails and transfers them to Google Sheets. It also automatically sets reminder dates and notifies managers when important tasks are approaching, thereby preventing tasks from being overlooked or missed.

[1052] System Configuration

[1053] This system has the following main functions:

[1054] 1. Obtaining and summarizing emails

[1055] The server automatically retrieves emails with specific labels and summarizes their content using an AI model. For example, an email that says "Please pick product A" would be summarized as "Picking product A".

[1056] 2. Transfer to a spreadsheet

[1057] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[1058] 3. Automatic setting of reminder dates

[1059] The server automatically sets a reminder date for tasks recorded in the spreadsheet, a certain number of days later. For example, it can set the reminder date to 5 days after the date of receipt.

[1060] 4. Notification of important tasks

[1061] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[1062] Hardware and software usage

[1063] Hardware: Warehouse management robots (e.g., Amazon Robotics)

[1064] Software: Google Apps Script, Gmail API, Google Sheets API, OpenAI API

[1065] Explanation of processing details

[1066] Email retrieval and summarization:

[1067] The server uses the Gmail API to monitor emails labeled "Important." It retrieves the email content and summarizes it using a generative AI model (e.g., OpenAI's text-davinci-003). For example, an email that says "Please pick item A" would be summarized as "Pick item A." An example of a specific prompt used is as follows:

[1068] Example of a prompt:

[1069] Email content:

[1070] "Please pick item A."

[1071] Please summarize.

[1072] Transferring to a spreadsheet:

[1073] The server uses the Google Sheets API to add the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Picking task for product A", the email received date and time "October 1, 2023", and the sender "warehouse@example.com" will be newly recorded.

[1074] Automatic reminder date setting:

[1075] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[1076] Important task notifications:

[1077] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using the Gmail API. For example, a task due tomorrow, "Picking Product A," will be notified to relevant parties.

[1078] This enables the logistics center to automatically summarize, record, and remind tasks, as well as notify users of important tasks.

[1079] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1080] Step 1:

[1081] Get email

[1082] The server uses the Gmail API to retrieve emails labeled "Important." The input is unread emails labeled "Important," and the output is the full content of the retrieved emails. The server periodically checks the mailbox and extracts new emails that match the criteria.

[1083] Step 2:

[1084] Summary of email content

[1085] The server uses a generative AI model (e.g., OpenAI's text-davinci-003) to summarize the content of the retrieved emails. The input is the body of the retrieved email, and the output is the summarized text. Specifically, the server prepares a prompt, sends it to the AI ​​model, and receives the summarization result.

[1086] Step 3:

[1087] Transfer to spreadsheet

[1088] The server uses the Google Sheets API to transfer summarized email content to a Google Spreadsheet. The inputs are the summarized email content, the date and time the email was received, and the sender's address; the output is a newly added row in the spreadsheet. The server writes this data to a specific sheet and row and records it as a new task.

[1089] Step 4:

[1090] Automatic reminder date setting

[1091] The server automatically sets reminder dates for tasks recorded in a spreadsheet based on the date and time they were received. The input is the date and time the task was received as recorded in the spreadsheet, and the output is the calculated reminder date. The server calculates the reminder date by adding a certain number of days from the task's received date and enters that date in the relevant column.

[1092] Step 5:

[1093] Important task notifications

[1094] The server scans the spreadsheet at a set time each day to identify tasks with approaching or important reminder dates. The input is task information recorded in the spreadsheet, and the output is notification emails to stakeholders. The server identifies tasks with approaching reminder dates and sends notifications to the email addresses of stakeholders associated with those tasks.

[1095] Examples of specific actions

[1096] The server retrieves emails labeled "important" and sends their content to an AI summarization engine for summarization.

[1097] For example, the action of summarizing an email that says "Please pick product A" to "Pick product A".

[1098] This operation records the summarized content in Google Sheets, with columns A (email received date and time), B (summarized content), and C (sender's email address).

[1099] If the task is received on "October 1, 2023," the system calculates the reminder date and enters it as "October 6, 2023" into the spreadsheet.

[1100] When the reminder date for "Picking Product A" approaches, an email notification is sent to relevant parties stating, "The task deadline is approaching."

[1101] 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.

[1102] This invention is a system that automatically summarizes the content of emails and records the summary in a spreadsheet, and also incorporates an emotion engine that recognizes the user's emotions to adjust notification content and reminder dates. This system improves work efficiency and enables task management that is adapted to the user's emotional state.

[1103] System Configuration

[1104] This system has the following main functions:

[1105] 1. Obtaining and summarizing emails

[1106] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[1107] 2. Transfer to a spreadsheet

[1108] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[1109] 3. Automatic setting of reminder dates

[1110] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[1111] 4. Notification of important tasks

[1112] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[1113] 5. Integration of the Emotional Engine

[1114] The server uses an emotion engine to analyze the user's emotional state.

[1115] 6. Customizing notification content

[1116] The server customizes the content of notifications according to the user's emotional state.

[1117] 7. Adjusting the reminder date

[1118] The server can adjust the reminder date based on the user's emotional state.

[1119] Explanation of program processing

[1120] These features are implemented using Google Apps Script.

[1121] Email acquisition and summarization

[1122] The server automatically retrieves emails with a specific label, "Important," using the Gmail service. It then sends the content of the retrieved emails to an AI-based summarization engine to generate a summary. For example, an email titled "Details about a new project with a client" would be summarized as "New project details."

[1123] Transfer to spreadsheet

[1124] The server adds the summarized content to a new row in a Google Spreadsheet. For example, the summary result "New Project Details," the email received date and time "October 1, 2023," and the sender "client@example.com" will be recorded.

[1125] Automatic reminder date setting

[1126] The server calculates the reminder date based on the task's reception date and time. For example, it might set the reminder date to 5 days after the reception date.

[1127] Important task notifications

[1128] The server scans the spreadsheet and detects tasks with upcoming reminder dates. For detected tasks, it sends email reminder notifications to the relevant parties. For example, if "scheduling a meeting" is due tomorrow, a notification will be sent to the relevant parties.

[1129] Emotional engine integration

[1130] The server analyzes the user's emotions using an emotion engine. For example, the emotion engine detects the emotional state "stress" from the context of the user's emails or the content entered in spreadsheets.

[1131] Customizing notification content

[1132] The server customizes the content of notifications based on the analysis results of the emotion engine. For example, if it detects that the user is stressed, it changes the tone of the notification email to soften it.

[1133] Adjusting the reminder date

[1134] The server can adjust the reminder date based on the user's emotional state. For example, if the server detects that the user is "very busy," it will change the reminder date from two days later to one day later.

[1135] Specific example

[1136] Example 1: Transferring data from Gmail to a task list and integrating sentiment recognition.

[1137] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, content such as "Regarding a new proposal for the project" would be summarized as "New proposal."

[1138] The server adds the summary results to a Google Spreadsheet. Simultaneously, the sentiment engine recognizes from the email context that the user is "focused."

[1139] Example 2: Setting reminder dates and customizing notifications

[1140] The server sets a reminder date of 5 days later for added tasks. For example, if the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[1141] When a user is in a "high-concentration" state, the server will customize the message sent as a reminder email to have a more pleasant tone, such as "This is a reminder for confirmation."

[1142] Example 3: Notification and reminder scheduling for important tasks

[1143] The server scans the spreadsheet at a set time each day to detect tasks whose reminder date is the next day. For example, if "schedule a meeting" is one of the tasks, that day would be tomorrow.

[1144] If the server detects that a user is "extremely busy," it will prompt the relevant user to prepare in advance by sending an early notification.

[1145] This invention not only automates email content summarization, transfer to spreadsheets, setting reminder dates, and task notifications, but also enables flexible task management that takes into account the user's emotional state. As a result, it is expected to improve work efficiency, reduce user stress, and increase productivity.

[1146] The following describes the processing flow.

[1147] Step 1:

[1148] The server uses the Gmail service to search for new emails with the specific label "Important".

[1149] Step 2:

[1150] The server retrieves the latest messages from each thread based on the search results.

[1151] Step 3:

[1152] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[1153] Step 4:

[1154] The server receives the summarization results from the AI ​​summarization engine. For example, an email titled "New Project Proposal" would be summarized as "Project Proposal."

