system

Abstract

We provide the system. [Solution] A natural language processing means that analyzes received electronic messages and automatically generates reply proposals based on their content, An evaluation method for evaluating the priority and urgency of electronic messages based on the analysis results, A notification means that generates individual notifications and sends them to the user's terminal for electronic messages that are assessed as high priority or urgent, A scheduling integration method that retrieves user schedule information from an external scheduling application and automatically generates a reply proposal including suggested dates and times, An interface means that sends multiple generated response options to the user's terminal, allowing the user to review and select one; A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to efficiently manage a large number of electronic messages and effectively utilize the user's time, prompt responses according to the importance of the messages and presentation of specific reply plans including schedule adjustment are required. However, performing them manually places a heavy burden on the user, so the development of a system for automating this is necessary. In particular, since messages with a high degree of urgency need to be responded to without delay, evaluation of the degree of urgency and enhancement of the notification function are issues.

Means for Solving the Problems

[0005] This invention provides a natural language processing means for analyzing received electronic messages and introduces a system that automatically evaluates the priority and urgency of messages based on their content. Based on this evaluation, it is possible to send individual notifications to messages with high priority or urgency, quickly informing the user. Furthermore, by providing a scheduling linkage means that can cooperate with external schedule management applications and generate specific reply proposals including the user's available time when scheduling adjustments are necessary, it provides efficient reply proposals. This reduces the burden on the user and improves work efficiency.

[0006] "Natural language processing means" refers to means that possess technologies for analyzing the content of electronic messages, extracting information based on context and keywords, and understanding it.

[0007] "Evaluation means" refers to algorithms and devices for determining the importance and urgency of electronic messages and evaluating their priority based on the analysis results.

[0008] "Notification means" refers to technologies and devices for individually informing users of the existence of electronic messages that have been assessed as high priority or urgency.

[0009] A "schedule integration method" is a means of obtaining a user's schedule information through communication with an external schedule management application and suggesting candidate dates and times for scheduling adjustments.

[0010] "Interface means" refers to means that provide a user interface for the user to review and select from multiple generated response options.

[0011] "Transmission means" refers to communication devices and functions for generating a final reply based on a reply draft selected or modified by the user and sending it to the recipient.

[0012] "Recording means" refers to a system and method for recording analyzed and evaluated electronic messages as logs and storing them in a database. [Brief explanation of the drawing]

[0013] [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]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides a system for efficiently managing received electronic messages and reducing the burden on users. When a server receives an electronic message, it uses natural language processing to analyze its content and determine the message's category and importance. Based on this analysis, individual notifications are generated for messages with high priority or urgency and sent to the user via the terminal.

[0035] In addition, the server automatically evaluates priority using evaluation tools and uses this information to generate appropriate response suggestions. For example, when a meeting invitation email is received, the server can use scheduling integration tools to retrieve the user's availability from an external scheduling application and automatically generate a response suggestion including possible dates and times.

[0036] The terminal presents the user with multiple response options sent from the server and supports confirmation and selection via an interface. In this way, the user can quickly provide an appropriate response.

[0037] For example, if a user receives an urgent email from a business partner, the server immediately analyzes its contents and assesses its urgency. If it contains important information, a push notification is sent to the device, allowing the user to access the email immediately. Furthermore, if scheduling adjustments are necessary, suggested dates and times reflecting the user's availability are displayed, enabling them to respond immediately.

[0038] Ultimately, the server confirms the reply selected by the user and sends the reply email to the recipient using the appropriate sending method. This entire system flow allows users to streamline their daily email processing and make better use of their time. This system supports businesses in managing large volumes of email and promotes fast and accurate communication.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] When the server receives a new electronic message, it first stores the message in its mail database. Then, it adds it to a queue as a message to be analyzed.

[0042] Step 2:

[0043] The server uses a natural language processing engine to analyze the body, subject, and sender information of received messages. This allows it to identify the message category (e.g., urgent, scheduling, information sharing) and extract the necessary information.

[0044] Step 3:

[0045] The server executes evaluation measures based on the analysis results to determine the priority and urgency of the message. This is done based on the detection of specific keywords and patterns, as well as the message sender.

[0046] Step 4:

[0047] Based on the evaluation results, the server sends individual notifications to user terminals for messages deemed to be of high urgency. This allows users to quickly become aware of important messages.

[0048] Step 5:

[0049] The server uses scheduling integration to retrieve user calendar information from external scheduling applications as needed. This generates candidate dates and times, which are then included in the reply suggestions.

[0050] Step 6:

[0051] Based on the analyzed information, the server generates several appropriate response options and sends them to the user's terminal. These response options may include a suggested schedule, if necessary.

[0052] Step 7:

[0053] The terminal displays the user with suggested replies received from the server and provides an interface for selection and editing. The user uses this interface to select the best reply and modify its content as needed.

[0054] Step 8:

[0055] Once the user confirms their reply, the device sends that information to the server, which then uses its sending method to send the final reply email to the recipient. The server then logs this reply.

[0056] (Example 1)

[0057] 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."

[0058] In today's information-saturated world, particularly through email, the sheer volume of incoming information presents users with the risk of overlooking important details and the problem of spending excessive time processing information. Furthermore, manual reply processes are inefficient, and this inefficiency is particularly evident in business settings where quick responses are crucial. The challenge lies in solving these problems and supporting appropriate responses while streamlining information processing.

[0059] 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.

[0060] In this invention, the server includes information processing means for analyzing received information and automatically generating a response based on its content; evaluation means for evaluating the priority and urgency of the information based on the analysis results; and notification means for generating individual notifications for information evaluated as high priority or high urgency and sending them to the terminal. This enables a rapid response according to the priority of the information, allowing the user to effectively manage important information and make quick decisions.

[0061] "Information processing means" refers to technology that has the function of analyzing received information and automatically generating appropriate response proposals based on its content.

[0062] An "evaluation tool" is a technology that has the function of objectively evaluating the importance and urgency of information based on the content of the analyzed information.

[0063] A "notification method" is a technology that generates individual warnings or notifications for information of high priority or urgency, and quickly transmits that information to the user's terminal.

[0064] "Integration means" refers to technology that has the function of obtaining schedule information from an external management system and generating appropriate response proposals based on that information.

[0065] "Connection means" refers to a technology that presents multiple generated response options to the user's device, allowing the user to make the appropriate selection from among them.

[0066] "Communication method" refers to the technology that finalizes the response selected or modified by the user and sends the information as the final response.

[0067] "Recording means" refers to technology that has the function of storing analyzed and evaluated information in a database or similar system for future reference and analysis.

[0068] This invention relates to a system that efficiently processes received information and supports user information management. This system mainly consists of three components: a server, a terminal, and a user.

