system

Abstract

We provide the system. [Solution] A means of analyzing received digital communications to extract tasks, A means for automatically generating reminders and deadline notifications based on the deadlines of extracted tasks, A means for generating answer candidates for the extracted tasks that can be answered automatically, A means of summarizing all digital communications and tasks received in a day, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0006] "Digital communication" refers to all means of transmitting and receiving information using electronic means, and specifically includes email and messaging apps.

[0007] "Analysis" refers to the process of understanding the content of received digital communications and extracting relevant information and tasks from them.

[0008] A "task" refers to a specific action or goal to be achieved, and includes concrete tasks such as "creating presentation materials" or "preparing for a meeting."

[0009] A "reminder" refers to a message or alert that is sent to a user based on a specific time or condition, intended to encourage them to perform a task.

[0010] A "deadline notification" is a notification that informs the user that the deadline for a task is approaching, and it serves to create a sense of urgency for the user.

[0011] "Automated response" refers to response messages or data that the system automatically generates based on the extracted tasks.

[0012] A "summary" refers to a concise compilation of all the information and tasks received during the day, focusing on the main points.

[0013] "Natural language processing" is a field of technology that converts human language into a format that is easily understood by machines and then analyzes it.

[0014] "Importance" refers to a measure used to evaluate the relative importance of tasks and information, and is used when determining the priority of work.

[0015] "Priority" refers to the criteria used to determine the order in which tasks are performed or addressed, and is primarily set based on urgency and importance. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0024] [First Embodiment]

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

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

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

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

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

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

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

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0037] This invention relates to a system that effectively manages tasks from various digital communication methods and improves operational efficiency. Specific embodiments of this system are described below.

[0038] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. The system mainly consists of three elements: a server, terminals, and users.

[0039] Receiving and analyzing messages and emails

[0040] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0041] Generating task reminders and deadline notifications

[0042] The server automatically generates reminders and deadline notifications based on the deadline information of the extracted tasks. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline.

[0043] Task automated response support

[0044] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[0045] Summary and development of daily communications

[0046] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. The generated summary report is provided to the user via their terminal. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[0047] Specific example

[0048] 1. The user receives an email saying, "Please submit your report by next Friday."

[0049] 2. The device forwards this email to the server.

[0050] 3. The server uses natural language processing technology to extract the task "Create a report".

[0051] 4. The server sets a reminder and sends notifications three days before and on Friday.

[0052] 5. The user receives a reminder on their device, checks the task, and takes action.

[0053] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[0054] The following describes the processing flow.

[0055] Step 1: Receiving emails and messages

[0056] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[0057] Step 2: Message Analysis

[0058] The server analyzes received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message, and extracts tasks and relevant information.

[0059] Step 3: Extract and save tasks

[0060] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[0061] Step 4: Generate reminders and deadline notifications

[0062] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline.

[0063] Step 5: Send the reminder

[0064] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[0065] Step 6: Generate automated responses

[0066] The server detects digital communications that can be automatically responded to. For example, in response to a request such as "Please send the latest sales data," the server searches the database for the relevant data and generates a list of possible responses.

[0067] Step 7: User verification for automated responses

[0068] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[0069] Step 8: Generate a summary of daily communications

[0070] The server aggregates all messages and tasks received during the day and generates a summary report. The report takes into account the importance and priority of the tasks and presents them concisely.

[0071] Step 9: Submit and view summary report

[0072] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[0073] (Example 1)

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

[0075] Traditional digital communication methods presented challenges in efficiently managing received messages and emails and ensuring important tasks weren't overlooked. In particular, extracting key information from a large volume of messages, setting timely reminders, and generating automated responses for quick replies were difficult. Summarizing key points from a daily stream of communications was also a significant burden for users. This situation led to decreased work efficiency and task delays.

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

[0077] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to extract important tasks from messages and emails without missing them and set reminders at the appropriate time. Furthermore, by generating automatic answers, quick responses are possible, reducing the burden on the user. In addition, by adding means for summarizing all digital communications and tasks received in a day, users can grasp important information at a glance, improving work efficiency.

[0078] "Received digital communications" refers to all text and data sent to a user's device via email, messaging apps, etc.

[0079] "Task extraction methods" refer to the process of identifying important tasks and appointments from received digital communications and recording them as tasks.

[0080] "Means for automatically generating reminders and deadline notifications" refers to a process that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information.

[0081] "Means for generating response candidates for items that can be answered automatically" refers to a process that extracts content from received digital communications that can be handled automatically and generates an appropriate response.

[0082] "Means of transferring digital communications to a server" refers to the process of sending the communication content received on the user's terminal to a server.

[0083] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is generally used to extract meaning from text.

[0084] "The means of summarizing and expanding" is the process of analyzing all digital communications and tasks received in a day and concisely summarizing the key points and tasks.

[0085] "Task importance and priority" refers to criteria that indicate the degree to which a task contributes to success and the priority of its processing.

[0086] A "server" is a computer system that processes and stores data and provides services to client terminals via a network.

[0087] A "terminal" is a device that a user directly operates to receive and transmit digital communications.

[0088] This invention relates to a system that effectively manages tasks from various digital communication methods and improves work efficiency. This system has the function of analyzing received digital communications to extract tasks, automatically generating reminders and deadline notifications for these tasks, and further generating automatic responses.

[0089] The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for analyzing incoming digital communications and extracting necessary tasks and information. This analysis requires advanced text data analysis capabilities using natural language processing techniques. Specific technologies used here include generative AI models such as the GPT model and the BERT model.

[0090] server:

[0091] The server is the primary processing facility for analyzing received digital communications. The server analyzes digital communications received from the user's terminal (e.g., emails and messaging app notifications) to extract important tasks. For example, if a user receives an email saying, "Please submit the report by next Friday," the server uses natural language processing techniques to extract the task "Create the report" and recognizes that the deadline is Friday.

[0092] Next, the server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. This ensures that important tasks are not missed and reminders are sent at the appropriate time. For example, for the task "Create a report," the server sets a reminder and sends notifications to the user's device three days before the deadline and on the deadline day.

[0093] Furthermore, for messages that can be answered automatically, the server automatically generates response suggestions. For example, if an email is received that says, "Please send me the latest sales data," the server searches for the latest data, generates the results as response suggestions, and presents them to the user.

[0094] Furthermore, the server aggregates all digital communications and tasks received throughout the day and generates a summary report. This summary report takes into account the importance and priority of the tasks and is ultimately provided to the user via their terminal. For example, at the end of the day, the user might be notified with a message such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[0095] Terminal:

[0096] A terminal is a device that users directly operate to receive and send various digital communications. The terminal has the function of automatically forwarding received content, such as emails and messaging apps, to a server. For example, when a user receives a message in Outlook, Gmail, LINE, or Slack, the terminal detects this and automatically forwards it to the server.

[0097] User:

[0098] Users are the ultimate beneficiaries of the system, receiving individual digital communications and taking actions based on them. Through their terminals, users can view reminders and summary reports provided by the server and manage their own schedules and tasks.

[0099] Specific example

[0100] 1. The user receives an email saying, "Please submit your report by next Friday."

[0101] 2. The device forwards this email to the server.

[0102] 3. The server uses natural language processing technology to extract the task "Create a report".

[0103] 4. The server sets a reminder and sends notifications three days before and on Friday.

[0104] 5. The user receives a reminder on their device, checks the task, and takes action.

[0105] Examples of prompts to input into a generative AI model

[0106] "Analyze the following email and extract the key task: 'Prepare the presentation materials for tomorrow's meeting.'"

[0107] "Please generate an appropriate automated response to this email: 'Please send us your 2023 sales data.'"

[0108] "Please summarize all messages received today."

[0109] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[0110] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0111] Step 1:

[0112] The user receives digital communication. Digital communication includes information received via email or messaging apps. In this case, the received message becomes input data. For example, the user receives an email saying, "Please submit the report by next Friday."

[0113] Step 2:

[0114] The terminal automatically detects received digital communications and forwards them to the server. The HTTP / HTTPS protocol is used for this forwarding. In this process, the input data is the received message, and the output data is the result of the forwarding to the server. Specifically, the terminal parses the email and sends the email data to the server.

[0115] Step 3:

[0116] The server analyzes the content of digital communications using natural language processing techniques (e.g., GPT or BERT models). The input data for this analysis is the received digital communications, and the output data is the analysis results and the extracted tasks. Specifically, the server analyzes an email that says, "Please submit the report by next Friday," and extracts the task, "Create a report."

[0117] Step 4:

[0118] The server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. In this process, task deadline information is the input data, and the automatically generated reminders and notifications are the output data. For example, the server sets the deadline for "Create Report" to Friday and instructs it to send reminders on Tuesday and on the day of the deadline.

[0119] Step 5:

[0120] The server identifies digital communications that can be automatically responded to from those it receives and generates response candidates. In this process, the input data is the received digital communications, and the output data is the generated response candidates. Specifically, if the server receives an email saying, "Please send the latest sales data," it searches for the latest data and generates the results as response candidates.

[0121] Step 6:

[0122] The server aggregates all digital communications and tasks received during the day and generates a summary report. In this process, the input data is all digital communications and tasks received during the day, and the output data is the summary report. The server generates the summary report for the user, taking into account the importance and priority of the tasks. For example, it may include content such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent response tasks."

[0123] In this way, the coordination of each step creates a system that significantly improves the user's work efficiency.

[0124] (Application Example 1)

[0125] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0126] In factory communication and task management, there are problems with missed instructions and delays, leading to decreased production efficiency. In particular, overlooking important tasks and failing to send timely and appropriate reminders and notifications are major causes of inefficient production processes. Furthermore, the large volume of information received makes it difficult to manage all tasks, so there is a need for an effective and real-time method for processing this information.

[0127] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0128] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, means for generating answer candidates for the extracted tasks that can be automatically answered, means for summarizing all digital communications and tasks received in a day, and means for providing instructions and status information in real time via smart devices in a factory environment. This prevents instructions from being missed within the factory and enables efficient task management and rapid information sharing.

[0129] "Received digital communications" refers to messages received through communication methods that are exchanged in digital format, such as email, messaging apps, and chat tools.

[0130] "Means for extracting tasks" refers to a function that uses natural language processing technology and other methods to extract important work instructions and tasks from received digital communications.

[0131] "Means for automatically generating reminders and deadline notifications" refers to a function that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information, according to the set deadline.

[0132] "Means for generating answer candidates for questions that can be answered automatically" refers to a function that generates appropriate answer candidates based on received digital communications, using pre-configured rules and databases.

[0133] "A means of summarizing all digital communications and tasks received in a day" refers to a function that aggregates all digital communications and extracted tasks received daily and creates a summary report based on importance and priority.

[0134] "Means of providing instructions and status information in real time via smart devices in a factory environment" refers to a function that uses mobile information terminals such as smart glasses and tablets to provide workers with instructions and on-site conditions within the factory in real time.

[0135] This invention is a system for streamlining communication and task management in a factory environment. Specific embodiments of the system are described below.

[0136] System Configuration

[0137] This system consists mainly of the following elements:

[0138] Server: Analyzes digital communications, extracts and manages tasks, generates reminders and notifications, creates automated responses, and generates summary reports.

[0139] Terminal: Receives digital communications and forwards them to the server. It also receives reminders and notifications from the server and displays them to the user.

[0140] User: Includes smart glasses worn by factory workers and portable information devices such as tablets used by factory workers.

[0141] Program processing and the technologies used

[0142] Natural language processing technology:

[0143] The server uses a natural language processing library (e.g., spaCy) to analyze the received digital communications and extract important task and deadline information.

[0144] Reminders and deadline notifications:

[0145] The server generates reminders and deadline notifications at the appropriate time based on task deadline information and sends them to the user's device using a push notification service (e.g., Firebase).

[0146] Automatic answer generation:

[0147] The server detects content in the received digital communications that can be automatically responded to and generates response candidates using pre-configured rules and databases.

[0148] Summary report generation:

[0149] The server aggregates all digital communications and tasks received in a day, creates a summary report based on their importance and priority, and provides it to the user.

[0150] Specific examples of hardware and software

[0151] Hardware:

[0152] Smart glasses (e.g., Google® Glass®, Microsoft® HoloLens®)

[0153] Servers within a factory (e.g., Linux®-based servers)

[0154] software:

[0155] Natural language processing libraries (e.g., spaCy, BERT)

[0156] Push notification service (e.g., Firebase)

[0157] Specific example

[0158] An example of its actual use is shown below.

[0159] Specific example 1:

[0160] Factory staff wear smart glasses and receive maintenance instruction emails. The contents of these emails are immediately sent to a server. The server uses a natural language processing model to extract the task "Submit Maintenance Report" and its deadline. A reminder is set, and a push notification is sent as the deadline approaches. Staff also review the task and send a reply confirming receipt of the task using an automatically generated response.

[0161] Specific example 2:

[0162] Factory staff receive a daily summary report detailing tasks they need to complete and any problems that arise. This summary report is generated by a server based on the importance and priority of each task and is provided via a terminal.

[0163] Example of a prompt

[0164] Prompt message:

[0165] "Design a state-of-the-art factory task management system. This system will provide factory staff with real-time process instructions, parts replenishment, machine failure information, and more via smart glasses. It will include features to analyze important tasks from emails and messages, set reminders, and generate automated responses."

[0166] In this way, the present invention specifically realizes a system that prevents tasks from being overlooked in a factory environment and provides support for efficiently carrying out operations.

[0167] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0168] Step 1:

[0169] The terminal detects digital communications (emails and messages) received from the user and forwards their contents to the server.

[0170] Input: Digital communication received by the user; Output: Transfer of digital communication to the server.

[0171] Step 2:

[0172] The server analyzes the content of received digital communications using natural language processing techniques and extracts task and deadline information. One example of a library used is spaCy.

[0173] Input: Content of digital communications; Output: Extracted task and deadline information.

[0174] Specific operation: The server loads a natural language processing model, parses the text of the digital communication, and extracts entities including tasks and due dates.

[0175] Step 3:

[0176] The server automatically generates reminders and deadline notifications based on the extracted tasks and deadline information. The generated notifications are sent to the device using a push notification service (e.g., Firebase).

[0177] Input: Extracted task and deadline information; Output: Generated reminders and deadline notifications.

[0178] Specific operation: Based on task deadline information, the server sets reminders and deadline notifications and schedules push notifications as needed.

[0179] Step 4:

[0180] The server generates answer candidates for tasks that can be answered automatically, using pre-configured rules and databases. These generated answer candidates are sent to the terminal and displayed to the user.

[0181] Input: Extracted tasks, Output: Generated answer candidates.

[0182] Specific operation: The server executes rule-based engines and database queries to generate automated response candidates and send them to the terminal.

[0183] Step 5:

[0184] The server aggregates all digital communications and tasks received during the day and generates a summary report based on importance and priority. This summary report is then provided to the user via their terminal.

[0185] Input: All digital communications received in a day and extracted tasks; Output: Generated summary report.

[0186] Specific operation: The server aggregates all incoming digital communications and extracted tasks, applies a weighting algorithm to calculate importance and priority, and generates a summary report.

[0187] Step 6:

[0188] The terminal provides users with real-time instructions and status information through smart devices (e.g., smart glasses or tablets) in a factory environment.

[0189] Input: Reminders, notifications, and summary reports sent from the server. Output: Notifications, instructions, and summary reports displayed on smart devices.

[0190] Specific operation: The terminal displays information received from the server on the user interface and pops up notifications at the appropriate times.

[0191] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0192] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[0193] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. Furthermore, by combining it with an emotion engine that recognizes emotions from the user's digital communications, it can respond according to the user's state. The system mainly consists of three elements: a server, a terminal, and the user.

[0194] Receiving and analyzing messages and emails

[0195] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0196] Recognition of emotions

[0197] The server uses an emotion engine to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "high stress" or "satisfied" from the context of the user's emails and messages.

[0198] Adjusting task priorities

[0199] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it reduces their workload by prioritizing tasks that are easier for them to handle.

[0200] Generating task reminders and deadline notifications

[0201] The server automatically generates reminders and deadline notifications based on the extracted task deadline information. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline. If the user is experiencing stress, adjustments will be made, such as sending reminders earlier.

[0202] Task automated response support

[0203] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[0204] Summary and development of daily communications

[0205] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. It also includes elements that reflect the user's emotional state as recognized by the emotion engine. The generated summary report is provided to the user via their device. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[0206] Specific example

[0207] 1. The user receives an email saying, "Please submit your report by next Friday."

[0208] 2. The device forwards this email to the server.

