System

Abstract

A system is provided.SOLUTION: The system includes a means for acquiring unread data, a means for analyzing the acquired unread data, a means for classifying the analyzed data on the basis of a subject, a means for summarizing the classified data, a means for extracting the summarized data as schedule information, and a means for displaying the extracted schedule information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's business environment, it is common for large amounts of unread communication data to accumulate after holidays or during busy periods. In such situations, it is difficult for users to process unread data quickly and efficiently, which can lead to the problem of important meetings and tasks being overlooked. This problem can reduce users' work efficiency and increase the risk of missing important information. [Means for solving the problem]

[0005] The present invention provides a system for automatically acquiring, analyzing, classifying, summarizing, and extracting unread data as schedule information. The system includes the following means.

[0006] 1. How to get unread data

[0007] 2. How to analyze the unread data

[0008] 3. A means of categorizing the analyzed data based on subject matter

[0009] 4. A means of summarizing classified data

[0010] 5. A means of extracting summarized data as schedule information

[0011] 6. A means of displaying extracted schedule information

[0012] This allows users to quickly grasp the contents of unread data and work efficiently without missing important meetings or tasks.

[0013] "Unread data" is data that has been received in a communication system but has not yet been viewed by a user.

[0014] "Acquiring" means gathering data from a system and making it available.

[0015] "Analyzing" is the process of examining the content of data, breaking it down and classifying it to understand its meaning and structure.

[0016] "Classifying" means grouping data based on specific criteria.

[0017] A "subject" is a label or title that indicates a summary or theme of the data content.

[0018] To summarize means to extract the main points or important parts from a large amount of information and summarize them concisely.

[0019] "Schedule information" is information about plans and tasks related to a specific date and time period.

[0020] "Displaying" means providing the processed data visually to the user.

[0021] A "system" is a collection of multiple devices and programs configured to perform a specific function. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The present invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires, analyzes, classifies, summarizes, and extracts schedule information from unread data, and finally displays it on the user's screen, allowing the user to quickly grasp the information.

[0044] Program processing

[0045] 1. Get unread data

[0046] The server connects to the user's communication database and retrieves the unread data, specifically, the server extracts the data that has been flagged as unread through a specific interface.

[0047] 2. Analysis of unread data

[0048] The server parses the raw data it receives, for example using the email.parser module, which converts each datum into an object, allowing it to programmatically manipulate its contents (subject, body, date and time, etc.).

[0049] 3. Data Classification

[0050] The server categorizes the analyzed data based on its subject. Specifically, it divides it into three categories: "meetings," "tasks," and "general." Data with a subject that includes "meeting" is classified as "meetings," and data with a subject that includes "task," "request," or "action" is classified as "tasks." All other data is classified as "general."

[0051] 4. Data Summarization

[0052] The server summarizes the classified data. For each category, it counts the number of data items and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[0053] 5. Extracting schedule information

[0054] The server analyzes the data related to "meetings" from the classified data and extracts schedule information. Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting.

[0055] 6. View summary and schedule information

[0056] The server transmits the generated summary and schedule information to the terminal, where it is displayed, allowing the user to efficiently organize data and manage important schedules.

[0057] Specific examples

[0058] For example, suppose a user starts the system after a long vacation, and the system finds 500 unread records and categorizes and summarizes them.

[0059] 1. Data Acquisition

[0060] The server connects to the mail server and retrieves 500 unread items.

[0061] 2. Data Analysis

[0062] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[0063] 3. Data Classification

[0064] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General."

[0065] 4. Data Summarization

[0066] The server generates a summary by listing the three most recent subjects for each category, for example, in the "Meetings" category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[0067] 5. Extracting schedule information

[0068] The server extracts date and time information from the meeting category and generates a schedule, for example, "Client Meeting" is "October 20, 2023, 9:00 AM."

[0069] 6.Display

[0070] The server transmits the generated summary and schedule information to the terminal, where the user can confirm them.

[0071] This allows users to efficiently check data and process it without missing important information or schedules.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[0075] Step 2:

[0076] The server parses the raw data it receives, converting each piece of data into an EmailMessage object using modules such as email.parser, and making it possible to extract fields such as the subject, body, and date and time.

[0077] Step 3:

[0078] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: "meetings," "tasks," and "general." For example, data with a subject containing "meeting" is classified as "meetings," and data with a subject containing "task," "request," or "action" is classified as "task."

[0079] Step 4:

[0080] The server summarizes the classified data. Specifically, it counts the number of data items in each category and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[0081] Step 5:

[0082] The server extracts schedule information from data in the "Meeting" category. Specifically, it analyzes the date and time of each data and extracts the meeting date, time, and title. Using the Date field in the email header as an example, date and time information can be obtained from there.

[0083] Step 6:

[0084] The server transmits the generated summary and schedule information to the terminal, where the information is displayed to the user, allowing the user to quickly check the contents of unread data and important schedules and work efficiently.

[0085] Specifically, if there are 500 unread data items after a long vacation, the server retrieves the data, analyzes, classifies, summarizes, and extracts schedule information, then displays it on the user's device for confirmation. This series of processes allows the user to effectively organize data without missing any important information.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] In today's information society, users need to quickly and efficiently process large amounts of unread data to avoid missing important information. However, conventional systems require cumbersome processes such as retrieving, analyzing, classifying, summarizing, and extracting and displaying schedule information from unread data. These processes are often performed manually, placing a heavy burden on users. Therefore, there is a need for an automated system that can effectively manage and quickly process unread data.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data using the email.parser module and converting each data into an object, means for categorizing the analyzed data using regular expressions based on keywords in the subject line, means for summarizing the categorized data, means for extracting the summarized data as schedule information, and means for displaying the extracted schedule information, thereby enabling a user to efficiently organize data through an automated process and quickly grasp important information and schedules.

[0091] "Unread data" refers to digital information that has not yet been read by the user.

[0092] A "server" refers to a computer system that provides services to clients over a network.

[0093] "Terminal" refers to a computer or smart device that is directly operated by a user.

[0094] "User" refers to a person who uses this system to manage and check unread data.

[0095] "Communication database" refers to a database that stores communication data such as user emails and messages.

[0096] "Analysis" refers to the process of structuring acquired data and converting it into an understandable format.

[0097] The "email.parser module" refers to a software library specialized in parsing emails.

[0098] An "object" is a unit of data handled within a program, and refers to something that has attributes and functions.

[0099] A "category" refers to a group of data classified based on specific criteria.

[0100] "Regular expressions" refers to a pattern matching technique for searching and replacing strings.

[0101] A "summary" refers to information that extracts the essence of data and summarizes it briefly.

[0102] "Schedule information" refers to information including the dates, times, and details of scheduled events.

[0103] A "natural language processing library" refers to a software tool for analyzing human language.

[0104] "GUI" is an abbreviation for Graphical User Interface, and refers to an interface that users can operate visually.

[0105] MODE FOR CARRYING OUT THE INVENTION

[0106] This invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires unread data, analyzes, classifies, summarizes, and extracts and displays schedule information, making it easier for users to quickly grasp the information.

[0107] System configuration

[0108] The server executes each process using the following specific hardware and software:

[0109] Hardware: High-performance server machines, database servers, network equipment

[0110] Software: Python, email.parser module, regular expression library, natural language processing library (NLP)

[0111] A terminal is a device that allows a user to check data, and is equipped with a user interface such as a computer or smartphone.

[0112] System Operation

[0113] 1. Get unread data

[0114] The server connects to the user's communication database and retrieves the unread data, using the IMAP protocol to extract data that has been flagged as unread.

[0115] 2. Analysis of unread data

[0116] The server uses the email.parser module to parse the retrieved unread data: the data is converted from raw string format into individual email objects, and information such as the subject, body, date and time is extracted.

[0117] 3. Data Classification

[0118] The server classifies the analyzed email data into three categories based on the subject: "meetings," "tasks," and "general." The classification is performed using regular expressions based on keywords in the subject.

[0119] 4. Data Summarization

[0120] The server summarizes the classified data, for example, extracting the subjects of the three most recent data items for each category and generating summary information.

[0121] 5. Extracting schedule information

[0122] The server then further analyzes the data related to "meetings" from the classified data to extract schedule information, and uses a natural language processing library to obtain date and time information and meeting titles from the email body.

[0123] 6. View summary and schedule information

[0124] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access.

[0125] Specific examples

[0126] For example, consider the case where a user starts up the system after a long vacation. The system finds 500 unread records, categorizes and summarizes them. The server connects to the mail server to retrieve the 500 unread records, and uses the email.parser module to parse each record and extract information such as the subject, body, date, and time.

[0127] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General." It extracts the three most recent subjects for each category and generates a summary. For example, in the "Meetings" category, it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting." It extracts date and time information from the Meetings category and generates schedule information such as "Client Meeting" at "October 20, 2023, 9:00."

[0128] The server transmits the generated summary and schedule information to the terminal, and the user can check them on the terminal.

[0129] Prompt Sentence Examples

[0130] Below are some examples of prompt sentences to input into the generative AI model.

[0131] "Retrieve unread emails from a specified database, parse, categorize, and summarize them. Also extract meeting schedules and generate information to display to the user."

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1:

[0134] Get unread data

[0135] The server connects to the user's communication database and retrieves unread data. Specifically, it connects to the email server using the IMAP protocol and extracts email data with the "UNSEEN" tag. The server uses the user's authentication information to log in to the email server and retrieve unread emails. The input to this process is the user's authentication information, and the output is a list of unread email data.

[0136] Specific behavior:

[0137] The server uses the server's address, port number, username, and password to connect to the IMAP server.

[0138] Use the "UNSEEN" tag as a search criterion to retrieve unread emails and save the data locally.

[0139] Step 2:

[0140] Analysis of unread data

[0141] The server uses the email.parser module to parse the received unread data. The server parses the received email data from raw strings and extracts information such as the subject, body, date and time. The input for parsing is the data of unread emails, and the output is a structured email object.

[0142] Specific behavior:

[0143] The server loads the email.parser library and parses the unread email data.

[0144] As a result of the analysis, information such as the subject, body, and sending date and time is extracted from each email and converted into structured data such as JSON format.

[0145] Step 3:

[0146] Data classification

[0147] The server categorizes the parsed email data based on the subject line. This process uses regular expressions to separate emails into three categories: "meetings," "tasks," and "general" based on keywords in the subject line. The input for classification is a structured email object, and the output is email data categorized by category.

[0148] Specific behavior:

[0149] The server analyzes the subject line using regular expressions, and if it matches certain keywords, it sorts the data into the corresponding category.

[0150] The classified data is stored as an array for each category.

[0151] Step 4:

[0152] Data Summarization

[0153] The server summarizes the data in each category. Specifically, it counts the number of data items in each category and extracts the subjects of the three most recent items. The input for the summary is the email data categorized by category, and the output is the summary information.

[0154] Specific behavior:

[0155] The number of data items in each category is counted, and the three most recent subjects are listed from the emails sorted by newest to oldest.

[0156] The server generates the summary information and stores it in the form of a list.

[0157] Step 5:

[0158] Extracting schedule information

[0159] The server extracts schedule information from the "Meeting" category. Specifically, it uses a natural language processing library to analyze the date and time information and title from the body of emails related to meetings. The input for schedule extraction is the email data related to meetings, and the output is the schedule information.

[0160] Specific behavior:

[0161] The server uses a natural language processing library to analyze the email body and detect date and time information and the title of the meeting.

[0162] The extracted information is saved as calendar-format data.

[0163] Step 6:

[0164] Viewing summary and schedule information

[0165] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access. The input of the display is the summary and schedule information, and the output is a visual display on the terminal.

[0166] Specific behavior:

[0167] The server sends the summary and schedule information to the terminal using an HTTP POST request.

[0168] The terminal parses the received data and displays it to the user in an appropriate format.

[0169] (Application example 1)

[0170] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0171] Store managers receive many messages and communications on a daily basis, but they face the challenge of finding it difficult to process the vast amount of information quickly and efficiently. They are particularly prone to overlooking urgent communications and important schedules, which can result in delays to important decisions and actions in store operations. Furthermore, because they lack a system in place to effectively manage these communications, they end up spending a lot of time manually checking emails and managing schedules.

[0172] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0173] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information and summary information, and means for displaying and notifying the extracted schedule information and summary information. This enables the store manager to quickly and efficiently process a large amount of messages and manage them without missing important schedules or emergency messages.

[0174] "Unread data" is data such as emails or messages that have not yet been read or processed.

[0175] "Means of acquisition" refers to the methods and technologies used to collect unread data from a server or device.

[0176] "Means of analysis" refers to methods and techniques for understanding the content of the acquired unread data and extracting important information.

[0177] A "classification method" is a method or technique that separates analyzed data into specific categories based on its subject or content.

[0178] "Summarization methods" are methods or techniques for concisely summarizing classified data and extracting only the important points.

[0179] "Means for extracting schedule information" refers to methods and techniques for extracting information about schedules and dates from the summarized data.

[0180] The "display and notification means" refers to a method or technique for displaying the extracted schedule information and summary information on a screen so that the user can check it, and for issuing a notification as necessary.

[0181] The "means for setting priorities according to urgency" refers to a method or technology for determining the priority of processing based on the importance or urgency of analyzed data.

[0182] In the embodiment of the present invention, the server constructs a system including the following means.

[0183] The server first obtains unread messages received by the store operator. Specifically, it connects to the store's mail server or other communication tools (e.g., Slack, WhatsApp) to collect unread messages. This process is performed using Python modules such as imaplib and email.

[0184] The server then parses the retrieved unread messages, extracting information such as the subject, body, and date and time from the message, and converts each piece of data into an object that can be manipulated. This process may involve using the email.parser module or other natural language processing libraries.

[0185] The parsed data is then sorted into categories such as "urgent," "routine tasks," and "general" based on the subject and content of the message. The server looks for specific keywords (e.g., "urgent" and "task") and sorts messages based on them, allowing users to determine which messages should be prioritized.

[0186] The classified data is summarized. The server summarizes important messages for each category and extracts and lists the key points, allowing users to quickly understand only the information they need from a vast amount of information.

[0187] The server then extracts schedule information from the summarized data, generating specific appointments from messages containing time and date information for meetings and important tasks, prioritizing them according to urgency.

[0188] The generated summary and schedule information are displayed and notified. The server sends this information to the user's smartphone or other device, allowing the user to manage the information efficiently. For example, the user can set up notifications to be received at regular intervals.

[0189] As a concrete example, consider the case where a store manager receives 100 unread messages after a holiday. 50 of them are classified as "urgent," 30 as "regular tasks," and 20 as "general." The summary information of the three messages and important schedule information (e.g., "The meeting will be held on October 15th at 10:00") are then notified to the user's smartphone.

[0190] Example prompt for a generative AI model:

[0191] Sort and organize your unread messages into the following categories:

[0192] Urgent (contains "urgent" in "subject" or "body")

[0193] Regular tasks (contains "task" in "subject" or "body")

[0194] General (other)

[0195] Summarize the three most recent messages for each category and extract meeting information from messages in the Recurring Tasks category.

[0196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0197] Step 1:

[0198] The server connects to the store operator's mail server or communication tool using a means to retrieve unread data and collects unread messages. Specifically, it uses imaplib and the email module to extract messages that are flagged as unread. It uses mail server authentication information as input and obtains a list of unread messages as output.

[0199] Step 2:

[0200] The server then parses the contents of each message using the parsing mechanism for the unread messages it retrieves. Specifically, it uses the email.parser module to extract information such as the message subject, body, and date and time. The input to this step is a list of unread messages, and the output is a set of parsed message information objects.

[0201] Step 3:

[0202] The server uses a subject-based classification method to categorize the parsed data into "urgent," "routine tasks," and "general" categories. Specifically, it checks whether the subject or body of each message contains specific keywords (e.g., "urgent" or "task"). The input to this step is a set of parsed message information objects, and the output is a list of messages sorted by category.

[0203] Step 4:

[0204] The server uses a means of summarizing the categorized data to summarize the three most recent messages for each category. Specifically, it extracts the key points within each category and generates a concise summary. The input to this step is a list of messages categorized by category, and the output is summary information for each category.

