system

Abstract

Provide a system. 【Solution means】 In an electronic communication management device, Means for analyzing the received electronic communication and performing category classification; Means for summarizing the content of the electronic communication; Means for setting priorities based on the electronic communication; Means for automatically generating a template sentence for the electronic communication; Means for notifying the user of unhandled electronic communications; Means for analyzing and classifying instructions within the factory; Means for summarizing the instructions and notifying the robot operation system; Means for determining the work order of instructions according to the priority; A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to the increase in electronic communication, especially e-mails, businesspersons have to process a large amount of information, and as a result, cases where important mails are overlooked or responses are delayed frequently occur. Therefore, there is a need for a technology to efficiently manage electronic communication, set priorities, and automatically generate reply contents. Furthermore, there is a problem that it is desired to prevent the follow-up for unresponded communications from being missed.

Means for Solving the Problems

[0005] This invention is solved by an electronic communications management device. This device automatically analyzes received electronic communications, categorizes them, and summarizes their content to efficiently set priorities. It also automatically generates reply templates and notifies the user of unanswered electronic communications, providing an environment where users can respond quickly. This prevents important emails from being overlooked or delays in responses, and enables effective management of electronic communications.

[0006] "Electronic communications" refers to information sent and received via the Internet and other digital networks, and includes, but is not limited to, email, messaging applications, and collaboration tools.

[0007] A "management device" refers to a system of electronic devices and software that efficiently classifies and organizes electronic communications and assists users in processing information.

[0008] "Analysis" refers to the process of verifying the content of electronic communications using computer programs and extracting or evaluating information.

[0009] "Category classification" refers to the process of automatically assigning electronic communications to specific groups or sets based on their content and characteristics.

[0010] "Summary" means making the overall content of electronic communication concise, extracting only the main points and expressing them briefly.

[0011] "Priority" refers to the evaluation order assigned to determine the sequence of processing and response among multiple electronic communications.

[0012] A "template message" refers to a pre-formatted text used to save time when replying to messages or entering information.

[0013] "Notification" refers to the act of informing users of important information or actions related to electronic communications through warnings or alerts. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The present invention is a system that enables efficient management of electronic communications, and specifically a device that supports classification, summarization, prioritization, automatic replies, and notifications in communications, primarily email.

[0036] The server first receives electronic communications via the Internet protocol. The received emails are parsed by a natural language processing module and automatically categorized based on their content into "Notifications," "Requires Action," and "Requires a Reply." This classification allows users to manage communications more efficiently.

[0037] Next, the server summarizes the content of each email and extracts only the essential information, allowing users to quickly grasp the information. For example, in the case of a meeting invitation email, the summarization function extracts the meeting participants, date, time, and location, and presents them to the user in an easy-to-understand manner.

[0038] When prioritizing received communications, the server calculates a score considering the sender's status and the email's deadline, and determines the processing order for each communication. Based on this, the device notifies the user of high-priority emails via push notification.

[0039] When an automated reply is required, the server uses a template generation module to automatically create a reply tailored to the characteristics of the email. For example, a client's question email will generate a template such as, "Thank you for your question. We will answer the following points..."

[0040] Furthermore, the device has a function that sets reminders and notifies the user when there are unaddressed emails or when important follow-up is required. This helps users prevent missed responses and clearly understand which emails should be prioritized.

[0041] Through these processes, users can reduce the time spent on communication management and focus on critical business issues. This system configuration makes it possible to reduce user stress and improve operational efficiency.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server receives new electronic communications. The received email is passed to a natural language processing module for analysis. This automatically classifies the email into three categories: "Notification," "Requires Action," or "Requires a Reply."

[0045] Step 2:

[0046] The server shortens the content of the analyzed email using a summarization algorithm. In this process, it extracts important information and keywords to generate a summary that the user can quickly understand.

[0047] Step 3:

[0048] The server prioritizes each email based on the classification and summarization results. This involves calculating a score that takes into account sender information, email urgency, response deadline, and other factors, and then creating a priority list.

[0049] Step 4:

[0050] The device receives priority information from the server and notifies the user about high-priority electronic communications. Notifications are made on mobile and desktop devices, and users are alerted through visual alerts and audio notifications.

[0051] Step 5:

[0052] The server generates an automated reply template for emails that require a response. This template takes into account the questions and information requests contained in the email and inserts context-appropriate variables into a pre-defined format to quickly prepare an appropriate reply.

[0053] Step 6:

[0054] The server uses a reminder function to monitor unanswered emails. If a specified period has passed, it issues another notification about the unanswered email and sends that information to the user's device to remind them.

[0055] Step 7:

[0056] Users receive notifications through their devices, check high-priority emails, and respond quickly. They can also use server-generated reply templates to respond to emails as needed, ensuring smooth workflow.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In modern society, where a vast amount of electronic communication is exchanged daily, users spend a significant amount of time and effort managing it. This burden is particularly heavy in business environments where classification, prioritization, and rapid response are required, highlighting the need for efficient information management. Furthermore, incorrect prioritization and missed responses are not uncommon. To address these problems, a system is needed that automates the management of electronic communications and reduces the burden on users.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the information of the electronic communications, and means for setting priorities based on the electronic communications. This enables automatic classification of electronic communications and efficient information management.

[0062] "Electronic communication" refers to digital data sent and received via email or messaging apps.

[0063] A "control system" refers to a system of hardware and software for processing and managing electronic communications.

[0064] "Analysis" refers to the process of understanding the content of electronic communications and extracting important information.

[0065] "Category classification" refers to the process of dividing electronic communications into specific groups based on their content.

[0066] "Summarizing information" refers to the process of extracting only the essential points of electronic communication and expressing them in a concise form.

[0067] "Setting priorities" refers to the process of evaluating the importance of electronic communications and determining the order in which they will be processed.

[0068] "Generating template sentences" refers to the process of automatically creating common response sentences.

[0069] "Notifying" refers to the process of informing a user of information.

[0070] "Natural language processing" refers to the technology of enabling computers to understand and analyze human language.

[0071] "Digital communication equipment" refers to electronic devices used for sending and receiving electronic communications.

[0072] This invention provides a system for efficiently managing electronic communications, particularly for automating the analysis and processing of emails. This system consists of a combination of hardware and software.

[0073] The server receives electronic communications via a communication network. This can utilize Internet protocols such as IMAP and SMTP. The server periodically checks the mailbox to retrieve new electronic communications.

[0074] The received communications are analyzed using a natural language processing module. Specifically, open-source libraries and cloud-based services (e.g., Google® Cloud Natural Language API, IBM Watson® Natural Language Understanding) are used to understand the content of emails and extract important information.

[0075] The analyzed data is categorized and sorted into categories such as "Notifications," "Items Requiring Action," and "Items Requiring a Reply," based on the content of the electronic communications. This allows users to efficiently manage their electronic communications.

[0076] Next, the server uses a summarization algorithm (e.g., Sumy or TextRank) to summarize the email body, extracting and displaying only the key points. This summary is displayed on the email viewing screen, allowing the user to quickly grasp the information.

[0077] Furthermore, a priority setting module prioritizes communications based on factors such as the sender information and urgency of the data. High-priority communications are immediately notified to the user using the device's push notification function.

[0078] If a reply is required, the server automatically generates a template message using a generative AI model (e.g., a GPT model). This model is given the prompt message, "You are the server for the email management system. Analyze the received emails using natural language processing, classify and summarize them, prioritize them, and generate an automatic reply as needed," and then generates a specific reply message.

[0079] Finally, the device provides alerts that allow users to prevent missed communications or items requiring follow-up by setting reminder notifications.

[0080] This system allows users to reduce the time spent managing communications and focus on more important tasks. For example, users can quickly check high-priority sales emails, reducing the risk of missing or mishandling them.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server receives electronic communications via the Internet protocol. The input is email from the communication network, and the server periodically checks its mailbox to retrieve new emails. The output is email data stored for analysis. This process manages the latest electronic communications within the system.

[0084] Step 2:

[0085] The server inputs received emails into a natural language processing module and analyzes their content. Specifically, it performs text analysis and extracts keywords and phrases. As a result, the emails are categorized as "Notifications," "Requires Action," and "Requires Reply." This process allows users to efficiently grasp the information.

[0086] Step 3:

[0087] The server inputs the analyzed email content into a summarization algorithm. This algorithm extracts the key points of the information and generates a shortened version. The output of this summarization process is concise and easily readable by the user. For example, a meeting invitation email might be summarized as "Meeting, October 10th, 2 PM, Meeting Room A."

[0088] Step 4:

[0089] The server inputs email data into a priority setting module and determines priority based on sender information and deadlines. As part of the data processing, it evaluates the sender's job title and the deadline stated in the email, and calculates a score. The output is a prioritized email list, which the terminal uses to notify the user of important emails.

[0090] Step 5:

[0091] The server generates automatic replies as needed. The input is an email that is deemed to require a reply, and prompts are given to the generation AI model to create a template. The output is the automatically generated reply email. For example, an inquiry email from a client will generate content such as, "Thank you for your question. We will answer the following points..."

[0092] Step 6:

[0093] The device sets reminders for unanswered or follow-up emails and notifies the user. The input is a list of high-priority emails, and the output is a notification alert to the user. This ensures that users don't forget to address important emails.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In modern factories, numerous instructions are communicated electronically, requiring rapid analysis, classification, and prioritization of these instructions for efficient work. Delays in instruction transmission and response lead to decreased productivity, necessitating systems to improve this process.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for setting priorities based on the electronic communications. This enables efficient management of instructions within the factory and rapid execution of important tasks.

[0099] "Electronic communications" refers to information that is transmitted and received in digital format via the internet or other means.

[0100] A "management device" is a device used within a system to receive, analyze, classify, and notify data.

[0101] "Means for categorization" refers to a function that performs the process of sorting received electronic communications into specific groups or categories based on their content.

[0102] A "summarization tool" is a function that extracts important information from the content of electronic communications and displays it in a shortened form.

[0103] A "means of setting priorities" is a function that determines the order in which communications are processed based on their urgency and importance.

[0104] "Means for generating template text" refers to a function that automatically creates standardized text and allows for quick replies.

[0105] "Notification methods" refer to functions that inform users of information in order to warn or alert them about unsupported or important communications.

[0106] "Means for analyzing and classifying instructions within a factory" refers to the function of understanding instructions within a factory and assigning them to the appropriate response category.

[0107] "Means of notifying the robot operation system" refers to a function that transmits the analyzed information to the robot control system to determine the next action.

[0108] "Means for determining the order of tasks" refers to a function that determines the order in which tasks should be performed based on received instructions, from the perspective of efficiency.

[0109] This invention is a system aimed at managing electronic communications and efficiently processing instructions within a factory. The server receives electronic communications via the Internet protocol and analyzes emails using a natural language processing module. The analyzed content is automatically categorized into "Notifications," "Items Requiring Action," and "Items Requiring a Reply." This allows users to efficiently manage electronic communications. Furthermore, the server generates email summaries and extracts specific information such as meeting invitations. This summarization function allows users to quickly grasp important information.

[0110] When setting priorities, the server considers sender information and communication deadlines, calculates a score, and determines the processing order. This allows the terminal to notify the user of high-priority emails via push notifications. If necessary, the server uses a template message generation module to create an automated reply message, such as "Thank you for your question. We will answer the following points..."

[0111] On the other hand, regarding instructions within the factory, the server analyzes the instructions and quickly transmits them to the factory robots. The instructions are analyzed and classified into "must be executed immediately," "requires confirmation," and "can be executed later," and the important parts are notified to the robot operation system. In addition, the order of work is determined according to urgency, and high-priority instructions can be executed first.

[0112] For example, if a factory manager sends an email instructing them to "inspect equipment A immediately," this email will be classified as "requires immediate action," and the factory robots can begin the inspection work right away. This entire process improves work efficiency within the factory and enables quicker responses.

[0113] Examples of prompts for a generative AI model include the following:

[0114] "Factory instruction email analysis: Equipment A inspection requires immediate action due to urgency assessment. Please create a summary and feedback template. Email body: 'Inspect Equipment A immediately.'"

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server receives electronic communications via the Internet Protocol. The input is unparsed email. The output is raw email data that needs to be parsed, and this data is processed in the next step.

[0118] Step 2:

[0119] The server parses received emails using a natural language processing module. The input is raw email data. The server analyzes the content of the email, tokenizes the document, and performs syntactic analysis. Based on this analysis, the output is data categorized as "Notifications," "Requires Action," and "Requires Reply."

[0120] Step 3:

[0121] The server summarizes the content of the analyzed emails. The input is analyzed email data with assigned categories. The server uses a summarization algorithm to extract important information from the text and summarize key points such as meeting participants, date, time, and location. The output is summarized data that clearly shows the information important to the user.

[0122] Step 4:

[0123] The server sets priorities based on the information it detects during the analysis process. The input is summarized email data. The server considers sender information, deadlines stated in the email, etc., and applies a scoring algorithm to calculate priorities. The output is email data with assigned priorities.

[0124] Step 5:

[0125] The server generates template messages for automatic replies as needed. The input is email data that has been prioritized and categorized. The template generation module selects the appropriate template based on the intent of the email and creates an automatic reply message. The output is the automatically generated reply message.

[0126] Step 6:

[0127] The device uses a notification system to inform the user of unsupported communications and high-priority emails. The input is the email information to be notified to the user. The device alerts the user using means such as push notifications and alerts. The output is the information received by the user as a notification.