[0069] The server is the core of information reception and analyzes received messages using information processing tools. This analysis utilizes natural language processing techniques, such as generative models widely used in AI. The server categorizes and evaluates importance, and based on the results, notifies terminals of high-priority information. The evaluation tool automatically determines the priority of information, and notifications are sent immediately as needed.

[0070] A terminal is a device that presents information sent from a server to the user. Using a connection method, multiple response options are presented to the user. The user can select the most appropriate response through the terminal's interface. This process speeds up daily information processing.

[0071] The user is the one who uses their device to check information and select a response. The response selected by the user is sent to the recipient via the server through a communication method. For example, if a user receives an urgent meeting invitation, the server analyzes the content and suggests possible dates and times in conjunction with the user's schedule. This allows the user to respond quickly and manage their schedule efficiently.

[0072] As a concrete example, consider inputting the following prompt into the AI ​​model.

[0073] "You have received a new meeting invitation email. The title is 'Project Progress Meeting'. Please suggest possible dates and times that take into account the user's availability."

[0074] Thus, this system enables efficient and effective information processing in complex information environments, providing support to enable users to take quick and appropriate actions.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives an electronic message. The received message is the input data. The server initiates natural language processing and uses a generative AI model to analyze the message content, determining its category and importance. The output is the message's category information and importance rating.

[0078] Step 2:

[0079] The server uses evaluation tools based on the analysis results to assess the priority and urgency of each message. The category information and importance ratings obtained from step 1 are used as input. Based on this assessment, the server determines which messages have high priority or urgency. The output is the priority and urgency evaluation data for each message.

[0080] Step 3:

[0081] The server generates individual notifications using notification methods for high-priority or high-urgency messages determined in step 2. This notification data is sent to the user's terminal. The input is priority and urgency evaluation data, and the output is notification information. Notifications sent to the terminal are immediately presented to the user.

[0082] Step 4:

[0083] The terminal presents the user with multiple response options received from the server. This includes user schedule information obtained by the server using a communication mechanism. Input consists of response option data from the server and user schedule information. The terminal uses this information and a connection mechanism to present options to the user. Output is the response option selected by the user.

[0084] Step 5:

[0085] The user selects the most suitable response from the options presented on the terminal. This selection is made by clicking on a specific response. The selected response is sent back to the server via a communication method. The input is the response selected by the user, and the output is the selection data sent to the server.

[0086] Step 6:

[0087] The server generates a final reply based on the user's selected reply. This final reply is configured to convey information to the sender. The server transmits it via a transmission method. The input is the selected reply data, and the output is the final reply message.

[0088] (Application Example 1)

[0089] 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."

[0090] In today's world, the sheer volume of electronic messages is increasing, requiring users to expend considerable effort managing them. Similarly, managing financial data has become increasingly complex, demanding efficient assessment of importance and appropriate action. However, traditional systems have struggled to integrate and manage this information effectively, enabling quick and appropriate responses.

[0091] 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.

[0092] In this invention, the server includes information processing means for analyzing received messages, evaluation means for assessing importance, notification means for generating and sending individual notifications, schedule adjustment means for adjusting schedules, and financial evaluation means for evaluating financial data. This enables efficient management of electronic messages and financial data, allowing users to make quick and appropriate decisions.

[0093] "Information processing means" refers to a device or program that has the function of analyzing a received electronic message and automatically generating a reply based on its content.

[0094] "Evaluation means" refers to a device or algorithm that has the function of evaluating the priority and urgency of electronic messages based on the analysis results.

[0095] A "notification means" is a device or program that has the function of generating individual notifications for electronic messages that are evaluated as having high priority or urgency, and sending them to the user's device.

[0096] A "schedule adjustment means" is a device or program that has the function of acquiring the user's schedule information from external schedule management application software and automatically generating a reply proposal that includes candidate dates and times.

[0097] "User interface means" refers to a device or program that provides a function to send multiple generated response options to a user device, allowing the user to review and select one.

[0098] A "financial evaluation tool" is a device or algorithm that has the function of evaluating highly important financial data based on the analysis of received information and automatically generating payment proposals based on that important financial data.

[0099] The system implementing this invention consists of a program that works in conjunction with several stages of information processing equipment. The overall process is centered around an information processing server and is carried out through interaction with terminal devices and users.

[0100] First, the server uses information processing tools to analyze received electronic messages with a natural language processing engine. Possible natural language processing engines used include general open-source libraries and cloud services (e.g., Google® Cloud Natural Language API). The analyzed information is then evaluated to determine its importance and priority. This process utilizes machine learning models to optimize the process based on patterns from past messages.

[0101] Next, the server uses a notification mechanism to send individual push notifications to the user's device for messages deemed to be of high importance. This notification uses a cloud messaging service such as Firebase Cloud Messaging. A scheduling mechanism is also used to retrieve the user's schedule information from other external scheduling applications and automatically generate a reply proposal that includes suggested dates and times for the user to review and select.

[0102] This information is displayed on the device through a user interface, designed to allow users to operate it intuitively. The device-side interface can be adapted to a wide range of devices by using cross-platform development frameworks such as React Native.

[0103] Finally, the financial evaluation tool analyzes the received financial data and generates a weighted payment plan. This process utilizes an AI model trained on the user's past payment history and uses the Stripe API to process payments.

[0104] As a concrete example, imagine a scenario where a user manages their monthly bills using this system on their smartphone. When the user receives a new bill, the server immediately analyzes it and suggests an appropriate payment plan. For example, the prompt might be, "Manage my monthly electricity bills and tell me the best payment plan."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server first analyzes the received electronic message. In this step, information processing tools are used, and the message text is given as input. The server uses a natural language processing engine to analyze the message content, extracting keywords and classifying categories. This allows it to understand the subject and purpose of the message, and the analysis results are output.

[0108] Step 2:

[0109] The server uses an evaluation tool to assess the importance and priority of messages based on the analyzed information. The input is the analysis results, which are the output of step 1. Here, a machine learning model learns from past message data and calculates an importance score. This results in an output that determines which messages should be processed first.

[0110] Step 3:

[0111] The server generates individual notifications for high-priority messages using a notification mechanism and sends them to the terminal. The high-priority messages obtained in step 2 serve as input for this process. The server constructs a notification message and sends it to the user's terminal via a cloud messaging service. This notification triggers an action on the terminal.

[0112] Step 4:

[0113] The terminal displays notification messages received from the server to the user. The input is the notification sent in step 3. The terminal visually represents the notification content through the user interface and displays it in a format that is easy for the user to understand. This allows the user to quickly check the message.

[0114] Step 5:

[0115] The server uses a scheduling mechanism to retrieve user schedule information from an external scheduling application and automatically generates a reply proposal including suggested dates and times. The input requires the user's schedule data and analysis results. The server uses a scheduling API to check free time, calculates suggested dates and times, and presents them as output.