[0209] 3. The server uses natural language processing technology to extract the task "Create a report".

[0210] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0211] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0212] 6. The server sets a reminder and sends notifications three days before and on Friday.

[0213] 7. The user checks reminders and the adjusted task list on their device and takes action.

[0214] In this way, the present invention prevents tasks from being overlooked and provides optimal task management tailored to the user's emotional state.

[0215] The following describes the processing flow.

[0216] Step 1: Receiving emails and messages

[0217] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[0218] Step 2: Message Analysis

[0219] The server analyzes the received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message and extracts tasks and relevant information. From an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" is extracted.

[0220] Step 3: Extract and save tasks

[0221] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[0222] Step 4: Recognizing Emotions

[0223] The server uses an emotion engine to recognize the user's emotions from the received digital communications. It analyzes the emotional state, such as "highly stressed" or "satisfied," from the context and expression of the user's message.

[0224] Step 5: Adjust task priorities

[0225] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. If the user is feeling stressed, it will prioritize tasks that are easier to handle.

[0226] Step 6: Generate reminders and deadline notifications

[0227] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline. If the user is experiencing stress, adjustments such as setting earlier reminders will be made.

[0228] Step 7: Send the reminder

[0229] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[0230] Step 8: Automated task response support

[0231] The server detects digital communications that can be automatically responded to. For example, in response to a message like "Please send the latest sales data," the server searches its database for the relevant data and generates a list of possible responses.

[0232] Step 9: User verification of automated responses

[0233] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[0234] Step 10: Generate a summary of daily communications

[0235] The server aggregates all messages and tasks received each day and generates a summary report. The report is concise and takes into account the importance and priority of each task. Furthermore, it includes elements that reflect the user's emotional state as recognized by the emotion engine.

[0236] Step 11: Submit and view summary report

[0237] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[0238] Specific example

[0239] 1. The user receives an email saying, "Please submit your report by next Friday."

[0240] 2. The device forwards this email to the server.

[0241] 3. The server uses natural language processing technology to extract the task "Create a report".

[0242] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0243] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0244] 6. The server sets a reminder and sends notifications three days before and on Friday.

[0245] 7. The user checks reminders and the adjusted task list on their device and takes action.

[0246] (Example 2)

[0247] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0248] Traditional task management systems are limited to extracting tasks and generating reminders from digital communications, and have the drawback of not being able to properly manage tasks while considering the user's emotional state. Furthermore, task prioritization and automated response generation are not based on the user's emotional state, which can increase the user's workload. In addition, there is a lack of functionality to recognize and appropriately reflect emotional states from digital communications, and this point needs to be addressed.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0250] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to adjust task priorities and reminder timing according to the user's emotional state. Furthermore, by incorporating means into the server for summarizing all digital communications and tasks received in a day, and means for recognizing the user's emotional state, the accuracy and appropriateness of task management can be improved, and the user's burden can be reduced.

[0251] "Digital communication" refers to communication conducted in digital formats such as email, messaging apps, and web chat.

[0252] A "task" refers to a specific task or process that a user extracts from digital communication.

[0253] A "reminder" refers to a function that notifies users of the deadline or due date for a specific task.

[0254] "Deadline notification" refers to a function that notifies the user when the deadline for a task is approaching.

[0255] "Automatic response" refers to a reply text that is automatically generated in response to an received digital communication.

[0256] "Emotion recognition" refers to the process of analyzing a user's emotional state from the content of digital communications.

[0257] "Priority adjustment" refers to adjusting the order in which tasks are executed based on their importance and the user's emotional state.

[0258] "Summarizing" refers to the process of concisely organizing all digital communications and tasks received during the day.

[0259] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[0260] An "emotion engine" refers to a technology that analyzes a user's digital communication content and recognizes their emotional state.

[0261] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[0262] Receiving and analyzing messages and emails

[0263] When a user receives an email or message, the device automatically detects it and forwards it to the server. Specifically, the device uses IMAP or SMTP protocols to retrieve the email content and forwards it to the server via HTTPS. The server uses a parsing module (e.g., Python's NLTK or spaCy) to analyze the content of the received message or email. This analysis uses natural language processing techniques to extract tasks and important information. For example, from an email that says "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0264] Recognition of emotions

[0265] The server uses an emotion engine (e.g., IBM Watson® Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "high stress" or "satisfied" from the context of emails and messages. The module uses the emotional tone of the text, word usage patterns, and context as evaluation criteria.

[0266] Adjusting task priorities

[0267] The server adjusts task priorities based on the results of emotion recognition. For example, if a user is identified as being "highly stressed," it prioritizes presenting tasks that are easier to tackle to reduce stress. This reduces the user's workload. Specifically, the algorithm presents tasks that can be addressed immediately first, especially for users with high stress levels.

[0268] Generating task reminders and deadline notifications

[0269] The server generates reminders and deadline notifications based on the extracted task deadline information. Specifically, it uses an algorithm that calculates the reminder dates and times by working backward from the task deadline. For example, if the task "Create presentation materials" is due on Friday, the server sets reminders for Monday and Thursday and sends them to the user's device. It can also adjust the timing of reminders to be earlier if the user is experiencing stress.

[0270] Task automated response support

[0271] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, where they can review, modify, and reply as needed.

[0272] Summary and development of daily communications

[0273] The server aggregates all messages and tasks received throughout the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks, and also reflects the results of sentiment recognition. For example, at the end of the day, it might be generated and provided to the user's terminal with a report such as, "The main tasks received today were: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[0274] Specific example

[0275] The following is a concrete example of this system.

[0276] 1. The user receives an email saying, "Please submit your report by next Friday."

[0277] 2. The device forwards this email to the server.

[0278] 3. The server uses natural language processing technology to extract the task "Create a report".

[0279] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0280] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0281] 6. The server sets a reminder and sends notifications three days before and on Friday. <00OO892> 7. The user checks the task list adjusted with the reminder on the terminal and responds.

[0283] Specific examples of prompt sentences

[0284] Examples of prompt sentences for the generative AI model are as follows:

[0285] Please analyze the content of the following email to extract tasks and analyze the user's emotional state:

[0286] Content of the email: <00OO906>

[0287] "Please submit the report by next Friday. Also, please prepare the meeting materials for Monday."

[0288] Extracted tasks:

[0289] 1. Creation of the report

[0290] 2. Preparation of the meeting materials for Monday

[0291] User's emotional state:

[0292] High stress

[0293] In this way, the present invention provides optimal task management based on the user's emotional state and can reduce the workload.

[0294] The flow of the specific process in Example 2 will be described with reference to FIG. 13.

[0295] ​​​​​​​​The terminal detects this reception event. Specifically, the terminal uses the IMAP or SMTP protocol to receive new emails from the mail server and issues a reception notification.

[0298] Input: Emails and messages received by the user.

[0299] Output: Reception event detected by the terminal.

[0300] Step 2:

[0301] The terminal transfers the detected reception event to the server.

[0302] As a specific method, the terminal transfers the content of the received email to the server via an HTTP request.

[0303] Input: Reception event and the content of the email.

[0304] Output: Data sent to the server.

[0305] Step 3:

[0306] The server passes the received data to the analysis module. The analysis module uses natural language processing techniques (e.g., NLTK or spaCy in Python) to analyze the content of the email.

[0307] In this analysis process, tokenization, part-of-speech tagging, and dependency parsing are performed to extract important tasks and information.

[0308] Input: The content of the email sent to the server.

[0309] Output: Analyzed tasks and important information.

[0310] Step 4:

[0311] The server uses an emotion engine (such as IBM Watson Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the analyzed data.

[0312] Specifically, it evaluates emotional tone and word usage patterns to analyze emotional states such as "high stress" or "satisfied."

[0313] Input: Analyzed tasks or information.

[0314] Output: User's emotional state.

[0315] Step 5:

[0316] The server adjusts task priorities based on the results of emotion recognition.

[0317] This includes an algorithm that prioritizes tasks that are easier for users with high stress levels to handle.

[0318] Input: User's emotional state and the analyzed task.

[0319] Output: A task list with adjusted priorities.

[0320] Step 6:

[0321] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks.

[0322] For example, you can set a reminder date and time by working backward from the task deadline.

[0323] Input: Task deadline information.

[0324] Output: Generated reminders and deadline notifications.

[0325] Step 7:

[0326] The device receives reminders and deadline notifications sent from the server and notifies the user.

[0327] Specifically, the device will use push notifications and email notification functions.

[0328] Input: Generated reminder and deadline notifications.

[0329] Output: Reminders and deadline notifications displayed on the user's device.

[0330] Step 8:

[0331] The server detects emails and messages that can be automatically responded to and generates appropriate response suggestions.

[0332] For example, in response to an email requesting "Please send the latest sales data," the system automatically searches for the latest data and generates a response.

[0333] Input: Received emails or messages.

[0334] Output: Generated answer candidates.

[0335] Step 9:

[0336] The server aggregates all messages and tasks received during the day and generates a summary report.

[0337] The summary report is based on the importance and priority of the tasks, and also reflects the results of sentiment recognition.

[0338] Input: Messages and tasks received during the day.

[0339] Output: Summary report.

[0340] Step 10:

[0341] The terminal receives a summary report sent from the server and provides it to the user.

[0342] As a specific method, the device will provide a summary report via a display screen or email.

[0343] Input: Summary report.

[0344] Output: A summary report displayed on the user's device.

[0345] (Application Example 2)

[0346] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0347] In today's digital communication environment, numerous tasks and pieces of information are sent and received through multiple communication methods (email, messaging apps, social networking services, etc.). However, effectively managing and appropriately responding to this information is difficult. In particular, there is a lack of adjustment of task priorities that take into account the user's emotional state, and a lack of information to help users maintain a better lifestyle.

[0348] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing received digital communications and extracting tasks; means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks; means for generating answer candidates for the extracted tasks that can be automatically answered; means for unfolding all digital communications and tasks received in a day as a summary; means for recognizing the user's emotions from the content of the analyzed digital communications and adjusting the priority of tasks according to that emotional state; and means for searching for and providing relevant information from external information sources based on the user's emotional state and the extracted tasks. This makes task management from multiple digital communication means simple and efficient, and enables optimal task management and information provision according to the user's emotional state.

[0349] "Digital communication" refers to information sent and received via electronic means such as email, messaging apps, and social networking services (SNS).

[0350] A "task" is a specific task or action that a user needs to perform, extracted from digital communications.

[0351] A "reminder" is a notification that serves as a reminder to inform a user of a specific task or event.

[0352] A "deadline notification" is a notification that informs a user of the deadline for completing a specific task.

[0353] An "automatic response" is a suggested response generated by the system in response to received digital communication, which the user can review and modify.

[0354] A "summary" is a concise report that aggregates and summarizes all digital communications and tasks received during the day.

[0355] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0356] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from the content of digital communications.

[0357] "Adjusting priorities" means dynamically changing the processing order of extracted tasks according to the user's emotional state.

[0358] An "external information source" is a resource that provides information obtained through external databases such as the internet or APIs.

[0359] "Information provision" refers to the act of delivering additional relevant information to users based on their emotional state and task content.

[0360] This invention provides a system consisting of three elements: a server, a terminal, and a user. This system analyzes digital communication content, recognizes the user's emotional state, and optimizes task management and information provision.

[0361] Server Processing

[0362] The server analyzes the received digital communications. Natural language processing techniques are used for the analysis, which extracts tasks. Specifically, from an email that says, "Please submit the report by next Friday," the task "Create a report" is extracted. Next, the server uses emotion recognition techniques to recognize the user's emotional state. If the user is feeling stressed, that emotion can be detected. For example, emotional states such as "highly stressed" or "satisfied" are analyzed from the context of the user's emails and messages.

[0363] Terminal processing

[0364] The terminal receives analysis results transferred from the server and provides users with reminders and deadline notifications. Reminders are automatically generated based on task deadlines and sent to the user at the appropriate time. For example, for a task such as "Create a report," notifications are sent three days before and on the day of the deadline. Furthermore, if the user is experiencing stress, adjustments are made, such as sending reminders earlier.

[0365] Providing information tailored to the user's emotional state.

[0366] The server can search for and provide relevant information from external sources based on the user's emotional state. For example, if a user is feeling "stressed," the server will automatically search for and provide articles that can help reduce stress. This allows the user to work on tasks while reducing their psychological burden.

[0367] Automated response support

[0368] The server generates suggested answers for tasks that can be answered automatically. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates suggested answers. The generated suggested answers are sent to the terminal, where the user can review, modify, and reply.

[0369] Specific example

[0370] Suppose a user receives the message, "Please prepare the meeting materials. I've been very busy lately and I'm feeling stressed." In this case, the server analyzes the message and extracts the task "Prepare meeting materials." It also uses emotion recognition technology to detect the user's emotional state of "high stress." Based on this information, the server adjusts priorities, prioritizing less urgent tasks and providing the user with articles on stress reduction methods.

[0371] Example of a prompt

[0372] "Please prepare the meeting materials. I've been really busy lately and I'm feeling stressed."

[0373] In this way, the system of the present invention can effectively manage the user's digital communication content and provide optimal task management and information according to the user's emotional state. This makes it possible to reduce the workload and provide the user with an efficient work environment.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server detects received digital communications and retrieves their content. The input is digital communications (e.g., emails, messages), and the output is the text data of the retrieved digital communications. Specifically, it accepts emails and messages transferred from terminals to the server and extracts their content in text format.

[0377] Step 2:

[0378] The server analyzes text data and extracts tasks. The input is text data, and the output is the extracted tasks. Specifically, it uses natural language processing techniques (e.g., a text analysis engine) to detect clear tasks such as "write a report" or "prepare meeting materials" from the content of digital communications.

[0379] Step 3:

[0380] The server recognizes the user's emotional state from the analyzed text data. The input is text data, and the output is the recognized emotional state (e.g., stress, euphoria). Specifically, it utilizes emotion recognition technology (e.g., an emotion analysis engine) to analyze keywords and context within the text and determine whether the user is feeling stressed or satisfied.

[0381] Step 4:

[0382] The server adjusts task priorities based on the recognized emotional state. The input is the extracted tasks and the recognized emotional state, and the output is the adjusted task priorities. Specifically, the task management module may move reminders earlier or reschedule lower-priority tasks to take precedence if the user is feeling stressed.

[0383] Step 5:

[0384] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks. The input is the extracted tasks, and the output is the generated reminders and deadline notifications. Specifically, based on the task's deadline information, it automatically generates notifications three days before or on the day of the specified deadline and sends them to the terminal.

[0385] Step 6:

[0386] The device provides users with reminders and deadline notifications sent from the server. The input is the notifications sent from the server, and the output is the reminders and deadline notifications displayed to the user. Specifically, it uses mobile notification functionality to display alerts on the user's smartphone.

[0387] Step 7:

[0388] The server aggregates all digital communications and tasks received during the day and generates a summary report. The input is all digital communications received during the day, and the output is the summary report. Specifically, the summary generation engine creates a summary based on the importance and priority of the tasks and provides it to the user.

[0389] Step 8:

[0390] The server searches for and provides relevant information from external sources based on the user's emotional state and extracted tasks. The input is the emotional state and task content, and the output is relevant information (e.g., articles, reference materials). Specifically, it uses external APIs to search for the latest information related to articles and tasks that can help reduce stress, and notifies the user.

[0391] Step 9:

[0392] Users check and respond to reminders and summary reports notified on their devices. Input is notifications from the device, and user actions are the output. Specifically, users check task lists and reminders displayed on their smartphones and take the necessary actions (e.g., complete tasks, read articles).

[0393] Through the above processing steps, users can efficiently manage tasks and receive optimal information based on digital communication content.

[0394] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0395] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0397] [Second Embodiment]

[0398] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0399] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0400] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0401] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0402] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0404] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0405] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0406] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0408] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0409] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0410] This invention relates to a system that effectively manages tasks from various digital communication methods and improves operational efficiency. Specific embodiments of this system are described below.

[0411] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. The system mainly consists of three elements: a server, terminals, and users.

[0412] Receiving and analyzing messages and emails

[0413] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0414] Generating task reminders and deadline notifications

[0415] The server automatically generates reminders and deadline notifications based on the deadline information of the extracted tasks. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline.

[0416] Task automated response support

[0417] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[0418] Summary and development of daily communications

[0419] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. The generated summary report is provided to the user via their terminal. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[0420] Specific example

[0421] 1. The user receives an email saying, "Please submit your report by next Friday."

[0422] 2. The device forwards this email to the server.

[0423] 3. The server uses natural language processing technology to extract the task "Create a report".

[0424] 4. The server sets a reminder and sends notifications three days before and on Friday.

