System

Abstract

To provide a system.SOLUTION: A system includes: means for collecting messages received via a plurality of different communication means, including at least two of emails, communication tools, chats, and messenger applications; means for centralizing the collected messages by converting the collected messages into a common data format; means for storing the centralized messages in a database in association with identification information indicating the type of communication means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, communication tools are divided into multiple areas, making it difficult to understand which tool received the message and where. It is also difficult to determine whether a message is work-related or personal, and to organize and manage tasks accordingly. [Means for solving the problem]

[0005] The present invention provides a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, and a means for managing messages as tasks based on their content. This allows users to see at a glance which tool a message came from and to manage tasks appropriately according to the message content. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention has a means for unifying and displaying messages from multiple communication tools. Specifically, it collects messages from email, communication tools, chats, messenger apps, etc. and displays them in a list on a single interface. This allows users to see at a glance which tool the message came from.

[0029] "Example 2"

[0030] The system of the present invention also has a means for distinguishing between work and personal messages based on their content. Specifically, it analyzes the sender and content of a message to automatically determine whether it is work-related or personal. For example, if the sender of a message is a business email address or if the message content contains work-related keywords, the message is determined to be work-related.

[0031] "Example 3"

[0032] Furthermore, the system of the present invention has a means for creating and managing tasks based on the content of messages. Specifically, the system analyzes the content of messages and automatically generates tasks based on the content. For example, if a message contains a phrase indicating an action, such as "prepare for a meeting," a task is generated based on that phrase and added to the task list. This allows users to manage tasks appropriately according to the content of the message.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The system collects messages from emails, communication tools, chats, messenger apps, etc.

[0036] Step 2: List all collected messages in one interface.

[0037] Step 3: Users can see at a glance which tool the message came from by looking at the list of messages.

[0038] "Example 2"

[0039] Step 1: The system analyzes the message's origin and content.

[0040] Step 2: Based on the analysis, it automatically determines whether the message is work-related or personal.

[0041] Step 3: For example, if the message was sent from a work email address or contains work-related keywords, the message is determined to be work-related.

[0042] "Example 3"

[0043] Step 1: The system analyzes the content of the message.

[0044] Step 2: Based on the analysis results, tasks are automatically generated based on the messages.

[0045] Step 3: If the message content contains an action phrase, such as "prepare for a meeting," generate a task based on that phrase and add it to your task list.

[0046] Step 4: This allows the user to manage tasks appropriately according to the content of the message.

[0047] Example 1

[0048] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0049] In today's business environment, it is common for users to receive messages using multiple communication methods. However, this can make message management cumbersome and increases the risk of missing important messages. It is also difficult to distinguish between work and personal messages based on their content, and to manage them as tasks.

[0050] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0051] In this invention, the server includes means for collecting messages from multiple communication means, means for unifying the collected messages, and means for saving the unified messages, which allows the user to check messages from multiple communication means on a single interface and manage them efficiently without missing important messages.

[0052] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0053] "Means for collecting messages" is a function for acquiring new messages from multiple communication means.

[0054] "Means for unifying messages" refers to a function for converting collected messages into a common data format and managing them in a unified manner.

[0055] The "means for storing messages" is a function for storing the unified messages in a storage device such as a database.

[0056] The "means for displaying messages" is a function for displaying saved messages on the interface so that the user can view them.

[0057] The "means for manipulating messages" is a function that allows a user to click on a displayed message to view details or to send a reply.

[0058] The "means for distinguishing between work and personal matters" is a function for determining whether a message is work-related or personal-related based on the content of the message.

[0059] The "means for dividing and managing messages into tasks" is a function for classifying messages into tasks based on their content and managing them according to their importance and urgency.

[0060] MODE FOR CARRYING OUT THE INVENTION

[0061] This invention is a system that unifies and displays messages from multiple communication means, classifies messages as work or personal based on their content, and manages them as tasks. A specific embodiment of this system is described below.

[0062] Server Roles

[0063] The server is responsible for collecting messages from multiple communication methods. Specifically, it obtains new messages using APIs such as email servers, communication tools (e.g., Slack, MICROSOFT® TEAMS®), chat apps (e.g., WhatsApp, LINE), and messenger apps (e.g., Facebook Messenger). The server centralizes these messages and converts them into a common data format. The converted messages are then stored in a relational database such as MySQL® or PostgreSQL.

[0064] Device Role

[0065] The terminal provides an interface for users to view and manipulate messages. Specifically, it displays a list of messages retrieved from the server through a web or mobile application. For example, a front end can be built using React or Vue.js, allowing users to visually check messages. Messages are color-coded by tool: blue for email, green for communication tools, and red for chat apps.

[0066] User operations

[0067] Users can use the device's interface to interact with messages. Specifically, they can click on a specific message to view details or send a reply. Users can also use the filter function to display only messages from specific communication channels. Furthermore, based on the content of the message, users can classify messages as work or personal and manage them as tasks. Tasks are created based on the importance and urgency of the message.

[0068] Specific examples

[0069] For example, consider a situation where a user receives messages from multiple communication channels while at work. By using this system, the user can view messages from email, Slack, WhatsApp, and Facebook Messenger in a single interface. This allows the user to see at a glance which communication channel the message came from and respond quickly.

[0070] Prompt Sentence Examples

[0071] Examples of prompts to be input to a generative AI model include:

[0072] "Please explain the system that consolidates and displays messages from multiple communication methods. Specifically, please specify what hardware and software are used and what data processing and calculations are performed. Please also provide specific examples."

[0073] Using this prompt, the generative AI model can generate a detailed description of the system.

[0074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0075] Program processing flow

[0076] Step 1: Collecting messages

[0077] The server collects messages from multiple communication methods. As input, it uses API endpoints such as email servers, communication tools (e.g., Slack, Microsoft Teams), chat apps (e.g., WhatsApp, LINE), and messenger apps (e.g., Facebook Messenger). The server sends requests to these APIs to obtain new messages. As output, it obtains the message data obtained from each communication method.

[0078] Step 2: Unified messaging

[0079] The server centralizes the collected messages. It uses the message data collected in step 1 as input. Specifically, it converts the messages obtained from each communication method into a common data format. For example, an email message has fields such as "sender," "subject," "body," and "received date and time," while a Slack message has fields such as "sender," "channel," "message content," and "sent date and time." The server unifies these fields and converts them into a single data format. The output is the centralized message data.

[0080] Step 3: Save the message

[0081] The server stores the centralized messages in a database. The message data centralized in step 2 is used as input. Specifically, a relational database such as MySQL or PostgreSQL is used to store the message data in a table. For example, the message table contains columns such as "Message ID," "Sender," "Content," "Tool Name," and "Received Date and Time," which uniquely identify each message. The output is the message data stored in the database.

[0082] Step 4: Displaying messages

[0083] The terminal provides an interface for users to view messages. It uses message data obtained from the server as input. Specifically, it displays a list of messages through a web or mobile application. For example, a front end can be built using React or Vue.js to allow users to visually check messages. Messages are color-coded by tool, with emails displayed in blue, communication tools in green, and chat apps in red. The output is a list of messages that the user can view.

[0084] Step 5: Working with messages

[0085] The user manipulates messages using the interface on the device. The input is the displayed list of messages. Specifically, the user can click on a specific message to view details or send a reply. The user can also use the filter function to display only messages from specific communication channels. Furthermore, the user can sort messages into work or personal messages based on their content and manage them as tasks. The output is the result of the user's manipulation.

[0086] (Application example 1)

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

[0088] In logistics centers, messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.) are scattered, making it difficult for staff to quickly grasp and respond to information. This also reduces work efficiency and can lead to important communications being overlooked.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0090] In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on their content, a means for integrating messages collected from multiple communication tools so that logistics center staff can understand them at a glance on their smartphones, and a means for unifying and displaying messages from multiple communication tools used within the logistics center. This allows logistics center staff to understand at a glance which tool a message came from and respond quickly.

[0091] "Multiple communication tools" refers to different types of messaging methods such as email, communication tools, chat, and messenger apps.

[0092] "Centralized display" refers to the ability to collect messages from multiple communication tools and display them in a single interface.

[0093] "Means of classification" refers to the ability to classify messages as work-related or personal based on their content.

[0094] "Means for creating and managing tasks" refers to the function of generating tasks based on the contents of messages and managing those tasks.

[0095] A "logistics center" refers to a facility where goods are stored, sorted, and delivered.

[0096] "Staff" refers to employees working at the logistics center.

[0097] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[0098] "Means of integration" refers to the ability to combine messages collected from multiple communication tools into one system.

[0099] "Responding quickly" means taking necessary action promptly after receiving a message.

[0100] The system for carrying out the present invention is for displaying messages from a plurality of communication tools in a unified manner in a distribution center. Specifically, the server includes the following means:

[0101] First, the server collects messages from multiple contact tools (email, communication tools, chat, messenger apps, etc.) by using Python's imaplib and email libraries to retrieve emails from the IMAP server, the slack_sdk library to collect Slack messages, and the WhatsApp API to collect WhatsApp messages.

[0102] The server then uses Flask to provide an API endpoint to centralize and display the collected messages, allowing staff at the distribution center to use their smartphones to view all messages in a single interface.

[0103] Additionally, the server distinguishes between work-related and personal-related messages based on their content, allowing staff to quickly understand and respond to important messages.

[0104] The server also generates and manages tasks based on the message content, allowing appropriate actions to be taken based on the importance and urgency of the message.

[0105] For example, when a logistics center staff member opens the "Logistics Message Integration App" on their smartphone, messages from email, Slack, and WhatsApp are displayed on one screen. This allows staff to see at a glance which tool the message came from and respond quickly.

[0106] An example prompt is:

[0107] When logistics center staff open the "Logistics Message Integration App" on their smartphones, messages from email, Slack, and WhatsApp are displayed on a single screen. This allows staff to see at a glance which tool the message came from and respond quickly.

[0108] In this way, the efficiency of message management in a logistics center can be greatly improved.

[0109] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0110] Step 1:

[0111] The server retrieves emails from an IMAP server. Specifically, it uses the imaplib and email libraries to log in to a specified email account and retrieve all emails from the inbox. The input is the mail server's authentication information, and the output is data including the email sender, subject, and body.

[0112] Step 2:

[0113] The server retrieves messages from Slack. Specifically, it uses the slack_sdk library to retrieve messages from a specified Slack channel. The input is a Slack API token and a channel ID, and the output is data including the user ID and message text.

[0114] Step 3:

[0115] The server retrieves messages from WhatsApp. Specifically, it uses the WhatsApp API to retrieve messages from a specified WhatsApp account. The input is a WhatsApp API token, and the output is data including the sender and message text.

[0116] Step 4:

[0117] The server centralizes all messages retrieved. Specifically, it consolidates messages retrieved from email, Slack, and WhatsApp into a single data structure. The input is message data retrieved from each communication tool, and the output is a consolidated message list.

[0118] Step 5:

[0119] The server provides the consolidated messages from an API endpoint using Flask. Specifically, we use the Flask framework to build an API that returns the consolidated message list in JSON format. The input is the consolidated message list, and the output is the JSON data provided by the API endpoint.

[0120] Step 6:

[0121] The user opens the "Logistics Message Integration App" on their smartphone. Specifically, they access the server's API endpoint using the smartphone's browser or a dedicated app. The input is the API endpoint URL, and the output is a list of integrated messages.

[0122] Step 7:

[0123] The user checks the displayed messages and responds as necessary. Specifically, the user checks the content of the messages and takes appropriate action based on their importance and urgency. The input is a list of consolidated messages, and the output is the user's action.

[0124] Example 2

[0125] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0126] In today's business environment, there is a need to efficiently manage messages from multiple communication methods (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centralize these messages, classify them as work or personal based on their content, and appropriately organize and manage them as tasks. In particular, there is a need to accurately analyze the sender and content of messages and quickly notify users of the classification results, but current systems cannot do this efficiently.

[0127] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0128] In this invention, the server includes a means for centralizing and displaying messages from multiple communication means, a means for analyzing the sender address of the message, a means for analyzing the content of the message using a generative AI model and classifying it as work or personal, a means for tasking and managing the message based on the message content, and a means for notifying the user's terminal of the classification results. This makes it possible to efficiently centrally manage messages from multiple communication means, appropriately classify and task the messages based on their content, and promptly notify the user.

[0129] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0130] "Source address" refers to information that indicates the email address or user ID of the sender of the message.

[0131] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze text data and understand and classify its content.

[0132] "Message content" refers to the main body of text data sent and received via communication means.

[0133] "Means for unifying and displaying" is a function for integrating and displaying messages obtained from multiple communication means in a single interface.

[0134] The "analysis means" is a function for analyzing the sender address and contents of a message and extracting its characteristics.

[0135] "Means for classification" is a function that automatically classifies messages as work-related or personal based on the analyzed content.

[0136] The "means for creating and managing tasks" is a function for generating tasks based on the sorted messages and managing those tasks.

[0137] The "means for notifying the classification result" is a function for notifying the user terminal of the results of the classified messages.

[0138] This invention relates to a system that consolidates and displays messages from multiple communication methods, analyzes the sender address and content of the messages, classifies them as work-related or personal, and then organizes and manages tasks based on the content. A specific embodiment of this system is shown below.

[0139] System configuration

[0140] The server is configured using the following hardware and software.

[0141] Hardware: A server machine with a powerful processor, sufficient memory, storage devices, and network interfaces.

[0142] Software: Mail servers (supporting the IMAP protocol), generative AI models (e.g., OpenAI's GPT-4), natural language processing libraries (e.g., NLTK and spaCy), notification systems (e.g., Pushbullet).

[0143] Receiving messages

[0144] The server receives messages sent by users. Specifically, it retrieves messages through the API of an email server or chat application. For example, it retrieves messages from a mail server using the IMAP protocol.

[0145] Source Analysis

[0146] The server analyzes the sender address of the message. Specifically, it analyzes the message header information and extracts the sender address. For example, if the sender address is a business email address such as "@company.com", it determines that the message is work-related.

[0147] Content analysis

[0148] The server analyzes the message content using natural language processing (NLP) techniques. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, it inputs the following prompt sentence into the generative AI model:

[0149] Prompt: "Please determine whether the following message is work-related or personal. Message: 'Please prepare the materials for tomorrow's meeting.'"

[0150] The server receives the response from the generative AI model and determines whether the message is work-related or personal.

[0151] Notification of classification results

[0152] The server classifies messages as "work" or "personal" based on the analysis results and notifies the user of the results via push notification or email. For example, it sends push notifications using the Pushbullet library.

[0153] Specific examples

[0154] When a user receives a message saying "Please prepare materials for tomorrow's meeting," the server processes the message as follows.

[0155] 1. Make sure the message is sent from a business email address with "@company.com" in it.

[0156] 2. Analyze the message content to determine whether it contains business-related keywords such as "meeting" or "materials."

[0157] 3. Based on this information, classify the message as "Work."

[0158] 4. The classification results are sent to the user's device.

[0159] In this way, messages from multiple communication methods can be efficiently managed in a centralized manner, appropriately categorized and organized into tasks based on their content, and promptly notified to the user.

[0160] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0161] Step 1:

[0162] Receiving messages

[0163] The server receives messages sent by users. Specifically, it retrieves messages through the API of an email server or chat application. For example, it retrieves messages from a mail server using the IMAP protocol.

[0164] Input: Message sent by the user

[0165] Output: Message data stored on the server

[0166] Step 2:

[0167] Source Analysis

[0168] The server analyzes the sender address of the message. Specifically, it analyzes the message header information and extracts the sender address. For example, if the sender address is a business email address such as "@company.com", it determines that the message is work-related.

[0169] Input: Message data stored on the server

[0170] Output: Analysis result of source address

[0171] Step 3:

[0172] Content analysis

[0173] The server analyzes the message content using natural language processing (NLP) techniques. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, it inputs the following prompt sentence into the generative AI model:

[0174] Prompt: "Please determine whether the following message is work-related or personal. Message: 'Please prepare the materials for tomorrow's meeting.'"

[0175] The server receives the response from the generative AI model and determines whether the message is work-related or personal.

[0176] Input: Message content

[0177] Output: Message classification (Work or Personal)

[0178] Step 4:

[0179] Notification of classification results

[0180] The server classifies messages as "work" or "personal" based on the analysis results and notifies the user of the results via push notification or email. For example, it sends push notifications using the Pushbullet library.

[0181] Input: Message classification results

[0182] Output: Notification sent to the user's device

[0183] Step 5:

[0184] Tasking and Management

[0185] The server generates and manages tasks based on the classified messages. Specifically, it prioritizes tasks based on the importance and urgency of the messages and notifies users at the appropriate time.

[0186] Input: Classified message

[0187] Output: Tasks registered in the task management system

[0188] The above is the specific processing flow of the program of this system.

[0189] (Application example 2)

[0190] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0191] Many users today use multiple communication tools, and work-related and personal messages are often mixed together. This makes message management cumbersome, and it is particularly difficult to determine whether messages about expenses are work-related or personal. Furthermore, if expenses are not properly classified, problems may arise in expense reimbursement and personal financial management. Therefore, there is a need for a system that automatically classifies expenses based on the content of messages and allows users to easily check them.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for analyzing the sender and content of the messages and automatically determining whether the expenditure is work-related or personal-related, and a means for displaying the results of the expenditure classification so that the user can confirm them. This allows the user to unify and manage messages from multiple communication tools and automatically classify and confirm expenditures.

[0193] "Contact tools" are means for sending and receiving messages such as email, communication tools, chat, and messenger apps.

[0194] A "message" is information such as text, images, audio, and video sent and received through a communication tool.

[0195] "Unified" refers to aggregating and displaying messages from multiple communication tools in a single interface.

[0196] "Sorting" refers to determining and classifying messages based on their content as work-related or personal.

[0197] "Tasking" refers to managing a message as a specific work item based on its content.

[0198] "Source" refers to information about the sender who sent the message.

[0199] "Analysis" refers to analyzing the source and content of a message and making a judgment based on specific conditions.

[0200] "Expenses" refers to information regarding the payment of money.

[0201] "Classification Results" refers to the results of classification of expenditures based on the content of a message.

[0202] "Display" refers to visually presenting the classification results so that the user can confirm them.

[0203] A system for implementing this invention includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for analyzing the sender and content of the messages and automatically determining whether the expenditure is work-related or personal-related, and a means for displaying the classified expenditure results so that the user can confirm them.

[0204] System Program

[0205] The program for this system is implemented using a programming language such as Python. The program performs the following processes:

[0206] 1. Single message:

[0207] The server collects messages from email, communication tools, chats, messenger apps, etc. and aggregates them into a single interface, allowing users to view messages from multiple communication tools in one place.

[0208] 2. Message sorting:

[0209] The server analyzes the sender and content of the message and automatically determines whether it is work-related or personal. For example, if the sender is a work email address or the message content contains work-related keywords such as "meeting expenses" or "travel expenses," the message will be classified as work-related.

[0210] 3. Tasking:

[0211] The server then categorizes the messages into tasks based on their importance and urgency, allowing users to prioritize important tasks.

[0212] 4. Classification of expenses:

[0213] The server analyzes the message sender and content to automatically determine whether the expense is work-related or personal. For example, if a payment notification is sent from a work email address or the message content contains work-related keywords such as "meeting expenses" or "travel expenses," the server will classify the expense as work-related.

[0214] 5. Displaying the classification results:

[0215] The server displays the results of the expense classification so that the user can check them, allowing the user to easily check the details of the expenses and manage them appropriately.

[0216] Hardware and software used

[0217] Hardware: Smartphones, servers

[0218] Software: Python, database management systems (e.g., MySQL), messaging APIs (e.g., Gmail API)

[0219] Specific examples

[0220] For example, consider the case where a user receives the following message:

[0221] From: "boss@work-email.com"

[0222] Message: "About the cost of your next meeting"

[0223] In this case, the server classifies the message as "work" and manages the expenditure as work-related.

[0224] Prompt Sentence Examples

[0225] Below is an example of a prompt sentence to input to the generative AI model.

[0226] From: "boss@work-email.com"

[0227] Message: "About the cost of your next meeting"

[0228] Is this message work-related or personal?

[0229] This prompt can be used to ask a generative AI model to classify the message.

[0230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0231] Step 1:

[0232] The server collects messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.). As input, it receives message data obtained through the API of each communication tool. As output, it stores these messages in a centralized database. Specifically, the server calls the API of each communication tool, obtains message data, and stores it in the database.

[0233] Step 2:

[0234] The server analyzes the sender and content of collected messages and automatically determines whether they are work-related or personal. As input, it receives message data stored in a database. As output, it generates a classification result (work or personal) for each message. Specifically, the server analyzes the message content using regular expressions and keyword matching, and stores the classification results in a database.

[0235] Step 3:

[0236] The server generates tasks based on the classified messages. As input, it receives message data including the classification results. As output, it generates tasked data. Specifically, the server evaluates the importance and urgency of the messages and stores them in a database as tasks.

[0237] Step 4:

[0238] The server analyzes the message sender and content and automatically determines whether the expenditure is work-related or personal. As input, it receives message data stored in a database. As output, it generates the expenditure classification results. Specifically, the server classifies expenditures based on the message content and stores the results in a database.

[0239] Step 5:

[0240] The server displays the expense classification results so that the user can check them. As input, the server receives data including the expense classification results. As output, the server generates data to be displayed on the user interface. Specifically, the server visually presents the classification results to the user through a web page or a mobile application.

[0241] Step 6:

[0242] The user inputs a prompt sentence into the generative AI model and requests that it classify the message. As input, the prompt sentence is sent to the generative AI model. As output, the classification result is received from the generative AI model. Specifically, the user inputs a prompt sentence like the following:

[0243] From: "boss@work-email.com"

[0244] Message: "About the cost of your next meeting"

[0245] Is this message work-related or personal?

[0246] This prompt can be used to ask a generative AI model to classify the message.

