system

Abstract

Provide a system. 【Solution means】 Means for analyzing recipient information and determining whether the recipient is the specified address, Means for analyzing subject information and determining whether it is a new subject, Means for determining past electronic messages received with the same subject and deleting the others if it is the final communication, Means for determining the presence or absence of attached data, Means for analyzing whether a specific character string is included in the electronic message, Means for analyzing financial-related information and classifying high-importance emails, Means for detecting suspicious behavior and generating a warning, Means for retaining only important electronic messages based on the above determination means and deleting the others, 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] In modern society, many people receive a huge amount of electronic messages every day, and it is required to efficiently extract important information from them and remove unnecessary information. However, in conventional methods, manual email sorting is the mainstream, which is time-consuming and labor-intensive. Therefore, there is a need to provide a system that retains only the information that the user truly needs and automatically deletes and manages unnecessary information.

Means for Solving the Problems

[0005] The system of the present invention provides means for analyzing recipient information to determine if the recipient is using a specified address, and further analyzes subject information to identify if it is a new subject. It then provides means for identifying the final exchange among electronic messages received with the same subject, retaining only the necessary emails, and deleting the others. It also detects the presence or absence of attachments and whether specific strings (e.g., "password") are included in the electronic message, enabling efficient management of only important electronic messages based on these determinations. As a result, users can quickly access important information and are not bothered by unnecessary information.

[0006] "Recipient information" refers to information that indicates the address to which an electronic message is sent.

[0007] "Subject information" refers to information that indicates a title used to summarize and express the content of an electronic message.

[0008] A "new subject line" refers to a subject line that has not been recorded before or is being received for the first time.

[0009] "Same subject line" refers to a message with a subject line that matches a message previously received.

[0010] "Last exchange" refers to the most recent electronic message in a series of exchanges received for the specified subject.

[0011] "Attached data" refers to documents, images, and other files added to an electronic message.

[0012] "Specific string of characters" refers to pre-set important words or phrases included in electronic messages, such as "passwords."

[0013] "Important electronic messages" refer to high-priority electronic messages that have been determined to need to be retained. [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 numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered 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 numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[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 system of this invention involves a server accessing a user's email account and executing a program that automatically organizes received electronic messages. For each received message, the server first analyzes the recipient information and determines whether the user's address is included in the "To" or "Cc" fields. This ensures that only messages addressed to the user are processed.

[0036] Next, the server analyzes the subject information of each incoming message to determine if it is a new subject or the same as a previously received subject. For messages with the same subject belonging to past exchanges, it determines if it is the final exchange, and any older messages that are not the final exchange are to be deleted.

[0037] Furthermore, the server checks for attachments, and if attachments are found, it retains the message as high-priority.

[0038] The server also analyzes the message body and checks if it contains a specific string (for example, a word like "password"). Messages that meet this condition are also retained.

[0039] As a concrete example, consider a scenario where a user receives an email about Project A. The server classifies this email as a new subject. Subsequently, if another email with the same subject, Project A, arrives, the server compares the dates and retains only the most recent one. Furthermore, if the other email has presentation materials attached, those are also retained. Finally, if the other message contains the word "password," this is also retained.

[0040] In this way, the server efficiently organizes the user's email environment and supports the quick retrieval of important information. Users can check only the most important emails remaining in their inbox, thereby improving work efficiency.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server receives electronic messages and analyzes the recipient information for each message. It then checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only messages addressed to that user.

[0044] Step 2:

[0045] The server parses the message subject and matches it against a list of subjects in the database. If the subject is new, the message is retained. If it matches an existing subject, the server then prepares to check the email's timestamp.

[0046] Step 3:

[0047] The server compares the timestamps of emails with the same subject. It retains the message with the most recent date and time as the final exchange, and deletes older messages as unnecessary data.

[0048] Step 4:

[0049] The server checks each message for attachments. Messages with attachments are deemed highly important and are retained.

[0050] Step 5:

[0051] The server searches for specific strings within the message body. It identifies and stores messages that contain specific strings such as "password" or "Pass".

[0052] Step 6:

[0053] The server will create a list of messages that do not meet the above conditions (1) to (8) and automatically delete them. Only important messages will remain in the user's inbox.

[0054] (Example 1)

[0055] 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."

[0056] Receiving emails often presents problems because it takes time to review the content, making it difficult to quickly identify important information. Furthermore, the accumulation of numerous messages makes management cumbersome, leading to decreased work efficiency.

[0057] 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.

[0058] In this invention, the server includes means for analyzing recipient information and determining destination information, means for analyzing subject information and determining novelty, and means for comparing the date and time of previously received messages with the same subject and deleting all but the most recent. As a result, only important electronic messages are efficiently retained, and users can quickly obtain the information they need.

[0059] "Recipient information" refers to information about the destination of an electronic message and is used to determine whether the message is intended for a specific address.

[0060] "Subject information" refers to the content written in the title portion of an electronic message, and serves as an indicator for determining the novelty and relevance of the message.

[0061] "Same subject line" refers to a message that has the same title as a previously received message, and this is a criterion for distinguishing whether it is a continuation of a previous exchange or a new message.

[0062] "Date and time comparison" is a process to check the reception dates and times of multiple messages to identify the most recent message, and based on this result, a decision is made to delete older messages.

[0063] "Data attachments" refer to additional files or documents sent along with an electronic message, and are used as one of the criteria for evaluating their importance.

[0064] "Detecting the presence of specific strings" is the process of analyzing the content of an electronic message to determine whether predefined important keywords or phrases are present in the text.

[0065] "Retaining or deleting a message" means selecting whether to retain the message as a record or delete it as unnecessary, based on an assessment of the message's importance.

[0066] This invention enables efficient organization of electronic messages in a server-managed mail system. The server accesses the user's mail account and analyzes the received messages. The hardware used is a standard server computer, and the software includes a "mail server software API" and the "Python programming language." Specifically, the system is configured in the following way.

[0067] The server uses the "Python IMAP library" to access the mail server and retrieve new messages from the user's mailbox. Recipient information is analyzed by examining the email header to determine if the user is specified as the recipient or to add to the recipient list. The "pandas" library is used to compare the message subject with past email subject data to determine if it is a new message.

[0068] The server runs a date and time-based comparison algorithm on past messages. This ensures that only the most recent messages are retained, and older messages are identified for deletion. It also uses the "email library" to check for attachments and determines the importance of each message based on that.

[0069] Furthermore, the message body is analyzed using the natural language processing library "nltk". By checking for the presence of specific keywords, such as "password," it is possible to identify messages containing highly confidential information and retain them according to their importance.

[0070] As a concrete example of its operation, if a user receives a new email related to Project A, the server adds this email as a new entry in the database. Subsequently, if other emails with the same subject arrive, the server deletes all but the most recent email and saves the newest one. If an email with a different subject arrives with presentation materials attached, the server saves it as important. Furthermore, messages containing the word "password" are also specially stored.

[0071] An example of a prompt might be, "Please organize my emails. Keep the most recent emails related to Project A, and also retain important messages with attachments and passwords." In this way, the system allows users to efficiently organize their mailboxes and quickly retrieve only the important information.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server accesses the user's email account. It uses the user's authentication information as input. The output is a list of unread messages in the inbox. Specifically, it uses the "Python IMAP library" to establish a session with the mail server and retrieve the unread email list.

[0075] Step 2:

[0076] The server parses the recipient information of each received message. The input is the list of unread messages obtained in step 1. The output is a subset of messages that the user has specified as either the recipient or added to the recipient list. Specifically, it parses the email header information and checks the "To" and "Cc" fields.

[0077] Step 3:

[0078] The server analyzes the message subject information to determine its novelty. It uses a subset of messages obtained in step 2 as input. The output is a list of new and existing subject messages. The "pandas" library is used to compare these messages against past subject data to determine whether they are new or existing.

[0079] Step 4:

[0080] The server compares the date and time of messages with the same subject. The input is the existing subject messages separated in step 3. The output is a list that retains only the most recent messages. Specifically, it parses the time information, creates a message data frame, and keeps only the most recent one.

[0081] Step 5:

[0082] The server checks if the message has an attachment. The input is the message subset obtained in step 2. The output is a list of messages that have attachments. The "email library" is used to parse parts of the message and check for the presence of attachments.

[0083] Step 6:

[0084] The server analyzes the message body and detects whether it contains specific keywords. The input is a subset of messages obtained in step 2, and the output is a list of messages containing the given keywords. The "nltk" library is used to perform text analysis and scan for keywords.

[0085] Step 7:

[0086] The server decides whether to retain or delete messages based on the results of the previous steps. The input is the result list from steps 4, 5, and 6. The output is the user's inbox, which contains only the important messages that should be kept. An algorithm is applied that retains messages deemed important in the database and deletes the rest.

[0087] (Application Example 1)

[0088] 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."

[0089] When receiving emails, important financial information can get buried, and suspicious messages such as phishing emails can pose a risk of harm to users. Therefore, it is necessary for users to manage their emails quickly and efficiently, not miss important information, and be vigilant against suspicious emails.

[0090] 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.

[0091] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is the specified address, means for analyzing subject information and determining whether the subject is new, means for analyzing financial information and classifying high-priority emails, and means for detecting suspicious activity and generating warnings. This enables users to quickly review important electronic messages and reduce the risk of fraud.

[0092] "Recipient information" refers to information about the destination of an electronic message and is data necessary to determine whether an email was sent to a specific address.

[0093] "Subject information" refers to the title information attached to an electronic message and serves as a criterion for determining whether it is a new message or related to past correspondence.

[0094] "Financial information" refers to information within electronic messages that relates to specific transactions or settlements, and is used to assess its importance and identify high-priority messages.

[0095] "Suspicious activity" refers to situations where electronic messages or their content deviate from normal patterns and may pose a potential risk to the user.

[0096] "Generating a warning" is the process of issuing notifications or alerts to inform users that suspicious behavior has been detected.

[0097] The system of this invention improves user convenience by automatically organizing the user's emails and prioritizing the display of high-priority messages. The server uses several hardware and software components to access the user's email account and analyze the email data.

[0098] The server processes email data using cloud services such as Amazon Web Services (AWS®) and Google® Cloud Platform. For email analysis, it uses Google's Gmail API or Microsoft®'s Graph API to analyze recipient and subject information. For specific keyword analysis, it uses Google's Natural Language API to analyze email bodies and detect financial information and suspicious activity.

[0099] This system allows users to quickly verify emails containing important financial information, reducing the risk of phishing scams. Furthermore, if suspicious content is detected, an immediate warning is generated and the user is notified.

[0100] For example, if a user receives an email regarding rent payment, the server will classify this email as financial information and display it preferentially. Furthermore, if the email contains words such as "phishing" or "bank account," a warning will be generated.

[0101] An example of a prompt to the generating AI model is as follows: "Automatically identify and filter important financial transaction emails from my email inbox, and detect and warn me about phishing emails."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server accesses the user's email account and retrieves data from received emails. The input data is the user's email account information, and the output is the unprocessed received email data. This data includes email recipient information, subject, body, attachments, etc.

[0105] Step 2:

[0106] The server analyzes recipient information to determine if the email was legitimately sent to the user. The input data is the recipient information of the received email, and it outputs a boolean value indicating whether the email is legitimate. This step uses the Gmail API or Graph API to check if the user's email address is included in the "To" or "Cc" fields.

[0107] Step 3:

[0108] The server analyzes the subject information to determine if it is a new subject or related to a past email. The input data is the email subject information, and the output is the subject status (new or continuing). If past emails with the same subject exist, only the latest message is flagged, and all others are considered for deletion.