[0205] Step 5:

[0206] The server then extracts date and time information related to meetings and important tasks using a means of extracting schedule information from the summarized data. Specifically, it uses natural language processing technology to analyze the date and time information in the messages and generate a schedule. The input for this step is the summary information for each category, and the output is the schedule information.

[0207] Step 6:

[0208] The server then displays the extracted schedule information and summary information and sends it to the user's smartphone or other device using a means for notification. Specifically, it generates a UI for visually displaying the information and notifies the user using push notifications as needed. The input to this step is the schedule information and summary information, and the output is the display and notification on the user's device.

[0209] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0210] This invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[0211] Program processing

[0212] 1. Get unread data

[0213] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[0214] 2. Analysis of unread data

[0215] The server parses the raw data it receives, for example using the email.parser module to convert each byte into an object so that fields such as the subject, body, date, etc. can be extracted.

[0216] 3. Data Classification

[0217] The server classifies the parsed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: meetings, tasks, and general. For example, if the subject contains "meeting," it will be classified as "meeting," and if it contains "task," "request," or "action," it will be classified as "task."

[0218] 4. Data Summarization

[0219] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[0220] 5. Extracting schedule information

[0221] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting. For example, it obtains date and time information from the Date field in the email header.

[0222] 6. Emotion Recognition by Emotion Engine

[0223] The server recognizes the user's emotions through an emotion engine, which uses techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state.

[0224] 7. Adjusting the display order

[0225] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, data of high importance is displayed first.

[0226] 8. Viewing summary and schedule information

[0227] The server transmits the generated summary and schedule information to the terminal, where they are displayed to the user, allowing the user to receive information in a manner that takes into consideration their emotional state.

[0228] Specific examples

[0229] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[0230] 1. Data Acquisition

[0231] The server connects to the mail server and retrieves 500 unread items.

[0232] 2. Data Analysis

[0233] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[0234] 3. Data Classification

[0235] The server categorizes the data based on the subject into 100 meetings, 200 tasks, and 200 general items.

[0236] 4. Data Summarization

[0237] The server generates a summary by listing the three most recent subjects for each category, for example, in the meetings category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[0238] 5. Extracting schedule information

[0239] The server extracts date and time information from the meeting category to generate a schedule. For example, it extracts information such as "Client Meeting" at 9:00 on October 20, 2023.

[0240] 6. Emotion Recognition by Emotion Engine

[0241] The server uses an emotion engine to analyze the user's emotions and recognizes that the user is feeling stressed.

[0242] 7. Adjusting the display order

[0243] The server takes into consideration that the user is feeling stressed and adjusts the order so that data with high importance is displayed with priority.

[0244] 8. Display

[0245] The server sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to quickly check important information without stress.

[0246] This series of processes enables effective data management and schedule adjustment that takes into account the user's emotional state.

[0247] The processing flow will be explained below.

[0248] Step 1:

[0249] The server connects to the user's communication database and retrieves unread data, specifically, the server sifts through the mail server or messaging platform to retrieve unread flagged data.

[0250] Step 2:

[0251] The server parses the retrieved unread data using modules such as email.parser, converts each data item into an object, and extracts fields such as the subject, body, and date and time.

[0252] Step 3:

[0253] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and classifies it into at least three categories: "meetings," "tasks," and "general." For example, if the subject contains "meeting," it is classified as "meeting," and if it contains "task," "request," or "action," it is classified as "task."

[0254] Step 4:

[0255] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[0256] Step 5:

[0257] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the meeting date, time, and title. For example, it obtains the meeting date and time from the Date field in the email header or the date and time expression in the body of the email.

[0258] Step 6:

[0259] The server uses an emotion engine to recognize the user's emotions. Specifically, it determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. For example, it analyzes the user's voice to infer emotions such as stress or joy.

[0260] Step 7:

[0261] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server displays the most important data first.

[0262] Step 8:

[0263] The server then sends the generated summary and schedule information to the device, where the user can view the information in a way that takes into account their emotional state. This allows the user to quickly check important information while reducing stress.

[0264] Example 2

[0265] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0266] Conventional unread data management systems display information without considering the user's emotional state, which can lead to information overload and stress. Furthermore, manual intervention is required to retrieve, analyze, classify, summarize, and schedule unread data, making efficient data management difficult.

[0267] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0268] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for recognizing the emotional state of the user, means for adjusting the display order based on the recognized emotion, and means for displaying the extracted schedule information and summarized data, thereby enabling efficient data management that takes the emotional state of the user into consideration and reducing stress.

[0269] "Unread data" refers to communication data (such as emails or messages) that the user has not yet viewed.

[0270] "Means of acquisition" refers to the function for collecting unread data from the user's communication database or mail server.

[0271] "Means for analysis" refers to the function of analyzing the acquired unread data and extracting information such as the subject, body, date and time.

[0272] "Means for classification" refers to the function for sorting analyzed data into specific categories (meetings, tasks, general, etc.) based on the subject.

[0273] "Means for summarizing" refers to the function of summarizing classified data and showing the latest subject and number of data for each category.

[0274] "Means for extracting schedule information" refers to the function of extracting date and time information from data related to meetings, etc., and organizing it as schedule information.

[0275] "Means for recognizing emotional state" refers to a function that determines the user's current emotion using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0276] The "means for adjusting the display order" refers to a function for optimizing the display order of information based on the recognized user emotion.

[0277] "Display means" refers to a function for providing extracted schedule information and summarized data to a user.

[0278] The present invention provides a system for efficiently managing unread data and displaying and notifying users based on their emotional state. This system includes a series of processes: acquiring unread data, analyzing, classifying, summarizing, extracting schedule information, and adjusting the display order based on the user's emotional state. The following describes how each process is implemented.

[0279] The server first connects to the user's communication database and retrieves the unread emails. This can be done using a mail server or other communication interface. For example, the IMAP protocol can be used to retrieve unread emails from a mail server. This connection is typically made using the imaplib module.

[0280] The server then parses the unread data, using the email.parser module to convert each email's subject, body, date and time fields into an extractable format, which then turns the data into an object with detailed content for easy further processing.

[0281] The server classifies the parsed data based on the subject line, for example, if the subject line contains "meeting" it will be classified as a "meeting," and if it contains "task," "request," or "action" it will be classified as a "task." This can be done using pattern matching algorithms or keyword lists.

[0282] Once the classification is complete, the server summarizes the data. It counts the number of data items in each category and lists the subjects of the three most recent items. This allows users to quickly grasp important information.

[0283] Next, the server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule. This process efficiently notifies users of important meeting schedules.

[0284] Furthermore, the server recognizes the user's emotional state through an emotion engine, which may use techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state, for example, using a voice recognition module and a facial expression analysis module.

[0285] Based on the recognized emotion, the server adjusts the display order of data. For example, if the user is feeling stressed, it will prioritize displaying data of high importance. This process enables optimal information provision that takes into account the user's emotional state.

[0286] Finally, the server sends the generated summary and schedule information to the terminal, where it is displayed to the user. The user can check important information in a way that takes into account their emotional state, enabling information management with reduced burden. This allows for efficient management of unread data and an improved user experience.

[0287] Specific examples

[0288] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[0289] Prompt Sentence Examples

[0290] "It takes the unread data, parses it, categorizes it, and summarizes it. It then schedules and displays meeting information. It also adjusts the display order based on the user's emotional state."

[0291] This series of processes is expected to enable effective data management and schedule adjustment that takes into account the user's emotional state.

[0292] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0293] Step 1:

[0294] The server connects to the user's communication database, specifically to a mail server or other communication interface, and extracts data that has been flagged as unread.

[0295] Input: User's mail server information, communication data flagged as unread

[0296] What happens: The server starts an IMAP session, queries the user's unread mail folder, receives a list of unread mail, and downloads the data for each mail.

[0297] Output: List of unread data

[0298] Step 2:

[0299] The server parses the retrieved unread data using the email.parser module to extract fields such as the subject, body, and date and time.

[0300] Input: Unread data

[0301] Specific operation: The server passes the raw data to email.parser, which extracts the subject, body, and date and time information from the resulting object.

[0302] Output: Parsed data object

[0303] Step 3:

[0304] The server classifies the parsed data based on the subject. For example, if the subject contains "meeting," it is classified as "conference," and if it contains "task," "request," or "action," it is classified as "task."

[0305] Input: Parsed data object

[0306] Specific operation: The server refers to the keyword list and sorts data into three categories (meetings, tasks, general) based on the subject, and stores the classification results in an internal database.

[0307] Output: Data with classification information

[0308] Step 4:

[0309] The server summarizes the classified data, counts the number of data items in each category, and extracts and lists the subjects of the three most recent data items.

[0310] Input: Data with classification information

[0311] Specific operation: The server counts the number of data in each category, extracts the latest three subjects from each category, and creates a list. The summary information is saved in the internal database.

[0312] Output: List of summary information

[0313] Step 5:

[0314] The server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule.

[0315] Input: Data classified as "Meeting"

[0316] Specific operation: The server parses the Date field from the meeting category data, extracts the date and time and the title, and saves the schedule information in the database.

[0317] Output: Schedule information

[0318] Step 6:

[0319] The server recognizes the user's emotional state through an emotion engine, which can use techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0320] Input: User voice data, facial expression data, keystroke data

[0321] Specific operation: The server acquires voice data, facial expression data, and keystroke data, passes this data to the emotion engine for analysis, and stores the analysis results in an internal database.

[0322] Output: Emotional state analysis results

[0323] Step 7:

[0324] The server adjusts the display order based on the recognized emotion, for example, if the user is feeling stressed, it will prioritize displaying data that is more important.

[0325] Input: Emotional state analysis results, summary information, schedule information

[0326] Specific operation: The server calculates the display order priority based on the emotion analysis results and sets the display order.

[0327] Output: Adjusted display order

[0328] Step 8:

[0329] The server then sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to receive information in a manner that takes into account their emotional state.

[0330] Input: Adjusted display order, summary information, schedule information

[0331] Specific operation: The server sends summary information and schedule information to the terminal, and the terminal updates the screen to display the received information.

[0332] Output: Summary and schedule information displayed to the user

[0333] (Application example 2)

[0334] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0335] In today's world, users tend to be overwhelmed by a large amount of unread data, making it difficult for them to quickly grasp important information. Furthermore, current systems cannot reduce the burden on users because information priorities change depending on the user's emotional state. Furthermore, many advertising systems do not display appropriate ads based on the user's current emotional state, resulting in ineffective ad delivery. This calls for improving user experience and maximizing advertising effectiveness.

[0336] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0337] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on a subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for displaying information based on the extracted schedule information and the user's emotional state, an emotion engine for recognizing the user's emotion, and means for displaying information in order of importance based on the emotion recognized by the emotion engine. This allows the user to effectively manage a large amount of unread data and enables optimal display of information and advertisements according to the user's emotional state.

[0338] The "means for obtaining unread data" is a function that allows the server to connect to the user's communication database and extract data that has been flagged as unread.

[0339] The "means for analyzing acquired unread data" is a function that can analyze the raw data acquired by the server and convert fields such as the subject, body, date and time into an extractable form.

[0340] "Means for classifying analyzed data based on subject" refers to a function in which the server examines the subject of the data and sorts it into pre-defined categories.

[0341] The "means for summarizing classified data" is a function in which the server counts the number of data items in each category and generates a summary of the latest data.

[0342] The "means for extracting summarized data as schedule information" is a function in which the server extracts date and time information from the classified data and generates a schedule.

[0343] The "means for displaying information based on the extracted schedule information and the emotional state of the user" is a function by which the server adjusts the display order of the schedule information and data according to the emotional state of the user.

[0344] The "emotion engine that recognizes the user's emotions" is a function that allows the server to determine the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0345] The "means for displaying information in order of importance based on the emotion recognized by the emotion engine" is a function for displaying information with a high degree of importance on the basis of the emotional state recognized by the server.

[0346] The present invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[0347] The server connects to the user's communication database to retrieve unread data and extracts data flagged as unread. It then parses the raw data and uses the email.parser module to convert fields such as subject, body, and date into extractable data. The parsed data is then classified into at least three categories based on the subject: "Meetings," "Tasks," and "General." A summary of the latest data for each category is then generated.

[0348] Of the classified data, date and time information is extracted from data in the "Meeting" category and organized as schedule information. At this time, the date and time information is obtained from the Date field in the email header. The server uses an emotion engine to determine the user's emotional state, and if the user is feeling stressed, adjusts the display order to prioritize data of high importance. This emotion engine uses techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0349] Finally, the generated summary and schedule information are sent to the user's device, where the information is displayed in a way that takes into account the user's emotional state. The system can also provide example prompts using a generative AI model.

[0350] Hardware and software used

[0351] Hardware: Smartphones, servers

[0352] Software: Python, email.parser module, datetime module, collections.defaultdict, numpy

[0353] Specific examples

[0354] For example, when a user starts the system after a long vacation, the system will find 500 unread records, classify and summarize them. Furthermore, if the emotion engine determines that the user is feeling stressed, it will prioritize displaying stress-reducing advertisements.

[0355] Prompt Sentence Examples

[0356] User data: User voice data, facial image data, and keystroke data after long hours of work

[0357] task:

[0358] 1. Process voice data to recognize emotions

[0359] 2. Process facial image data to recognize emotions

[0360] 3. Process keystroke data to recognize emotions

[0361] conditions:

[0362] If the user is feeling stressed, display a "stress relief ad"

[0363] Output format: Ad list

[0364] As described above, the present invention can improve the user experience by managing data and displaying information based on the emotional state of the user.

[0365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0366] Step 1:

[0367] The server connects to the user's communication database to retrieve unread data. As input, it takes unread data such as emails, messages, and notifications, and extracts data that has been flagged as unread. As output, it obtains a list of unread data.

[0368] Step 2:

[0369] The server parses the retrieved unread data. It receives the list of unread data as input and uses the email.parser module to convert fields such as subject, body, date and time into extractable data. The output is a list of parsed data.

[0370] Step 3:

[0371] The server categorizes the parsed data based on subject. It receives the parsed data as input, examines the subject, and categorizes it into at least three categories: meetings, tasks, and general. As output, it gets a list of categorized data.

[0372] Step 4:

[0373] The server summarizes the classified data. It receives the classified data as input, counts the number of data in each category, and generates a summary of the latest three data items. The summary information is provided as output.

[0374] Step 5:

[0375] The server extracts schedule information from data classified as "meetings." It receives data in the "meetings" category as input, obtains date and time information from the Date field in the email header, and generates a schedule. The schedule information is obtained as output.

[0376] Step 6:

[0377] The server uses an emotion engine to recognize the user's emotions. It receives user data such as voice data, facial expression data, and keystroke data as input, and determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. The user's emotional state is obtained as the output.

[0378] Step 7:

[0379] The server adjusts the display order of information based on the user's emotional state. It receives schedule information and the user's emotional state as input, and rearranges the information based on the output from the emotion engine to prioritize the display of information of higher importance. The adjusted display order is obtained as the output.

[0380] Step 8:

[0381] The server sends the generated summary and schedule information to the terminal. It receives the adjusted display order as input and sends the information to the user terminal. The terminal displays the information in a way that takes into account the user's emotional state, allowing the user to quickly check important information. As output, appropriate information is displayed to the user.

[0382] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0383] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0385] [Second embodiment]

[0386] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0387] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0388] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0389] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0390] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0391] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0392] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0393] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0394] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0395] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0396] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0397] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0398] The present invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires, analyzes, classifies, summarizes, and extracts schedule information from unread data, and finally displays it on the user's screen, allowing the user to quickly grasp the information.

[0399] Program processing

[0400] 1. Get unread data

[0401] The server connects to the user's communication database and retrieves the unread data, specifically, the server extracts the data that has been flagged as unread through a specific interface.

[0402] 2. Analysis of unread data

[0403] The server parses the raw data it receives, for example using the email.parser module, which converts each datum into an object, allowing it to programmatically manipulate its contents (subject, body, date and time, etc.).