[0116] Step 6:

[0117] The terminal presents the user with suggested replies, including possible dates and times sent from the server, and supports their selection. Here, the output from step 5 serves as input. On the terminal, the suggested replies are presented in a way that the user can freely review and select from, and the selection result is returned via the user interface.

[0118] Step 7:

[0119] The server analyzes the received financial data using financial evaluation tools and automatically generates data importance and payment plan candidates. Financial data is used as input for this process. An AI model is used to generate an appropriate payment plan, and the results are output.

[0120] Step 8:

[0121] The user reviews the plan provided through the device and makes a final selection. Based on instructions from the server, the device displays the plan and prepares to return the user's selection to the server.

[0122] Step 9:

[0123] The server confirms the final response or payment based on the information selected by the user, and completes the process. Here, the selection from the terminal is the input, and the server completes the entire process by performing the necessary actions.

[0124] 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.

[0125] This invention relates to a system that recognizes the emotional state of a sender by utilizing natural language processing means for analyzing the content of an electronic message upon receipt, and an emotion engine. The server analyzes the body, subject, sender information, and emotional expressions within the received message to determine the message's category (e.g., urgent, scheduling, information sharing), priority, and urgency.

[0126] The server utilizes an emotion engine to evaluate the emotional tone extracted from a message and uses that to generate a response. For example, if an email from a business partner expresses dissatisfaction or concern, the server can suggest a respectful response that takes the emotional state into account.

[0127] By using a scheduling integration method, when scheduling adjustments are necessary, the server can retrieve the user's availability from an external scheduling application and automatically generate a response plan including suggested dates and times. Furthermore, the terminal presents these response plans to the user, providing an interface that allows the user to select the most suitable plan and edit it as needed.

[0128] For example, when a user receives an inquiry email from a customer, the server analyzes the email and uses an emotion engine to recognize the customer's emotions. If it determines that the customer is anxious, the server generates a reassuring reply and suggests it to the user. The user can review the suggested reply, select the appropriate one, or further modify it to finalize their response.

[0129] Ultimately, the server uses the sending method to send a reply email to the recipient based on the user's submitted response. Additionally, the analyzed sentiment information and response history are stored in a database via recording devices, which can be used for future reference and analysis.

[0130] With the above configuration, the present invention can improve the efficiency of electronic message management, reduce the burden on users, and support communication that takes emotions into consideration.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] When the server receives a new electronic message, it stores its contents in the mail database and starts the analysis process. The received message is processed by a natural language processing engine, which extracts information from the message body and subject.

[0134] Step 2:

[0135] The server uses natural language processing to determine the category, importance, and urgency of messages. Through analysis, it identifies keywords such as "urgent" and "meeting scheduling," and uses this information to set the message priority.

[0136] Step 3:

[0137] The server uses an emotion engine to analyze the message content and evaluate the sender's emotions. This includes emotional states such as joy, dissatisfaction, and anger. For example, a message with many expressions of gratitude would be recognized as "joyful."

[0138] Step 4:

[0139] The server generates the most appropriate response based on the assessed priority, urgency, and emotion. The tone and content of the response are adjusted to match the perceived emotion. For example, if "dissatisfaction" is detected, a polite response focused on problem resolution will be generated.

[0140] Step 5:

[0141] The server uses scheduling integration to retrieve user schedule information from external scheduling applications as needed. This makes it possible to suggest reply options, including possible dates and times.

[0142] Step 6:

[0143] The terminal displays multiple generated response options to the user, allowing the user to review and select an option through the interface. The user can also edit the response options as needed.

[0144] Step 7:

[0145] After the user selects or modifies a reply, the device sends that information to the server. The server then creates a final reply based on that information and sends it to the recipient via email through the sending method.

[0146] Step 8:

[0147] The server records all analysis and message sentiment, and stores it in a database for future reference. This information can be used to improve and analyze future interactions.

[0148] (Example 2)

[0149] 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".

[0150] In recent years, the surge in digital messaging has demanded that recipients efficiently manage a large volume of messages and respond appropriately. However, manually prioritizing messages and crafting responses tailored to their content is time-consuming and laborious, hindering efficient communication. Furthermore, responding with consideration for emotions is difficult, posing a significant challenge, especially in business communication. Solutions to these problems are needed.

[0151] 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.

[0152] In this invention, the server includes language processing means for analyzing received digital messages and generating reply candidates based on their content; analysis means for evaluating the priority and urgency of digital messages based on the analysis results; and notification means for generating individual information for digital messages evaluated as having high priority or urgency and transmitting it to the user device. This makes it possible to quickly and accurately determine the content of received messages and present appropriate reply suggestions to the user.

[0153] A "digital message" is a document containing information that is sent and received electronically via the internet or other means.

[0154] "Language processing means" refers to technologies that analyze text within digital messages, understand and organize its content, and perform processing to generate reply candidates.

[0155] "Analysis means" refers to a system or technology for evaluating the content of a received message and determining its priority and urgency.

[0156] A "notification means" is a device or system that has the function of notifying user devices of information about messages deemed important or urgent.

[0157] A "management application program" refers to software or services for schedule management and scheduling, which enable integration with digital messaging.

[0158] "Integration means" refers to an interface or technology that enables information exchange with external systems and allows for processing and proposals based on received digital messages.

[0159] "Means of communication" refers to a device or system that has communication capabilities for sending a reply that the user has finally confirmed.

[0160] The invention will now be described in terms of embodiments. This system is composed of a combination of various technologies to efficiently receive, analyze, and generate replies to digital messages.

[0161] The server captures digital messages received via the internet and analyzes the message body, subject, and sender information. During this process, natural language processing technologies such as Google Cloud Natural Language API and IBM Watson® Natural Language Understanding are used to organize the message content into structured data.

[0162] The server then uses an emotion engine to evaluate the emotional tone of the message based on the analyzed data. The emotion engine used is a sentiment analysis tool such as Microsoft® Azure® Text Analytics, which scores the message as positive, negative, or neutral.

[0163] Once sentiment evaluation is complete, the server uses a generative AI model to generate the optimal response. Generative AI, such as OpenAI's GPT model, can be used, and prompts can be used to instruct the server to "consider the sentiment of the email and generate an appropriate response."

[0164] It can also integrate with external management application programs, for example, by using Google Calendar or Microsoft Outlook Calendar to retrieve schedule information. Based on this information, the server generates response suggestions that take into account the user's availability.

[0165] The generated multiple response options are presented to the user via the device. The device provides a user interface and is designed to allow the user to intuitively review, select, and modify the response options.

[0166] For example, if a user receives an email from a business partner requesting to schedule a meeting for next week, the server performs sentiment analysis and generates a reply in a friendly tone. It also checks the user's calendar and suggests possible dates and times. An example of a prompt used in this process is, "Generate a tone-conscious reply to schedule a meeting based on the digital message."