[0425] 5. The user receives a reminder on their device, checks the task, and takes action.

[0426] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[0427] The following describes the processing flow.

[0428] Step 1: Receiving emails and messages

[0429] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[0430] Step 2: Message Analysis

[0431] The server analyzes received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message, and extracts tasks and relevant information.

[0432] Step 3: Extract and save tasks

[0433] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[0434] Step 4: Generate reminders and deadline notifications

[0435] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline.

[0436] Step 5: Send the reminder

[0437] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[0438] Step 6: Generate automated responses

[0439] The server detects digital communications that can be automatically responded to. For example, in response to a request such as "Please send the latest sales data," the server searches the database for the relevant data and generates a list of possible responses.

[0440] Step 7: User verification for automated responses

[0441] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[0442] Step 8: Generate a summary of daily communications

[0443] The server aggregates all messages and tasks received during the day and generates a summary report. The report takes into account the importance and priority of the tasks and presents them concisely.

[0444] Step 9: Submit and view summary report

[0445] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[0446] (Example 1)

[0447] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0448] Traditional digital communication methods presented challenges in efficiently managing received messages and emails and ensuring important tasks weren't overlooked. In particular, extracting key information from a large volume of messages, setting timely reminders, and generating automated responses for quick replies were difficult. Summarizing key points from a daily stream of communications was also a significant burden for users. This situation led to decreased work efficiency and task delays.

[0449] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0450] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to extract important tasks from messages and emails without missing them and set reminders at the appropriate time. Furthermore, by generating automatic answers, quick responses are possible, reducing the burden on the user. In addition, by adding means for summarizing all digital communications and tasks received in a day, users can grasp important information at a glance, improving work efficiency.

[0451] "Received digital communications" refers to all text and data sent to a user's device via email, messaging apps, etc.

[0452] "Task extraction methods" refer to the process of identifying important tasks and appointments from received digital communications and recording them as tasks.

[0453] "Means for automatically generating reminders and deadline notifications" refers to a process that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information.

[0454] "Means for generating response candidates for items that can be answered automatically" refers to a process that extracts content from received digital communications that can be handled automatically and generates an appropriate response.

[0455] "Means of transferring digital communications to a server" refers to the process of sending the communication content received on the user's terminal to a server.

[0456] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is generally used to extract meaning from text.

[0457] "The means of summarizing and expanding" is the process of analyzing all digital communications and tasks received in a day and concisely summarizing the key points and tasks.

[0458] "Task importance and priority" refers to criteria that indicate the degree to which a task contributes to success and the priority of its processing.

[0459] A "server" is a computer system that processes and stores data and provides services to client terminals via a network.

[0460] A "terminal" is a device that a user directly operates to receive and transmit digital communications.

[0461] This invention relates to a system that effectively manages tasks from various digital communication methods and improves work efficiency. This system has the function of analyzing received digital communications to extract tasks, automatically generating reminders and deadline notifications for these tasks, and further generating automatic responses.

[0462] The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for analyzing incoming digital communications and extracting necessary tasks and information. This analysis requires advanced text data analysis capabilities using natural language processing techniques. Specific technologies used here include generative AI models such as the GPT model and the BERT model.

[0463] server:

[0464] The server is the primary processing facility for analyzing received digital communications. The server analyzes digital communications received from the user's terminal (e.g., emails and messaging app notifications) to extract important tasks. For example, if a user receives an email saying, "Please submit the report by next Friday," the server uses natural language processing techniques to extract the task "Create the report" and recognizes that the deadline is Friday.

[0465] Next, the server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. This ensures that important tasks are not missed and reminders are sent at the appropriate time. For example, for the task "Create a report," the server sets a reminder and sends notifications to the user's device three days before the deadline and on the deadline day.

[0466] Furthermore, for messages that can be answered automatically, the server automatically generates response suggestions. For example, if an email is received that says, "Please send me the latest sales data," the server searches for the latest data, generates the results as response suggestions, and presents them to the user.

[0467] Furthermore, the server aggregates all digital communications and tasks received throughout the day and generates a summary report. This summary report takes into account the importance and priority of the tasks and is ultimately provided to the user via their terminal. For example, at the end of the day, the user might be notified with a message such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[0468] Terminal:

[0469] A terminal is a device that users directly operate to receive and send various digital communications. The terminal has the function of automatically forwarding received content, such as emails and messaging apps, to a server. For example, when a user receives a message in Outlook, Gmail, LINE, or Slack, the terminal detects this and automatically forwards it to the server.

[0470] User:

[0471] Users are the ultimate beneficiaries of the system, receiving individual digital communications and taking actions based on them. Through their terminals, users can view reminders and summary reports provided by the server and manage their own schedules and tasks.

[0472] Specific example

[0473] 1. The user receives an email saying, "Please submit your report by next Friday."

[0474] 2. The device forwards this email to the server.

[0475] 3. The server uses natural language processing technology to extract the task "Create a report".

[0476] 4. The server sets a reminder and sends notifications three days before and on Friday.

[0477] 5. The user receives a reminder on their device, checks the task, and takes action.

[0478] Examples of prompts to input into a generative AI model

[0479] "Analyze the following email and extract the key task: 'Prepare the presentation materials for tomorrow's meeting.'"

[0480] "Please generate an appropriate automated response to this email: 'Please send us your 2023 sales data.'"

[0481] "Please summarize all messages received today."

[0482] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] The user receives digital communication. Digital communication includes information received via email or messaging apps. In this case, the received message becomes input data. For example, the user receives an email saying, "Please submit the report by next Friday."

[0486] Step 2:

[0487] The terminal automatically detects received digital communications and forwards them to the server. The HTTP / HTTPS protocol is used for this forwarding. In this process, the input data is the received message, and the output data is the result of the forwarding to the server. Specifically, the terminal parses the email and sends the email data to the server.

[0488] Step 3:

[0489] The server analyzes the content of digital communications using natural language processing techniques (e.g., GPT or BERT models). The input data for this analysis is the received digital communications, and the output data is the analysis results and the extracted tasks. Specifically, the server analyzes an email that says, "Please submit the report by next Friday," and extracts the task, "Create a report."

[0490] Step 4:

[0491] The server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. In this process, task deadline information is the input data, and the automatically generated reminders and notifications are the output data. For example, the server sets the deadline for "Create Report" to Friday and instructs it to send reminders on Tuesday and on the day of the deadline.

[0492] Step 5:

[0493] The server identifies digital communications that can be automatically responded to from those it receives and generates response candidates. In this process, the input data is the received digital communications, and the output data is the generated response candidates. Specifically, if the server receives an email saying, "Please send the latest sales data," it searches for the latest data and generates the results as response candidates.

[0494] Step 6:

[0495] The server aggregates all digital communications and tasks received during the day and generates a summary report. In this process, the input data is all digital communications and tasks received during the day, and the output data is the summary report. The server generates the summary report for the user, taking into account the importance and priority of the tasks. For example, it may include content such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent response tasks."

[0496] In this way, the coordination of each step creates a system that significantly improves the user's work efficiency.

[0497] (Application Example 1)

[0498] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0499] In factory communication and task management, there are problems with missed instructions and delays, leading to decreased production efficiency. In particular, overlooking important tasks and failing to send timely and appropriate reminders and notifications are major causes of inefficient production processes. Furthermore, the large volume of information received makes it difficult to manage all tasks, so there is a need for an effective and real-time method for processing this information.

[0500] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0501] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, means for generating answer candidates for the extracted tasks that can be automatically answered, means for summarizing all digital communications and tasks received in a day, and means for providing instructions and status information in real time via smart devices in a factory environment. This prevents instructions from being missed within the factory and enables efficient task management and rapid information sharing.

[0502] "Received digital communications" refers to messages received through communication methods that are exchanged in digital format, such as email, messaging apps, and chat tools.

[0503] "Means for extracting tasks" refers to a function that uses natural language processing technology and other methods to extract important work instructions and tasks from received digital communications.

[0504] "Means for automatically generating reminders and deadline notifications" refers to a function that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information, according to the set deadline.

[0505] "Means for generating answer candidates for questions that can be answered automatically" refers to a function that generates appropriate answer candidates based on received digital communications, using pre-configured rules and databases.

[0506] "A means of summarizing all digital communications and tasks received in a day" refers to a function that aggregates all digital communications and extracted tasks received daily and creates a summary report based on importance and priority.

[0507] "Means of providing instructions and status information in real time via smart devices in a factory environment" refers to a function that uses mobile information terminals such as smart glasses and tablets to provide workers with instructions and on-site conditions within the factory in real time.

[0508] This invention is a system for streamlining communication and task management in a factory environment. Specific embodiments of the system are described below.

[0509] System Configuration

[0510] This system consists mainly of the following elements:

[0511] Server: Analyzes digital communications, extracts and manages tasks, generates reminders and notifications, creates automated responses, and generates summary reports.

[0512] Terminal: Receives digital communications and forwards them to the server. It also receives reminders and notifications from the server and displays them to the user.

[0513] User: Includes smart glasses worn by factory workers and portable information devices such as tablets used by factory workers.

[0514] Program processing and the technologies used

[0515] Natural language processing technology:

[0516] The server uses a natural language processing library (e.g., spaCy) to analyze the received digital communications and extract important task and deadline information.

[0517] Reminders and deadline notifications:

[0518] The server generates reminders and deadline notifications at the appropriate time based on task deadline information and sends them to the user's device using a push notification service (e.g., Firebase).

[0519] Automatic answer generation:

[0520] The server detects content in the received digital communications that can be automatically responded to and generates response candidates using pre-configured rules and databases.

[0521] Summary report generation:

[0522] The server aggregates all digital communications and tasks received in a day, creates a summary report based on their importance and priority, and provides it to the user.

[0523] Specific examples of hardware and software

[0524] Hardware:

[0525] Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[0526] Servers within a factory (e.g., Linux-based servers)

[0527] software:

[0528] Natural language processing libraries (e.g., spaCy, BERT)

[0529] Push notification service (e.g., Firebase)

[0530] Specific example

[0531] An example of its actual use is shown below.

[0532] Specific example 1:

[0533] Factory staff wear smart glasses and receive maintenance instruction emails. The contents of these emails are immediately sent to a server. The server uses a natural language processing model to extract the task "Submit Maintenance Report" and its deadline. A reminder is set, and a push notification is sent as the deadline approaches. Staff also review the task and send a reply confirming receipt of the task using an automatically generated response.

[0534] Specific example 2:

[0535] Factory staff receive a daily summary report detailing tasks they need to complete and any problems that arise. This summary report is generated by a server based on the importance and priority of each task and is provided via a terminal.

[0536] Example of a prompt

[0537] Prompt message:

[0538] "Design a state-of-the-art factory task management system. This system will provide factory staff with real-time process instructions, parts replenishment, machine failure information, and more via smart glasses. It will include features to analyze important tasks from emails and messages, set reminders, and generate automated responses."

[0539] In this way, the present invention specifically realizes a system that prevents tasks from being overlooked in a factory environment and provides support for efficiently carrying out operations.

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] The terminal detects digital communications (emails and messages) received from the user and forwards their contents to the server.

[0543] Input: Digital communication received by the user; Output: Transfer of digital communication to the server.

[0544] Step 2:

[0545] The server analyzes the content of received digital communications using natural language processing techniques and extracts task and deadline information. One example of a library used is spaCy.

[0546] Input: Content of digital communications; Output: Extracted task and deadline information.

[0547] Specific operation: The server loads a natural language processing model, parses the text of the digital communication, and extracts entities including tasks and due dates.

[0548] Step 3:

[0549] The server automatically generates reminders and deadline notifications based on the extracted tasks and deadline information. The generated notifications are sent to the device using a push notification service (e.g., Firebase).

[0550] Input: Extracted task and deadline information; Output: Generated reminders and deadline notifications.

[0551] Specific operation: Based on task deadline information, the server sets reminders and deadline notifications and schedules push notifications as needed.

[0552] Step 4:

[0553] The server generates answer candidates for tasks that can be answered automatically, using pre-configured rules and databases. These generated answer candidates are sent to the terminal and displayed to the user.

[0554] Input: Extracted tasks, Output: Generated answer candidates.

[0555] Specific operation: The server executes rule-based engines and database queries to generate automated response candidates and send them to the terminal.

[0556] Step 5:

[0557] The server aggregates all digital communications and tasks received during the day and generates a summary report based on importance and priority. This summary report is then provided to the user via their terminal.

[0558] Input: All digital communications received in a day and extracted tasks; Output: Generated summary report.

[0559] Specific operation: The server aggregates all incoming digital communications and extracted tasks, applies a weighting algorithm to calculate importance and priority, and generates a summary report.

[0560] Step 6:

[0561] The terminal provides users with real-time instructions and status information through smart devices (e.g., smart glasses or tablets) in a factory environment.

[0562] Input: Reminders, notifications, and summary reports sent from the server. Output: Notifications, instructions, and summary reports displayed on smart devices.

[0563] Specific operation: The terminal displays information received from the server on the user interface and pops up notifications at the appropriate times.

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

[0565] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[0566] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. Furthermore, by combining it with an emotion engine that recognizes emotions from the user's digital communications, it can respond according to the user's state. The system mainly consists of three elements: a server, a terminal, and the user.

[0567] Receiving and analyzing messages and emails

[0568] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0569] Recognition of emotions

[0570] The server uses an emotion engine to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "high stress" or "satisfied" from the context of the user's emails and messages.

[0571] Adjusting task priorities

[0572] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it reduces their workload by prioritizing tasks that are easier for them to handle.

[0573] Generating task reminders and deadline notifications

[0574] The server automatically generates reminders and deadline notifications based on the extracted task deadline information. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline. If the user is experiencing stress, adjustments will be made, such as sending reminders earlier.

[0575] Task automated response support

[0576] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[0577] Summary and development of daily communications

[0578] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. It also includes elements that reflect the user's emotional state as recognized by the emotion engine. The generated summary report is provided to the user via their device. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[0579] Specific example

[0580] 1. The user receives an email saying, "Please submit your report by next Friday."

[0581] 2. The device forwards this email to the server.

[0582] 3. The server uses natural language processing technology to extract the task "Create a report".

[0583] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0584] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0585] 6. The server sets a reminder and sends notifications three days before and on Friday.

[0586] 7. The user checks reminders and the adjusted task list on their device and takes action.

[0587] In this way, the present invention prevents tasks from being overlooked and provides optimal task management tailored to the user's emotional state.

[0588] The following describes the processing flow.

[0589] Step 1: Receiving emails and messages

[0590] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[0591] Step 2: Message Analysis

[0592] The server analyzes the received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message and extracts tasks and relevant information. From an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" is extracted.

[0593] Step 3: Extract and save tasks

[0594] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[0595] Step 4: Recognizing Emotions

[0596] The server uses an emotion engine to recognize the user's emotions from the received digital communications. It analyzes the emotional state, such as "highly stressed" or "satisfied," from the context and expression of the user's message.

[0597] Step 5: Adjust task priorities

[0598] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. If the user is feeling stressed, it will prioritize tasks that are easier to handle.

[0599] Step 6: Generate reminders and deadline notifications

[0600] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline. If the user is experiencing stress, adjustments such as setting earlier reminders will be made.

[0601] Step 7: Send the reminder

[0602] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[0603] Step 8: Automated task response support

[0604] The server detects digital communications that can be automatically responded to. For example, in response to a message like "Please send the latest sales data," the server searches its database for the relevant data and generates a list of possible responses.

[0605] Step 9: User verification of automated responses

[0606] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[0607] Step 10: Generate a summary of daily communications

[0608] The server aggregates all messages and tasks received each day and generates a summary report. The report is concise and takes into account the importance and priority of each task. Furthermore, it includes elements that reflect the user's emotional state as recognized by the emotion engine.

[0609] Step 11: Submit and view summary report

[0610] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[0611] Specific example

[0612] 1. The user receives an email saying, "Please submit your report by next Friday."

[0613] 2. The device forwards this email to the server.

[0614] 3. The server uses natural language processing technology to extract the task "Create a report".

[0615] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0616] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0617] 6. The server sets a reminder and sends notifications three days before and on Friday.

[0618] 7. The user checks reminders and the adjusted task list on their device and takes action.

[0619] (Example 2)

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

[0621] Traditional task management systems are limited to extracting tasks and generating reminders from digital communications, and have the drawback of not being able to properly manage tasks while considering the user's emotional state. Furthermore, task prioritization and automated response generation are not based on the user's emotional state, which can increase the user's workload. In addition, there is a lack of functionality to recognize and appropriately reflect emotional states from digital communications, and this point needs to be addressed.