[0109] Step 4:

[0110] The server analyzes the email body using a natural language processing engine to extract financial information and specific keywords. The input data is the email body, and the output is a list of detected important keywords and whether or not financial information is present. For example, it uses Google's Natural Language API to extract keywords such as "payment" and "invoice."

[0111] Step 5:

[0112] The server checks for attachments and determines their importance. The input is the email attachment information, and it outputs a flag indicating the presence of an attachment. If an important document is attached, the email's priority is increased.

[0113] Step 6:

[0114] The server detects suspicious activity and issues warnings to the user as needed. Input data consists of well-structured email content and past message patterns, while output is whether or not a warning notification was issued. For example, a warning is generated if an abnormal pattern in the sender address or suspicious content is detected.

[0115] Step 7:

[0116] The server makes the final decision to prioritize saving important emails and delete others based on all analysis results. The input data is the output of each step, and the output is a cleaned list of received emails. This organizes the mailbox so that only information important to the user remains.

[0117] 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.

[0118] This invention relates to a system that uses a program in which a server accesses a user's email account and automatically organizes received messages. The server first analyzes the recipient information of received messages to check if the user's address is included. This allows it to sort out messages addressed to the user.

[0119] Next, the server analyzes the subject information and compares it with the database to determine if it is a new subject. If it is new, it is retained; if the same subject already exists, the timestamp is used to determine if it is the last exchange, and previous messages are deleted.

[0120] The server determines whether there are any attachments, and if there are, it prioritizes them as important and retains the email. It also analyzes the message body to see if it contains specific strings, such as "password," and retains emails containing such strings as well.

[0121] In addition, by using an emotion engine, the server analyzes the user's emotional state. This emotion engine detects the user's emotional response from the content and expression of messages and reclassifies the priority of messages according to the emotional state. Through this process, emotionally important messages are processed to stand out more to the user.

[0122] For example, suppose a user receives an urgent project email. The server analyzes the subject line, attachments, and specific words in the email body, and uses a sentiment engine to determine the user's emotion is "anxious." In this case, the server further increases the importance of the email and notifies the user. Conversely, emails deemed unimportant can be suppressed to avoid interfering with other tasks.

[0123] This system allows the server to provide users with an environment where they can efficiently manage important emails. Users can prioritize checking emails that are appropriately recognized as important, improving work efficiency.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server receives the email and examines the recipient information for each message. Here, it checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only the emails addressed to that user.

[0127] Step 2:

[0128] The server parses the message subject and compares it to existing subjects in the database. If the subject is new, the message is retained. If the subject already exists, the process proceeds to checking the timestamp.

[0129] Step 3:

[0130] The server compares the timestamps of messages with the same subject. It identifies the most recent message and deletes older messages as unnecessary data.

[0131] Step 4:

[0132] The server checks each message for attachments. Messages with attachments are deemed high-priority and are retained.

[0133] Step 5:

[0134] The server analyzes the message body to check if it contains a specific string ("password" or similar words). If so, it retains the message.

[0135] Step 6:

[0136] The server uses an emotion engine to analyze the content of messages. This analysis identifies the user's emotional state and reclassifies message priorities accordingly. Messages that are emotionally important to the user receive higher priority.

[0137] Step 7:

[0138] Finally, the server lists emails that do not meet the criteria and automatically deletes them. Only important messages, organized according to priority, remain in the user's inbox.

[0139] (Example 2)

[0140] 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".

[0141] There is a problem in that it is difficult for information users to quickly and efficiently select and prioritize important messages from the large volume of electronic messages they receive daily. Furthermore, there is a lack of means to identify emotionally important information and prioritize it accordingly.

[0142] 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.

[0143] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is a specified electronic communication address; means for analyzing subject information and determining whether the subject is new; and means for analyzing the content of electronic information, determining the emotional state of the information user, and reclassifying the priority of the information based on that. This enables the information user to quickly identify important electronic messages and, as needed, appropriately prioritize emotionally important information.

[0144] "Analyzing recipient information" is the process of checking whether the recipient field of an electronic message contains the specific electronic communication address of the information user.

[0145] "Analyzing subject information" is the process of examining the subject field of an electronic message to determine whether it is a new message or one that has been received before.

[0146] "Determining electronic information" refers to the process of checking past electronic information with the same subject based on specific conditions to determine whether it is the final exchange.

[0147] "Determining whether or not there are attachments" refers to the process of detecting whether or not files or documents are attached to an electronic message.

[0148] "Analyzing a specific string of characters" is the process of determining whether a particular keyword or phrase is included in the content of an electronic message.

[0149] "Analyzing the content of electronic information" is the process of evaluating the text of an entire electronic message and using it to identify the emotional state of the information user.

[0150] "Reclassifying information priority" is the process of resetting the importance of an received electronic message by considering factors such as the emotional state of the information user.

[0151] This invention is a system in which a server accesses a user's email account and automatically organizes received messages. First, the server uses the email protocol IMAP or POP3 to collect newly received messages from the user's mail server. This allows the user to retrieve messages in bulk.

[0152] Next, the server analyzes the recipient information of each received message. Here, it checks whether a specific telecommunications address is included in the "To" or "CC" fields. This identifies only messages addressed to the user and filters out irrelevant information.

[0153] Next, the server extracts the message subject and compares it against the database to determine if it's new or existing. Using SQL Server, it verifies the existence of the subject and helps reduce duplicate messages.

[0154] The server also checks if the message has attachments, analyzes their format, and assesses their importance. For example, it might decide to retain them if they are spreadsheets or important document formats.

[0155] Furthermore, the server uses a natural language processing library to analyze the message body and determine if it contains specific keywords (e.g., "password"). This makes it possible to identify messages of high importance.

[0156] Furthermore, it utilizes an emotion analysis engine to determine the emotional state of the information user from the message content. A generative AI model identifies emotions such as "tension" or "indifference" from the message and adjusts the message priority accordingly.

[0157] For example, when a user receives an urgent email related to a project, if the server determines from the subject line and sentiment analysis results that the message is "tense," it will increase the importance of the message and send a prompt notification to the user's terminal. On the other hand, emails that are judged to be "indifferent" by sentiment analysis can be given a lower importance rating.

[0158] An example of a prompt message is: "This email has an important subject line, and if the sentiment engine determines the user's emotion is 'stressed,' the notification priority will be reset."

[0159] In this way, the server realizes a system that efficiently manages and prioritizes the information that users need.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server accesses the email server to retrieve newly received electronic messages. It uses user authentication information as input. By logging into the SMTP server and collecting message data using the IMAP or POP3 protocol, it obtains a list of unread messages as output.

[0163] Step 2:

[0164] The server analyzes the recipient information of received emails. The input is the information contained in the recipient field. By analyzing this and comparing it with the user's registered e-mail address, the server selects messages addressed to the user as output.

[0165] Step 3:

[0166] The server extracts the subject line of each email and compares it against the database to determine if it is new or existing. The input is the subject line field of each message. By executing an SQL query and searching the database for existing subjects, the output is a list of unique subjects.

[0167] Step 4:

[0168] The server checks if an email has an attachment and evaluates its format. It uses the data of the attachment contained in the email as input. It analyzes the file format and name, determines whether it meets the importance criteria, and identifies messages with files that should be retained as output.

[0169] Step 5:

[0170] The server analyzes specific keywords in the email body. The input is the text data of the email body. By utilizing a natural language processing library to search for specific strings such as "password," it extracts messages of high importance as output.

[0171] Step 6:

[0172] The server uses an emotion analysis engine to analyze the user's emotional state from the content of the email. The input is the text data of the entire message. Using a generative AI model, it prepares to adjust the emotional priority of the message by outputting emotion labels (e.g., "Tense", "Indifferent").

[0173] Step 7:

[0174] The server reclassifies the email priorities based on the analysis results and determines the notification method. The input is the analysis results obtained at each step. The priority is reset, and appropriate notifications (e.g., immediate notification or notification suppression) are sent to the user terminal as output.

[0175] (Application Example 2)

[0176] 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".

[0177] In modern society, information overload makes it difficult to quickly grasp important information. This problem is particularly serious in environments with many information sources, such as smart cities, where citizens have difficulty receiving emergency information or information about events of interest in a timely manner. Therefore, there is a need for a system that efficiently organizes electronic messages and presents important information quickly.

[0178] 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.

[0179] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is at the specified address; means for analyzing subject information and determining whether the subject is new; means for determining past electronic messages received with the same subject and deleting others if they are the last exchange; means for determining whether there is attached data; means for analyzing whether a specific string is contained in the electronic message; means for analyzing emotional state and reclassifying priority; and means for retaining only important electronic messages and deleting others based on the aforementioned determination means. This enables citizens to receive and respond to urgent and highly relevant information quickly and appropriately.

[0180] "Recipient information" refers to information included in an electronic message, specifically an identifier for the person or organization to whom the message is sent.

[0181] "Subject information" refers to the text information used as the title of an electronic message, and this information serves as a clue to understanding the content and importance of the message.

[0182] "Attached data" refers to files, images, and other data formats that are sent along with an electronic message and are used to supplement the content of the message.

[0183] A "specific string of characters" refers to a particular word or phrase included in an electronic message that serves as a basis for determining the importance or relevance of the message.

[0184] "Emotional state" refers to the user's psychological reaction and attitude when receiving an electronic message, and is inferred from the content and expression of the message.

[0185] "Priority" is an indicator that shows the importance and urgency of an electronic message, and is determined based on the message's content and related information.

[0186] "Decision-making means" refers to the methods or processes used by a system to analyze electronic messages and make decisions based on that information.

[0187] An "important electronic message" is an electronic message that, based on analysis and evaluation, is deemed to require special attention from the recipient.

[0188] The system realizing this invention includes a program that receives user emails and automatically organizes them according to their importance. When analyzing emails, the server makes decisions based on recipient information, subject information, attachments, specific strings, and sentiment. The analysis of recipient information verifies whether the email is addressed to an address specified by the user. The analysis of subject information checks whether the same subject has been used before and determines whether it is a new or final exchange. If attachments are included, their importance is highly rated, and further importance is determined by detecting specific strings.

[0189] Using an emotion engine, the system analyzes the user's emotional state from text and expressions and reclassifies their importance. Based on this emotional state, notification priorities are adjusted to allow users to receive information efficiently. Suitable hardware includes information terminals such as smartphones and tablets, and the software uses Python and email analysis libraries. The emotion engine is implemented, for example, as a Python library to perform sentiment analysis.

[0190] As a concrete example, consider a scenario where a user receives a "flood warning" notification from a smart city. This email is displayed with priority due to its high urgency. On the other hand, an email inviting a library reading group is only appropriately presented if the user is analyzed as having a preference for cultural activities.

[0191] An example of a prompt message might be: "Generate a program that analyzes the following email content, labels high-priority information with 'Urgent Notification,' and organizes the information appropriately according to the user's level of interest."

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server analyzes the received email to extract recipient information. The entire email is used as input. The server analyzes the email's recipient field to determine if the recipient matches the specified address and outputs the result.

[0195] Step 2:

[0196] The server analyzes the subject information of received emails. The email subject is used as input. The server checks whether the subject has existed in the past against a database, determines whether it is a new or recent exchange, and outputs the result.

[0197] Step 3:

[0198] The server determines whether an email contains attachments. The entire email is used as input. The server checks for the presence of attachments and outputs the result. If attachments exist, their importance is set to high.

[0199] Step 4:

[0200] The server analyzes specific strings from the email body. The email body is used as input. The server searches for keywords (e.g., "password"), confirms their presence, and outputs them. If important keywords are included, the importance level is increased.