[0167] This system enables quick and appropriate responses to digital messages and reduces the administrative burden on users.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server receives digital messages via the internet. It takes the message body, subject, and sender information as input and preprocesses them. Specifically, it removes unnecessary spaces and special characters from the text and converts the message into a parseable format. As a result of this conversion process, the preprocessed text data is output.

[0171] Step 2:

[0172] The server analyzes the data by passing the pre-processed messages through natural language processing tools. The input is the output data from step 1, and keywords and phrases from the messages are extracted using natural language processing techniques. This outputs the message category (e.g., urgent, scheduling, information sharing) and priority. NLU tools are used for analysis.

[0173] Step 3:

[0174] The server uses an emotion engine to evaluate the emotional tone of the analyzed message. In this step, it receives the output from step 2 (keywords and phrases) as input and scores the sentiment based on it. As a result, the emotional state of the message (positive, negative, neutral) is output. This process uses a sentiment analysis tool.

[0175] Step 4:

[0176] The server utilizes a generative AI model to generate the optimal reply. Based on the category information and sentiment state obtained in steps 2 and 3, prompts are used to input the information into the AI ​​model. For example, a prompt such as "Consider the sentiment of the email and generate an appropriate reply" might be used. The output of this step is multiple reply options.

[0177] Step 5:

[0178] The server uses a scheduling mechanism to retrieve the user's availability from an external management application program. It then compares the user's schedule information with the response candidates generated in step 4 to create candidates that include suggested dates and times. The output in this step is a response proposal that reflects the schedule.

[0179] Step 6:

[0180] The terminal presents the user with multiple reply options sent from the server via the user interface. The user can review these as input and select or modify the best option. The selected or modified reply becomes the output of this step.

[0181] Step 7:

[0182] Finally, the server generates a reply that the user has confirmed and sends it as an email. The output from step 6 is used as input, and the transmission is performed using the communication protocol. Additionally, the analysis data and reply history are recorded and stored in a database for future reference. The output here consists of sent emails and recorded data.

[0183] (Application Example 2)

[0184] 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".

[0185] In email management, there is a problem in providing prompt and appropriate replies based on the content of received messages. Furthermore, there is a need to improve the quality of communication by providing replies that take the sender's emotions into consideration. This invention aims to solve these conventional problems by analyzing the emotional information contained in emails and generating appropriate reply proposals based on that analysis.

[0186] 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.

[0187] In this invention, the server includes a natural language processing means that analyzes received emails and automatically generates reply proposals based on their content; an evaluation means that evaluates the priority and urgency of electronic messages based on the analysis results; and an emotion recognition means that uses emotional properties extracted from the message to generate reply proposals appropriate to the emotional state. This enables the creation of efficient replies that take emotions into consideration.

[0188] "Email" refers to document data sent and received in digital format via the internet or other networks.

[0189] "Natural language processing means" refers to technologies or systems that enable computers to understand, analyze, and generate responses to human language.

[0190] "Evaluation means" refers to a method or device for determining the priority and urgency of related information based on analyzed data.

[0191] "Notification means" refers to a method or device for informing the user of important or urgent information based on evaluation results.

[0192] "Interface means" refers to a method or device for a user to review and select an appropriate response from multiple generated candidates.

[0193] "Schedule integration means" refers to a function that shares or retrieves a user's schedule with an external system and generates appropriate response suggestions.

[0194] "Emotion recognition means" refers to a function or process for analyzing emotional elements extracted from electronic messages and generating an appropriate response based on this analysis.

[0195] This invention is a system that automatically generates appropriate reply suggestions by using natural language processing technology on a server to analyze emails, identify the content of the message and the sender's emotional state, and then generate appropriate reply suggestions. An emotion engine is used for emotion recognition, evaluating the emotional properties extracted from the message. Based on the evaluated information, the priority and urgency of the email are also determined.

[0196] The server uses, for example, the Python programming language and leverages natural language processing libraries such as spaCy and transformers. This allows it to analyze email content, and the sentiment engine evaluates emotional properties to determine the tone of the message. Furthermore, the evaluation process generates suggested replies based on the sender's emotional state, enabling users to communicate in an emotionally sensitive manner without feeling stressed.

[0197] The terminal presents the user with multiple generated response options and provides an interface to support selection and editing. Through this interface, the user can review the presented response options and modify them as needed. The interface is designed with ease of use in mind, allowing for intuitive operation.

[0198] For example, if a user receives an email from a customer stating, "I am concerned about the recent delay in payment," the server analyzes the email and, using its sentiment engine, determines it to be "dissatisfied." Based on this, the server automatically generates a proposed reply offering an apology and prompt action, and presents it to the user. After the user reviews the proposed reply, they can press the send button, and the reply will be automatically sent.

[0199] By using a generative AI model, the accuracy of message analysis and sentiment evaluation is improved, enabling appropriate responses in a variety of communication scenarios. An example of a prompt is the instruction, "Customer message: I am concerned about the recent payment delay. Please generate a response." This prompt prompts the server to start the process of generating a response that takes sentiment and content into consideration.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The server receives the email data as input and first analyzes the text using natural language processing techniques. For example, it uses the spaCy library to break down the email content into morphemes and extract topics and keywords. This analysis result forms the basis for the next processing step.

[0203] Step 2:

[0204] The server uses an evaluation tool with the analyzed data as input to determine the priority and urgency of the message. In this step, for example, urgency is quantified based on keyword frequency and context, and the results are output as data.

[0205] Step 3:

[0206] The server uses emotion recognition tools to perform sentiment analysis. It evaluates the emotional tone from the input message text and classifies the emotion into categories such as "positive" or "negative" using, for example, a sentiment analysis model like Transformers. The output is a label of the emotional state.

[0207] Step 4:

[0208] The server takes the obtained emotional state and evaluation data as input and automatically generates appropriate response suggestions via a response suggestion generation mechanism. Using a generation AI model, it constructs response sentences that take emotions into consideration. The output is multiple generated response suggestions.

[0209] Step 5:

[0210] The device receives multiple generated response options and presents them to the user through a user interface. This interface provides the user with the ability to review, select, or further edit the response options.

[0211] Step 6:

[0212] The user uses the terminal interface to review the suggested reply and edit it as needed. They then finalize the reply and prepare to send it.

[0213] Step 7:

[0214] The server takes the user's confirmed reply as input and sends the reply email to the recipient via the sending method. Once the sending is complete, the result is recorded in the log.

[0215] Step 8:

[0216] The server stores the analyzed emails, their sentiment status, priority data, and reply history in a data storage unit. The stored data serves as a source of information for future reference and analysis.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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".

[0233] This invention provides a system for efficiently managing received electronic messages and reducing the burden on users. When a server receives an electronic message, it uses natural language processing to analyze its content and determine the message's category and importance. Based on this analysis, individual notifications are generated for messages with high priority or urgency and sent to the user via the terminal.