[1155] Step 5:

[1156] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[1157] Step 6:

[1158] The server then adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[1159] Step 7:

[1160] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later.

[1161] Step 8:

[1162] The server simply enters the calculated reminder date into the corresponding column in the spreadsheet.

[1163] Step 9:

[1164] The device sends the emotional state entered by the user into a spreadsheet to the emotion engine.

[1165] Step 10:

[1166] The server receives the analysis results from the emotion engine and recognizes the user's emotional state. For example, it might recognize the user as being in a "stressed state."

[1167] Step 11:

[1168] The server runs a script at a set time every day, scanning all tasks in the spreadsheet.

[1169] Step 12:

[1170] The server then detects tasks from the scanned tasks that have a reminder date for the following day.

[1171] Step 13:

[1172] The server adjusts the content of notifications based on the user's emotional state. For example, if the server detects that the user is in a "stressed state," it will soften the tone of the notification.

[1173] Step 14:

[1174] The server adjusts the reminder date based on the emotion engine's analysis. For example, if the server recognizes the user as "very busy," it will set the reminder date to one day earlier.

[1175] Step 15:

[1176] The server will send notifications to relevant parties based on the adjusted reminder date. For example, it might send an email with a message like, "This is a reminder to reschedule the meeting."

[1177] Through these steps, summarizing email content, transferring it to a spreadsheet, setting reminder dates, and sending task notifications are automated, resulting in a system that flexibly responds to the user's emotional state. This is expected to improve work efficiency, reduce user stress, and increase productivity.

[1178] (Example 2)

[1179] 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."

[1180] Traditional systems require manual summarization of email content and manual recording in spreadsheets to manage tasks, which reduces work efficiency. Furthermore, reminder dates and notification content are set uniformly without considering the user's emotional state, potentially negatively impacting user stress and work efficiency. Additionally, the manual selection of important tasks increases the likelihood of oversights and poor prioritization.

[1181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring emails that meet specific conditions, means for summarizing the contents of acquired emails, means for recording the summarized contents in spreadsheet software, means for automatically setting a reminder date based on the recorded contents, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotional state, means for customizing the content of the notification based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This not only automates email summarization and task management, but also enables flexible task management that adapts to the user's emotional state.

[1182] "Specific conditions" refer to rules or criteria for selecting appropriate emails, such as labels tagged on emails or filter settings.

[1183] "Email" refers to digital messages sent and received via the internet or other computer networks.

[1184] A "summary" is a text that extracts the main points of an email and presents them concisely.

[1185] A "spreadsheet program" is a software application used for organizing, analyzing, and creating graphs from data. It is commonly known as a spreadsheet.

[1186] A "reminder date" is the date on which notifications or alerts are sent in relation to tasks, appointments, etc.

[1187] A "notification" is a message or alert sent to a user based on a specific event or condition.

[1188] "Emotional state" refers to the user's psychological state and emotional tendencies, which are analyzed by the AI ​​engine.

[1189] "Automatic configuration" refers to the process in which the system automatically determines and applies values ​​and conditions without manual intervention.

[1190] "Transmission" refers to the act of delivering digital data to other devices or services via a network or communication channel.

[1191] "Analysis" is the process of thoroughly examining data and information to identify specific patterns and trends.

[1192] "Customization" refers to modifying the operation of a system or service to suit the user's needs or specific conditions.

[1193] "Adjustment" refers to the act of changing settings or values ​​based on specific criteria or conditions.

[1194] This invention relates to a system that retrieves important emails from email addresses based on specific criteria, automatically summarizes their content, and records it in a spreadsheet program. The system also includes a function to recognize the user's emotional state and adjust reminder dates and notification content accordingly. This invention is expected to improve work efficiency and reduce user stress. The system has the following main functions:

[1195] Get email

[1196] The server uses Google's Gmail API to automatically retrieve emails based on specific criteria. These criteria might include emails labeled as "Important," for example. The server periodically retrieves emails that meet these criteria and adds them to a list.

[1197] Summary of email content

[1198] The server sends the content of the retrieved emails to an AI summarization engine, which generates a summary. This AI summarization engine uses natural language processing (NLP) technology to create a concise summary of lengthy email content.

[1199] Specific example

[1200] For example, an email that says, "Let's have a meeting about a new project with the client," can be summarized as, "Meeting about the new project."

[1201] Recording the summary content into a spreadsheet program.

[1202] The server records the summarized content in a Google Spreadsheet. The information recorded includes the summary result, the date and time the email was received, and the sender's information.

[1203] Specific example

[1204] If the summary result is "Meeting for a new project," the email was received on "October 1, 2023," and the sender is "client@example.com," this information will be added to a new row in the spreadsheet.

[1205] Automatic reminder date setting

[1206] The server automatically calculates the reminder date based on the date and time the task was received. For example, it can set the reminder date to 5 days after the email was received.

[1207] Specific example

[1208] If the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[1209] Important task notifications

[1210] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. These notifications are sent via email.

[1211] Specific example

[1212] If the deadline for "scheduling a meeting" is tomorrow, a reminder notification will be sent to all relevant parties.

[1213] User sentiment analysis using an emotion engine

[1214] The server sends the user's emails and spreadsheet contents to the emotion engine to analyze the user's emotional state. The emotional state is expressed in terms such as "stressed" or "concentrated."

[1215] Specific example

[1216] When the emotion engine detects a user's emotional state, such as "busy" or "distracted," that information is recorded.

[1217] Customizing notification content

[1218] The server customizes notification content based on the user's emotional state. If it determines that the user is stressed, it changes the tone of the notification email to soften it.

[1219] Specific example

[1220] A message in a friendly tone is sent, such as, "This is a reminder for confirmation."

[1221] Adjusting the reminder date

[1222] The server adjusts the reminder date based on the user's emotional state. For example, if it detects that the user is extremely busy, it will set the reminder date earlier than originally scheduled.

[1223] Specific example

[1224] If the user is determined to be "extremely busy," the reminder date will be changed from 2 days later to 1 day later.

[1225] Example of a prompt

[1226] The following are examples of prompt statements used for generative AI models.

[1227] "Retrieve emails labeled 'Important' from Gmail, send them to a summarization engine, and have it generate a concise summary."

[1228] Thus, this system streamlines automatic email summarization and task management, and further enables flexible management that takes into account the user's emotional state. The above is a description of embodiments for carrying out the present invention.

[1229] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1230] Step 1:

[1231] The server uses the Gmail API to automatically retrieve emails that meet specific criteria. The input is a specific criterion (e.g., the "Important" label), and the output is a list of unread emails that match that criterion. Specifically, the server runs a scheduled task every hour, issuing a query to search for unread emails with the specified label. It retrieves the IDs of the matching emails and adds them to the list.

[1232] Step 2:

[1233] The server sends the content of the retrieved email to an AI summarization engine to generate a summary. The input is the email body, and the output is the summarized text. Specifically, the server extracts the email body as text, sends this text to the AI ​​summarization engine, and receives the summary result. For example, an email that says "Let's have a meeting about a new project with the client" would be summarized as "Meeting about a new project."

[1234] Step 3:

[1235] The server records the summarized content in a Google Spreadsheet. Inputs include the summary result, the email's received date and time, and sender information; output is this information recorded in the spreadsheet. Specifically, the server adds a new row to the spreadsheet and enters the summary result, received date and time, and sender information into the corresponding cells. For example, the summary result might be "Meeting about a new project," the email received date and time might be "October 1, 2023," and the sender might be "client@example.com."

[1236] Step 4:

[1237] The server automatically calculates the reminder date based on the task's reception date and time. The input is the task's reception date and time, and the output is the calculated reminder date. Specifically, the server calculates a date five days after the task's reception date and enters the reminder date into the corresponding cell in the spreadsheet. For example, if the email was received on "October 1, 2023," the reminder date would be "October 6, 2023."

[1238] Step 5:

[1239] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. The input is task information from the spreadsheet, and the output is the sent reminder notifications. Specifically, the server checks the spreadsheet's reminder dates at a fixed time each day, generates and sends notification emails for tasks with a reminder date the following day. For example, if the task "Schedule a meeting" is due tomorrow, a reminder notification will be sent to the relevant parties.