[0201] Step 5:

[0202] The server uses an emotion engine to analyze the emotional state of an email. The email body is used as input. The server performs emotion analysis, detects the user's emotional state, and reclassifies the email's priority based on the results before outputting it.

[0203] Step 6:

[0204] The server retains only the electronic messages deemed important based on these judgment results and deletes all other messages. The judgment results from each step are used as input. The server ultimately presents the retained messages to the user.

[0205] 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.

[0206] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] The system of this invention involves a server accessing a user's email account and executing a program that automatically organizes received electronic messages. For each received message, the server first analyzes the recipient information and determines whether the user's address is included in the "To" or "Cc" fields. This ensures that only messages addressed to the user are processed.

[0222] Next, the server analyzes the subject information of each incoming message to determine if it is a new subject or the same as a previously received subject. For messages with the same subject belonging to past exchanges, it determines if it is the final exchange, and any older messages that are not the final exchange are to be deleted.

[0223] Furthermore, the server checks for attachments, and if attachments are found, it retains the message as high-priority.

[0224] The server also analyzes the message body and checks if it contains a specific string (for example, a word like "password"). Messages that meet this condition are also retained.

[0225] As a concrete example, consider a scenario where a user receives an email about Project A. The server classifies this email as a new subject. Subsequently, if another email with the same subject, Project A, arrives, the server compares the dates and retains only the most recent one. Furthermore, if the other email has presentation materials attached, those are also retained. Finally, if the other message contains the word "password," this is also retained.

[0226] In this way, the server efficiently organizes the user's email environment and supports the quick retrieval of important information. Users can check only the most important emails remaining in their inbox, thereby improving work efficiency.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server receives electronic messages and analyzes the recipient information for each message. It then checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only messages addressed to that user.

[0230] Step 2:

[0231] The server parses the message subject and matches it against a list of subjects in the database. If the subject is new, the message is retained. If it matches an existing subject, the server then prepares to check the email's timestamp.

[0232] Step 3:

[0233] The server compares the timestamps of emails with the same subject. It retains the message with the most recent date and time as the final exchange, and deletes older messages as unnecessary data.

[0234] Step 4:

[0235] The server checks each message for attachments. Messages with attachments are deemed highly important and are retained.

[0236] Step 5:

[0237] The server searches for specific strings within the message body. It identifies and stores messages that contain specific strings such as "password" or "Pass".

[0238] Step 6:

[0239] The server will create a list of messages that do not meet the above conditions (1) to (8) and automatically delete them. Only important messages will remain in the user's inbox.

[0240] (Example 1)

[0241] 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."

[0242] Receiving emails often presents problems because it takes time to review the content, making it difficult to quickly identify important information. Furthermore, the accumulation of numerous messages makes management cumbersome, leading to decreased work efficiency.

[0243] 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.

[0244] In this invention, the server includes means for analyzing recipient information and determining destination information, means for analyzing subject information and determining novelty, and means for comparing the date and time of previously received messages with the same subject and deleting all but the most recent. As a result, only important electronic messages are efficiently retained, and users can quickly obtain the information they need.

[0245] "Recipient information" refers to information about the destination of an electronic message and is used to determine whether the message is intended for a specific address.

[0246] "Subject information" refers to the content written in the title portion of an electronic message, and serves as an indicator for determining the novelty and relevance of the message.

[0247] "Same subject line" refers to a message that has the same title as a previously received message, and this is a criterion for distinguishing whether it is a continuation of a previous exchange or a new message.

[0248] "Date and time comparison" is a process to check the reception dates and times of multiple messages to identify the most recent message, and based on this result, a decision is made to delete older messages.

[0249] "Data attachments" refer to additional files or documents sent along with an electronic message, and are used as one of the criteria for evaluating their importance.

[0250] "Detecting the presence of specific strings" is the process of analyzing the content of an electronic message to determine whether predefined important keywords or phrases are present in the text.

[0251] "Retaining or deleting a message" means selecting whether to retain the message as a record or delete it as unnecessary, based on an assessment of the message's importance.

[0252] This invention enables efficient organization of electronic messages in a server-managed mail system. The server accesses the user's mail account and analyzes the received messages. The hardware used is a standard server computer, and the software includes a "mail server software API" and the "Python programming language." Specifically, the system is configured in the following way.

[0253] The server uses the "Python IMAP library" to access the mail server and retrieve new messages from the user's mailbox. Recipient information is analyzed by examining the email header to determine if the user is specified as the recipient or to add to the recipient list. The "pandas" library is used to compare the message subject with past email subject data to determine if it is a new message.

[0254] The server runs a date and time-based comparison algorithm on past messages. This ensures that only the most recent messages are retained, and older messages are identified for deletion. It also uses the "email library" to check for attachments and determines the importance of each message based on that.

[0255] Furthermore, the message body is analyzed using the natural language processing library "nltk". By checking for the presence of specific keywords, such as "password," it is possible to identify messages containing highly confidential information and retain them according to their importance.

[0256] As a concrete example of its operation, if a user receives a new email related to Project A, the server adds this email as a new entry in the database. Subsequently, if other emails with the same subject arrive, the server deletes all but the most recent email and saves the newest one. If an email with a different subject arrives with presentation materials attached, the server saves it as important. Furthermore, messages containing the word "password" are also specially stored.

[0257] An example of a prompt might be, "Please organize my emails. Keep the most recent emails related to Project A, and also retain important messages with attachments and passwords." In this way, the system allows users to efficiently organize their mailboxes and quickly retrieve only the important information.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server accesses the user's email account. It uses the user's authentication information as input. The output is a list of unread messages in the inbox. Specifically, it uses the "Python IMAP library" to establish a session with the mail server and retrieve the unread email list.

[0261] Step 2:

[0262] The server parses the recipient information of each received message. The input is the list of unread messages obtained in step 1. The output is a subset of messages that the user has specified as either the recipient or added to the recipient list. Specifically, it parses the email header information and checks the "To" and "Cc" fields.

[0263] Step 3:

[0264] The server analyzes the message subject information to determine its novelty. It uses a subset of messages obtained in step 2 as input. The output is a list of new and existing subject messages. The "pandas" library is used to compare these messages against past subject data to determine whether they are new or existing.

[0265] Step 4:

[0266] The server compares the date and time of messages with the same subject. The input is the existing subject messages separated in step 3. The output is a list that retains only the most recent messages. Specifically, it parses the time information, creates a message data frame, and keeps only the most recent one.

[0267] Step 5:

[0268] The server checks if the message has an attachment. The input is the message subset obtained in step 2. The output is a list of messages that have attachments. The "email library" is used to parse parts of the message and check for the presence of attachments.

[0269] Step 6:

[0270] The server analyzes the message body and detects whether it contains specific keywords. The input is a subset of messages obtained in step 2, and the output is a list of messages containing the given keywords. The "nltk" library is used to perform text analysis and scan for keywords.

[0271] Step 7:

[0272] The server decides whether to retain or delete messages based on the results of the previous steps. The input is the result list from steps 4, 5, and 6. The output is the user's inbox, which contains only the important messages that should be kept. An algorithm is applied that retains messages deemed important in the database and deletes the rest.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] When receiving emails, important financial information can get buried, and suspicious messages such as phishing emails can pose a risk of harm to users. Therefore, it is necessary for users to manage their emails quickly and efficiently, not miss important information, and be vigilant against suspicious emails.

[0276] 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.

[0277] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is the specified address, means for analyzing subject information and determining whether the subject is new, means for analyzing financial information and classifying high-priority emails, and means for detecting suspicious activity and generating warnings. This enables users to quickly review important electronic messages and reduce the risk of fraud.

[0278] "Recipient information" refers to information about the destination of an electronic message and is data necessary to determine whether an email was sent to a specific address.

[0279] "Subject information" refers to the title information attached to an electronic message and serves as a criterion for determining whether it is a new message or related to past correspondence.

[0280] "Financial-related information" refers to information related to specific transactions or settlements within an electronic message, and is used to evaluate its importance and identify high-priority messages.

[0281] "Suspicious behavior" refers to the case where an electronic message or its content is different from the normal pattern, indicating a situation that may pose a potential risk to the user.

[0282] "Generating a warning" is a process of issuing notifications or alerts to inform the user that suspicious behavior has been detected.

[0283] The system of this invention automatically organizes the user's emails and preferentially displays high-importance messages, thereby improving the user's convenience. The server accesses the user's email account and uses some hardware and software to analyze the email data.

[0284] The server uses cloud services such as Amazon Web Services (AWS) and Google Cloud Platform to process the email data. For email analysis, Google's Gmail API or Microsoft's Graph API is used to analyze recipient information and subject information. For the analysis of specific keywords, Google's Natural Language API is used to analyze the email body to detect financial-related information and suspicious behavior.

[0285] With this system, the user can quickly check emails containing important financial information and reduce the risk of phishing fraud. Also, when suspicious content is detected, a warning is immediately generated and notified to the user.

[0286] As a specific example, when the user receives an email regarding rent payment, the server classifies this email as financial-related information and displays it preferentially. Also, if words such as "phishing" or "bank account" are included in the email, a warning is generated.

[0287] Examples of prompt sentences for the generation AI model are as follows: "Please automatically identify and classify important financial transaction emails from the email inbox and detect and warn phishing fraud emails."

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The server accesses the user's email account and obtains the data of received emails. The input data is the user's email account information, and the output is the unprocessed received email data. This data includes the recipient information, subject, body, attached data, etc. of the email.

[0291] Step 2:

[0292] The server analyzes the recipient information to determine whether the email is legitimately sent to the user. The input data is the recipient information of the received email, and the output is a true / false value indicating whether it is a legitimate email. In this step, the Gmail API or Graph API is used to check whether the user's email address is included in "To" or "Cc".

[0293] Step 3:

[0294] The server analyzes the subject information to determine whether it is a new subject or the relevance to past emails. The input data is the subject information of the email, and the output is the status of the subject (new or continued). If there is a past email with the same subject, only the latest message is flagged, and the others are candidates for deletion.

[0295] Step 4:

[0296] The server analyzes the email body using a natural language processing engine to extract financial information and specific keywords. The input data is the email body, and the output is a list of detected important keywords and whether or not financial information is present. For example, it uses Google's Natural Language API to extract keywords such as "payment" and "invoice."

[0297] Step 5:

[0298] The server checks for attachments and determines their importance. The input is the email attachment information, and it outputs a flag indicating the presence of an attachment. If an important document is attached, the email's priority is increased.

[0299] Step 6:

[0300] The server detects suspicious activity and issues warnings to the user as needed. Input data consists of well-structured email content and past message patterns, while output is whether or not a warning notification was issued. For example, a warning is generated if an abnormal pattern in the sender address or suspicious content is detected.

[0301] Step 7:

[0302] The server makes the final decision to prioritize saving important emails and delete others based on all analysis results. The input data is the output of each step, and the output is a cleaned list of received emails. This organizes the mailbox so that only information important to the user remains.

[0303] 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.

[0304] The present invention is a system that uses a program in which a server accesses a user's email account and automatically organizes received messages. First, for the received messages, the server analyzes the recipient information to check whether the user's address is included. Thereby, messages addressed to the user are selected.

[0305] Next, the server analyzes the subject information and determines whether it is a new subject by comparing it with a database. If it is new, it is retained. If the same subject exists, the server uses a timestamp to determine whether it is the final interaction and deletes the previous messages.

[0306] The server determines the presence or absence of attached data. If there is an attached file, the server considers it to be of high importance and retains the email. Also, the server analyzes whether a specific string, such as "password", is included in the message body, and if so, the message is also a retention target.

[0307] In addition, by using an emotion engine, the server analyzes the user's emotional state. This emotion engine detects the user's emotional reaction from the content and expression of the message and reclassifies the priority of the message according to the emotional state. Through this process, emotionally important messages are processed so as to be more prominent for the user.