[0404] 3. Data Classification

[0405] The server categorizes the analyzed data based on its subject. Specifically, it divides it into three categories: "meetings," "tasks," and "general." Data with a subject that includes "meeting" is classified as "meetings," and data with a subject that includes "task," "request," or "action" is classified as "tasks." All other data is classified as "general."

[0406] 4. Data Summarization

[0407] The server summarizes the classified data. For each category, it counts the number of data items and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[0408] 5. Extracting schedule information

[0409] The server analyzes the data related to "meetings" from the classified data and extracts schedule information. Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting.

[0410] 6. View summary and schedule information

[0411] The server transmits the generated summary and schedule information to the terminal, where it is displayed, allowing the user to efficiently organize data and manage important schedules.

[0412] Specific examples

[0413] For example, suppose a user starts the system after a long vacation, and the system finds 500 unread records and categorizes and summarizes them.

[0414] 1. Data Acquisition

[0415] The server connects to the mail server and retrieves 500 unread items.

[0416] 2. Data Analysis

[0417] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[0418] 3. Data Classification

[0419] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General."

[0420] 4. Data Summarization

[0421] The server generates a summary by listing the three most recent subjects for each category, for example, in the "Meetings" category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[0422] 5. Extracting schedule information

[0423] The server extracts date and time information from the meeting category and generates a schedule, for example, "Client Meeting" is "October 20, 2023, 9:00 AM."

[0424] 6.Display

[0425] The server transmits the generated summary and schedule information to the terminal, where the user can confirm them.

[0426] This allows users to efficiently check data and process it without missing important information or schedules.

[0427] The processing flow will be explained below.

[0428] Step 1:

[0429] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[0430] Step 2:

[0431] The server parses the raw data it receives, converting each piece of data into an EmailMessage object using modules such as email.parser, and making it possible to extract fields such as the subject, body, and date and time.

[0432] Step 3:

[0433] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: "meetings," "tasks," and "general." For example, data with a subject containing "meeting" is classified as "meetings," and data with a subject containing "task," "request," or "action" is classified as "task."

[0434] Step 4:

[0435] The server summarizes the classified data. Specifically, it counts the number of data items in each category and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[0436] Step 5:

[0437] The server extracts schedule information from data in the "Meeting" category. Specifically, it analyzes the date and time of each data and extracts the meeting date, time, and title. Using the Date field in the email header as an example, date and time information can be obtained from there.

[0438] Step 6:

[0439] The server transmits the generated summary and schedule information to the terminal, where the information is displayed to the user, allowing the user to quickly check the contents of unread data and important schedules and work efficiently.

[0440] Specifically, if there are 500 unread data items after a long vacation, the server retrieves the data, analyzes, classifies, summarizes, and extracts schedule information, then displays it on the user's device for confirmation. This series of processes allows the user to effectively organize data without missing any important information.

[0441] Example 1

[0442] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0443] In today's information society, users need to quickly and efficiently process large amounts of unread data to avoid missing important information. However, conventional systems require cumbersome processes such as retrieving, analyzing, classifying, summarizing, and extracting and displaying schedule information from unread data. These processes are often performed manually, placing a heavy burden on users. Therefore, there is a need for an automated system that can effectively manage and quickly process unread data.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0445] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data using the email.parser module and converting each data into an object, means for categorizing the analyzed data using regular expressions based on keywords in the subject line, means for summarizing the categorized data, means for extracting the summarized data as schedule information, and means for displaying the extracted schedule information, thereby enabling a user to efficiently organize data through an automated process and quickly grasp important information and schedules.

[0446] "Unread data" refers to digital information that has not yet been read by the user.

[0447] A "server" refers to a computer system that provides services to clients over a network.

[0448] "Terminal" refers to a computer or smart device that is directly operated by a user.

[0449] "User" refers to a person who uses this system to manage and check unread data.

[0450] "Communication database" refers to a database that stores communication data such as user emails and messages.

[0451] "Analysis" refers to the process of structuring acquired data and converting it into an understandable format.

[0452] The "email.parser module" refers to a software library specialized in parsing emails.

[0453] An "object" is a unit of data handled within a program, and refers to something that has attributes and functions.

[0454] A "category" refers to a group of data classified based on specific criteria.

[0455] "Regular expressions" refers to a pattern matching technique for searching and replacing strings.

[0456] A "summary" refers to information that extracts the essence of data and summarizes it briefly.

[0457] "Schedule information" refers to information including the dates, times, and details of scheduled events.

[0458] A "natural language processing library" refers to a software tool for analyzing human language.

[0459] "GUI" is an abbreviation for Graphical User Interface, and refers to an interface that users can operate visually.

[0460] MODE FOR CARRYING OUT THE INVENTION

[0461] This invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires unread data, analyzes, classifies, summarizes, and extracts and displays schedule information, making it easier for users to quickly grasp the information.

[0462] System configuration

[0463] The server executes each process using the following specific hardware and software:

[0464] Hardware: High-performance server machines, database servers, network equipment

[0465] Software: Python, email.parser module, regular expression library, natural language processing library (NLP)

[0466] A terminal is a device that allows a user to check data, and is equipped with a user interface such as a computer or smartphone.

[0467] System Operation

[0468] 1. Get unread data

[0469] The server connects to the user's communication database and retrieves the unread data, using the IMAP protocol to extract data that has been flagged as unread.

[0470] 2. Analysis of unread data

[0471] The server uses the email.parser module to parse the retrieved unread data: the data is converted from raw string format into individual email objects, and information such as the subject, body, date and time is extracted.

[0472] 3. Data Classification

[0473] The server classifies the analyzed email data into three categories based on the subject: "meetings," "tasks," and "general." The classification is performed using regular expressions based on keywords in the subject.

[0474] 4. Data Summarization

[0475] The server summarizes the classified data, for example, extracting the subjects of the three most recent data items for each category and generating summary information.

[0476] 5. Extracting schedule information

[0477] The server then further analyzes the data related to "meetings" from the classified data to extract schedule information, and uses a natural language processing library to obtain date and time information and meeting titles from the email body.

[0478] 6. View summary and schedule information

[0479] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access.

[0480] Specific examples

[0481] For example, consider the case where a user starts up the system after a long vacation. The system finds 500 unread records, categorizes and summarizes them. The server connects to the mail server to retrieve the 500 unread records, and uses the email.parser module to parse each record and extract information such as the subject, body, date, and time.

[0482] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General." It extracts the three most recent subjects for each category and generates a summary. For example, in the "Meetings" category, it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting." It extracts date and time information from the Meetings category and generates schedule information such as "Client Meeting" at "October 20, 2023, 9:00."

[0483] The server transmits the generated summary and schedule information to the terminal, and the user can check them on the terminal.

[0484] Prompt Sentence Examples

[0485] Below are some examples of prompt sentences to input into the generative AI model.

[0486] "Retrieve unread emails from a specified database, parse, categorize, and summarize them. Also extract meeting schedules and generate information to display to the user."

[0487] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0488] Step 1:

[0489] Get unread data

[0490] The server connects to the user's communication database and retrieves unread data. Specifically, it connects to the email server using the IMAP protocol and extracts email data with the "UNSEEN" tag. The server uses the user's authentication information to log in to the email server and retrieve unread emails. The input to this process is the user's authentication information, and the output is a list of unread email data.

[0491] Specific behavior:

[0492] The server uses the server's address, port number, username, and password to connect to the IMAP server.

[0493] Use the "UNSEEN" tag as a search criterion to retrieve unread emails and save the data locally.

[0494] Step 2:

[0495] Analysis of unread data

[0496] The server uses the email.parser module to parse the received unread data. The server parses the received email data from raw strings and extracts information such as the subject, body, date and time. The input for parsing is the data of unread emails, and the output is a structured email object.

[0497] Specific behavior:

[0498] The server loads the email.parser library and parses the unread email data.

[0499] As a result of the analysis, information such as the subject, body, and sending date and time is extracted from each email and converted into structured data such as JSON format.

[0500] Step 3:

[0501] Data classification

[0502] The server categorizes the parsed email data based on the subject line. This process uses regular expressions to separate emails into three categories: "meetings," "tasks," and "general" based on keywords in the subject line. The input for classification is a structured email object, and the output is email data categorized by category.

[0503] Specific behavior:

[0504] The server analyzes the subject line using regular expressions, and if it matches certain keywords, it sorts the data into the corresponding category.

[0505] The classified data is stored as an array for each category.

[0506] Step 4:

[0507] Data Summarization

[0508] The server summarizes the data in each category. Specifically, it counts the number of data items in each category and extracts the subjects of the three most recent items. The input for the summary is the email data categorized by category, and the output is the summary information.

[0509] Specific behavior:

[0510] The number of data items in each category is counted, and the three most recent subjects are listed from the emails sorted by newest to oldest.

[0511] The server generates the summary information and stores it in the form of a list.

[0512] Step 5:

[0513] Extracting schedule information

[0514] The server extracts schedule information from the "Meeting" category. Specifically, it uses a natural language processing library to analyze the date and time information and title from the body of emails related to meetings. The input for schedule extraction is the email data related to meetings, and the output is the schedule information.

[0515] Specific behavior:

[0516] The server uses a natural language processing library to analyze the email body and detect date and time information and the title of the meeting.

[0517] The extracted information is saved as calendar-format data.

[0518] Step 6:

[0519] Viewing summary and schedule information

[0520] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access. The input of the display is the summary and schedule information, and the output is a visual display on the terminal.

[0521] Specific behavior:

[0522] The server sends the summary and schedule information to the terminal using an HTTP POST request.

[0523] The terminal parses the received data and displays it to the user in an appropriate format.

[0524] (Application example 1)

[0525] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0526] Store managers receive many messages and communications on a daily basis, but they face the challenge of finding it difficult to process the vast amount of information quickly and efficiently. They are particularly prone to overlooking urgent communications and important schedules, which can result in delays to important decisions and actions in store operations. Furthermore, because they lack a system in place to effectively manage these communications, they end up spending a lot of time manually checking emails and managing schedules.

[0527] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0528] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information and summary information, and means for displaying and notifying the extracted schedule information and summary information. This enables the store manager to quickly and efficiently process a large amount of messages and manage them without missing important schedules or emergency messages.

[0529] "Unread data" is data such as emails or messages that have not yet been read or processed.

[0530] "Means of acquisition" refers to the methods and technologies used to collect unread data from a server or device.

[0531] "Means of analysis" refers to methods and techniques for understanding the content of the acquired unread data and extracting important information.

[0532] A "classification method" is a method or technique that separates analyzed data into specific categories based on its subject or content.

[0533] "Summarization methods" are methods or techniques for concisely summarizing classified data and extracting only the important points.

[0534] "Means for extracting schedule information" refers to methods and techniques for extracting information about schedules and dates from the summarized data.

[0535] The "display and notification means" refers to a method or technique for displaying the extracted schedule information and summary information on a screen so that the user can check it, and for issuing a notification as necessary.

[0536] The "means for setting priorities according to urgency" refers to a method or technology for determining the priority of processing based on the importance or urgency of analyzed data.

[0537] In the embodiment of the present invention, the server constructs a system including the following means.

[0538] The server first obtains unread messages received by the store operator. Specifically, it connects to the store's mail server or other communication tools (e.g., Slack, WhatsApp) to collect unread messages. This process is performed using Python modules such as imaplib and email.

[0539] The server then parses the retrieved unread messages, extracting information such as the subject, body, and date and time from the message, and converts each piece of data into an object that can be manipulated. This process may involve using the email.parser module or other natural language processing libraries.

[0540] The parsed data is then sorted into categories such as "urgent," "routine tasks," and "general" based on the subject and content of the message. The server looks for specific keywords (e.g., "urgent" and "task") and sorts messages based on them, allowing users to determine which messages should be prioritized.

[0541] The classified data is summarized. The server summarizes important messages for each category and extracts and lists the key points, allowing users to quickly understand only the information they need from a vast amount of information.

[0542] The server then extracts schedule information from the summarized data, generating specific appointments from messages containing time and date information for meetings and important tasks, prioritizing them according to urgency.

[0543] The generated summary and schedule information are displayed and notified. The server sends this information to the user's smartphone or other device, allowing the user to manage the information efficiently. For example, the user can set up notifications to be received at regular intervals.

[0544] As a concrete example, consider the case where a store manager receives 100 unread messages after a holiday. 50 of them are classified as "urgent," 30 as "regular tasks," and 20 as "general." The summary information of the three messages and important schedule information (e.g., "The meeting will be held on October 15th at 10:00") are then notified to the user's smartphone.

[0545] Example prompt for a generative AI model:

[0546] Sort and organize your unread messages into the following categories:

[0547] Urgent (contains "urgent" in "subject" or "body")

[0548] Regular tasks (contains "task" in "subject" or "body")

[0549] General (other)

[0550] Summarize the three most recent messages for each category and extract meeting information from messages in the Recurring Tasks category.

[0551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0552] Step 1:

[0553] The server connects to the store operator's mail server or communication tool using a means to retrieve unread data and collects unread messages. Specifically, it uses imaplib and the email module to extract messages that are flagged as unread. It uses mail server authentication information as input and obtains a list of unread messages as output.

[0554] Step 2:

[0555] The server then parses the contents of each message using the parsing mechanism for the unread messages it retrieves. Specifically, it uses the email.parser module to extract information such as the message subject, body, and date and time. The input to this step is a list of unread messages, and the output is a set of parsed message information objects.

[0556] Step 3:

[0557] The server uses a subject-based classification method to categorize the parsed data into "urgent," "routine tasks," and "general" categories. Specifically, it checks whether the subject or body of each message contains specific keywords (e.g., "urgent" or "task"). The input to this step is a set of parsed message information objects, and the output is a list of messages sorted by category.

[0558] Step 4:

[0559] The server uses a means of summarizing the categorized data to summarize the three most recent messages for each category. Specifically, it extracts the key points within each category and generates a concise summary. The input to this step is a list of messages categorized by category, and the output is summary information for each category.

[0560] Step 5:

[0561] The server then extracts date and time information related to meetings and important tasks using a means of extracting schedule information from the summarized data. Specifically, it uses natural language processing technology to analyze the date and time information in the messages and generate a schedule. The input for this step is the summary information for each category, and the output is the schedule information.

[0562] Step 6:

[0563] The server then displays the extracted schedule information and summary information and sends it to the user's smartphone or other device using a means for notification. Specifically, it generates a UI for visually displaying the information and notifies the user using push notifications as needed. The input to this step is the schedule information and summary information, and the output is the display and notification on the user's device.

[0564] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0565] This invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[0566] Program processing

[0567] 1. Get unread data

[0568] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[0569] 2. Analysis of unread data

[0570] The server parses the raw data it receives, for example using the email.parser module to convert each byte into an object so that fields such as the subject, body, date, etc. can be extracted.

[0571] 3. Data Classification

[0572] The server classifies the parsed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: meetings, tasks, and general. For example, if the subject contains "meeting," it will be classified as "meeting," and if it contains "task," "request," or "action," it will be classified as "task."

[0573] 4. Data Summarization

[0574] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[0575] 5. Extracting schedule information

[0576] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting. For example, it obtains date and time information from the Date field in the email header.

[0577] 6. Emotion Recognition by Emotion Engine

[0578] The server recognizes the user's emotions through an emotion engine, which uses techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state.

[0579] 7. Adjusting the display order

[0580] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, data of high importance is displayed first.

[0581] 8. Viewing summary and schedule information

[0582] The server transmits the generated summary and schedule information to the terminal, where they are displayed to the user, allowing the user to receive information in a manner that takes into consideration their emotional state.

[0583] Specific examples

[0584] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[0585] 1. Data Acquisition

[0586] The server connects to the mail server and retrieves 500 unread items.

[0587] 2. Data Analysis

[0588] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[0589] 3. Data Classification

[0590] The server categorizes the data based on the subject into 100 meetings, 200 tasks, and 200 general items.

[0591] 4. Data Summarization

[0592] The server generates a summary by listing the three most recent subjects for each category, for example, in the meetings category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[0593] 5. Extracting schedule information

[0594] The server extracts date and time information from the meeting category to generate a schedule. For example, it extracts information such as "Client Meeting" at 9:00 on October 20, 2023.