[0247] Example 3

[0248] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0249] In today's business environment, it is common to use multiple communication methods (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centrally manage messages from these communication methods, separate business and personal messages, and automatically generate and manage tasks based on the content of messages. As a result, users must manually check the content of messages and manually generate tasks, which makes efficient task management difficult.

[0250] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0251] In this invention, the server includes means for unifying and displaying messages from multiple communication means, means for classifying messages as business or personal based on their content, means for analyzing the content of the messages and automatically generating tasks based on the analysis results, and means for adding the generated tasks to a task list and managing them. This allows the user to unify and manage messages from multiple communication means, classify messages as business or personal, and automatically generate tasks based on the message content, thereby enabling efficient task management.

[0252] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0253] "Centralization" refers to consolidating and displaying messages from multiple communication methods in one place.

[0254] "Work" refers to the activities and tasks related to a job or occupation.

[0255] "Personal" refers to personal business or private activities.

[0256] "Discretion" refers to distinguishing between business and personal messages based on their content.

[0257] "Analysis" refers to analyzing the content of a message using natural language processing technology to understand its meaning.

[0258] A "task" refers to a specific action or work, a concrete work item that is generated based on the content of a message.

[0259] A "task list" is a list for displaying and managing generated tasks.

[0260] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0261] This invention is a system that unifies messages from multiple communication means, classifies messages as business or personal based on their content, analyzes the content of the messages, automatically generates tasks, and adds them to a task list for management.

[0262] Hardware and software used

[0263] Hardware

[0264] User's device (PC, smartphone, etc.)

[0265] software

[0266] Natural language processing technology (Google® Cloud Natural Language API, IBM Watson® Natural Language Understanding, etc.)

[0267] Program processing

[0268] Server Processing

[0269] The server analyzes messages received from users. Specifically, the server uses natural language processing technology to analyze the content of the messages and extract phrases that indicate actions. This analysis is performed using software such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[0270] Once the analysis is complete, the server automatically generates tasks based on the extracted phrases. The generated tasks are added to a task list and managed for the user to review later. For example, if a message contains the phrase "prepare for a meeting," the server will generate a task called "prepare for a meeting" and add it to the task list.

[0271] Terminal handling

[0272] The terminal is responsible for sending messages entered by the user to the server. When the user enters a message and presses the send button, the terminal sends the message to the server. For example, if the user enters "I need to prepare for tomorrow's meeting," the message is sent from the terminal to the server.

[0273] User Action

[0274] Users use their devices to input messages and manage tasks. When a user inputs a message, the message is sent to the server via the device. The server analyzes the message and generates a task, which the user can then view in a task list. For example, if a user inputs "Create presentation materials for next week," a task called "Create presentation materials" will be generated based on the input.

[0275] Specific examples

[0276] Prompt Sentence Examples

[0277] If you enter a message like "I need to prepare for tomorrow's meeting," the server will generate a task called "Prepare for meeting" and add it to your task list.

[0278] This system allows the user to automatically generate tasks based on the contents of messages and efficiently manage tasks. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0279] Step 1: User enters message

[0280] A user inputs a message using a terminal (such as a PC or smartphone). For example, the user inputs "I need to prepare for tomorrow's meeting." This input is saved as a text message on the terminal.

[0281] Step 2: The device sends a message to the server

[0282] When a user inputs a message and presses the send button, the device sends the message to the server. Specifically, the device sends the message data in text format to the server via an Internet connection. The input is the user's message, and the output is the message sent to the server.

[0283] Step 3: The server receives the message

[0284] The server receives messages sent from the terminal. The received messages are stored on the server as text data. The input is the message data from the terminal, and the output is the message data stored on the server.

[0285] Step 4: The server parses the message

[0286] The server analyzes the received message. Specifically, it uses natural language processing technology (such as Google Cloud Natural Language API or IBM Watson Natural Language Understanding) to analyze the content of the message and extract phrases that indicate actions. The input is the message data stored on the server, and the output is the extracted action phrase.

[0287] Step 5: The server creates a task

[0288] The server automatically generates tasks based on the analysis results. It creates specific tasks based on the extracted phrases. For example, it generates the task "prepare for a meeting" from the phrase "prepare for a meeting." The input is the extracted action phrase, and the output is the generated task.

[0289] Step 6: Server updates task list

[0290] The server adds the generated tasks to a task list, which is then saved in a database for later review by the user. The input is the generated tasks, and the output is the updated task list.

[0291] Step 7: User checks task list

[0292] The user uses the terminal to check the task list, which displays the tasks generated by the server. This allows the user to check and manage tasks that are automatically generated based on the message content. The input is the updated task list, and the output is the task list displayed on the user's screen.

[0293] (Application example 3)

[0294] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0295] Conventional message management systems lack the functionality to automatically generate tasks based on the content of messages and issue instructions to machines, which reduces work efficiency within factories. Furthermore, the inability to analyze the content of messages and generate appropriate tasks can lead to work delays and errors. This creates challenges that undermine productivity and safety within factories.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0297] In this invention, the server includes a means for consolidating and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the message content, a means for analyzing the message content, generating specific tasks for machines based on the analysis results, and adding the tasks to a task list, and a means for instructing the generated tasks to machines. This makes it possible to automatically generate appropriate tasks based on the message content and issue instructions to machines. This improves work efficiency in factories and reduces work delays and errors.

[0298] "Contact tools" are means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0299] "Message" refers to text information or instructions sent or received through a communication tool.

[0300] "Unification" refers to consolidating messages from multiple communication tools into one platform or interface.

[0301] "Discretion" refers to distinguishing between work-related and personal-related messages based on their content.

[0302] "Tasking" refers to analyzing the content of a message and generating specific tasks and instructions based on that content.

[0303] "Management" refers to adding generated tasks to a list and tracking their progress and completion.

[0304] "Analysis" refers to understanding the content of a message using techniques such as natural language processing and extracting meaning.

[0305] "Machinery" refers to robots and automated equipment used in factories.

[0306] A "task list" refers to a list for displaying and managing generated tasks in a list format.

[0307] "Instruction" refers to instructing a machine to take specific actions based on the generated task.

[0308] A system for carrying out the present invention includes an application that is installed on a robot used in a factory. A specific embodiment of this system will be described below.

[0309] System configuration

[0310] The system consists of the following main components:

[0311] 1. Server: Centralizes and displays messages from multiple communication tools.

[0312] 2. Natural Language Processing (NLP) engine: Analyzes the content of messages and generates tasks based on the analysis results.

[0313] 3. Task Manager: Add generated tasks to the task list and manage them.

[0314] 4. Robot Controller: Instructs the robot to execute the generated tasks.

[0315] Hardware and software used

[0316] Hardware: Factory robots

[0317] Software: Python, spaCy (natural language processing library), TaskManager (task management system), RobotController (robot control system)

[0318] Processing flow

[0319] 1. Receiving a message: The server receives a message from a communication tool such as email, a communication tool, a chat, or a messenger app.

[0320] 2. Message centralization: Received messages are aggregated and displayed on one platform.

[0321] 3. Message analysis: The content of the message is analyzed using a natural language processing engine (spaCy) to extract meaning.

[0322] 4. Task generation: Generate specific tasks based on the analysis results. For example, if a message saying "Perform maintenance on line 1" is received, generate a maintenance task.

[0323] 5. Add to task list: Add the generated task to the task list and manage it.

[0324] 6. Instruct the robot: Instruct the robot to perform the tasks added to the task list.

[0325] Specific examples

[0326] For example, if the following message is received:

[0327] Message: "Maintenance on Line 1"

[0328] Program processing: Parses messages, generates "maintenance" tasks, and issues instructions to the robot.

[0329] Example prompts to input to a generative AI model:

[0330] "A message has been received to perform maintenance on Line 1. Based on this message, please generate a maintenance task and issue instructions to the robot."

[0331] In this way, tasks can be automatically generated based on messages within the factory and instructions can be issued to robots, improving work efficiency within the factory and reducing work delays and errors.

[0332] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0333] Step 1:

[0334] The server receives messages from communication tools such as email, communication tools, chat, and messenger apps. The input is the message sent from each communication tool, and the output is the centralized message data. Specifically, the server retrieves the messages using the API of each communication tool and stores them in a database.

[0335] Step 2:

[0336] The server aggregates and displays received messages on a single platform. The input is unified message data, and the output is a unified message interface that users can view. Specifically, it retrieves messages from the database and displays them on a web interface or application screen.

[0337] Step 3:

[0338] The server uses a natural language processing engine (spaCy) to analyze the message content and extract meaning. The input is the unified message data, and the output is the analyzed message content. Specifically, it loads the spaCy model and analyzes the message text to extract important keywords and phrases.

[0339] Step 4:

[0340] The server generates specific tasks based on the analysis results. The input is the analyzed message content, and the output is the generated task. Specifically, the server determines the type and details of the task based on the extracted keywords and phrases, and generates a task object.

[0341] Step 5:

[0342] The server adds the created tasks to a task list and manages them. The input is the created task, and the output is the updated task list. Specifically, it saves the task object to a database and updates the task list.

[0343] Step 6:

[0344] The server instructs the robot to execute the tasks added to the task list. The input is the updated task list, and the output is the robot's execution status. Specifically, the server sends the generated tasks to the robot via the robot controller and monitors the execution status.

[0345] In this way, tasks can be automatically generated based on messages within the factory and instructions can be issued to robots, improving work efficiency within the factory and reducing work delays and errors.

[0346] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0347] "Example 1"

[0348] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing messages into tasks based on their content, and an emotion engine for recognizing a user's emotion. The emotion engine recognizes a user's emotion from, for example, the user's tone of voice, facial expression, or message context.

[0349] "Example 2"

[0350] In one embodiment, the emotion engine system provides the ability to adjust the priority of messages based on the user's emotions. For example, if a user receives a message that expresses anger or frustration, that message may be determined to have a high priority and displayed to the user for immediate attention.

[0351] "Example 3"

[0352] In one embodiment, the emotion engine system adjusts task priorities based on the user's emotions. For example, if a user receives a message expressing joy or excitement, tasks related to that message are deemed to have a high priority and are displayed at the top of the task list.

[0353] The processing flow of each embodiment will be described below.

[0354] "Example 1"

[0355] Step 1: The system consolidates and displays messages from multiple contact tools.

[0356] Step 2: The system then classifies the message as work or personal based on its content.

[0357] Step 3: The system then tasks and manages the messages based on their content.

[0358] Step 4: Finally, the emotion engine recognizes the user's emotions from their tone of voice, facial expressions, and the context of the message.

[0359] "Example 2"

[0360] Step 1: The system adjusts the priority of messages based on the user's sentiment.

[0361] Step 2: For example, if a user receives a message that expresses anger or frustration, the message is deemed to have a high priority.

[0362] Step 3: As a result, the message is displayed so that it immediately catches the user's eye.

[0363] Step 1: The system adjusts task priorities based on the user's emotions.

[0364] Step 2: For example, if a user receives a message expressing joy or excitement, the task associated with that message is deemed to have high priority.

[0365] Step 3: As a result, the task will appear at the top of your task list.

[0366] Example 1

[0367] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0368] In today's business environment, users use multiple communication methods (email, communication tools, chat, messaging applications, etc.) and must check messages from each tool separately. This makes checking and managing messages cumbersome and increases the risk of missing important messages. Users are also required to distinguish between work and personal messages based on their content and manage them as tasks, but doing this manually takes time and effort. It is also important to recognize users' emotions and provide appropriate feedback, but current systems do not adequately address this issue.

[0369] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0370] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for recognizing a user's emotions, and a means for providing feedback based on the recognized emotions. This allows a user to unify and manage messages from multiple communication means, efficiently manage tasks without missing important messages, and improve user satisfaction and productivity by recognizing the user's emotions and providing appropriate feedback.

[0371] "Communication means" refers to multiple means used by users to send and receive messages, such as email, communication tools, chat, messaging applications, etc.

[0372] "Means for centralized display" refers to a means for displaying messages collected from multiple communication means in a list on a single interface.

[0373] "Means for sorting" refers to means for analyzing the content of messages and classifying them as work-related or personal-related.

[0374] The "tasking and management means" refers to a means for generating a task based on the content of a message and managing the task.

[0375] "Means for recognizing emotions" refers to means for recognizing a user's emotions from the user's tone of voice, facial expression, message context, etc.

[0376] The "means for providing feedback" refers to a means for providing appropriate advice or information to a user based on the user's recognized emotions.

[0377] This invention is a system that displays messages from multiple communication methods in a unified manner, classifies messages as work-related or personal based on their content, and manages them as tasks. It also includes a function to recognize the user's emotions and provide appropriate feedback.

[0378] Message collection and centralized display

[0379] The server collects messages from multiple communication methods used by the user (e.g., email, communication tools, chat, messaging applications). To do this, it uses the API for each communication method. For example, it uses a mail API to collect emails, a communication API to collect communication tools, a chat API to collect chats, and a messaging API to collect messaging applications. The collected messages are stored in a database, and the terminal displays an interface that the user can access, displaying the collected messages in a list format.

[0380] Message Classification

[0381] The server analyzes the content of the collected messages and classifies them as work-related or personal. This is done using natural language processing (NLP) technology. Specifically, it uses a natural language processing API to analyze the content of the messages and determine the category. For example, a message saying "Please prepare materials for tomorrow's meeting" would be classified as work-related, while a message saying "Let's go see a movie this weekend" would be classified as personal.

[0382] Task Creation and Management

[0383] The server generates tasks based on the classified messages and adds them to the task management tool. Task management uses a task management API. For example, a new task can be generated using the task management tool's API and added to the user's task management board. The user can then view the generated tasks and manage their progress.

[0384] Emotional Recognition and Feedback

[0385] The server uses an emotion engine to recognize the user's emotions. This is done using voice recognition technology and facial recognition technology. For example, a voice recognition API is used to analyze the user's tone of voice, and a facial recognition API is used to analyze the user's facial expressions. The server then provides appropriate feedback to the user based on the recognized emotions. For example, if a user sends a message saying, "I'm very tired today," the emotion engine will recognize the user's fatigue and provide advice on how to relax.

[0386] Specific examples

[0387] When a user uses email, communication tools, chat, or messaging applications, the server collects messages from these tools and displays them in a single interface. Users can see at a glance which tool the message came from. They can also distinguish between work and personal messages based on their content and create and manage tasks. Furthermore, by recognizing users' emotions and providing appropriate feedback, user satisfaction and productivity can be improved.

[0388] Prompt Sentence Examples

[0389] "Collect messages from email, communication tools, chat, and messaging applications and display them in a single interface. Classify messages as work or personal based on their content, generate and manage tasks, and recognize user emotions and provide appropriate feedback."

[0390] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0391] Step 1:

[0392] The server collects messages from multiple communication means.

[0393] Input: Message data from email, communication tools, chat, and messaging application APIs.

[0394] Specific operation: The server calls the API of each communication method to obtain new messages. For example, it uses an email API to obtain new emails and a communication tool API to obtain new chat messages.

[0395] Output: Collected message data is stored in a database.

[0396] Step 2:

[0397] The server retrieves the messages from the database for centralized display of the collected messages.

[0398] Input: Message data stored in the database.

[0399] Specific operation: The server retrieves the collected messages from the database and sends them to the interface that the user accesses.

[0400] Output: A centralized list of messages that are displayed on your terminal.

[0401] Step 3:

[0402] The server analyzes the content of the collected messages and distinguishes between business and personal messages.

[0403] Input: Message data retrieved from the database.

[0404] Specific operation: The server calls a natural language processing API to analyze the content of the message and determine the category. For example, a message saying "Please prepare the materials for tomorrow's meeting" will be classified as work, and a message saying "Let's go see a movie this weekend" will be classified as personal.

[0405] Output: Classified message data.

[0406] Step 4:

[0407] The server generates tasks based on the classified messages and adds them to the task management tool.

[0408] Input: Classified message data.

[0409] Specific operation: The server calls the task management API to create a new task and adds it to the user's task management board. For example, it uses the API of a task management tool to create a task called "Prepare materials for the meeting" and adds it to the user's task management board.

[0410] Output: The new task added to the task management tool.

[0411] Step 5:

[0412] The server uses an emotion engine to recognize the user's emotions.

[0413] Input: The user's tone of voice, facial expression, and message context.

[0414] Specific operation: The server calls the speech recognition API to analyze the user's tone of voice and the face recognition API to analyze the user's facial expression. For example, if you send a message saying "I'm very tired today," the emotion engine will recognize the user's tiredness.

[0415] Output: Recognized user emotion data.

[0416] Step 6:

[0417] The server provides appropriate feedback to the user based on the recognized emotion.

[0418] Input: Recognized user emotion data.

[0419] Specific operation: The server provides the user with advice and information to help them relax based on the recognized emotions. For example, if the server recognizes that the user is tired, it will suggest relaxing music or a break.

[0420] Output: Feedback provided to the user.

[0421] (Application example 1)

[0422] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0423] Conventional factory robots rarely have the ability to consolidate and display messages from multiple communication tools, distinguish between work and personal messages based on their content, or turn messages into tasks and manage them based on their content. Furthermore, they lack the ability to recognize the emotions of factory staff and prompt appropriate responses, making it difficult to improve communication efficiency or respond appropriately based on staff emotions.

[0424] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means including an emotion engine for recognizing the user's emotions, and a means for recognizing the emotions of staff in the factory and encouraging appropriate responses. This makes it possible to improve the efficiency of communication in the factory and to respond appropriately based on the emotions of staff.

[0425] "Multiple communication tools" refers to different types of communication methods such as email, communication tools, chat, and messenger apps.

[0426] "A means for centralized display" refers to the ability to integrate and display messages from multiple communication tools on a single interface.

[0427] "Sort-out" refers to the ability to automatically classify messages as work-related or personal based on their content.

[0428] "Means for creating and managing tasks" refers to the function of generating tasks based on the contents of messages and managing those tasks.

[0429] An "emotion engine" refers to technology that recognizes a user's emotions from the user's tone of voice, facial expression, message context, etc.

[0430] "Means to recognize the emotions of staff in the factory and encourage appropriate responses" refers to a function that uses an emotion engine to recognize the emotions of staff working in the factory and take appropriate responses based on those emotions.

[0431] The system for carrying out the present invention is configured as an application installed on a factory robot. A specific embodiment of the system will be described below.

[0432] System configuration

[0433] The server includes a means for unifying and displaying messages from a plurality of communication tools, a means for classifying messages as work or personal based on their contents, a means for tasking and managing messages based on their contents, a means including an emotion engine for recognizing the emotions of users, and a means for recognizing the emotions of staff in the factory and encouraging them to respond appropriately.

[0434] Hardware and software used

[0435] Hardware: Factory robots (e.g. KUKA, Fanuc)

[0436] Software: Python, smtplib, imaplib, email library, TextBlob, json

[0437] Data processing and calculation

[0438] The server performs the following data processing and calculations.

[0439] 1. Get email:

[0440] The server retrieves emails from the IMAP server, using the imaplib library.

[0441] 2. Message analysis:

[0442] The server analyzes the content of the emails and performs sentiment analysis using the TextBlob library.

[0443] 3. Single message:

[0444] The server centralizes the analysis results and displays them in a unified interface, using the json library.

[0445] 4. Emotion Engine:

[0446] The server uses emotion recognition technology to recognize the user's emotions from the user's tone of voice, facial expressions, and message context.

[0447] 5. Promoting appropriate responses:

[0448] The server recognizes the emotions of the staff in the factory and responds appropriately based on those emotions.

[0449] Specific examples

[0450] For example, if a factory worker tells a robot, "The production line is running late today," the robot will register the message as a task and notify the manager. If the robot recognizes that a worker is tired, it will send a message encouraging them to take a break.

[0451] Prompt Sentence Examples

[0452] Design a system in which, when a staff member speaks to a robot in a factory, the robot will recognize the staff member's emotions and respond appropriately. For example, if a staff member says, "The production line is behind schedule today," the robot will register that message as a task and notify the manager.

[0453] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0454] Step 1:

[0455] The server retrieves emails from the IMAP server. Specifically, it uses the imaplib library to connect to the mail server and log in to the user's email account. It then searches through all emails in the inbox and retrieves the ID of each email. This gives a list containing the email's metadata (sender, subject, date and time, etc.).

[0456] Input: IMAP server connection information, user email account information

[0457] Output: List of email metadata

[0458] Step 2:

[0459] The server analyzes the content of the received emails and performs sentiment analysis. Specifically, it extracts the body of each email using the email library and performs sentiment analysis using the TextBlob library. As a result of the sentiment analysis, it obtains a sentiment score (positive, negative, neutral) for each email.

[0460] Input: List of email metadata

[0461] Output: Email data with sentiment scores

[0462] Step 3:

[0463] The server centralizes the analysis results and displays them on a unified interface. Specifically, it converts email data with sentiment scores into JSON format using the json library and displays it in a user-accessible interface that can be accessed through a web browser or a dedicated application.

[0464] Input: Email data with sentiment scores

[0465] Output: Display data on a unified interface

[0466] Step 4:

[0467] The server recognizes the user's emotions from the user's tone of voice, facial expressions, and message context. Specifically, emotion recognition technology is used to analyze audio and video data to identify the user's emotional state. This allows the user's emotional state to be understood in real time.

[0468] Input: Audio data, video data

[0469] Output: User's emotional state data

[0470] Step 5:

[0471] The server recognizes the emotions of factory staff and responds appropriately based on those emotions. Specifically, based on emotional state data, it sends messages encouraging staff to take a break if they feel stressed. It also notifies managers if important tasks are delayed.

[0472] Input: User's emotional state data

[0473] Output: Appropriate response message, notify administrator

[0474] Example 2

[0475] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0476] In today's business environment, there is a need to efficiently manage messages from multiple communication channels (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centrally display these messages, distinguish between work and personal messages based on their content, and adjust their priority based on their emotions. It is also important to organize and manage messages into tasks based on their content, but current systems are unable to provide these functions in an integrated manner. This leads to problems such as users missing important messages and task management becoming cumbersome.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0478] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as work-related or personal based on their content, a means for analyzing the content of the messages and adjusting the priority of the messages based on the emotions of the users, and a means for organizing and managing tasks based on the content of the messages. This allows the user to unify and manage messages from multiple communication means, and efficiently manage tasks without missing important messages.