[0622] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0623] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to adjust task priorities and reminder timing according to the user's emotional state. Furthermore, by incorporating means into the server for summarizing all digital communications and tasks received in a day, and means for recognizing the user's emotional state, the accuracy and appropriateness of task management can be improved, and the user's burden can be reduced.

[0624] "Digital communication" refers to communication conducted in digital formats such as email, messaging apps, and web chat.

[0625] A "task" refers to a specific task or process that a user extracts from digital communication.

[0626] A "reminder" refers to a function that notifies users of the deadline or due date for a specific task.

[0627] "Deadline notification" refers to a function that notifies the user when the deadline for a task is approaching.

[0628] "Automatic response" refers to a reply text that is automatically generated in response to an received digital communication.

[0629] "Emotion recognition" refers to the process of analyzing a user's emotional state from the content of digital communications.

[0630] "Priority adjustment" refers to adjusting the order in which tasks are executed based on their importance and the user's emotional state.

[0631] "Summarizing" refers to the process of concisely organizing all digital communications and tasks received during the day.

[0632] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[0633] An "emotion engine" refers to a technology that analyzes a user's digital communication content and recognizes their emotional state.

[0634] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[0635] Receiving and analyzing messages and emails

[0636] When a user receives an email or message, the device automatically detects it and forwards it to the server. Specifically, the device uses IMAP or SMTP protocols to retrieve the email content and forwards it to the server via HTTPS. The server uses a parsing module (e.g., Python's NLTK or spaCy) to analyze the content of the received message or email. This analysis uses natural language processing techniques to extract tasks and important information. For example, from an email that says "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0637] Recognition of emotions

[0638] The server uses an emotion engine (such as IBM Watson Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "highly stressed" or "satisfied" from the context of emails and messages. The module uses the emotional tone of the text, word usage patterns, and context as evaluation criteria.

[0639] Adjusting task priorities

[0640] The server adjusts task priorities based on the results of emotion recognition. For example, if a user is identified as being "highly stressed," it prioritizes presenting tasks that are easier to tackle to reduce stress. This reduces the user's workload. Specifically, the algorithm presents tasks that can be addressed immediately first, especially for users with high stress levels.

[0641] Generating task reminders and deadline notifications

[0642] The server generates reminders and deadline notifications based on the extracted task deadline information. Specifically, it uses an algorithm that calculates the reminder dates and times by working backward from the task deadline. For example, if the task "Create presentation materials" is due on Friday, the server sets reminders for Monday and Thursday and sends them to the user's device. It can also adjust the timing of reminders to be earlier if the user is experiencing stress.

[0643] Task automated response support

[0644] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, where they can review, modify, and reply as needed.

[0645] Summary and development of daily communications

[0646] The server aggregates all messages and tasks received throughout the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks, and also reflects the results of sentiment recognition. For example, at the end of the day, it might be generated and provided to the user's terminal with a report such as, "The main tasks received today were: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[0647] Specific example

[0648] The following is a concrete example of this system.

[0649] 1. The user receives an email saying, "Please submit your report by next Friday."

[0650] 2. The device forwards this email to the server.

[0651] 3. The server uses natural language processing technology to extract the task "Create a report".

[0652] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0653] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0654] 6. The server sets a reminder and sends notifications three days before and on Friday.

[0655] 7. The user checks reminders and the adjusted task list on their device and takes action.

[0656] Examples of prompt statements

[0657] Examples of prompts for a generative AI model are as follows:

[0658] Analyze the content of the following email to extract tasks and analyze the user's emotional state:

[0659] Email content:

[0660] "Please submit the report by next Friday. Also, please prepare the materials for Monday's meeting."

[0661] Extracted tasks:

[0662] 1. Prepare the report

[0663] 2. Preparing meeting materials for Monday

[0664] User's emotional state:

[0665] High stress levels

[0666] In this way, the present invention provides optimal task management based on the user's emotional state, thereby reducing the workload.

[0667] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0668] Step 1:

[0669] The user receives emails and messages.

[0670] The device detects this incoming event. Specifically, the device receives new emails from the mail server using the IMAP or SMTP protocol and sends a notification of receipt.

[0671] Input: Emails and messages received by the user.

[0672] Output: Received events detected by the terminal.

[0673] Step 2:

[0674] The terminal forwards the detected incoming event to the server.

[0675] Specifically, the terminal forwards the content of the received email to the server via an HTTP request.

[0676] Input: Received event and email content.

[0677] Output: Data sent to the server.

[0678] Step 3:

[0679] The server passes the received data to the analysis module. The analysis module uses natural language processing techniques (such as Python's NLTK or spaCy) to analyze the content of the email.

[0680] This analysis process involves tokenization, part-of-speech tagging, and dependency analysis to extract important tasks and information.

[0681] Input: The content of the email sent to the server.

[0682] Output: Analyzed tasks and important information.

[0683] Step 4:

[0684] The server uses an emotion engine (such as IBM Watson Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the analyzed data.

[0685] Specifically, it evaluates emotional tone and word usage patterns to analyze emotional states such as "high stress" or "satisfied."

[0686] Input: Analyzed tasks or information.

[0687] Output: User's emotional state.

[0688] Step 5:

[0689] The server adjusts task priorities based on the results of emotion recognition.

[0690] This includes an algorithm that prioritizes tasks that are easier for users with high stress levels to handle.

[0691] Input: User's emotional state and the analyzed task.

[0692] Output: A task list with adjusted priorities.

[0693] Step 6:

[0694] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks.

[0695] For example, you can set a reminder date and time by working backward from the task deadline.

[0696] Input: Task deadline information.

[0697] Output: Generated reminders and deadline notifications.

[0698] Step 7:

[0699] The device receives reminders and deadline notifications sent from the server and notifies the user.

[0700] Specifically, the device will use push notifications and email notification functions.

[0701] Input: Generated reminder and deadline notifications.

[0702] Output: Reminders and deadline notifications displayed on the user's device.

[0703] Step 8:

[0704] The server detects emails and messages that can be automatically responded to and generates appropriate response suggestions.

[0705] For example, in response to an email requesting "Please send the latest sales data," the system automatically searches for the latest data and generates a response.

[0706] Input: Received emails or messages.

[0707] Output: Generated answer candidates.

[0708] Step 9:

[0709] The server aggregates all messages and tasks received during the day and generates a summary report.

[0710] The summary report is based on the importance and priority of the tasks, and also reflects the results of sentiment recognition.

[0711] Input: Messages and tasks received during the day.

[0712] Output: Summary report.

[0713] Step 10:

[0714] The terminal receives a summary report sent from the server and provides it to the user.

[0715] As a specific method, the device will provide a summary report via a display screen or email.

[0716] Input: Summary report.

[0717] Output: A summary report displayed on the user's device.

[0718] (Application Example 2)

[0719] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0720] In today's digital communication environment, numerous tasks and pieces of information are sent and received through multiple communication methods (email, messaging apps, social networking services, etc.). However, effectively managing and appropriately responding to this information is difficult. In particular, there is a lack of adjustment of task priorities that take into account the user's emotional state, and a lack of information to help users maintain a better lifestyle.

[0721] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing received digital communications and extracting tasks; means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks; means for generating answer candidates for the extracted tasks that can be automatically answered; means for unfolding all digital communications and tasks received in a day as a summary; means for recognizing the user's emotions from the content of the analyzed digital communications and adjusting the priority of tasks according to that emotional state; and means for searching for and providing relevant information from external information sources based on the user's emotional state and the extracted tasks. This makes task management from multiple digital communication means simple and efficient, and enables optimal task management and information provision according to the user's emotional state.

[0722] "Digital communication" refers to information sent and received via electronic means such as email, messaging apps, and social networking services (SNS).

[0723] A "task" is a specific task or action that a user needs to perform, extracted from digital communications.

[0724] A "reminder" is a notification that serves as a reminder to inform a user of a specific task or event.

[0725] A "deadline notification" is a notification that informs a user of the deadline for completing a specific task.

[0726] An "automatic response" is a suggested response generated by the system in response to received digital communication, which the user can review and modify.

[0727] A "summary" is a concise report that aggregates and summarizes all digital communications and tasks received during the day.

[0728] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0729] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from the content of digital communications.

[0730] "Adjusting priorities" means dynamically changing the processing order of extracted tasks according to the user's emotional state.

[0731] An "external information source" is a resource that provides information obtained through external databases such as the internet or APIs.

[0732] "Information provision" refers to the act of delivering additional relevant information to users based on their emotional state and task content.

[0733] This invention provides a system consisting of three elements: a server, a terminal, and a user. This system analyzes digital communication content, recognizes the user's emotional state, and optimizes task management and information provision.

[0734] Server Processing

[0735] The server analyzes the received digital communications. Natural language processing techniques are used for the analysis, which extracts tasks. Specifically, from an email that says, "Please submit the report by next Friday," the task "Create a report" is extracted. Next, the server uses emotion recognition techniques to recognize the user's emotional state. If the user is feeling stressed, that emotion can be detected. For example, emotional states such as "highly stressed" or "satisfied" are analyzed from the context of the user's emails and messages.

[0736] Terminal processing

[0737] The terminal receives analysis results transferred from the server and provides users with reminders and deadline notifications. Reminders are automatically generated based on task deadlines and sent to the user at the appropriate time. For example, for a task such as "Create a report," notifications are sent three days before and on the day of the deadline. Furthermore, if the user is experiencing stress, adjustments are made, such as sending reminders earlier.

[0738] Providing information tailored to the user's emotional state.

[0739] The server can search for and provide relevant information from external sources based on the user's emotional state. For example, if a user is feeling "stressed," the server will automatically search for and provide articles that can help reduce stress. This allows the user to work on tasks while reducing their psychological burden.

[0740] Automated response support

[0741] The server generates suggested answers for tasks that can be answered automatically. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates suggested answers. The generated suggested answers are sent to the terminal, where the user can review, modify, and reply.

[0742] Specific example

[0743] Suppose a user receives the message, "Please prepare the meeting materials. I've been very busy lately and I'm feeling stressed." In this case, the server analyzes the message and extracts the task "Prepare meeting materials." It also uses emotion recognition technology to detect the user's emotional state of "high stress." Based on this information, the server adjusts priorities, prioritizing less urgent tasks and providing the user with articles on stress reduction methods.

[0744] Example of a prompt

[0745] "Please prepare the meeting materials. I've been really busy lately and I'm feeling stressed."

[0746] In this way, the system of the present invention can effectively manage the user's digital communication content and provide optimal task management and information according to the user's emotional state. This makes it possible to reduce the workload and provide the user with an efficient work environment.

[0747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0748] Step 1:

[0749] The server detects received digital communications and retrieves their content. The input is digital communications (e.g., emails, messages), and the output is the text data of the retrieved digital communications. Specifically, it accepts emails and messages transferred from terminals to the server and extracts their content in text format.

[0750] Step 2:

[0751] The server analyzes text data and extracts tasks. The input is text data, and the output is the extracted tasks. Specifically, it uses natural language processing techniques (e.g., a text analysis engine) to detect clear tasks such as "write a report" or "prepare meeting materials" from the content of digital communications.

[0752] Step 3:

[0753] The server recognizes the user's emotional state from the analyzed text data. The input is text data, and the output is the recognized emotional state (e.g., stress, euphoria). Specifically, it utilizes emotion recognition technology (e.g., an emotion analysis engine) to analyze keywords and context within the text and determine whether the user is feeling stressed or satisfied.

[0754] Step 4:

[0755] The server adjusts task priorities based on the recognized emotional state. The input is the extracted tasks and the recognized emotional state, and the output is the adjusted task priorities. Specifically, the task management module may move reminders earlier or reschedule lower-priority tasks to take precedence if the user is feeling stressed.

[0756] Step 5:

[0757] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks. The input is the extracted tasks, and the output is the generated reminders and deadline notifications. Specifically, based on the task's deadline information, it automatically generates notifications three days before or on the day of the specified deadline and sends them to the terminal.

[0758] Step 6:

[0759] The device provides users with reminders and deadline notifications sent from the server. The input is the notifications sent from the server, and the output is the reminders and deadline notifications displayed to the user. Specifically, it uses mobile notification functionality to display alerts on the user's smartphone.

[0760] Step 7:

[0761] The server aggregates all digital communications and tasks received during the day and generates a summary report. The input is all digital communications received during the day, and the output is the summary report. Specifically, the summary generation engine creates a summary based on the importance and priority of the tasks and provides it to the user.

[0762] Step 8:

[0763] The server searches for and provides relevant information from external sources based on the user's emotional state and extracted tasks. The input is the emotional state and task content, and the output is relevant information (e.g., articles, reference materials). Specifically, it uses external APIs to search for the latest information related to articles and tasks that can help reduce stress, and notifies the user.

[0764] Step 9:

[0765] Users check and respond to reminders and summary reports notified on their devices. Input is notifications from the device, and user actions are the output. Specifically, users check task lists and reminders displayed on their smartphones and take the necessary actions (e.g., complete tasks, read articles).

[0766] Through the above processing steps, users can efficiently manage tasks and receive optimal information based on digital communication content.

[0767] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0768] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0769] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0770] [Third Embodiment]

[0771] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0772] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0773] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0774] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0775] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0776] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0777] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0778] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0779] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0780] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0781] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0782] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0783] This invention relates to a system that effectively manages tasks from various digital communication methods and improves operational efficiency. Specific embodiments of this system are described below.

[0784] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. The system mainly consists of three elements: a server, terminals, and users.

[0785] Receiving and analyzing messages and emails

[0786] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0787] Generating task reminders and deadline notifications

[0788] The server automatically generates reminders and deadline notifications based on the deadline information of the extracted tasks. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline.

[0789] Task automated response support

[0790] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[0791] Summary and development of daily communications

[0792] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. The generated summary report is provided to the user via their terminal. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[0793] Specific example

[0794] 1. The user receives an email saying, "Please submit your report by next Friday."

[0795] 2. The device forwards this email to the server.

[0796] 3. The server uses natural language processing technology to extract the task "Create a report".

[0797] 4. The server sets a reminder and sends notifications three days before and on Friday.

[0798] 5. The user receives a reminder on their device, checks the task, and takes action.

[0799] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[0800] The following describes the processing flow.

[0801] Step 1: Receiving emails and messages

[0802] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[0803] Step 2: Message Analysis

[0804] The server analyzes received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message, and extracts tasks and relevant information.

[0805] Step 3: Extract and save tasks

[0806] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[0807] Step 4: Generate reminders and deadline notifications

[0808] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline.

[0809] Step 5: Send the reminder

[0810] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[0811] Step 6: Generate automated responses

[0812] The server detects digital communications that can be automatically responded to. For example, in response to a request such as "Please send the latest sales data," the server searches the database for the relevant data and generates a list of possible responses.

[0813] Step 7: User verification for automated responses

[0814] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[0815] Step 8: Generate a summary of daily communications

[0816] The server aggregates all messages and tasks received during the day and generates a summary report. The report takes into account the importance and priority of the tasks and presents them concisely.

[0817] Step 9: Submit and view summary report

[0818] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[0819] (Example 1)

[0820] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0821] Traditional digital communication methods presented challenges in efficiently managing received messages and emails and ensuring important tasks weren't overlooked. In particular, extracting key information from a large volume of messages, setting timely reminders, and generating automated responses for quick replies were difficult. Summarizing key points from a daily stream of communications was also a significant burden for users. This situation led to decreased work efficiency and task delays.

[0822] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0823] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to extract important tasks from messages and emails without missing them and set reminders at the appropriate time. Furthermore, by generating automatic answers, quick responses are possible, reducing the burden on the user. In addition, by adding means for summarizing all digital communications and tasks received in a day, users can grasp important information at a glance, improving work efficiency.

[0824] "Received digital communications" refers to all text and data sent to a user's device via email, messaging apps, etc.

[0825] "Task extraction methods" refer to the process of identifying important tasks and appointments from received digital communications and recording them as tasks.

[0826] "Means for automatically generating reminders and deadline notifications" refers to a process that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information.

[0827] "Means for generating response candidates for items that can be answered automatically" refers to a process that extracts content from received digital communications that can be handled automatically and generates an appropriate response.

[0828] "Means of transferring digital communications to a server" refers to the process of sending the communication content received on the user's terminal to a server.

[0829] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is generally used to extract meaning from text.

[0830] "The means of summarizing and expanding" is the process of analyzing all digital communications and tasks received in a day and concisely summarizing the key points and tasks.

[0831] "Task importance and priority" refers to criteria that indicate the degree to which a task contributes to success and the priority of its processing.