[0234] In addition, the server automatically evaluates priority using evaluation tools and uses this information to generate appropriate response suggestions. For example, when a meeting invitation email is received, the server can use scheduling integration tools to retrieve the user's availability from an external scheduling application and automatically generate a response suggestion including possible dates and times.

[0235] The terminal presents the user with multiple response options sent from the server and supports confirmation and selection via an interface. In this way, the user can quickly provide an appropriate response.

[0236] For example, if a user receives an urgent email from a business partner, the server immediately analyzes its contents and assesses its urgency. If it contains important information, a push notification is sent to the device, allowing the user to access the email immediately. Furthermore, if scheduling adjustments are necessary, suggested dates and times reflecting the user's availability are displayed, enabling them to respond immediately.

[0237] Ultimately, the server confirms the reply selected by the user and sends the reply email to the recipient using the appropriate sending method. This entire system flow allows users to streamline their daily email processing and make better use of their time. This system supports businesses in managing large volumes of email and promotes fast and accurate communication.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] When the server receives a new electronic message, it first stores the message in its mail database. Then, it adds it to a queue as a message to be analyzed.

[0241] Step 2:

[0242] The server uses a natural language processing engine to analyze the body, subject, and sender information of received messages. This allows it to identify the message category (e.g., urgent, scheduling, information sharing) and extract the necessary information.

[0243] Step 3:

[0244] The server executes evaluation measures based on the analysis results to determine the priority and urgency of the message. This is done based on the detection of specific keywords and patterns, as well as the message sender.

[0245] Step 4:

[0246] Based on the evaluation results, the server sends individual notifications to user terminals for messages deemed to be of high urgency. This allows users to quickly become aware of important messages.

[0247] Step 5:

[0248] The server uses scheduling integration to retrieve user calendar information from external scheduling applications as needed. This generates candidate dates and times, which are then included in the reply suggestions.

[0249] Step 6:

[0250] Based on the analyzed information, the server generates several appropriate response options and sends them to the user's terminal. These response options may include a suggested schedule, if necessary.

[0251] Step 7:

[0252] The terminal displays the user with suggested replies received from the server and provides an interface for selection and editing. The user uses this interface to select the best reply and modify its content as needed.

[0253] Step 8:

[0254] Once the user confirms their reply, the device sends that information to the server, which then uses its sending method to send the final reply email to the recipient. The server then logs this reply.

[0255] (Example 1)

[0256] 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."

[0257] In today's information-saturated world, particularly through email, the sheer volume of incoming information presents users with the risk of overlooking important details and the problem of spending excessive time processing information. Furthermore, manual reply processes are inefficient, and this inefficiency is particularly evident in business settings where quick responses are crucial. The challenge lies in solving these problems and supporting appropriate responses while streamlining information processing.

[0258] 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.

[0259] In this invention, the server includes information processing means for analyzing received information and automatically generating a response based on its content; evaluation means for evaluating the priority and urgency of the information based on the analysis results; and notification means for generating individual notifications for information evaluated as high priority or high urgency and sending them to the terminal. This enables a rapid response according to the priority of the information, allowing the user to effectively manage important information and make quick decisions.

[0260] "Information processing means" refers to technology that has the function of analyzing received information and automatically generating appropriate response proposals based on its content.

[0261] An "evaluation tool" is a technology that has the function of objectively evaluating the importance and urgency of information based on the content of the analyzed information.

[0262] A "notification method" is a technology that generates individual warnings or notifications for information of high priority or urgency, and quickly transmits that information to the user's terminal.

[0263] "Integration means" refers to technology that has the function of obtaining schedule information from an external management system and generating appropriate response proposals based on that information.

[0264] "Connection means" refers to a technology that presents multiple generated response options to the user's device, allowing the user to make the appropriate selection from among them.

[0265] "Communication method" refers to the technology that finalizes the response selected or modified by the user and sends the information as the final response.

[0266] "Recording means" refers to technology that has the function of storing analyzed and evaluated information in a database or similar system for future reference and analysis.

[0267] This invention relates to a system that efficiently processes received information and supports user information management. This system mainly consists of three components: a server, a terminal, and a user.

[0268] The server is the core of information reception and analyzes received messages using information processing tools. This analysis utilizes natural language processing techniques, such as generative models widely used in AI. The server categorizes and evaluates importance, and based on the results, notifies terminals of high-priority information. The evaluation tool automatically determines the priority of information, and notifications are sent immediately as needed.

[0269] A terminal is a device that presents information sent from a server to the user. Using a connection method, multiple response options are presented to the user. The user can select the most appropriate response through the terminal's interface. This process speeds up daily information processing.

[0270] The user is the one who uses their device to check information and select a response. The response selected by the user is sent to the recipient via the server through a communication method. For example, if a user receives an urgent meeting invitation, the server analyzes the content and suggests possible dates and times in conjunction with the user's schedule. This allows the user to respond quickly and manage their schedule efficiently.

[0271] As a concrete example, consider inputting the following prompt into the AI ​​model.

[0272] "You have received a new meeting invitation email. The title is 'Project Progress Meeting'. Please suggest possible dates and times that take into account the user's availability."

[0273] Thus, this system enables efficient and effective information processing in complex information environments, providing support to enable users to take quick and appropriate actions.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The server receives an electronic message. The received message is the input data. The server initiates natural language processing and uses a generative AI model to analyze the message content, determining its category and importance. The output is the message's category information and importance rating.

[0277] Step 2:

[0278] The server uses evaluation means based on the analysis results to evaluate the priority and urgency of each message. As input, the category information and importance evaluation obtained from Step 1 are used. Based on this evaluation, it is determined which messages are of high priority or high urgency. The output is the evaluation data of the priority and urgency of each message.

[0279] Step 3:

[0280] For messages determined to be of high priority or high urgency in Step 2, the server generates individual notifications using notification means. These notification data are sent to the user's terminal. The input is the evaluation data of priority and urgency, and the output is notification information. The notifications sent to the terminal are immediately presented to the user.

[0281] Step 4:

[0282] The terminal presents multiple reply proposals received from the server to the user. The user's schedule information obtained by the server using cooperation means is also included. The input is the reply proposal data from the server and the user's schedule information. The terminal uses this to utilize connection means and presents options to the user. The output is the reply proposal selected by the user.

[0283] Step 5:

[0284] The user selects the optimal one from the reply proposals presented on the terminal. This selection is made by clicking on a specific reply proposal. The selected reply proposal is sent back to the server by transmission means. The input is the reply proposal selected by the user, and the output is the selection data to the server.