[0479] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0480] A "message" is information such as text, images, audio, and video sent and received through a communication means.

[0481] "Unification" refers to the integration of messages from multiple communication channels and displaying and managing them in a single interface.

[0482] "Sorting" refers to automatically classifying messages as work-related or personal based on their content and source.

[0483] "Sentiment analysis" is the process of analyzing the content of a message and detecting the emotions (e.g., joy, anger, sadness, etc.) contained therein.

[0484] "Priority adjustment" refers to changing the order in which messages are displayed and the notification method based on the results of sentiment analysis.

[0485] "Tasking" refers to managing a message as a specific work item based on its content.

[0486] "Management" refers to tracking tasked work items, checking progress, and updating or correcting them as needed.

[0487] This invention is a system that unifies and displays messages from multiple communication means, classifies messages as work or personal based on their contents, adjusts the priority of messages based on emotions, and manages them as tasks based on their contents.

[0488] Hardware and software used

[0489] Hardware: Servers, terminals (PCs, smartphones)

[0490] Software: Message analysis engine, emotion engine, generative AI model

[0491] System Overview

[0492] Unified messaging

[0493] The server retrieves messages from multiple communication methods (email, communication tools, chat, messenger apps, etc.) and displays them in a unified manner, allowing users to view all messages in a single interface.

[0494] Message sorting

[0495] The server analyzes the sender address and content of received messages and automatically distinguishes between work-related and personal messages, specifically based on business email addresses and work-related keywords.

[0496] Message Priority Adjustment

[0497] The server uses an emotion engine to analyze the content of messages and detect the user's emotions (e.g., anger, dissatisfaction, etc.). Messages are prioritized based on the intensity of the emotions, and the terminal displays high-priority messages so that they are immediately visible to the user.

[0498] Tasking a message

[0499] The server manages messages by dividing them into tasks based on their content, and by the importance and urgency of the messages.

[0500] Specific examples

[0501] Message sorting

[0502] Example: If a user receives a message from "boss@company.com" with the content "Project progress report", the server will determine that the message is work-related. The server will recognize the address "boss@company.com" as a business email address and will determine that the keyword "project progress report" is work-related.

[0503] Message Priority Adjustment

[0504] Example: If a user receives the message "This project is not progressing at all!", the emotion engine will detect "anger" and mark the message as high priority. The device will then display the message in a popup notification.

[0505] Prompt Sentence Examples

[0506] Prompt: "Analyze the following message and determine whether it is work-related or personal. Also, prioritize the message based on the user's sentiment. Message: 'This project is not progressing at all!'"

[0507] In this way, the system can automatically sort messages based on their content, adjust message priorities based on the user's emotions, and efficiently manage tasks.

[0508] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0509] Step 1:

[0510] Receiving messages

[0511] The server retrieves new messages from multiple communication methods (email, communication tools, chat, messenger apps, etc.).

[0512] Input: Message data from each communication method.

[0513] Output: A list of retrieved messages.

[0514] Specific operation: The server calls the API of each communication method and polls for new messages.

[0515] Step 2:

[0516] Verifying the sender address of a message

[0517] The server checks the sender address of the received message.

[0518] Input: A list of received messages.

[0519] Output: The source address of each message.

[0520] What happens next: The server parses the message header information and extracts the sender address.

[0521] Step 3:

[0522] Message content analysis

[0523] The server analyzes the body of the message and checks whether it contains business-related keywords.

[0524] Input: The body of each message.

[0525] Output: Analysis results for each message (presence or absence of keywords).

[0526] What happens: The server uses a natural language processing (NLP) engine to extract keywords from the message body.

[0527] Step 4:

[0528] Message Classification

[0529] The server automatically determines whether a message is work-related or personal based on the sender's address and content.

[0530] Input: The source address and parsing results for each message.

[0531] Output: Classification of each message (work-related or personal).

[0532] What it does: The server uses predefined rules and machine learning models to classify messages as "work" or "personal."

[0533] Step 5:

[0534] Message sentiment analysis

[0535] The server analyzes the content of the received message using an emotion engine.

[0536] Input: The body of each message.

[0537] Output: Sentiment score for each message.

[0538] Specific operation: The server calls the sentiment analysis API and obtains the sentiment score of the message.

[0539] Step 6:

[0540] Emotional intensity assessment

[0541] The server evaluates the intensity of the emotion based on the emotion score obtained from the emotion engine.

[0542] Input: Sentiment score for each message.

[0543] Output: Sentiment intensity (high, medium, low) for each message.

[0544] Specific behavior: The server compares the emotion score with a threshold and classifies the emotion intensity as "high," "medium," or "low."

[0545] Step 7:

[0546] Message Priority Settings

[0547] The server prioritizes messages based on the intensity of the emotion.

[0548] Input: Sentiment intensity of each message.

[0549] Output: The priority of each message (high, medium, low).

[0550] Specific behavior: The server sets the priority of the message as "high," "medium," or "low" depending on the intensity of the emotion.

[0551] Step 8:

[0552] Displaying messages

[0553] The terminal displays high priority messages in a way that makes them immediately visible to the user.

[0554] Input: The priority of each message.

[0555] Output: The message that is displayed to the user.

[0556] What it does: Your device uses notifications to pop up high-priority messages.

[0557] Step 9:

[0558] Tasking a message

[0559] The server tasks and manages messages based on their content.

[0560] Input: The body and priority of each message.

[0561] Output: Messages managed as tasks.

[0562] Specific operation: The server analyzes the contents of the message and registers it in the task management system.

[0563] In this way, the system can automatically sort messages based on their content, adjust message priorities based on the user's emotions, and efficiently manage tasks.

[0564] (Application example 2)

[0565] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0566] In today's brick-and-mortar stores, it is difficult for staff to efficiently manage messages from multiple communication tools. They are also required to distinguish between work and personal messages based on their content, and adjust priorities based on their emotions. However, there is no automated system for this. This puts important messages at risk of being overlooked, potentially resulting in poor customer service.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0568] In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing tasks based on their content, a means for analyzing the sentiment of messages and adjusting their priorities, and a means for enabling store staff to efficiently manage messages, thereby enabling the staff to respond promptly and appropriately without missing important messages.

[0569] "Contact tools" are means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0570] A "message" is information sent or received in the form of text, images, audio, video, etc.

[0571] "Centralization" refers to consolidating data from multiple sources in one place and managing it in a unified manner.

[0572] "Display" refers to the visual presentation of data or information to a user.

[0573] "Segregation" refers to the classification of data or information based on specific criteria.

[0574] "Work" refers to activities and tasks related to a job or function.

[0575] "Private" refers to personal activities and tasks.

[0576] "Tasking" refers to converting the content of a message into specific tasks or action items.

[0577] "Management" refers to efficiently organizing data and information and manipulating and controlling it as needed.

[0578] "Emotion" refers to the user's psychological state that can be read from the content of the message.

[0579] "Analysis" refers to the detailed investigation of data or information to understand its meaning and structure.

[0580] "Priority" is an indicator of the importance and urgency of a task or message.

[0581] A "physical store" refers to a sales or service establishment that exists in a physical location and can be visited in person by customers.

[0582] "Staff" refers to employees and personnel working in physical stores.

[0583] "Efficient" means achieving maximum results with minimum time and effort.

[0584] The following system configuration will be described as an embodiment of the present invention.

[0585] System Configuration

[0586] Hardware

[0587] Server: A central processing unit for unifying and analyzing messages.

[0588] Devices: Devices used by staff, such as smartphones or smart glasses.

[0589] Network: The communications infrastructure that connects servers and devices.

[0590] software

[0591] Message Management System: A program that centralizes, categorizes, tasks, analyzes sentiment, and prioritizes messages.

[0592] Sentiment Analysis Engine: A sentiment analysis library such as TextBlob.

[0593] User interface: The application that allows staff to view and respond to messages.

[0594] Processing flow

[0595] 1. Single message

[0596] The server collects and centralizes messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.).

[0597] This allows staff to view all messages in one interface.

[0598] 2. Message Classification

[0599] The server analyzes the message's origin and content and automatically determines whether it is work-related or personal.

[0600] For example, if keywords such as "customer," "order," "complaint," and "inquiry" are included, they are classified as work-related.

[0601] 3. Tasking and Management

[0602] The server generates and manages tasks based on the classified messages.

[0603] Tasks are prioritized based on the importance and urgency of the message.

[0604] 4. Sentiment analysis and priority adjustment

[0605] The server analyzes the sentiment of the message using a sentiment analysis engine such as TextBlob.

[0606] Based on the analysis, messages that express anger or frustration are set as a high priority.

[0607] For example, a message that reads "Complaint from a customer. Very dissatisfied." will be displayed as a high priority.

[0608] 5. User Interface

[0609] Through the application installed on the device, staff can check messages according to priority and respond quickly.

[0610] Specific examples

[0611] For example, if a store staff member is using a smartphone, the following prompt sentence will be generated:

[0612] Analyze messages from customers, determine whether they are work-related or personal, and adjust the priority using the sentiment engine. For example, a message saying "This is a complaint from a customer. They are very unhappy" is considered work-related and has high priority.

[0613] Based on this prompt, the server analyzes the message and assigns appropriate classification and priority to it, allowing staff to respond quickly and appropriately without missing important messages.

[0614] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0615] Step 1:

[0616] The server collects and centralizes messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.).

[0617] Input: Message data from each contact tool.

[0618] Data processing: Convert message data into a unified format and store it in a database.

[0619] Output: Unified message data.

[0620] Specific operation: Uses API to retrieve messages from each communication tool, converts them into a unified format, and saves them in a database.

[0621] Step 2:

[0622] The server analyzes the message's origin and content and automatically determines whether it is work-related or personal.

[0623] Input: Unified message data.

[0624] Data processing: Analyze message content and classify it based on keywords.

[0625] Output: Classified message data (work-related or personal).

[0626] Specific operation: Analyzes message content using natural language processing (NLP) technology to detect keywords such as "customer," "order," "complaint," and "inquiry."

[0627] Step 3:

[0628] The server generates and manages tasks based on the classified messages.

[0629] Input: Classified message data.

[0630] Data transformation: Convert messages into tasks and prioritize them based on importance and urgency.

[0631] Output: Tasked message data.

[0632] Specific operation: Analyze the message content, register it as a task in the task management system, and set the priority.

[0633] Step 4:

[0634] The server analyzes the sentiment of the message using a sentiment analysis engine such as TextBlob.

[0635] Input: Tasked message data.

[0636] Data processing: Analyze the sentiment of the message using a sentiment analysis engine and calculate the polarity.

[0637] Output: Sentiment analysis result (polarity value).

[0638] What it does: It uses the TextBlob library to analyze the sentiment of the message and get the polarity value.

[0639] Step 5:

[0640] Based on the analysis, the server assigns a high priority to messages that express anger or frustration.

[0641] Input: Sentiment analysis result (polarity value).

[0642] Data processing: Set priorities based on polarity values.

[0643] Output: Prioritized task data.

[0644] Specific behavior: If the polarity value is below a certain threshold, set the priority of the task to high.

[0645] Step 6:

[0646] Through an application installed on the device, staff can check messages according to priority and respond quickly.

[0647] Input: Prioritized task data.

[0648] Data manipulation: View tasks based on priority.

[0649] Output: A list of tasks that can be viewed by staff.

[0650] Specific operation: Staff are notified of priority tasks via smartphone or smart glasses applications, encouraging them to take action.

[0651] Example 3

[0652] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0653] In today's business environment, it is common to use multiple communication methods (e.g., email, communication tools, chat, and messenger applications). However, it is difficult to centrally manage messages from these communication methods, separate business and personal messages, and automatically generate and manage tasks based on the content of messages. Furthermore, the lack of a function to adjust task priorities based on the user's emotions makes efficient task management problematic.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0655] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as business or personal based on their content, a means for analyzing the content of the messages and automatically generating tasks, a means for analyzing the user's emotions and adjusting the priority of the tasks, and a means for adding the generated tasks to a task list and displaying them. This makes it possible to unify the management of messages from multiple communication means, separate business and personal messages, and further adjust the priority of tasks based on the user's emotions.

[0656] "Communication means" refers to means by which users send and receive messages, such as email, communication tools, chat, and messenger applications.

[0657] "Unification" refers to integrating messages from multiple communication methods and displaying them in a single interface.

[0658] "Work" refers to the activities of a user related to their job or work.

[0659] "Personal" refers to activities undertaken by a user for personal purposes.

[0660] "Analyzing the content of a message" refers to using natural language processing techniques to analyze the text of a message and understand its meaning and intent.

[0661] "Automatically generating tasks" refers to automatically listing specific tasks or actions that the user should perform based on the content of the message.

[0662] "Analyzing the user's emotions" refers to using emotion analysis technology to determine the user's emotional state from the content and expression of the message.

[0663] "Adjusting task priority" refers to setting the importance and urgency of the generated task based on the user's feelings and other factors.

[0664] A "task list" refers to a list that displays a list of tasks that a user needs to perform.

[0665] "Display" refers to visually showing the generated task or message on the user's terminal.

[0666] This invention is a system that consolidates and displays messages from multiple communication means, classifies messages as business or personal based on their content, analyzes the content of the messages to automatically generate tasks, and analyzes the user's emotions to adjust the priority of the tasks.

[0667] System configuration

[0668] Hardware

[0669] Server: Receives messages, analyzes them, generates tasks, analyzes emotions, and manages tasks.

[0670] Device: The task list is displayed on the device used by the user (PC, smartphone, tablet, etc.).

[0671] software

[0672] Natural language processing engine: Used to analyze the content of messages. Specifically, it uses Google Cloud Natural Language API and Microsoft Azure (registered trademark) Text Analytics.

[0673] Sentiment analysis engine: Used to analyze user emotions. Specifically, it uses IBM Watson Tone Analyzer and Amazon Comprehend.

[0674] Database: Used to store generated tasks and messages.

[0675] System Operation

[0676] Receiving messages

[0677] The server receives messages sent by users, which may be sent via email, communication tools, chat, messenger applications, or the like.

[0678] Message content analysis

[0679] The server uses a natural language processing engine to analyze the content of the received message, for example, by using the Google Cloud Natural Language API to parse the message and extract action phrases.

[0680] Creating a task

[0681] The server generates tasks based on the action phrases extracted by the NLP engine. For example, if the phrase "prepare for a meeting" is extracted, the server generates a task called "prepare for a meeting."

[0682] Emotion Analysis

[0683] The server uses a sentiment analysis engine to analyze the user's emotions from the message content. For example, it uses IBM Watson Tone Analyzer to analyze the emotions of the message and determine that the user is excited.

[0684] Task Priority Setting

[0685] The server prioritizes tasks based on the results of the sentiment analysis engine. For example, if the user is excited, the associated task will be deemed to have a high priority.

[0686] Adding and viewing to the task list

[0687] The server saves the created task in a database and adds it to a task list, and the terminal updates the user's task list to display the new task.

[0688] Specific examples

[0689] Consider a case where a user sends a message saying, "I have to prepare for a meeting next week." When this message is received, the server uses the Google Cloud Natural Language API to extract the phrase "prepare for a meeting" and generates a task. It then analyzes the user's emotions using IBM Watson Tone Analyzer. If the user is determined to be excited, the task is given high priority.

[0690] Example prompts to input to a generative AI model:

[0691] I received a message saying "I need to prepare for a meeting next week." Generate tasks based on this message and prioritize them based on the user's sentiment.

[0692] In this way, the user can manage tasks appropriately according to the contents of the message. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0693] Step 1: Receiving a message

[0694] The server receives messages sent by users. These messages are sent via email, communication tools, chat, messenger applications, etc. The input is the message from the user, and the output is the received message data.

[0695] Step 2: Parsing the message content

[0696] The server uses a natural language processing engine to analyze the content of received messages. Specifically, it uses the Google Cloud Natural Language API to analyze messages and extract action phrases. The input is the received message data, and the output is the extracted action phrase.

[0697] Step 3: Creating a task

[0698] The server generates tasks based on the action phrases extracted by the NLP engine. For example, if the phrase "prepare for a meeting" is extracted, the server generates a task called "prepare for a meeting." The input is the extracted action phrase, and the output is the generated task data.

[0699] Step 4: Sentiment Analysis

[0700] The server uses an emotion analysis engine to analyze the user's emotion from the message content. Specifically, it uses IBM Watson Tone Analyzer to analyze the emotion of the message and determine whether the user is excited. The input is the received message data, and the output is the analyzed emotion data.

[0701] Step 5: Prioritizing tasks

[0702] The server prioritizes tasks based on the results of the emotion analysis engine. For example, if a user is excited, the related tasks are determined to have a high priority. The inputs are the generated task data and the analyzed emotion data, and the output is the prioritized task data.

[0703] Step 6: Add and view in the task list

[0704] The server saves the generated tasks in a database and adds them to a task list. The terminal updates the user's task list and displays the new tasks. The input is the prioritized task data, and the output is the updated task list.

[0705] (Application example 3)

[0706] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0707] While conventional task management systems can generate tasks by analyzing message content, they lack the ability to adjust task priorities based on the user's emotions. This makes it difficult to flexibly manage tasks based on the user's emotions and circumstances, which can hinder efficient work execution. Furthermore, the inability to centrally manage messages from multiple communication methods leads to problems such as information dispersion and cumbersome management.

[0708] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0709] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as business or personal based on their content, a means for organizing and managing tasks based on their content, and a means for analyzing the emotions in the messages and adjusting the priority of the tasks. This enables flexible task management according to the user's emotions and situation, and enables efficient work execution.

[0710] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0711] "Centralization" refers to integrating data from multiple different sources and consolidating it in one place.

[0712] "Display" refers to the visual presentation of data or information to a user.

[0713] "Segregation" refers to the classification of data or information based on specific criteria.

[0714] "Work" refers to the activities and tasks related to a job or occupation.

[0715] "Personal" refers to an individual's private activities and tasks.

[0716] "Tasking" refers to generating specific work items based on the content of a message.

[0717] "Management" refers to the process for organizing, tracking, and completing the tasks that are generated.

[0718] "Emotion" refers to the psychological state or feeling that a user expresses through a message.

[0719] "Analysis" refers to the detailed investigation of data or information to understand its meaning and structure.

[0720] "Priority" refers to the criteria for determining the order of tasks or work based on their importance or urgency.

[0721] "Adjustment" refers to changing a setting or state based on specific criteria or conditions.

[0722] The system for implementing this invention consolidates and displays messages from multiple communication means, classifies messages as business or personal based on their content, and automatically generates and manages tasks based on the message content. It also has the function of analyzing the sentiment of messages and adjusting task priorities.

[0723] Hardware and software used

[0724] Hardware: Factory robots, servers, user devices (smartphones, tablets, PCs, etc.)

[0725] Software: Python, TextBlob (sentiment analysis library), messaging API (email, chat tools, etc.)

[0726] Data processing and calculation

[0727] Centralized message display

[0728] The server collects messages from multiple communication methods, consolidates them, and displays them on the user's device, allowing the user to view all messages from a single interface.

[0729] Message sorting

[0730] The server analyzes the content of messages and automatically classifies them as business-related or personal. For example, if a message contains the phrase "preparing for a meeting," it will be classified as business-related.

[0731] Automated task creation and management

[0732] The server automatically generates a task based on the message content and adds it to the task list. For example, if a message saying "Prepare for a meeting" is received, a task called "Prepare for a meeting" is generated based on the message content.

[0733] Sentiment analysis and priority adjustment

[0734] The server uses TextBlob to analyze the sentiment of the message and determine whether it is positive or negative. Based on the results of the sentiment analysis, the priority of the generated task is dynamically adjusted. For example, if a user sends a positive message such as "I'm so excited about today!", the priority of the task related to that message is set high.

[0735] Specific examples

[0736] Example message

[0737] Message: "Getting ready for a meeting. I'm so excited for today!"

[0738] Generated task: "Prepare for meeting"

[0739] Priority: High

[0740] Prompt Sentence Examples

[0741] Message: "Getting ready for a meeting. I'm so excited for today!"

[0742] Generated task: "Prepare for meeting"

[0743] Priority: High

[0744] In this way, the server analyzes messages, automatically generates tasks, and sets priorities, enabling flexible task management according to the user's emotions and circumstances, thereby enabling efficient work execution.

[0745] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0746] Step 1:

[0747] The server collects messages from multiple communication methods (email, chat tools, etc.).

[0748] Input: Message data from each communication method

[0749] Output: Unified message list

[0750] Specific operation: The server retrieves messages using the API of each communication method and combines them into a single list.

[0751] Step 2:

[0752] The server analyzes the collected messages and distinguishes between business-related and personal messages.

[0753] Input: Centralized message list

[0754] Output: Sorted message list (work-related, personal)

[0755] What it does: The server uses natural language processing (NLP) techniques to analyze the content of messages and classify them as business-related or personal based on specific keywords and phrases.

[0756] Step 3:

[0757] The server automatically generates tasks from business-related messages and adds them to a task list.

[0758] Input: Work-related message

[0759] Output: Generated task list

[0760] Specific operation: The server analyzes work-related messages, extracts action phrases, generates tasks, and adds them to the task list.

[0761] Step 4:

[0762] The server analyzes the sentiment of the message and adjusts the priority of the task.

[0763] Input: Work-related messages, generated task lists

[0764] Output: Priority-adjusted task list

[0765] Specific behavior: The server analyzes the sentiment of the message using a sentiment analysis library such as TextBlob, and increases the priority of the task if the sentiment is positive, and decreases the priority if the sentiment is negative.

[0766] Step 5:

[0767] The server displays the task list with adjusted priorities on the user terminal.

[0768] Input: Priority-adjusted task list

[0769] Output: Task list displayed on the user's terminal

[0770] Specific operation: The server sends the task list to the user's terminal and displays it so that the user can visually check it.