[0595] 6. Emotion Recognition by Emotion Engine

[0596] The server uses an emotion engine to analyze the user's emotions and recognizes that the user is feeling stressed.

[0597] 7. Adjusting the display order

[0598] The server takes into consideration that the user is feeling stressed and adjusts the order so that data with high importance is displayed with priority.

[0599] 8. Display

[0600] The server sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to quickly check important information without stress.

[0601] This series of processes enables effective data management and schedule adjustment that takes into account the user's emotional state.

[0602] The processing flow will be explained below.

[0603] Step 1:

[0604] The server connects to the user's communication database and retrieves unread data, specifically, the server sifts through the mail server or messaging platform to retrieve unread flagged data.

[0605] Step 2:

[0606] The server parses the retrieved unread data using modules such as email.parser, converts each data item into an object, and extracts fields such as the subject, body, and date and time.

[0607] Step 3:

[0608] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and classifies it into at least three categories: "meetings," "tasks," and "general." For example, if the subject contains "meeting," it is classified as "meeting," and if it contains "task," "request," or "action," it is classified as "task."

[0609] Step 4:

[0610] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[0611] Step 5:

[0612] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the meeting date, time, and title. For example, it obtains the meeting date and time from the Date field in the email header or the date and time expression in the body of the email.

[0613] Step 6:

[0614] The server uses an emotion engine to recognize the user's emotions. Specifically, it determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. For example, it analyzes the user's voice to infer emotions such as stress or joy.

[0615] Step 7:

[0616] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server displays the most important data first.

[0617] Step 8:

[0618] The server then sends the generated summary and schedule information to the device, where the user can view the information in a way that takes into account their emotional state. This allows the user to quickly check important information while reducing stress.

[0619] Example 2

[0620] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0621] Conventional unread data management systems display information without considering the user's emotional state, which can lead to information overload and stress. Furthermore, manual intervention is required to retrieve, analyze, classify, summarize, and schedule unread data, making efficient data management difficult.

[0622] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0623] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for recognizing the emotional state of the user, means for adjusting the display order based on the recognized emotion, and means for displaying the extracted schedule information and summarized data, thereby enabling efficient data management that takes the emotional state of the user into consideration and reducing stress.

[0624] "Unread data" refers to communication data (such as emails or messages) that the user has not yet viewed.

[0625] "Means of acquisition" refers to the function for collecting unread data from the user's communication database or mail server.

[0626] "Means for analysis" refers to the function of analyzing the acquired unread data and extracting information such as the subject, body, date and time.

[0627] "Means for classification" refers to the function for sorting analyzed data into specific categories (meetings, tasks, general, etc.) based on the subject.

[0628] "Means for summarizing" refers to the function of summarizing classified data and showing the latest subject and number of data for each category.

[0629] "Means for extracting schedule information" refers to the function of extracting date and time information from data related to meetings, etc., and organizing it as schedule information.

[0630] "Means for recognizing emotional state" refers to a function that determines the user's current emotion using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0631] The "means for adjusting the display order" refers to a function for optimizing the display order of information based on the recognized user emotion.

[0632] "Display means" refers to a function for providing extracted schedule information and summarized data to a user.

[0633] The present invention provides a system for efficiently managing unread data and displaying and notifying users based on their emotional state. This system includes a series of processes: acquiring unread data, analyzing, classifying, summarizing, extracting schedule information, and adjusting the display order based on the user's emotional state. The following describes how each process is implemented.

[0634] The server first connects to the user's communication database and retrieves the unread emails. This can be done using a mail server or other communication interface. For example, the IMAP protocol can be used to retrieve unread emails from a mail server. This connection is typically made using the imaplib module.

[0635] The server then parses the unread data, using the email.parser module to convert each email's subject, body, date and time fields into an extractable format, which then turns the data into an object with detailed content for easy further processing.

[0636] The server classifies the parsed data based on the subject line, for example, if the subject line contains "meeting" it will be classified as a "meeting," and if it contains "task," "request," or "action" it will be classified as a "task." This can be done using pattern matching algorithms or keyword lists.

[0637] Once the classification is complete, the server summarizes the data. It counts the number of data items in each category and lists the subjects of the three most recent items. This allows users to quickly grasp important information.

[0638] Next, the server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule. This process efficiently notifies users of important meeting schedules.

[0639] Furthermore, the server recognizes the user's emotional state through an emotion engine, which may use techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state, for example, using a voice recognition module and a facial expression analysis module.

[0640] Based on the recognized emotion, the server adjusts the display order of data. For example, if the user is feeling stressed, it will prioritize displaying data of high importance. This process enables optimal information provision that takes into account the user's emotional state.

[0641] Finally, the server sends the generated summary and schedule information to the terminal, where it is displayed to the user. The user can check important information in a way that takes into account their emotional state, enabling information management with reduced burden. This allows for efficient management of unread data and an improved user experience.

[0642] Specific examples

[0643] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[0644] Prompt Sentence Examples

[0645] "It takes the unread data, parses it, categorizes it, and summarizes it. It then schedules and displays meeting information. It also adjusts the display order based on the user's emotional state."

[0646] This series of processes is expected to enable effective data management and schedule adjustment that takes into account the user's emotional state.

[0647] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0648] Step 1:

[0649] The server connects to the user's communication database, specifically to a mail server or other communication interface, and extracts data that has been flagged as unread.

[0650] Input: User's mail server information, communication data flagged as unread

[0651] What happens: The server starts an IMAP session, queries the user's unread mail folder, receives a list of unread mail, and downloads the data for each mail.

[0652] Output: List of unread data

[0653] Step 2:

[0654] The server parses the retrieved unread data using the email.parser module to extract fields such as the subject, body, and date and time.

[0655] Input: Unread data

[0656] Specific operation: The server passes the raw data to email.parser, which extracts the subject, body, and date and time information from the resulting object.

[0657] Output: Parsed data object

[0658] Step 3:

[0659] The server classifies the parsed data based on the subject. For example, if the subject contains "meeting," it is classified as "conference," and if it contains "task," "request," or "action," it is classified as "task."

[0660] Input: Parsed data object

[0661] Specific operation: The server refers to the keyword list and sorts data into three categories (meetings, tasks, general) based on the subject, and stores the classification results in an internal database.

[0662] Output: Data with classification information

[0663] Step 4:

[0664] The server summarizes the classified data, counts the number of data items in each category, and extracts and lists the subjects of the three most recent data items.

[0665] Input: Data with classification information

[0666] Specific operation: The server counts the number of data in each category, extracts the latest three subjects from each category, and creates a list. The summary information is saved in the internal database.

[0667] Output: List of summary information

[0668] Step 5:

[0669] The server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule.

[0670] Input: Data classified as "Meeting"

[0671] Specific operation: The server parses the Date field from the meeting category data, extracts the date and time and the title, and saves the schedule information in the database.

[0672] Output: Schedule information

[0673] Step 6:

[0674] The server recognizes the user's emotional state through an emotion engine, which can use techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0675] Input: User voice data, facial expression data, keystroke data

[0676] Specific operation: The server acquires voice data, facial expression data, and keystroke data, passes this data to the emotion engine for analysis, and stores the analysis results in an internal database.

[0677] Output: Emotional state analysis results

[0678] Step 7:

[0679] The server adjusts the display order based on the recognized emotion, for example, if the user is feeling stressed, it will prioritize displaying data that is more important.

[0680] Input: Emotional state analysis results, summary information, schedule information

[0681] Specific operation: The server calculates the display order priority based on the emotion analysis results and sets the display order.

[0682] Output: Adjusted display order

[0683] Step 8:

[0684] The server then sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to receive information in a manner that takes into account their emotional state.

[0685] Input: Adjusted display order, summary information, schedule information

[0686] Specific operation: The server sends summary information and schedule information to the terminal, and the terminal updates the screen to display the received information.

[0687] Output: Summary and schedule information displayed to the user

[0688] (Application example 2)

[0689] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0690] In today's world, users tend to be overwhelmed by a large amount of unread data, making it difficult for them to quickly grasp important information. Furthermore, current systems cannot reduce the burden on users because information priorities change depending on the user's emotional state. Furthermore, many advertising systems do not display appropriate ads based on the user's current emotional state, resulting in ineffective ad delivery. This calls for improving user experience and maximizing advertising effectiveness.

[0691] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0692] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on a subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for displaying information based on the extracted schedule information and the user's emotional state, an emotion engine for recognizing the user's emotion, and means for displaying information in order of importance based on the emotion recognized by the emotion engine. This allows the user to effectively manage a large amount of unread data and enables optimal display of information and advertisements according to the user's emotional state.

[0693] The "means for obtaining unread data" is a function that allows the server to connect to the user's communication database and extract data that has been flagged as unread.

[0694] The "means for analyzing acquired unread data" is a function that can analyze the raw data acquired by the server and convert fields such as the subject, body, date and time into an extractable form.

[0695] "Means for classifying analyzed data based on subject" refers to a function in which the server examines the subject of the data and sorts it into pre-defined categories.

[0696] The "means for summarizing classified data" is a function in which the server counts the number of data items in each category and generates a summary of the latest data.

[0697] The "means for extracting summarized data as schedule information" is a function in which the server extracts date and time information from the classified data and generates a schedule.

[0698] The "means for displaying information based on the extracted schedule information and the emotional state of the user" is a function by which the server adjusts the display order of the schedule information and data according to the emotional state of the user.

[0699] The "emotion engine that recognizes the user's emotions" is a function that allows the server to determine the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0700] The "means for displaying information in order of importance based on the emotion recognized by the emotion engine" is a function for displaying information with a high degree of importance on the basis of the emotional state recognized by the server.

[0701] The present invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[0702] The server connects to the user's communication database to retrieve unread data and extracts data flagged as unread. It then parses the raw data and uses the email.parser module to convert fields such as subject, body, and date into extractable data. The parsed data is then classified into at least three categories based on the subject: "Meetings," "Tasks," and "General." A summary of the latest data for each category is then generated.

[0703] Of the classified data, date and time information is extracted from data in the "Meeting" category and organized as schedule information. At this time, the date and time information is obtained from the Date field in the email header. The server uses an emotion engine to determine the user's emotional state, and if the user is feeling stressed, adjusts the display order to prioritize data of high importance. This emotion engine uses techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0704] Finally, the generated summary and schedule information are sent to the user's device, where the information is displayed in a way that takes into account the user's emotional state. The system can also provide example prompts using a generative AI model.

[0705] Hardware and software used

[0706] Hardware: Smartphones, servers

[0707] Software: Python, email.parser module, datetime module, collections.defaultdict, numpy

[0708] Specific examples

[0709] For example, when a user starts the system after a long vacation, the system will find 500 unread records, classify and summarize them. Furthermore, if the emotion engine determines that the user is feeling stressed, it will prioritize displaying stress-reducing advertisements.

[0710] Prompt Sentence Examples

[0711] User data: User voice data, facial image data, and keystroke data after long hours of work

[0712] task:

[0713] 1. Process voice data to recognize emotions

[0714] 2. Process facial image data to recognize emotions

[0715] 3. Process keystroke data to recognize emotions

[0716] conditions:

[0717] If the user is feeling stressed, display a "stress relief ad"

[0718] Output format: Ad list

[0719] As described above, the present invention can improve the user experience by managing data and displaying information based on the emotional state of the user.

[0720] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0721] Step 1:

[0722] The server connects to the user's communication database to retrieve unread data. As input, it takes unread data such as emails, messages, and notifications, and extracts data that has been flagged as unread. As output, it obtains a list of unread data.

[0723] Step 2:

[0724] The server parses the retrieved unread data. It receives the list of unread data as input and uses the email.parser module to convert fields such as subject, body, date and time into extractable data. The output is a list of parsed data.

[0725] Step 3:

[0726] The server categorizes the parsed data based on subject. It receives the parsed data as input, examines the subject, and categorizes it into at least three categories: meetings, tasks, and general. As output, it gets a list of categorized data.

[0727] Step 4:

[0728] The server summarizes the classified data. It receives the classified data as input, counts the number of data in each category, and generates a summary of the latest three data items. The summary information is provided as output.

[0729] Step 5:

[0730] The server extracts schedule information from data classified as "meetings." It receives data in the "meetings" category as input, obtains date and time information from the Date field in the email header, and generates a schedule. The schedule information is obtained as output.

[0731] Step 6:

[0732] The server uses an emotion engine to recognize the user's emotions. It receives user data such as voice data, facial expression data, and keystroke data as input, and determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. The user's emotional state is obtained as the output.

[0733] Step 7:

[0734] The server adjusts the display order of information based on the user's emotional state. It receives schedule information and the user's emotional state as input, and rearranges the information based on the output from the emotion engine to prioritize the display of information of higher importance. The adjusted display order is obtained as the output.

[0735] Step 8:

[0736] The server sends the generated summary and schedule information to the terminal. It receives the adjusted display order as input and sends the information to the user terminal. The terminal displays the information in a way that takes into account the user's emotional state, allowing the user to quickly check important information. As output, appropriate information is displayed to the user.

[0737] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0738] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0739] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0740] [Third embodiment]

[0741] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0742] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0743] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0744] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0745] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0746] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0747] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0748] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0749] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0750] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0751] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0752] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0753] The present invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires, analyzes, classifies, summarizes, and extracts schedule information from unread data, and finally displays it on the user's screen, allowing the user to quickly grasp the information.

[0754] Program processing

[0755] 1. Get unread data

[0756] The server connects to the user's communication database and retrieves the unread data, specifically, the server extracts the data that has been flagged as unread through a specific interface.

[0757] 2. Analysis of unread data

[0758] The server parses the raw data it receives, for example using the email.parser module, which converts each datum into an object, allowing it to programmatically manipulate its contents (subject, body, date and time, etc.).

[0759] 3. Data Classification

[0760] The server categorizes the analyzed data based on its subject. Specifically, it divides it into three categories: "meetings," "tasks," and "general." Data with a subject that includes "meeting" is classified as "meetings," and data with a subject that includes "task," "request," or "action" is classified as "tasks." All other data is classified as "general."

[0761] 4. Data Summarization

[0762] The server summarizes the classified data. For each category, it counts the number of data items and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[0763] 5. Extracting schedule information

[0764] The server analyzes the data related to "meetings" from the classified data and extracts schedule information. Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting.

[0765] 6. View summary and schedule information

[0766] The server transmits the generated summary and schedule information to the terminal, where it is displayed, allowing the user to efficiently organize data and manage important schedules.

[0767] Specific examples

[0768] For example, suppose a user starts the system after a long vacation, and the system finds 500 unread records and categorizes and summarizes them.

[0769] 1. Data Acquisition

[0770] The server connects to the mail server and retrieves 500 unread items.

[0771] 2. Data Analysis

[0772] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[0773] 3. Data Classification

[0774] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General."

[0775] 4. Data Summarization

[0776] The server generates a summary by listing the three most recent subjects for each category, for example, in the "Meetings" category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[0777] 5. Extracting schedule information

[0778] The server extracts date and time information from the meeting category and generates a schedule, for example, "Client Meeting" is "October 20, 2023, 9:00 AM."

[0779] 6.Display

[0780] The server transmits the generated summary and schedule information to the terminal, where the user can confirm them.

[0781] This allows users to efficiently check data and process it without missing important information or schedules.

[0782] The processing flow will be explained below.

[0783] Step 1:

[0784] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[0785] Step 2:

[0786] The server parses the raw data it receives, converting each piece of data into an EmailMessage object using modules such as email.parser, and making it possible to extract fields such as the subject, body, and date and time.

[0787] Step 3:

[0788] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: "meetings," "tasks," and "general." For example, data with a subject containing "meeting" is classified as "meetings," and data with a subject containing "task," "request," or "action" is classified as "task."

[0789] Step 4:

[0790] The server summarizes the classified data. Specifically, it counts the number of data items in each category and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[0791] Step 5:

[0792] The server extracts schedule information from data in the "Meeting" category. Specifically, it analyzes the date and time of each data and extracts the meeting date, time, and title. Using the Date field in the email header as an example, date and time information can be obtained from there.