[0285] Step 6:

[0286] The server generates the final reply based on the reply proposal selected by the user. This final reply is configured to convey information to the sender. The server sends this by transmission means. The input is the selected reply proposal data, and the output is the final reply message.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0289] In modern times, the number of electronic messages has been increasing, and users require a great deal of effort to manage them. Also, the management of financial data has similarly become complicated, and it is required to efficiently evaluate the importance and appropriately respond to it. However, in conventional systems, it has been difficult to integrally manage this information and quickly and appropriately respond to it.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0291] In this invention, the server includes information processing means for analyzing the received message, evaluation means for evaluating the importance, notification means for generating and transmitting an individual notification, schedule adjustment means for adjusting the schedule, and financial evaluation means for evaluating the financial data. Thereby, electronic messages and financial data can be efficiently managed, and users can make quick and appropriate decisions.

[0292] The "information processing means" is a device or program having a function of analyzing the received electronic message and automatically generating a reply draft based on its content.

[0293] The "evaluation means" is a device or algorithm having a function of evaluating the priority and urgency of an electronic message based on the analysis result.

[0294] The "notification means" is a device or program having a function of generating an individual notification for an electronic message evaluated as having a high priority or high urgency and transmitting it to the user device.

[0295] A "schedule adjustment means" is a device or program that has the function of acquiring the user's schedule information from external schedule management application software and automatically generating a reply proposal that includes candidate dates and times.

[0296] "User interface means" refers to a device or program that provides a function to send multiple generated response options to a user device, allowing the user to review and select one.

[0297] A "financial evaluation tool" is a device or algorithm that has the function of evaluating highly important financial data based on the analysis of received information and automatically generating payment proposals based on that important financial data.

[0298] The system implementing this invention consists of a program that works in conjunction with several stages of information processing equipment. The overall process is centered around an information processing server and is carried out through interaction with terminal devices and users.

[0299] First, the server uses information processing tools to analyze received electronic messages with a natural language processing engine. Possible natural language processing engines used include common open-source libraries and cloud services (e.g., Google Cloud Natural Language API). The analyzed information is then evaluated to determine its importance and priority. This process utilizes machine learning models to optimize the process based on patterns from past messages.

[0300] Next, the server uses a notification mechanism to send individual push notifications to the user's device for messages deemed to be of high importance. This notification uses a cloud messaging service such as Firebase Cloud Messaging. A scheduling mechanism is also used to retrieve the user's schedule information from other external scheduling applications and automatically generate a reply proposal that includes suggested dates and times for the user to review and select.

[0301] By means of the user interface, these pieces of information are displayed on the terminal and designed to be intuitively operable by the user. The terminal-side interface can support a wide range of devices by using a cross-platform development framework such as React Native.

[0302] Finally, the financial evaluation means analyzes the received financial data and generates a weighted payment plan. For this process, an AI model that has learned the user's past payment history is utilized, and payment processing is carried out using the Stripe API.

[0303] As a specific example, assume a scenario where a user manages monthly bills using this system on a smartphone. When the user receives a new bill, the server immediately analyzes it and presents an appropriate payment plan. For example, it is conceivable to input "Manage the monthly electricity bill and tell me the optimal payment plan." as a prompt.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The server first analyzes the received electronic message. In this step, the information processing means is used, and the message text is given as input. The server uses a natural language processing engine to analyze the content of the message, perform keyword extraction and category classification. Thereby, the theme and purpose of the message are grasped, and the analysis result is obtained as output.

[0307] Step 2:

[0308] The server uses an evaluation tool to assess the importance and priority of messages based on the analyzed information. The input is the analysis results, which are the output of step 1. Here, a machine learning model learns from past message data and calculates an importance score. This results in an output that determines which messages should be processed first.

[0309] Step 3:

[0310] The server generates individual notifications for high-priority messages using a notification mechanism and sends them to the terminal. The high-priority messages obtained in step 2 serve as input for this process. The server constructs a notification message and sends it to the user's terminal via a cloud messaging service. This notification triggers an action on the terminal.

[0311] Step 4:

[0312] The terminal displays notification messages received from the server to the user. The input is the notification sent in step 3. The terminal visually represents the notification content through the user interface and displays it in a format that is easy for the user to understand. This allows the user to quickly check the message.

[0313] Step 5:

[0314] The server uses a scheduling mechanism to retrieve user schedule information from an external scheduling application and automatically generates a reply proposal including suggested dates and times. The input requires the user's schedule data and analysis results. The server uses a scheduling API to check free time, calculates suggested dates and times, and presents them as output.

[0315] Step 6:

[0316] The terminal presents the user with suggested replies, including possible dates and times sent from the server, and supports their selection. Here, the output from step 5 serves as input. On the terminal, the suggested replies are presented in a way that the user can freely review and select from, and the selection result is returned via the user interface.

[0317] Step 7:

[0318] The server analyzes the received financial data using financial evaluation tools and automatically generates data importance and payment plan candidates. Financial data is used as input for this process. An AI model is used to generate an appropriate payment plan, and the results are output.

[0319] Step 8:

[0320] The user reviews the plan provided through the device and makes a final selection. Based on instructions from the server, the device displays the plan and prepares to return the user's selection to the server.

[0321] Step 9:

[0322] The server confirms the final response or payment based on the information selected by the user, and completes the process. Here, the selection from the terminal is the input, and the server completes the entire process by performing the necessary actions.

[0323] 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.

[0324] This invention relates to a system that recognizes the emotional state of a sender by utilizing natural language processing means for analyzing the content of an electronic message upon receipt, and an emotion engine. The server analyzes the body, subject, sender information, and emotional expressions within the received message to determine the message's category (e.g., urgent, scheduling, information sharing), priority, and urgency.

[0325] The server utilizes an emotion engine to evaluate the emotional tone extracted from a message and uses that to generate a response. For example, if an email from a business partner expresses dissatisfaction or concern, the server can suggest a respectful response that takes the emotional state into account.

[0326] By using a scheduling integration method, when scheduling adjustments are necessary, the server can retrieve the user's availability from an external scheduling application and automatically generate a response plan including suggested dates and times. Furthermore, the terminal presents these response plans to the user, providing an interface that allows the user to select the most suitable plan and edit it as needed.

[0327] For example, when a user receives an inquiry email from a customer, the server analyzes the email and uses an emotion engine to recognize the customer's emotions. If it determines that the customer is anxious, the server generates a reassuring reply and suggests it to the user. The user can review the suggested reply, select the appropriate one, or further modify it to finalize their response.

[0328] Ultimately, the server uses the sending method to send a reply email to the recipient based on the user's submitted response. Additionally, the analyzed sentiment information and response history are stored in a database via recording devices, which can be used for future reference and analysis.

[0329] With the above configuration, the present invention can improve the efficiency of electronic message management, reduce the burden on users, and support communication that takes emotions into consideration.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] When the server receives a new electronic message, it stores its contents in the mail database and starts the analysis process. The received message is processed by a natural language processing engine, which extracts information from the message body and subject.

[0333] Step 2:

[0334] The server uses natural language processing to determine the category, importance, and urgency of messages. Through analysis, it identifies keywords such as "urgent" and "meeting scheduling," and uses this information to set the message priority.