[1240] Step 6:

[1241] The server sends the user's emails and spreadsheet contents to an emotion engine to analyze the user's emotional state. The input is the user's email text and task information from the spreadsheet, and the output is the analysis of the emotional state. Specifically, the server extracts the user's emails and spreadsheet contents, sends them to the emotion engine, and records the returned emotional state (e.g., "stressed," "focused") in a spreadsheet or database.

[1242] Step 7:

[1243] The server customizes notification content based on the user's emotional state. Input includes the results of the emotion engine's analysis and the standard notification content, while output is the customized notification content. Specifically, if the server determines that the user is "stressed," it softens the wording of the notification email. For example, it might change it to something like, "This is a reminder."

[1244] Step 8:

[1245] The server adjusts the reminder date based on the user's emotional state. The inputs are the results of the emotion engine's analysis and the existing reminder date; the output is the adjusted reminder date. Specifically, if the server detects that the user is "very busy," it will move the reminder date earlier than originally set. For example, it might change the reminder date from two days later to one day later.

[1246] The above outlines the specific flow of the processing steps in this system's program.

[1247] (Application Example 2)

[1248] 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."

[1249] Traditional email summarization systems were useful for summarizing email content and automating reminders, but they failed to consider the user's emotional state and couldn't deliver important notifications at the right time. As a result, important tasks and notifications were often missed, and work efficiency could not be sufficiently improved. Furthermore, it was difficult for users to receive timely notifications when they were stressed or busy.

[1250] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1251] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotions using an emotion recognition engine, means for customizing the notification content based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This enables task management and notifications adapted to the user's emotional state, improving work efficiency and reducing user stress.

[1252] "Email" refers to digital messages sent and received over the internet.

[1253] To "summarize" means to extract the essential parts of information and describe them concisely.

[1254] A "spreadsheet" is a digital document in tabular format, consisting of rows and columns, and is primarily used for organizing and calculating data.

[1255] A "reminder date" is a date and time designated for sending a notification about a specific action or task.

[1256] A "notification" is a message or alert used to convey specific information to a recipient.

[1257] An "emotion recognition engine" is a software technology used to analyze and recognize a user's emotional state.

[1258] "To analyze" means to handle data and information and to thoroughly understand its contents.

[1259] "Customizing" means changing the content or functionality according to specific conditions or requirements.

[1260] A "task" is a job or activity performed to achieve a specific objective.

[1261] A "server" is a computer system that provides services over a network.

[1262] This invention is a task management and notification system that takes into account the user's emotional state, and is implemented using the following main hardware and software.

[1263] Hardware and software configuration

[1264] 1. Server

[1265] Google Apps Script: A scripting language for integrating with Google Sheets.

[1266] Gmail API: Retrieving emails and sorting them based on specific labels

[1267] Emotion recognition engine: Software for analyzing a user's emotional state in real time.

[1268] smtplib: A Python library for sending email notifications.

[1269] OpenCV: An open-source library for analyzing emotions from facial expressions and voice tone.

[1270] Program processing flow

[1271] The server will perform the following steps:

[1272] 1. Obtaining and summarizing emails

[1273] The server uses the Gmail API to automatically retrieve emails with specific labels (e.g., Important), and sends their content to an AI-based summarization engine to generate a summary. For example, if the email is titled "Regarding a New Project Proposal," the summary will be "New Proposal."

[1274] 2. Recording in a spreadsheet

[1275] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded. For example, the summary result might be "New Proposal," the email received on "October 1, 2023," and the sender might be "example@domain.com."

[1276] 3. Automatic setting of reminder dates

[1277] The system automatically sets a reminder date based on the recorded information. For example, it can set a reminder date five days after the date of receipt.

[1278] 4. User sentiment analysis using an emotion recognition engine

[1279] The server uses an emotion recognition engine to analyze the user's emotional state. It recognizes emotional states (e.g., stress, concentration) from facial expressions and tone of voice.

[1280] 5. Customizing notification content

[1281] The content of notifications will be customized based on the user's emotional state. For example, if the user is feeling stressed, the tone of the notification email will be softened.

[1282] 6. Adjusting the reminder date

[1283] The reminder date can be adjusted based on the user's emotional state. For example, if the user is detected as "very busy," the reminder date can be changed from two days later to one day later.

[1284] 7. Sending notifications

[1285] Based on the reminder date, notifications will be automatically sent to the relevant parties. For example, if the deadline for "scheduling a meeting" is tomorrow, a notification will be sent to the relevant parties.

[1286] Specific example

[1287] Example 1: Urgent task notification

[1288] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, "New project proposal from your boss" would be summarized as "New project proposal."

[1289] The server adds the summary results to a Google Spreadsheet, and at the same time, the sentiment engine recognizes that the user is "focused."

[1290] Example 2: Setting reminder dates and customizing notifications

[1291] The server will set a reminder date five days later. For example, if the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[1292] If a user is experiencing "stress," the content of the reminder email will be customized to a more gentle tone, such as "This is a reminder for confirmation."

[1293] Example 3: Example of a system response

[1294] Prompt message: "The driver is stressed. Please soften the notification and send an email."

[1295] Result: "Attention! Driver, you are feeling stressed. Take a short break. How about taking some time for a cup of tea?"

[1296] This invention enables task management and notifications that adapt to the user's emotional state, thereby improving work efficiency and reducing user stress.

[1297] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1298] Step 1:

[1299] The server automatically retrieves emails labeled "Important" using the Gmail API. Since these retrieved emails contain detailed information, the retrieved email data is used as input and summarized in the next step.

[1300] Step 2:

[1301] The server sends the retrieved email content to an AI-based summarization engine, which generates a summary. This summarization engine uses natural language processing techniques to extract the essential content of the email. For example, an email titled "Details about a new project with a client" would be summarized as "New project details." This summary is then recorded in a spreadsheet in the next step.

[1302] Step 3:

[1303] The server records the summarized email content in a Google Spreadsheet. Here, relevant data such as the summarized text, the date and time the email was received, and the sender are added to a new row in the spreadsheet. For example, the summarized result "New Project Details", the email received date and time "October 1, 2023", and the sender "example@domain.com" are recorded.

[1304] Step 4:

[1305] The server automatically sets reminder dates for tasks recorded in the spreadsheet. Specifically, it calculates and sets a reminder date a certain number of days (e.g., 5 days) after the date of receipt. This reminder date serves as the basis for sending notifications.

[1306] Step 5:

[1307] The server analyzes the user's emotional state using an emotion recognition engine. This analysis is a process that recognizes the user's emotional state (e.g., stress, concentration) from their facial expressions and tone of voice. The resulting emotional data is then used to customize subsequent notifications.

[1308] Step 6:

[1309] The server customizes notification content based on the user's emotional state. For example, if the user is feeling "stressed," the content of the notification email will be changed to soften the message. This customized notification content can be communicated to the user more effectively.

[1310] Step 7:

[1311] The server takes the user's emotional state into consideration and adjusts the reminder date as needed. For example, if the user is perceived as "extremely busy," the reminder date might be changed from two days later to one day later to ensure the user doesn't miss important tasks.

[1312] Step 8:

[1313] The server automatically sends notifications to relevant parties based on the reminder date. This process uses smtplib to send emails. For example, if the deadline for "scheduling a meeting" is the next day, the server will notify the relevant parties accordingly. This notification allows them to prepare appropriately.

[1314] 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.

[1315] 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.

[1316] 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.

[1317] [Fourth Embodiment]

[1318] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1319] 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.

[1320] 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).

[1321] 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.

[1322] 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.

[1323] 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).

[1324] 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.

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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".

[1331] This invention is a system that automatically summarizes the content of emails and transfers the summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates for tasks and sending notifications for important tasks.

[1332] System Configuration

[1333] This system has the following main functions:

[1334] 1. Obtaining and summarizing emails

[1335] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[1336] 2. Transfer to a spreadsheet

[1337] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[1338] 3. Automatic setting of reminder dates

[1339] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[1340] 4. Notification of important tasks

[1341] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[1342] Explanation of program processing

[1343] These features are implemented using Google Apps Script.