[0793] Step 6:

[0794] The server transmits the generated summary and schedule information to the terminal, where the information is displayed to the user, allowing the user to quickly check the contents of unread data and important schedules and work efficiently.

[0795] Specifically, if there are 500 unread data items after a long vacation, the server retrieves the data, analyzes, classifies, summarizes, and extracts schedule information, then displays it on the user's device for confirmation. This series of processes allows the user to effectively organize data without missing any important information.

[0796] Example 1

[0797] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0798] In today's information society, users need to quickly and efficiently process large amounts of unread data to avoid missing important information. However, conventional systems require cumbersome processes such as retrieving, analyzing, classifying, summarizing, and extracting and displaying schedule information from unread data. These processes are often performed manually, placing a heavy burden on users. Therefore, there is a need for an automated system that can effectively manage and quickly process unread data.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0800] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data using the email.parser module and converting each data into an object, means for categorizing the analyzed data using regular expressions based on keywords in the subject line, means for summarizing the categorized data, means for extracting the summarized data as schedule information, and means for displaying the extracted schedule information, thereby enabling a user to efficiently organize data through an automated process and quickly grasp important information and schedules.

[0801] "Unread data" refers to digital information that has not yet been read by the user.

[0802] A "server" refers to a computer system that provides services to clients over a network.

[0803] "Terminal" refers to a computer or smart device that is directly operated by a user.

[0804] "User" refers to a person who uses this system to manage and check unread data.

[0805] "Communication database" refers to a database that stores communication data such as user emails and messages.

[0806] "Analysis" refers to the process of structuring acquired data and converting it into an understandable format.

[0807] The "email.parser module" refers to a software library specialized in parsing emails.

[0808] An "object" is a unit of data handled within a program, and refers to something that has attributes and functions.

[0809] A "category" refers to a group of data classified based on specific criteria.

[0810] "Regular expressions" refers to a pattern matching technique for searching and replacing strings.

[0811] A "summary" refers to information that extracts the essence of data and summarizes it briefly.

[0812] "Schedule information" refers to information including the dates, times, and details of scheduled events.

[0813] A "natural language processing library" refers to a software tool for analyzing human language.

[0814] "GUI" is an abbreviation for Graphical User Interface, and refers to an interface that users can operate visually.

[0815] MODE FOR CARRYING OUT THE INVENTION

[0816] This invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires unread data, analyzes, classifies, summarizes, and extracts and displays schedule information, making it easier for users to quickly grasp the information.

[0817] System configuration

[0818] The server executes each process using the following specific hardware and software:

[0819] Hardware: High-performance server machines, database servers, network equipment

[0820] Software: Python, email.parser module, regular expression library, natural language processing library (NLP)

[0821] A terminal is a device that allows a user to check data, and is equipped with a user interface such as a computer or smartphone.

[0822] System Operation

[0823] 1. Get unread data

[0824] The server connects to the user's communication database and retrieves the unread data, using the IMAP protocol to extract data that has been flagged as unread.

[0825] 2. Analysis of unread data

[0826] The server uses the email.parser module to parse the retrieved unread data: the data is converted from raw string format into individual email objects, and information such as the subject, body, date and time is extracted.

[0827] 3. Data Classification

[0828] The server classifies the analyzed email data into three categories based on the subject: "meetings," "tasks," and "general." The classification is performed using regular expressions based on keywords in the subject.

[0829] 4. Data Summarization

[0830] The server summarizes the classified data, for example, extracting the subjects of the three most recent data items for each category and generating summary information.

[0831] 5. Extracting schedule information

[0832] The server then further analyzes the data related to "meetings" from the classified data to extract schedule information, and uses a natural language processing library to obtain date and time information and meeting titles from the email body.

[0833] 6. View summary and schedule information

[0834] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access.

[0835] Specific examples

[0836] For example, consider the case where a user starts up the system after a long vacation. The system finds 500 unread records, categorizes and summarizes them. The server connects to the mail server to retrieve the 500 unread records, and uses the email.parser module to parse each record and extract information such as the subject, body, date, and time.

[0837] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General." It extracts the three most recent subjects for each category and generates a summary. For example, in the "Meetings" category, it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting." It extracts date and time information from the Meetings category and generates schedule information such as "Client Meeting" at "October 20, 2023, 9:00."

[0838] The server transmits the generated summary and schedule information to the terminal, and the user can check them on the terminal.

[0839] Prompt Sentence Examples

[0840] Below are some examples of prompt sentences to input into the generative AI model.

[0841] "Retrieve unread emails from a specified database, parse, categorize, and summarize them. Also extract meeting schedules and generate information to display to the user."

[0842] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0843] Step 1:

[0844] Get unread data

[0845] The server connects to the user's communication database and retrieves unread data. Specifically, it connects to the email server using the IMAP protocol and extracts email data with the "UNSEEN" tag. The server uses the user's authentication information to log in to the email server and retrieve unread emails. The input to this process is the user's authentication information, and the output is a list of unread email data.

[0846] Specific behavior:

[0847] The server uses the server's address, port number, username, and password to connect to the IMAP server.

[0848] Use the "UNSEEN" tag as a search criterion to retrieve unread emails and save the data locally.

[0849] Step 2:

[0850] Analysis of unread data

[0851] The server uses the email.parser module to parse the received unread data. The server parses the received email data from raw strings and extracts information such as the subject, body, date and time. The input for parsing is the data of unread emails, and the output is a structured email object.

[0852] Specific behavior:

[0853] The server loads the email.parser library and parses the unread email data.

[0854] As a result of the analysis, information such as the subject, body, and sending date and time is extracted from each email and converted into structured data such as JSON format.

[0855] Step 3:

[0856] Data classification

[0857] The server categorizes the parsed email data based on the subject line. This process uses regular expressions to separate emails into three categories: "meetings," "tasks," and "general" based on keywords in the subject line. The input for classification is a structured email object, and the output is email data categorized by category.

[0858] Specific behavior:

[0859] The server analyzes the subject line using regular expressions, and if it matches certain keywords, it sorts the data into the corresponding category.

[0860] The classified data is stored as an array for each category.

[0861] Step 4:

[0862] Data Summarization

[0863] The server summarizes the data in each category. Specifically, it counts the number of data items in each category and extracts the subjects of the three most recent items. The input for the summary is the email data categorized by category, and the output is the summary information.

[0864] Specific behavior:

[0865] The number of data items in each category is counted, and the three most recent subjects are listed from the emails sorted by newest to oldest.

[0866] The server generates the summary information and stores it in the form of a list.

[0867] Step 5:

[0868] Extracting schedule information

[0869] The server extracts schedule information from the "Meeting" category. Specifically, it uses a natural language processing library to analyze the date and time information and title from the body of emails related to meetings. The input for schedule extraction is the email data related to meetings, and the output is the schedule information.

[0870] Specific behavior:

[0871] The server uses a natural language processing library to analyze the email body and detect date and time information and the title of the meeting.

[0872] The extracted information is saved as calendar-format data.

[0873] Step 6:

[0874] Viewing summary and schedule information

[0875] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access. The input of the display is the summary and schedule information, and the output is a visual display on the terminal.

[0876] Specific behavior:

[0877] The server sends the summary and schedule information to the terminal using an HTTP POST request.

[0878] The terminal parses the received data and displays it to the user in an appropriate format.

[0879] (Application example 1)

[0880] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0881] Store managers receive many messages and communications on a daily basis, but they face the challenge of finding it difficult to process the vast amount of information quickly and efficiently. They are particularly prone to overlooking urgent communications and important schedules, which can result in delays to important decisions and actions in store operations. Furthermore, because they lack a system in place to effectively manage these communications, they end up spending a lot of time manually checking emails and managing schedules.

[0882] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0883] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information and summary information, and means for displaying and notifying the extracted schedule information and summary information. This enables the store manager to quickly and efficiently process a large amount of messages and manage them without missing important schedules or emergency messages.

[0884] "Unread data" is data such as emails or messages that have not yet been read or processed.

[0885] "Means of acquisition" refers to the methods and technologies used to collect unread data from a server or device.

[0886] "Means of analysis" refers to methods and techniques for understanding the content of the acquired unread data and extracting important information.

[0887] A "classification method" is a method or technique that separates analyzed data into specific categories based on its subject or content.

[0888] "Summarization methods" are methods or techniques for concisely summarizing classified data and extracting only the important points.

[0889] "Means for extracting schedule information" refers to methods and techniques for extracting information about schedules and dates from the summarized data.

[0890] The "display and notification means" refers to a method or technique for displaying the extracted schedule information and summary information on a screen so that the user can check it, and for issuing a notification as necessary.

[0891] The "means for setting priorities according to urgency" refers to a method or technology for determining the priority of processing based on the importance or urgency of analyzed data.

[0892] In the embodiment of the present invention, the server constructs a system including the following means.

[0893] The server first obtains unread messages received by the store operator. Specifically, it connects to the store's mail server or other communication tools (e.g., Slack, WhatsApp) to collect unread messages. This process is performed using Python modules such as imaplib and email.

[0894] The server then parses the retrieved unread messages, extracting information such as the subject, body, and date and time from the message, and converts each piece of data into an object that can be manipulated. This process may involve using the email.parser module or other natural language processing libraries.

[0895] The parsed data is then sorted into categories such as "urgent," "routine tasks," and "general" based on the subject and content of the message. The server looks for specific keywords (e.g., "urgent" and "task") and sorts messages based on them, allowing users to determine which messages should be prioritized.

[0896] The classified data is summarized. The server summarizes important messages for each category and extracts and lists the key points, allowing users to quickly understand only the information they need from a vast amount of information.

[0897] The server then extracts schedule information from the summarized data, generating specific appointments from messages containing time and date information for meetings and important tasks, prioritizing them according to urgency.

[0898] The generated summary and schedule information are displayed and notified. The server sends this information to the user's smartphone or other device, allowing the user to manage the information efficiently. For example, the user can set up notifications to be received at regular intervals.

[0899] As a concrete example, consider the case where a store manager receives 100 unread messages after a holiday. 50 of them are classified as "urgent," 30 as "regular tasks," and 20 as "general." The summary information of the three messages and important schedule information (e.g., "The meeting will be held on October 15th at 10:00") are then notified to the user's smartphone.

[0900] Example prompt for a generative AI model:

[0901] Sort and organize your unread messages into the following categories:

[0902] Urgent (contains "urgent" in "subject" or "body")

[0903] Regular tasks (contains "task" in "subject" or "body")

[0904] General (other)

[0905] Summarize the three most recent messages for each category and extract meeting information from messages in the Recurring Tasks category.

[0906] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0907] Step 1:

[0908] The server connects to the store operator's mail server or communication tool using a means to retrieve unread data and collects unread messages. Specifically, it uses imaplib and the email module to extract messages that are flagged as unread. It uses mail server authentication information as input and obtains a list of unread messages as output.

[0909] Step 2:

[0910] The server then parses the contents of each message using the parsing mechanism for the unread messages it retrieves. Specifically, it uses the email.parser module to extract information such as the message subject, body, and date and time. The input to this step is a list of unread messages, and the output is a set of parsed message information objects.

[0911] Step 3:

[0912] The server uses a subject-based classification method to categorize the parsed data into "urgent," "routine tasks," and "general" categories. Specifically, it checks whether the subject or body of each message contains specific keywords (e.g., "urgent" or "task"). The input to this step is a set of parsed message information objects, and the output is a list of messages sorted by category.

[0913] Step 4:

[0914] The server uses a means of summarizing the categorized data to summarize the three most recent messages for each category. Specifically, it extracts the key points within each category and generates a concise summary. The input to this step is a list of messages categorized by category, and the output is summary information for each category.

[0915] Step 5:

[0916] The server then extracts date and time information related to meetings and important tasks using a means of extracting schedule information from the summarized data. Specifically, it uses natural language processing technology to analyze the date and time information in the messages and generate a schedule. The input for this step is the summary information for each category, and the output is the schedule information.

[0917] Step 6:

[0918] The server then displays the extracted schedule information and summary information and sends it to the user's smartphone or other device using a means for notification. Specifically, it generates a UI for visually displaying the information and notifies the user using push notifications as needed. The input to this step is the schedule information and summary information, and the output is the display and notification on the user's device.

[0919] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0920] This invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[0921] Program processing

[0922] 1. Get unread data

[0923] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[0924] 2. Analysis of unread data

[0925] The server parses the raw data it receives, for example using the email.parser module to convert each byte into an object so that fields such as the subject, body, date, etc. can be extracted.

[0926] 3. Data Classification

[0927] The server classifies the parsed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: meetings, tasks, and general. For example, if the subject contains "meeting," it will be classified as "meeting," and if it contains "task," "request," or "action," it will be classified as "task."

[0928] 4. Data Summarization

[0929] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[0930] 5. Extracting schedule information

[0931] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting. For example, it obtains date and time information from the Date field in the email header.

[0932] 6. Emotion Recognition by Emotion Engine

[0933] The server recognizes the user's emotions through an emotion engine, which uses techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state.

[0934] 7. Adjusting the display order

[0935] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, data of high importance is displayed first.

[0936] 8. Viewing summary and schedule information

[0937] The server transmits the generated summary and schedule information to the terminal, where they are displayed to the user, allowing the user to receive information in a manner that takes into consideration their emotional state.

[0938] Specific examples

[0939] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[0940] 1. Data Acquisition

[0941] The server connects to the mail server and retrieves 500 unread items.

[0942] 2. Data Analysis

[0943] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[0944] 3. Data Classification

[0945] The server categorizes the data based on the subject into 100 meetings, 200 tasks, and 200 general items.

[0946] 4. Data Summarization

[0947] The server generates a summary by listing the three most recent subjects for each category, for example, in the meetings category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[0948] 5. Extracting schedule information

[0949] The server extracts date and time information from the meeting category to generate a schedule. For example, it extracts information such as "Client Meeting" at 9:00 on October 20, 2023.

[0950] 6. Emotion Recognition by Emotion Engine

[0951] The server uses an emotion engine to analyze the user's emotions and recognizes that the user is feeling stressed.

[0952] 7. Adjusting the display order

[0953] The server takes into consideration that the user is feeling stressed and adjusts the order so that data with high importance is displayed with priority.

[0954] 8. Display

[0955] The server sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to quickly check important information without stress.

[0956] This series of processes enables effective data management and schedule adjustment that takes into account the user's emotional state.

[0957] The processing flow will be explained below.

[0958] Step 1:

[0959] The server connects to the user's communication database and retrieves unread data, specifically, the server sifts through the mail server or messaging platform to retrieve unread flagged data.

[0960] Step 2:

[0961] The server parses the retrieved unread data using modules such as email.parser, converts each data item into an object, and extracts fields such as the subject, body, and date and time.

[0962] Step 3:

[0963] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and classifies it into at least three categories: "meetings," "tasks," and "general." For example, if the subject contains "meeting," it is classified as "meeting," and if it contains "task," "request," or "action," it is classified as "task."

[0964] Step 4:

[0965] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[0966] Step 5:

[0967] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the meeting date, time, and title. For example, it obtains the meeting date and time from the Date field in the email header or the date and time expression in the body of the email.

[0968] Step 6:

[0969] The server uses an emotion engine to recognize the user's emotions. Specifically, it determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. For example, it analyzes the user's voice to infer emotions such as stress or joy.

[0970] Step 7:

[0971] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server displays the most important data first.

[0972] Step 8:

[0973] The server then sends the generated summary and schedule information to the device, where the user can view the information in a way that takes into account their emotional state. This allows the user to quickly check important information while reducing stress.

[0974] Example 2

[0975] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0976] Conventional unread data management systems display information without considering the user's emotional state, which can lead to information overload and stress. Furthermore, manual intervention is required to retrieve, analyze, classify, summarize, and schedule unread data, making efficient data management difficult.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0978] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for recognizing the emotional state of the user, means for adjusting the display order based on the recognized emotion, and means for displaying the extracted schedule information and summarized data, thereby enabling efficient data management that takes the emotional state of the user into consideration and reducing stress.

[0979] "Unread data" refers to communication data (such as emails or messages) that the user has not yet viewed.