[0335] Step 3:

[0336] The server uses an emotion engine to analyze the message content and evaluate the sender's emotions. This includes emotional states such as joy, dissatisfaction, and anger. For example, a message with many expressions of gratitude would be recognized as "joyful."

[0337] Step 4:

[0338] The server generates the most appropriate response based on the assessed priority, urgency, and emotion. The tone and content of the response are adjusted to match the perceived emotion. For example, if "dissatisfaction" is detected, a polite response focused on problem resolution will be generated.

[0339] Step 5:

[0340] The server uses scheduling integration to retrieve user schedule information from external scheduling applications as needed. This makes it possible to suggest reply options, including possible dates and times.

[0341] Step 6:

[0342] The terminal displays multiple generated response options to the user, allowing the user to review and select an option through the interface. The user can also edit the response options as needed.

[0343] Step 7:

[0344] After the user selects or modifies a reply, the device sends that information to the server. The server then creates a final reply based on that information and sends it to the recipient via email through the sending method.

[0345] Step 8:

[0346] The server records all analysis and message sentiment, and stores it in a database for future reference. This information can be used to improve and analyze future interactions.

[0347] (Example 2)

[0348] 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".

[0349] In recent years, the surge in digital messaging has demanded that recipients efficiently manage a large volume of messages and respond appropriately. However, manually prioritizing messages and crafting responses tailored to their content is time-consuming and laborious, hindering efficient communication. Furthermore, responding with consideration for emotions is difficult, posing a significant challenge, especially in business communication. Solutions to these problems are needed.

[0350] 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.

[0351] In this invention, the server includes language processing means for analyzing received digital messages and generating reply candidates based on their content; analysis means for evaluating the priority and urgency of digital messages based on the analysis results; and notification means for generating individual information for digital messages evaluated as having high priority or urgency and transmitting it to the user device. This makes it possible to quickly and accurately determine the content of received messages and present appropriate reply suggestions to the user.

[0352] A "digital message" is a document containing information that is sent and received electronically via the internet or other means.

[0353] "Language processing means" refers to technologies that analyze text within digital messages, understand and organize its content, and perform processing to generate reply candidates.

[0354] "Analysis means" refers to a system or technology for evaluating the content of a received message and determining its priority and urgency.

[0355] A "notification means" is a device or system that has the function of notifying user devices of information about messages deemed important or urgent.

[0356] A "management application program" refers to software or services for schedule management and scheduling, which enable integration with digital messaging.

[0357] "Integration means" refers to an interface or technology that enables information exchange with external systems and allows for processing and proposals based on received digital messages.

[0358] "Means of communication" refers to a device or system that has communication capabilities for sending a reply that the user has finally confirmed.

[0359] The invention will now be described in terms of embodiments. This system is composed of a combination of various technologies to efficiently receive, analyze, and generate replies to digital messages.

[0360] The server captures digital messages received via the internet and analyzes the message body, subject, and sender information. During this process, natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding are used to organize the message content into structured data.

[0361] The server then uses an emotion engine to evaluate the emotional tone of the message based on the analyzed data. The emotion engine used is a sentiment analysis tool such as Microsoft Azure Text Analytics, which scores the message as having positive, negative, or neutral sentiment.

[0362] Once sentiment assessment is complete, the server uses a generative AI model to generate the optimal response. Generative AI, such as OpenAI's GPT model, can be used, and prompts can be used to instruct the server to "consider the sentiment of the email and generate an appropriate response."

[0363] It can also integrate with external management application programs, for example, by using Google Calendar or Microsoft Outlook Calendar to retrieve schedule information. Based on this information, the server generates response suggestions that take into account the user's availability.

[0364] The generated multiple response options are presented to the user via the device. The device provides a user interface and is designed to allow the user to intuitively review, select, and modify the response options.

[0365] For example, if a user receives an email from a business partner requesting to schedule a meeting for next week, the server performs sentiment analysis and generates a reply in a friendly tone. It also checks the user's calendar and suggests possible dates and times. An example of a prompt used in this process is, "Generate a tone-conscious reply to schedule a meeting based on the digital message."

[0366] This system enables quick and appropriate responses to digital messages and reduces the administrative burden on users.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The server receives digital messages via the internet. It takes the message body, subject, and sender information as input and preprocesses them. Specifically, it removes unnecessary spaces and special characters from the text and converts the message into a parseable format. As a result of this conversion process, the preprocessed text data is output.

[0370] Step 2:

[0371] The server analyzes the data by passing the pre-processed messages through natural language processing tools. The input is the output data from step 1, and keywords and phrases from the messages are extracted using natural language processing techniques. This outputs the message category (e.g., urgent, scheduling, information sharing) and priority. NLU tools are used for analysis.

[0372] Step 3:

[0373] The server uses an emotion engine to evaluate the emotional tone of the analyzed message. In this step, it receives the output from step 2 (keywords and phrases) as input and scores the sentiment based on it. As a result, the emotional state of the message (positive, negative, neutral) is output. This process uses a sentiment analysis tool.

[0374] Step 4:

[0375] The server utilizes a generative AI model to generate the optimal reply. Based on the category information and sentiment state obtained in steps 2 and 3, prompts are used to input the information into the AI ​​model. For example, a prompt such as "Consider the sentiment of the email and generate an appropriate reply" might be used. The output of this step is multiple reply options.

[0376] Step 5:

[0377] The server uses a scheduling mechanism to retrieve the user's availability from an external management application program. It then compares the user's schedule information with the response candidates generated in step 4 to create candidates that include suggested dates and times. The output in this step is a response proposal that reflects the schedule.

[0378] Step 6:

[0379] The terminal presents the user with multiple reply options sent from the server via the user interface. The user can review these as input and select or modify the best option. The selected or modified reply becomes the output of this step.

[0380] Step 7:

[0381] Finally, the server generates a reply that the user has confirmed and sends it as an email. The output from step 6 is used as input, and the transmission is performed using the communication protocol. Additionally, the analysis data and reply history are recorded and stored in a database for future reference. The output here consists of sent emails and recorded data.

[0382] (Application Example 2)

[0383] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0384] In email management, there is a problem in providing prompt and appropriate replies based on the content of received messages. Furthermore, there is a need to improve the quality of communication by providing replies that take the sender's emotions into consideration. This invention aims to solve these conventional problems by analyzing the emotional information contained in emails and generating appropriate reply proposals based on that analysis.

[0385] 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.

[0386] In this invention, the server includes a natural language processing means that analyzes received emails and automatically generates reply proposals based on their content; an evaluation means that evaluates the priority and urgency of electronic messages based on the analysis results; and an emotion recognition means that uses emotional properties extracted from the message to generate reply proposals appropriate to the emotional state. This enables the creation of efficient replies that take emotions into consideration.