[0128] Step 7:

[0129] The server receives and analyzes instructions from within the factory. The input is instruction emails issued within the factory. The server uses natural language processing to analyze these instructions and classifies them into "Requires immediate execution," "Requires confirmation," and "Can be executed later." The output is data with the instruction category determined.

[0130] Step 8:

[0131] The terminal notifies the robot operation system of the analyzed instructions and directs it to take appropriate action. The input is instruction data with a determined category. The robot operation system initiates actions to perform tasks according to priority. The output is the efficient and rapid completion of processes within the factory.

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] This invention incorporates an emotion engine that recognizes and adjusts user emotions into a system that enables efficient management of electronic communications. This allows for communication management based on the user's emotional state, enabling flexible responses tailored to individual situations.

[0134] The server first receives electronic communications via the Internet protocol. The received emails are parsed via a natural language processing module and automatically classified based on their content. After classification, the server summarizes the content and extracts important information. At this point, to understand the user's emotional state, the emotion engine analyzes the user's facial expression data, voice data, or keyboard input speed to estimate the current emotional state.

[0135] Next, the server uses the information obtained from the emotion engine to adjust priorities and notification methods. For example, if a user is stressed, the server may reduce notifications for high-priority emails or change the notification method to a quieter one. Conversely, when the user is relaxed, notifications are sent as usual.

[0136] The device receives notification information from the server and displays alerts to the user in the most optimal format. Based on the results presented by the emotion engine, the notification interface is dynamically adjusted to ensure the user receives the notifications in a less stressful way.

[0137] Furthermore, the server automatically generates a template message tailored to the purpose of the email when it recognizes that a reply is required. This template message is adjusted according to the user's emotional state, and may, for example, adopt a gentle tone to alleviate stress.

[0138] This system also includes a feature to monitor unanswered emails and send reminders as needed. The timing and format of these reminders are also adjusted based on the analysis results of the emotion engine. This allows users to reduce the hassle of receiving emails and enjoy priority settings that are tailored to their emotions.

[0139] Through this process, users can efficiently manage business-related communications while reducing emotional burden. Individualized responses tailored to emotional states lead to improved work efficiency and a more personalized experience.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The server receives electronic communications via the internet. The emails are input into a natural language processing module for analysis, which automatically categorizes the content into "Notification," "Action Required," and "Reply Required."

[0143] Step 2:

[0144] The server passes the classified emails to a summarization module, which generates a summary containing key information. This summary is then presented to the user to support quick decision-making.

[0145] Step 3:

[0146] Based on data obtained from the user's device, such as input from a facial recognition camera or voice analysis device, the server activates an emotion engine. This determines the user's current emotional state.

[0147] Step 4:

[0148] The server uses the results of the emotion engine's analysis to prioritize emails and set notification formats. For example, if a user is feeling stressed, it may refrain from sending important notifications or send them in a gentler manner.

[0149] Step 5:

[0150] The device receives notification information from the server and presents it to the user in a format that best suits the user's emotional state. This allows the user to receive the necessary information in the most appropriate situation.

[0151] Step 6:

[0152] The server automatically generates template responses for emails that require a reply. These responses are tailored to the user's emotional state to ensure a stress-free experience.

[0153] Step 7:

[0154] The server sets a reminder for unprocessed emails and notifies the user again. The timing and format of this notification can also be flexibly changed according to the user's emotional state.

[0155] Step 8:

[0156] Users manage their electronic communications while reducing stress through emotionally sensitive communication content and notifications. Each process is implemented in a way that is sensitive to the user's psychological state.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] In electronic communication processing, there are challenges in prioritizing and adjusting notification methods while considering the user's emotions and state, as well as the need for efficient communication management while reducing stress. Current systems do not adequately achieve flexible processing that responds to the individual user's state.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for analyzing the user's state. This enables flexible notification methods and priority adjustments according to the user's state. Furthermore, this results in less stressful communication processing and management.

[0162] "Received electronic communications" refers to electronic information such as emails and messages acquired by communication devices.

[0163] "Analysis" refers to the process of analyzing the content of electronic communications and extracting necessary information and features.

[0164] "Category classification" refers to the process of grouping analyzed electronic communications based on their content and importance, and assigning them to specific categories.

[0165] "Summarization" refers to the act of extracting and shortening important information while maintaining the overall meaning of electronic communication.

[0166] "User state" refers to the user's emotions and psychological condition, and serves as an indicator for the system to adapt to these factors.

[0167] "Notification method" refers to the means or format used to transmit information or messages to users.

[0168] "Priority adjustment" refers to the process of determining the priority of processing received electronic communications and optimizing them to meet user needs.

[0169] "Means of generating documents" refers to algorithms or processes for writing appropriate templates and content for electronic communication.

[0170] "Unanswered electronic communications" refers to electronic messages that the user has not yet processed and that require a response or action.

[0171] "Communication equipment" refers to physical devices or equipment used to send, receive, and process electronic communications.

[0172] This invention aims to implement an electronic communication system that takes into account the emotional state of the user. The system mainly consists of a server and terminals, which work together in appropriate coordination.

[0173] The server receives electronic communications using Internet protocols. For email analysis, it uses Python's NLTK library or Google's Cloud Natural Language API as natural language processing modules. The analyzed electronic communications are categorized based on their content, and important information is extracted and summarized. In addition, the server uses an emotion engine to analyze the user's facial expressions, voice data, or keyboard input speed to evaluate the user's emotional state. For the emotion engine, it can use OpenAI's Sentiment Analysis API or Microsoft's Azure Emotion API.

[0174] Based on the analysis results, the server prioritizes electronic communications and adjusts notification methods. If the user is experiencing stress, notifications will be reduced or silent notifications will be sent depending on their importance. Once the user's stress level decreases, the server will resume normal notifications.

[0175] The device dynamically changes its notification interface to effectively convey information received from the server to the user. To minimize user stress, the device can choose a quiet and unobtrusive notification method.

[0176] For electronic communications requiring a response, the server automatically generates template messages using an AI model. The generated documents may reflect considerations for reducing the user's emotional state and stress. An example of a specific prompt message might be, "Generate a response that takes user stress reduction into consideration."

[0177] Finally, the server monitors unresolved electronic communications and sets reminders as needed. These reminders are flexibly adjusted according to the user's emotional state, enabling comfortable email management. This system reduces the emotional burden of managing electronic communications, providing a more personalized experience for users.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The server receives electronic communications via the Internet protocol. The data received as input is the content of emails. After reception, the emails are parsed using a natural language processing module. Specifically, the grammatical structure is analyzed and keywords are extracted using the Python NLTK library. The output is the category and summarized content of the analyzed emails.

[0181] Step 2:

[0182] The server categorizes received electronic communications based on the analysis results. The input is the summary information obtained in step 1. Categorization considers the urgency of the email and the importance of the sender. Specifically, it uses text mining techniques to refer to similar past communication data. The output is a list of emails with assigned priorities.

[0183] Step 3:

[0184] The server performs analysis to evaluate the user's current emotional state. Inputs include the user's facial expression data, voice data, and keyboard input speed. The emotion engine utilizes OpenAI's emotion analysis API to process this data and estimate the emotional state. The output is evaluation data indicating the emotional state, such as stress or relaxation.

[0185] Step 4:

[0186] The server adjusts notification methods and priorities based on the user's emotional state. The input is the information obtained in steps 2 and 3. Specifically, if the server determines that the user is experiencing high stress, it will process notifications by reducing their volume or other actions. The output is the adjusted notification schedule and prioritized email settings.

[0187] Step 5:

[0188] The device receives notification information from the server and presents it to the user in the most appropriate format. Inputs include the notification schedule and email priority. Specifically, the device communicates to the user via voice notification, vibration, or visual alert. Output is the implementation of notifications designed to minimize stress.

[0189] Step 6:

[0190] The server automatically generates template responses to electronic communications requiring a reply. The input consists of the email content and the user's emotional state. Using a generation AI model, the prompt "Generate a reply that considers reducing user stress" is applied to create an appropriate template response. The output is a template response tailored to the user's emotional state.

[0191] Step 7:

[0192] The server monitors unanswered emails and sets reminders as needed. Inputs include a list of unanswered emails and the user's emotional state. Based on the analysis, reminders are set at the optimal time. Specifically, it adjusts the reminder frequency by referring to past reply history. The output is a reminder notification that takes the user's emotional state into consideration.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0195] With the increase in information and communication in modern times, the amount of electronic communication individuals receive has become enormous, and many users experience stress in processing it. In particular, there is a need to appropriately distinguish between urgent and non-urgent communications and to respond appropriately according to one's emotional state. However, conventional systems lack the ability to adjust priorities based on emotional state and to suggest less stressful notification methods. As a result, users may end up feeling emotionally burdened.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0197] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for adjusting notification methods using an emotion engine that analyzes the user's emotional state. This enables users to have optimal notification settings that are tailored to their own emotions and to manage communications in a way that reduces stress.

[0198] An "efficient electronic communications management device" is an information processing system equipped with functions to analyze received electronic communications and appropriately classify, summarize, and prioritize them.

[0199] A "means for performing category classification" refers to a processing device that has the function of dividing received electronic communications into specific categories based on their content.

[0200] A "means of summarizing content" refers to a system that has the function of condensing the vast amount of information received from electronic communications into a concise format.

[0201] A "means for setting priorities" is a device that has the function of determining the order in which to process received electronic communications according to their importance.

[0202] "Means of adjusting notification methods using an emotion engine" refers to a system that estimates the user's emotional state from their facial expressions and voice, and selects the most appropriate notification method based on that.

[0203] A "means for generating template statements" refers to a software process that has the function of automatically generating response statements in response to received electronic communications.

[0204] "Means for estimating emotions using a user's facial expressions or voice" refers to an analytical device equipped with the function of analyzing data such as the user's facial features and tone of voice to identify the user's emotional state.

[0205] A "means of suggesting payment methods" refers to a system that has a process for presenting appropriate payment methods based on the user's emotional state.

[0206] A description of the embodiment for carrying out the invention will be provided.

[0207] The system in this invention is a device for the efficient management of electronic communications and can be implemented using specific hardware and software. The server receives electronic communications via a network, analyzes their content, and categorizes them. Natural language processing is utilized for the analysis, and NLP libraries and APIs may be used. Generative AI models can be used to summarize the content of emails, and a database management system may be used on the server side in the process of extracting important information.

[0208] To recognize a user's emotions, hardware such as the smartphone's camera and microphone are used. The emotion engine software analyzes facial expression and voice data acquired from these devices. It estimates whether the user is experiencing stress and adjusts the notification method and priority accordingly. Specifically, it performs actions such as switching to silent mode when delivering notifications via the display device or speaker. This operation is synchronized on the user's device, ensuring that information is transmitted at the optimal time.

[0209] Furthermore, in the template sentence generation process, the system automatically creates response suggestions. These may include a tone that is appropriate and stress-relieving, selected by the generated AI model based on emotional information.

[0210] For example, if a user receives an important message during a busy time, the system will sense their stress level from their facial expression and immediately refrain from sending a notification. Furthermore, when the user is relaxed after work, the system will use a gentler tone in the template message and automatically send a notification. This reduces the user's emotional burden while achieving efficient communication management.

[0211] An example of a prompt is, "Design an AI model that infers emotions from a user's facial expression image and returns tags such as 'stress' or 'relaxed'." Using this example makes it easier to start designing the AI ​​model necessary for emotion recognition.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The server receives electronic communications over the network. The input is electronic communication data, acquired using the Internet Protocol. The output is unprocessed message data awaiting analysis.

[0215] Step 2:

[0216] The server analyzes received electronic communications using a natural language processing library and performs categorization. Based on the content of the communications, they are categorized into business, private, spam, etc. The input is unprocessed message data, and the output is data with category labels attached. Specifically, it applies a text analysis algorithm.

[0217] Step 3:

[0218] The server summarizes the content of the analyzed electronic communications and extracts important information using a natural language generation AI model. The input is electronic communications data with category labels, and the output is summarized text. The generation AI model performs the summarization using prompt sentences.

[0219] Step 4:

[0220] The device activates the smartphone's camera and microphone to estimate the user's emotions and acquire facial and audio data. The input is the user's real-time video and audio, and the output is raw data that is fed into the emotion engine. This operation includes camera control and audio sampling.

[0221] Step 5:

[0222] The device uses an emotion engine to analyze the user's emotional state and performs digital signal processing. The input is raw data, and the output is emotion tags such as "stress" and "relaxed." The emotion engine extracts facial and vocal features.

[0223] Step 6:

[0224] The server adjusts notification methods based on the user's emotional state. Inputs include emotional tags and summarized text, which determine notification priority and method. Outputs are the adjusted notification settings, specifically changes to notification volume and vibration mode.

[0225] Step 7:

[0226] The server automatically generates template sentences, adjusting the tone to reflect the user's emotional state. Input consists of summarized text and emotion tags, while output is a template reply. A generative AI model handles language generation.

[0227] Step 8:

[0228] The device displays customized notifications to the user. Input consists of notification settings and template text, while output is a visual or auditory notification to the user. Specific actions include on-screen displays and audio alerts.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] The present invention is a system that enables efficient management of electronic communications, and specifically a device that supports classification, summarization, prioritization, automatic replies, and notifications in communications, primarily email.