[0832] A "server" is a computer system that processes and stores data and provides services to client terminals via a network.

[0833] A "terminal" is a device that a user directly operates to receive and transmit digital communications.

[0834] This invention relates to a system that effectively manages tasks from various digital communication methods and improves work efficiency. This system has the function of analyzing received digital communications to extract tasks, automatically generating reminders and deadline notifications for these tasks, and further generating automatic responses.

[0835] The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for analyzing incoming digital communications and extracting necessary tasks and information. This analysis requires advanced text data analysis capabilities using natural language processing techniques. Specific technologies used here include generative AI models such as the GPT model and the BERT model.

[0836] server:

[0837] The server is the primary processing facility for analyzing received digital communications. The server analyzes digital communications received from the user's terminal (e.g., emails and messaging app notifications) to extract important tasks. For example, if a user receives an email saying, "Please submit the report by next Friday," the server uses natural language processing techniques to extract the task "Create the report" and recognizes that the deadline is Friday.

[0838] Next, the server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. This ensures that important tasks are not missed and reminders are sent at the appropriate time. For example, for the task "Create a report," the server sets a reminder and sends notifications to the user's device three days before the deadline and on the deadline day.

[0839] Furthermore, for messages that can be answered automatically, the server automatically generates response suggestions. For example, if an email is received that says, "Please send me the latest sales data," the server searches for the latest data, generates the results as response suggestions, and presents them to the user.

[0840] Furthermore, the server aggregates all digital communications and tasks received throughout the day and generates a summary report. This summary report takes into account the importance and priority of the tasks and is ultimately provided to the user via their terminal. For example, at the end of the day, the user might be notified with a message such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[0841] Terminal:

[0842] A terminal is a device that users directly operate to receive and send various digital communications. The terminal has the function of automatically forwarding received content, such as emails and messaging apps, to a server. For example, when a user receives a message in Outlook, Gmail, LINE, or Slack, the terminal detects this and automatically forwards it to the server.

[0843] User:

[0844] Users are the ultimate beneficiaries of the system, receiving individual digital communications and taking actions based on them. Through their terminals, users can view reminders and summary reports provided by the server and manage their own schedules and tasks.

[0845] Specific example

[0846] 1. The user receives an email saying, "Please submit your report by next Friday."

[0847] 2. The device forwards this email to the server.

[0848] 3. The server uses natural language processing technology to extract the task "Create a report".

[0849] 4. The server sets a reminder and sends notifications three days before and on Friday.

[0850] 5. The user receives a reminder on their device, checks the task, and takes action.

[0851] Examples of prompts to input into a generative AI model

[0852] "Analyze the following email and extract the key task: 'Prepare the presentation materials for tomorrow's meeting.'"

[0853] "Please generate an appropriate automated response to this email: 'Please send us your 2023 sales data.'"

[0854] "Please summarize all messages received today."

[0855] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[0856] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0857] Step 1:

[0858] The user receives digital communication. Digital communication includes information received via email or messaging apps. In this case, the received message becomes input data. For example, the user receives an email saying, "Please submit the report by next Friday."

[0859] Step 2:

[0860] The terminal automatically detects received digital communications and forwards them to the server. The HTTP / HTTPS protocol is used for this forwarding. In this process, the input data is the received message, and the output data is the result of the forwarding to the server. Specifically, the terminal parses the email and sends the email data to the server.

[0861] Step 3:

[0862] The server analyzes the content of digital communications using natural language processing techniques (e.g., GPT or BERT models). The input data for this analysis is the received digital communications, and the output data is the analysis results and the extracted tasks. Specifically, the server analyzes an email that says, "Please submit the report by next Friday," and extracts the task, "Create a report."

[0863] Step 4:

[0864] The server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. In this process, task deadline information is the input data, and the automatically generated reminders and notifications are the output data. For example, the server sets the deadline for "Create Report" to Friday and instructs it to send reminders on Tuesday and on the day of the deadline.

[0865] Step 5:

[0866] The server identifies digital communications that can be automatically responded to from those it receives and generates response candidates. In this process, the input data is the received digital communications, and the output data is the generated response candidates. Specifically, if the server receives an email saying, "Please send the latest sales data," it searches for the latest data and generates the results as response candidates.

[0867] Step 6:

[0868] The server aggregates all digital communications and tasks received during the day and generates a summary report. In this process, the input data is all digital communications and tasks received during the day, and the output data is the summary report. The server generates the summary report for the user, taking into account the importance and priority of the tasks. For example, it may include content such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent response tasks."

[0869] In this way, the coordination of each step creates a system that significantly improves the user's work efficiency.

[0870] (Application Example 1)

[0871] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0872] In factory communication and task management, there are problems with missed instructions and delays, leading to decreased production efficiency. In particular, overlooking important tasks and failing to send timely and appropriate reminders and notifications are major causes of inefficient production processes. Furthermore, the large volume of information received makes it difficult to manage all tasks, so there is a need for an effective and real-time method for processing this information.

[0873] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0874] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, means for generating answer candidates for the extracted tasks that can be automatically answered, means for summarizing all digital communications and tasks received in a day, and means for providing instructions and status information in real time via smart devices in a factory environment. This prevents instructions from being missed within the factory and enables efficient task management and rapid information sharing.

[0875] "Received digital communications" refers to messages received through communication methods that are exchanged in digital format, such as email, messaging apps, and chat tools.

[0876] "Means for extracting tasks" refers to a function that uses natural language processing technology and other methods to extract important work instructions and tasks from received digital communications.

[0877] "Means for automatically generating reminders and deadline notifications" refers to a function that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information, according to the set deadline.

[0878] "Means for generating answer candidates for questions that can be answered automatically" refers to a function that generates appropriate answer candidates based on received digital communications, using pre-configured rules and databases.

[0879] "A means of summarizing all digital communications and tasks received in a day" refers to a function that aggregates all digital communications and extracted tasks received daily and creates a summary report based on importance and priority.

[0880] "Means of providing instructions and status information in real time via smart devices in a factory environment" refers to a function that uses mobile information terminals such as smart glasses and tablets to provide workers with instructions and on-site conditions within the factory in real time.

[0881] This invention is a system for streamlining communication and task management in a factory environment. Specific embodiments of the system are described below.

[0882] System Configuration

[0883] This system consists mainly of the following elements:

[0884] Server: Analyzes digital communications, extracts and manages tasks, generates reminders and notifications, creates automated responses, and generates summary reports.

[0885] Terminal: Receives digital communications and forwards them to the server. It also receives reminders and notifications from the server and displays them to the user.

[0886] User: Includes smart glasses worn by factory workers and portable information devices such as tablets used by factory workers.

[0887] Program processing and the technologies used

[0888] Natural language processing technology:

[0889] The server uses a natural language processing library (e.g., spaCy) to analyze the received digital communications and extract important task and deadline information.

[0890] Reminders and deadline notifications:

[0891] The server generates reminders and deadline notifications at the appropriate time based on task deadline information and sends them to the user's device using a push notification service (e.g., Firebase).

[0892] Automatic answer generation:

[0893] The server detects content in the received digital communications that can be automatically responded to and generates response candidates using pre-configured rules and databases.

[0894] Summary report generation:

[0895] The server aggregates all digital communications and tasks received in a day, creates a summary report based on their importance and priority, and provides it to the user.

[0896] Specific examples of hardware and software

[0897] Hardware:

[0898] Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[0899] Servers within a factory (e.g., Linux-based servers)

[0900] software:

[0901] Natural language processing libraries (e.g., spaCy, BERT)

[0902] Push notification service (e.g., Firebase)

[0903] Specific example

[0904] An example of its actual use is shown below.

[0905] Specific example 1:

[0906] Factory staff wear smart glasses and receive maintenance instruction emails. The contents of these emails are immediately sent to a server. The server uses a natural language processing model to extract the task "Submit Maintenance Report" and its deadline. A reminder is set, and a push notification is sent as the deadline approaches. Staff also review the task and send a reply confirming receipt of the task using an automatically generated response.

[0907] Specific example 2:

[0908] Factory staff receive a daily summary report detailing tasks they need to complete and any problems that arise. This summary report is generated by a server based on the importance and priority of each task and is provided via a terminal.

[0909] Example of a prompt

[0910] Prompt message:

[0911] "Design a state-of-the-art factory task management system. This system will provide factory staff with real-time process instructions, parts replenishment, machine failure information, and more via smart glasses. It will include features to analyze important tasks from emails and messages, set reminders, and generate automated responses."

[0912] In this way, the present invention specifically realizes a system that prevents tasks from being overlooked in a factory environment and provides support for efficiently carrying out operations.

[0913] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0914] Step 1:

[0915] The terminal detects digital communications (emails and messages) received from the user and forwards their contents to the server.

[0916] Input: Digital communication received by the user; Output: Transfer of digital communication to the server.

[0917] Step 2:

[0918] The server analyzes the content of received digital communications using natural language processing techniques and extracts task and deadline information. One example of a library used is spaCy.

[0919] Input: Content of digital communications; Output: Extracted task and deadline information.

[0920] Specific operation: The server loads a natural language processing model, parses the text of the digital communication, and extracts entities including tasks and due dates.

[0921] Step 3:

[0922] The server automatically generates reminders and deadline notifications based on the extracted tasks and deadline information. The generated notifications are sent to the device using a push notification service (e.g., Firebase).

[0923] Input: Extracted task and deadline information; Output: Generated reminders and deadline notifications.

[0924] Specific operation: Based on task deadline information, the server sets reminders and deadline notifications and schedules push notifications as needed.

[0925] Step 4:

[0926] The server generates answer candidates for tasks that can be answered automatically, using pre-configured rules and databases. These generated answer candidates are sent to the terminal and displayed to the user.

[0927] Input: Extracted tasks, Output: Generated answer candidates.

[0928] Specific operation: The server executes rule-based engines and database queries to generate automated response candidates and send them to the terminal.

[0929] Step 5:

[0930] The server aggregates all digital communications and tasks received during the day and generates a summary report based on importance and priority. This summary report is then provided to the user via their terminal.

[0931] Input: All digital communications received in a day and extracted tasks; Output: Generated summary report.

[0932] Specific operation: The server aggregates all incoming digital communications and extracted tasks, applies a weighting algorithm to calculate importance and priority, and generates a summary report.

[0933] Step 6:

[0934] The terminal provides users with real-time instructions and status information through smart devices (e.g., smart glasses or tablets) in a factory environment.

[0935] Input: Reminders, notifications, and summary reports sent from the server. Output: Notifications, instructions, and summary reports displayed on smart devices.

[0936] Specific operation: The terminal displays information received from the server on the user interface and pops up notifications at the appropriate times.

[0937] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0938] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[0939] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. Furthermore, by combining it with an emotion engine that recognizes emotions from the user's digital communications, it can respond according to the user's state. The system mainly consists of three elements: a server, a terminal, and the user.

[0940] Receiving and analyzing messages and emails

[0941] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[0942] Recognition of emotions

[0943] The server uses an emotion engine to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "high stress" or "satisfied" from the context of the user's emails and messages.

[0944] Adjusting task priorities

[0945] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it reduces their workload by prioritizing tasks that are easier for them to handle.

[0946] Generating task reminders and deadline notifications

[0947] The server automatically generates reminders and deadline notifications based on the extracted task deadline information. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline. If the user is experiencing stress, adjustments will be made, such as sending reminders earlier.

[0948] Task automated response support

[0949] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[0950] Summary and development of daily communications

[0951] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. It also includes elements that reflect the user's emotional state as recognized by the emotion engine. The generated summary report is provided to the user via their device. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[0952] Specific example

[0953] 1. The user receives an email saying, "Please submit your report by next Friday."

[0954] 2. The device forwards this email to the server.

[0955] 3. The server uses natural language processing technology to extract the task "Create a report".

[0956] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0957] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0958] 6. The server sets a reminder and sends notifications three days before and on Friday.

[0959] 7. The user checks reminders and the adjusted task list on their device and takes action.

[0960] In this way, the present invention prevents tasks from being overlooked and provides optimal task management tailored to the user's emotional state.

[0961] The following describes the processing flow.

[0962] Step 1: Receiving emails and messages

[0963] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[0964] Step 2: Message Analysis

[0965] The server analyzes the received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message and extracts tasks and relevant information. From an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" is extracted.

[0966] Step 3: Extract and save tasks

[0967] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[0968] Step 4: Recognizing Emotions

[0969] The server uses an emotion engine to recognize the user's emotions from the received digital communications. It analyzes the emotional state, such as "highly stressed" or "satisfied," from the context and expression of the user's message.

[0970] Step 5: Adjust task priorities

[0971] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. If the user is feeling stressed, it will prioritize tasks that are easier to handle.

[0972] Step 6: Generate reminders and deadline notifications

[0973] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline. If the user is experiencing stress, adjustments such as setting earlier reminders will be made.

[0974] Step 7: Send the reminder

[0975] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[0976] Step 8: Automated task response support

[0977] The server detects digital communications that can be automatically responded to. For example, in response to a message like "Please send the latest sales data," the server searches its database for the relevant data and generates a list of possible responses.

[0978] Step 9: User verification of automated responses

[0979] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[0980] Step 10: Generate a summary of daily communications

[0981] The server aggregates all messages and tasks received each day and generates a summary report. The report is concise and takes into account the importance and priority of each task. Furthermore, it includes elements that reflect the user's emotional state as recognized by the emotion engine.

[0982] Step 11: Submit and view summary report

[0983] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[0984] Specific example

[0985] 1. The user receives an email saying, "Please submit your report by next Friday."

[0986] 2. The device forwards this email to the server.

[0987] 3. The server uses natural language processing technology to extract the task "Create a report".

[0988] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[0989] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[0990] 6. The server sets a reminder and sends notifications three days before and on Friday.

[0991] 7. The user checks reminders and the adjusted task list on their device and takes action.

[0992] (Example 2)

[0993] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0994] Traditional task management systems are limited to extracting tasks and generating reminders from digital communications, and have the drawback of not being able to properly manage tasks while considering the user's emotional state. Furthermore, task prioritization and automated response generation are not based on the user's emotional state, which can increase the user's workload. In addition, there is a lack of functionality to recognize and appropriately reflect emotional states from digital communications, and this point needs to be addressed.

[0995] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0996] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to adjust task priorities and reminder timing according to the user's emotional state. Furthermore, by incorporating means into the server for summarizing all digital communications and tasks received in a day, and means for recognizing the user's emotional state, the accuracy and appropriateness of task management can be improved, and the user's burden can be reduced.

[0997] "Digital communication" refers to communication conducted in digital formats such as email, messaging apps, and web chat.

[0998] A "task" refers to a specific task or process that a user extracts from digital communication.

[0999] A "reminder" refers to a function that notifies users of the deadline or due date for a specific task.

[1000] "Deadline notification" refers to a function that notifies the user when the deadline for a task is approaching.

[1001] "Automatic response" refers to a reply text that is automatically generated in response to an received digital communication.

[1002] "Emotion recognition" refers to the process of analyzing a user's emotional state from the content of digital communications.

[1003] "Priority adjustment" refers to adjusting the order in which tasks are executed based on their importance and the user's emotional state.

[1004] "Summarizing" refers to the process of concisely organizing all digital communications and tasks received during the day.

[1005] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[1006] An "emotion engine" refers to a technology that analyzes a user's digital communication content and recognizes their emotional state.

[1007] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[1008] Receiving and analyzing messages and emails

[1009] When a user receives an email or message, the device automatically detects it and forwards it to the server. Specifically, the device uses IMAP or SMTP protocols to retrieve the email content and forwards it to the server via HTTPS. The server uses a parsing module (e.g., Python's NLTK or spaCy) to analyze the content of the received message or email. This analysis uses natural language processing techniques to extract tasks and important information. For example, from an email that says "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[1010] Recognition of emotions

[1011] The server uses an emotion engine (such as IBM Watson Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "highly stressed" or "satisfied" from the context of emails and messages. The module uses the emotional tone of the text, word usage patterns, and context as evaluation criteria.

[1012] Adjusting task priorities

[1013] The server adjusts task priorities based on the results of emotion recognition. For example, if a user is identified as being "highly stressed," it prioritizes presenting tasks that are easier to tackle to reduce stress. This reduces the user's workload. Specifically, the algorithm presents tasks that can be addressed immediately first, especially for users with high stress levels.

[1014] Generating task reminders and deadline notifications

[1015] The server generates reminders and deadline notifications based on the extracted task deadline information. Specifically, it uses an algorithm that calculates the reminder dates and times by working backward from the task deadline. For example, if the task "Create presentation materials" is due on Friday, the server sets reminders for Monday and Thursday and sends them to the user's device. It can also adjust the timing of reminders to be earlier if the user is experiencing stress.