[0308] As a specific example, when the user receives an email with a high project urgency, assume that the server determines the user's emotion as "nervous" using the emotion engine in addition to analyzing the subject, the presence or absence of attached files, and specific words in the text. In this case, the server further increases the importance of the email and notifies the user. Conversely, emails determined to be uninterested can suppress notifications so as not to interfere with other work.

[0309] With this system, the server can provide an environment in which the user can efficiently manage important emails. The user can preferentially check emails with appropriately recognized importance, improving work efficiency.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] The server receives the email and examines the recipient information for each message. Here, it checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only the emails addressed to that user.

[0313] Step 2:

[0314] The server parses the message subject and compares it to existing subjects in the database. If the subject is new, the message is retained. If the subject already exists, the process proceeds to checking the timestamp.

[0315] Step 3:

[0316] The server compares the timestamps of messages with the same subject. It identifies the most recent message and deletes older messages as unnecessary data.

[0317] Step 4:

[0318] The server checks each message for attachments. Messages with attachments are deemed high-priority and are retained.

[0319] Step 5:

[0320] The server analyzes the message body to check if it contains a specific string ("password" or similar words). If so, it retains the message.

[0321] Step 6:

[0322] The server uses an emotion engine to analyze the content of messages. This analysis identifies the user's emotional state and reclassifies message priorities accordingly. Messages that are emotionally important to the user receive higher priority.

[0323] Step 7:

[0324] Finally, the server lists emails that do not meet the criteria and automatically deletes them. Only important messages, organized according to priority, remain in the user's inbox.

[0325] (Example 2)

[0326] 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".

[0327] There is a problem in that it is difficult for information users to quickly and efficiently select and prioritize important messages from the large volume of electronic messages they receive daily. Furthermore, there is a lack of means to identify emotionally important information and prioritize it accordingly.

[0328] 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.

[0329] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is a specified electronic communication address; means for analyzing subject information and determining whether the subject is new; and means for analyzing the content of electronic information, determining the emotional state of the information user, and reclassifying the priority of the information based on that. This enables the information user to quickly identify important electronic messages and, as needed, appropriately prioritize emotionally important information.

[0330] "Analyzing recipient information" is the process of checking whether the recipient field of an electronic message contains the specific electronic communication address of the information user.

[0331] "Analyzing subject information" is the process of examining the subject field of an electronic message to determine whether it is a new message or one that has been received before.

[0332] "Determining electronic information" refers to the process of checking past electronic information with the same subject based on specific conditions to determine whether it is the final exchange.

[0333] "Determining whether or not there are attachments" refers to the process of detecting whether or not files or documents are attached to an electronic message.

[0334] "Analyzing a specific string of characters" is the process of determining whether a particular keyword or phrase is included in the content of an electronic message.

[0335] "Analyzing the content of electronic information" is the process of evaluating the text of an entire electronic message and using it to identify the emotional state of the information user.

[0336] "Reclassifying information priority" is the process of resetting the importance of an received electronic message by considering factors such as the emotional state of the information user.

[0337] This invention is a system in which a server accesses a user's email account and automatically organizes received messages. First, the server uses the email protocol IMAP or POP3 to collect newly received messages from the user's mail server. This allows the user to retrieve messages in bulk.

[0338] Next, the server analyzes the recipient information of each received message. Here, it checks whether a specific telecommunications address is included in the "To" or "CC" fields. This identifies only messages addressed to the user and filters out irrelevant information.

[0339] Next, the server extracts the message subject and compares it against the database to determine if it's new or existing. Using SQL Server, it verifies the existence of the subject and helps reduce duplicate messages.

[0340] The server also checks if the message has attachments, analyzes their format, and assesses their importance. For example, it might decide to retain them if they are spreadsheets or important document formats.

[0341] Furthermore, the server uses a natural language processing library to analyze the message body and determine if it contains specific keywords (e.g., "password"). This makes it possible to identify messages of high importance.

[0342] Furthermore, it utilizes an emotion analysis engine to determine the emotional state of the information user from the message content. A generative AI model identifies emotions such as "tension" or "indifference" from the message and adjusts the message priority accordingly.

[0343] For example, when a user receives an urgent email related to a project, if the server determines from the subject line and sentiment analysis results that the message is "tense," it will increase the importance of the message and send a prompt notification to the user's terminal. On the other hand, emails that are judged to be "indifferent" by sentiment analysis can be given a lower importance rating.

[0344] An example of a prompt message is: "This email has an important subject line, and if the sentiment engine determines the user's emotion is 'stressed,' the notification priority will be reset."

[0345] In this way, the server realizes a system that efficiently manages and prioritizes the information that users need.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The server accesses the email server to retrieve newly received electronic messages. It uses user authentication information as input. By logging into the SMTP server and collecting message data using the IMAP or POP3 protocol, it obtains a list of unread messages as output.

[0349] Step 2:

[0350] The server analyzes the recipient information of received emails. The input is the information contained in the recipient field. By analyzing this and comparing it with the user's registered e-mail address, the server selects messages addressed to the user as output.

[0351] Step 3:

[0352] The server extracts the subject line of each email and compares it against the database to determine if it is new or existing. The input is the subject line field of each message. By executing an SQL query and searching the database for existing subjects, the output is a list of unique subjects.

[0353] Step 4:

[0354] The server checks if an email has an attachment and evaluates its format. It uses the data of the attachment contained in the email as input. It analyzes the file format and name, determines whether it meets the importance criteria, and identifies messages with files that should be retained as output.

[0355] Step 5:

[0356] The server analyzes specific keywords in the email body. The input is the text data of the email body. By utilizing a natural language processing library to search for specific strings such as "password," it extracts messages of high importance as output.

[0357] Step 6:

[0358] The server uses an emotion analysis engine to analyze the user's emotional state from the content of the email. The input is the text data of the entire message. Using a generative AI model, it prepares to adjust the emotional priority of the message by outputting emotion labels (e.g., "Tense", "Indifferent").

[0359] Step 7:

[0360] The server reclassifies the email priorities based on the analysis results and determines the notification method. The input is the analysis results obtained at each step. The priority is reset, and appropriate notifications (e.g., immediate notification or notification suppression) are sent to the user terminal as output.

[0361] (Application Example 2)

[0362] 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 as the "terminal".

[0363] In modern society, information overload makes it difficult to quickly grasp important information. This problem is particularly serious in environments with many information sources, such as smart cities, where citizens have difficulty receiving emergency information or information about events of interest in a timely manner. Therefore, there is a need for a system that efficiently organizes electronic messages and presents important information quickly.

[0364] 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.

[0365] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is at the specified address; means for analyzing subject information and determining whether the subject is new; means for determining past electronic messages received with the same subject and deleting others if they are the last exchange; means for determining whether there is attached data; means for analyzing whether a specific string is contained in the electronic message; means for analyzing emotional state and reclassifying priority; and means for retaining only important electronic messages and deleting others based on the aforementioned determination means. This enables citizens to receive and respond to urgent and highly relevant information quickly and appropriately.

[0366] "Recipient information" refers to information included in an electronic message, specifically an identifier for the person or organization to whom the message is sent.

[0367] "Subject information" refers to the text information used as the title of an electronic message, and this information serves as a clue to understanding the content and importance of the message.

[0368] "Attached data" refers to files, images, and other data formats that are sent along with an electronic message and are used to supplement the content of the message.

[0369] A "specific string of characters" refers to a particular word or phrase included in an electronic message that serves as a basis for determining the importance or relevance of the message.

[0370] "Emotional state" refers to the user's psychological reaction and attitude when receiving an electronic message, and is inferred from the content and expression of the message.

[0371] "Priority" is an indicator that shows the importance and urgency of an electronic message, and is determined based on the message's content and related information.

[0372] "Decision-making means" refers to the methods or processes used by a system to analyze electronic messages and make decisions based on that information.

[0373] An "important electronic message" is an electronic message that, based on analysis and evaluation, is deemed to require special attention from the recipient.

[0374] The system realizing this invention includes a program that receives user emails and automatically organizes them according to their importance. When analyzing emails, the server makes decisions based on recipient information, subject information, attachments, specific strings, and sentiment. The analysis of recipient information verifies whether the email is addressed to an address specified by the user. The analysis of subject information checks whether the same subject has been used before and determines whether it is a new or final exchange. If attachments are included, their importance is highly rated, and further importance is determined by detecting specific strings.

[0375] Using an emotion engine, the system analyzes the user's emotional state from text and expressions and reclassifies their importance. Based on this emotional state, notification priorities are adjusted to allow users to receive information efficiently. Suitable hardware includes information terminals such as smartphones and tablets, and the software uses Python and email analysis libraries. The emotion engine is implemented, for example, as a Python library to perform sentiment analysis.

[0376] As a concrete example, consider a scenario where a user receives a "flood warning" notification from a smart city. This email is displayed with priority due to its high urgency. On the other hand, an email inviting a library reading group is only appropriately presented if the user is analyzed as having a preference for cultural activities.

[0377] An example of a prompt message might be: "Generate a program that analyzes the following email content, labels high-priority information with 'Urgent Notification,' and organizes the information appropriately according to the user's level of interest."

[0378] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0379] Step 1:

[0380] The server analyzes the received email to extract recipient information. The entire email is used as input. The server analyzes the email's recipient field to determine if the recipient matches the specified address and outputs the result.

[0381] Step 2:

[0382] The server analyzes the subject information of received emails. The email subject is used as input. The server checks whether the subject has existed in the past against a database, determines whether it is a new or recent exchange, and outputs the result.

[0383] Step 3:

[0384] The server determines whether an email contains attachments. The entire email is used as input. The server checks for the presence of attachments and outputs the result. If attachments exist, their importance is set to high.

[0385] Step 4:

[0386] The server analyzes specific strings from the email body. The email body is used as input. The server searches for keywords (e.g., "password"), confirms their presence, and outputs them. If important keywords are included, the importance level is increased.

[0387] Step 5:

[0388] The server uses an emotion engine to analyze the emotional state of an email. The email body is used as input. The server performs emotion analysis, detects the user's emotional state, and reclassifies the email's priority based on the results before outputting it.

[0389] Step 6:

[0390] The server retains only the electronic messages deemed important based on these judgment results and deletes all other messages. The judgment results from each step are used as input. The server ultimately presents the retained messages to the user.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] [Third Embodiment]

[0395] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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".

[0407] The system of this invention involves a server accessing a user's email account and executing a program that automatically organizes received electronic messages. For each received message, the server first analyzes the recipient information and determines whether the user's address is included in the "To" or "Cc" fields. This ensures that only messages addressed to the user are processed.

[0408] Next, the server analyzes the subject information of each incoming message to determine if it is a new subject or the same as a previously received subject. For messages with the same subject belonging to past exchanges, it determines if it is the final exchange, and any older messages that are not the final exchange are to be deleted.

[0409] Furthermore, the server checks for attachments, and if attachments are found, it retains the message as high-priority.

[0410] The server also analyzes the message body and checks if it contains a specific string (for example, a word like "password"). Messages that meet this condition are also retained.

[0411] As a concrete example, consider a scenario where a user receives an email about Project A. The server classifies this email as a new subject. Subsequently, if another email with the same subject, Project A, arrives, the server compares the dates and retains only the most recent one. Furthermore, if the other email has presentation materials attached, those are also retained. Finally, if the other message contains the word "password," this is also retained.

[0412] In this way, the server efficiently organizes the user's email environment and supports the quick retrieval of important information. Users can check only the most important emails remaining in their inbox, thereby improving work efficiency.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The server receives electronic messages and analyzes the recipient information for each message. It then checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only messages addressed to that user.