[0246] The server first receives electronic communications via the Internet protocol. The received emails are parsed by a natural language processing module and automatically categorized based on their content into "Notifications," "Requires Action," and "Requires a Reply." This classification allows users to manage communications more efficiently.

[0247] Next, the server summarizes the content of each email and extracts only the essential information, allowing users to quickly grasp the information. For example, in the case of a meeting invitation email, the summarization function extracts the meeting participants, date, time, and location, and presents them to the user in an easy-to-understand manner.

[0248] When prioritizing received communications, the server calculates a score considering the sender's status and the email's deadline, and determines the processing order for each communication. Based on this, the device notifies the user of high-priority emails via push notification.

[0249] When an automated reply is required, the server uses a template generation module to automatically create a reply tailored to the characteristics of the email. For example, a client's question email will generate a template such as, "Thank you for your question. We will answer the following points..."

[0250] Furthermore, the device has a function that sets reminders and notifies the user when there are unaddressed emails or when important follow-up is required. This helps users prevent missed responses and clearly understand which emails should be prioritized.

[0251] Through these processes, users can reduce the time spent on communication management and focus on critical business issues. This system configuration makes it possible to reduce user stress and improve operational efficiency.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The server receives new electronic communications. The received email is passed to a natural language processing module for analysis. This automatically classifies the email into three categories: "Notification," "Requires Action," or "Requires a Reply."

[0255] Step 2:

[0256] The server shortens the content of the analyzed email using a summarization algorithm. In this process, it extracts important information and keywords to generate a summary that the user can quickly understand.

[0257] Step 3:

[0258] The server prioritizes each email based on the classification and summarization results. This involves calculating a score that takes into account sender information, email urgency, response deadline, and other factors, and then creating a priority list.

[0259] Step 4:

[0260] The device receives priority information from the server and notifies the user about high-priority electronic communications. Notifications are made on mobile and desktop devices, and users are alerted through visual alerts and audio notifications.

[0261] Step 5:

[0262] The server generates an automated reply template for emails that require a response. This template takes into account the questions and information requests contained in the email and inserts context-appropriate variables into a pre-defined format to quickly prepare an appropriate reply.

[0263] Step 6:

[0264] The server uses a reminder function to monitor unanswered emails. If a specified period has passed, it issues another notification about the unanswered email and sends that information to the user's device to remind them.

[0265] Step 7:

[0266] Users receive notifications through their devices, check high-priority emails, and respond quickly. They can also use server-generated reply templates to respond to emails as needed, ensuring smooth workflow.

[0267] (Example 1)

[0268] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] In modern society, where a vast amount of electronic communication is exchanged daily, users spend a significant amount of time and effort managing it. This burden is particularly heavy in business environments where classification, prioritization, and rapid response are required, highlighting the need for efficient information management. Furthermore, incorrect prioritization and missed responses are not uncommon. To address these problems, a system is needed that automates the management of electronic communications and reduces the burden on users.

[0270] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0271] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the information of the electronic communications, and means for setting priorities based on the electronic communications. This enables automatic classification of electronic communications and efficient information management.

[0272] "Electronic communication" refers to digital data sent and received via email or messaging apps.

[0273] A "control system" refers to a system of hardware and software for processing and managing electronic communications.

[0274] "Analysis" refers to the process of understanding the content of electronic communications and extracting important information.

[0275] "Category classification" refers to the process of dividing electronic communications into specific groups based on their content.

[0276] "Summarize information" refers to the process of extracting only the important points of an electronic communication and presenting them in a concise form.

[0277] "Set priorities" refers to the process of evaluating the importance of an electronic communication and determining the order of processing.

[0278] "Generate template sentences" refers to the process of automatically creating general response sentences.

[0279] "Notify" refers to the process of informing a user of information.

[0280] "Natural language processing" refers to the technology of enabling a computer to understand human language and perform analysis.

[0281] "Digital communication device" refers to an electronic device for transmitting and receiving electronic communications.

[0282] The present invention is a system for efficiently managing electronic communications, particularly for automating the analysis and processing of emails. This system is composed of a combination of hardware and software.

[0283] The server receives electronic communications via a communication network. For this, IMAP or SMTP of the Internet protocol can be used. The server periodically checks the mailbox and acquires new electronic communications.

[0284] The received communications are analyzed using a natural language processing module. Specifically, open-source libraries or cloud-based services (e.g., Google Cloud Natural Language API, IBM Watson Natural Language Understanding) are used to understand the content of the email and extract important information.

[0285] The analyzed data is categorized and sorted into categories such as "Notifications", "Items Requiring Action", and "Items Requiring Reply" based on the content of electronic communications. This enables users to efficiently manage their electronic communications.

[0286] Next, the server utilizes summarization algorithms (e.g., Sumy or TextRank) to summarize the email body, extracting only the important points for display. This summary is shown on the email viewing screen, allowing users to quickly grasp the information.

[0287] Furthermore, the priority setting module sets priorities considering the sender information and urgency of the communication data. High-priority communications are immediately notified to the user using the terminal's push notification function.

[0288] If a reply is required, the server automatically generates a template message using a generative AI model (e.g., GPT model). This model is given the prompt "You are the server of an email management system. Analyze the received emails using natural language processing, classify, summarize, set priorities, and generate an automatic reply if necessary." and generates a specific reply message.

[0289] Finally, the terminal sets reminder notifications for unaddressed communications and items requiring follow-up, providing alerts that prevent users from missing any actions.

[0290] This system creates an environment where users can shorten the time spent on communication management and focus on more important tasks. For example, users can quickly check high-priority sales emails, reducing the risk of oversight or incorrect responses.

[0291] The flow of the specific process in Example 1 will be described using FIG. 11.

[0292] Step 1:

[0293] The server receives electronic communications via the Internet protocol. The input is email from the communication network, and the server periodically checks its mailbox to retrieve new emails. The output is email data stored for analysis. This process manages the latest electronic communications within the system.

[0294] Step 2:

[0295] The server inputs received emails into a natural language processing module and analyzes their content. Specifically, it performs text analysis and extracts keywords and phrases. As a result, the emails are categorized as "Notifications," "Requires Action," and "Requires Reply." This process allows users to efficiently grasp the information.

[0296] Step 3:

[0297] The server inputs the analyzed email content into a summarization algorithm. This algorithm extracts the key points of the information and generates a shortened version. The output of this summarization process is concise and easily readable by the user. For example, a meeting invitation email might be summarized as "Meeting, October 10th, 2 PM, Meeting Room A."

[0298] Step 4:

[0299] The server inputs email data into a priority setting module and determines priority based on sender information and deadlines. As part of the data processing, it evaluates the sender's job title and the deadline stated in the email, and calculates a score. The output is a prioritized email list, which the terminal uses to notify the user of important emails.

[0300] Step 5:

[0301] The server generates an automatic reply as needed. The input is an email for which a reply is determined to be necessary, and a prompt sentence is given to the generation AI model to create a template sentence. The output is an automatically created reply email. For example, for an inquiry email from a client, content such as "Thank you for your question. We will answer the following points..." is generated.

[0302] Step 6:

[0303] The terminal sets a reminder for unhandled emails and emails that require follow-up, and notifies the user. The input is a high-priority email list, and the output is a notification alert to the user. With this operation, the user can ensure that they do not forget to handle important emails.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] In modern factories, a large number of instructions are carried out through electronic communication, and it is required to quickly analyze, classify these instructions, and perform work with appropriate priorities for efficient operation. Since delays in instruction transmission and response cause a decrease in productivity, a system for improving this is necessary.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example is realized by the following means.

[0308] In this invention, the server includes means for analyzing the received electronic communication to perform category classification, means for summarizing the content of the electronic communication, and means for setting a priority based on the electronic communication. Thereby, it becomes possible to efficiently manage instructions in the factory and quickly perform important work.

[0309] "Electronic communications" refers to information that is transmitted and received in digital format via the internet or other means.

[0310] A "management device" is a device used within a system to receive, analyze, classify, and notify data.

[0311] "Means for categorization" refers to a function that performs the process of sorting received electronic communications into specific groups or categories based on their content.

[0312] A "summarization tool" is a function that extracts important information from the content of electronic communications and displays it in a shortened form.

[0313] A "means of setting priorities" is a function that determines the order in which communications are processed based on their urgency and importance.

[0314] "Means for generating template text" refers to a function that automatically creates standardized text and allows for quick replies.

[0315] "Notification methods" refer to functions that inform users of information in order to warn or alert them about unsupported or important communications.

[0316] "Means for analyzing and classifying instructions within a factory" refers to the function of understanding instructions within a factory and assigning them to the appropriate response category.

[0317] "Means of notifying the robot operation system" refers to a function that transmits the analyzed information to the robot control system to determine the next action.

[0318] "Means for determining the order of tasks" refers to a function that determines the order in which tasks should be performed based on received instructions, from the perspective of efficiency.

[0319] This invention is a system aimed at managing electronic communications and efficiently processing instructions within a factory. The server receives electronic communications via the Internet protocol and analyzes emails using a natural language processing module. The analyzed content is automatically categorized into "Notifications," "Items Requiring Action," and "Items Requiring a Reply." This allows users to efficiently manage electronic communications. Furthermore, the server generates email summaries and extracts specific information such as meeting invitations. This summarization function allows users to quickly grasp important information.

[0320] When setting priorities, the server considers sender information and communication deadlines, calculates a score, and determines the processing order. This allows the terminal to notify the user of high-priority emails via push notifications. If necessary, the server uses a template message generation module to create an automated reply message, such as "Thank you for your question. We will answer the following points..."

[0321] On the other hand, regarding instructions within the factory, the server analyzes the instructions and quickly transmits them to the factory robots. The instructions are analyzed and classified into "must be executed immediately," "requires confirmation," and "can be executed later," and the important parts are notified to the robot operation system. In addition, the order of work is determined according to urgency, and high-priority instructions can be executed first.

[0322] For example, if a factory manager sends an email instructing them to "inspect equipment A immediately," this email will be classified as "requires immediate action," and the factory robots can begin the inspection work right away. This entire process improves work efficiency within the factory and enables quicker responses.

[0323] Examples of prompts for a generative AI model include the following:

[0324] "Factory instruction email analysis: Equipment A inspection requires immediate action due to urgency assessment. Please create a summary and feedback template. Email body: 'Inspect Equipment A immediately.'"

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The server receives electronic communications via the Internet Protocol. The input is unparsed email. The output is raw email data that needs to be parsed, and this data is processed in the next step.

[0328] Step 2:

[0329] The server parses received emails using a natural language processing module. The input is raw email data. The server analyzes the content of the email, tokenizes the document, and performs syntactic analysis. Based on this analysis, the output is data categorized as "Notifications," "Requires Action," and "Requires Reply."

[0330] Step 3:

[0331] The server summarizes the content of the analyzed emails. The input is analyzed email data with assigned categories. The server uses a summarization algorithm to extract important information from the text and summarize key points such as meeting participants, date, time, and location. The output is summarized data that clearly shows the information important to the user.

[0332] Step 4:

[0333] The server sets priorities based on the information it detects during the analysis process. The input is summarized email data. The server considers sender information, deadlines stated in the email, etc., and applies a scoring algorithm to calculate priorities. The output is email data with assigned priorities.

[0334] Step 5:

[0335] The server generates template messages for automatic replies as needed. The input is email data that has been prioritized and categorized. The template generation module selects the appropriate template based on the intent of the email and creates an automatic reply message. The output is the automatically generated reply message.

[0336] Step 6:

[0337] The device uses a notification system to inform the user of unsupported communications and high-priority emails. The input is the email information to be notified to the user. The device alerts the user using means such as push notifications and alerts. The output is the information received by the user as a notification.

[0338] Step 7:

[0339] The server receives and analyzes instructions from within the factory. The input is instruction emails issued within the factory. The server uses natural language processing to analyze these instructions and classifies them into "Requires immediate execution," "Requires confirmation," and "Can be executed later." The output is data with the instruction category determined.

[0340] Step 8:

[0341] The terminal notifies the robot operation system of the analyzed instructions and directs it to take appropriate action. The input is instruction data with a determined category. The robot operation system initiates actions to perform tasks according to priority. The output is the efficient and rapid completion of processes within the factory.

[0342] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0343] This invention incorporates an emotion engine that recognizes and adjusts user emotions into a system that enables efficient management of electronic communications. This allows for communication management based on the user's emotional state, enabling flexible responses tailored to individual situations.

[0344] The server first receives electronic communications via the Internet protocol. The received emails are parsed via a natural language processing module and automatically classified based on their content. After classification, the server summarizes the content and extracts important information. At this point, to understand the user's emotional state, the emotion engine analyzes the user's facial expression data, voice data, or keyboard input speed to estimate the current emotional state.

[0345] Next, the server uses the information obtained from the emotion engine to adjust priorities and notification methods. For example, if a user is stressed, the server may reduce notifications for high-priority emails or change the notification method to a quieter one. Conversely, when the user is relaxed, notifications are sent as usual.

[0346] The device receives notification information from the server and displays alerts to the user in the most optimal format. Based on the results presented by the emotion engine, the notification interface is dynamically adjusted to ensure the user receives the notifications in a less stressful way.

[0347] Furthermore, the server automatically generates a template message tailored to the purpose of the email when it recognizes that a reply is required. This template message is adjusted according to the user's emotional state, and may, for example, adopt a gentle tone to alleviate stress.