[1016] Task automated response support

[1017] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, where they can review, modify, and reply as needed.

[1018] Summary and development of daily communications

[1019] The server aggregates all messages and tasks received throughout the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks, and also reflects the results of sentiment recognition. For example, at the end of the day, it might be generated and provided to the user's terminal with a report such as, "The main tasks received today were: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[1020] Specific example

[1021] The following is a concrete example of this system.

[1022] 1. The user receives an email saying, "Please submit your report by next Friday."

[1023] 2. The device forwards this email to the server.

[1024] 3. The server uses natural language processing technology to extract the task "Create a report".

[1025] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[1026] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[1027] 6. The server sets a reminder and sends notifications three days before and on Friday.

[1028] 7. The user checks reminders and the adjusted task list on their device and takes action.

[1029] Examples of prompt statements

[1030] Examples of prompts for a generative AI model are as follows:

[1031] Analyze the content of the following email to extract tasks and analyze the user's emotional state:

[1032] Email content:

[1033] "Please submit the report by next Friday. Also, please prepare the materials for Monday's meeting."

[1034] Extracted tasks:

[1035] 1. Prepare the report

[1036] 2. Preparing meeting materials for Monday

[1037] User's emotional state:

[1038] High stress levels

[1039] In this way, the present invention provides optimal task management based on the user's emotional state, thereby reducing the workload.

[1040] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1041] Step 1:

[1042] The user receives emails and messages.

[1043] The device detects this incoming event. Specifically, the device receives new emails from the mail server using the IMAP or SMTP protocol and sends a notification of receipt.

[1044] Input: Emails and messages received by the user.

[1045] Output: Received events detected by the terminal.

[1046] Step 2:

[1047] The terminal forwards the detected incoming event to the server.

[1048] Specifically, the terminal forwards the content of the received email to the server via an HTTP request.

[1049] Input: Received event and email content.

[1050] Output: Data sent to the server.

[1051] Step 3:

[1052] The server passes the received data to the analysis module. The analysis module uses natural language processing techniques (such as Python's NLTK or spaCy) to analyze the content of the email.

[1053] This analysis process involves tokenization, part-of-speech tagging, and dependency analysis to extract important tasks and information.

[1054] Input: The content of the email sent to the server.

[1055] Output: Analyzed tasks and important information.

[1056] Step 4:

[1057] The server uses an emotion engine (such as IBM Watson Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the analyzed data.

[1058] Specifically, it evaluates emotional tone and word usage patterns to analyze emotional states such as "high stress" or "satisfied."

[1059] Input: Analyzed tasks or information.

[1060] Output: User's emotional state.

[1061] Step 5:

[1062] The server adjusts task priorities based on the results of emotion recognition.

[1063] This includes an algorithm that prioritizes tasks that are easier for users with high stress levels to handle.

[1064] Input: User's emotional state and the analyzed task.

[1065] Output: A task list with adjusted priorities.

[1066] Step 6:

[1067] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks.

[1068] For example, you can set a reminder date and time by working backward from the task deadline.

[1069] Input: Task deadline information.

[1070] Output: Generated reminders and deadline notifications.

[1071] Step 7:

[1072] The device receives reminders and deadline notifications sent from the server and notifies the user.

[1073] Specifically, the device will use push notifications and email notification functions.

[1074] Input: Generated reminder and deadline notifications.

[1075] Output: Reminders and deadline notifications displayed on the user's device.

[1076] Step 8:

[1077] The server detects emails and messages that can be automatically responded to and generates appropriate response suggestions.

[1078] For example, in response to an email requesting "Please send the latest sales data," the system automatically searches for the latest data and generates a response.

[1079] Input: Received emails or messages.

[1080] Output: Generated answer candidates.

[1081] Step 9:

[1082] The server aggregates all messages and tasks received during the day and generates a summary report.

[1083] The summary report is based on the importance and priority of the tasks, and also reflects the results of sentiment recognition.

[1084] Input: Messages and tasks received during the day.

[1085] Output: Summary report.

[1086] Step 10:

[1087] The terminal receives a summary report sent from the server and provides it to the user.

[1088] As a specific method, the device will provide a summary report via a display screen or email.

[1089] Input: Summary report.

[1090] Output: A summary report displayed on the user's device.

[1091] (Application Example 2)

[1092] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1093] In today's digital communication environment, numerous tasks and pieces of information are sent and received through multiple communication methods (email, messaging apps, social networking services, etc.). However, effectively managing and appropriately responding to this information is difficult. In particular, there is a lack of adjustment of task priorities that take into account the user's emotional state, and a lack of information to help users maintain a better lifestyle.

[1094] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing received digital communications and extracting tasks; means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks; means for generating answer candidates for the extracted tasks that can be automatically answered; means for unfolding all digital communications and tasks received in a day as a summary; means for recognizing the user's emotions from the content of the analyzed digital communications and adjusting the priority of tasks according to that emotional state; and means for searching for and providing relevant information from external information sources based on the user's emotional state and the extracted tasks. This makes task management from multiple digital communication means simple and efficient, and enables optimal task management and information provision according to the user's emotional state.

[1095] "Digital communication" refers to information sent and received via electronic means such as email, messaging apps, and social networking services (SNS).

[1096] A "task" is a specific task or action that a user needs to perform, extracted from digital communications.

[1097] A "reminder" is a notification that serves as a reminder to inform a user of a specific task or event.

[1098] A "deadline notification" is a notification that informs a user of the deadline for completing a specific task.

[1099] An "automatic response" is a suggested response generated by the system in response to received digital communication, which the user can review and modify.

[1100] A "summary" is a concise report that aggregates and summarizes all digital communications and tasks received during the day.

[1101] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[1102] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from the content of digital communications.

[1103] "Adjusting priorities" means dynamically changing the processing order of extracted tasks according to the user's emotional state.

[1104] An "external information source" is a resource that provides information obtained through external databases such as the internet or APIs.

[1105] "Information provision" refers to the act of delivering additional relevant information to users based on their emotional state and task content.

[1106] This invention provides a system consisting of three elements: a server, a terminal, and a user. This system analyzes digital communication content, recognizes the user's emotional state, and optimizes task management and information provision.

[1107] Server Processing

[1108] The server analyzes the received digital communications. Natural language processing techniques are used for the analysis, which extracts tasks. Specifically, from an email that says, "Please submit the report by next Friday," the task "Create a report" is extracted. Next, the server uses emotion recognition techniques to recognize the user's emotional state. If the user is feeling stressed, that emotion can be detected. For example, emotional states such as "highly stressed" or "satisfied" are analyzed from the context of the user's emails and messages.

[1109] Terminal processing

[1110] The terminal receives analysis results transferred from the server and provides users with reminders and deadline notifications. Reminders are automatically generated based on task deadlines and sent to the user at the appropriate time. For example, for a task such as "Create a report," notifications are sent three days before and on the day of the deadline. Furthermore, if the user is experiencing stress, adjustments are made, such as sending reminders earlier.

[1111] Providing information tailored to the user's emotional state.

[1112] The server can search for and provide relevant information from external sources based on the user's emotional state. For example, if a user is feeling "stressed," the server will automatically search for and provide articles that can help reduce stress. This allows the user to work on tasks while reducing their psychological burden.

[1113] Automated response support

[1114] The server generates suggested answers for tasks that can be answered automatically. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates suggested answers. The generated suggested answers are sent to the terminal, where the user can review, modify, and reply.

[1115] Specific example

[1116] Suppose a user receives the message, "Please prepare the meeting materials. I've been very busy lately and I'm feeling stressed." In this case, the server analyzes the message and extracts the task "Prepare meeting materials." It also uses emotion recognition technology to detect the user's emotional state of "high stress." Based on this information, the server adjusts priorities, prioritizing less urgent tasks and providing the user with articles on stress reduction methods.

[1117] Example of a prompt

[1118] "Please prepare the meeting materials. I've been really busy lately and I'm feeling stressed."

[1119] In this way, the system of the present invention can effectively manage the user's digital communication content and provide optimal task management and information according to the user's emotional state. This makes it possible to reduce the workload and provide the user with an efficient work environment.

[1120] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1121] Step 1:

[1122] The server detects received digital communications and retrieves their content. The input is digital communications (e.g., emails, messages), and the output is the text data of the retrieved digital communications. Specifically, it accepts emails and messages transferred from terminals to the server and extracts their content in text format.

[1123] Step 2:

[1124] The server analyzes text data and extracts tasks. The input is text data, and the output is the extracted tasks. Specifically, it uses natural language processing techniques (e.g., a text analysis engine) to detect clear tasks such as "write a report" or "prepare meeting materials" from the content of digital communications.

[1125] Step 3:

[1126] The server recognizes the user's emotional state from the analyzed text data. The input is text data, and the output is the recognized emotional state (e.g., stress, euphoria). Specifically, it utilizes emotion recognition technology (e.g., an emotion analysis engine) to analyze keywords and context within the text and determine whether the user is feeling stressed or satisfied.

[1127] Step 4:

[1128] The server adjusts task priorities based on the recognized emotional state. The input is the extracted tasks and the recognized emotional state, and the output is the adjusted task priorities. Specifically, the task management module may move reminders earlier or reschedule lower-priority tasks to take precedence if the user is feeling stressed.

[1129] Step 5:

[1130] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks. The input is the extracted tasks, and the output is the generated reminders and deadline notifications. Specifically, based on the task's deadline information, it automatically generates notifications three days before or on the day of the specified deadline and sends them to the terminal.

[1131] Step 6:

[1132] The device provides users with reminders and deadline notifications sent from the server. The input is the notifications sent from the server, and the output is the reminders and deadline notifications displayed to the user. Specifically, it uses mobile notification functionality to display alerts on the user's smartphone.

[1133] Step 7:

[1134] The server aggregates all digital communications and tasks received during the day and generates a summary report. The input is all digital communications received during the day, and the output is the summary report. Specifically, the summary generation engine creates a summary based on the importance and priority of the tasks and provides it to the user.

[1135] Step 8:

[1136] The server searches for and provides relevant information from external sources based on the user's emotional state and extracted tasks. The input is the emotional state and task content, and the output is relevant information (e.g., articles, reference materials). Specifically, it uses external APIs to search for the latest information related to articles and tasks that can help reduce stress, and notifies the user.

[1137] Step 9:

[1138] Users check and respond to reminders and summary reports notified on their devices. Input is notifications from the device, and user actions are the output. Specifically, users check task lists and reminders displayed on their smartphones and take the necessary actions (e.g., complete tasks, read articles).

[1139] Through the above processing steps, users can efficiently manage tasks and receive optimal information based on digital communication content.

[1140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1141] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1142] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1143] [Fourth Embodiment]

[1144] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1145] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1148] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1151] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1153] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1154] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1155] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1156] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1157] This invention relates to a system that effectively manages tasks from various digital communication methods and improves operational efficiency. Specific embodiments of this system are described below.

[1158] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. The system mainly consists of three elements: a server, terminals, and users.

[1159] Receiving and analyzing messages and emails

[1160] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[1161] Generating task reminders and deadline notifications

[1162] The server automatically generates reminders and deadline notifications based on the deadline information of the extracted tasks. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline.

[1163] Task automated response support

[1164] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[1165] Summary and development of daily communications

[1166] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. The generated summary report is provided to the user via their terminal. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[1167] Specific example

[1168] 1. The user receives an email saying, "Please submit your report by next Friday."

[1169] 2. The device forwards this email to the server.

[1170] 3. The server uses natural language processing technology to extract the task "Create a report".

[1171] 4. The server sets a reminder and sends notifications three days before and on Friday.

[1172] 5. The user receives a reminder on their device, checks the task, and takes action.

[1173] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[1174] The following describes the processing flow.

[1175] Step 1: Receiving emails and messages

[1176] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[1177] Step 2: Message Analysis

[1178] The server analyzes received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message, and extracts tasks and relevant information.

[1179] Step 3: Extract and save tasks

[1180] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[1181] Step 4: Generate reminders and deadline notifications

[1182] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline.

[1183] Step 5: Send the reminder

[1184] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[1185] Step 6: Generate automated responses

[1186] The server detects digital communications that can be automatically responded to. For example, in response to a request such as "Please send the latest sales data," the server searches the database for the relevant data and generates a list of possible responses.

[1187] Step 7: User verification for automated responses

[1188] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[1189] Step 8: Generate a summary of daily communications

[1190] The server aggregates all messages and tasks received during the day and generates a summary report. The report takes into account the importance and priority of the tasks and presents them concisely.

[1191] Step 9: Submit and view summary report

[1192] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[1193] (Example 1)

[1194] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1195] Traditional digital communication methods presented challenges in efficiently managing received messages and emails and ensuring important tasks weren't overlooked. In particular, extracting key information from a large volume of messages, setting timely reminders, and generating automated responses for quick replies were difficult. Summarizing key points from a daily stream of communications was also a significant burden for users. This situation led to decreased work efficiency and task delays.

[1196] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1197] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to extract important tasks from messages and emails without missing them and set reminders at the appropriate time. Furthermore, by generating automatic answers, quick responses are possible, reducing the burden on the user. In addition, by adding means for summarizing all digital communications and tasks received in a day, users can grasp important information at a glance, improving work efficiency.

[1198] "Received digital communications" refers to all text and data sent to a user's device via email, messaging apps, etc.

[1199] "Task extraction methods" refer to the process of identifying important tasks and appointments from received digital communications and recording them as tasks.

[1200] "Means for automatically generating reminders and deadline notifications" refers to a process that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information.

[1201] "Means for generating response candidates for items that can be answered automatically" refers to a process that extracts content from received digital communications that can be handled automatically and generates an appropriate response.

[1202] "Means of transferring digital communications to a server" refers to the process of sending the communication content received on the user's terminal to a server.

[1203] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is generally used to extract meaning from text.

[1204] "The means of summarizing and expanding" is the process of analyzing all digital communications and tasks received in a day and concisely summarizing the key points and tasks.

[1205] "Task importance and priority" refers to criteria that indicate the degree to which a task contributes to success and the priority of its processing.

[1206] A "server" is a computer system that processes and stores data and provides services to client terminals via a network.

[1207] A "terminal" is a device that a user directly operates to receive and transmit digital communications.

[1208] This invention relates to a system that effectively manages tasks from various digital communication methods and improves work efficiency. This system has the function of analyzing received digital communications to extract tasks, automatically generating reminders and deadline notifications for these tasks, and further generating automatic responses.

[1209] The system primarily consists of three elements: a server, a terminal, and a user. The server is responsible for analyzing incoming digital communications and extracting necessary tasks and information. This analysis requires advanced text data analysis capabilities using natural language processing techniques. Specific technologies used here include generative AI models such as the GPT model and the BERT model.

[1210] server:

[1211] The server is the primary processing facility for analyzing received digital communications. The server analyzes digital communications received from the user's terminal (e.g., emails and messaging app notifications) to extract important tasks. For example, if a user receives an email saying, "Please submit the report by next Friday," the server uses natural language processing techniques to extract the task "Create the report" and recognizes that the deadline is Friday.

[1212] Next, the server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. This ensures that important tasks are not missed and reminders are sent at the appropriate time. For example, for the task "Create a report," the server sets a reminder and sends notifications to the user's device three days before the deadline and on the deadline day.

[1213] Furthermore, for messages that can be answered automatically, the server automatically generates response suggestions. For example, if an email is received that says, "Please send me the latest sales data," the server searches for the latest data, generates the results as response suggestions, and presents them to the user.

[1214] Furthermore, the server aggregates all digital communications and tasks received throughout the day and generates a summary report. This summary report takes into account the importance and priority of the tasks and is ultimately provided to the user via their terminal. For example, at the end of the day, the user might be notified with a message such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent tasks."

[1215] Terminal:

[1216] A terminal is a device that users directly operate to receive and send various digital communications. The terminal has the function of automatically forwarding received content, such as emails and messaging apps, to a server. For example, when a user receives a message in Outlook, Gmail, LINE, or Slack, the terminal detects this and automatically forwards it to the server.

[1217] User:

[1218] Users are the ultimate beneficiaries of the system, receiving individual digital communications and taking actions based on them. Through their terminals, users can view reminders and summary reports provided by the server and manage their own schedules and tasks.

[1219] Specific example

[1220] 1. The user receives an email saying, "Please submit your report by next Friday."

[1221] 2. The device forwards this email to the server.