[1344] Email acquisition and summarization

[1345] The server uses the Gmail service to monitor emails with the specific label "Important." It retrieves the content of the emails and summarizes it using an AI-based summarization engine. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[1346] Transfer to spreadsheet

[1347] The server adds the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Project proposal with client", the email received date and time "October 1, 2023", and the sender "client@example.com" will be newly recorded.

[1348] Automatic reminder date setting

[1349] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[1350] Important task notifications

[1351] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using Gmail. For example, a task due tomorrow, "Schedule a meeting with the client," will be notified to the relevant parties.

[1352] Specific example

[1353] Example 1: Transferring data from Gmail to a task list

[1354] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, an email titled "Proposal for a new project with a client" would be summarized as "Project proposal with a client."

[1355] The server adds the summarized content as a new row in a Google Spreadsheet. Enter the date and time the email was received in column A, the summarized content in column B, and the sender's email address in column C.

[1356] Example 2: Automatic setting of reminder dates

[1357] The server checks the last added row and calculates the reminder date five days after the task's receipt date. For example, if the receipt date is "October 1, 2023", the reminder date will be "October 6, 2023".

[1358] The server enters the calculated reminder date into the relevant column D.

[1359] Example 3: Notification of important tasks

[1360] The server scans the spreadsheet at a set time each day to detect tasks whose reminder date is the following day. For example, if a task such as "Schedule a meeting with a client" is found, it sets up notifications for the relevant parties.

[1361] Based on the detected task, the server sends an email to the relevant parties requesting them to "confirm the meeting date with the client."

[1362] This system automates email processing, task logging, reminder setting, and important task notifications, significantly improving work efficiency.

[1363] The following describes the processing flow.

[1364] Step 1:

[1365] The server uses the Gmail service to search for new emails with the specific label "Important".

[1366] Step 2:

[1367] The server retrieves the latest messages from each thread based on the search results.

[1368] Step 3:

[1369] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[1370] Step 4:

[1371] The server receives the summarization results from the AI ​​summarization engine. For example, an email whose full text is "Proposal for a new project" is summarized as "Project proposal".

[1372] Step 5:

[1373] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[1374] Step 6:

[1375] The server adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[1376] Step 7:

[1377] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later from that date.

[1378] Step 8:

[1379] The server enters the calculated reminder date into the corresponding column in the spreadsheet.

[1380] Step 9:

[1381] The server runs a script at a set time every day, scanning all tasks in the spreadsheet.

[1382] Step 10:

[1383] The server detects tasks from the scanned tasks that have a reminder date for the next day.

[1384] Step 11:

[1385] The server retrieves contact information for those involved based on the detected critical tasks.

[1386] Step 12:

[1387] The server will send a reminder email to the relevant contacts. The email will include a message such as, "Please confirm the meeting date with the client."

[1388] Through these steps, the process of summarizing email content, transferring it to a spreadsheet, setting reminder dates, and notifying important tasks is automated. This not only improves work efficiency but also reduces the risk of human error.

[1389] (Example 1)

[1390] 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".

[1391] Traditional email management systems require users to manually summarize received emails, transfer the content to task management systems or spreadsheets, and set reminder dates. This process is time-consuming and labor-intensive, and its efficiency significantly decreases, especially when handling large volumes of emails. Furthermore, there is a risk of overlooking important tasks or setting incorrect reminders. To address these challenges, a system is needed that centrally and automatically summarizes emails, automatically transfers the content, sets reminder dates, and notifies users of important tasks.

[1392] 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.

[1393] In this invention, the server includes means for summarizing content from emails, means for transferring the summarized content to spreadsheet data, means for automatically setting reminder dates based on the recorded content, means for automatically sending notifications based on the reminder dates, means for monitoring emails with specific identifiers, means for automatically shortening content using a summarization engine, and means for adding data to new rows in a spreadsheet. This automates email management and task management, enabling increased work efficiency and preventing important tasks from being overlooked.

[1394] "Email" refers to messages sent and received electronically.

[1395] "Methods for summarizing content" refer to techniques for shortening the body of an email and extracting important information.

[1396] "Spreadsheet data" refers to software or files used to store data in a format consisting of rows and columns.

[1397] A "reminder date" is the date on which you set a notification or reminder about a particular task or event.

[1398] "Means for automatically sending notifications" refers to a function that automatically sends notification messages based on set conditions.

[1399] A "specific identifier" refers to a tag or label used to identify an email based on specific conditions or attributes.

[1400] A "means of monitoring" refers to a method that has a monitoring function that reacts when specific conditions are met.

[1401] A "summarization engine" is an algorithm or program used to shorten the content of a text and extract only the important parts.

[1402] A "spreadsheet" is a spreadsheet software or file format used to manage data using rows and columns.

[1403] "Method for adding data to a new row" refers to the function of adding new information as a row to a spreadsheet.

[1404] "Recorded content" refers to data that stores the summarized content of emails and related information.

[1405] "Automatic configuration" refers to a function that allows the system to automatically configure settings based on specific conditions.

[1406] An "important task" is a task that has a higher priority than other tasks and requires attention.

[1407] Modes for carrying out the invention

[1408] This invention is a system that automatically summarizes the content of emails and transfers that summary to a spreadsheet. It also improves work efficiency by automatically setting reminder dates and sending notifications for important tasks. This system has the following main functions:

[1409] Email acquisition and summarization

[1410] The server uses Google Apps Script to monitor emails via the Gmail API. It identifies emails with the specific identifier "Important" and retrieves their content. The retrieved content is sent to a summarization engine using a generative AI model, which outputs it in a shortened and summarized form.

[1411] Specific example:

[1412] Assume you receive a new email with the specific identifier "Important" and the subject line "Proposal for a New Project with a Client." The content of this email can be summarized as "Project Proposal with a Client."

[1413] Transferring data to a spreadsheet.

[1414] The server transfers the summarized email content to a spreadsheet (e.g., Google Sheets). Specifically, it records the following information in a new row:

[1415] Date and time of receipt

[1416] Summary

[1417] Sender's email address

[1418] Specific example:

[1419] The summarized content, "Project proposal with client," the date received, "October 1, 2023," and the sender, "client@example.com," are recorded in columns A, B, and C, respectively.

[1420] Automatic reminder date setting

[1421] The server automatically calculates the reminder date based on the task's reception date and time recorded in the spreadsheet data, and enters it into the relevant column of the data. The reminder date is set a specified number of days after the reception date.

[1422] Specific example:

[1423] If the received date is "October 1, 2023," the reminder date will be set to "October 6, 2023" and entered in column D of the spreadsheet.

[1424] Important task notifications

[1425] The server scans spreadsheet data at a set time each day to identify tasks with upcoming reminder dates or those of high importance. It then automatically sends notifications to the relevant stakeholders.

[1426] Specific example:

[1427] For the task "Schedule a meeting with the client," which has a reminder date of "October 6, 2023," send a notification email to the relevant parties stating, "Please confirm the meeting date with the client."

[1428] Overall flow

[1429] This system's program is implemented using Google Apps Script. The server works in conjunction with the Gmail API, a summarization engine, and the Google Sheets API to automatically monitor, summarize, transcribe, set reminder dates for, and send notifications for emails. This allows users to efficiently manage their emails and track tasks without any hassle.

[1430] This system is expected to significantly improve work efficiency by automating email processing, task recording, reminder date setting, and important task notifications.

[1431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1432] Step 1:

[1433] Get email

[1434] The server uses Google Apps Script to initiate a session with the Gmail API. It monitors email folders that have a specific label set to "Important." When a new email is detected, it retrieves its data (sender, received date and time, subject, and body).

[1435] Input: New email with the specific label "Important"

[1436] Output: Email data (sender, received date and time, subject, body)

[1437] Specific actions:

[1438] The server uses the Gmail API to check for new emails labeled "Important." For example, it might find a new email with the subject line "Proposal for a new project with a client."

[1439] Step 2:

[1440] Summary of email content

[1441] The server sends the body of the retrieved email to the AI ​​summarization engine. The summarization engine analyzes the email content, extracts the important parts, and generates a shortened summary.