[0348] This system also includes a feature to monitor unanswered emails and send reminders as needed. The timing and format of these reminders are also adjusted based on the analysis results of the emotion engine. This allows users to reduce the hassle of receiving emails and enjoy priority settings that are tailored to their emotions.

[0349] Through this process, users can efficiently manage business-related communications while reducing emotional burden. Individualized responses tailored to emotional states lead to improved work efficiency and a more personalized experience.

[0350] The following describes the processing flow.

[0351] Step 1:

[0352] The server receives electronic communications via the internet. The emails are input into a natural language processing module for analysis, which automatically categorizes the content into "Notification," "Action Required," and "Reply Required."

[0353] Step 2:

[0354] The server passes the classified emails to a summarization module, which generates a summary containing key information. This summary is then presented to the user to support quick decision-making.

[0355] Step 3:

[0356] Based on data obtained from the user's device, such as input from a facial recognition camera or voice analysis device, the server activates an emotion engine. This determines the user's current emotional state.

[0357] Step 4:

[0358] The server uses the results of the emotion engine's analysis to prioritize emails and set notification formats. For example, if a user is feeling stressed, it may refrain from sending important notifications or send them in a gentler manner.

[0359] Step 5:

[0360] The device receives notification information from the server and presents it to the user in a format that best suits the user's emotional state. This allows the user to receive the necessary information in the most appropriate situation.

[0361] Step 6:

[0362] The server automatically generates template responses for emails that require a reply. These responses are tailored to the user's emotional state to ensure a stress-free experience.

[0363] Step 7:

[0364] The server sets a reminder for unprocessed emails and notifies the user again. The timing and format of this notification can also be flexibly changed according to the user's emotional state.

[0365] Step 8:

[0366] Users manage their electronic communications while reducing stress through emotionally sensitive communication content and notifications. Each process is implemented in a way that is sensitive to the user's psychological state.

[0367] (Example 2)

[0368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0369] In electronic communication processing, there are challenges in prioritizing and adjusting notification methods while considering the user's emotions and state, as well as the need for efficient communication management while reducing stress. Current systems do not adequately achieve flexible processing that responds to the individual user's state.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for analyzing the user's state. This enables flexible notification methods and priority adjustments according to the user's state. Furthermore, this results in less stressful communication processing and management.

[0372] "Received electronic communications" refers to electronic information such as emails and messages acquired by communication devices.

[0373] "Analysis" refers to the process of analyzing the content of electronic communications and extracting necessary information and features.

[0374] "Category classification" refers to the process of grouping analyzed electronic communications based on their content and importance, and assigning them to specific categories.

[0375] "Summarization" refers to the act of extracting and shortening important information while maintaining the overall meaning of electronic communication.

[0376] "User state" refers to the user's emotions and psychological condition, and serves as an indicator for the system to adapt to these factors.

[0377] "Notification method" refers to the means or format used to transmit information or messages to users.

[0378] "Priority adjustment" refers to the process of determining the priority of processing received electronic communications and optimizing them to meet user needs.

[0379] "Means of generating documents" refers to algorithms or processes for writing appropriate templates and content for electronic communication.

[0380] "Unanswered electronic communications" refers to electronic messages that the user has not yet processed and that require a response or action.

[0381] "Communication equipment" refers to physical devices or equipment used to send, receive, and process electronic communications.

[0382] This invention aims to implement an electronic communication system that takes into account the emotional state of the user. The system mainly consists of a server and terminals, which work together in appropriate coordination.

[0383] The server receives electronic communications using internet protocols. For email analysis, it uses Python's NLTK library or Google's Cloud Natural Language API as natural language processing modules. The analyzed electronic communications are categorized based on their content, and important information is extracted and summarized. In addition, the server uses an emotion engine to analyze the user's facial expressions, voice data, or keyboard input speed to evaluate the user's emotional state. For the emotion engine, OpenAI's Sentiment Analysis API or Microsoft's Azure Emotion API can be used.

[0384] Based on the analysis results, the server prioritizes electronic communications and adjusts notification methods. If the user is experiencing stress, notifications will be reduced or silent notifications will be sent depending on their importance. Once the user's stress level decreases, the server will resume normal notifications.

[0385] The device dynamically changes its notification interface to effectively convey information received from the server to the user. To minimize user stress, the device can choose a quiet and unobtrusive notification method.

[0386] For electronic communications requiring a response, the server automatically generates template messages using an AI model. The generated documents may reflect considerations for reducing the user's emotional state and stress. An example of a specific prompt message might be, "Generate a response that takes user stress reduction into consideration."

[0387] Finally, the server monitors unresolved electronic communications and sets reminders as needed. These reminders are flexibly adjusted according to the user's emotional state, enabling comfortable email management. This system reduces the emotional burden of managing electronic communications, providing a more personalized experience for users.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The server receives electronic communications via the Internet protocol. The data received as input is the content of emails. After reception, the emails are parsed using a natural language processing module. Specifically, the grammatical structure is analyzed and keywords are extracted using the Python NLTK library. The output is the category and summarized content of the analyzed emails.

[0391] Step 2:

[0392] The server categorizes received electronic communications based on the analysis results. The input is the summary information obtained in step 1. Categorization considers the urgency of the email and the importance of the sender. Specifically, it uses text mining techniques to refer to similar past communication data. The output is a list of emails with assigned priorities.

[0393] Step 3:

[0394] The server performs analysis to evaluate the user's current emotional state. Inputs include the user's facial expression data, voice data, and keyboard input speed. The emotion engine utilizes OpenAI's emotion analysis API to process this data and estimate the emotional state. The output is evaluation data indicating the emotional state, such as stress or relaxation.

[0395] Step 4:

[0396] The server adjusts notification methods and priorities based on the user's emotional state. The input is the information obtained in steps 2 and 3. Specifically, if the server determines that the user is experiencing high stress, it will process notifications by reducing their volume or other actions. The output is the adjusted notification schedule and prioritized email settings.

[0397] Step 5:

[0398] The device receives notification information from the server and presents it to the user in the most appropriate format. Inputs include the notification schedule and email priority. Specifically, the device communicates to the user via voice notification, vibration, or visual alert. Output is the implementation of notifications designed to minimize stress.

[0399] Step 6:

[0400] The server automatically generates template responses to electronic communications requiring a reply. The input consists of the email content and the user's emotional state. Using a generation AI model, the prompt "Generate a reply that considers reducing user stress" is applied to create an appropriate template response. The output is a template response tailored to the user's emotional state.

[0401] Step 7:

[0402] The server monitors unanswered emails and sets reminders as needed. Inputs include a list of unanswered emails and the user's emotional state. Based on the analysis, reminders are set at the optimal time. Specifically, it adjusts the reminder frequency by referring to past reply history. The output is a reminder notification that takes the user's emotional state into consideration.

[0403] (Application Example 2)

[0404] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0405] With the increase in information and communication in modern times, the amount of electronic communication individuals receive has become enormous, and many users experience stress in processing it. In particular, there is a need to appropriately distinguish between urgent and non-urgent communications and to respond appropriately according to one's emotional state. However, conventional systems lack the ability to adjust priorities based on emotional state and to suggest less stressful notification methods. As a result, users may end up feeling emotionally burdened.

[0406] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0407] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for adjusting notification methods using an emotion engine that analyzes the user's emotional state. This enables users to have optimal notification settings that are tailored to their own emotions and to manage communications in a way that reduces stress.

[0408] An "efficient electronic communications management device" is an information processing system equipped with functions to analyze received electronic communications and appropriately classify, summarize, and prioritize them.

[0409] A "means for performing category classification" refers to a processing device that has the function of dividing received electronic communications into specific categories based on their content.

[0410] A "means of summarizing content" refers to a system that has the function of condensing the vast amount of information received from electronic communications into a concise format.

[0411] A "means for setting priorities" is a device that has the function of determining the order in which to process received electronic communications according to their importance.

[0412] "Means of adjusting notification methods using an emotion engine" refers to a system that estimates the user's emotional state from their facial expressions and voice, and selects the most appropriate notification method based on that.

[0413] A "means for generating template statements" refers to a software process that has the function of automatically generating response statements in response to received electronic communications.

[0414] "Means for estimating emotions using a user's facial expressions or voice" refers to an analytical device equipped with the function of analyzing data such as the user's facial features and tone of voice to identify the user's emotional state.

[0415] A "means of suggesting payment methods" refers to a system that has a process for presenting appropriate payment methods based on the user's emotional state.

[0416] A description of the embodiment for carrying out the invention will be provided.

[0417] The system in this invention is a device for the efficient management of electronic communications and can be implemented using specific hardware and software. The server receives electronic communications via a network, analyzes their content, and categorizes them. Natural language processing is utilized for the analysis, and NLP libraries and APIs may be used. Generative AI models can be used to summarize the content of emails, and a database management system may be used on the server side in the process of extracting important information.

[0418] To recognize a user's emotions, hardware such as the smartphone's camera and microphone are used. The emotion engine software analyzes facial expression and voice data acquired from these devices. It estimates whether the user is experiencing stress and adjusts the notification method and priority accordingly. Specifically, it performs actions such as switching to silent mode when delivering notifications via the display device or speaker. This operation is synchronized on the user's device, ensuring that information is transmitted at the optimal time.

[0419] Furthermore, in the template sentence generation process, the system automatically creates response suggestions. These may include a tone that is appropriate and stress-relieving, selected by the generated AI model based on emotional information.

[0420] For example, if a user receives an important message during a busy time, the system will sense their stress level from their facial expression and immediately refrain from sending a notification. Furthermore, when the user is relaxed after work, the system will use a gentler tone in the template message and automatically send a notification. This reduces the user's emotional burden while achieving efficient communication management.

[0421] An example of a prompt is, "Design an AI model that infers emotions from a user's facial expression image and returns tags such as 'stress' or 'relaxed'." Using this example makes it easier to start designing the AI ​​model necessary for emotion recognition.

[0422] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0423] Step 1:

[0424] The server receives electronic communications over the network. The input is electronic communication data, acquired using the Internet Protocol. The output is unprocessed message data awaiting analysis.

[0425] Step 2:

[0426] The server analyzes received electronic communications using a natural language processing library and performs categorization. Based on the content of the communications, they are categorized into business, private, spam, etc. The input is unprocessed message data, and the output is data with category labels attached. Specifically, it applies a text analysis algorithm.

[0427] Step 3:

[0428] The server summarizes the content of the analyzed electronic communications and extracts important information using a natural language generation AI model. The input is electronic communications data with category labels, and the output is summarized text. The generation AI model performs the summarization using prompt sentences.

[0429] Step 4:

[0430] The device activates the smartphone's camera and microphone to estimate the user's emotions and acquire facial and audio data. The input is the user's real-time video and audio, and the output is raw data that is fed into the emotion engine. This operation includes camera control and audio sampling.

[0431] Step 5:

[0432] The device uses an emotion engine to analyze the user's emotional state and performs digital signal processing. The input is raw data, and the output is emotion tags such as "stress" and "relaxed." The emotion engine extracts facial and vocal features.

[0433] Step 6:

[0434] The server adjusts notification methods based on the user's emotional state. Inputs include emotional tags and summarized text, which determine notification priority and method. Outputs are the adjusted notification settings, specifically changes to notification volume and vibration mode.

[0435] Step 7:

[0436] The server automatically generates template sentences, adjusting the tone to reflect the user's emotional state. Input consists of summarized text and emotion tags, while output is a template reply. A generative AI model handles language generation.

[0437] Step 8:

[0438] The device displays customized notifications to the user. Input consists of notification settings and template text, while output is a visual or auditory notification to the user. Specific actions include on-screen displays and audio alerts.

[0439] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0440] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0441] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0442] [Third Embodiment]

[0443] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0444] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0445] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0446] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0447] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0448] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0449] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0450] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0451] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0452] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0453] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0454] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0455] The present invention is a system that enables efficient management of electronic communications, and specifically a device that supports classification, summarization, prioritization, automatic replies, and notifications in communications, primarily email.

[0456] The server first receives electronic communications via the Internet protocol. The received emails are parsed by a natural language processing module and automatically categorized based on their content into "Notifications," "Requires Action," and "Requires a Reply." This classification allows users to manage communications more efficiently.

[0457] Next, the server summarizes the content of each email and extracts only the important information, allowing users to quickly grasp the information. For example, in the case of a meeting invitation email, the summarization function extracts the meeting participants, date, time, and location, and presents them to the user in an easy-to-understand manner.

[0458] When prioritizing received communications, the server calculates a score considering the sender's status and the email's deadline, and determines the processing order for each communication. Based on this, the device notifies the user of high-priority emails via push notification.

[0459] When an automated reply is required, the server uses a template generation module to automatically create a reply that is appropriate for the characteristics of the email. For example, a question email from a client will generate a template such as, "Thank you for your question. We will answer the following points..."

[0460] Furthermore, the device has a function that sets reminders and notifies the user when there are unaddressed emails or when important follow-up is required. This helps users prevent missed responses and clearly understand which emails should be prioritized.

[0461] Through these processes, users can reduce the time spent on communication management and focus on critical business issues. This system configuration makes it possible to reduce user stress and improve operational efficiency.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The server receives new electronic communications. The received email is passed to a natural language processing module for analysis. This automatically classifies the email into three categories: "Notification," "Requires Action," or "Requires a Reply."

[0465] Step 2:

[0466] The server shortens the content of the analyzed email using a summarization algorithm. In this process, it extracts important information and keywords to generate a summary that the user can quickly understand.