[1222] 3. The server uses natural language processing technology to extract the task "Create a report".

[1223] 4. The server sets a reminder and sends notifications three days before and on Friday.

[1224] 5. The user receives a reminder on their device, checks the task, and takes action.

[1225] Examples of prompts to input into a generative AI model

[1226] "Analyze the following email and extract the key task: 'Prepare the presentation materials for tomorrow's meeting.'"

[1227] "Please generate an appropriate automated response to this email: 'Please send us your 2023 sales data.'"

[1228] "Please summarize all messages received today."

[1229] In this way, the present invention provides support to prevent tasks from being overlooked and to carry out work efficiently.

[1230] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1231] Step 1:

[1232] The user receives digital communication. Digital communication includes information received via email or messaging apps. In this case, the received message becomes input data. For example, the user receives an email saying, "Please submit the report by next Friday."

[1233] Step 2:

[1234] The terminal automatically detects received digital communications and forwards them to the server. The HTTP / HTTPS protocol is used for this forwarding. In this process, the input data is the received message, and the output data is the result of the forwarding to the server. Specifically, the terminal parses the email and sends the email data to the server.

[1235] Step 3:

[1236] The server analyzes the content of digital communications using natural language processing techniques (e.g., GPT or BERT models). The input data for this analysis is the received digital communications, and the output data is the analysis results and the extracted tasks. Specifically, the server analyzes an email that says, "Please submit the report by next Friday," and extracts the task, "Create a report."

[1237] Step 4:

[1238] The server automatically generates reminders and deadline notifications based on the deadlines of the extracted tasks. In this process, task deadline information is the input data, and the automatically generated reminders and notifications are the output data. For example, the server sets the deadline for "Create Report" to Friday and instructs it to send reminders on Tuesday and on the day of the deadline.

[1239] Step 5:

[1240] The server identifies digital communications that can be automatically responded to from those it receives and generates response candidates. In this process, the input data is the received digital communications, and the output data is the generated response candidates. Specifically, if the server receives an email saying, "Please send the latest sales data," it searches for the latest data and generates the results as response candidates.

[1241] Step 6:

[1242] The server aggregates all digital communications and tasks received during the day and generates a summary report. In this process, the input data is all digital communications and tasks received during the day, and the output data is the summary report. The server generates the summary report for the user, taking into account the importance and priority of the tasks. For example, it may include content such as, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two urgent response tasks."

[1243] In this way, the coordination of each step creates a system that significantly improves the user's work efficiency.

[1244] (Application Example 1)

[1245] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1246] In factory communication and task management, there are problems with missed instructions and delays, leading to decreased production efficiency. In particular, overlooking important tasks and failing to send timely and appropriate reminders and notifications are major causes of inefficient production processes. Furthermore, the large volume of information received makes it difficult to manage all tasks, so there is a need for an effective and real-time method for processing this information.

[1247] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1248] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, means for generating answer candidates for the extracted tasks that can be automatically answered, means for summarizing all digital communications and tasks received in a day, and means for providing instructions and status information in real time via smart devices in a factory environment. This prevents instructions from being missed within the factory and enables efficient task management and rapid information sharing.

[1249] "Received digital communications" refers to messages received through communication methods that are exchanged in digital format, such as email, messaging apps, and chat tools.

[1250] "Means for extracting tasks" refers to a function that uses natural language processing technology and other methods to extract important work instructions and tasks from received digital communications.

[1251] "Means for automatically generating reminders and deadline notifications" refers to a function that automatically creates and sends reminders and deadline notifications based on the extracted task deadline information, according to the set deadline.

[1252] "Means for generating answer candidates for questions that can be answered automatically" refers to a function that generates appropriate answer candidates based on received digital communications, using pre-configured rules and databases.

[1253] "A means of summarizing all digital communications and tasks received in a day" refers to a function that aggregates all digital communications and extracted tasks received daily and creates a summary report based on importance and priority.

[1254] "Means of providing instructions and status information in real time via smart devices in a factory environment" refers to a function that uses mobile information terminals such as smart glasses and tablets to provide workers with instructions and on-site conditions within the factory in real time.

[1255] This invention is a system for streamlining communication and task management in a factory environment. Specific embodiments of the system are described below.

[1256] System Configuration

[1257] This system consists mainly of the following elements:

[1258] Server: Analyzes digital communications, extracts and manages tasks, generates reminders and notifications, creates automated responses, and generates summary reports.

[1259] Terminal: Receives digital communications and forwards them to the server. It also receives reminders and notifications from the server and displays them to the user.

[1260] User: Includes smart glasses worn by factory workers and portable information devices such as tablets used by factory workers.

[1261] Program processing and the technologies used

[1262] Natural language processing technology:

[1263] The server uses a natural language processing library (e.g., spaCy) to analyze the received digital communications and extract important task and deadline information.

[1264] Reminders and deadline notifications:

[1265] The server generates reminders and deadline notifications at the appropriate time based on task deadline information and sends them to the user's device using a push notification service (e.g., Firebase).

[1266] Automatic answer generation:

[1267] The server detects content in the received digital communications that can be automatically responded to and generates response candidates using pre-configured rules and databases.

[1268] Summary report generation:

[1269] The server aggregates all digital communications and tasks received in a day, creates a summary report based on their importance and priority, and provides it to the user.

[1270] Specific examples of hardware and software

[1271] Hardware:

[1272] Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[1273] Servers within a factory (e.g., Linux-based servers)

[1274] software:

[1275] Natural language processing libraries (e.g., spaCy, BERT)

[1276] Push notification service (e.g., Firebase)

[1277] Specific example

[1278] An example of its actual use is shown below.

[1279] Specific example 1:

[1280] Factory staff wear smart glasses and receive maintenance instruction emails. The contents of these emails are immediately sent to a server. The server uses a natural language processing model to extract the task "Submit Maintenance Report" and its deadline. A reminder is set, and a push notification is sent as the deadline approaches. Staff also review the task and send a reply confirming receipt of the task using an automatically generated response.

[1281] Specific example 2:

[1282] Factory staff receive a daily summary report detailing tasks they need to complete and any problems that arise. This summary report is generated by a server based on the importance and priority of each task and is provided via a terminal.

[1283] Example of a prompt

[1284] Prompt message:

[1285] "Design a state-of-the-art factory task management system. This system will provide factory staff with real-time process instructions, parts replenishment, machine failure information, and more via smart glasses. It will include features to analyze important tasks from emails and messages, set reminders, and generate automated responses."

[1286] In this way, the present invention specifically realizes a system that prevents tasks from being overlooked in a factory environment and provides support for efficiently carrying out operations.

[1287] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1288] Step 1:

[1289] The terminal detects digital communications (emails and messages) received from the user and forwards their contents to the server.

[1290] Input: Digital communication received by the user; Output: Transfer of digital communication to the server.

[1291] Step 2:

[1292] The server analyzes the content of received digital communications using natural language processing techniques and extracts task and deadline information. One example of a library used is spaCy.

[1293] Input: Content of digital communications; Output: Extracted task and deadline information.

[1294] Specific operation: The server loads a natural language processing model, parses the text of the digital communication, and extracts entities including tasks and due dates.

[1295] Step 3:

[1296] The server automatically generates reminders and deadline notifications based on the extracted tasks and deadline information. The generated notifications are sent to the device using a push notification service (e.g., Firebase).

[1297] Input: Extracted task and deadline information; Output: Generated reminders and deadline notifications.

[1298] Specific operation: Based on task deadline information, the server sets reminders and deadline notifications and schedules push notifications as needed.

[1299] Step 4:

[1300] The server generates answer candidates for tasks that can be answered automatically, using pre-configured rules and databases. These generated answer candidates are sent to the terminal and displayed to the user.

[1301] Input: Extracted tasks, Output: Generated answer candidates.

[1302] Specific operation: The server executes rule-based engines and database queries to generate automated response candidates and send them to the terminal.

[1303] Step 5:

[1304] The server aggregates all digital communications and tasks received during the day and generates a summary report based on importance and priority. This summary report is then provided to the user via their terminal.

[1305] Input: All digital communications received in a day and extracted tasks; Output: Generated summary report.

[1306] Specific operation: The server aggregates all incoming digital communications and extracted tasks, applies a weighting algorithm to calculate importance and priority, and generates a summary report.

[1307] Step 6:

[1308] The terminal provides users with real-time instructions and status information through smart devices (e.g., smart glasses or tablets) in a factory environment.

[1309] Input: Reminders, notifications, and summary reports sent from the server. Output: Notifications, instructions, and summary reports displayed on smart devices.

[1310] Specific operation: The terminal displays information received from the server on the user interface and pops up notifications at the appropriate times.

[1311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1312] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[1313] This system analyzes content received from multiple digital communication media (e.g., email and messaging apps) and extracts and manages important tasks. Furthermore, by combining it with an emotion engine that recognizes emotions from the user's digital communications, it can respond according to the user's state. The system mainly consists of three elements: a server, a terminal, and the user.

[1314] Receiving and analyzing messages and emails

[1315] When a user receives an email or message, the device automatically detects it and forwards it to the server. The server uses an analysis module to analyze the content of the received message or email. This analysis employs natural language processing techniques to extract tasks and important information. For example, from an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[1316] Recognition of emotions

[1317] The server uses an emotion engine to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "high stress" or "satisfied" from the context of the user's emails and messages.

[1318] Adjusting task priorities

[1319] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. For example, if a user is feeling stressed, it reduces their workload by prioritizing tasks that are easier for them to handle.

[1320] Generating task reminders and deadline notifications

[1321] The server automatically generates reminders and deadline notifications based on the extracted task deadline information. Reminders are sent to the user's device at the appropriate time according to the set deadline. For example, for a task such as "Create presentation materials," the user will receive notifications three days before the deadline and on the day of the deadline. If the user is experiencing stress, adjustments will be made, such as sending reminders earlier.

[1322] Task automated response support

[1323] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, allowing them to review, modify, and reply.

[1324] Summary and development of daily communications

[1325] The server aggregates all messages and tasks received during the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks. It also includes elements that reflect the user's emotional state as recognized by the emotion engine. The generated summary report is provided to the user via their device. For example, at the end of the day, it might display something like, "The main tasks received today are as follows: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[1326] Specific example

[1327] 1. The user receives an email saying, "Please submit your report by next Friday."

[1328] 2. The device forwards this email to the server.

[1329] 3. The server uses natural language processing technology to extract the task "Create a report".

[1330] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[1331] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[1332] 6. The server sets a reminder and sends notifications three days before and on Friday.

[1333] 7. The user checks reminders and the adjusted task list on their device and takes action.

[1334] In this way, the present invention prevents tasks from being overlooked and provides optimal task management tailored to the user's emotional state.

[1335] The following describes the processing flow.

[1336] Step 1: Receiving emails and messages

[1337] The user receives a new message via a digital communication medium (e.g., email or messaging app). The device monitors and detects this and forwards the received data to the server.

[1338] Step 2: Message Analysis

[1339] The server analyzes the received digital communications using a natural language processing module. The natural language processing (NLP) engine understands keywords and context within the message and extracts tasks and relevant information. From an email that says, "Please prepare the presentation materials for next week," the task "Create presentation materials" is extracted.

[1340] Step 3: Extract and save tasks

[1341] The server stores the tasks extracted from the analysis results in an internal database. Metadata such as content, deadline, and priority is attached to each task.

[1342] Step 4: Recognizing Emotions

[1343] The server uses an emotion engine to recognize the user's emotions from the received digital communications. It analyzes the emotional state, such as "highly stressed" or "satisfied," from the context and expression of the user's message.

[1344] Step 5: Adjust task priorities

[1345] The server adjusts task priorities based on the user's emotions, as recognized by the emotion engine. If the user is feeling stressed, it will prioritize tasks that are easier to handle.

[1346] Step 6: Generate reminders and deadline notifications

[1347] The server checks the deadlines of tasks stored in the database and automatically generates reminders and deadline notifications. Reminders are set the day before the specified deadline, and deadline notifications are set on the day of the deadline. If the user is experiencing stress, adjustments such as setting earlier reminders will be made.

[1348] Step 7: Send the reminder

[1349] When it's time to send a reminder, the server sends the reminder data to the device. The device then converts this into a notification format and displays it to the user.

[1350] Step 8: Automated task response support

[1351] The server detects digital communications that can be automatically responded to. For example, in response to a message like "Please send the latest sales data," the server searches its database for the relevant data and generates a list of possible responses.

[1352] Step 9: User verification of automated responses

[1353] The device presents the user with generated answer suggestions. The user reviews the answers, makes corrections as needed, and finally submits them.

[1354] Step 10: Generate a summary of daily communications

[1355] The server aggregates all messages and tasks received each day and generates a summary report. The report is concise and takes into account the importance and priority of each task. Furthermore, it includes elements that reflect the user's emotional state as recognized by the emotion engine.

[1356] Step 11: Submit and view summary report

[1357] The server generates a summary report and sends it to the terminal. The terminal displays the summary report to the user, providing information to help them understand the day's main tasks and notifications.

[1358] Specific example

[1359] 1. The user receives an email saying, "Please submit your report by next Friday."

[1360] 2. The device forwards this email to the server.

[1361] 3. The server uses natural language processing technology to extract the task "Create a report".

[1362] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[1363] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[1364] 6. The server sets a reminder and sends notifications three days before and on Friday.

[1365] 7. The user checks reminders and the adjusted task list on their device and takes action.

[1366] (Example 2)

[1367] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1368] Traditional task management systems are limited to extracting tasks and generating reminders from digital communications, and have the drawback of not being able to properly manage tasks while considering the user's emotional state. Furthermore, task prioritization and automated response generation are not based on the user's emotional state, which can increase the user's workload. In addition, there is a lack of functionality to recognize and appropriately reflect emotional states from digital communications, and this point needs to be addressed.

[1369] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1370] In this invention, the server includes means for analyzing received digital communications to extract tasks, means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks, and means for generating answer candidates for the extracted tasks that can be automatically answered. This makes it possible to adjust task priorities and reminder timing according to the user's emotional state. Furthermore, by incorporating means into the server for summarizing all digital communications and tasks received in a day, and means for recognizing the user's emotional state, the accuracy and appropriateness of task management can be improved, and the user's burden can be reduced.

[1371] "Digital communication" refers to communication conducted in digital formats such as email, messaging apps, and web chat.

[1372] A "task" refers to a specific task or process that a user extracts from digital communication.

[1373] A "reminder" refers to a function that notifies users of the deadline or due date for a specific task.

[1374] "Deadline notification" refers to a function that notifies the user when the deadline for a task is approaching.

[1375] "Automatic response" refers to a reply text that is automatically generated in response to an received digital communication.

[1376] "Emotion recognition" refers to the process of analyzing a user's emotional state from the content of digital communications.

[1377] "Priority adjustment" refers to adjusting the order in which tasks are executed based on their importance and the user's emotional state.

[1378] "Summarizing" refers to the process of concisely organizing all digital communications and tasks received during the day.

[1379] "Natural language processing technology" refers to technologies that enable computers to understand, interpret, and generate human language.

[1380] An "emotion engine" refers to a technology that analyzes a user's digital communication content and recognizes their emotional state.

[1381] This invention relates to a system that effectively manages tasks from various digital communication methods and further optimizes task management and notifications by recognizing the user's emotions. Specific embodiments of this system are described below.

[1382] Receiving and analyzing messages and emails

[1383] When a user receives an email or message, the device automatically detects it and forwards it to the server. Specifically, the device uses IMAP or SMTP protocols to retrieve the email content and forwards it to the server via HTTPS. The server uses a parsing module (e.g., Python's NLTK or spaCy) to analyze the content of the received message or email. This analysis uses natural language processing techniques to extract tasks and important information. For example, from an email that says "Please prepare the presentation materials for next week," the task "Create presentation materials" might be extracted.

[1384] Recognition of emotions

[1385] The server uses an emotion engine (such as IBM Watson Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the received digital communications. For example, it analyzes emotional states such as "highly stressed" or "satisfied" from the context of emails and messages. The module uses the emotional tone of the text, word usage patterns, and context as evaluation criteria.

[1386] Adjusting task priorities

[1387] The server adjusts task priorities based on the results of emotion recognition. For example, if a user is identified as being "highly stressed," it prioritizes presenting tasks that are easier to tackle to reduce stress. This reduces the user's workload. Specifically, the algorithm presents tasks that can be addressed immediately first, especially for users with high stress levels.