[0771] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0772] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0774] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0775] [Second embodiment]

[0776] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0777] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0778] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0779] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0780] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0781] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0782] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0783] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0784] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0785] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0786] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0787] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0788] "Example 1"

[0789] The system of the present invention has a means for unifying and displaying messages from multiple communication tools. Specifically, it collects messages from email, communication tools, chats, messenger apps, etc. and displays them in a list on a single interface. This allows users to see at a glance which tool the message came from.

[0790] "Example 2"

[0791] The system of the present invention also has a means for distinguishing between work and personal messages based on their content. Specifically, it analyzes the sender and content of a message to automatically determine whether it is work-related or personal. For example, if the sender of a message is a business email address or if the message content contains work-related keywords, the message is determined to be work-related.

[0792] "Example 3"

[0793] Furthermore, the system of the present invention has a means for creating and managing tasks based on the content of messages. Specifically, the system analyzes the content of messages and automatically generates tasks based on the content. For example, if a message contains a phrase indicating an action, such as "prepare for a meeting," a task is generated based on that phrase and added to the task list. This allows users to manage tasks appropriately according to the content of the message.

[0794] The processing flow of each embodiment will be described below.

[0795] "Example 1"

[0796] Step 1: The system collects messages from emails, communication tools, chats, messenger apps, etc.

[0797] Step 2: List all collected messages in one interface.

[0798] Step 3: Users can see at a glance which tool the message came from by looking at the list of messages.

[0799] "Example 2"

[0800] Step 1: The system analyzes the message's origin and content.

[0801] Step 2: Based on the analysis, it automatically determines whether the message is work-related or personal.

[0802] Step 3: For example, if the message was sent from a work email address or contains work-related keywords, the message is determined to be work-related.

[0803] "Example 3"

[0804] Step 1: The system analyzes the content of the message.

[0805] Step 2: Based on the analysis results, tasks are automatically generated based on the messages.

[0806] Step 3: If the message content contains an action phrase, such as "prepare for a meeting," generate a task based on that phrase and add it to your task list.

[0807] Step 4: This allows the user to manage tasks appropriately according to the content of the message.

[0808] Example 1

[0809] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0810] In today's business environment, it is common for users to receive messages using multiple communication methods. However, this can make message management cumbersome and increases the risk of missing important messages. It is also difficult to distinguish between work and personal messages based on their content, and to manage them as tasks.

[0811] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0812] In this invention, the server includes means for collecting messages from multiple communication means, means for unifying the collected messages, and means for saving the unified messages, which allows the user to check messages from multiple communication means on a single interface and manage them efficiently without missing important messages.

[0813] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0814] "Means for collecting messages" is a function for acquiring new messages from multiple communication means.

[0815] "Means for unifying messages" refers to a function for converting collected messages into a common data format and managing them in a unified manner.

[0816] The "means for storing messages" is a function for storing the unified messages in a storage device such as a database.

[0817] The "means for displaying messages" is a function for displaying saved messages on the interface so that the user can view them.

[0818] The "means for manipulating messages" is a function that allows a user to click on a displayed message to view details or to send a reply.

[0819] The "means for distinguishing between work and personal matters" is a function for determining whether a message is work-related or personal-related based on the content of the message.

[0820] The "means for dividing and managing messages into tasks" is a function for classifying messages into tasks based on their content and managing them according to their importance and urgency.

[0821] MODE FOR CARRYING OUT THE INVENTION

[0822] This invention is a system that unifies and displays messages from multiple communication means, classifies messages as work or personal based on their content, and manages them as tasks. A specific embodiment of this system is described below.

[0823] Server Roles

[0824] The server is responsible for collecting messages from multiple communication methods. Specifically, it obtains new messages using APIs from email servers, communication tools (e.g., Slack, Microsoft Teams), chat apps (e.g., WhatsApp, LINE), messenger apps (e.g., Facebook Messenger), etc. The server centralizes these messages and converts them into a common data format. The converted messages are then stored in a relational database such as MySQL or PostgreSQL.

[0825] Device Role

[0826] The terminal provides an interface for users to view and manipulate messages. Specifically, it displays a list of messages retrieved from the server through a web or mobile application. For example, a front end can be built using React or Vue.js, allowing users to visually check messages. Messages are color-coded by tool: blue for email, green for communication tools, and red for chat apps.

[0827] User operations

[0828] Users can use the device's interface to interact with messages. Specifically, they can click on a specific message to view details or send a reply. Users can also use the filter function to display only messages from specific communication channels. Furthermore, based on the content of the message, users can classify messages as work or personal and manage them as tasks. Tasks are created based on the importance and urgency of the message.

[0829] Specific examples

[0830] For example, consider a situation where a user receives messages from multiple communication channels while at work. By using this system, the user can view messages from email, Slack, WhatsApp, and Facebook Messenger in a single interface. This allows the user to see at a glance which communication channel the message came from and respond quickly.

[0831] Prompt Sentence Examples

[0832] Examples of prompts to be input to a generative AI model include:

[0833] "Please explain the system that consolidates and displays messages from multiple communication methods. Specifically, please specify what hardware and software are used and what data processing and calculations are performed. Please also provide specific examples."

[0834] Using this prompt, the generative AI model can generate a detailed description of the system.

[0835] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0836] Program processing flow

[0837] Step 1: Collecting messages

[0838] The server collects messages from multiple communication methods. As input, it uses API endpoints such as email servers, communication tools (e.g., Slack, Microsoft Teams), chat apps (e.g., WhatsApp, LINE), and messenger apps (e.g., Facebook Messenger). The server sends requests to these APIs to obtain new messages. As output, it obtains the message data obtained from each communication method.

[0839] Step 2: Unified messaging

[0840] The server centralizes the collected messages. It uses the message data collected in step 1 as input. Specifically, it converts the messages obtained from each communication method into a common data format. For example, an email message has fields such as "sender," "subject," "body," and "received date and time," while a Slack message has fields such as "sender," "channel," "message content," and "sent date and time." The server unifies these fields and converts them into a single data format. The output is the centralized message data.

[0841] Step 3: Save the message

[0842] The server stores the centralized messages in a database. The message data centralized in step 2 is used as input. Specifically, a relational database such as MySQL or PostgreSQL is used to store the message data in a table. For example, the message table contains columns such as "Message ID," "Sender," "Content," "Tool Name," and "Received Date and Time," which uniquely identify each message. The output is the message data stored in the database.

[0843] Step 4: Displaying messages

[0844] The terminal provides an interface for users to view messages. It uses message data obtained from the server as input. Specifically, it displays a list of messages through a web or mobile application. For example, a front end can be built using React or Vue.js to allow users to visually check messages. Messages are color-coded by tool, with emails displayed in blue, communication tools in green, and chat apps in red. The output is a list of messages that the user can view.

[0845] Step 5: Working with messages

[0846] The user manipulates messages using the interface on the device. The input is the displayed list of messages. Specifically, the user can click on a specific message to view details or send a reply. The user can also use the filter function to display only messages from specific communication channels. Furthermore, the user can sort messages into work or personal messages based on their content and manage them as tasks. The output is the result of the user's manipulation.

[0847] (Application example 1)

[0848] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0849] In logistics centers, messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.) are scattered, making it difficult for staff to quickly grasp and respond to information. This also reduces work efficiency and can lead to important communications being overlooked.

[0850] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0851] In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on their content, a means for integrating messages collected from multiple communication tools so that logistics center staff can understand them at a glance on their smartphones, and a means for unifying and displaying messages from multiple communication tools used within the logistics center. This allows logistics center staff to understand at a glance which tool a message came from and respond quickly.

[0852] "Multiple communication tools" refers to different types of messaging methods such as email, communication tools, chat, and messenger apps.

[0853] "Centralized display" refers to the ability to collect messages from multiple communication tools and display them in a single interface.

[0854] "Means of classification" refers to the ability to classify messages as work-related or personal based on their content.

[0855] "Means for creating and managing tasks" refers to the function of generating tasks based on the contents of messages and managing those tasks.

[0856] A "logistics center" refers to a facility where goods are stored, sorted, and delivered.

[0857] "Staff" refers to employees working at the logistics center.

[0858] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[0859] "Means of integration" refers to the ability to combine messages collected from multiple communication tools into one system.

[0860] "Responding quickly" means taking necessary action promptly after receiving a message.

[0861] The system for carrying out the present invention is for displaying messages from a plurality of communication tools in a unified manner in a distribution center. Specifically, the server includes the following means:

[0862] First, the server collects messages from multiple contact tools (email, communication tools, chat, messenger apps, etc.) by using Python's imaplib and email libraries to retrieve emails from the IMAP server, the slack_sdk library to collect Slack messages, and the WhatsApp API to collect WhatsApp messages.

[0863] The server then uses Flask to provide an API endpoint to centralize and display the collected messages, allowing staff at the distribution center to use their smartphones to view all messages in a single interface.

[0864] Additionally, the server distinguishes between work-related and personal-related messages based on their content, allowing staff to quickly understand and respond to important messages.

[0865] The server also generates and manages tasks based on the message content, allowing appropriate actions to be taken based on the importance and urgency of the message.

[0866] For example, when a logistics center staff member opens the "Logistics Message Integration App" on their smartphone, messages from email, Slack, and WhatsApp are displayed on one screen. This allows staff to see at a glance which tool the message came from and respond quickly.

[0867] An example prompt is:

[0868] When logistics center staff open the "Logistics Message Integration App" on their smartphones, messages from email, Slack, and WhatsApp are displayed on a single screen. This allows staff to see at a glance which tool the message came from and respond quickly.

[0869] In this way, the efficiency of message management in a logistics center can be greatly improved.

[0870] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0871] Step 1:

[0872] The server retrieves emails from an IMAP server. Specifically, it uses the imaplib and email libraries to log in to a specified email account and retrieve all emails from the inbox. The input is the mail server's authentication information, and the output is data including the email sender, subject, and body.

[0873] Step 2:

[0874] The server retrieves messages from Slack. Specifically, it uses the slack_sdk library to retrieve messages from a specified Slack channel. The input is a Slack API token and a channel ID, and the output is data including the user ID and message text.

[0875] Step 3:

[0876] The server retrieves messages from WhatsApp. Specifically, it uses the WhatsApp API to retrieve messages from a specified WhatsApp account. The input is a WhatsApp API token, and the output is data including the sender and message text.

[0877] Step 4:

[0878] The server centralizes all messages retrieved. Specifically, it consolidates messages retrieved from email, Slack, and WhatsApp into a single data structure. The input is message data retrieved from each communication tool, and the output is a consolidated message list.

[0879] Step 5:

[0880] The server provides the consolidated messages from an API endpoint using Flask. Specifically, we use the Flask framework to build an API that returns the consolidated message list in JSON format. The input is the consolidated message list, and the output is the JSON data provided by the API endpoint.

[0881] Step 6:

[0882] The user opens the "Logistics Message Integration App" on their smartphone. Specifically, they access the server's API endpoint using the smartphone's browser or a dedicated app. The input is the API endpoint URL, and the output is a list of integrated messages.

[0883] Step 7:

[0884] The user checks the displayed messages and responds as necessary. Specifically, the user checks the content of the messages and takes appropriate action based on their importance and urgency. The input is a list of consolidated messages, and the output is the user's action.

[0885] Example 2

[0886] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0887] In today's business environment, there is a need to efficiently manage messages from multiple communication methods (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centralize these messages, classify them as work or personal based on their content, and appropriately organize and manage them as tasks. In particular, there is a need to accurately analyze the sender and content of messages and quickly notify users of the classification results, but current systems cannot do this efficiently.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0889] In this invention, the server includes a means for centralizing and displaying messages from multiple communication means, a means for analyzing the sender address of the message, a means for analyzing the content of the message using a generative AI model and classifying it as work or personal, a means for tasking and managing the message based on the message content, and a means for notifying the user's terminal of the classification results. This makes it possible to efficiently centrally manage messages from multiple communication means, appropriately classify and task the messages based on their content, and promptly notify the user.

[0890] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[0891] "Source address" refers to information that indicates the email address or user ID of the sender of the message.

[0892] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze text data and understand and classify its content.

[0893] "Message content" refers to the main body of text data sent and received via communication means.

[0894] "Means for unifying and displaying" is a function for integrating and displaying messages obtained from multiple communication means in a single interface.

[0895] The "analysis means" is a function for analyzing the sender address and contents of a message and extracting its characteristics.

[0896] "Means for classification" is a function that automatically classifies messages as work-related or personal based on the analyzed content.

[0897] The "means for creating and managing tasks" is a function for generating tasks based on the sorted messages and managing those tasks.

[0898] The "means for notifying the classification result" is a function for notifying the user terminal of the results of the classified messages.

[0899] This invention relates to a system that consolidates and displays messages from multiple communication methods, analyzes the sender address and content of the messages, classifies them as work-related or personal, and then organizes and manages tasks based on the content. A specific embodiment of this system is shown below.

[0900] System configuration

[0901] The server is configured using the following hardware and software.

[0902] Hardware: A server machine with a powerful processor, sufficient memory, storage devices, and network interfaces.

[0903] Software: Mail servers (supporting the IMAP protocol), generative AI models (e.g., OpenAI's GPT-4), natural language processing libraries (e.g., NLTK or spaCy), notification systems (e.g., Pushbullet).

[0904] Receiving messages

[0905] The server receives messages sent by users. Specifically, it retrieves messages through the API of an email server or chat application. For example, it retrieves messages from a mail server using the IMAP protocol.

[0906] Source Analysis

[0907] The server analyzes the sender address of the message. Specifically, it analyzes the message header information and extracts the sender address. For example, if the sender address is a business email address such as "@company.com", it determines that the message is work-related.

[0908] Content analysis

[0909] The server analyzes the message content using natural language processing (NLP) techniques. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, it inputs the following prompt sentence into the generative AI model:

[0910] Prompt: "Please determine whether the following message is work-related or personal. Message: 'Please prepare the materials for tomorrow's meeting.'"

[0911] The server receives the response from the generative AI model and determines whether the message is work-related or personal.

[0912] Notification of classification results

[0913] The server classifies messages as "work" or "personal" based on the analysis results and notifies the user of the results via push notification or email. For example, it sends push notifications using the Pushbullet library.

[0914] Specific examples

[0915] When a user receives a message saying "Please prepare materials for tomorrow's meeting," the server processes the message as follows.

[0916] 1. Make sure the message is sent from a business email address with "@company.com" in it.

[0917] 2. Analyze the message content to determine whether it contains business-related keywords such as "meeting" or "materials."

[0918] 3. Based on this information, classify the message as "Work."

[0919] 4. The classification results are sent to the user's device.

[0920] In this way, messages from multiple communication methods can be efficiently managed in a centralized manner, appropriately categorized and organized into tasks based on their content, and promptly notified to the user.

[0921] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0922] Step 1:

[0923] Receiving messages

[0924] The server receives messages sent by users. Specifically, it retrieves messages through the API of an email server or chat application. For example, it retrieves messages from a mail server using the IMAP protocol.

[0925] Input: Message sent by the user

[0926] Output: Message data stored on the server

[0927] Step 2:

[0928] Source Analysis

[0929] The server analyzes the sender address of the message. Specifically, it analyzes the message header information and extracts the sender address. For example, if the sender address is a business email address such as "@company.com", it determines that the message is work-related.

[0930] Input: Message data stored on the server

[0931] Output: Analysis result of source address

[0932] Step 3:

[0933] Content analysis

[0934] The server analyzes the message content using natural language processing (NLP) techniques. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, it inputs the following prompt sentence into the generative AI model:

[0935] Prompt: "Please determine whether the following message is work-related or personal. Message: 'Please prepare the materials for tomorrow's meeting.'"

[0936] The server receives the response from the generative AI model and determines whether the message is work-related or personal.

[0937] Input: Message content

[0938] Output: Message classification (Work or Personal)

[0939] Step 4:

[0940] Notification of classification results

[0941] The server classifies messages as "work" or "personal" based on the analysis results and notifies the user of the results via push notification or email. For example, it sends push notifications using the Pushbullet library.

[0942] Input: Message classification results

[0943] Output: Notification sent to the user's device

[0944] Step 5:

[0945] Tasking and Management

[0946] The server generates and manages tasks based on the classified messages. Specifically, it prioritizes tasks based on the importance and urgency of the messages and notifies users at the appropriate time.

[0947] Input: Classified message

[0948] Output: Tasks registered in the task management system

[0949] The above is the specific processing flow of the program of this system.

[0950] (Application example 2)

[0951] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0952] Many users today use multiple communication tools, and work-related and personal messages are often mixed together. This makes message management cumbersome, and it is particularly difficult to determine whether messages about expenses are work-related or personal. Furthermore, if expenses are not properly classified, problems may arise in expense reimbursement and personal financial management. Therefore, there is a need for a system that automatically classifies expenses based on the content of messages and allows users to easily check them.

[0953] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for analyzing the sender and content of the messages and automatically determining whether the expenditure is work-related or personal-related, and a means for displaying the results of the expenditure classification so that the user can confirm them. This allows the user to unify and manage messages from multiple communication tools and automatically classify and confirm expenditures.

[0954] "Contact tools" are means for sending and receiving messages such as email, communication tools, chat, and messenger apps.

[0955] A "message" is information such as text, images, audio, and video sent and received through a communication tool.

[0956] "Unified" refers to aggregating and displaying messages from multiple communication tools in a single interface.

[0957] "Sorting" refers to determining and classifying messages based on their content as work-related or personal.

[0958] "Tasking" refers to managing a message as a specific work item based on its content.

[0959] "Source" refers to information about the sender who sent the message.

[0960] "Analysis" refers to analyzing the source and content of a message and making a judgment based on specific conditions.

[0961] "Expenses" refers to information regarding the payment of money.

[0962] "Classification Results" refers to the results of classification of expenditures based on the content of a message.

[0963] "Display" refers to visually presenting the classification results so that the user can confirm them.

[0964] A system for implementing this invention includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for analyzing the sender and content of the messages and automatically determining whether the expenditure is work-related or personal-related, and a means for displaying the classified expenditure results so that the user can confirm them.

[0965] System Program

[0966] The program for this system is implemented using a programming language such as Python. The program performs the following processes:

[0967] 1. Single message:

[0968] The server collects messages from email, communication tools, chats, messenger apps, etc. and aggregates them into a single interface, allowing users to view messages from multiple communication tools in one place.

[0969] 2. Message sorting:

[0970] The server analyzes the sender and content of the message and automatically determines whether it is work-related or personal. For example, if the sender is a work email address or the message content contains work-related keywords such as "meeting expenses" or "travel expenses," the message will be classified as work-related.

[0971] 3. Tasking:

[0972] The server then categorizes the messages into tasks based on their importance and urgency, allowing users to prioritize important tasks.

[0973] 4. Classification of expenses:

[0974] The server analyzes the message sender and content to automatically determine whether the expense is work-related or personal. For example, if a payment notification is sent from a work email address or the message content contains work-related keywords such as "meeting expenses" or "travel expenses," the server will classify the expense as work-related.

[0975] 5. Displaying the classification results:

[0976] The server displays the results of the expense classification so that the user can check them, allowing the user to easily check the details of the expenses and manage them appropriately.

[0977] Hardware and software used

[0978] Hardware: Smartphones, servers

[0979] Software: Python, database management systems (e.g., MySQL), messaging APIs (e.g., Gmail API)

[0980] Specific examples

[0981] For example, consider the case where a user receives the following message:

[0982] From: "boss@work-email.com"

[0983] Message: "About the cost of your next meeting"

[0984] In this case, the server classifies the message as "work" and manages the expenditure as work-related.

[0985] Prompt Sentence Examples

[0986] Below is an example of a prompt sentence to input to the generative AI model.

[0987] From: "boss@work-email.com"

[0988] Message: "About the cost of your next meeting"

[0989] Is this message work-related or personal?

[0990] This prompt can be used to ask a generative AI model to classify the message.

[0991] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0992] Step 1:

[0993] The server collects messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.). As input, it receives message data obtained through the API of each communication tool. As output, it stores these messages in a centralized database. Specifically, the server calls the API of each communication tool, obtains message data, and stores it in the database.

[0994] Step 2:

[0995] The server analyzes the sender and content of collected messages and automatically determines whether they are work-related or personal. As input, it receives message data stored in a database. As output, it generates a classification result (work or personal) for each message. Specifically, the server analyzes the message content using regular expressions and keyword matching, and stores the classification results in a database.

[0996] Step 3:

[0997] The server generates tasks based on the classified messages. As input, it receives message data including the classification results. As output, it generates tasked data. Specifically, the server evaluates the importance and urgency of the messages and stores them in a database as tasks.

[0998] Step 4:

[0999] The server analyzes the message sender and content and automatically determines whether the expenditure is work-related or personal. As input, it receives message data stored in a database. As output, it generates the expenditure classification results. Specifically, the server classifies expenditures based on the message content and stores the results in a database.

[1000] Step 5:

[1001] The server displays the expense classification results so that the user can check them. As input, the server receives data including the expense classification results. As output, the server generates data to be displayed on the user interface. Specifically, the server visually presents the classification results to the user through a web page or a mobile application.

[1002] Step 6:

[1003] The user inputs a prompt sentence into the generative AI model and requests that it classify the message. As input, the prompt sentence is sent to the generative AI model. As output, the classification result is received from the generative AI model. Specifically, the user inputs a prompt sentence like the following:

[1004] From: "boss@work-email.com"

[1005] Message: "About the cost of your next meeting"

[1006] Is this message work-related or personal?

[1007] This prompt can be used to ask a generative AI model to classify the message.

[1008] Example 3

[1009] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1010] In today's business environment, it is common to use multiple communication methods (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centrally manage messages from these communication methods, separate business and personal messages, and automatically generate and manage tasks based on the content of messages. As a result, users must manually check the content of messages and manually generate tasks, which makes efficient task management difficult.