[0416] Step 2:

[0417] The server parses the message subject and matches it against a list of subjects in the database. If the subject is new, the message is retained. If it matches an existing subject, the server then prepares to check the email's timestamp.

[0418] Step 3:

[0419] The server compares the timestamps of emails with the same subject. It retains the message with the most recent date and time as the final exchange, and deletes older messages as unnecessary data.

[0420] Step 4:

[0421] The server checks each message for attachments. Messages with attachments are deemed highly important and are retained.

[0422] Step 5:

[0423] The server searches for specific strings within the message body. It identifies and stores messages that contain specific strings such as "password" or "Pass".

[0424] Step 6:

[0425] The server will create a list of messages that do not meet the above conditions (1) to (8) and automatically delete them. Only important messages will remain in the user's inbox.

[0426] (Example 1)

[0427] 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."

[0428] Receiving emails often presents problems because it takes time to review the content, making it difficult to quickly identify important information. Furthermore, the accumulation of numerous messages makes management cumbersome, leading to decreased work efficiency.

[0429] 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.

[0430] In this invention, the server includes means for analyzing recipient information and determining destination information, means for analyzing subject information and determining novelty, and means for comparing the date and time of previously received messages with the same subject and deleting all but the most recent. As a result, only important electronic messages are efficiently retained, and users can quickly obtain the information they need.

[0431] "Recipient information" refers to information about the destination of an electronic message and is used to determine whether the message is intended for a specific address.

[0432] "Subject information" refers to the content written in the title portion of an electronic message, and serves as an indicator for determining the novelty and relevance of the message.

[0433] "Same subject line" refers to a message that has the same title as a previously received message, and this is a criterion for distinguishing whether it is a continuation of a previous exchange or a new message.

[0434] "Date and time comparison" is a process to check the reception dates and times of multiple messages to identify the most recent message, and based on this result, a decision is made to delete older messages.

[0435] "Data attachments" refer to additional files or documents sent along with an electronic message, and are used as one of the criteria for evaluating their importance.

[0436] "Detecting the presence of specific strings" is the process of analyzing the content of an electronic message to determine whether predefined important keywords or phrases are present in the text.

[0437] "Retaining or deleting a message" means selecting whether to retain the message as a record or delete it as unnecessary, based on an assessment of the message's importance.

[0438] This invention enables efficient organization of electronic messages in a server-managed mail system. The server accesses the user's mail account and analyzes the received messages. The hardware used is a standard server computer, and the software includes a "mail server software API" and the "Python programming language." Specifically, the system is configured in the following way.

[0439] The server uses the "Python IMAP library" to access the mail server and retrieve new messages from the user's mailbox. Recipient information is analyzed by examining the email header to determine if the user is specified as the recipient or to add to the recipient list. The "pandas" library is used to compare the message subject with past email subject data to determine if it is a new message.

[0440] The server runs a date and time-based comparison algorithm on past messages. This ensures that only the most recent messages are retained, and older messages are identified for deletion. It also uses the "email library" to check for attachments and determines the importance of each message based on that.

[0441] Furthermore, the message body is analyzed using the natural language processing library "nltk". By checking for the presence of specific keywords, such as "password," it is possible to identify messages containing highly confidential information and retain them according to their importance.

[0442] As a concrete example of its operation, if a user receives a new email related to Project A, the server adds this email as a new entry in the database. Subsequently, if other emails with the same subject arrive, the server deletes all but the most recent email and saves the newest one. If an email with a different subject arrives with presentation materials attached, the server saves it as important. Furthermore, messages containing the word "password" are also specially stored.

[0443] An example of a prompt might be, "Please organize my emails. Keep the most recent emails related to Project A, and also retain important messages with attachments and passwords." In this way, the system allows users to efficiently organize their mailboxes and quickly retrieve only the important information.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The server accesses the user's email account. It uses the user's authentication information as input. The output is a list of unread messages in the inbox. Specifically, it uses the "Python IMAP library" to establish a session with the mail server and retrieve the unread email list.

[0447] Step 2:

[0448] The server parses the recipient information of each received message. The input is the list of unread messages obtained in step 1. The output is a subset of messages that the user has specified as either the recipient or added to the recipient list. Specifically, it parses the email header information and checks the "To" and "Cc" fields.

[0449] Step 3:

[0450] The server analyzes the message subject information to determine its novelty. It uses a subset of messages obtained in step 2 as input. The output is a list of new and existing subject messages. The "pandas" library is used to compare these messages against past subject data to determine whether they are new or existing.

[0451] Step 4:

[0452] The server compares the date and time of messages with the same subject. The input is the existing subject messages separated in step 3. The output is a list that retains only the most recent messages. Specifically, it parses the time information, creates a message data frame, and keeps only the most recent one.

[0453] Step 5:

[0454] The server checks if the message has an attachment. The input is the message subset obtained in step 2. The output is a list of messages that have attachments. The "email library" is used to parse parts of the message and check for the presence of attachments.

[0455] Step 6:

[0456] The server analyzes the message body and detects whether it contains specific keywords. The input is a subset of messages obtained in step 2, and the output is a list of messages containing the given keywords. The "nltk" library is used to perform text analysis and scan for keywords.

[0457] Step 7:

[0458] The server decides whether to retain or delete messages based on the results of the previous steps. The input is the result list from steps 4, 5, and 6. The output is the user's inbox, which contains only the important messages that should be kept. An algorithm is applied that retains messages deemed important in the database and deletes the rest.

[0459] (Application Example 1)

[0460] 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."

[0461] When receiving emails, important financial information can get buried, and suspicious messages such as phishing emails can pose a risk of harm to users. Therefore, it is necessary for users to manage their emails quickly and efficiently, not miss important information, and be vigilant against suspicious emails.

[0462] 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.

[0463] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is the specified address, means for analyzing subject information and determining whether the subject is new, means for analyzing financial information and classifying high-priority emails, and means for detecting suspicious activity and generating warnings. This enables users to quickly review important electronic messages and reduce the risk of fraud.

[0464] "Recipient information" refers to information about the destination of an electronic message and is data necessary to determine whether an email was sent to a specific address.

[0465] "Subject information" refers to the title information attached to an electronic message and serves as a criterion for determining whether it is a new message or related to past correspondence.

[0466] "Financial information" refers to information within electronic messages that relates to specific transactions or settlements, and is used to assess its importance and identify high-priority messages.

[0467] "Suspicious activity" refers to situations where electronic messages or their content deviate from normal patterns and may pose a potential risk to the user.

[0468] "Generating a warning" is the process of issuing notifications or alerts to inform users that suspicious behavior has been detected.

[0469] The system of this invention improves user convenience by automatically organizing the user's emails and prioritizing the display of high-priority messages. The server uses several hardware and software components to access the user's email account and analyze the email data.

[0470] The server processes email data using cloud services such as Amazon Web Services (AWS) and Google Cloud Platform. For email analysis, it uses Google's Gmail API or Microsoft's Graph API to analyze recipient and subject information. For specific keyword analysis, it uses Google's Natural Language API to analyze email bodies and detect financial information and suspicious activity.

[0471] This system allows users to quickly verify emails containing important financial information, reducing the risk of phishing scams. Furthermore, if suspicious content is detected, an immediate warning is generated and the user is notified.

[0472] For example, if a user receives an email regarding rent payment, the server will classify this email as financial information and display it preferentially. Furthermore, if the email contains words such as "phishing" or "bank account," a warning will be generated.

[0473] An example of a prompt to the generating AI model is as follows: "Automatically identify and filter important financial transaction emails from my email inbox, and detect and warn me about phishing emails."

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] The server accesses the user's email account and retrieves data from received emails. The input data is the user's email account information, and the output is the unprocessed received email data. This data includes email recipient information, subject, body, attachments, etc.

[0477] Step 2:

[0478] The server analyzes recipient information to determine if the email was legitimately sent to the user. The input data is the recipient information of the received email, and it outputs a boolean value indicating whether the email is legitimate. This step uses the Gmail API or Graph API to check if the user's email address is included in the "To" or "Cc" fields.

[0479] Step 3:

[0480] The server analyzes the subject information to determine if it is a new subject or related to a past email. The input data is the email subject information, and the output is the subject status (new or continuing). If past emails with the same subject exist, only the latest message is flagged, and all others are considered for deletion.

[0481] Step 4:

[0482] The server analyzes the email body using a natural language processing engine to extract financial information and specific keywords. The input data is the email body, and the output is a list of detected important keywords and whether or not financial information is present. For example, it uses Google's Natural Language API to extract keywords such as "payment" and "invoice."

[0483] Step 5:

[0484] The server checks for attachments and determines their importance. The input is the email attachment information, and it outputs a flag indicating the presence of an attachment. If an important document is attached, the email's priority is increased.

[0485] Step 6:

[0486] The server detects suspicious activity and issues warnings to the user as needed. Input data consists of well-structured email content and past message patterns, while output is whether or not a warning notification was issued. For example, a warning is generated if an abnormal pattern in the sender address or suspicious content is detected.

[0487] Step 7:

[0488] The server makes the final decision to prioritize saving important emails and delete others based on all analysis results. The input data is the output of each step, and the output is a cleaned list of received emails. This organizes the mailbox so that only information important to the user remains.

[0489] 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.

[0490] This invention relates to a system that uses a program in which a server accesses a user's email account and automatically organizes received messages. The server first analyzes the recipient information of received messages to check if the user's address is included. This allows it to sort out messages addressed to the user.

[0491] Next, the server analyzes the subject information and compares it with the database to determine if it is a new subject. If it is new, it is retained; if the same subject already exists, the timestamp is used to determine if it is the last exchange, and previous messages are deleted.

[0492] The server determines whether there are any attachments, and if there are, it prioritizes them as important and retains the email. It also analyzes the message body to see if it contains specific strings, such as "password," and retains emails containing such strings as well.

[0493] In addition, by using an emotion engine, the server analyzes the user's emotional state. This emotion engine detects the user's emotional response from the content and expression of messages and reclassifies the priority of messages according to the emotional state. Through this process, emotionally important messages are processed to stand out more to the user.

[0494] For example, suppose a user receives an urgent project email. The server analyzes the subject line, attachments, and specific words in the email body, and uses a sentiment engine to determine the user's emotion is "anxious." In this case, the server further increases the importance of the email and notifies the user. Conversely, emails deemed unimportant can be suppressed to avoid interfering with other tasks.

[0495] This system allows the server to provide users with an environment where they can efficiently manage important emails. Users can prioritize checking emails that are appropriately recognized as important, improving work efficiency.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The server receives the email and examines the recipient information for each message. Here, it checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only the emails addressed to that user.

[0499] Step 2:

[0500] The server parses the message subject and compares it to existing subjects in the database. If the subject is new, the message is retained. If the subject already exists, the process proceeds to checking the timestamp.

[0501] Step 3:

[0502] The server compares the timestamps of messages with the same subject. It identifies the most recent message and deletes older messages as unnecessary data.

[0503] Step 4:

[0504] The server checks each message for attachments. Messages with attachments are deemed high-priority and are retained.

[0505] Step 5:

[0506] The server analyzes the message body to check if it contains a specific string ("password" or similar words). If so, it retains the message.

[0507] Step 6:

[0508] The server uses an emotion engine to analyze the content of messages. This analysis identifies the user's emotional state and reclassifies message priorities accordingly. Messages that are emotionally important to the user receive higher priority.

[0509] Step 7:

[0510] Finally, the server lists emails that do not meet the criteria and automatically deletes them. Only important messages, organized according to priority, remain in the user's inbox.