[0467] Step 3:

[0468] The server prioritizes each email based on the classification and summarization results. This involves calculating a score that takes into account sender information, email urgency, response deadline, and other factors, and then creating a priority list.

[0469] Step 4:

[0470] The device receives priority information from the server and notifies the user about high-priority electronic communications. Notifications are made on mobile and desktop devices, and users are alerted through visual alerts and audio notifications.

[0471] Step 5:

[0472] The server generates an automated reply template for emails that require a response. This takes into account the questions and information requests contained in the email and inserts situation-appropriate variables into a pre-defined format to quickly prepare an appropriate reply.

[0473] Step 6:

[0474] The server uses a reminder function to monitor unanswered emails. If a specified period has passed, it issues another notification about the unanswered email and sends that information to the user's device to remind them.

[0475] Step 7:

[0476] Users receive notifications through their devices, check high-priority emails, and respond quickly. They can also reply to emails using server-generated reply templates as needed, ensuring smooth workflow.

[0477] (Example 1)

[0478] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0479] In modern society, where a vast amount of electronic communication is exchanged daily, users spend a significant amount of time and effort managing it. This burden is particularly heavy in business environments where classification, prioritization, and rapid response are required, highlighting the need for efficient information management. Furthermore, incorrect prioritization and missed responses are not uncommon. To address these problems, a system is needed that automates the management of electronic communications and reduces the burden on users.

[0480] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0481] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the information of the electronic communications, and means for setting priorities based on the electronic communications. This enables automatic classification of electronic communications and efficient information management.

[0482] "Electronic communication" refers to digital data sent and received via email or messaging apps.

[0483] A "control system" refers to a system of hardware and software for processing and managing electronic communications.

[0484] "Analysis" refers to the process of understanding the content of electronic communications and extracting important information.

[0485] "Category classification" refers to the process of dividing electronic communications into specific groups based on their content.

[0486] "Summarizing information" refers to the process of extracting only the essential points of electronic communication and expressing them in a concise form.

[0487] "Setting priorities" refers to the process of evaluating the importance of electronic communications and determining the order in which they will be processed.

[0488] "Generating template sentences" refers to the process of automatically creating common response sentences.

[0489] "Notifying" refers to the process of informing a user of information.

[0490] "Natural language processing" refers to the technology of enabling computers to understand and analyze human language.

[0491] "Digital communication equipment" refers to electronic devices used for sending and receiving electronic communications.

[0492] This invention provides a system for efficiently managing electronic communications, particularly for automating the analysis and processing of emails. This system consists of a combination of hardware and software.

[0493] The server receives electronic communications via a communication network. This can utilize Internet protocols such as IMAP and SMTP. The server periodically checks the mailbox to retrieve new electronic communications.

[0494] The received communications are analyzed using a natural language processing module. Specifically, open-source libraries and cloud-based services (e.g., Google Cloud Natural Language API, IBM Watson Natural Language Understanding) are used to understand the content of emails and extract important information.

[0495] The analyzed data is categorized and sorted into categories such as "Notifications," "Items Requiring Action," and "Items Requiring a Reply," based on the content of the electronic communications. This allows users to efficiently manage their electronic communications.

[0496] Next, the server uses a summarization algorithm (e.g., Sumy or TextRank) to summarize the email body, extracting and displaying only the key points. This summary is displayed on the email viewing screen, allowing the user to quickly grasp the information.

[0497] Furthermore, a priority setting module prioritizes communications based on factors such as the sender information and urgency of the data. High-priority communications are immediately notified to the user using the device's push notification function.

[0498] If a reply is required, the server automatically generates a template message using a generative AI model (e.g., a GPT model). This model is given the prompt message, "You are the server for the email management system. Analyze the received emails using natural language processing, classify and summarize them, prioritize them, and generate an automated reply as needed," and then generates a specific reply message.

[0499] Finally, the device provides alerts that allow users to prevent missed communications or items requiring follow-up by setting reminder notifications.

[0500] This system allows users to reduce the time spent managing communications and focus on more important tasks. For example, users can quickly check high-priority sales emails, reducing the risk of missing or mishandling them.

[0501] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0502] Step 1:

[0503] The server receives electronic communications via the Internet protocol. The input is email from the communication network, and the server periodically checks its mailbox to retrieve new emails. The output is email data stored for analysis. This process manages the latest electronic communications within the system.

[0504] Step 2:

[0505] The server inputs received emails into a natural language processing module and analyzes their content. Specifically, it performs text analysis and extracts keywords and phrases. As a result, the emails are categorized as "Notifications," "Requires Action," and "Requires Reply." This process allows users to efficiently grasp the information.

[0506] Step 3:

[0507] The server inputs the analyzed email content into a summarization algorithm. This algorithm extracts the key points of the information and generates a shortened version. The output of this summarization process is concise and easily readable by the user. For example, a meeting invitation email might be summarized as "Meeting, October 10th, 2 PM, Meeting Room A."

[0508] Step 4:

[0509] The server inputs email data into a priority setting module and determines priority based on sender information and deadlines. As part of the data processing, it evaluates the sender's job title and the deadline stated in the email, and calculates a score. The output is a prioritized email list, which the terminal uses to notify the user of important emails.

[0510] Step 5:

[0511] The server generates automatic replies as needed. The input is an email that is deemed to require a reply, and prompts are given to the generation AI model to create a template. The output is the automatically generated reply email. For example, an inquiry email from a client will generate content such as, "Thank you for your question. We will answer the following points..."

[0512] Step 6:

[0513] The device sets reminders for unanswered or follow-up emails and notifies the user. The input is a list of high-priority emails, and the output is a notification alert to the user. This ensures that users don't forget to address important emails.

[0514] (Application Example 1)

[0515] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0516] In modern factories, numerous instructions are communicated electronically, requiring rapid analysis, classification, and prioritization of these instructions for efficient work. Delays in instruction transmission and response lead to decreased productivity, necessitating systems to improve this process.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0518] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for setting priorities based on the electronic communications. This enables efficient management of instructions within the factory and rapid execution of important tasks.

[0519] "Electronic communications" refers to information that is transmitted and received in digital format via the internet or other means.

[0520] A "management device" is a device used within a system to receive, analyze, classify, and notify data.

[0521] "Means for categorization" refers to a function that performs the process of sorting received electronic communications into specific groups or categories based on their content.

[0522] A "summarization tool" is a function that extracts important information from the content of electronic communications and displays it in a shortened form.

[0523] A "means of setting priorities" is a function that determines the order in which communications are processed based on their urgency and importance.

[0524] "Means for generating template text" refers to a function that automatically creates standardized text and allows for quick replies.

[0525] "Notification methods" refer to functions that inform users of information in order to warn or alert them about unsupported or important communications.

[0526] "Means for analyzing and classifying instructions within a factory" refers to the function of understanding instructions within a factory and assigning them to the appropriate response category.

[0527] "Means of notifying the robot operation system" refers to a function that transmits the analyzed information to the robot control system to determine the next action.

[0528] "Means for determining the order of tasks" refers to a function that determines the order in which tasks should be performed based on received instructions, from the perspective of efficiency.

[0529] This invention is a system aimed at managing electronic communications and efficiently processing instructions within a factory. The server receives electronic communications via the Internet protocol and analyzes emails using a natural language processing module. The analyzed content is automatically categorized into "Notifications," "Items Requiring Action," and "Items Requiring a Reply." This allows users to efficiently manage electronic communications. Furthermore, the server generates email summaries and extracts specific information such as meeting invitations. This summarization function allows users to quickly grasp important information.

[0530] When setting priorities, the server considers sender information and communication deadlines, calculates a score, and determines the processing order. This allows the terminal to notify the user of high-priority emails via push notifications. If necessary, the server uses a template message generation module to create an automated reply message, such as "Thank you for your question. We will answer the following points..."

[0531] On the other hand, regarding instructions within the factory, the server analyzes the instructions and quickly transmits them to the factory robots. The instructions are analyzed and classified into "must be executed immediately," "requires confirmation," and "can be executed later," and the important parts are notified to the robot operation system. In addition, the order of work is determined according to urgency, and high-priority instructions can be executed first.

[0532] For example, if a factory manager sends an email instructing them to "inspect equipment A immediately," this email will be classified as "requires immediate action," and the factory robots can begin the inspection work right away. This entire process improves work efficiency within the factory and enables quicker responses.

[0533] Examples of prompts for a generative AI model include the following:

[0534] "Factory instruction email analysis: Equipment A inspection requires immediate action due to urgency assessment. Please create a summary and feedback template. Email body: 'Inspect Equipment A immediately.'"

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] The server receives electronic communications via the Internet Protocol. The input is unparsed email. The output is raw email data that needs to be parsed, and this data is processed in the next step.

[0538] Step 2:

[0539] The server parses received emails using a natural language processing module. The input is raw email data. The server analyzes the content of the email, tokenizes the document, and performs syntactic analysis. Based on this analysis, the output is data categorized as "Notifications," "Requires Action," and "Requires Reply."

[0540] Step 3:

[0541] The server summarizes the content of the analyzed emails. The input is analyzed email data with assigned categories. The server uses a summarization algorithm to extract important information from the text and summarize key points such as meeting participants, date, time, and location. The output is summarized data that clearly shows the information important to the user.

[0542] Step 4:

[0543] The server sets priorities based on the information it detects during the analysis process. The input is summarized email data. The server considers sender information, deadlines stated in the email, etc., and applies a scoring algorithm to calculate priorities. The output is email data with assigned priorities.

[0544] Step 5:

[0545] The server generates template messages for automatic replies as needed. The input is email data that has been prioritized and categorized. The template generation module selects the appropriate template based on the intent of the email and creates an automatic reply message. The output is the automatically generated reply message.

[0546] Step 6:

[0547] The device uses a notification system to inform the user of unsupported communications and high-priority emails. The input is the email information to be notified to the user. The device uses means such as push notifications and alerts to draw the user's attention. The output is the information received by the user as a notification.

[0548] Step 7:

[0549] The server receives and analyzes instructions from within the factory. The input is instruction emails issued within the factory. The server uses natural language processing to analyze these instructions and classifies them into "Requires immediate execution," "Requires confirmation," and "Can be executed later." The output is data with the instruction category determined.

[0550] Step 8:

[0551] The terminal notifies the robot operation system of the analyzed instructions and directs it to take appropriate action. The input is instruction data with a determined category. The robot operation system initiates actions to perform tasks according to priority. The output is the efficient and rapid completion of processes within the factory.

[0552] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0553] This invention incorporates an emotion engine that recognizes and adjusts user emotions into a system that enables efficient management of electronic communications. This allows for communication management based on the user's emotional state, enabling flexible responses tailored to individual situations.

[0554] The server first receives electronic communications via the Internet protocol. The received emails are parsed via a natural language processing module and automatically classified based on their content. After classification, the server summarizes the content and extracts important information. At this point, to understand the user's emotional state, the emotion engine analyzes the user's facial expression data, voice data, or keyboard input speed to estimate the current emotional state.

[0555] Next, the server uses the information obtained from the emotion engine to adjust priorities and notification methods. For example, if a user is stressed, the server may reduce notifications for high-priority emails or change the notification method to a quieter one. Conversely, when the user is relaxed, notifications are sent as usual.

[0556] The device receives notification information from the server and displays alerts to the user in the most optimal format. Based on the results presented by the emotion engine, the notification interface is dynamically adjusted to ensure the user receives the notifications in a less stressful way.

[0557] Furthermore, the server automatically generates a template message tailored to the purpose of the email when it recognizes that a reply is required. This template message is adjusted according to the user's emotional state, and may, for example, adopt a gentle tone to alleviate stress.

[0558] This system also includes a feature to monitor unanswered emails and send reminders as needed. The timing and format of these reminders are also adjusted based on the analysis results of the emotion engine. This allows users to reduce the hassle of receiving emails and enjoy priority settings that are tailored to their emotions.

[0559] Through this process, users can efficiently manage business communications while reducing emotional burden. Individualized responses tailored to emotional states lead to improved work efficiency and a more personalized experience.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] The server receives electronic communications via the internet. It inputs the emails into a natural language processing module for analysis, automatically classifying the content into "Notification," "Action Required," and "Reply Required."

[0563] Step 2:

[0564] The server passes the classified emails to a summarization module, which generates a summary containing extracted key information. This summary is then presented to the user to support quick decision-making.

[0565] Step 3:

[0566] Based on data obtained from the user's device, such as input from a facial recognition camera or voice analysis device, the server activates an emotion engine. This determines the user's current emotional state.

[0567] Step 4:

[0568] The server uses the results of the emotion engine's analysis to prioritize emails and set notification formats. For example, if a user is feeling stressed, it may refrain from sending important notifications or send them in a gentler manner.

[0569] Step 5:

[0570] The device receives notification information from the server and presents it to the user in a format that best suits the user's emotional state. This allows the user to receive the necessary information in the most appropriate situation.

[0571] Step 6:

[0572] The server automatically generates template messages for emails that require a reply. These messages are tailored to the user's emotional state to ensure a stress-free experience.

[0573] Step 7:

[0574] The server sets a reminder for unanswered emails and notifies the user again. The timing and format of this notification can also be flexibly changed according to the user's emotional state.

[0575] Step 8:

[0576] Users can manage their electronic communications while reducing stress through emotionally sensitive communication content and notifications. Each process is implemented in a way that is sensitive to the user's psychological state.