[1011] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1012] In this invention, the server includes means for unifying and displaying messages from multiple communication means, means for classifying messages as business or personal based on their content, means for analyzing the content of the messages and automatically generating tasks based on the analysis results, and means for adding the generated tasks to a task list and managing them. This allows the user to unify and manage messages from multiple communication means, classify messages as business or personal, and automatically generate tasks based on the message content, thereby enabling efficient task management.

[1013] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1014] "Centralization" refers to consolidating and displaying messages from multiple communication methods in one place.

[1015] "Work" refers to the activities and tasks related to a job or occupation.

[1016] "Personal" refers to personal business or private activities.

[1017] "Discretion" refers to distinguishing between business and personal messages based on their content.

[1018] "Analysis" refers to analyzing the content of a message using natural language processing technology to understand its meaning.

[1019] A "task" refers to a specific action or work, a concrete work item that is generated based on the content of a message.

[1020] A "task list" is a list for displaying and managing generated tasks.

[1021] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[1022] This invention is a system that unifies messages from multiple communication means, classifies messages as business or personal based on their content, analyzes the content of the messages, automatically generates tasks, and adds them to a task list for management.

[1023] Hardware and software used

[1024] Hardware

[1025] User's device (PC, smartphone, etc.)

[1026] software

[1027] Natural language processing technology (Google Cloud Natural Language API, IBM Watson Natural Language Understanding, etc.)

[1028] Program processing

[1029] Server Processing

[1030] The server analyzes messages received from users. Specifically, the server uses natural language processing technology to analyze the content of the messages and extract phrases that indicate actions. This analysis is performed using software such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[1031] Once the analysis is complete, the server automatically generates tasks based on the extracted phrases. The generated tasks are added to a task list and managed for the user to review later. For example, if a message contains the phrase "prepare for a meeting," the server will generate a task called "prepare for a meeting" and add it to the task list.

[1032] Terminal handling

[1033] The terminal is responsible for sending messages entered by the user to the server. When the user enters a message and presses the send button, the terminal sends the message to the server. For example, if the user enters "I need to prepare for tomorrow's meeting," the message is sent from the terminal to the server.

[1034] User Action

[1035] Users use their devices to input messages and manage tasks. When a user inputs a message, the message is sent to the server via the device. The server analyzes the message and generates a task, which the user can then view in a task list. For example, if a user inputs "Create presentation materials for next week," a task called "Create presentation materials" will be generated based on the input.

[1036] Specific examples

[1037] Prompt Sentence Examples

[1038] If you enter a message like "I need to prepare for tomorrow's meeting," the server will generate a task called "Prepare for meeting" and add it to your task list.

[1039] This system allows the user to automatically generate tasks based on the contents of messages and efficiently manage tasks. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1040] Step 1: User enters message

[1041] A user inputs a message using a terminal (such as a PC or smartphone). For example, the user inputs "I need to prepare for tomorrow's meeting." This input is saved as a text message on the terminal.

[1042] Step 2: The device sends a message to the server

[1043] When a user inputs a message and presses the send button, the device sends the message to the server. Specifically, the device sends the message data in text format to the server via an Internet connection. The input is the user's message, and the output is the message sent to the server.

[1044] Step 3: The server receives the message

[1045] The server receives messages sent from the terminal. The received messages are stored on the server as text data. The input is the message data from the terminal, and the output is the message data stored on the server.

[1046] Step 4: The server parses the message

[1047] The server analyzes the received message. Specifically, it uses natural language processing technology (such as Google Cloud Natural Language API or IBM Watson Natural Language Understanding) to analyze the content of the message and extract phrases that indicate actions. The input is the message data stored on the server, and the output is the extracted action phrase.

[1048] Step 5: The server creates a task

[1049] The server automatically generates tasks based on the analysis results. It creates specific tasks based on the extracted phrases. For example, it generates the task "prepare for a meeting" from the phrase "prepare for a meeting." The input is the extracted action phrase, and the output is the generated task.

[1050] Step 6: Server updates task list

[1051] The server adds the generated tasks to a task list, which is then saved in a database for later review by the user. The input is the generated tasks, and the output is the updated task list.

[1052] Step 7: User checks task list

[1053] The user uses the terminal to check the task list, which displays the tasks generated by the server. This allows the user to check and manage tasks that are automatically generated based on the message content. The input is the updated task list, and the output is the task list displayed on the user's screen.

[1054] (Application example 3)

[1055] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1056] Conventional message management systems lack the functionality to automatically generate tasks based on the content of messages and issue instructions to machines, which reduces work efficiency within factories. Furthermore, the inability to analyze the content of messages and generate appropriate tasks can lead to work delays and errors. This creates challenges that undermine productivity and safety within factories.

[1057] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1058] In this invention, the server includes a means for consolidating and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the message content, a means for analyzing the message content, generating specific tasks for machines based on the analysis results, and adding the tasks to a task list, and a means for instructing the generated tasks to machines. This makes it possible to automatically generate appropriate tasks based on the message content and issue instructions to machines. This improves work efficiency in factories and reduces work delays and errors.

[1059] "Contact tools" are means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1060] "Message" refers to text information or instructions sent or received through a communication tool.

[1061] "Unification" refers to consolidating messages from multiple communication tools into one platform or interface.

[1062] "Discretion" refers to distinguishing between work-related and personal-related messages based on their content.

[1063] "Tasking" refers to analyzing the content of a message and generating specific tasks and instructions based on that content.

[1064] "Management" refers to adding generated tasks to a list and tracking their progress and completion.

[1065] "Analysis" refers to understanding the content of a message using techniques such as natural language processing and extracting meaning.

[1066] "Machinery" refers to robots and automated equipment used in factories.

[1067] A "task list" refers to a list for displaying and managing generated tasks in a list format.

[1068] "Instruction" refers to instructing a machine to take specific actions based on the generated task.

[1069] A system for carrying out the present invention includes an application that is installed on a robot used in a factory. A specific embodiment of this system will be described below.

[1070] System configuration

[1071] The system consists of the following main components:

[1072] 1. Server: Centralizes and displays messages from multiple communication tools.

[1073] 2. Natural Language Processing (NLP) engine: Analyzes the content of messages and generates tasks based on the analysis results.

[1074] 3. Task Manager: Add generated tasks to the task list and manage them.

[1075] 4. Robot Controller: Instructs the robot to execute the generated tasks.

[1076] Hardware and software used

[1077] Hardware: Factory robots

[1078] Software: Python, spaCy (natural language processing library), TaskManager (task management system), RobotController (robot control system)

[1079] Processing flow

[1080] 1. Receiving a message: The server receives a message from a communication tool such as email, a communication tool, a chat, or a messenger app.

[1081] 2. Message centralization: Received messages are aggregated and displayed on one platform.

[1082] 3. Message analysis: The content of the message is analyzed using a natural language processing engine (spaCy) to extract meaning.

[1083] 4. Task generation: Generate specific tasks based on the analysis results. For example, if a message saying "Perform maintenance on line 1" is received, generate a maintenance task.

[1084] 5. Add to task list: Add the generated task to the task list and manage it.

[1085] 6. Instruct the robot: Instruct the robot to perform the tasks added to the task list.

[1086] Specific examples

[1087] For example, if the following message is received:

[1088] Message: "Maintenance on Line 1"

[1089] Program processing: Parses messages, generates "maintenance" tasks, and issues instructions to the robot.

[1090] Example prompts to input to a generative AI model:

[1091] "A message has been received to perform maintenance on Line 1. Based on this message, please generate a maintenance task and issue instructions to the robot."

[1092] In this way, tasks can be automatically generated based on messages within the factory and instructions can be issued to robots, improving work efficiency within the factory and reducing work delays and errors.

[1093] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1094] Step 1:

[1095] The server receives messages from communication tools such as email, communication tools, chat, and messenger apps. The input is the message sent from each communication tool, and the output is the centralized message data. Specifically, the server retrieves the messages using the API of each communication tool and stores them in a database.

[1096] Step 2:

[1097] The server aggregates and displays received messages on a single platform. The input is unified message data, and the output is a unified message interface that users can view. Specifically, it retrieves messages from the database and displays them on a web interface or application screen.

[1098] Step 3:

[1099] The server uses a natural language processing engine (spaCy) to analyze the message content and extract meaning. The input is the unified message data, and the output is the analyzed message content. Specifically, it loads the spaCy model and analyzes the message text to extract important keywords and phrases.

[1100] Step 4:

[1101] The server generates specific tasks based on the analysis results. The input is the analyzed message content, and the output is the generated task. Specifically, the server determines the type and details of the task based on the extracted keywords and phrases, and generates a task object.

[1102] Step 5:

[1103] The server adds the created tasks to a task list and manages them. The input is the created task, and the output is the updated task list. Specifically, it saves the task object to a database and updates the task list.

[1104] Step 6:

[1105] The server instructs the robot to execute the tasks added to the task list. The input is the updated task list, and the output is the robot's execution status. Specifically, the server sends the generated tasks to the robot via the robot controller and monitors the execution status.

[1106] In this way, tasks can be automatically generated based on messages within the factory and instructions can be issued to robots, improving work efficiency within the factory and reducing work delays and errors.

[1107] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1108] "Example 1"

[1109] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing messages into tasks based on their content, and an emotion engine for recognizing a user's emotion. The emotion engine recognizes a user's emotion from, for example, the user's tone of voice, facial expression, or message context.

[1110] "Example 2"

[1111] In one embodiment, the emotion engine system provides the ability to adjust the priority of messages based on the user's emotions. For example, if a user receives a message that expresses anger or frustration, that message may be determined to have a high priority and displayed to the user for immediate attention.

[1112] "Example 3"

[1113] In one embodiment, the emotion engine system adjusts task priorities based on the user's emotions. For example, if a user receives a message expressing joy or excitement, tasks related to that message are deemed to have a high priority and are displayed at the top of the task list.

[1114] The processing flow of each embodiment will be described below.

[1115] "Example 1"

[1116] Step 1: The system consolidates and displays messages from multiple contact tools.

[1117] Step 2: The system then classifies the message as work or personal based on its content.

[1118] Step 3: The system then tasks and manages the messages based on their content.

[1119] Step 4: Finally, the emotion engine recognizes the user's emotions from their tone of voice, facial expressions, and the context of the message.

[1120] "Example 2"

[1121] Step 1: The system adjusts the priority of messages based on the user's sentiment.

[1122] Step 2: For example, if a user receives a message that expresses anger or frustration, the message is deemed to have a high priority.

[1123] Step 3: As a result, the message is displayed so that it immediately catches the user's eye.

[1124] Step 1: The system adjusts task priorities based on the user's emotions.

[1125] Step 2: For example, if a user receives a message expressing joy or excitement, the task associated with that message is deemed to have high priority.

[1126] Step 3: As a result, the task will appear at the top of your task list.

[1127] Example 1

[1128] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1129] In today's business environment, users use multiple communication methods (email, communication tools, chat, messaging applications, etc.) and must check messages from each tool separately. This makes checking and managing messages cumbersome and increases the risk of missing important messages. Users are also required to distinguish between work and personal messages based on their content and manage them as tasks, but doing this manually takes time and effort. It is also important to recognize users' emotions and provide appropriate feedback, but current systems do not adequately address this issue.

[1130] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1131] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for recognizing a user's emotions, and a means for providing feedback based on the recognized emotions. This allows a user to unify and manage messages from multiple communication means, efficiently manage tasks without missing important messages, and improve user satisfaction and productivity by recognizing the user's emotions and providing appropriate feedback.

[1132] "Communication means" refers to multiple means used by users to send and receive messages, such as email, communication tools, chat, messaging applications, etc.

[1133] "Means for centralized display" refers to a means for displaying messages collected from multiple communication means in a list on a single interface.

[1134] "Means for sorting" refers to means for analyzing the content of messages and classifying them as work-related or personal-related.

[1135] The "tasking and management means" refers to a means for generating a task based on the content of a message and managing the task.

[1136] "Means for recognizing emotions" refers to means for recognizing a user's emotions from the user's tone of voice, facial expression, message context, etc.

[1137] The "means for providing feedback" refers to a means for providing appropriate advice or information to a user based on the user's recognized emotions.

[1138] This invention is a system that displays messages from multiple communication methods in a unified manner, classifies messages as work-related or personal based on their content, and manages them as tasks. It also includes a function to recognize the user's emotions and provide appropriate feedback.

[1139] Message collection and centralized display

[1140] The server collects messages from multiple communication methods used by the user (e.g., email, communication tools, chat, messaging applications). To do this, it uses the API for each communication method. For example, it uses a mail API to collect emails, a communication API to collect communication tools, a chat API to collect chats, and a messaging API to collect messaging applications. The collected messages are stored in a database, and the terminal displays an interface that the user can access, displaying the collected messages in a list format.

[1141] Message Classification

[1142] The server analyzes the content of the collected messages and classifies them as work-related or personal. This is done using natural language processing (NLP) technology. Specifically, it uses a natural language processing API to analyze the content of the messages and determine the category. For example, a message saying "Please prepare materials for tomorrow's meeting" would be classified as work-related, while a message saying "Let's go see a movie this weekend" would be classified as personal.

[1143] Task Creation and Management

[1144] The server generates tasks based on the classified messages and adds them to the task management tool. Task management uses a task management API. For example, a new task can be generated using the task management tool's API and added to the user's task management board. The user can then view the generated tasks and manage their progress.

[1145] Emotional Recognition and Feedback

[1146] The server uses an emotion engine to recognize the user's emotions. This is done using voice recognition technology and facial recognition technology. For example, a voice recognition API is used to analyze the user's tone of voice, and a facial recognition API is used to analyze the user's facial expressions. The server then provides appropriate feedback to the user based on the recognized emotions. For example, if a user sends a message saying, "I'm very tired today," the emotion engine will recognize the user's fatigue and provide advice on how to relax.

[1147] Specific examples

[1148] When a user uses email, communication tools, chat, or messaging applications, the server collects messages from these tools and displays them in a single interface. Users can see at a glance which tool the message came from. They can also distinguish between work and personal messages based on their content and create and manage tasks. Furthermore, by recognizing users' emotions and providing appropriate feedback, user satisfaction and productivity can be improved.

[1149] Prompt Sentence Examples

[1150] "Collect messages from email, communication tools, chat, and messaging applications and display them in a single interface. Classify messages as work or personal based on their content, generate and manage tasks, and recognize user emotions and provide appropriate feedback."

[1151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1152] Step 1:

[1153] The server collects messages from multiple communication means.

[1154] Input: Message data from email, communication tools, chat, and messaging application APIs.

[1155] Specific operation: The server calls the API of each communication method to obtain new messages. For example, it uses an email API to obtain new emails and a communication tool API to obtain new chat messages.

[1156] Output: Collected message data is stored in a database.

[1157] Step 2:

[1158] The server retrieves the messages from the database for centralized display of the collected messages.

[1159] Input: Message data stored in the database.

[1160] Specific operation: The server retrieves the collected messages from the database and sends them to the interface that the user accesses.

[1161] Output: A centralized list of messages that are displayed on your terminal.

[1162] Step 3:

[1163] The server analyzes the content of the collected messages and distinguishes between business and personal messages.

[1164] Input: Message data retrieved from the database.

[1165] Specific operation: The server calls a natural language processing API to analyze the content of the message and determine the category. For example, a message saying "Please prepare the materials for tomorrow's meeting" will be classified as work, and a message saying "Let's go see a movie this weekend" will be classified as personal.

[1166] Output: Classified message data.

[1167] Step 4:

[1168] The server generates tasks based on the classified messages and adds them to the task management tool.

[1169] Input: Classified message data.

[1170] Specific operation: The server calls the task management API to create a new task and adds it to the user's task management board. For example, it uses the API of a task management tool to create a task called "Prepare materials for the meeting" and adds it to the user's task management board.

[1171] Output: The new task added to the task management tool.

[1172] Step 5:

[1173] The server uses an emotion engine to recognize the user's emotions.

[1174] Input: The user's tone of voice, facial expression, and message context.

[1175] Specific operation: The server calls the speech recognition API to analyze the user's tone of voice and the face recognition API to analyze the user's facial expression. For example, if you send a message saying "I'm very tired today," the emotion engine will recognize the user's tiredness.

[1176] Output: Recognized user emotion data.

[1177] Step 6:

[1178] The server provides appropriate feedback to the user based on the recognized emotion.

[1179] Input: Recognized user emotion data.

[1180] Specific operation: The server provides the user with advice and information to help them relax based on the recognized emotions. For example, if the server recognizes that the user is tired, it will suggest relaxing music or a break.

[1181] Output: Feedback provided to the user.

[1182] (Application example 1)

[1183] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1184] Conventional factory robots rarely have the ability to consolidate and display messages from multiple communication tools, distinguish between work and personal messages based on their content, or turn messages into tasks and manage them based on their content. Furthermore, they lack the ability to recognize the emotions of factory staff and prompt appropriate responses, making it difficult to improve communication efficiency or respond appropriately based on staff emotions.

[1185] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means including an emotion engine for recognizing the user's emotions, and a means for recognizing the emotions of staff in the factory and encouraging appropriate responses. This makes it possible to improve the efficiency of communication in the factory and to respond appropriately based on the emotions of staff.

[1186] "Multiple communication tools" refers to different types of communication methods such as email, communication tools, chat, and messenger apps.

[1187] "A means for centralized display" refers to the ability to integrate and display messages from multiple communication tools on a single interface.

[1188] "Sort-out" refers to the ability to automatically classify messages as work-related or personal based on their content.

[1189] "Means for creating and managing tasks" refers to the function of generating tasks based on the contents of messages and managing those tasks.

[1190] An "emotion engine" refers to technology that recognizes a user's emotions from the user's tone of voice, facial expression, message context, etc.

[1191] "Means to recognize the emotions of staff in the factory and encourage appropriate responses" refers to a function that uses an emotion engine to recognize the emotions of staff working in the factory and take appropriate responses based on those emotions.

[1192] The system for carrying out the present invention is configured as an application installed on a factory robot. A specific embodiment of the system will be described below.

[1193] System configuration

[1194] The server includes a means for unifying and displaying messages from a plurality of communication tools, a means for classifying messages as work or personal based on their contents, a means for tasking and managing messages based on their contents, a means including an emotion engine for recognizing the emotions of users, and a means for recognizing the emotions of staff in the factory and encouraging them to respond appropriately.

[1195] Hardware and software used

[1196] Hardware: Factory robots (e.g. KUKA, Fanuc)

[1197] Software: Python, smtplib, imaplib, email library, TextBlob, json

[1198] Data processing and calculation

[1199] The server performs the following data processing and calculations.

[1200] 1. Get email:

[1201] The server retrieves emails from the IMAP server, using the imaplib library.

[1202] 2. Message analysis:

[1203] The server analyzes the content of the emails and performs sentiment analysis using the TextBlob library.

[1204] 3. Single message:

[1205] The server centralizes the analysis results and displays them in a unified interface, using the json library.

[1206] 4. Emotion Engine:

[1207] The server uses emotion recognition technology to recognize the user's emotions from the user's tone of voice, facial expressions, and message context.

[1208] 5. Promoting appropriate responses:

[1209] The server recognizes the emotions of the staff in the factory and responds appropriately based on those emotions.

[1210] Specific examples

[1211] For example, if a factory worker tells a robot, "The production line is running late today," the robot will register the message as a task and notify the manager. If the robot recognizes that a worker is tired, it will send a message encouraging them to take a break.

[1212] Prompt Sentence Examples

[1213] Design a system in which, when a staff member speaks to a robot in a factory, the robot will recognize the staff member's emotions and respond appropriately. For example, if a staff member says, "The production line is behind schedule today," the robot will register that message as a task and notify the manager.

[1214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1215] Step 1:

[1216] The server retrieves emails from the IMAP server. Specifically, it uses the imaplib library to connect to the mail server and log in to the user's email account. It then searches through all emails in the inbox and retrieves the ID of each email. This gives a list containing the email's metadata (sender, subject, date and time, etc.).

[1217] Input: IMAP server connection information, user email account information

[1218] Output: List of email metadata

[1219] Step 2:

[1220] The server analyzes the content of the received emails and performs sentiment analysis. Specifically, it extracts the body of each email using the email library and performs sentiment analysis using the TextBlob library. As a result of the sentiment analysis, it obtains a sentiment score (positive, negative, neutral) for each email.

[1221] Input: List of email metadata

[1222] Output: Email data with sentiment scores

[1223] Step 3:

[1224] The server centralizes the analysis results and displays them on a unified interface. Specifically, it converts email data with sentiment scores into JSON format using the json library and displays it in a user-accessible interface that can be accessed through a web browser or a dedicated application.

[1225] Input: Email data with sentiment scores

[1226] Output: Display data on a unified interface

[1227] Step 4:

[1228] The server recognizes the user's emotions from the user's tone of voice, facial expressions, and message context. Specifically, emotion recognition technology is used to analyze audio and video data to identify the user's emotional state. This allows the user's emotional state to be understood in real time.

[1229] Input: Audio data, video data

[1230] Output: User's emotional state data

[1231] Step 5:

[1232] The server recognizes the emotions of factory staff and responds appropriately based on those emotions. Specifically, based on emotional state data, it sends messages encouraging staff to take a break if they feel stressed. It also notifies managers if important tasks are delayed.

[1233] Input: User's emotional state data

[1234] Output: Appropriate response message, notify administrator

[1235] Example 2

[1236] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1237] In today's business environment, there is a need to efficiently manage messages from multiple communication channels (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centrally display these messages, distinguish between work and personal messages based on their content, and adjust their priority based on their emotions. It is also important to organize and manage messages into tasks based on their content, but current systems are unable to provide these functions in an integrated manner. This leads to problems such as users missing important messages and task management becoming cumbersome.

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

[1239] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as work-related or personal based on their content, a means for analyzing the content of the messages and adjusting the priority of the messages based on the emotions of the users, and a means for organizing and managing tasks based on the content of the messages. This allows the user to unify and manage messages from multiple communication means, and efficiently manage tasks without missing important messages.