[1442] Input: Email body

[1443] Output: Summary

[1444] Specific actions:

[1445] The server sends the email body to the AI ​​summarization engine and receives a summary titled "Project proposal with client."

[1446] Step 3:

[1447] Transfer to spreadsheet

[1448] The server transfers the summarized email content into a spreadsheet. Using the Google Sheets API, the following data is added to a new row: date and time received (column A), summary text (column B), and sender's email address (column C).

[1449] Input: Date and time received, summary, sender's email address

[1450] Output: A new row is added to the spreadsheet.

[1451] Specific actions:

[1452] The server uses the spreadsheet API to add a new row with "October 1, 2023" in column A, "Project Proposal with Client" in column B, and "client@example.com" in column C.

[1453] Step 4:

[1454] Automatic reminder date setting

[1455] The server reads the last row added to the spreadsheet, calculates the reminder date (a specified number of days after the task's receipt date), and enters it into column D of the spreadsheet.

[1456] Input: Received date and time

[1457] Output: Reminder date

[1458] Specific actions:

[1459] The server calculates the reminder date, "October 6, 2023," which is 5 days after the reception date of "October 1, 2023," and enters it in column D.

[1460] Step 5:

[1461] Important task notifications

[1462] The server scans the spreadsheet at a set time every day to identify tasks with upcoming reminder dates or those of high importance. For identified tasks, it uses the Gmail API to send notification emails to the relevant parties.

[1463] Input: Spreadsheet data

[1464] Output: Notification email

[1465] Specific actions:

[1466] The server finds the task "Schedule a meeting with the client" with a reminder date of "October 6, 2023" and sends an email to the relevant parties requesting them to "Confirm the meeting date with the client."

[1467] (Application Example 1)

[1468] 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".

[1469] Logistics centers face the challenge of managing numerous tasks efficiently. Traditional methods require manual recording, management, and reminders, leading to decreased work efficiency. Furthermore, there's a risk of overlooking important tasks. To address these issues, a system is needed that automatically summarizes, records, and reminds tasks, and also notifies managers.

[1470] 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.

[1471] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, and means for notifying the administrator of the task details. This enables the automatic summarization, recording, reminder, and notification of important tasks at the logistics center.

[1472] "Email" refers to digital messages sent and received over the internet.

[1473] A "summary" is a short, concise compilation of long or complex information.

[1474] A "spreadsheet" is a document in spreadsheet software that uses a table format with rows and columns arranged in a regular pattern.

[1475] A "reminder date" is a specific date used to remind someone of a designated task or event.

[1476] A "notification" is an alert or message designed to inform a recipient of specific information or a message.

[1477] A "server" is a computer system that manages, stores, and provides information over a network.

[1478] A "task" is a unit of work or activity that needs to be performed to achieve a specific objective.

[1479] An "administrator" is someone responsible for the operation and supervision of a system or process.

[1480] A "logistics center" is a facility that receives, stores, and ships goods.

[1481] "Automatic configuration" refers to the process by which a system automatically sets values ​​and conditions without user intervention.

[1482] "Management" is the process of planning, organizing, and controlling tasks and resources in order to achieve specific goals.

[1483] This invention is a system aimed at improving the efficiency of task management in logistics centers. This system automatically summarizes task details from emails and transfers them to Google Sheets. It also automatically sets reminder dates and notifies managers when important tasks are approaching, thereby preventing tasks from being overlooked or missed.

[1484] System Configuration

[1485] This system has the following main functions:

[1486] 1. Obtaining and summarizing emails

[1487] The server automatically retrieves emails with specific labels and summarizes their content using an AI model. For example, an email that says "Please pick product A" would be summarized as "Picking product A".

[1488] 2. Transfer to a spreadsheet

[1489] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[1490] 3. Automatic setting of reminder dates

[1491] The server automatically sets a reminder date for tasks recorded in the spreadsheet, a certain number of days later. For example, it can set the reminder date to 5 days after the date of receipt.

[1492] 4. Notification of important tasks

[1493] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[1494] Hardware and software usage

[1495] Hardware: Warehouse management robots (e.g., Amazon Robotics)

[1496] Software: Google Apps Script, Gmail API, Google Sheets API, OpenAI API

[1497] Explanation of processing details

[1498] Email retrieval and summarization:

[1499] The server uses the Gmail API to monitor emails labeled "Important." It retrieves the email content and summarizes it using a generative AI model (e.g., OpenAI's text-davinci-003). For example, an email that says "Please pick item A" would be summarized as "Pick item A." An example of a specific prompt used is as follows:

[1500] Example of a prompt:

[1501] Email content:

[1502] "Please pick item A."

[1503] Please summarize.

[1504] Transferring to a spreadsheet:

[1505] The server uses the Google Sheets API to add the summarized content as a new row to a specific sheet in Google Sheets. For example, the summarized content "Picking task for product A", the email received date and time "October 1, 2023", and the sender "warehouse@example.com" will be newly recorded.

[1506] Automatic reminder date setting:

[1507] The server calculates the reminder date based on the task's reception date and automatically enters it into the relevant column in the spreadsheet. For example, it might set the reminder date to 5 days after the reception date.

[1508] Important task notifications:

[1509] The server scans the spreadsheet at a set time each day to identify tasks with approaching reminder dates or those of high importance. For identified tasks, it sends notifications to relevant parties using the Gmail API. For example, a task due tomorrow, "Picking Product A," will be notified to relevant parties.

[1510] This enables the logistics center to automatically summarize, record, and remind tasks, as well as notify users of important tasks.

[1511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1512] Step 1:

[1513] Get email

[1514] The server uses the Gmail API to retrieve emails labeled "Important." The input is unread emails labeled "Important," and the output is the full content of the retrieved emails. The server periodically checks the mailbox and extracts new emails that match the criteria.

[1515] Step 2:

[1516] Summary of email content

[1517] The server uses a generative AI model (e.g., OpenAI's text-davinci-003) to summarize the content of the retrieved emails. The input is the body of the retrieved email, and the output is the summarized text. Specifically, the server prepares a prompt, sends it to the AI ​​model, and receives the summarization result.

[1518] Step 3:

[1519] Transfer to spreadsheet

[1520] The server uses the Google Sheets API to transfer summarized email content to a Google Spreadsheet. The inputs are the summarized email content, the date and time the email was received, and the sender's address; the output is a newly added row in the spreadsheet. The server writes this data to a specific sheet and row and records it as a new task.

[1521] Step 4:

[1522] Automatic reminder date setting

[1523] The server automatically sets reminder dates for tasks recorded in a spreadsheet based on the date and time they were received. The input is the date and time the task was received as recorded in the spreadsheet, and the output is the calculated reminder date. The server calculates the reminder date by adding a certain number of days from the task's received date and enters that date in the relevant column.

[1524] Step 5:

[1525] Important task notifications

[1526] The server scans the spreadsheet at a set time each day to identify tasks with approaching or important reminder dates. The input is task information recorded in the spreadsheet, and the output is notification emails to stakeholders. The server identifies tasks with approaching reminder dates and sends notifications to the email addresses of stakeholders associated with those tasks.

[1527] Examples of specific actions

[1528] The server retrieves emails labeled "important" and sends their content to an AI summarization engine for summarization.

[1529] For example, the action of summarizing an email that says "Please pick product A" to "Pick product A".

[1530] This operation records the summarized content in Google Sheets, with columns A (email received date and time), B (summarized content), and C (sender's email address).

[1531] If the task is received on "October 1, 2023," the system calculates the reminder date and enters it as "October 6, 2023" into the spreadsheet.

[1532] When the reminder date for "Picking Product A" approaches, an email notification is sent to relevant parties stating, "The task deadline is approaching."

[1533] 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.

[1534] This invention is a system that automatically summarizes the content of emails and records the summary in a spreadsheet, and also incorporates an emotion engine that recognizes the user's emotions to adjust notification content and reminder dates. This system improves work efficiency and enables task management that is adapted to the user's emotional state.

[1535] System Configuration

[1536] This system has the following main functions:

[1537] 1. Obtaining and summarizing emails

[1538] The server automatically retrieves emails with specific labels and uses AI to summarize their content.

[1539] 2. Transfer to a spreadsheet

[1540] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded.