[0577] (Example 2)

[0578] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] In electronic communication processing, there are challenges in prioritizing and adjusting notification methods while considering the user's emotions and state, as well as the need for efficient communication management while reducing stress. Current systems do not adequately achieve flexible processing that responds to the individual user's state.

[0580] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0581] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for analyzing the user's state. This enables flexible notification methods and priority adjustments according to the user's state. Furthermore, this results in less stressful communication processing and management.

[0582] "Received electronic communications" refers to electronic information such as emails and messages acquired by communication devices.

[0583] "Analysis" refers to the process of analyzing the content of electronic communications and extracting necessary information and features.

[0584] "Category classification" refers to the process of grouping analyzed electronic communications based on their content and importance, and assigning them to specific categories.

[0585] "Summarization" refers to the act of extracting and shortening important information while maintaining the overall meaning of electronic communication.

[0586] "User state" refers to the user's emotions and psychological condition, and serves as an indicator for the system to adapt to these factors.

[0587] "Notification method" refers to the means or format used to transmit information or messages to users.

[0588] "Priority adjustment" refers to the process of determining the priority of processing received electronic communications and optimizing them to meet user needs.

[0589] "Means of generating documents" refers to algorithms or processes for writing appropriate templates and content for electronic communication.

[0590] "Unanswered electronic communications" refers to electronic messages that the user has not yet processed and that require a response or action.

[0591] "Communication equipment" refers to physical devices or equipment used to send, receive, and process electronic communications.

[0592] This invention aims to implement an electronic communication system that takes into account the emotional state of the user. The system mainly consists of a server and terminals, which work together in appropriate coordination.

[0593] The server receives electronic communications using internet protocols. For email analysis, it uses Python's NLTK library or Google's Cloud Natural Language API as natural language processing modules. The analyzed electronic communications are categorized based on their content, and important information is extracted and summarized. In addition, the server uses an emotion engine to analyze the user's facial expressions, voice data, or keyboard input speed to evaluate the user's emotional state. For the emotion engine, OpenAI's Sentiment Analysis API or Microsoft's Azure Emotion API can be used.

[0594] Based on the analysis results, the server prioritizes electronic communications and adjusts notification methods. If the user is experiencing stress, notifications will be reduced or silent notifications will be sent depending on their importance. Once the user's stress level decreases, the server will resume normal notifications.

[0595] The device dynamically changes its notification interface to effectively convey information received from the server to the user. To minimize user stress, the device can choose a quiet and unobtrusive notification method.

[0596] For electronic communications requiring a response, the server automatically generates template messages using an AI model. The generated documents may reflect considerations for reducing the user's emotional state and stress. An example of a specific prompt message might be, "Generate a response that takes user stress reduction into consideration."

[0597] Finally, the server monitors unresolved electronic communications and sets reminders as needed. These reminders are flexibly adjusted according to the user's emotional state, enabling comfortable email management. This system reduces the emotional burden of managing electronic communications, providing a more personalized experience for users.

[0598] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0599] Step 1:

[0600] The server receives electronic communications via the Internet protocol. The data received as input is the content of emails. After reception, the emails are parsed using a natural language processing module. Specifically, the grammatical structure is analyzed and keywords are extracted using the Python NLTK library. The output is the category and summarized content of the analyzed emails.

[0601] Step 2:

[0602] The server categorizes received electronic communications based on the analysis results. The input is the summary information obtained in step 1. Categorization considers the urgency of the email and the importance of the sender. Specifically, it uses text mining techniques to refer to similar past communication data. The output is a list of emails with assigned priorities.

[0603] Step 3:

[0604] The server performs analysis to evaluate the user's current emotional state. Inputs include the user's facial expression data, voice data, and keyboard input speed. The emotion engine utilizes OpenAI's emotion analysis API to process this data and estimate the emotional state. The output is evaluation data indicating the emotional state, such as stress or relaxation.

[0605] Step 4:

[0606] The server adjusts notification methods and priorities based on the user's emotional state. The input is the information obtained in steps 2 and 3. Specifically, if the server determines that the user is experiencing high stress, it will process notifications by reducing their volume or other actions. The output is the adjusted notification schedule and prioritized email settings.

[0607] Step 5:

[0608] The device receives notification information from the server and presents it to the user in the most appropriate format. Inputs include the notification schedule and email priority. Specifically, the device communicates to the user via voice notification, vibration, or visual alert. Output is the implementation of notifications designed to minimize stress.

[0609] Step 6:

[0610] The server automatically generates template responses to electronic communications requiring a reply. The input consists of the email content and the user's emotional state. Using a generation AI model, the prompt "Generate a reply that considers reducing user stress" is applied to create an appropriate template response. The output is a template response tailored to the user's emotional state.

[0611] Step 7:

[0612] The server monitors unanswered emails and sets reminders as needed. Inputs include a list of unanswered emails and the user's emotional state. Based on the analysis, reminders are set at the optimal time. Specifically, it adjusts the reminder frequency by referring to past reply history. The output is a reminder notification that takes the user's emotional state into consideration.

[0613] (Application Example 2)

[0614] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0615] With the increase in information and communication in modern times, the amount of electronic communication individuals receive has become enormous, and many users experience stress in processing it. In particular, there is a need to appropriately distinguish between urgent and non-urgent communications and to respond appropriately according to one's emotional state. However, conventional systems lack the ability to adjust priorities based on emotional state and to suggest less stressful notification methods. As a result, users may end up feeling emotionally burdened.

[0616] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0617] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for adjusting notification methods using an emotion engine that analyzes the user's emotional state. This enables users to have optimal notification settings that are tailored to their own emotions and to manage communications in a way that reduces stress.

[0618] An "efficient electronic communications management device" is an information processing system equipped with functions to analyze received electronic communications and appropriately classify, summarize, and prioritize them.

[0619] A "means for performing category classification" refers to a processing device that has the function of dividing received electronic communications into specific categories based on their content.

[0620] A "means of summarizing content" refers to a system that has the function of condensing the vast amount of information received from electronic communications into a concise format.

[0621] A "means for setting priorities" is a device that has the function of determining the order in which to process received electronic communications according to their importance.

[0622] "Means of adjusting notification methods using an emotion engine" refers to a system that estimates the user's emotional state from their facial expressions and voice, and selects the most appropriate notification method based on that.

[0623] A "means for generating template statements" refers to a software process that has the function of automatically generating response statements in response to received electronic communications.

[0624] "Means for estimating emotions using a user's facial expressions or voice" refers to an analytical device equipped with the function of analyzing data such as the user's facial features and tone of voice to identify the user's emotional state.

[0625] A "means of suggesting payment methods" refers to a system that has a process for presenting appropriate payment methods based on the user's emotional state.

[0626] A description of the embodiment for carrying out the invention will be provided.

[0627] The system in this invention is a device for the efficient management of electronic communications and can be implemented using specific hardware and software. The server receives electronic communications via a network, analyzes their content, and categorizes them. Natural language processing is utilized for the analysis, and NLP libraries and APIs may be used. Generative AI models can be used to summarize the content of emails, and a database management system may be used on the server side in the process of extracting important information.

[0628] To recognize a user's emotions, hardware such as the smartphone's camera and microphone are used. The emotion engine software analyzes facial expression and voice data acquired from these devices. It estimates whether the user is experiencing stress and adjusts the notification method and priority accordingly. Specifically, it performs actions such as switching to silent mode when delivering notifications via the display device or speaker. This operation is synchronized on the user's device, ensuring that information is transmitted at the optimal time.

[0629] Furthermore, in the template sentence generation process, the system automatically creates response suggestions. These may include a tone that is appropriate and stress-relieving, selected by the generated AI model based on emotional information.

[0630] For example, if a user receives an important message during a busy time, the system will sense their stress level from their facial expression and immediately refrain from sending a notification. Furthermore, when the user is relaxed after work, the system will use a gentler tone in the template message and automatically send a notification. This reduces the user's emotional burden while achieving efficient communication management.

[0631] An example of a prompt is, "Design an AI model that infers emotions from a user's facial expression image and returns tags such as 'stress' or 'relaxed'." Using this example makes it easier to start designing the AI ​​model necessary for emotion recognition.

[0632] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0633] Step 1:

[0634] The server receives electronic communications over the network. The input is electronic communication data, acquired using the Internet Protocol. The output is unprocessed message data awaiting analysis.

[0635] Step 2:

[0636] The server analyzes received electronic communications using a natural language processing library and performs categorization. Based on the content of the communications, they are categorized into business, private, spam, etc. The input is unprocessed message data, and the output is data with category labels attached. Specifically, it applies a text analysis algorithm.

[0637] Step 3:

[0638] The server summarizes the content of the analyzed electronic communications and extracts important information using a natural language generation AI model. The input is electronic communications data with category labels, and the output is summarized text. The generation AI model performs the summarization using prompt sentences.

[0639] Step 4:

[0640] The device activates the smartphone's camera and microphone to estimate the user's emotions and acquire facial and audio data. The input is the user's real-time video and audio, and the output is raw data that is fed into the emotion engine. This operation includes camera control and audio sampling.

[0641] Step 5:

[0642] The device uses an emotion engine to analyze the user's emotional state and performs digital signal processing. The input is raw data, and the output is emotion tags such as "stress" and "relaxed." The emotion engine extracts facial and vocal features.

[0643] Step 6:

[0644] The server adjusts notification methods based on the user's emotional state. Inputs include emotional tags and summarized text, which determine notification priority and method. Outputs are the adjusted notification settings, specifically changes to notification volume and vibration mode.

[0645] Step 7:

[0646] The server automatically generates template sentences, adjusting the tone to reflect the user's emotional state. Input consists of summarized text and emotion tags, while output is a template reply. A generative AI model handles language generation.

[0647] Step 8:

[0648] The device displays customized notifications to the user. Input consists of notification settings and template text, while output is a visual or auditory notification to the user. Specific actions include on-screen displays and audio alerts.

[0649] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0650] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0651] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0652] [Fourth Embodiment]

[0653] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0654] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0655] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0656] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0657] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0658] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0659] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0660] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0661] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0662] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0663] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0664] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0665] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] The present invention is a system that enables efficient management of electronic communications, and specifically a device that supports classification, summarization, prioritization, automatic replies, and notifications in communications, primarily email.

[0667] The server first receives electronic communications via the Internet protocol. The received emails are parsed by a natural language processing module and automatically categorized based on their content into "Notifications," "Requires Action," and "Requires a Reply." This classification allows users to manage communications more efficiently.

[0668] Next, the server summarizes the content of each email and extracts only the essential information, allowing users to quickly grasp the information. For example, in the case of a meeting invitation email, the summarization function extracts the meeting participants, date, time, and location, and presents them to the user in an easy-to-understand manner.

[0669] When prioritizing received communications, the server calculates a score considering the sender's status and the email's deadline, and determines the processing order for each communication. Based on this, the device notifies the user of high-priority emails via push notification.

[0670] When an automated reply is required, the server uses a template generation module to automatically create a reply tailored to the characteristics of the email. For example, a client's question email will generate a template such as, "Thank you for your question. We will answer the following points..."

[0671] Furthermore, the device has a function that sets reminders and notifies the user when there are unaddressed emails or when important follow-up is required. This helps users prevent missed responses and clearly understand which emails should be prioritized.

[0672] Through these processes, users can reduce the time spent on communication management and focus on critical business issues. This system configuration makes it possible to reduce user stress and improve operational efficiency.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The server receives new electronic communications. The received email is passed to a natural language processing module for analysis. This automatically classifies the email into three categories: "Notification," "Requires Action," or "Requires a Reply."

[0676] Step 2:

[0677] The server shortens the content of the analyzed email using a summarization algorithm. In this process, it extracts important information and keywords to generate a summary that the user can quickly understand.

[0678] Step 3:

[0679] The server prioritizes each email based on the classification and summarization results. This involves calculating a score that takes into account sender information, email urgency, response deadline, and other factors, and then creating a priority list.

[0680] Step 4:

[0681] The device receives priority information from the server and notifies the user about high-priority electronic communications. Notifications are made on mobile and desktop devices, and users are alerted through visual alerts and audio notifications.

[0682] Step 5:

[0683] The server generates an automated reply template for emails that require a response. This template takes into account the questions and information requests contained in the email and inserts context-appropriate variables into a pre-defined format to quickly prepare an appropriate reply.

[0684] Step 6:

[0685] The server uses a reminder function to monitor unanswered emails. If a specified period has passed, it issues another notification about the unanswered email and sends that information to the user's device to remind them.

[0686] Step 7:

[0687] Users receive notifications through their devices, check high-priority emails, and respond quickly. They can also reply to emails using server-generated reply templates as needed, ensuring smooth workflow.

[0688] (Example 1)

[0689] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] In modern society, where a vast amount of electronic communication is exchanged daily, users spend a significant amount of time and effort managing it. This burden is particularly heavy in business environments where classification, prioritization, and rapid response are required, highlighting the need for efficient information management. Furthermore, incorrect prioritization and missed responses are not uncommon. To address these problems, a system is needed that automates the management of electronic communications and reduces the burden on users.

[0691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0692] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the information of the electronic communications, and means for setting priorities based on the electronic communications. This enables automatic classification of electronic communications and efficient information management.

[0693] "Electronic communication" refers to digital data sent and received via email or messaging apps.

[0694] A "control system" refers to a system of hardware and software for processing and managing electronic communications.