[0511] (Example 2)

[0512] 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."

[0513] There is a problem in that it is difficult for information users to quickly and efficiently select and prioritize important messages from the large volume of electronic messages they receive daily. Furthermore, there is a lack of means to identify emotionally important information and prioritize it accordingly.

[0514] 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.

[0515] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is a specified electronic communication address; means for analyzing subject information and determining whether the subject is new; and means for analyzing the content of electronic information, determining the emotional state of the information user, and reclassifying the priority of the information based on that. This enables the information user to quickly identify important electronic messages and, as needed, appropriately prioritize emotionally important information.

[0516] "Analyzing recipient information" is the process of checking whether the recipient field of an electronic message contains the specific electronic communication address of the information user.

[0517] "Analyzing subject information" is the process of examining the subject field of an electronic message to determine whether it is a new message or one that has been received before.

[0518] "Determining electronic information" refers to the process of checking past electronic information with the same subject based on specific conditions to determine whether it is the final exchange.

[0519] "Determining whether or not there are attachments" refers to the process of detecting whether or not files or documents are attached to an electronic message.

[0520] "Analyzing a specific string of characters" is the process of determining whether a particular keyword or phrase is included in the content of an electronic message.

[0521] "Analyzing the content of electronic information" is the process of evaluating the text of an entire electronic message and using it to identify the emotional state of the information user.

[0522] "Reclassifying information priority" is the process of resetting the importance of an received electronic message by considering factors such as the emotional state of the information user.

[0523] This invention is a system in which a server accesses a user's email account and automatically organizes received messages. First, the server uses the email protocol IMAP or POP3 to collect newly received messages from the user's mail server. This allows the user to retrieve messages in bulk.

[0524] Next, the server analyzes the recipient information of each received message. Here, it checks whether a specific telecommunications address is included in the "To" or "CC" fields. This identifies only messages addressed to the user and filters out irrelevant information.

[0525] Next, the server extracts the message subject and compares it against the database to determine if it's new or existing. Using SQL Server, it verifies the existence of the subject and helps reduce duplicate messages.

[0526] The server also checks if the message has attachments, analyzes their format, and assesses their importance. For example, it might decide to retain them if they are spreadsheets or important document formats.

[0527] Furthermore, the server uses a natural language processing library to analyze the message body and determine if it contains specific keywords (e.g., "password"). This makes it possible to identify messages of high importance.

[0528] Furthermore, it utilizes an emotion analysis engine to determine the emotional state of the information user from the message content. A generative AI model identifies emotions such as "tension" or "indifference" from the message and adjusts the message priority accordingly.

[0529] For example, when a user receives an urgent email related to a project, if the server determines from the subject line and sentiment analysis results that the message is "tense," it will increase the importance of the message and send a prompt notification to the user's terminal. On the other hand, emails that are judged to be "indifferent" by sentiment analysis can be given a lower importance rating.

[0530] An example of a prompt message is: "This email has an important subject line, and if the sentiment engine determines the user's emotion is 'stressed,' the notification priority will be reset."

[0531] In this way, the server realizes a system that efficiently manages and prioritizes the information that users need.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The server accesses the email server to retrieve newly received electronic messages. It uses user authentication information as input. By logging into the SMTP server and collecting message data using the IMAP or POP3 protocol, it obtains a list of unread messages as output.

[0535] Step 2:

[0536] The server analyzes the recipient information of received emails. The input is the information contained in the recipient field. By analyzing this and comparing it with the user's registered e-mail address, the server selects messages addressed to the user as output.

[0537] Step 3:

[0538] The server extracts the subject line of each email and compares it against the database to determine if it is new or existing. The input is the subject line field of each message. By executing an SQL query and searching the database for existing subjects, the output is a list of unique subjects.

[0539] Step 4:

[0540] The server checks if an email has an attachment and evaluates its format. It uses the data of the attachment contained in the email as input. It analyzes the file format and name, determines whether it meets the importance criteria, and identifies messages with files that should be retained as output.

[0541] Step 5:

[0542] The server analyzes specific keywords in the email body. The input is the text data of the email body. By utilizing a natural language processing library to search for specific strings such as "password," it extracts messages of high importance as output.

[0543] Step 6:

[0544] The server uses an emotion analysis engine to analyze the user's emotional state from the content of the email. The input is the text data of the entire message. Using a generative AI model, it prepares to adjust the emotional priority of the message by outputting emotion labels (e.g., "Tense", "Indifferent").

[0545] Step 7:

[0546] The server reclassifies the email priorities based on the analysis results and determines the notification method. The input is the analysis results obtained at each step. The priority is reset, and appropriate notifications (e.g., immediate notification or notification suppression) are sent to the user terminal as output.

[0547] (Application Example 2)

[0548] 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."

[0549] In modern society, information overload makes it difficult to quickly grasp important information. This problem is particularly serious in environments with many information sources, such as smart cities, where citizens have difficulty receiving emergency information or information about events of interest in a timely manner. Therefore, there is a need for a system that efficiently organizes electronic messages and presents important information quickly.

[0550] 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.

[0551] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is at the specified address; means for analyzing subject information and determining whether the subject is new; means for determining past electronic messages received with the same subject and deleting others if they are the last exchange; means for determining whether there is attached data; means for analyzing whether a specific string is contained in the electronic message; means for analyzing emotional state and reclassifying priority; and means for retaining only important electronic messages and deleting others based on the aforementioned determination means. This enables citizens to receive and respond to urgent and highly relevant information quickly and appropriately.

[0552] "Recipient information" refers to information included in an electronic message, specifically an identifier for the person or organization to whom the message is sent.

[0553] "Subject information" refers to the text information used as the title of an electronic message, and this information serves as a clue to understanding the content and importance of the message.

[0554] "Attached data" refers to files, images, and other data formats that are sent along with an electronic message and are used to supplement the content of the message.

[0555] A "specific string of characters" refers to a particular word or phrase included in an electronic message that serves as a basis for determining the importance or relevance of the message.

[0556] "Emotional state" refers to the user's psychological reaction and attitude when receiving an electronic message, and is inferred from the content and expression of the message.

[0557] "Priority" is an indicator that shows the importance and urgency of an electronic message, and is determined based on the message's content and related information.

[0558] "Decision-making means" refers to the methods or processes used by a system to analyze electronic messages and make decisions based on that information.

[0559] An "important electronic message" is an electronic message that, based on analysis and evaluation, is deemed to require special attention from the recipient.

[0560] The system realizing this invention includes a program that receives user emails and automatically organizes them according to their importance. When analyzing emails, the server makes decisions based on recipient information, subject information, attachments, specific strings, and sentiment. The analysis of recipient information verifies whether the email is addressed to an address specified by the user. The analysis of subject information checks whether the same subject has been used before and determines whether it is a new or final exchange. If attachments are included, their importance is highly rated, and further importance is determined by detecting specific strings.

[0561] Using an emotion engine, the system analyzes the user's emotional state from text and expressions and reclassifies their importance. Based on this emotional state, notification priorities are adjusted to allow users to receive information efficiently. Suitable hardware includes information terminals such as smartphones and tablets, and the software uses Python and email analysis libraries. The emotion engine is implemented, for example, as a Python library to perform sentiment analysis.

[0562] As a concrete example, consider a scenario where a user receives a "flood warning" notification from a smart city. This email is displayed with priority due to its high urgency. On the other hand, an email inviting a library reading group is only appropriately presented if the user is analyzed as having a preference for cultural activities.

[0563] An example of a prompt message might be: "Generate a program that analyzes the following email content, labels high-priority information with 'Urgent Notification,' and organizes the information appropriately according to the user's level of interest."

[0564] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0565] Step 1:

[0566] The server analyzes the received email to extract recipient information. The entire email is used as input. The server analyzes the email's recipient field to determine if the recipient matches the specified address and outputs the result.

[0567] Step 2:

[0568] The server analyzes the subject information of received emails. The email subject is used as input. The server checks whether the subject has existed in the past against a database, determines whether it is a new or recent exchange, and outputs the result.

[0569] Step 3:

[0570] The server determines whether an email contains attachments. The entire email is used as input. The server checks for the presence of attachments and outputs the result. If attachments exist, their importance is set to high.

[0571] Step 4:

[0572] The server analyzes specific strings from the email body. The email body is used as input. The server searches for keywords (e.g., "password"), confirms their presence, and outputs them. If important keywords are included, the importance level is increased.

[0573] Step 5:

[0574] The server uses an emotion engine to analyze the emotional state of an email. The email body is used as input. The server performs emotion analysis, detects the user's emotional state, and reclassifies the email's priority based on the results before outputting it.

[0575] Step 6:

[0576] The server retains only the electronic messages deemed important based on these judgment results and deletes all other messages. The judgment results from each step are used as input. The server ultimately presents the retained messages to the user.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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".

[0594] The system of this invention involves a server accessing a user's email account and executing a program that automatically organizes received electronic messages. For each received message, the server first analyzes the recipient information and determines whether the user's address is included in the "To" or "Cc" fields. This ensures that only messages addressed to the user are processed.

[0595] Next, the server analyzes the subject information of each incoming message to determine if it is a new subject or the same as a previously received subject. For messages with the same subject belonging to past exchanges, it determines if it is the final exchange, and any older messages that are not the final exchange are to be deleted.

[0596] Furthermore, the server checks for attachments, and if attachments are found, it retains the message as high-priority.

[0597] The server also analyzes the message body and checks if it contains a specific string (for example, a word like "password"). Messages that meet this condition are also retained.

[0598] As a concrete example, consider a scenario where a user receives an email about Project A. The server classifies this email as a new subject. Subsequently, if another email with the same subject, Project A, arrives, the server compares the dates and retains only the most recent one. Furthermore, if the other email has presentation materials attached, those are also retained. Finally, if the other message contains the word "password," this is also retained.

[0599] In this way, the server efficiently organizes the user's email environment and supports the quick retrieval of important information. Users can check only the most important emails remaining in their inbox, thereby improving work efficiency.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] The server receives electronic messages and analyzes the recipient information for each message. It then checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only messages addressed to that user.

[0603] Step 2:

[0604] The server parses the message subject and matches it against a list of subjects in the database. If the subject is new, the message is retained. If it matches an existing subject, the server then prepares to check the email's timestamp.

[0605] Step 3:

[0606] The server compares the timestamps of emails with the same subject. It retains the message with the most recent date and time as the final exchange, and deletes older messages as unnecessary data.

[0607] Step 4:

[0608] The server checks each message for attachments. Messages with attachments are deemed highly important and are retained.

[0609] Step 5:

[0610] The server searches for specific strings within the message body. It identifies and stores messages that contain specific strings such as "password" or "Pass".

[0611] Step 6:

[0612] The server will create a list of messages that do not meet the above conditions (1) to (8) and automatically delete them. Only important messages will remain in the user's inbox.

[0613] (Example 1)

[0614] 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".

[0615] Receiving emails often presents problems because it takes time to review the content, making it difficult to quickly identify important information. Furthermore, the accumulation of numerous messages makes management cumbersome, leading to decreased work efficiency.

[0616] 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.

[0617] In this invention, the server includes means for analyzing recipient information and determining destination information, means for analyzing subject information and determining novelty, and means for comparing the date and time of previously received messages with the same subject and deleting all but the most recent. As a result, only important electronic messages are efficiently retained, and users can quickly obtain the information they need.

[0618] "Recipient information" refers to information about the destination of an electronic message and is used to determine whether the message is intended for a specific address.

[0619] "Subject information" refers to the content written in the title portion of an electronic message, and serves as an indicator for determining the novelty and relevance of the message.

[0620] "Same subject line" refers to a message that has the same title as a previously received message, and this is a criterion for distinguishing whether it is a continuation of a previous exchange or a new message.