[1541] 3. Automatic setting of reminder dates

[1542] The server automatically sets reminder dates for tasks recorded in the spreadsheet, a certain number of days later.

[1543] 4. Notification of important tasks

[1544] The server scans for tasks with upcoming reminder dates and important tasks, and automatically sends notifications to the relevant parties.

[1545] 5. Integration of the Emotional Engine

[1546] The server uses an emotion engine to analyze the user's emotional state.

[1547] 6. Customizing notification content

[1548] The server customizes the content of notifications according to the user's emotional state.

[1549] 7. Adjusting the reminder date

[1550] The server can adjust the reminder date based on the user's emotional state.

[1551] Explanation of program processing

[1552] These features are implemented using Google Apps Script.

[1553] Email acquisition and summarization

[1554] The server automatically retrieves emails with a specific label, "Important," using the Gmail service. It then sends the content of the retrieved emails to an AI-based summarization engine to generate a summary. For example, an email titled "Details about a new project with a client" would be summarized as "New project details."

[1555] Transfer to spreadsheet

[1556] The server adds the summarized content to a new row in a Google Spreadsheet. For example, the summary result "New Project Details," the email received date and time "October 1, 2023," and the sender "client@example.com" will be recorded.

[1557] Automatic reminder date setting

[1558] The server calculates the reminder date based on the task's reception date and time. For example, it might set the reminder date to 5 days after the reception date.

[1559] Important task notifications

[1560] The server scans the spreadsheet and detects tasks with upcoming reminder dates. For detected tasks, it sends email reminder notifications to the relevant parties. For example, if "scheduling a meeting" is due tomorrow, a notification will be sent to the relevant parties.

[1561] Emotional engine integration

[1562] The server analyzes the user's emotions using an emotion engine. For example, the emotion engine detects the emotional state "stress" from the context of the user's emails or the content entered in spreadsheets.

[1563] Customizing notification content

[1564] The server customizes the content of notifications based on the analysis results of the emotion engine. For example, if it detects that the user is stressed, it changes the tone of the notification email to soften it.

[1565] Adjusting the reminder date

[1566] The server can adjust the reminder date based on the user's emotional state. For example, if the server detects that the user is "very busy," it will change the reminder date from two days later to one day later.

[1567] Specific example

[1568] Example 1: Transferring data from Gmail to a task list and integrating sentiment recognition.

[1569] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, content such as "Regarding a new proposal for the project" would be summarized as "New proposal."

[1570] The server adds the summary results to a Google Spreadsheet. Simultaneously, the sentiment engine recognizes from the email context that the user is "focused."

[1571] Example 2: Setting reminder dates and customizing notifications

[1572] The server sets a reminder date of 5 days later for added tasks. For example, if the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[1573] When a user is in a "high-concentration" state, the server will customize the message sent as a reminder email to have a more pleasant tone, such as "This is a reminder for confirmation."

[1574] Example 3: Notification and reminder scheduling for important tasks

[1575] The server scans the spreadsheet at a set time each day to detect tasks whose reminder date is the next day. For example, if "schedule a meeting" is one of the tasks, that day would be tomorrow.

[1576] If the server detects that a user is "extremely busy," it will prompt the relevant user to prepare in advance by sending an early notification.

[1577] This invention not only automates email content summarization, transfer to spreadsheets, setting reminder dates, and task notifications, but also enables flexible task management that takes into account the user's emotional state. As a result, it is expected to improve work efficiency, reduce user stress, and increase productivity.

[1578] The following describes the processing flow.

[1579] Step 1:

[1580] The server uses the Gmail service to search for new emails with the specific label "Important".

[1581] Step 2:

[1582] The server retrieves the latest messages from each thread based on the search results.

[1583] Step 3:

[1584] The server extracts the body of the retrieved message and sends it to the AI ​​summarization engine to request a summary.

[1585] Step 4:

[1586] The server receives the summarization results from the AI ​​summarization engine. For example, an email titled "New Project Proposal" would be summarized as "Project Proposal."

[1587] Step 5:

[1588] The server opens a Google Spreadsheet and retrieves the sheet named "Tasks".

[1589] Step 6:

[1590] The server then adds the summary results, the date and time the email was received, and the sender's email address to a new line.

[1591] Step 7:

[1592] The server reads the task's received date from the last row of the spreadsheet and calculates the reminder date five days later.

[1593] Step 8:

[1594] The server simply enters the calculated reminder date into the corresponding column in the spreadsheet.

[1595] Step 9:

[1596] The device sends the emotional state entered by the user into a spreadsheet to the emotion engine.

[1597] Step 10:

[1598] The server receives the analysis results from the emotion engine and recognizes the user's emotional state. For example, it might recognize the user as being in a "stressed state."

[1599] Step 11:

[1600] The server runs a script at a set time every day, scanning all tasks in the spreadsheet.

[1601] Step 12:

[1602] The server then detects tasks from the scanned tasks that have a reminder date for the following day.

[1603] Step 13:

[1604] The server adjusts the content of notifications based on the user's emotional state. For example, if the server detects that the user is in a "stressed state," it will soften the tone of the notification.

[1605] Step 14:

[1606] The server adjusts the reminder date based on the emotion engine's analysis. For example, if the server recognizes the user as "very busy," it will set the reminder date to one day earlier.

[1607] Step 15:

[1608] The server will send notifications to relevant parties based on the adjusted reminder date. For example, it might send an email with a message like, "This is a reminder to reschedule the meeting."

[1609] Through these steps, summarizing email content, transferring it to a spreadsheet, setting reminder dates, and sending task notifications are automated, resulting in a system that flexibly responds to the user's emotional state. This is expected to improve work efficiency, reduce user stress, and increase productivity.

[1610] (Example 2)

[1611] 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".

[1612] Traditional systems require manual summarization of email content and manual recording in spreadsheets to manage tasks, which reduces work efficiency. Furthermore, reminder dates and notification content are set uniformly without considering the user's emotional state, potentially negatively impacting user stress and work efficiency. Additionally, the manual selection of important tasks increases the likelihood of oversights and poor prioritization.

[1613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for automatically acquiring emails that meet specific conditions, means for summarizing the contents of acquired emails, means for recording the summarized contents in spreadsheet software, means for automatically setting a reminder date based on the recorded contents, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotional state, means for customizing the content of the notification based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This not only automates email summarization and task management, but also enables flexible task management that adapts to the user's emotional state.

[1614] "Specific conditions" refer to rules or criteria for selecting appropriate emails, such as labels tagged on emails or filter settings.

[1615] "Email" refers to digital messages sent and received via the internet or other computer networks.

[1616] A "summary" is a text that extracts the main points of an email and presents them concisely.

[1617] A "spreadsheet program" is a software application used for organizing, analyzing, and creating graphs from data. It is commonly known as a spreadsheet.

[1618] A "reminder date" is the date on which notifications or alerts are sent in relation to tasks, appointments, etc.

[1619] A "notification" is a message or alert sent to a user based on a specific event or condition.

[1620] "Emotional state" refers to the user's psychological state and emotional tendencies, which are analyzed by the AI ​​engine.

[1621] "Automatic configuration" refers to the process in which the system automatically determines and applies values ​​and conditions without manual intervention.

[1622] "Transmission" refers to the act of delivering digital data to other devices or services via a network or communication channel.

[1623] "Analysis" is the process of thoroughly examining data and information to identify specific patterns and trends.

[1624] "Customization" refers to modifying the operation of a system or service to suit the user's needs or specific conditions.

[1625] "Adjustment" refers to the act of changing settings or values ​​based on specific criteria or conditions.

[1626] This invention relates to a system that retrieves important emails from email addresses based on specific criteria, automatically summarizes their content, and records it in a spreadsheet program. The system also includes a function to recognize the user's emotional state and adjust reminder dates and notification content accordingly. This invention is expected to improve work efficiency and reduce user stress. The system has the following main functions:

[1627] Get email

[1628] The server uses Google's Gmail API to automatically retrieve emails based on specific criteria. These criteria might include emails labeled as "Important," for example. The server periodically retrieves emails that meet these criteria and adds them to a list.