[0695] "Analysis" refers to the process of understanding the content of electronic communications and extracting important information.

[0696] "Category classification" refers to the process of dividing electronic communications into specific groups based on their content.

[0697] "Summarizing information" refers to the process of extracting only the essential points of electronic communication and expressing them in a concise form.

[0698] "Setting priorities" refers to the process of evaluating the importance of electronic communications and determining the order in which they will be processed.

[0699] "Generating template sentences" refers to the process of automatically creating common response sentences.

[0700] "Notifying" refers to the process of informing a user of information.

[0701] "Natural language processing" refers to the technology of enabling computers to understand and analyze human language.

[0702] "Digital communication equipment" refers to electronic devices used for sending and receiving electronic communications.

[0703] This invention provides a system for efficiently managing electronic communications, particularly for automating the analysis and processing of emails. This system consists of a combination of hardware and software.

[0704] The server receives electronic communications via a communication network. This can utilize Internet protocols such as IMAP and SMTP. The server periodically checks the mailbox to retrieve new electronic communications.

[0705] The received communications are analyzed using a natural language processing module. Specifically, open-source libraries and cloud-based services (e.g., Google Cloud Natural Language API, IBM Watson Natural Language Understanding) are used to understand the content of emails and extract important information.

[0706] The analyzed data is categorized and sorted into categories such as "Notifications," "Items Requiring Action," and "Items Requiring a Reply," based on the content of the electronic communications. This allows users to manage their electronic communications efficiently.

[0707] Next, the server uses a summarization algorithm (e.g., Sumy or TextRank) to summarize the email body, extracting and displaying only the key points. This summary is displayed on the email viewing screen, allowing the user to quickly grasp the information.

[0708] Furthermore, a priority setting module prioritizes communications based on sender information and urgency. High-priority communications are immediately notified to the user using the device's push notification function.

[0709] If a reply is required, the server automatically generates a template message using a generative AI model (e.g., a GPT model). This model is given the prompt message, "You are the server for the email management system. Analyze the received emails using natural language processing, classify and summarize them, prioritize them, and generate an automated reply as needed," and then generates a specific reply message.

[0710] Finally, the device provides alerts that allow users to prevent missed communications or follow-ups by setting reminder notifications for unaddressed communications or items requiring attention.

[0711] This system allows users to reduce the time spent managing communications and focus on more important tasks. For example, users can quickly check high-priority sales emails, reducing the risk of missing or mishandling them.

[0712] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0713] Step 1:

[0714] The server receives electronic communications via the Internet protocol. The input is email from the communication network, and the server periodically checks its mailbox to retrieve new emails. The output is email data stored for analysis. This process manages the latest electronic communications within the system.

[0715] Step 2:

[0716] The server inputs received emails into a natural language processing module and analyzes their content. Specifically, it performs text analysis and extracts keywords and phrases. As a result, the emails are categorized as "Notifications," "Requires Action," and "Requires Reply." This process allows users to efficiently grasp the information.

[0717] Step 3:

[0718] The server inputs the analyzed email content into a summarization algorithm. This algorithm extracts the key points of the information and generates a shortened version. The output of this summarization process is concise and easily readable by the user. For example, a meeting invitation email might be summarized as "Meeting, October 10th, 2 PM, Meeting Room A."

[0719] Step 4:

[0720] The server inputs email data into a priority setting module and determines priority based on sender information and deadlines. As part of the data processing, it evaluates the sender's job title and the deadline stated in the email, and calculates a score. The output is a prioritized email list, which the terminal uses to notify the user of important emails.

[0721] Step 5:

[0722] The server generates automatic replies as needed. The input is an email that is deemed to require a reply, and prompts are given to the generation AI model to create a template. The output is the automatically generated reply email. For example, an inquiry email from a client will generate content such as, "Thank you for your question. We will answer the following points..."

[0723] Step 6:

[0724] The device sets reminders for unanswered or follow-up emails and notifies the user. The input is a list of high-priority emails, and the output is a notification alert to the user. This ensures that users don't forget to address important emails.

[0725] (Application Example 1)

[0726] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] In modern factories, numerous instructions are communicated electronically, requiring rapid analysis, classification, and prioritization of these instructions for efficient work. Delays in instruction transmission and response lead to decreased productivity, necessitating systems to improve this process.

[0728] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0729] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for setting priorities based on the electronic communications. This enables efficient management of instructions within the factory and rapid execution of important tasks.

[0730] "Electronic communications" refers to information that is transmitted and received in digital format via the internet or other means.

[0731] A "management device" is a device used within a system to receive, analyze, classify, and notify data.

[0732] "Means for categorization" refers to a function that performs the process of sorting received electronic communications into specific groups or categories based on their content.

[0733] A "summarization tool" is a function that extracts important information from the content of electronic communications and displays it in a shortened form.

[0734] A "means of setting priorities" is a function that determines the order in which communications are processed based on their urgency and importance.

[0735] "Means for generating template text" refers to a function that automatically creates standardized text and allows for quick replies.

[0736] "Notification methods" refer to functions that inform users of information in order to warn or alert them about unsupported or important communications.

[0737] "Means for analyzing and classifying instructions within a factory" refers to the function of understanding instructions within a factory and assigning them to the appropriate response category.

[0738] "Means of notifying the robot operation system" refers to a function that transmits the analyzed information to the robot control system to determine the next action.

[0739] "Means for determining the order of tasks" refers to a function that determines the order in which tasks should be performed based on received instructions, from the perspective of efficiency.

[0740] This invention is a system aimed at managing electronic communications and efficiently processing instructions within a factory. The server receives electronic communications via the Internet protocol and analyzes emails using a natural language processing module. The analyzed content is automatically categorized into "Notifications," "Items Requiring Action," and "Items Requiring a Reply." This allows users to efficiently manage electronic communications. Furthermore, the server generates email summaries and extracts specific information such as meeting invitations. This summarization function allows users to quickly grasp important information.

[0741] When setting priorities, the server considers sender information and communication deadlines, calculates a score, and determines the processing order. This allows the terminal to notify the user of high-priority emails via push notifications. If necessary, the server uses a template message generation module to create an automated reply message, such as "Thank you for your question. We will answer the following points..."

[0742] On the other hand, regarding instructions within the factory, the server analyzes the instructions and quickly transmits them to the factory robots. The instructions are analyzed and classified into "must be executed immediately," "requires confirmation," and "can be executed later," and the important parts are notified to the robot operation system. In addition, the order of work is determined according to urgency, and high-priority instructions can be executed first.

[0743] For example, if a factory manager sends an email instructing them to "inspect equipment A immediately," this email will be classified as "requires immediate action," and the factory robots can begin the inspection work right away. This entire process improves work efficiency within the factory and enables quicker responses.

[0744] Examples of prompts for a generative AI model include the following:

[0745] "Factory instruction email analysis: Equipment A inspection requires immediate action due to urgency assessment. Please create a summary and feedback template. Email body: 'Inspect Equipment A immediately.'"

[0746] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0747] Step 1:

[0748] The server receives electronic communications via the Internet protocol. The input is unparsed email. The output is raw email data that needs to be parsed, and this data is processed in the next step.

[0749] Step 2:

[0750] The server parses received emails using a natural language processing module. The input is raw email data. The server analyzes the content of the email, tokenizes the document, and performs syntactic analysis. Based on this analysis, the output is data categorized as "Notifications," "Requires Action," and "Requires Reply."

[0751] Step 3:

[0752] The server summarizes the content of the analyzed emails. The input is analyzed email data with assigned categories. The server uses a summarization algorithm to extract important information from the text and summarize key points such as meeting participants, date, time, and location. The output is summarized data that clearly shows the information important to the user.

[0753] Step 4:

[0754] The server sets priorities based on the information it detects during the analysis process. The input is summarized email data. The server considers sender information, deadlines stated in the email, etc., and applies a scoring algorithm to calculate priorities. The output is email data with assigned priorities.

[0755] Step 5:

[0756] The server generates template messages for automatic replies as needed. The input is email data that has been prioritized and categorized. The template generation module selects the appropriate template based on the intent of the email and creates an automatic reply message. The output is the automatically generated reply message.

[0757] Step 6:

[0758] The device uses a notification system to inform the user of unsupported communications and high-priority emails. The input is the email information to be notified to the user. The device uses means such as push notifications and alerts to draw the user's attention. The output is the information received by the user as a notification.

[0759] Step 7:

[0760] The server receives and analyzes instructions from within the factory. The input is instruction emails issued within the factory. The server uses natural language processing to analyze these instructions and classifies them into "Requires immediate execution," "Requires confirmation," and "Can be executed later." The output is data with the instruction category determined.

[0761] Step 8:

[0762] The terminal notifies the robot operation system of the analyzed instructions and directs it to take appropriate action. The input is instruction data with a determined category. The robot operation system initiates actions to perform tasks according to priority. The output is the efficient and rapid completion of processes within the factory.

[0763] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0764] This invention incorporates an emotion engine that recognizes and adjusts user emotions into a system that enables efficient management of electronic communications. This allows for communication management based on the user's emotional state, enabling flexible responses tailored to individual situations.

[0765] The server first receives electronic communications via the Internet protocol. The received emails are parsed via a natural language processing module and automatically classified based on their content. After classification, the server summarizes the content and extracts important information. At this point, to understand the user's emotional state, the emotion engine analyzes the user's facial expression data, voice data, or keyboard input speed to estimate the current emotional state.

[0766] Next, the server uses the information obtained from the emotion engine to adjust priorities and notification methods. For example, if a user is stressed, the server may reduce notifications for high-priority emails or change the notification method to a quieter one. Conversely, when the user is relaxed, notifications are sent as usual.

[0767] The device receives notification information from the server and displays alerts to the user in the most optimal format. Based on the results presented by the emotion engine, the notification interface is dynamically adjusted to ensure the user receives the notifications in a less stressful way.

[0768] Furthermore, the server automatically generates a template message tailored to the purpose of the email when it recognizes that a reply is required. This template message is adjusted according to the user's emotional state, and may, for example, adopt a gentle tone to alleviate stress.

[0769] This system also includes a feature to monitor unanswered emails and send reminders as needed. The timing and format of these reminders are also adjusted based on the analysis results of the emotion engine. This allows users to reduce the hassle of receiving emails and enjoy priority settings that are tailored to their emotions.

[0770] Through this process, users can efficiently manage business communications while reducing emotional burden. Individualized responses tailored to emotional states lead to improved work efficiency and a more personalized experience.

[0771] The following describes the processing flow.

[0772] Step 1:

[0773] The server receives electronic communications via the internet. It inputs the emails into a natural language processing module for analysis, automatically classifying the content into "Notification," "Action Required," and "Reply Required."

[0774] Step 2:

[0775] The server passes the classified emails to a summarization module, which generates a summary containing extracted key information. This summary is then presented to the user to support quick decision-making.

[0776] Step 3:

[0777] Based on data obtained from the user's device, such as input from a facial recognition camera or voice analysis device, the server activates an emotion engine. This determines the user's current emotional state.

[0778] Step 4:

[0779] The server uses the results of the emotion engine's analysis to prioritize emails and set notification formats. For example, if a user is feeling stressed, it may refrain from sending important notifications or send them in a gentler manner.

[0780] Step 5:

[0781] The device receives notification information from the server and presents it to the user in a format that best suits the user's emotional state. This allows the user to receive the necessary information in the most appropriate situation.

[0782] Step 6:

[0783] The server automatically generates template responses for emails that require a reply. These responses are tailored to the user's emotional state to ensure a stress-free experience.

[0784] Step 7:

[0785] The server sets a reminder for unprocessed emails and notifies the user again. The timing and format of this notification can also be flexibly changed according to the user's emotional state.

[0786] Step 8:

[0787] Users manage their electronic communications while reducing stress through emotionally sensitive communication content and notifications. Each process is implemented in a way that is sensitive to the user's psychological state.

[0788] (Example 2)

[0789] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0790] In electronic communication processing, there are challenges in prioritizing and adjusting notification methods while considering the user's emotions and state, as well as the need for efficient communication management while reducing stress. Current systems do not adequately achieve flexible processing that responds to the individual user's state.

[0791] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0792] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for analyzing the user's state. This enables flexible notification methods and priority adjustments according to the user's state. Furthermore, this results in less stressful communication processing and management.

[0793] "Received electronic communications" refers to electronic information such as emails and messages acquired by communication devices.

[0794] "Analysis" refers to the process of analyzing the content of electronic communications and extracting necessary information and features.

[0795] "Category classification" refers to the process of grouping analyzed electronic communications based on their content and importance, and assigning them to specific categories.

[0796] "Summarization" refers to the act of extracting and shortening important information while maintaining the overall meaning of electronic communication.

[0797] "User state" refers to the user's emotions and psychological condition, and serves as an indicator for the system to adapt to these factors.

[0798] "Notification method" refers to the means or format used to transmit information or messages to users.

[0799] "Priority adjustment" refers to the process of determining the priority of processing received electronic communications and optimizing them to meet user needs.

[0800] "Means of generating documents" refers to algorithms or processes for writing appropriate templates and content for electronic communication.

[0801] "Unanswered electronic communications" refers to electronic messages that the user has not yet processed and that require a response or action.

[0802] "Communication equipment" refers to physical devices or equipment used to send, receive, and process electronic communications.