[0621] "Date and time comparison" is a process to check the reception dates and times of multiple messages to identify the most recent message, and based on this result, a decision is made to delete older messages.

[0622] "Data attachments" refer to additional files or documents sent along with an electronic message, and are used as one of the criteria for evaluating their importance.

[0623] "Detecting the presence of specific strings" is the process of analyzing the content of an electronic message to determine whether predefined important keywords or phrases are present in the text.

[0624] "Retaining or deleting a message" means selecting whether to retain the message as a record or delete it as unnecessary, based on an assessment of the message's importance.

[0625] This invention enables efficient organization of electronic messages in a server-managed mail system. The server accesses the user's mail account and analyzes the received messages. The hardware used is a standard server computer, and the software includes a "mail server software API" and the "Python programming language." Specifically, the system is configured in the following way.

[0626] The server uses the "Python IMAP library" to access the mail server and retrieve new messages from the user's mailbox. Recipient information is analyzed by examining the email header to determine if the user is specified as the recipient or to add to the recipient list. The "pandas" library is used to compare the message subject with past email subject data to determine if it is a new message.

[0627] The server runs a date and time-based comparison algorithm on past messages. This ensures that only the most recent messages are retained, and older messages are identified for deletion. It also uses the "email library" to check for attachments and determines the importance of each message based on that.

[0628] Furthermore, the message body is analyzed using the natural language processing library "nltk". By checking for the presence of specific keywords, such as "password," it is possible to identify messages containing highly confidential information and retain them according to their importance.

[0629] As a concrete example of its operation, if a user receives a new email related to Project A, the server adds this email as a new entry in the database. Subsequently, if other emails with the same subject arrive, the server deletes all but the most recent email and saves the newest one. If an email with a different subject arrives with presentation materials attached, the server saves it as important. Furthermore, messages containing the word "password" are also specially stored.

[0630] An example of a prompt might be, "Please organize my emails. Keep the most recent emails related to Project A, and also retain important messages with attachments and passwords." In this way, the system allows users to efficiently organize their mailboxes and quickly retrieve only the important information.

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] The server accesses the user's email account. It uses the user's authentication information as input. The output is a list of unread messages in the inbox. Specifically, it uses the "Python IMAP library" to establish a session with the mail server and retrieve the unread email list.

[0634] Step 2:

[0635] The server parses the recipient information of each received message. The input is the list of unread messages obtained in step 1. The output is a subset of messages that the user has specified as either the recipient or added to the recipient list. Specifically, it parses the email header information and checks the "To" and "Cc" fields.

[0636] Step 3:

[0637] The server analyzes the message subject information to determine its novelty. It uses a subset of messages obtained in step 2 as input. The output is a list of new and existing subject messages. The "pandas" library is used to compare these messages against past subject data to determine whether they are new or existing.

[0638] Step 4:

[0639] The server compares the date and time of messages with the same subject. The input is the existing subject messages separated in step 3. The output is a list that retains only the most recent messages. Specifically, it parses the time information, creates a message data frame, and keeps only the most recent one.

[0640] Step 5:

[0641] The server checks if the message has an attachment. The input is the message subset obtained in step 2. The output is a list of messages that have attachments. The "email library" is used to parse parts of the message and check for the presence of attachments.

[0642] Step 6:

[0643] The server analyzes the message body and detects whether it contains specific keywords. The input is a subset of messages obtained in step 2, and the output is a list of messages containing the given keywords. The "nltk" library is used to perform text analysis and scan for keywords.

[0644] Step 7:

[0645] The server decides whether to retain or delete messages based on the results of the previous steps. The input is the result list from steps 4, 5, and 6. The output is the user's inbox, which contains only the important messages that should be kept. An algorithm is applied that retains messages deemed important in the database and deletes the rest.

[0646] (Application Example 1)

[0647] 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".

[0648] When receiving emails, important financial information can get buried, and suspicious messages such as phishing emails can pose a risk of harm to users. Therefore, it is necessary for users to manage their emails quickly and efficiently, not miss important information, and be vigilant against suspicious emails.

[0649] 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.

[0650] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is the specified address, means for analyzing subject information and determining whether the subject is new, means for analyzing financial information and classifying high-priority emails, and means for detecting suspicious activity and generating warnings. This enables users to quickly review important electronic messages and reduce the risk of fraud.

[0651] "Recipient information" refers to information about the destination of an electronic message and is data necessary to determine whether an email was sent to a specific address.

[0652] "Subject information" refers to the title information attached to an electronic message and serves as a criterion for determining whether it is a new message or related to past correspondence.

[0653] "Financial information" refers to information within electronic messages that relates to specific transactions or settlements, and is used to assess its importance and identify high-priority messages.

[0654] "Suspicious activity" refers to situations where electronic messages or their content deviate from normal patterns and may pose a potential risk to the user.

[0655] "Generating a warning" is the process of issuing notifications or alerts to inform users that suspicious behavior has been detected.

[0656] The system of this invention improves user convenience by automatically organizing the user's emails and prioritizing the display of high-priority messages. The server uses several hardware and software components to access the user's email account and analyze the email data.

[0657] The server processes email data using cloud services such as Amazon Web Services (AWS) and Google Cloud Platform. For email analysis, it uses Google's Gmail API or Microsoft's Graph API to analyze recipient and subject information. For specific keyword analysis, it uses Google's Natural Language API to analyze email bodies and detect financial information and suspicious activity.

[0658] This system allows users to quickly verify emails containing important financial information, reducing the risk of phishing scams. Furthermore, if suspicious content is detected, an immediate warning is generated and the user is notified.

[0659] For example, if a user receives an email regarding rent payment, the server will classify this email as financial information and display it preferentially. Furthermore, if the email contains words such as "phishing" or "bank account," a warning will be generated.

[0660] An example of a prompt to the generating AI model is as follows: "Automatically identify and filter important financial transaction emails from my email inbox, and detect and warn me about phishing emails."

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] The server accesses the user's email account and retrieves data from received emails. The input data is the user's email account information, and the output is the unprocessed received email data. This data includes email recipient information, subject, body, attachments, etc.

[0664] Step 2:

[0665] The server analyzes recipient information to determine if the email was legitimately sent to the user. The input data is the recipient information of the received email, and it outputs a boolean value indicating whether the email is legitimate. This step uses the Gmail API or Graph API to check if the user's email address is included in the "To" or "Cc" fields.

[0666] Step 3:

[0667] The server analyzes the subject information to determine if it is a new subject or related to a past email. The input data is the email subject information, and the output is the subject status (new or continuing). If past emails with the same subject exist, only the latest message is flagged, and all others are considered for deletion.

[0668] Step 4:

[0669] The server analyzes the email body using a natural language processing engine to extract financial information and specific keywords. The input data is the email body, and the output is a list of detected important keywords and whether or not financial information is present. For example, it uses Google's Natural Language API to extract keywords such as "payment" and "invoice."

[0670] Step 5:

[0671] The server checks for attachments and determines their importance. The input is the email attachment information, and it outputs a flag indicating the presence of an attachment. If an important document is attached, the email's priority is increased.

[0672] Step 6:

[0673] The server detects suspicious activity and issues warnings to the user as needed. Input data consists of well-structured email content and past message patterns, while output is whether or not a warning notification was issued. For example, a warning is generated if an abnormal pattern in the sender address or suspicious content is detected.

[0674] Step 7:

[0675] The server makes the final decision to prioritize saving important emails and delete others based on all analysis results. The input data is the output of each step, and the output is a cleaned list of received emails. This organizes the mailbox so that only information important to the user remains.

[0676] 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.

[0677] This invention relates to a system that uses a program in which a server accesses a user's email account and automatically organizes received messages. The server first analyzes the recipient information of received messages to check if the user's address is included. This allows it to sort out messages addressed to the user.

[0678] Next, the server analyzes the subject information and compares it with the database to determine if it is a new subject. If it is new, it is retained; if the same subject already exists, the timestamp is used to determine if it is the last exchange, and previous messages are deleted.

[0679] The server determines whether there are any attachments, and if there are, it prioritizes them as important and retains the email. It also analyzes the message body to see if it contains specific strings, such as "password," and retains emails containing such strings as well.

[0680] In addition, by using an emotion engine, the server analyzes the user's emotional state. This emotion engine detects the user's emotional response from the content and expression of messages and reclassifies the priority of messages according to the emotional state. Through this process, emotionally important messages are processed to stand out more to the user.

[0681] For example, suppose a user receives an urgent project email. The server analyzes the subject line, attachments, and specific words in the email body, and uses a sentiment engine to determine the user's emotion is "anxious." In this case, the server further increases the importance of the email and notifies the user. Conversely, emails deemed unimportant can be suppressed to avoid interfering with other tasks.

[0682] This system allows the server to provide users with an environment where they can efficiently manage important emails. Users can prioritize checking emails that are appropriately recognized as important, improving work efficiency.

[0683] The following describes the processing flow.

[0684] Step 1:

[0685] The server receives the email and examines the recipient information for each message. Here, it checks if the user's address is included in the "To" or "Cc" field of the email, and filters out only the emails addressed to that user.

[0686] Step 2:

[0687] The server parses the message subject and compares it to existing subjects in the database. If the subject is new, the message is retained. If the subject already exists, the process proceeds to checking the timestamp.

[0688] Step 3:

[0689] The server compares the timestamps of messages with the same subject. It identifies the most recent message and deletes older messages as unnecessary data.

[0690] Step 4:

[0691] The server checks each message for attachments. Messages with attachments are deemed high-priority and are retained.

[0692] Step 5:

[0693] The server analyzes the message body to check if it contains a specific string ("password" or similar words). If so, it retains the message.

[0694] Step 6:

[0695] The server uses an emotion engine to analyze the content of messages. This analysis identifies the user's emotional state and reclassifies message priorities accordingly. Messages that are emotionally important to the user receive higher priority.

[0696] Step 7:

[0697] Finally, the server lists emails that do not meet the criteria and automatically deletes them. Only important messages, organized according to priority, remain in the user's inbox.

[0698] (Example 2)

[0699] 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".

[0700] There is a problem in that it is difficult for information users to quickly and efficiently select and prioritize important messages from the large volume of electronic messages they receive daily. Furthermore, there is a lack of means to identify emotionally important information and prioritize it accordingly.

[0701] 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.

[0702] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is a specified electronic communication address; means for analyzing subject information and determining whether the subject is new; and means for analyzing the content of electronic information, determining the emotional state of the information user, and reclassifying the priority of the information based on that. This enables the information user to quickly identify important electronic messages and, as needed, appropriately prioritize emotionally important information.

[0703] "Analyzing recipient information" is the process of checking whether the recipient field of an electronic message contains the specific electronic communication address of the information user.

[0704] "Analyzing subject information" is the process of examining the subject field of an electronic message to determine whether it is a new message or one that has been received before.

[0705] "Determining electronic information" refers to the process of checking past electronic information with the same subject based on specific conditions to determine whether it is the final exchange.

[0706] "Determining whether or not there are attachments" refers to the process of detecting whether or not files or documents are attached to an electronic message.

[0707] "Analyzing a specific string of characters" is the process of determining whether a particular keyword or phrase is included in the content of an electronic message.

[0708] "Analyzing the content of electronic information" is the process of evaluating the text of an entire electronic message and using it to identify the emotional state of the information user.

[0709] "Reclassifying information priority" is the process of resetting the importance of an received electronic message by considering factors such as the emotional state of the information user.

[0710] This invention is a system in which a server accesses a user's email account and automatically organizes received messages. First, the server uses the email protocol IMAP or POP3 to collect newly received messages from the user's mail server. This allows the user to retrieve messages in bulk.