[1388] Generating task reminders and deadline notifications

[1389] The server generates reminders and deadline notifications based on the extracted task deadline information. Specifically, it uses an algorithm that calculates the reminder dates and times by working backward from the task deadline. For example, if the task "Create presentation materials" is due on Friday, the server sets reminders for Monday and Thursday and sends them to the user's device. It can also adjust the timing of reminders to be earlier if the user is experiencing stress.

[1390] Task automated response support

[1391] The server detects messages and emails that can be automatically answered and generates appropriate responses. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates potential responses. These generated responses are sent to the user's device, where they can review, modify, and reply as needed.

[1392] Summary and development of daily communications

[1393] The server aggregates all messages and tasks received throughout the day and generates a summary report. This summary report is structured based on the importance and priority of the tasks, and also reflects the results of sentiment recognition. For example, at the end of the day, it might be generated and provided to the user's terminal with a report such as, "The main tasks received today were: preparing meeting materials, sharing sales performance data, and two stress / urgent tasks."

[1394] Specific example

[1395] The following is a concrete example of this system.

[1396] 1. The user receives an email saying, "Please submit your report by next Friday."

[1397] 2. The device forwards this email to the server.

[1398] 3. The server uses natural language processing technology to extract the task "Create a report".

[1399] 4. The server uses an emotion engine to analyze the user's emotions and detect if the user is experiencing stress.

[1400] 5. The server adjusts task priorities and speeds up reminder notifications to make them easier for users to handle.

[1401] 6. The server sets a reminder and sends notifications three days before and on Friday.

[1402] 7. The user checks reminders and the adjusted task list on their device and takes action.

[1403] Examples of prompt statements

[1404] Examples of prompts for a generative AI model are as follows:

[1405] Analyze the content of the following email to extract tasks and analyze the user's emotional state:

[1406] Email content:

[1407] "Please submit the report by next Friday. Also, please prepare the materials for Monday's meeting."

[1408] Extracted tasks:

[1409] 1. Prepare the report

[1410] 2. Preparing meeting materials for Monday

[1411] User's emotional state:

[1412] High stress levels

[1413] In this way, the present invention provides optimal task management based on the user's emotional state, thereby reducing the workload.

[1414] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1415] Step 1:

[1416] The user receives emails and messages.

[1417] The device detects this incoming event. Specifically, the device receives new emails from the mail server using the IMAP or SMTP protocol and sends a notification of receipt.

[1418] Input: Emails and messages received by the user.

[1419] Output: Received events detected by the terminal.

[1420] Step 2:

[1421] The terminal forwards the detected incoming event to the server.

[1422] Specifically, the terminal forwards the content of the received email to the server via an HTTP request.

[1423] Input: Received event and email content.

[1424] Output: Data sent to the server.

[1425] Step 3:

[1426] The server passes the received data to the analysis module. The analysis module uses natural language processing techniques (such as Python's NLTK or spaCy) to analyze the content of the email.

[1427] This analysis process involves tokenization, part-of-speech tagging, and dependency analysis to extract important tasks and information.

[1428] Input: The content of the email sent to the server.

[1429] Output: Analyzed tasks and important information.

[1430] Step 4:

[1431] The server uses an emotion engine (such as IBM Watson Tone Analyzer or Google Cloud Natural Language API) to recognize the user's emotions from the analyzed data.

[1432] Specifically, it evaluates emotional tone and word usage patterns to analyze emotional states such as "high stress" or "satisfied."

[1433] Input: Analyzed tasks or information.

[1434] Output: User's emotional state.

[1435] Step 5:

[1436] The server adjusts task priorities based on the results of emotion recognition.

[1437] This includes an algorithm that prioritizes tasks that are easier for users with high stress levels to handle.

[1438] Input: User's emotional state and the analyzed task.

[1439] Output: A task list with adjusted priorities.

[1440] Step 6:

[1441] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks.

[1442] For example, you can set a reminder date and time by working backward from the task deadline.

[1443] Input: Task deadline information.

[1444] Output: Generated reminders and deadline notifications.

[1445] Step 7:

[1446] The device receives reminders and deadline notifications sent from the server and notifies the user.

[1447] Specifically, the device will use push notifications and email notification functions.

[1448] Input: Generated reminder and deadline notifications.

[1449] Output: Reminders and deadline notifications displayed on the user's device.

[1450] Step 8:

[1451] The server detects emails and messages that can be automatically responded to and generates appropriate response suggestions.

[1452] For example, in response to an email requesting "Please send the latest sales data," the system automatically searches for the latest data and generates a response.

[1453] Input: Received emails or messages.

[1454] Output: Generated answer candidates.

[1455] Step 9:

[1456] The server aggregates all messages and tasks received during the day and generates a summary report.

[1457] The summary report is based on the importance and priority of the tasks, and also reflects the results of sentiment recognition.

[1458] Input: Messages and tasks received during the day.

[1459] Output: Summary report.

[1460] Step 10:

[1461] The terminal receives a summary report sent from the server and provides it to the user.

[1462] As a specific method, the device will provide a summary report via a display screen or email.

[1463] Input: Summary report.

[1464] Output: A summary report displayed on the user's device.

[1465] (Application Example 2)

[1466] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1467] In today's digital communication environment, numerous tasks and pieces of information are sent and received through multiple communication methods (email, messaging apps, social networking services, etc.). However, effectively managing and appropriately responding to this information is difficult. In particular, there is a lack of adjustment of task priorities that take into account the user's emotional state, and a lack of information to help users maintain a better lifestyle.

[1468] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for analyzing received digital communications and extracting tasks; means for automatically generating reminders and deadline notifications based on the deadlines of the extracted tasks; means for generating answer candidates for the extracted tasks that can be automatically answered; means for unfolding all digital communications and tasks received in a day as a summary; means for recognizing the user's emotions from the content of the analyzed digital communications and adjusting the priority of tasks according to that emotional state; and means for searching for and providing relevant information from external information sources based on the user's emotional state and the extracted tasks. This makes task management from multiple digital communication means simple and efficient, and enables optimal task management and information provision according to the user's emotional state.

[1469] "Digital communication" refers to information sent and received via electronic means such as email, messaging apps, and social networking services (SNS).

[1470] A "task" is a specific task or action that a user needs to perform, extracted from digital communications.

[1471] A "reminder" is a notification that serves as a reminder to inform a user of a specific task or event.

[1472] A "deadline notification" is a notification that informs a user of the deadline for completing a specific task.

[1473] An "automatic response" is a suggested response generated by the system in response to received digital communication, which the user can review and modify.

[1474] A "summary" is a concise report that aggregates and summarizes all digital communications and tasks received during the day.

[1475] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[1476] "Emotion recognition" is a technology that analyzes and recognizes a user's emotional state from the content of digital communications.

[1477] "Adjusting priorities" means dynamically changing the processing order of extracted tasks according to the user's emotional state.

[1478] An "external information source" is a resource that provides information obtained through external databases such as the internet or APIs.

[1479] "Information provision" refers to the act of delivering additional relevant information to users based on their emotional state and task content.

[1480] This invention provides a system consisting of three elements: a server, a terminal, and a user. This system analyzes digital communication content, recognizes the user's emotional state, and optimizes task management and information provision.

[1481] Server Processing

[1482] The server analyzes the received digital communications. Natural language processing techniques are used for the analysis, which extracts tasks. Specifically, from an email that says, "Please submit the report by next Friday," the task "Create a report" is extracted. Next, the server uses emotion recognition techniques to recognize the user's emotional state. If the user is feeling stressed, that emotion can be detected. For example, emotional states such as "highly stressed" or "satisfied" are analyzed from the context of the user's emails and messages.

[1483] Terminal processing

[1484] The terminal receives analysis results transferred from the server and provides users with reminders and deadline notifications. Reminders are automatically generated based on task deadlines and sent to the user at the appropriate time. For example, for a task such as "Create a report," notifications are sent three days before and on the day of the deadline. Furthermore, if the user is experiencing stress, adjustments are made, such as sending reminders earlier.

[1485] Providing information tailored to the user's emotional state.

[1486] The server can search for and provide relevant information from external sources based on the user's emotional state. For example, if a user is feeling "stressed," the server will automatically search for and provide articles that can help reduce stress. This allows the user to work on tasks while reducing their psychological burden.

[1487] Automated response support

[1488] The server generates suggested answers for tasks that can be answered automatically. For example, in response to an email requesting "Please send the latest sales data," the server automatically searches for the latest data and generates suggested answers. The generated suggested answers are sent to the terminal, where the user can review, modify, and reply.

[1489] Specific example

[1490] Suppose a user receives the message, "Please prepare the meeting materials. I've been very busy lately and I'm feeling stressed." In this case, the server analyzes the message and extracts the task "Prepare meeting materials." It also uses emotion recognition technology to detect the user's emotional state of "high stress." Based on this information, the server adjusts priorities, prioritizing less urgent tasks and providing the user with articles on stress reduction methods.

[1491] Example of a prompt

[1492] "Please prepare the meeting materials. I've been really busy lately and I'm feeling stressed."

[1493] In this way, the system of the present invention can effectively manage the user's digital communication content and provide optimal task management and information according to the user's emotional state. This makes it possible to reduce the workload and provide the user with an efficient work environment.

[1494] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1495] Step 1:

[1496] The server detects received digital communications and retrieves their content. The input is digital communications (e.g., emails, messages), and the output is the text data of the retrieved digital communications. Specifically, it accepts emails and messages transferred from terminals to the server and extracts their content in text format.

[1497] Step 2:

[1498] The server analyzes text data and extracts tasks. The input is text data, and the output is the extracted tasks. Specifically, it uses natural language processing techniques (e.g., a text analysis engine) to detect clear tasks such as "write a report" or "prepare meeting materials" from the content of digital communications.

[1499] Step 3:

[1500] The server recognizes the user's emotional state from the analyzed text data. The input is text data, and the output is the recognized emotional state (e.g., stress, euphoria). Specifically, it utilizes emotion recognition technology (e.g., an emotion analysis engine) to analyze keywords and context within the text and determine whether the user is feeling stressed or satisfied.

[1501] Step 4:

[1502] The server adjusts task priorities based on the recognized emotional state. The input is the extracted tasks and the recognized emotional state, and the output is the adjusted task priorities. Specifically, the task management module may move reminders earlier or reschedule lower-priority tasks to take precedence if the user is feeling stressed.

[1503] Step 5:

[1504] The server generates reminders and deadline notifications based on the deadline information of the extracted tasks. The input is the extracted tasks, and the output is the generated reminders and deadline notifications. Specifically, based on the task's deadline information, it automatically generates notifications three days before or on the day of the specified deadline and sends them to the terminal.

[1505] Step 6:

[1506] The device provides users with reminders and deadline notifications sent from the server. The input is the notifications sent from the server, and the output is the reminders and deadline notifications displayed to the user. Specifically, it uses mobile notification functionality to display alerts on the user's smartphone.

[1507] Step 7:

[1508] The server aggregates all digital communications and tasks received during the day and generates a summary report. The input is all digital communications received during the day, and the output is the summary report. Specifically, the summary generation engine creates a summary based on the importance and priority of the tasks and provides it to the user.

[1509] Step 8:

[1510] The server searches for and provides relevant information from external sources based on the user's emotional state and extracted tasks. The input is the emotional state and task content, and the output is relevant information (e.g., articles, reference materials). Specifically, it uses external APIs to search for the latest information related to articles and tasks that can help reduce stress, and notifies the user.

[1511] Step 9:

[1512] Users check and respond to reminders and summary reports notified on their devices. Input is notifications from the device, and user actions are the output. Specifically, users check task lists and reminders displayed on their smartphones and take the necessary actions (e.g., complete tasks, read articles).

[1513] Through the above processing steps, users can efficiently manage tasks and receive optimal information based on digital communication content.

[1514] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1515] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1516] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1517] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1518] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1519] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1520] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1521] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1522] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1523] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1524] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1525] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1526] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1527] 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.

[1528] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1529] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1530] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1531] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1532] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1533] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1534] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1535] The following is further disclosed regarding the embodiments described above.

[1536] (Claim 1)

[1537] A means of analyzing received digital communications to extract tasks,

[1538] A means for automatically generating reminders and deadline notifications based on the deadlines of extracted tasks,

[1539] A means for generating answer candidates for the extracted tasks that can be answered automatically,

[1540] A means of summarizing all digital communications and tasks received in a day,

[1541] A system that includes this.

[1542] (Claim 2)

[1543] The system according to claim 1, which uses natural language processing techniques when extracting tasks from received digital communications.

[1544] (Claim 3)

[1545] The system according to claim 1, which generates summaries based on the importance and priority of tasks when summarizing all digital communications and tasks received in a day.

[1546]

[1547] "Example 1"

[1548] (Claim 1)

[1549] A means of analyzing received digital communications to extract tasks,

[1550] A means for automatically generating reminders and deadline notifications based on the deadlines of extracted tasks,

[1551] A means for generating answer candidates for the extracted tasks that can be answered automatically,

[1552] A means of summarizing all digital communications and tasks received in a day,

[1553] A means of transferring received digital communications to a server,

[1554] A means of analyzing received messages using natural language processing technology and extracting important information,

[1555] A system that includes this.

[1556] (Claim 2)

[1557] The system according to claim 1, which uses natural language processing techniques when extracting tasks from received digital communications.

[1558] (Claim 3)

[1559] The system according to claim 1, which generates summaries based on the importance and priority of tasks when summarizing all digital communications and tasks received in a day.

[1560] "Application Example 1"

[1561] (Claim 1)

[1562] A means of analyzing received digital communications to extract tasks,

[1563] A means for automatically generating reminders and deadline notifications based on the deadlines of extracted tasks,

[1564] A means for generating answer candidates for the extracted tasks that can be answered automatically,

[1565] A means of summarizing all digital communications and tasks received in a day,

[1566] A means of providing instructions and status information in real time via smart devices in a factory environment,

[1567] A system that includes this.

[1568] (Claim 2)

[1569] The system according to claim 1, which uses natural language processing techniques when extracting tasks from received digital communications.

[1570] (Claim 3)

[1571] The system according to claim 1, which generates summaries based on the importance and priority of tasks when summarizing all digital communications and tasks received in a day.

[1572] "Example 2 of combining an emotion engine"

[1573] (Claim 1)

[1574] A means of analyzing received digital communications to extract tasks,

[1575] A means for automatically generating reminders and deadline notifications based on the deadlines of extracted tasks,

[1576] A means for generating answer candidates for the extracted tasks that can be answered automatically,

[1577] A means of summarizing all digital communications and tasks received in a day,

[1578] A means of recognizing the user's emotions from received digital communications,

[1579] A means of adjusting task priorities based on recognized user emotions,

[1580] A system that includes this.

[1581] (Claim 2)

[1582] The system according to claim 1, which uses natural language processing techniques when extracting tasks from received digital communications.

[1583] (Claim 3)

[1584] The system according to claim 1, which generates summaries based on the importance and priority of tasks when summarizing all digital communications and tasks received in a day.

[1585] (Claim 4)

[1586] The system according to claim 1, which uses emotion recognition technology when analyzing the user's emotional state from received digital communications.

[1587] (Claim 5)

[1588] The system according to claim 1, which adjusts the timing of reminders and deadline notifications according to the user's emotional state.

[1589] "Application example 2 when combining with an emotional engine"

[1590] (Claim 1)

[1591] A means of analyzing received digital communications to extract tasks,

[1592] A means for automatically generating reminders and deadline notifications based on the deadlines of extracted tasks,

[1593] A means for generating answer candidates for the extracted tasks that can be answered automatically,

[1594] A means of summarizing all digital communications and tasks received in a day,

[1595] A means of recognizing the user's emotions from the content of analyzed digital communications and adjusting task priorities according to that emotional state,

[1596] A means of retrieving and providing relevant information from external sources based on the user's emotional state and extracted tasks,

[1597] A system that includes this.

[1598] (Claim 2)

[1599] The system according to claim 1, which uses natural language processing techniques when extracting tasks from received digital communications.

[1600] (Claim 3)

[1601] The system according to claim 1, comprising means for generating summaries based on the importance and priority of tasks when summarizing all digital communications and tasks received in a day, and means for reflecting emotional elements in the summaries based on the emotional state of the user. [Explanation of Symbols]

[1602] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing received digital communications to extract tasks, A means for automatically generating reminders and deadline notifications based on the deadlines of extracted tasks, A means for generating answer candidates for the extracted tasks that can be answered automatically, A means of summarizing all digital communications and tasks received in a day, A system that includes this.

2. The system according to claim 1, which uses natural language processing techniques when extracting tasks from received digital communications.

3. The system according to claim 1, which generates a summary based on the importance and priority of tasks when summarizing all digital communications and tasks received in a day.

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