[0980] "Means of acquisition" refers to the function for collecting unread data from the user's communication database or mail server.

[0981] "Means for analysis" refers to the function of analyzing the acquired unread data and extracting information such as the subject, body, date and time.

[0982] "Means for classification" refers to the function for sorting analyzed data into specific categories (meetings, tasks, general, etc.) based on the subject.

[0983] "Means for summarizing" refers to the function of summarizing classified data and showing the latest subject and number of data for each category.

[0984] "Means for extracting schedule information" refers to the function of extracting date and time information from data related to meetings, etc., and organizing it as schedule information.

[0985] "Means for recognizing emotional state" refers to a function that determines the user's current emotion using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[0986] The "means for adjusting the display order" refers to a function for optimizing the display order of information based on the recognized user emotion.

[0987] "Display means" refers to a function for providing extracted schedule information and summarized data to a user.

[0988] The present invention provides a system for efficiently managing unread data and displaying and notifying users based on their emotional state. This system includes a series of processes: acquiring unread data, analyzing, classifying, summarizing, extracting schedule information, and adjusting the display order based on the user's emotional state. The following describes how each process is implemented.

[0989] The server first connects to the user's communication database and retrieves the unread emails. This can be done using a mail server or other communication interface. For example, the IMAP protocol can be used to retrieve unread emails from a mail server. This connection is typically made using the imaplib module.

[0990] The server then parses the unread data, using the email.parser module to convert each email's subject, body, date and time fields into an extractable format, which then turns the data into an object with detailed content for easy further processing.

[0991] The server classifies the parsed data based on the subject line, for example, if the subject line contains "meeting" it will be classified as a "meeting," and if it contains "task," "request," or "action" it will be classified as a "task." This can be done using pattern matching algorithms or keyword lists.

[0992] Once the classification is complete, the server summarizes the data. It counts the number of data items in each category and lists the subjects of the three most recent items. This allows users to quickly grasp important information.

[0993] Next, the server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule. This process efficiently notifies users of important meeting schedules.

[0994] Furthermore, the server recognizes the user's emotional state through an emotion engine, which may use techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state, for example, using a voice recognition module and a facial expression analysis module.

[0995] Based on the recognized emotion, the server adjusts the display order of data. For example, if the user is feeling stressed, it will prioritize displaying data of high importance. This process enables optimal information provision that takes into account the user's emotional state.

[0996] Finally, the server sends the generated summary and schedule information to the terminal, where it is displayed to the user. The user can check important information in a way that takes into account their emotional state, enabling information management with reduced burden. This allows for efficient management of unread data and an improved user experience.

[0997] Specific examples

[0998] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[0999] Prompt Sentence Examples

[1000] "It takes the unread data, parses it, categorizes it, and summarizes it. It then schedules and displays meeting information. It also adjusts the display order based on the user's emotional state."

[1001] This series of processes is expected to enable effective data management and schedule adjustment that takes into account the user's emotional state.

[1002] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1003] Step 1:

[1004] The server connects to the user's communication database, specifically to a mail server or other communication interface, and extracts data that has been flagged as unread.

[1005] Input: User's mail server information, communication data flagged as unread

[1006] What happens: The server starts an IMAP session, queries the user's unread mail folder, receives a list of unread mail, and downloads the data for each mail.

[1007] Output: List of unread data

[1008] Step 2:

[1009] The server parses the retrieved unread data using the email.parser module to extract fields such as the subject, body, and date and time.

[1010] Input: Unread data

[1011] Specific operation: The server passes the raw data to email.parser, which extracts the subject, body, and date and time information from the resulting object.

[1012] Output: Parsed data object

[1013] Step 3:

[1014] The server classifies the parsed data based on the subject. For example, if the subject contains "meeting," it is classified as "conference," and if it contains "task," "request," or "action," it is classified as "task."

[1015] Input: Parsed data object

[1016] Specific operation: The server refers to the keyword list and sorts data into three categories (meetings, tasks, general) based on the subject, and stores the classification results in an internal database.

[1017] Output: Data with classification information

[1018] Step 4:

[1019] The server summarizes the classified data, counts the number of data items in each category, and extracts and lists the subjects of the three most recent data items.

[1020] Input: Data with classification information

[1021] Specific operation: The server counts the number of data in each category, extracts the latest three subjects from each category, and creates a list. The summary information is saved in the internal database.

[1022] Output: List of summary information

[1023] Step 5:

[1024] The server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule.

[1025] Input: Data classified as "Meeting"

[1026] Specific operation: The server parses the Date field from the meeting category data, extracts the date and time and the title, and saves the schedule information in the database.

[1027] Output: Schedule information

[1028] Step 6:

[1029] The server recognizes the user's emotional state through an emotion engine, which can use techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[1030] Input: User voice data, facial expression data, keystroke data

[1031] Specific operation: The server acquires voice data, facial expression data, and keystroke data, passes this data to the emotion engine for analysis, and stores the analysis results in an internal database.

[1032] Output: Emotional state analysis results

[1033] Step 7:

[1034] The server adjusts the display order based on the recognized emotion, for example, if the user is feeling stressed, it will prioritize displaying data that is more important.

[1035] Input: Emotional state analysis results, summary information, schedule information

[1036] Specific operation: The server calculates the display order priority based on the emotion analysis results and sets the display order.

[1037] Output: Adjusted display order

[1038] Step 8:

[1039] The server then sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to receive information in a manner that takes into account their emotional state.

[1040] Input: Adjusted display order, summary information, schedule information

[1041] Specific operation: The server sends summary information and schedule information to the terminal, and the terminal updates the screen to display the received information.

[1042] Output: Summary and schedule information displayed to the user

[1043] (Application example 2)

[1044] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1045] In today's world, users tend to be overwhelmed by a large amount of unread data, making it difficult for them to quickly grasp important information. Furthermore, current systems cannot reduce the burden on users because information priorities change depending on the user's emotional state. Furthermore, many advertising systems do not display appropriate ads based on the user's current emotional state, resulting in ineffective ad delivery. This calls for improving user experience and maximizing advertising effectiveness.

[1046] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1047] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on a subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for displaying information based on the extracted schedule information and the user's emotional state, an emotion engine for recognizing the user's emotion, and means for displaying information in order of importance based on the emotion recognized by the emotion engine. This allows the user to effectively manage a large amount of unread data and enables optimal display of information and advertisements according to the user's emotional state.

[1048] The "means for obtaining unread data" is a function that allows the server to connect to the user's communication database and extract data that has been flagged as unread.

[1049] The "means for analyzing acquired unread data" is a function that can analyze the raw data acquired by the server and convert fields such as the subject, body, date and time into an extractable form.

[1050] "Means for classifying analyzed data based on subject" refers to a function in which the server examines the subject of the data and sorts it into pre-defined categories.

[1051] The "means for summarizing classified data" is a function in which the server counts the number of data items in each category and generates a summary of the latest data.

[1052] The "means for extracting summarized data as schedule information" is a function in which the server extracts date and time information from the classified data and generates a schedule.

[1053] The "means for displaying information based on the extracted schedule information and the emotional state of the user" is a function by which the server adjusts the display order of the schedule information and data according to the emotional state of the user.

[1054] The "emotion engine that recognizes the user's emotions" is a function that allows the server to determine the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[1055] The "means for displaying information in order of importance based on the emotion recognized by the emotion engine" is a function for displaying information with a high degree of importance on the basis of the emotional state recognized by the server.

[1056] The present invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[1057] The server connects to the user's communication database to retrieve unread data and extracts data flagged as unread. It then parses the raw data and uses the email.parser module to convert fields such as subject, body, and date into extractable data. The parsed data is then classified into at least three categories based on the subject: "Meetings," "Tasks," and "General." A summary of the latest data for each category is then generated.

[1058] Of the classified data, date and time information is extracted from data in the "Meeting" category and organized as schedule information. At this time, the date and time information is obtained from the Date field in the email header. The server uses an emotion engine to determine the user's emotional state, and if the user is feeling stressed, adjusts the display order to prioritize data of high importance. This emotion engine uses techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[1059] Finally, the generated summary and schedule information are sent to the user's device, where the information is displayed in a way that takes into account the user's emotional state. The system can also provide example prompts using a generative AI model.

[1060] Hardware and software used

[1061] Hardware: Smartphones, servers

[1062] Software: Python, email.parser module, datetime module, collections.defaultdict, numpy

[1063] Specific examples

[1064] For example, when a user starts the system after a long vacation, the system will find 500 unread records, classify and summarize them. Furthermore, if the emotion engine determines that the user is feeling stressed, it will prioritize displaying stress-reducing advertisements.

[1065] Prompt Sentence Examples

[1066] User data: User voice data, facial image data, and keystroke data after long hours of work

[1067] task:

[1068] 1. Process voice data to recognize emotions

[1069] 2. Process facial image data to recognize emotions

[1070] 3. Process keystroke data to recognize emotions

[1071] conditions:

[1072] If the user is feeling stressed, display a "stress relief ad"

[1073] Output format: Ad list

[1074] As described above, the present invention can improve the user experience by managing data and displaying information based on the emotional state of the user.

[1075] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1076] Step 1:

[1077] The server connects to the user's communication database to retrieve unread data. As input, it takes unread data such as emails, messages, and notifications, and extracts data that has been flagged as unread. As output, it obtains a list of unread data.

[1078] Step 2:

[1079] The server parses the retrieved unread data. It receives the list of unread data as input and uses the email.parser module to convert fields such as subject, body, date and time into extractable data. The output is a list of parsed data.

[1080] Step 3:

[1081] The server categorizes the parsed data based on subject. It receives the parsed data as input, examines the subject, and categorizes it into at least three categories: meetings, tasks, and general. As output, it gets a list of categorized data.

[1082] Step 4:

[1083] The server summarizes the classified data. It receives the classified data as input, counts the number of data in each category, and generates a summary of the latest three data items. The summary information is provided as output.

[1084] Step 5:

[1085] The server extracts schedule information from data classified as "meetings." It receives data in the "meetings" category as input, obtains date and time information from the Date field in the email header, and generates a schedule. The schedule information is obtained as output.

[1086] Step 6:

[1087] The server uses an emotion engine to recognize the user's emotions. It receives user data such as voice data, facial expression data, and keystroke data as input, and determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. The user's emotional state is obtained as the output.

[1088] Step 7:

[1089] The server adjusts the display order of information based on the user's emotional state. It receives schedule information and the user's emotional state as input, and rearranges the information based on the output from the emotion engine to prioritize the display of information of higher importance. The adjusted display order is obtained as the output.

[1090] Step 8:

[1091] The server sends the generated summary and schedule information to the terminal. It receives the adjusted display order as input and sends the information to the user terminal. The terminal displays the information in a way that takes into account the user's emotional state, allowing the user to quickly check important information. As output, appropriate information is displayed to the user.

[1092] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1094] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1095] [Fourth embodiment]

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

[1097] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1099] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1100] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1103] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1104] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1105] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1107] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1108] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1109] The present invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires, analyzes, classifies, summarizes, and extracts schedule information from unread data, and finally displays it on the user's screen, allowing the user to quickly grasp the information.

[1110] Program processing

[1111] 1. Get unread data

[1112] The server connects to the user's communication database and retrieves the unread data, specifically, the server extracts the data that has been flagged as unread through a specific interface.

[1113] 2. Analysis of unread data

[1114] The server parses the raw data it receives, for example using the email.parser module, which converts each datum into an object, allowing it to programmatically manipulate its contents (subject, body, date and time, etc.).

[1115] 3. Data Classification

[1116] The server categorizes the analyzed data based on its subject. Specifically, it divides it into three categories: "meetings," "tasks," and "general." Data with a subject that includes "meeting" is classified as "meetings," and data with a subject that includes "task," "request," or "action" is classified as "tasks." All other data is classified as "general."

[1117] 4. Data Summarization

[1118] The server summarizes the classified data. For each category, it counts the number of data items and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[1119] 5. Extracting schedule information

[1120] The server analyzes the data related to "meetings" from the classified data and extracts schedule information. Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting.

[1121] 6. View summary and schedule information

[1122] The server transmits the generated summary and schedule information to the terminal, where it is displayed, allowing the user to efficiently organize data and manage important schedules.

[1123] Specific examples

[1124] For example, suppose a user starts the system after a long vacation, and the system finds 500 unread records and categorizes and summarizes them.

[1125] 1. Data Acquisition

[1126] The server connects to the mail server and retrieves 500 unread items.

[1127] 2. Data Analysis

[1128] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[1129] 3. Data Classification

[1130] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General."

[1131] 4. Data Summarization

[1132] The server generates a summary by listing the three most recent subjects for each category, for example, in the "Meetings" category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[1133] 5. Extracting schedule information

[1134] The server extracts date and time information from the meeting category and generates a schedule, for example, "Client Meeting" is "October 20, 2023, 9:00 AM."

[1135] 6.Display

[1136] The server transmits the generated summary and schedule information to the terminal, where the user can confirm them.

[1137] This allows users to efficiently check data and process it without missing important information or schedules.

[1138] The processing flow will be explained below.

[1139] Step 1:

[1140] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[1141] Step 2:

[1142] The server parses the raw data it receives, converting each piece of data into an EmailMessage object using modules such as email.parser, and making it possible to extract fields such as the subject, body, and date and time.

[1143] Step 3:

[1144] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: "meetings," "tasks," and "general." For example, data with a subject containing "meeting" is classified as "meetings," and data with a subject containing "task," "request," or "action" is classified as "task."

[1145] Step 4:

[1146] The server summarizes the classified data. Specifically, it counts the number of data items in each category and lists the subjects of the three most recent data items. This allows users to grasp the important information of each category at a glance.

[1147] Step 5:

[1148] The server extracts schedule information from data in the "Meeting" category. Specifically, it analyzes the date and time of each data and extracts the meeting date, time, and title. Using the Date field in the email header as an example, date and time information can be obtained from there.

[1149] Step 6:

[1150] The server transmits the generated summary and schedule information to the terminal, where the information is displayed to the user, allowing the user to quickly check the contents of unread data and important schedules and work efficiently.

[1151] Specifically, if there are 500 unread data items after a long vacation, the server retrieves the data, analyzes, classifies, summarizes, and extracts schedule information, then displays it on the user's device for confirmation. This series of processes allows the user to effectively organize data without missing any important information.

[1152] Example 1

[1153] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1154] In today's information society, users need to quickly and efficiently process large amounts of unread data to avoid missing important information. However, conventional systems require cumbersome processes such as retrieving, analyzing, classifying, summarizing, and extracting and displaying schedule information from unread data. These processes are often performed manually, placing a heavy burden on users. Therefore, there is a need for an automated system that can effectively manage and quickly process unread data.

[1155] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1156] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data using the email.parser module and converting each data into an object, means for categorizing the analyzed data using regular expressions based on keywords in the subject line, means for summarizing the categorized data, means for extracting the summarized data as schedule information, and means for displaying the extracted schedule information, thereby enabling a user to efficiently organize data through an automated process and quickly grasp important information and schedules.

[1157] "Unread data" refers to digital information that has not yet been read by the user.

[1158] A "server" refers to a computer system that provides services to clients over a network.

[1159] "Terminal" refers to a computer or smart device that is directly operated by a user.

[1160] "User" refers to a person who uses this system to manage and check unread data.

[1161] "Communication database" refers to a database that stores communication data such as user emails and messages.

[1162] "Analysis" refers to the process of structuring acquired data and converting it into an understandable format.

[1163] The "email.parser module" refers to a software library specialized in parsing emails.

[1164] An "object" is a unit of data handled within a program, and refers to something that has attributes and functions.

[1165] A "category" refers to a group of data classified based on specific criteria.

[1166] "Regular expressions" refers to a pattern matching technique for searching and replacing strings.

[1167] A "summary" refers to information that extracts the essence of data and summarizes it briefly.

[1168] "Schedule information" refers to information including the dates, times, and details of scheduled events.

[1169] A "natural language processing library" refers to a software tool for analyzing human language.

[1170] "GUI" is an abbreviation for Graphical User Interface, and refers to an interface that users can operate visually.