[0711] Next, the server analyzes the recipient information of each received message. Here, it checks whether a specific telecommunications address is included in the "To" or "CC" fields. This identifies only messages addressed to the user and filters out irrelevant information.

[0712] Next, the server extracts the message subject and compares it against the database to determine if it's new or existing. Using SQL Server, it verifies the existence of the subject and helps reduce duplicate messages.

[0713] The server also checks if the message has attachments, analyzes their format, and assesses their importance. For example, it might decide to retain them if they are spreadsheets or important document formats.

[0714] Furthermore, the server uses a natural language processing library to analyze the message body and determine if it contains specific keywords (e.g., "password"). This makes it possible to identify messages of high importance.

[0715] Furthermore, it utilizes an emotion analysis engine to determine the emotional state of the information user from the message content. A generative AI model identifies emotions such as "tension" or "indifference" from the message and adjusts the message priority accordingly.

[0716] For example, when a user receives an urgent email related to a project, if the server determines from the subject line and sentiment analysis results that the message is "tense," it will increase the importance of the message and send a prompt notification to the user's terminal. On the other hand, emails that are judged to be "indifferent" by sentiment analysis can be given a lower importance rating.

[0717] An example of a prompt message is: "This email has an important subject line, and if the sentiment engine determines the user's emotion is 'stressed,' the notification priority will be reset."

[0718] In this way, the server realizes a system that efficiently manages and prioritizes the information that users need.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The server accesses the email server to retrieve newly received electronic messages. It uses user authentication information as input. By logging into the SMTP server and collecting message data using the IMAP or POP3 protocol, it obtains a list of unread messages as output.

[0722] Step 2:

[0723] The server analyzes the recipient information of received emails. The input is the information contained in the recipient field. By analyzing this and comparing it with the user's registered e-mail address, the server selects messages addressed to the user as output.

[0724] Step 3:

[0725] The server extracts the subject line of each email and compares it against the database to determine if it is new or existing. The input is the subject line field of each message. By executing an SQL query and searching the database for existing subjects, the output is a list of unique subjects.

[0726] Step 4:

[0727] The server checks if an email has an attachment and evaluates its format. It uses the data of the attachment contained in the email as input. It analyzes the file format and name, determines whether it meets the importance criteria, and identifies messages with files that should be retained as output.

[0728] Step 5:

[0729] The server analyzes specific keywords in the email body. The input is the text data of the email body. By utilizing a natural language processing library to search for specific strings such as "password," it extracts messages of high importance as output.

[0730] Step 6:

[0731] The server uses an emotion analysis engine to analyze the user's emotional state from the content of the email. The input is the text data of the entire message. Using a generative AI model, it prepares to adjust the emotional priority of the message by outputting emotion labels (e.g., "Tense", "Indifferent").

[0732] Step 7:

[0733] The server reclassifies the email priorities based on the analysis results and determines the notification method. The input is the analysis results obtained at each step. The priority is reset, and appropriate notifications (e.g., immediate notification or notification suppression) are sent to the user terminal as output.

[0734] (Application Example 2)

[0735] 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".

[0736] In modern society, information overload makes it difficult to quickly grasp important information. This problem is particularly serious in environments with many information sources, such as smart cities, where citizens have difficulty receiving emergency information or information about events of interest in a timely manner. Therefore, there is a need for a system that efficiently organizes electronic messages and presents important information quickly.

[0737] 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.

[0738] In this invention, the server includes means for analyzing recipient information and determining whether the recipient is at the specified address; means for analyzing subject information and determining whether the subject is new; means for determining past electronic messages received with the same subject and deleting others if they are the last exchange; means for determining whether there is attached data; means for analyzing whether a specific string is contained in the electronic message; means for analyzing emotional state and reclassifying priority; and means for retaining only important electronic messages and deleting others based on the aforementioned determination means. This enables citizens to receive and respond to urgent and highly relevant information quickly and appropriately.

[0739] "Recipient information" refers to information included in an electronic message, specifically an identifier for the person or organization to whom the message is sent.

[0740] "Subject information" refers to the text information used as the title of an electronic message, and this information serves as a clue to understanding the content and importance of the message.

[0741] "Attached data" refers to files, images, and other data formats that are sent along with an electronic message and are used to supplement the content of the message.

[0742] A "specific string of characters" refers to a particular word or phrase included in an electronic message that serves as a basis for determining the importance or relevance of the message.

[0743] "Emotional state" refers to the user's psychological reaction and attitude when receiving an electronic message, and is inferred from the content and expression of the message.

[0744] "Priority" is an indicator that shows the importance and urgency of an electronic message, and is determined based on the message's content and related information.

[0745] "Decision-making means" refers to the methods or processes used by a system to analyze electronic messages and make decisions based on that information.

[0746] An "important electronic message" is an electronic message that, based on analysis and evaluation, is deemed to require special attention from the recipient.

[0747] The system realizing this invention includes a program that receives user emails and automatically organizes them according to their importance. When analyzing emails, the server makes decisions based on recipient information, subject information, attachments, specific strings, and sentiment. The analysis of recipient information verifies whether the email is addressed to an address specified by the user. The analysis of subject information checks whether the same subject has been used before and determines whether it is a new or final exchange. If attachments are included, their importance is highly rated, and further importance is determined by detecting specific strings.

[0748] Using an emotion engine, the system analyzes the user's emotional state from text and expressions and reclassifies their importance. Based on this emotional state, notification priorities are adjusted to allow users to receive information efficiently. Suitable hardware includes information terminals such as smartphones and tablets, and the software uses Python and email analysis libraries. The emotion engine is implemented, for example, as a Python library to perform sentiment analysis.

[0749] As a concrete example, consider a scenario where a user receives a "flood warning" notification from a smart city. This email is displayed with priority due to its high urgency. On the other hand, an email inviting a library reading group is only appropriately presented if the user is analyzed as having a preference for cultural activities.

[0750] An example of a prompt message might be: "Generate a program that analyzes the following email content, labels high-priority information with 'Urgent Notification,' and organizes the information appropriately according to the user's level of interest."

[0751] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0752] Step 1:

[0753] The server analyzes the received email to extract recipient information. The entire email is used as input. The server analyzes the email's recipient field to determine if the recipient matches the specified address and outputs the result.

[0754] Step 2:

[0755] The server analyzes the subject information of received emails. The email subject is used as input. The server checks whether the subject has existed in the past against a database, determines whether it is a new or recent exchange, and outputs the result.

[0756] Step 3:

[0757] The server determines whether an email contains attachments. The entire email is used as input. The server checks for the presence of attachments and outputs the result. If attachments exist, their importance is set to high.

[0758] Step 4:

[0759] The server analyzes specific strings from the email body. The email body is used as input. The server searches for keywords (e.g., "password"), confirms their presence, and outputs them. If important keywords are included, the importance level is increased.

[0760] Step 5:

[0761] The server uses an emotion engine to analyze the emotional state of an email. The email body is used as input. The server performs emotion analysis, detects the user's emotional state, and reclassifies the email's priority based on the results before outputting it.

[0762] Step 6:

[0763] The server retains only the electronic messages deemed important based on these judgment results and deletes all other messages. The judgment results from each step are used as input. The server ultimately presents the retained messages to the user.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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."

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] The following is further disclosed regarding the embodiments described above.

[0786] (Claim 1)

[0787] A means for analyzing recipient information and determining whether the recipient is at the specified address,

[0788] A means of analyzing subject information to determine if it is a new subject,

[0789] A method for determining past electronic messages received with the same subject line and deleting others if they are the last correspondence,

[0790] A means for determining whether or not attached data is present,

[0791] A means of analyzing whether a specific string is contained within an electronic message,

[0792] A means for retaining only important electronic messages and deleting others based on the aforementioned determination means,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, further comprising means for determining whether the recipient field contains an address specified in the recipient information analysis.

[0796] (Claim 3)

[0797] The system according to claim 1, further comprising means for determining whether a particular string contains the word "password" or a similar word.

[0798] "Example 1"

[0799] (Claim 1)

[0800] A means for analyzing recipient information and determining destination information,

[0801] A means of determining novelty by analyzing subject information,

[0802] A method to compare the date and time of previously received emails with the same subject line and delete all but the most recent ones,

[0803] A means to check attached data and determine its importance,

[0804] A means for analyzing the content and detecting whether a specific string is included,

[0805] Based on these determinations, the importance of the information is evaluated, and means are provided for retaining or deleting the message.

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising means for checking the recipient field and determining that it contains specified identification information.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising means for determining whether the content contains a "password" or a similar identifier.

[0811] "Application Example 1"

[0812] (Claim 1)

[0813] A means for analyzing recipient information and determining whether the recipient is at the specified address,

[0814] A means of analyzing subject information to determine if it is a new subject,

[0815] A method for determining past electronic messages received with the same subject line and deleting others if they are the last correspondence,

[0816] A means for determining whether or not attached data is present,

[0817] A means of analyzing whether a specific string is contained within an electronic message,

[0818] A method for analyzing financial information and classifying high-priority emails,

[0819] A means for detecting suspicious activity and generating a warning,

[0820] A means for retaining only important electronic messages and deleting others based on the aforementioned determination means,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for determining whether the recipient field contains an address specified in the recipient information analysis.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for determining whether a particular string contains the word "password" or a similar word.

[0826] "Example 2 of combining an emotion engine"

[0827] (Claim 1)

[0828] A means for analyzing recipient information and determining whether the recipient is the specified electronic communication address,

[0829] A means of analyzing subject information to determine if it is a new subject,

[0830] A means to determine past electronic information received with the same subject line and delete others if they are the final exchange,

[0831] A means for determining whether or not attached information is present,

[0832] A means of analyzing whether a specific string of characters is contained within electronic information,

[0833] A means for analyzing the content of electronic information, determining the emotional state of the information user, and reclassifying the priority of the information based on that,

[0834] A means for retaining only important electronic information and deleting everything else based on the aforementioned determination means,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising means for determining whether the recipient field contains an electronic communication address specified in the recipient information analysis.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising means for determining whether a specific string contains a “password” or a similar word.

[0840] "Application example 2 when combining with an emotional engine"

[0841] (Claim 1)

[0842] A means for analyzing recipient information and determining whether the recipient is at the specified address,

[0843] A means of analyzing subject information to determine if it is a new subject,

[0844] A method for determining past electronic messages received with the same subject line and deleting others if they are the last correspondence,

[0845] A means for determining whether or not attached data is present,

[0846] A means of analyzing whether a specific string is contained within an electronic message,

[0847] A means of analyzing emotional states and reclassifying priorities,

[0848] A means for retaining only important electronic messages and deleting others based on the aforementioned determination means,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising means for determining whether the recipient field contains an address specified in the recipient information analysis.

[0852] (Claim 3)

[0853] The system according to claim 1, further comprising means for determining whether a particular string contains the word "password" or a similar word. [Explanation of Symbols]

[0854] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for analyzing recipient information and determining whether the recipient is at the specified address, A means of analyzing subject information to determine if it is a new subject, A method for determining past electronic messages received with the same subject line and deleting others if they are the last correspondence, A means for determining whether or not attached data is present, A means of analyzing whether a specific string is contained within an electronic message, A method for analyzing financial information and classifying high-priority emails, A means for detecting suspicious activity and generating a warning, A means for retaining only important electronic messages and deleting others based on the aforementioned determination means, A system that includes this.

2. The system according to claim 1, further comprising means for determining whether the recipient field contains an address specified in the recipient information analysis.

3. The system according to claim 1, further comprising means for determining whether a specific string contains the word "password" or a similar word.

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