[1240] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1241] A "message" is information such as text, images, audio, and video sent and received through a communication means.

[1242] "Unification" refers to the integration of messages from multiple communication channels and displaying and managing them in a single interface.

[1243] "Sorting" refers to automatically classifying messages as work-related or personal based on their content and source.

[1244] "Sentiment analysis" is the process of analyzing the content of a message and detecting the emotions (e.g., joy, anger, sadness, etc.) contained therein.

[1245] "Priority adjustment" refers to changing the order in which messages are displayed and the notification method based on the results of sentiment analysis.

[1246] "Tasking" refers to managing a message as a specific work item based on its content.

[1247] "Management" refers to tracking tasked work items, checking progress, and updating or correcting them as needed.

[1248] This invention is a system that unifies and displays messages from multiple communication means, classifies messages as work or personal based on their contents, adjusts the priority of messages based on emotions, and manages them as tasks based on their contents.

[1249] Hardware and software used

[1250] Hardware: Servers, terminals (PCs, smartphones)

[1251] Software: Message analysis engine, emotion engine, generative AI model

[1252] System Overview

[1253] Unified messaging

[1254] The server retrieves messages from multiple communication methods (email, communication tools, chat, messenger apps, etc.) and displays them in a unified manner, allowing users to view all messages in a single interface.

[1255] Message sorting

[1256] The server analyzes the sender address and content of received messages and automatically distinguishes between work-related and personal messages, specifically based on business email addresses and work-related keywords.

[1257] Message Priority Adjustment

[1258] The server uses an emotion engine to analyze the content of messages and detect the user's emotions (e.g., anger, dissatisfaction, etc.). Messages are prioritized based on the intensity of the emotions, and the terminal displays high-priority messages so that they are immediately visible to the user.

[1259] Tasking a message

[1260] The server manages messages by dividing them into tasks based on their content, and by the importance and urgency of the messages.

[1261] Specific examples

[1262] Message sorting

[1263] Example: If a user receives a message from "boss@company.com" with the content "Project progress report", the server will determine that the message is work-related. The server will recognize the address "boss@company.com" as a business email address and will determine that the keyword "project progress report" is work-related.

[1264] Message Priority Adjustment

[1265] Example: If a user receives the message "This project is not progressing at all!", the emotion engine will detect "anger" and mark the message as high priority. The device will then display the message in a popup notification.

[1266] Prompt Sentence Examples

[1267] Prompt: "Analyze the following message and determine whether it is work-related or personal. Also, prioritize the message based on the user's sentiment. Message: 'This project is not progressing at all!'"

[1268] In this way, the system can automatically sort messages based on their content, adjust message priorities based on the user's emotions, and efficiently manage tasks.

[1269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1270] Step 1:

[1271] Receiving messages

[1272] The server retrieves new messages from multiple communication methods (email, communication tools, chat, messenger apps, etc.).

[1273] Input: Message data from each communication method.

[1274] Output: A list of retrieved messages.

[1275] Specific operation: The server calls the API of each communication method and polls for new messages.

[1276] Step 2:

[1277] Verifying the sender address of a message

[1278] The server checks the sender address of the received message.

[1279] Input: A list of received messages.

[1280] Output: The source address of each message.

[1281] What happens next: The server parses the message header information and extracts the sender address.

[1282] Step 3:

[1283] Message content analysis

[1284] The server analyzes the body of the message and checks whether it contains business-related keywords.

[1285] Input: The body of each message.

[1286] Output: Analysis results for each message (presence or absence of keywords).

[1287] What happens: The server uses a natural language processing (NLP) engine to extract keywords from the message body.

[1288] Step 4:

[1289] Message Classification

[1290] The server automatically determines whether a message is work-related or personal based on the sender's address and content.

[1291] Input: The source address and parsing results for each message.

[1292] Output: Classification of each message (work-related or personal).

[1293] What it does: The server uses predefined rules and machine learning models to classify messages as "work" or "personal."

[1294] Step 5:

[1295] Message sentiment analysis

[1296] The server analyzes the content of the received message using an emotion engine.

[1297] Input: The body of each message.

[1298] Output: Sentiment score for each message.

[1299] Specific operation: The server calls the sentiment analysis API and obtains the sentiment score of the message.

[1300] Step 6:

[1301] Emotional intensity assessment

[1302] The server evaluates the intensity of the emotion based on the emotion score obtained from the emotion engine.

[1303] Input: Sentiment score for each message.

[1304] Output: Sentiment intensity (high, medium, low) for each message.

[1305] Specific behavior: The server compares the emotion score with a threshold and classifies the emotion intensity as "high," "medium," or "low."

[1306] Step 7:

[1307] Message Priority Settings

[1308] The server prioritizes messages based on the intensity of the emotion.

[1309] Input: Sentiment intensity of each message.

[1310] Output: The priority of each message (high, medium, low).

[1311] Specific behavior: The server sets the priority of the message as "high," "medium," or "low" depending on the intensity of the emotion.

[1312] Step 8:

[1313] Displaying messages

[1314] The terminal displays high priority messages in a way that makes them immediately visible to the user.

[1315] Input: The priority of each message.

[1316] Output: The message that is displayed to the user.

[1317] What it does: Your device uses notifications to pop up high-priority messages.

[1318] Step 9:

[1319] Tasking a message

[1320] The server tasks and manages messages based on their content.

[1321] Input: The body and priority of each message.

[1322] Output: Messages managed as tasks.

[1323] Specific operation: The server analyzes the contents of the message and registers it in the task management system.

[1324] In this way, the system can automatically sort messages based on their content, adjust message priorities based on the user's emotions, and efficiently manage tasks.

[1325] (Application example 2)

[1326] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1327] In today's brick-and-mortar stores, it is difficult for staff to efficiently manage messages from multiple communication tools. They are also required to distinguish between work and personal messages based on their content, and adjust priorities based on their emotions. However, there is no automated system for this. This puts important messages at risk of being overlooked, potentially resulting in poor customer service.

[1328] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1329] In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing tasks based on their content, a means for analyzing the sentiment of messages and adjusting their priorities, and a means for enabling store staff to efficiently manage messages, thereby enabling the staff to respond promptly and appropriately without missing important messages.

[1330] "Contact tools" are means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1331] A "message" is information sent or received in the form of text, images, audio, video, etc.

[1332] "Centralization" refers to consolidating data from multiple sources in one place and managing it in a unified manner.

[1333] "Display" refers to the visual presentation of data or information to a user.

[1334] "Segregation" refers to the classification of data or information based on specific criteria.

[1335] "Work" refers to activities and tasks related to a job or function.

[1336] "Private" refers to personal activities and tasks.

[1337] "Tasking" refers to converting the content of a message into specific tasks or action items.

[1338] "Management" refers to efficiently organizing data and information and manipulating and controlling it as needed.

[1339] "Emotion" refers to the user's psychological state that can be read from the content of the message.

[1340] "Analysis" refers to the detailed investigation of data or information to understand its meaning and structure.

[1341] "Priority" is an indicator of the importance and urgency of a task or message.

[1342] A "physical store" refers to a sales or service establishment that exists in a physical location and can be visited in person by customers.

[1343] "Staff" refers to employees and personnel working in physical stores.

[1344] "Efficient" means achieving maximum results with minimum time and effort.

[1345] The following system configuration will be described as an embodiment of the present invention.

[1346] System Configuration

[1347] Hardware

[1348] Server: A central processing unit for unifying and analyzing messages.

[1349] Devices: Devices used by staff, such as smartphones or smart glasses.

[1350] Network: The communications infrastructure that connects servers and devices.

[1351] software

[1352] Message Management System: A program that centralizes, categorizes, tasks, analyzes sentiment, and prioritizes messages.

[1353] Sentiment Analysis Engine: A sentiment analysis library such as TextBlob.

[1354] User interface: The application that allows staff to view and respond to messages.

[1355] Processing flow

[1356] 1. Single message

[1357] The server collects and centralizes messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.).

[1358] This allows staff to view all messages in one interface.

[1359] 2. Message Classification

[1360] The server analyzes the message's origin and content and automatically determines whether it is work-related or personal.

[1361] For example, if keywords such as "customer," "order," "complaint," and "inquiry" are included, they are classified as work-related.

[1362] 3. Tasking and Management

[1363] The server generates and manages tasks based on the classified messages.

[1364] Tasks are prioritized based on the importance and urgency of the message.

[1365] 4. Sentiment analysis and priority adjustment

[1366] The server analyzes the sentiment of the message using a sentiment analysis engine such as TextBlob.

[1367] Based on the analysis, messages that express anger or frustration are set as a high priority.

[1368] For example, a message that reads "Complaint from a customer. Very dissatisfied." will be displayed as a high priority.

[1369] 5. User Interface

[1370] Through the application installed on the device, staff can check messages according to priority and respond quickly.

[1371] Specific examples

[1372] For example, if a store staff member is using a smartphone, the following prompt sentence will be generated:

[1373] Analyze messages from customers, determine whether they are work-related or personal, and adjust the priority using the sentiment engine. For example, a message saying "This is a complaint from a customer. They are very unhappy" is considered work-related and has high priority.

[1374] Based on this prompt, the server analyzes the message and assigns appropriate classification and priority to it, allowing staff to respond quickly and appropriately without missing important messages.

[1375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1376] Step 1:

[1377] The server collects and centralizes messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.).

[1378] Input: Message data from each contact tool.

[1379] Data processing: Convert message data into a unified format and store it in a database.

[1380] Output: Unified message data.

[1381] Specific operation: Uses API to retrieve messages from each communication tool, converts them into a unified format, and saves them in a database.

[1382] Step 2:

[1383] The server analyzes the message's origin and content and automatically determines whether it is work-related or personal.

[1384] Input: Unified message data.

[1385] Data processing: Analyze message content and classify it based on keywords.

[1386] Output: Classified message data (work-related or personal).

[1387] Specific operation: Analyzes message content using natural language processing (NLP) technology to detect keywords such as "customer," "order," "complaint," and "inquiry."

[1388] Step 3:

[1389] The server generates and manages tasks based on the classified messages.

[1390] Input: Classified message data.

[1391] Data transformation: Convert messages into tasks and prioritize them based on importance and urgency.

[1392] Output: Tasked message data.

[1393] Specific operation: Analyze the message content, register it as a task in the task management system, and set the priority.

[1394] Step 4:

[1395] The server analyzes the sentiment of the message using a sentiment analysis engine such as TextBlob.

[1396] Input: Tasked message data.

[1397] Data processing: Analyze the sentiment of the message using a sentiment analysis engine and calculate the polarity.

[1398] Output: Sentiment analysis result (polarity value).

[1399] What it does: It uses the TextBlob library to analyze the sentiment of the message and get the polarity value.

[1400] Step 5:

[1401] Based on the analysis, the server assigns a high priority to messages that express anger or frustration.

[1402] Input: Sentiment analysis result (polarity value).

[1403] Data processing: Set priorities based on polarity values.

[1404] Output: Prioritized task data.

[1405] Specific behavior: If the polarity value is below a certain threshold, set the priority of the task to high.

[1406] Step 6:

[1407] Through an application installed on the device, staff can check messages according to priority and respond quickly.

[1408] Input: Prioritized task data.

[1409] Data manipulation: View tasks based on priority.

[1410] Output: A list of tasks that can be viewed by staff.

[1411] Specific operation: Staff are notified of priority tasks via smartphone or smart glasses applications, encouraging them to take action.

[1412] Example 3

[1413] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1414] In today's business environment, it is common to use multiple communication methods (e.g., email, communication tools, chat, and messenger applications). However, it is difficult to centrally manage messages from these communication methods, separate business and personal messages, and automatically generate and manage tasks based on the content of messages. Furthermore, the lack of a function to adjust task priorities based on the user's emotions makes efficient task management problematic.

[1415] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1416] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as business or personal based on their content, a means for analyzing the content of the messages and automatically generating tasks, a means for analyzing the user's emotions and adjusting the priority of the tasks, and a means for adding the generated tasks to a task list and displaying them. This makes it possible to unify the management of messages from multiple communication means, separate business and personal messages, and further adjust the priority of tasks based on the user's emotions.

[1417] "Communication means" refers to means by which users send and receive messages, such as email, communication tools, chat, and messenger applications.

[1418] "Unification" refers to integrating messages from multiple communication methods and displaying them in a single interface.

[1419] "Work" refers to the activities of a user related to their job or work.

[1420] "Personal" refers to activities undertaken by a user for personal purposes.

[1421] "Analyzing the content of a message" refers to using natural language processing techniques to analyze the text of a message and understand its meaning and intent.

[1422] "Automatically generating tasks" refers to automatically listing specific tasks or actions that the user should perform based on the content of the message.

[1423] "Analyzing the user's emotions" refers to using emotion analysis technology to determine the user's emotional state from the content and expression of the message.

[1424] "Adjusting task priority" refers to setting the importance and urgency of the generated task based on the user's feelings and other factors.

[1425] A "task list" refers to a list that displays a list of tasks that a user needs to perform.

[1426] "Display" refers to visually showing the generated task or message on the user's terminal.

[1427] This invention is a system that consolidates and displays messages from multiple communication means, classifies messages as business or personal based on their content, analyzes the content of the messages to automatically generate tasks, and analyzes the user's emotions to adjust the priority of the tasks.

[1428] System configuration

[1429] Hardware

[1430] Server: Receives messages, analyzes them, generates tasks, analyzes emotions, and manages tasks.

[1431] Device: The task list is displayed on the device used by the user (PC, smartphone, tablet, etc.).

[1432] software

[1433] Natural language processing engine: Used to analyze the content of messages, specifically using Google Cloud Natural Language API and Microsoft Azure Text Analytics.

[1434] Sentiment analysis engine: Used to analyze user emotions. Specifically, it uses IBM Watson Tone Analyzer and Amazon Comprehend.

[1435] Database: Used to store generated tasks and messages.

[1436] System Operation

[1437] Receiving messages

[1438] The server receives messages sent by users, which may be sent via email, communication tools, chat, messenger applications, or the like.

[1439] Message content analysis

[1440] The server uses a natural language processing engine to analyze the content of the received message, for example, by using the Google Cloud Natural Language API to parse the message and extract action phrases.

[1441] Creating a task

[1442] The server generates tasks based on the action phrases extracted by the NLP engine. For example, if the phrase "prepare for a meeting" is extracted, the server generates a task called "prepare for a meeting."

[1443] Emotion Analysis

[1444] The server uses a sentiment analysis engine to analyze the user's emotions from the message content. For example, it uses IBM Watson Tone Analyzer to analyze the emotions of the message and determine that the user is excited.

[1445] Task Priority Setting

[1446] The server prioritizes tasks based on the results of the sentiment analysis engine. For example, if the user is excited, the associated task will be deemed to have a high priority.

[1447] Adding and viewing to the task list

[1448] The server saves the created task in a database and adds it to a task list, and the terminal updates the user's task list to display the new task.

[1449] Specific examples

[1450] Consider a case where a user sends a message saying, "I have to prepare for a meeting next week." When this message is received, the server uses the Google Cloud Natural Language API to extract the phrase "prepare for a meeting" and generates a task. It then analyzes the user's emotions using IBM Watson Tone Analyzer. If the user is determined to be excited, the task is given high priority.

[1451] Example prompts to input to a generative AI model:

[1452] I received a message saying "I need to prepare for a meeting next week." Generate tasks based on this message and prioritize them based on the user's sentiment.

[1453] In this way, the user can manage tasks appropriately according to the contents of the message. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1454] Step 1: Receiving a message

[1455] The server receives messages sent by users. These messages are sent via email, communication tools, chat, messenger applications, etc. The input is the message from the user, and the output is the received message data.

[1456] Step 2: Parsing the message content

[1457] The server uses a natural language processing engine to analyze the content of received messages. Specifically, it uses the Google Cloud Natural Language API to analyze messages and extract action phrases. The input is the received message data, and the output is the extracted action phrase.

[1458] Step 3: Creating a task

[1459] The server generates tasks based on the action phrases extracted by the NLP engine. For example, if the phrase "prepare for a meeting" is extracted, the server generates a task called "prepare for a meeting." The input is the extracted action phrase, and the output is the generated task data.

[1460] Step 4: Sentiment Analysis

[1461] The server uses an emotion analysis engine to analyze the user's emotion from the message content. Specifically, it uses IBM Watson Tone Analyzer to analyze the emotion of the message and determine whether the user is excited. The input is the received message data, and the output is the analyzed emotion data.

[1462] Step 5: Prioritizing tasks

[1463] The server prioritizes tasks based on the results of the emotion analysis engine. For example, if a user is excited, the related tasks are determined to have a high priority. The inputs are the generated task data and the analyzed emotion data, and the output is the prioritized task data.

[1464] Step 6: Add and view in the task list

[1465] The server saves the generated tasks in a database and adds them to a task list. The terminal updates the user's task list and displays the new tasks. The input is the prioritized task data, and the output is the updated task list.

[1466] (Application example 3)

[1467] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1468] While conventional task management systems can generate tasks by analyzing message content, they lack the ability to adjust task priorities based on the user's emotions. This makes it difficult to flexibly manage tasks based on the user's emotions and circumstances, which can hinder efficient work execution. Furthermore, the inability to centrally manage messages from multiple communication methods leads to problems such as information dispersion and cumbersome management.

[1469] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1470] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as business or personal based on their content, a means for organizing and managing tasks based on their content, and a means for analyzing the emotions in the messages and adjusting the priority of the tasks. This enables flexible task management according to the user's emotions and situation, and enables efficient work execution.

[1471] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1472] "Centralization" refers to integrating data from multiple different sources and consolidating it in one place.

[1473] "Display" refers to the visual presentation of data or information to a user.

[1474] "Segregation" refers to the classification of data or information based on specific criteria.

[1475] "Work" refers to the activities and tasks related to a job or occupation.

[1476] "Personal" refers to an individual's private activities and tasks.

[1477] "Tasking" refers to generating specific work items based on the content of a message.

[1478] "Management" refers to the process for organizing, tracking, and completing the tasks that are generated.

[1479] "Emotion" refers to the psychological state or feeling that a user expresses through a message.

[1480] "Analysis" refers to the detailed investigation of data or information to understand its meaning and structure.

[1481] "Priority" refers to the criteria for determining the order of tasks or work based on their importance or urgency.

[1482] "Adjustment" refers to changing a setting or state based on specific criteria or conditions.

[1483] The system for implementing this invention consolidates and displays messages from multiple communication means, classifies messages as business or personal based on their content, and automatically generates and manages tasks based on the message content. It also has the function of analyzing the sentiment of messages and adjusting task priorities.

[1484] Hardware and software used

[1485] Hardware: Factory robots, servers, user devices (smartphones, tablets, PCs, etc.)

[1486] Software: Python, TextBlob (sentiment analysis library), messaging API (email, chat tools, etc.)

[1487] Data processing and calculation

[1488] Centralized message display

[1489] The server collects messages from multiple communication methods, consolidates them, and displays them on the user's device, allowing the user to view all messages from a single interface.

[1490] Message sorting

[1491] The server analyzes the content of messages and automatically classifies them as business-related or personal. For example, if a message contains the phrase "preparing for a meeting," it will be classified as business-related.

[1492] Automated task creation and management

[1493] The server automatically generates a task based on the message content and adds it to the task list. For example, if a message saying "Prepare for a meeting" is received, a task called "Prepare for a meeting" is generated based on the message content.

[1494] Sentiment analysis and priority adjustment

[1495] The server uses TextBlob to analyze the sentiment of the message and determine whether it is positive or negative. Based on the results of the sentiment analysis, the priority of the generated task is dynamically adjusted. For example, if a user sends a positive message such as "I'm so excited about today!", the priority of the task related to that message is set high.

[1496] Specific examples

[1497] Example message

[1498] Message: "Getting ready for a meeting. I'm so excited for today!"

[1499] Generated task: "Prepare for meeting"

[1500] Priority: High

[1501] Prompt Sentence Examples

[1502] Message: "Getting ready for a meeting. I'm so excited for today!"

[1503] Generated task: "Prepare for meeting"

[1504] Priority: High

[1505] In this way, the server analyzes messages, automatically generates tasks, and sets priorities, enabling flexible task management according to the user's emotions and circumstances, thereby enabling efficient work execution.

[1506] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1507] Step 1:

[1508] The server collects messages from multiple communication methods (email, chat tools, etc.).

[1509] Input: Message data from each communication method

[1510] Output: Unified message list

[1511] Specific operation: The server retrieves messages using the API of each communication method and combines them into a single list.

[1512] Step 2:

[1513] The server analyzes the collected messages and distinguishes between business-related and personal messages.

[1514] Input: Centralized message list

[1515] Output: Sorted message list (work-related, personal)

[1516] What it does: The server uses natural language processing (NLP) techniques to analyze the content of messages and classify them as business-related or personal based on specific keywords and phrases.

[1517] Step 3:

[1518] The server automatically generates tasks from business-related messages and adds them to a task list.

[1519] Input: Work-related message

[1520] Output: Generated task list

[1521] Specific operation: The server analyzes work-related messages, extracts action phrases, generates tasks, and adds them to the task list.

[1522] Step 4:

[1523] The server analyzes the sentiment of the message and adjusts the priority of the task.

[1524] Input: Work-related messages, generated task lists

[1525] Output: Priority-adjusted task list

[1526] Specific behavior: The server analyzes the sentiment of the message using a sentiment analysis library such as TextBlob, and increases the priority of the task if the sentiment is positive, and decreases the priority if the sentiment is negative.

[1527] Step 5:

[1528] The server displays the task list with adjusted priorities on the user terminal.

[1529] Input: Priority-adjusted task list

[1530] Output: Task list displayed on the user's terminal

[1531] Specific operation: The server sends the task list to the user's terminal and displays it so that the user can visually check it.