[1629] Summary of email content

[1630] The server sends the content of the retrieved emails to an AI summarization engine, which generates a summary. This AI summarization engine uses natural language processing (NLP) technology to create a concise summary of lengthy email content.

[1631] Specific example

[1632] For example, an email that says, "Let's have a meeting about a new project with the client," can be summarized as, "Meeting about the new project."

[1633] Recording the summary content into a spreadsheet program.

[1634] The server records the summarized content in a Google Spreadsheet. The information recorded includes the summary result, the date and time the email was received, and the sender's information.

[1635] Specific example

[1636] If the summary result is "Meeting for a new project," the email was received on "October 1, 2023," and the sender is "client@example.com," this information will be added to a new row in the spreadsheet.

[1637] Automatic reminder date setting

[1638] The server automatically calculates the reminder date based on the date and time the task was received. For example, it can set the reminder date to 5 days after the email was received.

[1639] Specific example

[1640] If the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[1641] Important task notifications

[1642] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. These notifications are sent via email.

[1643] Specific example

[1644] If the deadline for "scheduling a meeting" is tomorrow, a reminder notification will be sent to all relevant parties.

[1645] User sentiment analysis using an emotion engine

[1646] The server sends the user's emails and spreadsheet contents to the emotion engine to analyze the user's emotional state. The emotional state is expressed in terms such as "stressed" or "concentrated."

[1647] Specific example

[1648] When the emotion engine detects a user's emotional state, such as "busy" or "distracted," that information is recorded.

[1649] Customizing notification content

[1650] The server customizes notification content based on the user's emotional state. If it determines that the user is stressed, it changes the tone of the notification email to soften it.

[1651] Specific example

[1652] A message in a friendly tone is sent, such as, "This is a reminder for confirmation."

[1653] Adjusting the reminder date

[1654] The server adjusts the reminder date based on the user's emotional state. For example, if it detects that the user is extremely busy, it will set the reminder date earlier than originally scheduled.

[1655] Specific example

[1656] If the user is determined to be "extremely busy," the reminder date will be changed from 2 days later to 1 day later.

[1657] Example of a prompt

[1658] The following are examples of prompt statements used for generative AI models.

[1659] "Retrieve emails labeled 'Important' from Gmail, send them to a summarization engine, and have it generate a concise summary."

[1660] Thus, this system streamlines automatic email summarization and task management, and further enables flexible management that takes into account the user's emotional state. The above is a description of embodiments for carrying out the present invention.

[1661] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1662] Step 1:

[1663] The server uses the Gmail API to automatically retrieve emails that meet specific criteria. The input is a specific criterion (e.g., the "Important" label), and the output is a list of unread emails that match that criterion. Specifically, the server runs a scheduled task every hour, issuing a query to search for unread emails with the specified label. It retrieves the IDs of the matching emails and adds them to the list.

[1664] Step 2:

[1665] The server sends the content of the retrieved email to an AI summarization engine to generate a summary. The input is the email body, and the output is the summarized text. Specifically, the server extracts the email body as text, sends this text to the AI ​​summarization engine, and receives the summary result. For example, an email that says "Let's have a meeting about a new project with the client" would be summarized as "Meeting about a new project."

[1666] Step 3:

[1667] The server records the summarized content in a Google Spreadsheet. Inputs include the summary result, the email's received date and time, and sender information; output is this information recorded in the spreadsheet. Specifically, the server adds a new row to the spreadsheet and enters the summary result, received date and time, and sender information into the corresponding cells. For example, the summary result might be "Meeting about a new project," the email received date and time might be "October 1, 2023," and the sender might be "client@example.com."

[1668] Step 4:

[1669] The server automatically calculates the reminder date based on the task's reception date and time. The input is the task's reception date and time, and the output is the calculated reminder date. Specifically, the server calculates a date five days after the task's reception date and enters the reminder date into the corresponding cell in the spreadsheet. For example, if the email was received on "October 1, 2023," the reminder date would be "October 6, 2023."

[1670] Step 5:

[1671] The server periodically scans the spreadsheet to detect tasks with approaching reminder dates and sends notifications to relevant parties. The input is task information from the spreadsheet, and the output is the sent reminder notifications. Specifically, the server checks the spreadsheet's reminder dates at a fixed time each day, generates and sends notification emails for tasks with a reminder date the following day. For example, if the task "Schedule a meeting" is due tomorrow, a reminder notification will be sent to the relevant parties.

[1672] Step 6:

[1673] The server sends the user's emails and spreadsheet contents to an emotion engine to analyze the user's emotional state. The input is the user's email text and task information from the spreadsheet, and the output is the analysis of the emotional state. Specifically, the server extracts the user's emails and spreadsheet contents, sends them to the emotion engine, and records the returned emotional state (e.g., "stressed," "focused") in a spreadsheet or database.

[1674] Step 7:

[1675] The server customizes notification content based on the user's emotional state. Input includes the results of the emotion engine's analysis and the standard notification content, while output is the customized notification content. Specifically, if the server determines that the user is "stressed," it softens the wording of the notification email. For example, it might change it to something like, "This is a reminder."

[1676] Step 8:

[1677] The server adjusts the reminder date based on the user's emotional state. The inputs are the results of the emotion engine's analysis and the existing reminder date; the output is the adjusted reminder date. Specifically, if the server detects that the user is "very busy," it will move the reminder date earlier than originally set. For example, it might change the reminder date from two days later to one day later.

[1678] The above outlines the specific flow of the processing steps in this system's program.

[1679] (Application Example 2)

[1680] 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".

[1681] Traditional email summarization systems were useful for summarizing email content and automating reminders, but they failed to consider the user's emotional state and couldn't deliver important notifications at the right time. As a result, important tasks and notifications were often missed, and work efficiency could not be sufficiently improved. Furthermore, it was difficult for users to receive timely notifications when they were stressed or busy.

[1682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1683] In this invention, the server includes means for summarizing content from email, means for recording the summarized content in a spreadsheet, means for automatically setting a reminder date based on the recorded content, means for automatically sending a notification based on the reminder date, means for analyzing the user's emotions using an emotion recognition engine, means for customizing the notification content based on the user's emotional state, and means for adjusting the reminder date based on the user's emotional state. This enables task management and notifications adapted to the user's emotional state, improving work efficiency and reducing user stress.

[1684] "Email" refers to digital messages sent and received over the internet.

[1685] To "summarize" means to extract the essential parts of information and describe them concisely.

[1686] A "spreadsheet" is a digital document in tabular format, consisting of rows and columns, and is primarily used for organizing and calculating data.

[1687] A "reminder date" is a date and time designated for sending a notification about a specific action or task.

[1688] A "notification" is a message or alert used to convey specific information to a recipient.

[1689] An "emotion recognition engine" is a software technology used to analyze and recognize a user's emotional state.

[1690] "To analyze" means to handle data and information and to thoroughly understand its contents.

[1691] "Customizing" means changing the content or functionality according to specific conditions or requirements.

[1692] A "task" is a job or activity performed to achieve a specific objective.

[1693] A "server" is a computer system that provides services over a network.

[1694] This invention is a task management and notification system that takes into account the user's emotional state, and is implemented using the following main hardware and software.

[1695] Hardware and software configuration

[1696] 1. Server

[1697] Google Apps Script: A scripting language for integrating with Google Sheets.

[1698] Gmail API: Retrieving emails and sorting them based on specific labels

[1699] Emotion recognition engine: Software for analyzing a user's emotional state in real time.

[1700] smtplib: A Python library for sending email notifications.

[1701] OpenCV: An open-source library for analyzing emotions from facial expressions and voice tone.

[1702] Program processing flow

[1703] The server will perform the following steps:

[1704] 1. Obtaining and summarizing emails

[1705] The server uses the Gmail API to automatically retrieve emails with specific labels (e.g., Important), and sends their content to an AI-based summarization engine to generate a summary. For example, if the email is titled "Regarding a New Project Proposal," the summary will be "New Proposal."

[1706] 2. Recording in a spreadsheet

[1707] The server records the summarized email content in a Google Spreadsheet. The date and time the email was received and the sender are also recorded. For example, the summary result might be "New Proposal," the email received on "October 1, 2023," and the sender might be "example@domain.com."