[1171] MODE FOR CARRYING OUT THE INVENTION

[1172] This invention provides a system for effectively managing and quickly processing unread data. This system automatically acquires unread data, analyzes, classifies, summarizes, and extracts and displays schedule information, making it easier for users to quickly grasp the information.

[1173] System configuration

[1174] The server executes each process using the following specific hardware and software:

[1175] Hardware: High-performance server machines, database servers, network equipment

[1176] Software: Python, email.parser module, regular expression library, natural language processing library (NLP)

[1177] A terminal is a device that allows a user to check data, and is equipped with a user interface such as a computer or smartphone.

[1178] System Operation

[1179] 1. Get unread data

[1180] The server connects to the user's communication database and retrieves the unread data, using the IMAP protocol to extract data that has been flagged as unread.

[1181] 2. Analysis of unread data

[1182] The server uses the email.parser module to parse the retrieved unread data: the data is converted from raw string format into individual email objects, and information such as the subject, body, date and time is extracted.

[1183] 3. Data Classification

[1184] The server classifies the analyzed email data into three categories based on the subject: "meetings," "tasks," and "general." The classification is performed using regular expressions based on keywords in the subject.

[1185] 4. Data Summarization

[1186] The server summarizes the classified data, for example, extracting the subjects of the three most recent data items for each category and generating summary information.

[1187] 5. Extracting schedule information

[1188] The server then further analyzes the data related to "meetings" from the classified data to extract schedule information, and uses a natural language processing library to obtain date and time information and meeting titles from the email body.

[1189] 6. View summary and schedule information

[1190] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access.

[1191] Specific examples

[1192] For example, consider the case where a user starts up the system after a long vacation. The system finds 500 unread records, categorizes and summarizes them. The server connects to the mail server to retrieve the 500 unread records, and uses the email.parser module to parse each record and extract information such as the subject, body, date, and time.

[1193] The server categorizes the data based on the subject into 100 "Meetings," 200 "Tasks," and 200 "General." It extracts the three most recent subjects for each category and generates a summary. For example, in the "Meetings" category, it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting." It extracts date and time information from the Meetings category and generates schedule information such as "Client Meeting" at "October 20, 2023, 9:00."

[1194] The server transmits the generated summary and schedule information to the terminal, and the user can check them on the terminal.

[1195] Prompt Sentence Examples

[1196] Below are some examples of prompt sentences to input into the generative AI model.

[1197] "Retrieve unread emails from a specified database, parse, categorize, and summarize them. Also extract meeting schedules and generate information to display to the user."

[1198] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1199] Step 1:

[1200] Get unread data

[1201] The server connects to the user's communication database and retrieves unread data. Specifically, it connects to the email server using the IMAP protocol and extracts email data with the "UNSEEN" tag. The server uses the user's authentication information to log in to the email server and retrieve unread emails. The input to this process is the user's authentication information, and the output is a list of unread email data.

[1202] Specific behavior:

[1203] The server uses the server's address, port number, username, and password to connect to the IMAP server.

[1204] Use the "UNSEEN" tag as a search criterion to retrieve unread emails and save the data locally.

[1205] Step 2:

[1206] Analysis of unread data

[1207] The server uses the email.parser module to parse the received unread data. The server parses the received email data from raw strings and extracts information such as the subject, body, date and time. The input for parsing is the data of unread emails, and the output is a structured email object.

[1208] Specific behavior:

[1209] The server loads the email.parser library and parses the unread email data.

[1210] As a result of the analysis, information such as the subject, body, and sending date and time is extracted from each email and converted into structured data such as JSON format.

[1211] Step 3:

[1212] Data classification

[1213] The server categorizes the parsed email data based on the subject line. This process uses regular expressions to separate emails into three categories: "meetings," "tasks," and "general" based on keywords in the subject line. The input for classification is a structured email object, and the output is email data categorized by category.

[1214] Specific behavior:

[1215] The server analyzes the subject line using regular expressions, and if it matches certain keywords, it sorts the data into the corresponding category.

[1216] The classified data is stored as an array for each category.

[1217] Step 4:

[1218] Data Summarization

[1219] The server summarizes the data in each category. Specifically, it counts the number of data items in each category and extracts the subjects of the three most recent items. The input for the summary is the email data categorized by category, and the output is the summary information.

[1220] Specific behavior:

[1221] The number of data items in each category is counted, and the three most recent subjects are listed from the emails sorted by newest to oldest.

[1222] The server generates the summary information and stores it in the form of a list.

[1223] Step 5:

[1224] Extracting schedule information

[1225] The server extracts schedule information from the "Meeting" category. Specifically, it uses a natural language processing library to analyze the date and time information and title from the body of emails related to meetings. The input for schedule extraction is the email data related to meetings, and the output is the schedule information.

[1226] Specific behavior:

[1227] The server uses a natural language processing library to analyze the email body and detect date and time information and the title of the meeting.

[1228] The extracted information is saved as calendar-format data.

[1229] Step 6:

[1230] Viewing summary and schedule information

[1231] The server sends the generated summary and schedule information to the terminal, which displays the received information in a GUI for easy user access. The input of the display is the summary and schedule information, and the output is a visual display on the terminal.

[1232] Specific behavior:

[1233] The server sends the summary and schedule information to the terminal using an HTTP POST request.

[1234] The terminal parses the received data and displays it to the user in an appropriate format.

[1235] (Application example 1)

[1236] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1237] Store managers receive many messages and communications on a daily basis, but they face the challenge of finding it difficult to process the vast amount of information quickly and efficiently. They are particularly prone to overlooking urgent communications and important schedules, which can result in delays to important decisions and actions in store operations. Furthermore, because they lack a system in place to effectively manage these communications, they end up spending a lot of time manually checking emails and managing schedules.

[1238] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1239] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information and summary information, and means for displaying and notifying the extracted schedule information and summary information. This enables the store manager to quickly and efficiently process a large amount of messages and manage them without missing important schedules or emergency messages.

[1240] "Unread data" is data such as emails or messages that have not yet been read or processed.

[1241] "Means of acquisition" refers to the methods and technologies used to collect unread data from a server or device.

[1242] "Means of analysis" refers to methods and techniques for understanding the content of the acquired unread data and extracting important information.

[1243] A "classification method" is a method or technique that separates analyzed data into specific categories based on its subject or content.

[1244] "Summarization methods" are methods or techniques for concisely summarizing classified data and extracting only the important points.

[1245] "Means for extracting schedule information" refers to methods and techniques for extracting information about schedules and dates from the summarized data.

[1246] The "display and notification means" refers to a method or technique for displaying the extracted schedule information and summary information on a screen so that the user can check it, and for issuing a notification as necessary.

[1247] The "means for setting priorities according to urgency" refers to a method or technology for determining the priority of processing based on the importance or urgency of analyzed data.

[1248] In the embodiment of the present invention, the server constructs a system including the following means.

[1249] The server first obtains unread messages received by the store operator. Specifically, it connects to the store's mail server or other communication tools (e.g., Slack, WhatsApp) to collect unread messages. This process is performed using Python modules such as imaplib and email.

[1250] The server then parses the retrieved unread messages, extracting information such as the subject, body, and date and time from the message, and converts each piece of data into an object that can be manipulated. This process may involve using the email.parser module or other natural language processing libraries.

[1251] The parsed data is then sorted into categories such as "urgent," "routine tasks," and "general" based on the subject and content of the message. The server looks for specific keywords (e.g., "urgent" and "task") and sorts messages based on them, allowing users to determine which messages should be prioritized.

[1252] The classified data is summarized. The server summarizes important messages for each category and extracts and lists the key points, allowing users to quickly understand only the information they need from a vast amount of information.

[1253] The server then extracts schedule information from the summarized data, generating specific appointments from messages containing time and date information for meetings and important tasks, prioritizing them according to urgency.

[1254] The generated summary and schedule information are displayed and notified. The server sends this information to the user's smartphone or other device, allowing the user to manage the information efficiently. For example, the user can set up notifications to be received at regular intervals.

[1255] As a concrete example, consider the case where a store manager receives 100 unread messages after a holiday. 50 of them are classified as "urgent," 30 as "regular tasks," and 20 as "general." The summary information of the three messages and important schedule information (e.g., "The meeting will be held on October 15th at 10:00") are then notified to the user's smartphone.

[1256] Example prompt for a generative AI model:

[1257] Sort and organize your unread messages into the following categories:

[1258] Urgent (contains "urgent" in "subject" or "body")

[1259] Regular tasks (contains "task" in "subject" or "body")

[1260] General (other)

[1261] Summarize the three most recent messages for each category and extract meeting information from messages in the Recurring Tasks category.

[1262] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1263] Step 1:

[1264] The server connects to the store operator's mail server or communication tool using a means to retrieve unread data and collects unread messages. Specifically, it uses imaplib and the email module to extract messages that are flagged as unread. It uses mail server authentication information as input and obtains a list of unread messages as output.

[1265] Step 2:

[1266] The server then parses the contents of each message using the parsing mechanism for the unread messages it retrieves. Specifically, it uses the email.parser module to extract information such as the message subject, body, and date and time. The input to this step is a list of unread messages, and the output is a set of parsed message information objects.

[1267] Step 3:

[1268] The server uses a subject-based classification method to categorize the parsed data into "urgent," "routine tasks," and "general" categories. Specifically, it checks whether the subject or body of each message contains specific keywords (e.g., "urgent" or "task"). The input to this step is a set of parsed message information objects, and the output is a list of messages sorted by category.

[1269] Step 4:

[1270] The server uses a means of summarizing the categorized data to summarize the three most recent messages for each category. Specifically, it extracts the key points within each category and generates a concise summary. The input to this step is a list of messages categorized by category, and the output is summary information for each category.

[1271] Step 5:

[1272] The server then extracts date and time information related to meetings and important tasks using a means of extracting schedule information from the summarized data. Specifically, it uses natural language processing technology to analyze the date and time information in the messages and generate a schedule. The input for this step is the summary information for each category, and the output is the schedule information.

[1273] Step 6:

[1274] The server then displays the extracted schedule information and summary information and sends it to the user's smartphone or other device using a means for notification. Specifically, it generates a UI for visually displaying the information and notifies the user using push notifications as needed. The input to this step is the schedule information and summary information, and the output is the display and notification on the user's device.

[1275] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1276] This invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[1277] Program processing

[1278] 1. Get unread data

[1279] The server connects to the user's communication database to retrieve unread data, specifically, the server connects to a mail server or other communication interface and extracts data that has been flagged as unread.

[1280] 2. Analysis of unread data

[1281] The server parses the raw data it receives, for example using the email.parser module to convert each byte into an object so that fields such as the subject, body, date, etc. can be extracted.

[1282] 3. Data Classification

[1283] The server classifies the parsed data based on its subject. Specifically, it examines the subject of the data and sorts it into at least three categories: meetings, tasks, and general. For example, if the subject contains "meeting," it will be classified as "meeting," and if it contains "task," "request," or "action," it will be classified as "task."

[1284] 4. Data Summarization

[1285] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[1286] 5. Extracting schedule information

[1287] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the date, time, and title of the meeting. For example, it obtains date and time information from the Date field in the email header.

[1288] 6. Emotion Recognition by Emotion Engine

[1289] The server recognizes the user's emotions through an emotion engine, which uses techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state.

[1290] 7. Adjusting the display order

[1291] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, data of high importance is displayed first.

[1292] 8. Viewing summary and schedule information

[1293] The server transmits the generated summary and schedule information to the terminal, where they are displayed to the user, allowing the user to receive information in a manner that takes into consideration their emotional state.

[1294] Specific examples

[1295] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[1296] 1. Data Acquisition

[1297] The server connects to the mail server and retrieves 500 unread items.

[1298] 2. Data Analysis

[1299] The server analyzes each piece of data and extracts information such as the subject, body, date and time.

[1300] 3. Data Classification

[1301] The server categorizes the data based on the subject into 100 meetings, 200 tasks, and 200 general items.

[1302] 4. Data Summarization

[1303] The server generates a summary by listing the three most recent subjects for each category, for example, in the meetings category it lists the subjects "Client Meeting," "Project Planning Meeting," and "Staff Meeting."

[1304] 5. Extracting schedule information

[1305] The server extracts date and time information from the meeting category to generate a schedule. For example, it extracts information such as "Client Meeting" at 9:00 on October 20, 2023.

[1306] 6. Emotion Recognition by Emotion Engine

[1307] The server uses an emotion engine to analyze the user's emotions and recognizes that the user is feeling stressed.

[1308] 7. Adjusting the display order

[1309] The server takes into consideration that the user is feeling stressed and adjusts the order so that data with high importance is displayed with priority.

[1310] 8. Display

[1311] The server sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to quickly check important information without stress.

[1312] This series of processes enables effective data management and schedule adjustment that takes into account the user's emotional state.

[1313] The processing flow will be explained below.

[1314] Step 1:

[1315] The server connects to the user's communication database and retrieves unread data, specifically, the server sifts through the mail server or messaging platform to retrieve unread flagged data.

[1316] Step 2:

[1317] The server parses the retrieved unread data using modules such as email.parser, converts each data item into an object, and extracts fields such as the subject, body, and date and time.

[1318] Step 3:

[1319] The server classifies the analyzed data based on its subject. Specifically, it examines the subject of the data and classifies it into at least three categories: "meetings," "tasks," and "general." For example, if the subject contains "meeting," it is classified as "meeting," and if it contains "task," "request," or "action," it is classified as "task."

[1320] Step 4:

[1321] The server summarizes the classified data, counts the number of data items in each category, and lists the subjects of the three most recent data items, allowing users to quickly grasp important information.

[1322] Step 5:

[1323] The server extracts schedule information from data classified as "meetings." Specifically, it analyzes the date and time information in the data and extracts the meeting date, time, and title. For example, it obtains the meeting date and time from the Date field in the email header or the date and time expression in the body of the email.

[1324] Step 6:

[1325] The server uses an emotion engine to recognize the user's emotions. Specifically, it determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. For example, it analyzes the user's voice to infer emotions such as stress or joy.

[1326] Step 7:

[1327] The server adjusts the display order of schedule information and data based on the user's emotions recognized by the emotion engine. For example, if the user is feeling stressed, the server displays the most important data first.

[1328] Step 8:

[1329] The server then sends the generated summary and schedule information to the device, where the user can view the information in a way that takes into account their emotional state. This allows the user to quickly check important information while reducing stress.

[1330] Example 2

[1331] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1332] Conventional unread data management systems display information without considering the user's emotional state, which can lead to information overload and stress. Furthermore, manual intervention is required to retrieve, analyze, classify, summarize, and schedule unread data, making efficient data management difficult.

[1333] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1334] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on the subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for recognizing the emotional state of the user, means for adjusting the display order based on the recognized emotion, and means for displaying the extracted schedule information and summarized data, thereby enabling efficient data management that takes the emotional state of the user into consideration and reducing stress.

[1335] "Unread data" refers to communication data (such as emails or messages) that the user has not yet viewed.

[1336] "Means of acquisition" refers to the function for collecting unread data from the user's communication database or mail server.

[1337] "Means for analysis" refers to the function of analyzing the acquired unread data and extracting information such as the subject, body, date and time.

[1338] "Means for classification" refers to the function for sorting analyzed data into specific categories (meetings, tasks, general, etc.) based on the subject.

[1339] "Means for summarizing" refers to the function of summarizing classified data and showing the latest subject and number of data for each category.

[1340] "Means for extracting schedule information" refers to the function of extracting date and time information from data related to meetings, etc., and organizing it as schedule information.

[1341] "Means for recognizing emotional state" refers to a function that determines the user's current emotion using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[1342] The "means for adjusting the display order" refers to a function for optimizing the display order of information based on the recognized user emotion.

[1343] "Display means" refers to a function for providing extracted schedule information and summarized data to a user.

[1344] The present invention provides a system for efficiently managing unread data and displaying and notifying users based on their emotional state. This system includes a series of processes: acquiring unread data, analyzing, classifying, summarizing, extracting schedule information, and adjusting the display order based on the user's emotional state. The following describes how each process is implemented.