[1532] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1533] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1534] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1535] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1536] [Third embodiment]

[1537] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1538] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1539] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1540] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1541] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1542] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1543] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1544] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1545] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1546] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1547] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1548] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1549] "Example 1"

[1550] The system of the present invention has a means for unifying and displaying messages from multiple communication tools. Specifically, it collects messages from email, communication tools, chats, messenger apps, etc. and displays them in a list on a single interface. This allows users to see at a glance which tool the message came from.

[1551] "Example 2"

[1552] The system of the present invention also has a means for distinguishing between work and personal messages based on their content. Specifically, it analyzes the sender and content of a message to automatically determine whether it is work-related or personal. For example, if the sender of a message is a business email address or if the message content contains work-related keywords, the message is determined to be work-related.

[1553] "Example 3"

[1554] Furthermore, the system of the present invention has a means for creating and managing tasks based on the content of messages. Specifically, the system analyzes the content of messages and automatically generates tasks based on the content. For example, if a message contains a phrase indicating an action, such as "prepare for a meeting," a task is generated based on that phrase and added to the task list. This allows users to manage tasks appropriately according to the content of the message.

[1555] The processing flow of each embodiment will be described below.

[1556] "Example 1"

[1557] Step 1: The system collects messages from emails, communication tools, chats, messenger apps, etc.

[1558] Step 2: List all collected messages in one interface.

[1559] Step 3: Users can see at a glance which tool the message came from by looking at the list of messages.

[1560] "Example 2"

[1561] Step 1: The system analyzes the message's origin and content.

[1562] Step 2: Based on the analysis, it automatically determines whether the message is work-related or personal.

[1563] Step 3: For example, if the message was sent from a work email address or contains work-related keywords, the message is determined to be work-related.

[1564] "Example 3"

[1565] Step 1: The system analyzes the content of the message.

[1566] Step 2: Based on the analysis results, tasks are automatically generated based on the messages.

[1567] Step 3: If the message content contains an action phrase, such as "prepare for a meeting," generate a task based on that phrase and add it to your task list.

[1568] Step 4: This allows the user to manage tasks appropriately according to the content of the message.

[1569] Example 1

[1570] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1571] In today's business environment, it is common for users to receive messages using multiple communication methods. However, this can make message management cumbersome and increases the risk of missing important messages. It is also difficult to distinguish between work and personal messages based on their content, and to manage them as tasks.

[1572] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1573] In this invention, the server includes means for collecting messages from multiple communication means, means for unifying the collected messages, and means for saving the unified messages, which allows the user to check messages from multiple communication means on a single interface and manage them efficiently without missing important messages.

[1574] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1575] "Means for collecting messages" is a function for acquiring new messages from multiple communication means.

[1576] "Means for unifying messages" refers to a function for converting collected messages into a common data format and managing them in a unified manner.

[1577] The "means for storing messages" is a function for storing the unified messages in a storage device such as a database.

[1578] The "means for displaying messages" is a function for displaying saved messages on the interface so that the user can view them.

[1579] The "means for manipulating messages" is a function that allows a user to click on a displayed message to view details or to send a reply.

[1580] The "means for distinguishing between work and personal matters" is a function for determining whether a message is work-related or personal-related based on the content of the message.

[1581] The "means for dividing and managing messages into tasks" is a function for classifying messages into tasks based on their content and managing them according to their importance and urgency.

[1582] MODE FOR CARRYING OUT THE INVENTION

[1583] This invention is a system that unifies and displays messages from multiple communication means, classifies messages as work or personal based on their content, and manages them as tasks. A specific embodiment of this system is described below.

[1584] Server Roles

[1585] The server is responsible for collecting messages from multiple communication methods. Specifically, it obtains new messages using APIs from email servers, communication tools (e.g., Slack, Microsoft Teams), chat apps (e.g., WhatsApp, LINE), messenger apps (e.g., Facebook Messenger), etc. The server centralizes these messages and converts them into a common data format. The converted messages are then stored in a relational database such as MySQL or PostgreSQL.

[1586] Device Role

[1587] The terminal provides an interface for users to view and manipulate messages. Specifically, it displays a list of messages retrieved from the server through a web or mobile application. For example, a front end can be built using React or Vue.js, allowing users to visually check messages. Messages are color-coded by tool: blue for email, green for communication tools, and red for chat apps.

[1588] User operations

[1589] Users can use the device's interface to interact with messages. Specifically, they can click on a specific message to view details or send a reply. Users can also use the filter function to display only messages from specific communication channels. Furthermore, based on the content of the message, users can classify messages as work or personal and manage them as tasks. Tasks are created based on the importance and urgency of the message.

[1590] Specific examples

[1591] For example, consider a situation where a user receives messages from multiple communication channels while at work. By using this system, the user can view messages from email, Slack, WhatsApp, and Facebook Messenger in a single interface. This allows the user to see at a glance which communication channel the message came from and respond quickly.

[1592] Prompt Sentence Examples

[1593] Examples of prompts to be input to a generative AI model include:

[1594] "Please explain the system that consolidates and displays messages from multiple communication methods. Specifically, please specify what hardware and software are used and what data processing and calculations are performed. Please also provide specific examples."

[1595] Using this prompt, the generative AI model can generate a detailed description of the system.

[1596] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1597] Program processing flow

[1598] Step 1: Collecting messages

[1599] The server collects messages from multiple communication methods. As input, it uses API endpoints such as email servers, communication tools (e.g., Slack, Microsoft Teams), chat apps (e.g., WhatsApp, LINE), and messenger apps (e.g., Facebook Messenger). The server sends requests to these APIs to obtain new messages. As output, it obtains the message data obtained from each communication method.

[1600] Step 2: Unified messaging

[1601] The server centralizes the collected messages. It uses the message data collected in step 1 as input. Specifically, it converts the messages obtained from each communication method into a common data format. For example, an email message has fields such as "sender," "subject," "body," and "received date and time," while a Slack message has fields such as "sender," "channel," "message content," and "sent date and time." The server unifies these fields and converts them into a single data format. The output is the centralized message data.

[1602] Step 3: Save the message

[1603] The server stores the centralized messages in a database. The message data centralized in step 2 is used as input. Specifically, a relational database such as MySQL or PostgreSQL is used to store the message data in a table. For example, the message table contains columns such as "Message ID," "Sender," "Content," "Tool Name," and "Received Date and Time," which uniquely identify each message. The output is the message data stored in the database.

[1604] Step 4: Displaying messages

[1605] The terminal provides an interface for users to view messages. It uses message data obtained from the server as input. Specifically, it displays a list of messages through a web or mobile application. For example, a front end can be built using React or Vue.js to allow users to visually check messages. Messages are color-coded by tool, with emails displayed in blue, communication tools in green, and chat apps in red. The output is a list of messages that the user can view.

[1606] Step 5: Working with messages

[1607] The user manipulates messages using the interface on the device. The input is the displayed list of messages. Specifically, the user can click on a specific message to view details or send a reply. The user can also use the filter function to display only messages from specific communication channels. Furthermore, the user can sort messages into work or personal messages based on their content and manage them as tasks. The output is the result of the user's manipulation.

[1608] (Application example 1)

[1609] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1610] In logistics centers, messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.) are scattered, making it difficult for staff to quickly grasp and respond to information. This also reduces work efficiency and can lead to important communications being overlooked.

[1611] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1612] In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on their content, a means for integrating messages collected from multiple communication tools so that logistics center staff can understand them at a glance on their smartphones, and a means for unifying and displaying messages from multiple communication tools used within the logistics center. This allows logistics center staff to understand at a glance which tool a message came from and respond quickly.

[1613] "Multiple communication tools" refers to different types of messaging methods such as email, communication tools, chat, and messenger apps.

[1614] "Centralized display" refers to the ability to collect messages from multiple communication tools and display them in a single interface.

[1615] "Means of classification" refers to the ability to classify messages as work-related or personal based on their content.

[1616] "Means for creating and managing tasks" refers to the function of generating tasks based on the contents of messages and managing those tasks.

[1617] A "logistics center" refers to a facility where goods are stored, sorted, and delivered.

[1618] "Staff" refers to employees working at the logistics center.

[1619] A "smartphone" refers to a mobile phone that can connect to the Internet and use applications.

[1620] "Means of integration" refers to the ability to combine messages collected from multiple communication tools into one system.

[1621] "Responding quickly" means taking necessary action promptly after receiving a message.

[1622] The system for carrying out the present invention is for displaying messages from a plurality of communication tools in a unified manner in a distribution center. Specifically, the server includes the following means:

[1623] First, the server collects messages from multiple contact tools (email, communication tools, chat, messenger apps, etc.) by using Python's imaplib and email libraries to retrieve emails from the IMAP server, the slack_sdk library to collect Slack messages, and the WhatsApp API to collect WhatsApp messages.

[1624] The server then uses Flask to provide an API endpoint to centralize and display the collected messages, allowing staff at the distribution center to use their smartphones to view all messages in a single interface.

[1625] Additionally, the server distinguishes between work-related and personal-related messages based on their content, allowing staff to quickly understand and respond to important messages.

[1626] The server also generates and manages tasks based on the message content, allowing appropriate actions to be taken based on the importance and urgency of the message.

[1627] For example, when a logistics center staff member opens the "Logistics Message Integration App" on their smartphone, messages from email, Slack, and WhatsApp are displayed on one screen. This allows staff to see at a glance which tool the message came from and respond quickly.

[1628] An example prompt is:

[1629] When logistics center staff open the "Logistics Message Integration App" on their smartphones, messages from email, Slack, and WhatsApp are displayed on a single screen. This allows staff to see at a glance which tool the message came from and respond quickly.

[1630] In this way, the efficiency of message management in a logistics center can be greatly improved.

[1631] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1632] Step 1:

[1633] The server retrieves emails from an IMAP server. Specifically, it uses the imaplib and email libraries to log in to a specified email account and retrieve all emails from the inbox. The input is the mail server's authentication information, and the output is data including the email sender, subject, and body.

[1634] Step 2:

[1635] The server retrieves messages from Slack. Specifically, it uses the slack_sdk library to retrieve messages from a specified Slack channel. The input is a Slack API token and a channel ID, and the output is data including the user ID and message text.

[1636] Step 3:

[1637] The server retrieves messages from WhatsApp. Specifically, it uses the WhatsApp API to retrieve messages from a specified WhatsApp account. The input is a WhatsApp API token, and the output is data including the sender and message text.

[1638] Step 4:

[1639] The server centralizes all messages retrieved. Specifically, it consolidates messages retrieved from email, Slack, and WhatsApp into a single data structure. The input is message data retrieved from each communication tool, and the output is a consolidated message list.

[1640] Step 5:

[1641] The server provides the consolidated messages from an API endpoint using Flask. Specifically, we use the Flask framework to build an API that returns the consolidated message list in JSON format. The input is the consolidated message list, and the output is the JSON data provided by the API endpoint.

[1642] Step 6:

[1643] The user opens the "Logistics Message Integration App" on their smartphone. Specifically, they access the server's API endpoint using the smartphone's browser or a dedicated app. The input is the API endpoint URL, and the output is a list of integrated messages.

[1644] Step 7:

[1645] The user checks the displayed messages and responds as necessary. Specifically, the user checks the content of the messages and takes appropriate action based on their importance and urgency. The input is a list of consolidated messages, and the output is the user's action.

[1646] Example 2

[1647] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1648] In today's business environment, there is a need to efficiently manage messages from multiple communication methods (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centralize these messages, classify them as work or personal based on their content, and appropriately organize and manage them as tasks. In particular, there is a need to accurately analyze the sender and content of messages and quickly notify users of the classification results, but current systems cannot do this efficiently.

[1649] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1650] In this invention, the server includes a means for centralizing and displaying messages from multiple communication means, a means for analyzing the sender address of the message, a means for analyzing the content of the message using a generative AI model and classifying it as work or personal, a means for tasking and managing the message based on the message content, and a means for notifying the user's terminal of the classification results. This makes it possible to efficiently centrally manage messages from multiple communication means, appropriately classify and task the messages based on their content, and promptly notify the user.

[1651] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1652] "Source address" refers to information that indicates the email address or user ID of the sender of the message.

[1653] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze text data and understand and classify its content.

[1654] "Message content" refers to the main body of text data sent and received via communication means.

[1655] "Means for unifying and displaying" is a function for integrating and displaying messages obtained from multiple communication means in a single interface.

[1656] The "analysis means" is a function for analyzing the sender address and contents of a message and extracting its characteristics.

[1657] "Means for classification" is a function that automatically classifies messages as work-related or personal based on the analyzed content.

[1658] The "means for creating and managing tasks" is a function for generating tasks based on the sorted messages and managing those tasks.

[1659] The "means for notifying the classification result" is a function for notifying the user terminal of the results of the classified messages.

[1660] This invention relates to a system that consolidates and displays messages from multiple communication methods, analyzes the sender address and content of the messages, classifies them as work-related or personal, and then organizes and manages tasks based on the content. A specific embodiment of this system is shown below.

[1661] System configuration

[1662] The server is configured using the following hardware and software.

[1663] Hardware: A server machine with a powerful processor, sufficient memory, storage devices, and network interfaces.

[1664] Software: Mail servers (supporting the IMAP protocol), generative AI models (e.g., OpenAI's GPT-4), natural language processing libraries (e.g., NLTK or spaCy), notification systems (e.g., Pushbullet).

[1665] Receiving messages

[1666] The server receives messages sent by users. Specifically, it retrieves messages through the API of an email server or chat application. For example, it retrieves messages from a mail server using the IMAP protocol.

[1667] Source Analysis

[1668] The server analyzes the sender address of the message. Specifically, it analyzes the message header information and extracts the sender address. For example, if the sender address is a business email address such as "@company.com", it determines that the message is work-related.

[1669] Content analysis

[1670] The server analyzes the message content using natural language processing (NLP) techniques. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, it inputs the following prompt sentence into the generative AI model:

[1671] Prompt: "Please determine whether the following message is work-related or personal. Message: 'Please prepare the materials for tomorrow's meeting.'"

[1672] The server receives the response from the generative AI model and determines whether the message is work-related or personal.

[1673] Notification of classification results

[1674] The server classifies messages as "work" or "personal" based on the analysis results and notifies the user of the results via push notification or email. For example, it sends push notifications using the Pushbullet library.

[1675] Specific examples

[1676] When a user receives a message saying "Please prepare materials for tomorrow's meeting," the server processes the message as follows.

[1677] 1. Make sure the message is sent from a business email address with "@company.com" in it.

[1678] 2. Analyze the message content to determine whether it contains business-related keywords such as "meeting" or "materials."

[1679] 3. Based on this information, classify the message as "Work."

[1680] 4. The classification results are sent to the user's device.

[1681] In this way, messages from multiple communication methods can be efficiently managed in a centralized manner, appropriately categorized and organized into tasks based on their content, and promptly notified to the user.

[1682] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1683] Step 1:

[1684] Receiving messages

[1685] The server receives messages sent by users. Specifically, it retrieves messages through the API of an email server or chat application. For example, it retrieves messages from a mail server using the IMAP protocol.

[1686] Input: Message sent by the user

[1687] Output: Message data stored on the server

[1688] Step 2:

[1689] Source Analysis

[1690] The server analyzes the sender address of the message. Specifically, it analyzes the message header information and extracts the sender address. For example, if the sender address is a business email address such as "@company.com", it determines that the message is work-related.

[1691] Input: Message data stored on the server

[1692] Output: Analysis result of source address

[1693] Step 3:

[1694] Content analysis

[1695] The server analyzes the message content using natural language processing (NLP) techniques. For this analysis, it uses a generative AI model (e.g., OpenAI's GPT-4). Specifically, it inputs the following prompt sentence into the generative AI model:

[1696] Prompt: "Please determine whether the following message is work-related or personal. Message: 'Please prepare the materials for tomorrow's meeting.'"

[1697] The server receives the response from the generative AI model and determines whether the message is work-related or personal.

[1698] Input: Message content

[1699] Output: Message classification (Work or Personal)

[1700] Step 4:

[1701] Notification of classification results

[1702] The server classifies messages as "work" or "personal" based on the analysis results and notifies the user of the results via push notification or email. For example, it sends push notifications using the Pushbullet library.

[1703] Input: Message classification results

[1704] Output: Notification sent to the user's device

[1705] Step 5:

[1706] Tasking and Management

[1707] The server generates and manages tasks based on the classified messages. Specifically, it prioritizes tasks based on the importance and urgency of the messages and notifies users at the appropriate time.

[1708] Input: Classified message

[1709] Output: Tasks registered in the task management system

[1710] The above is the specific processing flow of the program of this system.

[1711] (Application example 2)

[1712] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1713] Many users today use multiple communication tools, and work-related and personal messages are often mixed together. This makes message management cumbersome, and it is particularly difficult to determine whether messages about expenses are work-related or personal. Furthermore, if expenses are not properly classified, problems may arise in expense reimbursement and personal financial management. Therefore, there is a need for a system that automatically classifies expenses based on the content of messages and allows users to easily check them.

[1714] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for analyzing the sender and content of the messages and automatically determining whether the expenditure is work-related or personal-related, and a means for displaying the results of the expenditure classification so that the user can confirm them. This allows the user to unify and manage messages from multiple communication tools and automatically classify and confirm expenditures.

[1715] "Contact tools" are means for sending and receiving messages such as email, communication tools, chat, and messenger apps.

[1716] A "message" is information such as text, images, audio, and video sent and received through a communication tool.

[1717] "Unified" refers to aggregating and displaying messages from multiple communication tools in a single interface.

[1718] "Sorting" refers to determining and classifying messages based on their content as work-related or personal.

[1719] "Tasking" refers to managing a message as a specific work item based on its content.

[1720] "Source" refers to information about the sender who sent the message.

[1721] "Analysis" refers to analyzing the source and content of a message and making a judgment based on specific conditions.

[1722] "Expenses" refers to information regarding the payment of money.

[1723] "Classification Results" refers to the results of classification of expenditures based on the content of a message.

[1724] "Display" refers to visually presenting the classification results so that the user can confirm them.

[1725] A system for implementing this invention includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for analyzing the sender and content of the messages and automatically determining whether the expenditure is work-related or personal-related, and a means for displaying the classified expenditure results so that the user can confirm them.

[1726] System Program

[1727] The program for this system is implemented using a programming language such as Python. The program performs the following processes:

[1728] 1. Single message:

[1729] The server collects messages from email, communication tools, chats, messenger apps, etc. and aggregates them into a single interface, allowing users to view messages from multiple communication tools in one place.

[1730] 2. Message sorting:

[1731] The server analyzes the sender and content of the message and automatically determines whether it is work-related or personal. For example, if the sender is a work email address or the message content contains work-related keywords such as "meeting expenses" or "travel expenses," the message will be classified as work-related.

[1732] 3. Tasking:

[1733] The server then categorizes the messages into tasks based on their importance and urgency, allowing users to prioritize important tasks.

[1734] 4. Classification of expenses:

[1735] The server analyzes the message sender and content to automatically determine whether the expense is work-related or personal. For example, if a payment notification is sent from a work email address or the message content contains work-related keywords such as "meeting expenses" or "travel expenses," the server will classify the expense as work-related.

[1736] 5. Displaying the classification results:

[1737] The server displays the results of the expense classification so that the user can check them, allowing the user to easily check the details of the expenses and manage them appropriately.

[1738] Hardware and software used

[1739] Hardware: Smartphones, servers

[1740] Software: Python, database management systems (e.g., MySQL), messaging APIs (e.g., Gmail API)

[1741] Specific examples

[1742] For example, consider the case where a user receives the following message:

[1743] From: "boss@work-email.com"

[1744] Message: "About the cost of your next meeting"

[1745] In this case, the server classifies the message as "work" and manages the expenditure as work-related.

[1746] Prompt Sentence Examples

[1747] Below is an example of a prompt sentence to input to the generative AI model.

[1748] From: "boss@work-email.com"

[1749] Message: "About the cost of your next meeting"

[1750] Is this message work-related or personal?

[1751] This prompt can be used to ask a generative AI model to classify the message.

[1752] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1753] Step 1:

[1754] The server collects messages from multiple communication tools (email, communication tools, chat, messenger apps, etc.). As input, it receives message data obtained through the API of each communication tool. As output, it stores these messages in a centralized database. Specifically, the server calls the API of each communication tool, obtains message data, and stores it in the database.

[1755] Step 2:

[1756] The server analyzes the sender and content of collected messages and automatically determines whether they are work-related or personal. As input, it receives message data stored in a database. As output, it generates a classification result (work or personal) for each message. Specifically, the server analyzes the message content using regular expressions and keyword matching, and stores the classification results in a database.

[1757] Step 3:

[1758] The server generates tasks based on the classified messages. As input, it receives message data including the classification results. As output, it generates tasked data. Specifically, the server evaluates the importance and urgency of the messages and stores them in a database as tasks.

[1759] Step 4:

[1760] The server analyzes the message sender and content and automatically determines whether the expenditure is work-related or personal. As input, it receives message data stored in a database. As output, it generates the expenditure classification results. Specifically, the server classifies expenditures based on the message content and stores the results in a database.

[1761] Step 5:

[1762] The server displays the expense classification results so that the user can check them. As input, the server receives data including the expense classification results. As output, the server generates data to be displayed on the user interface. Specifically, the server visually presents the classification results to the user through a web page or a mobile application.

[1763] Step 6:

[1764] The user inputs a prompt sentence into the generative AI model and requests that it classify the message. As input, the prompt sentence is sent to the generative AI model. As output, the classification result is received from the generative AI model. Specifically, the user inputs a prompt sentence like the following:

[1765] From: "boss@work-email.com"

[1766] Message: "About the cost of your next meeting"

[1767] Is this message work-related or personal?

[1768] This prompt can be used to ask a generative AI model to classify the message.

[1769] Example 3

[1770] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1771] In today's business environment, it is common to use multiple communication methods (email, communication tools, chat, messenger apps, etc.). However, it is difficult to centrally manage messages from these communication methods, separate business and personal messages, and automatically generate and manage tasks based on the content of messages. As a result, users must manually check the content of messages and manually generate tasks, which makes efficient task management difficult.