[1708] 3. Automatic setting of reminder dates

[1709] The system automatically sets a reminder date based on the recorded information. For example, it can set a reminder date five days after the date of receipt.

[1710] 4. User sentiment analysis using an emotion recognition engine

[1711] The server uses an emotion recognition engine to analyze the user's emotional state. It recognizes emotional states (e.g., stress, concentration) from facial expressions and tone of voice.

[1712] 5. Customizing notification content

[1713] The content of notifications will be customized based on the user's emotional state. For example, if the user is feeling stressed, the tone of the notification email will be softened.

[1714] 6. Adjusting the reminder date

[1715] The reminder date can be adjusted based on the user's emotional state. For example, if the user is detected as "very busy," the reminder date can be changed from two days later to one day later.

[1716] 7. Sending notifications

[1717] Based on the reminder date, notifications will be automatically sent to the relevant parties. For example, if the deadline for "scheduling a meeting" is tomorrow, a notification will be sent to the relevant parties.

[1718] Specific example

[1719] Example 1: Urgent task notification

[1720] When the server receives a new email labeled "Important," it retrieves its content and sends it to an AI summarization engine for summarization. For example, "New project proposal from your boss" would be summarized as "New project proposal."

[1721] The server adds the summary results to a Google Spreadsheet, and at the same time, the sentiment engine recognizes that the user is "focused."

[1722] Example 2: Setting reminder dates and customizing notifications

[1723] The server will set a reminder date five days later. For example, if the email is received on "October 1, 2023," the reminder date will be "October 6, 2023."

[1724] If a user is experiencing "stress," the content of the reminder email will be customized to a more gentle tone, such as "This is a reminder for confirmation."

[1725] Example 3: Example of a system response

[1726] Prompt message: "The driver is stressed. Please soften the notification and send an email."

[1727] Result: "Attention! Driver, you are feeling stressed. Take a short break. How about taking some time for a cup of tea?"

[1728] This invention enables task management and notifications that adapt to the user's emotional state, thereby improving work efficiency and reducing user stress.

[1729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1730] Step 1:

[1731] The server automatically retrieves emails labeled "Important" using the Gmail API. Since these retrieved emails contain detailed information, the retrieved email data is used as input and summarized in the next step.

[1732] Step 2:

[1733] The server sends the retrieved email content to an AI-based summarization engine, which generates a summary. This summarization engine uses natural language processing techniques to extract the essential content of the email. For example, an email titled "Details about a new project with a client" would be summarized as "New project details." This summary is then recorded in a spreadsheet in the next step.

[1734] Step 3:

[1735] The server records the summarized email content in a Google Spreadsheet. Here, relevant data such as the summarized text, the date and time the email was received, and the sender are added to a new row in the spreadsheet. For example, the summarized result "New Project Details", the email received date and time "October 1, 2023", and the sender "example@domain.com" are recorded.

[1736] Step 4:

[1737] The server automatically sets reminder dates for tasks recorded in the spreadsheet. Specifically, it calculates and sets a reminder date a certain number of days (e.g., 5 days) after the date of receipt. This reminder date serves as the basis for sending notifications.

[1738] Step 5:

[1739] The server analyzes the user's emotional state using an emotion recognition engine. This analysis is a process that recognizes the user's emotional state (e.g., stress, concentration) from their facial expressions and tone of voice. The resulting emotional data is then used to customize subsequent notifications.

[1740] Step 6:

[1741] The server customizes notification content based on the user's emotional state. For example, if the user is feeling "stressed," the content of the notification email will be changed to soften the message. This customized notification content can be communicated to the user more effectively.

[1742] Step 7:

[1743] The server takes the user's emotional state into consideration and adjusts the reminder date as needed. For example, if the user is perceived as "extremely busy," the reminder date might be changed from two days later to one day later to ensure the user doesn't miss important tasks.

[1744] Step 8:

[1745] The server automatically sends notifications to relevant parties based on the reminder date. This process uses smtplib to send emails. For example, if the deadline for "scheduling a meeting" is the next day, the server will notify the relevant parties accordingly. This notification allows them to prepare appropriately.

[1746] 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.

[1747] 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.

[1748] 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 robot 414.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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."

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] 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.

[1760] 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.

[1761] 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.

[1762] 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.

[1763] 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.

[1764] 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.

[1765] 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.

[1766] 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.

[1767] The following is further disclosed regarding the embodiments described above.

[1768] (Claim 1)

[1769] Means of summarizing content from emails,

[1770] A means of recording the summarized content in a spreadsheet,

[1771] A means to automatically set a reminder date based on the recorded information,

[1772] A means of automatically sending notifications based on the reminder date,

[1773] A system that includes this.

[1774] (Claim 2)

[1775] The system according to claim 1, further comprising means for sorting emails based on specific conditions.

[1776] (Claim 3)

[1777] The system according to claim 1, further comprising means for automatically identifying important tasks from recorded content.

[1778] "Example 1"

[1779] (Claim 1)

[1780] Means of summarizing content from emails,

[1781] A means of transferring the summarized content into spreadsheet data,

[1782] A means to automatically set a reminder date based on the recorded information,

[1783] A means of automatically sending notifications based on the reminder date,

[1784] Means for monitoring emails with specific identifiers,

[1785] A means of automatically shortening content using a summarization engine,

[1786] How to add data to a new row in a spreadsheet,

[1787] A system that includes this.

[1788] (Claim 2)

[1789] The system according to claim 1, further comprising means for sorting emails based on specific conditions.

[1790] (Claim 3)

[1791] The system according to claim 1, further comprising means for automatically identifying important tasks from recorded content.

[1792] "Application Example 1"

[1793] (Claim 1)

[1794] Means of summarizing content from emails,

[1795] A means of recording the summarized content in a spreadsheet,

[1796] A means to automatically set a reminder date based on the recorded information,

[1797] A means of automatically sending notifications based on the reminder date,

[1798] A means of notifying the administrator of the task details,

[1799] A system that includes this.

[1800] (Claim 2)

[1801] The system according to claim 1, further comprising means for sorting emails based on specific conditions.

[1802] (Claim 3)

[1803] The system according to claim 1, further comprising means for automatically identifying important tasks from recorded content.

[1804] "Example 2 of combining an emotion engine"

[1805] (Claim 1)

[1806] A means of automatically retrieving emails that meet specific conditions,

[1807] A means of summarizing the contents of the acquired emails,

[1808] A means of recording the summarized content in a spreadsheet program,

[1809] A means to automatically set a reminder date based on the recorded information,

[1810] A means of automatically sending notifications based on the reminder date,

[1811] A means of analyzing the emotional state of users,

[1812] A means of customizing notification content based on the user's emotional state,

[1813] A means of adjusting the reminder date based on the user's emotional state,

[1814] A system that includes this.

[1815] (Claim 2)

[1816] The system according to claim 1, which automatically identifies important tasks from recorded content.

[1817] (Claim 3)

[1818] The system according to claim 1, further comprising a method for requesting emails to be sorted based on specific conditions.

[1819] "Application example 2 when combining with an emotional engine"

[1820] (Claim 1)

[1821] Means of summarizing content from emails,

[1822] A means of recording the summarized content in a spreadsheet,

[1823] A means to automatically set a reminder date based on the recorded information,

[1824] A means of automatically sending notifications based on the reminder date,

[1825] A means of analyzing a user's emotions using an emotion recognition engine,

[1826] A means of customizing notification content based on the user's emotional state,

[1827] A means of adjusting the reminder date based on the user's emotional state,

[1828] A system that includes this.

[1829] (Claim 2)

[1830] The system according to claim 1, further comprising means for sorting emails based on specific conditions.

[1831] (Claim 3)

[1832] The system according to claim 1, further comprising means for automatically identifying important tasks from recorded content. [Explanation of Symbols]

[1833] 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 summarizing content from emails, A means of recording the summarized content in a spreadsheet, A means to automatically set a reminder date based on the recorded information, A means of automatically sending notifications based on the reminder date, A system that includes this.

2. The system according to claim 1, further comprising means for sorting emails based on specific conditions.

3. The system according to claim 1, further comprising means for automatically identifying important tasks from recorded content.

Images