[1345] The server first connects to the user's communication database and retrieves the unread emails. This can be done using a mail server or other communication interface. For example, the IMAP protocol can be used to retrieve unread emails from a mail server. This connection is typically made using the imaplib module.

[1346] The server then parses the unread data, using the email.parser module to convert each email's subject, body, date and time fields into an extractable format, which then turns the data into an object with detailed content for easy further processing.

[1347] The server classifies the parsed data based on the subject line, for example, if the subject line contains "meeting" it will be classified as a "meeting," and if it contains "task," "request," or "action" it will be classified as a "task." This can be done using pattern matching algorithms or keyword lists.

[1348] Once the classification is complete, the server summarizes the data. It counts the number of data items in each category and lists the subjects of the three most recent items. This allows users to quickly grasp important information.

[1349] Next, the server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule. This process efficiently notifies users of important meeting schedules.

[1350] Furthermore, the server recognizes the user's emotional state through an emotion engine, which may use techniques such as voice recognition, facial expression analysis, and keystroke analysis to determine the user's current emotional state, for example, using a voice recognition module and a facial expression analysis module.

[1351] Based on the recognized emotion, the server adjusts the display order of data. For example, if the user is feeling stressed, it will prioritize displaying data of high importance. This process enables optimal information provision that takes into account the user's emotional state.

[1352] Finally, the server sends the generated summary and schedule information to the terminal, where it is displayed to the user. The user can check important information in a way that takes into account their emotional state, enabling information management with reduced burden. This allows for efficient management of unread data and an improved user experience.

[1353] Specific examples

[1354] For example, suppose a user starts the system after a long vacation. The system finds 500 unread items, categorizes and summarizes them. Furthermore, if the emotion engine determines that the user is feeling stressed, it prioritizes the display of important information.

[1355] Prompt Sentence Examples

[1356] "It takes the unread data, parses it, categorizes it, and summarizes it. It then schedules and displays meeting information. It also adjusts the display order based on the user's emotional state."

[1357] This series of processes is expected to enable effective data management and schedule adjustment that takes into account the user's emotional state.

[1358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1359] Step 1:

[1360] The server connects to the user's communication database, specifically to a mail server or other communication interface, and extracts data that has been flagged as unread.

[1361] Input: User's mail server information, communication data flagged as unread

[1362] What happens: The server starts an IMAP session, queries the user's unread mail folder, receives a list of unread mail, and downloads the data for each mail.

[1363] Output: List of unread data

[1364] Step 2:

[1365] The server parses the retrieved unread data using the email.parser module to extract fields such as the subject, body, and date and time.

[1366] Input: Unread data

[1367] Specific operation: The server passes the raw data to email.parser, which extracts the subject, body, and date and time information from the resulting object.

[1368] Output: Parsed data object

[1369] Step 3:

[1370] The server classifies the parsed data based on the subject. For example, if the subject contains "meeting," it is classified as "conference," and if it contains "task," "request," or "action," it is classified as "task."

[1371] Input: Parsed data object

[1372] Specific operation: The server refers to the keyword list and sorts data into three categories (meetings, tasks, general) based on the subject, and stores the classification results in an internal database.

[1373] Output: Data with classification information

[1374] Step 4:

[1375] The server summarizes the classified data, counts the number of data items in each category, and extracts and lists the subjects of the three most recent data items.

[1376] Input: Data with classification information

[1377] Specific operation: The server counts the number of data in each category, extracts the latest three subjects from each category, and creates a list. The summary information is saved in the internal database.

[1378] Output: List of summary information

[1379] Step 5:

[1380] The server extracts schedule information from the data classified as "meetings." Specifically, it obtains date and time information from the Date field in the email header and organizes it as a schedule.

[1381] Input: Data classified as "Meeting"

[1382] Specific operation: The server parses the Date field from the meeting category data, extracts the date and time and the title, and saves the schedule information in the database.

[1383] Output: Schedule information

[1384] Step 6:

[1385] The server recognizes the user's emotional state through an emotion engine, which can use techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[1386] Input: User voice data, facial expression data, keystroke data

[1387] Specific operation: The server acquires voice data, facial expression data, and keystroke data, passes this data to the emotion engine for analysis, and stores the analysis results in an internal database.

[1388] Output: Emotional state analysis results

[1389] Step 7:

[1390] The server adjusts the display order based on the recognized emotion, for example, if the user is feeling stressed, it will prioritize displaying data that is more important.

[1391] Input: Emotional state analysis results, summary information, schedule information

[1392] Specific operation: The server calculates the display order priority based on the emotion analysis results and sets the display order.

[1393] Output: Adjusted display order

[1394] Step 8:

[1395] The server then sends the generated summary and schedule information to the terminal, where it is displayed to the user, allowing the user to receive information in a manner that takes into account their emotional state.

[1396] Input: Adjusted display order, summary information, schedule information

[1397] Specific operation: The server sends summary information and schedule information to the terminal, and the terminal updates the screen to display the received information.

[1398] Output: Summary and schedule information displayed to the user

[1399] (Application example 2)

[1400] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1401] In today's world, users tend to be overwhelmed by a large amount of unread data, making it difficult for them to quickly grasp important information. Furthermore, current systems cannot reduce the burden on users because information priorities change depending on the user's emotional state. Furthermore, many advertising systems do not display appropriate ads based on the user's current emotional state, resulting in ineffective ad delivery. This calls for improving user experience and maximizing advertising effectiveness.

[1402] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1403] In this invention, the server includes means for acquiring unread data, means for analyzing the acquired unread data, means for classifying the analyzed data based on a subject, means for summarizing the classified data, means for extracting the summarized data as schedule information, means for displaying information based on the extracted schedule information and the user's emotional state, an emotion engine for recognizing the user's emotion, and means for displaying information in order of importance based on the emotion recognized by the emotion engine. This allows the user to effectively manage a large amount of unread data and enables optimal display of information and advertisements according to the user's emotional state.

[1404] The "means for obtaining unread data" is a function that allows the server to connect to the user's communication database and extract data that has been flagged as unread.

[1405] The "means for analyzing acquired unread data" is a function that can analyze the raw data acquired by the server and convert fields such as the subject, body, date and time into an extractable form.

[1406] "Means for classifying analyzed data based on subject" refers to a function in which the server examines the subject of the data and sorts it into pre-defined categories.

[1407] The "means for summarizing classified data" is a function in which the server counts the number of data items in each category and generates a summary of the latest data.

[1408] The "means for extracting summarized data as schedule information" is a function in which the server extracts date and time information from the classified data and generates a schedule.

[1409] The "means for displaying information based on the extracted schedule information and the emotional state of the user" is a function by which the server adjusts the display order of the schedule information and data according to the emotional state of the user.

[1410] The "emotion engine that recognizes the user's emotions" is a function that allows the server to determine the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[1411] The "means for displaying information in order of importance based on the emotion recognized by the emotion engine" is a function for displaying information with a high degree of importance on the basis of the emotional state recognized by the server.

[1412] The present invention is a system for efficiently managing unread data, and also combines it with an emotion engine that recognizes the user's emotions and adjusts the display and notifications accordingly. This system can automatically acquire, analyze, classify, summarize, and extract unread data as schedule information, and display it in an optimal way according to the user's emotions.

[1413] The server connects to the user's communication database to retrieve unread data and extracts data flagged as unread. It then parses the raw data and uses the email.parser module to convert fields such as subject, body, and date into extractable data. The parsed data is then classified into at least three categories based on the subject: "Meetings," "Tasks," and "General." A summary of the latest data for each category is then generated.

[1414] Of the classified data, date and time information is extracted from data in the "Meeting" category and organized as schedule information. At this time, the date and time information is obtained from the Date field in the email header. The server uses an emotion engine to determine the user's emotional state, and if the user is feeling stressed, adjusts the display order to prioritize data of high importance. This emotion engine uses techniques such as voice recognition, facial expression analysis, and keystroke analysis.

[1415] Finally, the generated summary and schedule information are sent to the user's device, where the information is displayed in a way that takes into account the user's emotional state. The system can also provide example prompts using a generative AI model.

[1416] Hardware and software used

[1417] Hardware: Smartphones, servers

[1418] Software: Python, email.parser module, datetime module, collections.defaultdict, numpy

[1419] Specific examples

[1420] For example, when a user starts the system after a long vacation, the system will find 500 unread records, classify and summarize them. Furthermore, if the emotion engine determines that the user is feeling stressed, it will prioritize displaying stress-reducing advertisements.

[1421] Prompt Sentence Examples

[1422] User data: User voice data, facial image data, and keystroke data after long hours of work

[1423] task:

[1424] 1. Process voice data to recognize emotions

[1425] 2. Process facial image data to recognize emotions

[1426] 3. Process keystroke data to recognize emotions

[1427] conditions:

[1428] If the user is feeling stressed, display a "stress relief ad"

[1429] Output format: Ad list

[1430] As described above, the present invention can improve the user experience by managing data and displaying information based on the emotional state of the user.

[1431] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1432] Step 1:

[1433] The server connects to the user's communication database to retrieve unread data. As input, it takes unread data such as emails, messages, and notifications, and extracts data that has been flagged as unread. As output, it obtains a list of unread data.

[1434] Step 2:

[1435] The server parses the retrieved unread data. It receives the list of unread data as input and uses the email.parser module to convert fields such as subject, body, date and time into extractable data. The output is a list of parsed data.

[1436] Step 3:

[1437] The server categorizes the parsed data based on subject. It receives the parsed data as input, examines the subject, and categorizes it into at least three categories: meetings, tasks, and general. As output, it gets a list of categorized data.

[1438] Step 4:

[1439] The server summarizes the classified data. It receives the classified data as input, counts the number of data in each category, and generates a summary of the latest three data items. The summary information is provided as output.

[1440] Step 5:

[1441] The server extracts schedule information from data classified as "meetings." It receives data in the "meetings" category as input, obtains date and time information from the Date field in the email header, and generates a schedule. The schedule information is obtained as output.

[1442] Step 6:

[1443] The server uses an emotion engine to recognize the user's emotions. It receives user data such as voice data, facial expression data, and keystroke data as input, and determines the user's emotional state using techniques such as voice recognition, facial expression analysis, and keystroke analysis. The user's emotional state is obtained as the output.

[1444] Step 7:

[1445] The server adjusts the display order of information based on the user's emotional state. It receives schedule information and the user's emotional state as input, and rearranges the information based on the output from the emotion engine to prioritize the display of information of higher importance. The adjusted display order is obtained as the output.

[1446] Step 8:

[1447] The server sends the generated summary and schedule information to the terminal. It receives the adjusted display order as input and sends the information to the user terminal. The terminal displays the information in a way that takes into account the user's emotional state, allowing the user to quickly check important information. As output, appropriate information is displayed to the user.

[1448] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1449] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1450] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1451] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1452] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1453] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1454] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1455] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1456] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1457] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1458] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1459] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1460] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1461] 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.

[1462] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1463] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1464] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1465] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1466] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1467] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1468] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1469] The following is further disclosed regarding the above embodiment.

[1470] (Claim 1)

[1471] A means for obtaining unread data;

[1472] A means for analyzing the retrieved unread data;

[1473] means for categorizing the analyzed data based on subject matter;

[1474] a means for summarizing the classified data;

[1475] means for extracting the summarized data as schedule information;

[1476] The system includes a means for displaying the extracted schedule information.

[1477] (Claim 2)

[1478] 2. The system of claim 1, further comprising means for classifying unread data into at least three categories based on the subject of the unread data: meetings, tasks, and general.

[1479] (Claim 3)

[1480] 2. The system according to claim 1, further comprising means for extracting date and time information from data relating to a meeting and organizing the extracted date and time information as schedule information.

[1481] "Example 1"

[1482] (Claim 1)

[1483] A means for obtaining unread data;

[1484] A means for analyzing the retrieved unread data;

[1485] means for categorizing the analyzed data based on subject matter;

[1486] a means for summarizing the classified data;

[1487] means for extracting the summarized data as schedule information;

[1488] The system includes a means for displaying the extracted schedule information.

[1489] (Claim 2)

[1490] 2. The system of claim 1, further comprising means for classifying unread data into at least three categories based on the subject of the unread data: meetings, tasks, and general.

[1491] (Claim 3)

[1492] 2. The system according to claim 1, further comprising means for extracting date and time information from data relating to a meeting and organizing the extracted date and time information as schedule information.

[1493] (Claim 4)

[1494] 2. The system of claim 1, further comprising: means for parsing the retrieved unread data using an email.parser module and converting each piece of data into an object.

[1495] (Claim 5)

[1496] 10. The system of claim 1, further comprising means for classifying the analyzed data into categories based on keywords in the subject line using regular expressions.

[1497] (Claim 6)

[1498] 2. The system of claim 1, further comprising means for extracting the subject matter of the latest three records for each category and generating a summary.

[1499] (Claim 7)

[1500] 10. The system of claim 1, further comprising means for extracting date and time information and titles from data relating to a meeting using a natural language processing library.

[1501] (Claim 8)

[1502] 2. The system according to claim 1, further comprising means for transmitting the generated summary and schedule information to a terminal and displaying the summary and schedule information on a GUI.

[1503] "Application Example 1"

[1504] (Claim 1)

[1505] A means for obtaining unread data;

[1506] A means for analyzing the retrieved unread data;

[1507] means for categorizing the analyzed data based on subject matter;

[1508] a means for summarizing the classified data;

[1509] means for extracting the summarized data as schedule information;

[1510] The system includes means for displaying and communicating the extracted schedule and summary information.

[1511] (Claim 2)

[1512] 2. The system according to claim 1, further comprising: means for classifying the unread data into at least three categories, namely, meetings, tasks, and general, based on the subject of the unread data; and means for acquiring the unread data from the mail server at regular intervals.

[1513] (Claim 3)

[1514] 2. The system according to claim 1, further comprising means for extracting date and time information from data relating to meetings, organizing the information as schedule information, and setting priorities according to urgency.

[1515] "Example 2: Combining Emotion Engines"

[1516] (Claim 1)

[1517] A means for obtaining unread data;

[1518] A means for analyzing the retrieved unread data;

[1519] means for categorizing the analyzed data based on subject matter;

[1520] a means for summarizing the classified data;

[1521] means for extracting the summarized data as schedule information;

[1522] means for recognizing the emotional state of a user;

[1523] means for adjusting the display order based on the recognized emotion;

[1524] The system includes a means for displaying the extracted schedule information and summarized data.

[1525] (Claim 2)

[1526] 2. The system of claim 1, further comprising means for classifying unread data into at least three categories based on the subject of the unread data: meetings, tasks, and general.

[1527] (Claim 3)

[1528] 2. The system according to claim 1, further comprising means for extracting date and time information from data relating to a meeting and organizing the extracted date and time information as schedule information.

[1529] "Application example 2 when combining emotion engines"

[1530] (Claim 1)

[1531] A means for obtaining unread data;

[1532] A means for analyzing the retrieved unread data;

[1533] means for categorizing the analyzed data based on subject matter;

[1534] a means for summarizing the classified data;

[1535] means for extracting the summarized data as schedule information;

[1536] means for displaying information based on the extracted schedule information and the user's emotional state;

[1537] an emotion engine that recognizes the user's emotions;

[1538] A system including means for displaying information in order of importance based on emotions recognized by an emotion engine.

[1539] (Claim 2)

[1540] 2. The system of claim 1, further comprising means for classifying unread data into at least three categories based on the subject of the unread data: meetings, tasks, and general.

[1541] (Claim 3)

[1542] 2. The system according to claim 1, further comprising means for extracting date and time information from data relating to a meeting and organizing the extracted date and time information as schedule information. [Explanation of symbols]

[1543] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for obtaining unread data; A means for analyzing the retrieved unread data; means for categorizing the analyzed data based on subject matter; a means for summarizing the classified data; means for extracting the summarized data as schedule information; The system includes a means for displaying the extracted schedule information.

2. 2. The system according to claim 1, further comprising means for classifying the unread data into at least three categories, namely, meetings, tasks, and general, based on the subject of the unread data.

3. 2. The system according to claim 1, further comprising means for extracting date and time information from data relating to a meeting and organizing the extracted date and time information as schedule information.

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