[1772] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1773] In this invention, the server includes means for unifying and displaying messages from multiple communication means, means for classifying messages as business or personal based on their content, means for analyzing the content of the messages and automatically generating tasks based on the analysis results, and means for adding the generated tasks to a task list and managing them. This allows the user to unify and manage messages from multiple communication means, classify messages as business or personal, and automatically generate tasks based on the message content, thereby enabling efficient task management.

[1774] "Means of communication" refers to means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1775] "Centralization" refers to consolidating and displaying messages from multiple communication methods in one place.

[1776] "Work" refers to the activities and tasks related to a job or occupation.

[1777] "Personal" refers to personal business or private activities.

[1778] "Discretion" refers to distinguishing between business and personal messages based on their content.

[1779] "Analysis" refers to analyzing the content of a message using natural language processing technology to understand its meaning.

[1780] A "task" refers to a specific action or work, a concrete work item that is generated based on the content of a message.

[1781] A "task list" is a list for displaying and managing generated tasks.

[1782] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[1783] This invention is a system that unifies messages from multiple communication means, classifies messages as business or personal based on their content, analyzes the content of the messages, automatically generates tasks, and adds them to a task list for management.

[1784] Hardware and software used

[1785] Hardware

[1786] User's device (PC, smartphone, etc.)

[1787] software

[1788] Natural language processing technology (Google Cloud Natural Language API, IBM Watson Natural Language Understanding, etc.)

[1789] Program processing

[1790] Server Processing

[1791] The server analyzes messages received from users. Specifically, the server uses natural language processing technology to analyze the content of the messages and extract phrases that indicate actions. This analysis is performed using software such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[1792] Once the analysis is complete, the server automatically generates tasks based on the extracted phrases. The generated tasks are added to a task list and managed for the user to review later. For example, if a message contains the phrase "prepare for a meeting," the server will generate a task called "prepare for a meeting" and add it to the task list.

[1793] Terminal handling

[1794] The terminal is responsible for sending messages entered by the user to the server. When the user enters a message and presses the send button, the terminal sends the message to the server. For example, if the user enters "I need to prepare for tomorrow's meeting," the message is sent from the terminal to the server.

[1795] User Action

[1796] Users use their devices to input messages and manage tasks. When a user inputs a message, the message is sent to the server via the device. The server analyzes the message and generates a task, which the user can then view in a task list. For example, if a user inputs "Create presentation materials for next week," a task called "Create presentation materials" will be generated based on the input.

[1797] Specific examples

[1798] Prompt Sentence Examples

[1799] If you enter a message like "I need to prepare for tomorrow's meeting," the server will generate a task called "Prepare for meeting" and add it to your task list.

[1800] This system allows the user to automatically generate tasks based on the contents of messages and efficiently manage tasks. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1801] Step 1: User enters message

[1802] A user inputs a message using a terminal (such as a PC or smartphone). For example, the user inputs "I need to prepare for tomorrow's meeting." This input is saved as a text message on the terminal.

[1803] Step 2: The device sends a message to the server

[1804] When a user inputs a message and presses the send button, the device sends the message to the server. Specifically, the device sends the message data in text format to the server via an Internet connection. The input is the user's message, and the output is the message sent to the server.

[1805] Step 3: The server receives the message

[1806] The server receives messages sent from the terminal. The received messages are stored on the server as text data. The input is the message data from the terminal, and the output is the message data stored on the server.

[1807] Step 4: The server parses the message

[1808] The server analyzes the received message. Specifically, it uses natural language processing technology (such as Google Cloud Natural Language API or IBM Watson Natural Language Understanding) to analyze the content of the message and extract phrases that indicate actions. The input is the message data stored on the server, and the output is the extracted action phrase.

[1809] Step 5: The server creates a task

[1810] The server automatically generates tasks based on the analysis results. It creates specific tasks based on the extracted phrases. For example, it generates the task "prepare for a meeting" from the phrase "prepare for a meeting." The input is the extracted action phrase, and the output is the generated task.

[1811] Step 6: Server updates task list

[1812] The server adds the generated tasks to a task list, which is then saved in a database for later review by the user. The input is the generated tasks, and the output is the updated task list.

[1813] Step 7: User checks task list

[1814] The user uses the terminal to check the task list, which displays the tasks generated by the server. This allows the user to check and manage tasks that are automatically generated based on the message content. The input is the updated task list, and the output is the task list displayed on the user's screen.

[1815] (Application example 3)

[1816] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1817] Conventional message management systems lack the functionality to automatically generate tasks based on the content of messages and issue instructions to machines, which reduces work efficiency within factories. Furthermore, the inability to analyze the content of messages and generate appropriate tasks can lead to work delays and errors. This creates challenges that undermine productivity and safety within factories.

[1818] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1819] In this invention, the server includes a means for consolidating and displaying messages from multiple communication tools, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the message content, a means for analyzing the message content, generating specific tasks for machines based on the analysis results, and adding the tasks to a task list, and a means for instructing the generated tasks to machines. This makes it possible to automatically generate appropriate tasks based on the message content and issue instructions to machines. This improves work efficiency in factories and reduces work delays and errors.

[1820] "Contact tools" are means for sending and receiving messages, such as email, communication tools, chat, and messenger apps.

[1821] "Message" refers to text information or instructions sent or received through a communication tool.

[1822] "Unification" refers to consolidating messages from multiple communication tools into one platform or interface.

[1823] "Discretion" refers to distinguishing between work-related and personal-related messages based on their content.

[1824] "Tasking" refers to analyzing the content of a message and generating specific tasks and instructions based on that content.

[1825] "Management" refers to adding generated tasks to a list and tracking their progress and completion.

[1826] "Analysis" refers to understanding the content of a message using techniques such as natural language processing and extracting meaning.

[1827] "Machinery" refers to robots and automated equipment used in factories.

[1828] A "task list" refers to a list for displaying and managing generated tasks in a list format.

[1829] "Instruction" refers to instructing a machine to take specific actions based on the generated task.

[1830] A system for carrying out the present invention includes an application that is installed on a robot used in a factory. A specific embodiment of this system will be described below.

[1831] System configuration

[1832] The system consists of the following main components:

[1833] 1. Server: Centralizes and displays messages from multiple communication tools.

[1834] 2. Natural Language Processing (NLP) engine: Analyzes the content of messages and generates tasks based on the analysis results.

[1835] 3. Task Manager: Add generated tasks to the task list and manage them.

[1836] 4. Robot Controller: Instructs the robot to execute the generated tasks.

[1837] Hardware and software used

[1838] Hardware: Factory robots

[1839] Software: Python, spaCy (natural language processing library), TaskManager (task management system), RobotController (robot control system)

[1840] Processing flow

[1841] 1. Receiving a message: The server receives a message from a communication tool such as email, a communication tool, a chat, or a messenger app.

[1842] 2. Message centralization: Received messages are aggregated and displayed on one platform.

[1843] 3. Message analysis: The content of the message is analyzed using a natural language processing engine (spaCy) to extract meaning.

[1844] 4. Task generation: Generate specific tasks based on the analysis results. For example, if a message saying "Perform maintenance on line 1" is received, generate a maintenance task.

[1845] 5. Add to task list: Add the generated task to the task list and manage it.

[1846] 6. Instruct the robot: Instruct the robot to perform the tasks added to the task list.

[1847] Specific examples

[1848] For example, if the following message is received:

[1849] Message: "Maintenance on Line 1"

[1850] Program processing: Parses messages, generates "maintenance" tasks, and issues instructions to the robot.

[1851] Example prompts to input to a generative AI model:

[1852] "A message has been received to perform maintenance on Line 1. Based on this message, please generate a maintenance task and issue instructions to the robot."

[1853] In this way, tasks can be automatically generated based on messages within the factory and instructions can be issued to robots, improving work efficiency within the factory and reducing work delays and errors.

[1854] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1855] Step 1:

[1856] The server receives messages from communication tools such as email, communication tools, chat, and messenger apps. The input is the message sent from each communication tool, and the output is the centralized message data. Specifically, the server retrieves the messages using the API of each communication tool and stores them in a database.

[1857] Step 2:

[1858] The server aggregates and displays received messages on a single platform. The input is unified message data, and the output is a unified message interface that users can view. Specifically, it retrieves messages from the database and displays them on a web interface or application screen.

[1859] Step 3:

[1860] The server uses a natural language processing engine (spaCy) to analyze the message content and extract meaning. The input is the unified message data, and the output is the analyzed message content. Specifically, it loads the spaCy model and analyzes the message text to extract important keywords and phrases.

[1861] Step 4:

[1862] The server generates specific tasks based on the analysis results. The input is the analyzed message content, and the output is the generated task. Specifically, the server determines the type and details of the task based on the extracted keywords and phrases, and generates a task object.

[1863] Step 5:

[1864] The server adds the created tasks to a task list and manages them. The input is the created task, and the output is the updated task list. Specifically, it saves the task object to a database and updates the task list.

[1865] Step 6:

[1866] The server instructs the robot to execute the tasks added to the task list. The input is the updated task list, and the output is the robot's execution status. Specifically, the server sends the generated tasks to the robot via the robot controller and monitors the execution status.

[1867] In this way, tasks can be automatically generated based on messages within the factory and instructions can be issued to robots, improving work efficiency within the factory and reducing work delays and errors.

[1868] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1869] "Example 1"

[1870] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing messages into tasks based on their content, and an emotion engine for recognizing a user's emotion. The emotion engine recognizes a user's emotion from, for example, the user's tone of voice, facial expression, or message context.

[1871] "Example 2"

[1872] In one embodiment, the emotion engine system provides the ability to adjust the priority of messages based on the user's emotions. For example, if a user receives a message that expresses anger or frustration, that message may be determined to have a high priority and displayed to the user for immediate attention.

[1873] "Example 3"

[1874] In one embodiment, the emotion engine system adjusts task priorities based on the user's emotions. For example, if a user receives a message expressing joy or excitement, tasks related to that message are deemed to have a high priority and are displayed at the top of the task list.

[1875] The processing flow of each embodiment will be described below.

[1876] "Example 1"

[1877] Step 1: The system consolidates and displays messages from multiple contact tools.

[1878] Step 2: The system then classifies the message as work or personal based on its content.

[1879] Step 3: The system then tasks and manages the messages based on their content.

[1880] Step 4: Finally, the emotion engine recognizes the user's emotions from their tone of voice, facial expressions, and the context of the message.

[1881] "Example 2"

[1882] Step 1: The system adjusts the priority of messages based on the user's sentiment.

[1883] Step 2: For example, if a user receives a message that expresses anger or frustration, the message is deemed to have a high priority.

[1884] Step 3: As a result, the message is displayed so that it immediately catches the user's eye.

[1885] Step 1: The system adjusts task priorities based on the user's emotions.

[1886] Step 2: For example, if a user receives a message expressing joy or excitement, the task associated with that message is deemed to have high priority.

[1887] Step 3: As a result, the task will appear at the top of your task list.

[1888] Example 1

[1889] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1890] In today's business environment, users use multiple communication methods (email, communication tools, chat, messaging applications, etc.) and must check messages from each tool separately. This makes checking and managing messages cumbersome and increases the risk of missing important messages. Users are also required to distinguish between work and personal messages based on their content and manage them as tasks, but doing this manually takes time and effort. It is also important to recognize users' emotions and provide appropriate feedback, but current systems do not adequately address this issue.

[1891] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1892] In this invention, the server includes a means for unifying and displaying messages from multiple communication means, a means for classifying messages as work-related or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means for recognizing a user's emotions, and a means for providing feedback based on the recognized emotions. This allows a user to unify and manage messages from multiple communication means, efficiently manage tasks without missing important messages, and improve user satisfaction and productivity by recognizing the user's emotions and providing appropriate feedback.

[1893] "Communication means" refers to multiple means used by users to send and receive messages, such as email, communication tools, chat, messaging applications, etc.

[1894] "Means for centralized display" refers to a means for displaying messages collected from multiple communication means in a list on a single interface.

[1895] "Means for sorting" refers to means for analyzing the content of messages and classifying them as work-related or personal-related.

[1896] The "tasking and management means" refers to a means for generating a task based on the content of a message and managing the task.

[1897] "Means for recognizing emotions" refers to means for recognizing a user's emotions from the user's tone of voice, facial expression, message context, etc.

[1898] The "means for providing feedback" refers to a means for providing appropriate advice or information to a user based on the user's recognized emotions.

[1899] This invention is a system that displays messages from multiple communication methods in a unified manner, classifies messages as work-related or personal based on their content, and manages them as tasks. It also includes a function to recognize the user's emotions and provide appropriate feedback.

[1900] Message collection and centralized display

[1901] The server collects messages from multiple communication methods used by the user (e.g., email, communication tools, chat, messaging applications). To do this, it uses the API for each communication method. For example, it uses a mail API to collect emails, a communication API to collect communication tools, a chat API to collect chats, and a messaging API to collect messaging applications. The collected messages are stored in a database, and the terminal displays an interface that the user can access, displaying the collected messages in a list format.

[1902] Message Classification

[1903] The server analyzes the content of the collected messages and classifies them as work-related or personal. This is done using natural language processing (NLP) technology. Specifically, it uses a natural language processing API to analyze the content of the messages and determine the category. For example, a message saying "Please prepare materials for tomorrow's meeting" would be classified as work-related, while a message saying "Let's go see a movie this weekend" would be classified as personal.

[1904] Task Creation and Management

[1905] The server generates tasks based on the classified messages and adds them to the task management tool. Task management uses a task management API. For example, a new task can be generated using the task management tool's API and added to the user's task management board. The user can then view the generated tasks and manage their progress.

[1906] Emotional Recognition and Feedback

[1907] The server uses an emotion engine to recognize the user's emotions. This is done using voice recognition technology and facial recognition technology. For example, a voice recognition API is used to analyze the user's tone of voice, and a facial recognition API is used to analyze the user's facial expressions. The server then provides appropriate feedback to the user based on the recognized emotions. For example, if a user sends a message saying, "I'm very tired today," the emotion engine will recognize the user's fatigue and provide advice on how to relax.

[1908] Specific examples

[1909] When a user uses email, communication tools, chat, or messaging applications, the server collects messages from these tools and displays them in a single interface. Users can see at a glance which tool the message came from. They can also distinguish between work and personal messages based on their content and create and manage tasks. Furthermore, by recognizing users' emotions and providing appropriate feedback, user satisfaction and productivity can be improved.

[1910] Prompt Sentence Examples

[1911] "Collect messages from email, communication tools, chat, and messaging applications and display them in a single interface. Classify messages as work or personal based on their content, generate and manage tasks, and recognize user emotions and provide appropriate feedback."

[1912] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1913] Step 1:

[1914] The server collects messages from multiple communication means.

[1915] Input: Message data from email, communication tools, chat, and messaging application APIs.

[1916] Specific operation: The server calls the API of each communication method to obtain new messages. For example, it uses an email API to obtain new emails and a communication tool API to obtain new chat messages.

[1917] Output: Collected message data is stored in a database.

[1918] Step 2:

[1919] The server retrieves the messages from the database for centralized display of the collected messages.

[1920] Input: Message data stored in the database.

[1921] Specific operation: The server retrieves the collected messages from the database and sends them to the interface that the user accesses.

[1922] Output: A centralized list of messages that are displayed on your terminal.

[1923] Step 3:

[1924] The server analyzes the content of the collected messages and distinguishes between business and personal messages.

[1925] Input: Message data retrieved from the database.

[1926] Specific operation: The server calls a natural language processing API to analyze the content of the message and determine the category. For example, a message saying "Please prepare the materials for tomorrow's meeting" will be classified as work, and a message saying "Let's go see a movie this weekend" will be classified as personal.

[1927] Output: Classified message data.

[1928] Step 4:

[1929] The server generates tasks based on the classified messages and adds them to the task management tool.

[1930] Input: Classified message data.

[1931] Specific operation: The server calls the task management API to create a new task and adds it to the user's task management board. For example, it uses the API of a task management tool to create a task called "Prepare materials for the meeting" and adds it to the user's task management board.

[1932] Output: The new task added to the task management tool.

[1933] Step 5:

[1934] The server uses an emotion engine to recognize the user's emotions.

[1935] Input: The user's tone of voice, facial expression, and message context.

[1936] Specific operation: The server calls the speech recognition API to analyze the user's tone of voice and the face recognition API to analyze the user's facial expression. For example, if you send a message saying "I'm very tired today," the emotion engine will recognize the user's tiredness.

[1937] Output: Recognized user emotion data.

[1938] Step 6:

[1939] The server provides appropriate feedback to the user based on the recognized emotion.

[1940] Input: Recognized user emotion data.

[1941] Specific operation: The server provides the user with advice and information to help them relax based on the recognized emotions. For example, if the server recognizes that the user is tired, it will suggest relaxing music or a break.

[1942] Output: Feedback provided to the user.

[1943] (Application example 1)

[1944] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1945] Conventional factory robots rarely have the ability to consolidate and display messages from multiple communication tools, distinguish between work and personal messages based on their content, or turn messages into tasks and manage them based on their content. Furthermore, they lack the ability to recognize the emotions of factory staff and prompt appropriate responses, making it difficult to improve communication efficiency or respond appropriately based on staff emotions.

[1946] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for unifying and displaying messages from multiple communication tools, a means for classifying messages as work or personal based on their content, a means for organizing and managing tasks based on the content of the messages, a means including an emotion engine for recognizing the user's emotions, and a means for recognizing the emotions of staff in the factory and encouraging appropriate responses. This makes it possible to improve the efficiency of communication in the factory and to respond appropriately based on the emotions of staff.

[1947] "Multiple communication tools" refers to different types of communication methods such as email, communication tools, chat, and messenger apps.

[1948] "A means for centralized display" refers to the ability to integrate and display messages from multiple communication tools on a single interface.

[1949] "Sort-out" refers to the ability to automatically classify messages as work-related or personal based on their content.

[1950] "Means for creating and managing tasks" refers to the function of generating tasks based on the contents of messages and managing those tasks.

[1951] An "emotion engine" refers to technology that recognizes a user's emotions from the user's tone of voice, facial expression, message context, etc.

[1952] "Means to recognize the emotions of staff in the factory and encourage appropriate responses" refers to a function that uses an emotion engine to recognize the emotions of staff working in the factory and take appropriate responses based on those emotions.

[1953] The system for carrying out the present invention is configured as an application installed on a factory robot. A specific embodiment of the system will be described below.

[1954] System configuration

[1955] The server includes a means for unifying and displaying messages from a plurality of communication tools, a means for classifying messages as work or personal based on their contents, a means for tasking and managing messages based on their contents, a means including an emotion engine for recognizing the emotions of users, and a means for recognizing the emotions of staff in the factory and encouraging them to respond appropriately.

[1956] Hardware and software used

[1957] Hardware: Factory robots (e.g. KUKA, Fanuc)

[1958] Software: Python, smtplib, imaplib, email library, TextBlob, json

[1959] Data processing and calculation

[1960] The server performs the following data processing and calculations.

[1961] 1. Get email:

[1962] The server retrieves emails from the IMAP server, using the imaplib library.

[1963] 2. Message analysis:

[1964] The server analyzes the content of the emails and performs sentiment analysis using the TextBlob library.

[1965] 3. Single message:

[1966] The server centralizes the analysis results and displays them in a unified interface, using the json library.

[1967] 4. Emotion Engine:

[1968] The server uses emotion recognition technology to recognize the user's emotions from the user's tone of voice, facial expressions, and message context.

[1969] 5. Promoting appropriate responses:

[1970] The server recognizes the emotions of the staff in the factory and responds appropriately based on those emotions.

[1971] Specific examples

[1972] For example, if a factory worker tells a robot, "The production line is running late today," the robot will register the message as a task and notify the manager. If the robot recognizes that a worker is tired, it will send a message encouraging them to take a break.

[1973] Prompt Sentence Examples

[1974] Design a system in which, when a staff member speaks to a robot in a factory, the robot will recognize the staff member's emotions and respond appropriately. For example, if a staff member says, "The production line is behind schedule today," the robot will register that message as a task and notify the manager.

[1975] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1976] Step 1:

[1977] The server retrieves emails from the IMAP server. Specifically, it uses the imaplib library to connect to the mail server and log in to the user's email account. It then searches through all emails in the inbox and retrieves the ID of each email. This gives a list containing the email's metadata (sender, subject, date and time, etc.).

[1978] Input: IMAP server connection information, user email account information

[1979] Output: List of email metadata

[1980] Step 2:

[1981] The server analyzes the content of the received emails and performs sentiment analysis. Specifically, it extracts the body of each email using the email library and performs sentiment analysis using the TextBlob library. As a result of the sentiment analysis, it obtains a sentiment score (positive, negative, neutral) for each email.

[1982] Input: List of email metadata

[1983] Output: Email data with sentiment scores

[1984] Step 3:

[1985] The server centralizes the analysis results and displays them on a unified interface. Specifically, it converts email data with sentiment scores into JSON format using the json library and displays it in a user-accessible interface that can be accessed through a web browser or a dedicated application.

[1986] Input: Email data with sentiment scores

[1987] Output: Display data on a unified interface

[1988] Step 4:

[1989] The server recognizes the user's emotions from the user's tone of voice, facial expressions, and message context. Specifically, emotion recognition technology is used to analyze audio and video data to identify the user's emotional state. This allows ...

Claims

1. A means for collecting messages received in a plurality of different communication means including at least two or more of email, a communication tool, a chat, and a messenger app; means for centralizing the collected messages by converting the collected messages into a common data format; a means for storing the unified message in a database in association with identification information indicating a type of communication means; A system including:

2. A means for collecting messages received via a plurality of different communication means; A means for classifying the collected messages as work-related or personal-related based on the sender address and the contents of the messages; A means for notifying the classification result; A system including:

3. A means for collecting messages received via a plurality of different communication means; A means for generating a task by analyzing the contents of the acquired message using a natural language processing technique and extracting a phrase indicating an action; A means for adding the extracted tasks to a task list; A system including:

Images