[0803] This invention aims to implement an electronic communication system that takes into account the emotional state of the user. The system mainly consists of a server and terminals, which work together in appropriate coordination.

[0804] The server receives electronic communications using internet protocols. For email analysis, it uses Python's NLTK library or Google's Cloud Natural Language API as natural language processing modules. The analyzed electronic communications are categorized based on their content, and important information is extracted and summarized. In addition, the server uses an emotion engine to analyze the user's facial expressions, voice data, or keyboard input speed to evaluate the user's emotional state. For the emotion engine, OpenAI's Sentiment Analysis API or Microsoft's Azure Emotion API can be used.

[0805] Based on the analysis results, the server prioritizes electronic communications and adjusts notification methods. If the user is experiencing stress, notifications will be reduced or silent notifications will be sent depending on their importance. Once the user's stress level decreases, the server will resume normal notifications.

[0806] The device dynamically changes its notification interface to effectively convey information received from the server to the user. To minimize user stress, the device can choose a quiet and unobtrusive notification method.

[0807] For electronic communications requiring a response, the server automatically generates template messages using an AI model. The generated documents may reflect considerations for reducing the user's emotional state and stress. An example of a specific prompt message might be, "Generate a response that takes user stress reduction into consideration."

[0808] Finally, the server monitors unresolved electronic communications and sets reminders as needed. These reminders are flexibly adjusted according to the user's emotional state, enabling comfortable email management. This system reduces the emotional burden of managing electronic communications, providing a more personalized experience for users.

[0809] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0810] Step 1:

[0811] The server receives electronic communications via the Internet protocol. The data received as input is the content of emails. After reception, the emails are parsed using a natural language processing module. Specifically, the grammatical structure is analyzed and keywords are extracted using the Python NLTK library. The output is the category and summarized content of the analyzed emails.

[0812] Step 2:

[0813] The server categorizes received electronic communications based on the analysis results. The input is the summary information obtained in step 1. Categorization considers the urgency of the email and the importance of the sender. Specifically, it uses text mining techniques to refer to similar past communication data. The output is a list of emails with assigned priorities.

[0814] Step 3:

[0815] The server performs analysis to evaluate the user's current emotional state. Inputs include the user's facial expression data, voice data, and keyboard input speed. The emotion engine utilizes OpenAI's emotion analysis API to process this data and estimate the emotional state. The output is evaluation data indicating the emotional state, such as stress or relaxation.

[0816] Step 4:

[0817] The server adjusts notification methods and priorities based on the user's emotional state. The input is the information obtained in steps 2 and 3. Specifically, if the server determines that the user is experiencing high stress, it will process notifications by reducing their volume or other actions. The output is the adjusted notification schedule and prioritized email settings.

[0818] Step 5:

[0819] The device receives notification information from the server and presents it to the user in the most appropriate format. Inputs include the notification schedule and email priority. Specifically, the device communicates to the user via voice notification, vibration, or visual alert. Output is the implementation of notifications designed to minimize stress.

[0820] Step 6:

[0821] The server automatically generates template responses to electronic communications requiring a reply. The input consists of the email content and the user's emotional state. Using a generation AI model, the prompt "Generate a reply that considers reducing user stress" is applied to create an appropriate template response. The output is a template response tailored to the user's emotional state.

[0822] Step 7:

[0823] The server monitors unanswered emails and sets reminders as needed. Inputs include a list of unanswered emails and the user's emotional state. Based on the analysis, reminders are set at the optimal time. Specifically, it adjusts the reminder frequency by referring to past reply history. The output is a reminder notification that takes the user's emotional state into consideration.

[0824] (Application Example 2)

[0825] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0826] With the increase in information and communication in modern times, the amount of electronic communication individuals receive has become enormous, and many users experience stress in processing it. In particular, there is a need to appropriately distinguish between urgent and non-urgent communications and to respond appropriately according to one's emotional state. However, conventional systems lack the ability to adjust priorities based on emotional state and to suggest less stressful notification methods. As a result, users may end up feeling emotionally burdened.

[0827] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0828] In this invention, the server includes means for analyzing and categorizing received electronic communications, means for summarizing the content of electronic communications, and means for adjusting notification methods using an emotion engine that analyzes the user's emotional state. This enables users to have optimal notification settings that are tailored to their own emotions and to manage communications in a way that reduces stress.

[0829] An "efficient electronic communications management device" is an information processing system equipped with functions for analyzing received electronic communications and appropriately classifying, summarizing, and prioritizing them.

[0830] A "means for performing category classification" refers to a processing device that has the function of dividing received electronic communications into specific categories based on their content.

[0831] A "means of summarizing content" refers to a system that has the function of condensing the vast amount of information received from electronic communications into a concise format.

[0832] A "means for setting priorities" is a device that has the function of determining the order in which to process received electronic communications according to their importance.

[0833] "Means of adjusting notification methods using an emotion engine" refers to a system that estimates the user's emotional state from their facial expressions and voice, and selects the most appropriate notification method based on that.

[0834] A "means for generating template statements" refers to a software process that has the function of automatically generating response statements in response to received electronic communications.

[0835] "Means for estimating emotions using a user's facial expressions or voice" refers to an analytical device equipped with the function of analyzing data such as the user's facial features and tone of voice to identify the user's emotional state.

[0836] A "means of suggesting payment methods" refers to a system that has a process for presenting appropriate payment methods based on the user's emotional state.

[0837] A description of the embodiment for carrying out the invention will be provided.

[0838] The system in this invention is a device for the efficient management of electronic communications and can be implemented using specific hardware and software. The server receives electronic communications via a network, analyzes their content, and categorizes them. Natural language processing is utilized for the analysis, and NLP libraries and APIs may be used. Generative AI models can be used to summarize the content of emails, and a database management system may be used on the server side in the process of extracting important information.

[0839] To recognize a user's emotions, hardware such as the smartphone's camera and microphone are used. The emotion engine software analyzes facial expression and voice data acquired from these devices. It estimates whether the user is experiencing stress and adjusts the notification method and priority accordingly. Specifically, it performs actions such as switching to silent mode when delivering notifications via the display device or speaker. This operation is synchronized on the user's device, ensuring that information is transmitted at the optimal time.

[0840] Furthermore, in the template sentence generation process, the system automatically creates response suggestions. These may include a tone that is appropriate and stress-relieving, selected by the generated AI model based on emotional information.

[0841] For example, if a user receives an important message during a busy time, the system will sense their stress level from their facial expression and immediately refrain from sending a notification. Furthermore, when the user is relaxed after work, the system will use a gentler tone in the template message and automatically send a notification. This reduces the user's emotional burden while achieving efficient communication management.

[0842] An example of a prompt is, "Design an AI model that infers emotions from a user's facial expression image and returns tags such as 'stress' or 'relaxed'." Using this example makes it easier to start designing the AI ​​model necessary for emotion recognition.

[0843] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0844] Step 1:

[0845] The server receives electronic communications over the network. The input is electronic communication data, acquired using the Internet Protocol. The output is unprocessed message data awaiting analysis.

[0846] Step 2:

[0847] The server analyzes received electronic communications using a natural language processing library and performs categorization. Based on the content of the communications, they are categorized into business, private, spam, etc. The input is unprocessed message data, and the output is data with category labels attached. Specifically, it applies a text analysis algorithm.

[0848] Step 3:

[0849] The server summarizes the content of the analyzed electronic communications and extracts important information using a natural language generation AI model. The input is electronic communications data with category labels, and the output is summarized text. The generation AI model performs the summarization using prompt sentences.

[0850] Step 4:

[0851] The device activates the smartphone's camera and microphone to estimate the user's emotions and acquire facial and audio data. The input is the user's real-time video and audio, and the output is raw data that is fed into the emotion engine. This operation includes camera control and audio sampling.

[0852] Step 5:

[0853] The device uses an emotion engine to analyze the user's emotional state and performs digital signal processing. The input is raw data, and the output is emotion tags such as "stress" and "relaxed." The emotion engine extracts facial and vocal features.

[0854] Step 6:

[0855] The server adjusts notification methods based on the user's emotional state. Inputs include emotional tags and summarized text, which determine notification priority and method. Outputs are the adjusted notification settings, specifically changes to notification volume and vibration mode.

[0856] Step 7:

[0857] The server automatically generates template sentences, adjusting the tone to reflect the user's emotional state. Input consists of summarized text and emotion tags, while output is a template reply. A generative AI model handles language generation.

[0858] Step 8:

[0859] The device displays customized notifications to the user. Input consists of notification settings and template text, while output is a visual or auditory notification to the user. Specific actions include on-screen displays and audio alerts.

[0860] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0861] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0862] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0863] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0864] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0865] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0866] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0867] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0868] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0869] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0870] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0871] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0872] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0873] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0874] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0875] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0876] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0877] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0878] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0879] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0880] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0881] The following is further disclosed regarding the embodiments described above.

[0882] (Claim 1)

[0883] In an electronic communications management device,

[0884] A means of analyzing received electronic communications and classifying them into categories,

[0885] A means for summarizing the content of the aforementioned electronic communication,

[0886] Means for setting priorities based on the aforementioned electronic communication,

[0887] A means for automatically generating template text for the aforementioned electronic communication,

[0888] A means of notifying users of unsupported electronic communications,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, which sets priority based on the sender information and expiration date of received electronic communications.

[0892] (Claim 3)

[0893] The system according to claim 1, wherein the system notifies the user using other digital communication devices that work in conjunction with the analyzed electronic communications.

[0894] "Example 1"

[0895] (Claim 1)

[0896] In electronic communications control devices,

[0897] A means of analyzing received electronic communications and classifying them into categories,

[0898] A means for summarizing the information of the aforementioned electronic communications,

[0899] Means for setting priority based on the aforementioned electronic communication,

[0900] A means for automatically generating template text for the aforementioned electronic communication,

[0901] A means of notifying users of unsupported electronic communications,

[0902] A means of analyzing received electronic communications using natural language processing,

[0903] A means of notifying users of high-priority electronic communications,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, which sets priority based on the sender information and expiration date of received electronic communications.

[0907] (Claim 3)

[0908] The system according to claim 1, which notifies the user using other digital communication devices that work in conjunction with the analyzed electronic communications.

[0909] "Application Example 1"

[0910] (Claim 1)

[0911] In an electronic communications management device,

[0912] A means of analyzing received electronic communications and classifying them into categories,

[0913] A means for summarizing the content of the aforementioned electronic communication,

[0914] Means for setting priorities based on the aforementioned electronic communication,

[0915] A means for automatically generating template text for the aforementioned electronic communication,

[0916] A means of notifying users of unsupported electronic communications,

[0917] A means of analyzing and classifying instructions within the factory,

[0918] Means for summarizing the aforementioned instructions and notifying the robot operation system,

[0919] A means of determining the order of tasks in the instructions according to priority,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, which sets priority based on the sender information and expiration date of received electronic communications.

[0923] (Claim 3)

[0924] The system according to claim 1, wherein a notification is given to the user using another digital communication device that works in conjunction with the analyzed electronic communication.

[0925] "Example 2 of combining an emotion engine"

[0926] (Claim 1)

[0927] A means of analyzing received electronic communications and classifying them into categories,

[0928] A means for summarizing the content of the aforementioned electronic communication,

[0929] Means for analyzing the user's state,

[0930] Means for adjusting the notification method and priority based on the user's status,

[0931] Means for automatically generating documents in response to the aforementioned electronic communication,

[0932] A means of notifying users of unsupported electronic communications,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, which sets priority based on the sender information and expiration date of received electronic communications.

[0936] (Claim 3)

[0937] The system according to claim 1, which notifies the user using other communication devices that work in conjunction with the analyzed electronic communications.

[0938] "Application example 2 when combining with an emotional engine"

[0939] (Claim 1)

[0940] In an efficient management device for electronic communications,

[0941] A means of analyzing received electronic communications and classifying them into categories,

[0942] A means for summarizing the content of the aforementioned electronic communication,

[0943] Means for setting priorities based on the aforementioned electronic communication,

[0944] A means of adjusting notification methods using an emotion engine that analyzes the user's emotional state,

[0945] A means for automatically generating template text for the aforementioned electronic communication,

[0946] A means of providing template sentences in a gentle tone based on the user's emotional state,

[0947] A means of notifying users of unsupported electronic communications,

[0948] A means of estimating emotions using the user's facial expressions or voice,

[0949] A means of proposing payment methods based on the user's emotional state,

[0950] An information processing system that includes this.

[0951] (Claim 2)

[0952] The information processing system according to claim 1, which sets priorities considering the sender information and expiration date of received electronic communications.

[0953] (Claim 3)

[0954] The information processing system according to claim 1, which notifies the user using other digital communication devices that work in conjunction with the analyzed electronic communications. [Explanation of symbols]

[0955] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. In an electronic communications management device, A means of analyzing received electronic communications and classifying them into categories, A means for summarizing the content of the aforementioned electronic communication, Means for setting priorities based on the aforementioned electronic communication, A means for automatically generating template text for the aforementioned electronic communication, A means of notifying users of unsupported electronic communications, A means of analyzing and classifying instructions within the factory, Means for summarizing the aforementioned instructions and notifying the robot operation system, A means of determining the order of tasks in the instructions according to priority, A system that includes this.

2. The system according to claim 1, which sets priority based on the sender information and expiration date of received electronic communications.

3. The system according to claim 1, wherein a notification is given to the user using another digital communication device that works in conjunction with the analyzed electronic communication.

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