[0387] "Email" refers to document data sent and received in digital format via the internet or other networks.

[0388] "Natural language processing means" refers to technologies or systems that enable computers to understand, analyze, and generate responses to human language.

[0389] "Evaluation means" refers to a method or device for determining the priority and urgency of related information based on analyzed data.

[0390] "Notification means" refers to a method or device for informing the user of important or urgent information based on evaluation results.

[0391] "Interface means" refers to a method or device for a user to review and select an appropriate response from multiple generated candidates.

[0392] "Schedule integration means" refers to a function that shares or retrieves a user's schedule with an external system and generates appropriate response suggestions.

[0393] "Emotion recognition means" refers to a function or process for analyzing emotional elements extracted from electronic messages and generating an appropriate response based on this analysis.

[0394] This invention is a system that automatically generates appropriate reply suggestions by using natural language processing technology on a server to analyze emails, identify the content of the message and the sender's emotional state, and then generate appropriate reply suggestions. An emotion engine is used for emotion recognition, evaluating the emotional properties extracted from the message. Based on the evaluated information, the priority and urgency of the email are also determined.

[0395] The server uses, for example, the Python programming language and leverages natural language processing libraries such as spaCy and transformers. This allows it to analyze email content, and the sentiment engine evaluates emotional properties to determine the tone of the message. Furthermore, the evaluation process generates suggested replies based on the sender's emotional state, enabling users to communicate in an emotionally sensitive manner without feeling stressed.

[0396] The terminal presents the user with multiple generated response options and provides an interface to support selection and editing. Through this interface, the user can review the presented response options and modify them as needed. The interface is designed with ease of use in mind, allowing for intuitive operation.

[0397] For example, if a user receives an email from a customer stating, "I am concerned about the recent delay in payment," the server analyzes the email and, using its sentiment engine, determines it to be "dissatisfied." Based on this, the server automatically generates a proposed reply offering an apology and prompt action, and presents it to the user. After the user reviews the proposed reply, they can press the send button, and the reply will be automatically sent.

[0398] By using a generative AI model, the accuracy of message analysis and sentiment evaluation is improved, enabling appropriate responses in a variety of communication scenarios. An example of a prompt is the instruction, "Customer message: I am concerned about the recent payment delay. Please generate a response." This prompt prompts the server to start the process of generating a response that takes sentiment and content into consideration.

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The server receives the email data as input and first analyzes the text using natural language processing techniques. For example, it uses the spaCy library to break down the email content into morphemes and extract topics and keywords. This analysis result forms the basis for the next processing step.

[0402] Step 2:

[0403] The server uses an evaluation tool with the analyzed data as input to determine the priority and urgency of the message. In this step, for example, urgency is quantified based on keyword frequency and context, and the results are output as data.

[0404] Step 3:

[0405] The server uses emotion recognition tools to perform sentiment analysis. It evaluates the emotional tone from the input message text and classifies the emotion into categories such as "positive" or "negative" using, for example, a sentiment analysis model like Transformers. The output is a label of the emotional state.

[0406] Step 4:

[0407] The server takes the obtained emotional state and evaluation data as input and automatically generates appropriate response suggestions via a response suggestion generation mechanism. Using a generation AI model, it constructs response sentences that take emotions into consideration. The output is multiple generated response suggestions.

[0408] Step 5:

[0409] The device receives multiple generated response options and presents them to the user through a user interface. This interface provides the user with the ability to review, select, or further edit the response options.

[0410] Step 6:

[0411] The user uses the terminal interface to review the suggested reply and edit it as needed. They then finalize the reply and prepare to send it.

[0412] Step 7:

[0413] The server takes the user's confirmed reply as input and sends the reply email to the recipient via the sending method. Once the sending is complete, the result is recorded in the log.

[0414] Step 8:

[0415] The server stores the analyzed emails, their sentiment status, priority data, and reply history in a data storage unit. The stored data serves as a source of information for future reference and analysis.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] This invention provides a system for efficiently managing received electronic messages and reducing the burden on users. When a server receives an electronic message, it uses natural language processing to analyze its content and determine the message's category and importance. Based on this analysis, individual notifications are generated for messages with high priority or urgency and sent to the user via the terminal.

[0433] In addition, the server automatically evaluates priority using evaluation tools and uses this information to generate appropriate response suggestions. For example, when a meeting invitation email is received, the server can use scheduling integration tools to retrieve the user's availability from an external scheduling application and automatically generate a response suggestion including possible dates and times.

[0434] The terminal presents the user with multiple response options sent from the server and supports confirmation and selection via an interface. In this way, the user can quickly provide an appropriate response.

[0435] For example, if a user receives an urgent email from a business partner, the server immediately analyzes its contents and assesses its urgency. If it contains important information, a push notification is sent to the device, allowing the user to access the email immediately. Furthermore, if scheduling adjustments are necessary, suggested dates and times reflecting the user's availability are displayed, enabling them to respond immediately.

[0436] Ultimately, the server confirms the reply selected by the user and sends the reply email to the recipient using the appropriate sending method. This entire system flow allows users to streamline their daily email processing and make better use of their time. This system supports businesses in managing large volumes of email and promotes fast and accurate communication.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] When the server receives a new electronic message, it first stores the message in its mail database. Then, it adds it to a queue as a message to be analyzed.

[0440] Step 2:

[0441] The server uses a natural language processing engine to analyze the body, subject, and sender information of received messages. This allows it to identify the message category (e.g., urgent, scheduling, information sharing) and extract the necessary information.

[0442] Step 3:

[0443] The server executes evaluation measures based on the analysis results to determine the priority and urgency of the message. This is done based on the detection of specific keywords and patterns, as well as the message sender.

[0444] Step 4:

[0445] Based on the evaluation results, the server sends individual notifications to user terminals for messages deemed to be of high urgency. This allows users to quickly become aware of important messages.

[0446] Step 5:

[0447] The server uses scheduling integration to retrieve user calendar information from external scheduling applications as needed. This generates candidate dates and times, which are then included in the reply suggestions.

[0448] Step 6:

[0449] Based on the analyzed information, the server generates several appropriate response options and sends them to the user's terminal. These response options may include a suggested schedule, if necessary.

[0450] Step 7:

[0451] The terminal displays the user with suggested replies received from the server and provides an interface for selection and editing. The user uses this interface to select the best reply and modify its content as needed.

[0452] Step 8:

[0453] Once the user confirms their reply, the device sends that information to the server, which then uses its sending method to send the final reply email to the recipient. The server then logs this reply.

[0454] (Example 1)

[0455] 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."

[0456] In today's information-saturated world, particularly through email, the sheer volume of incoming information presents users with the risk of overlooking important details and the problem of spending excessive time processing information. Furthermore, manual reply processes are inefficient, and this inefficiency is particularly evident in business settings where quick responses are crucial. The challenge lies in solving these problems and supporting appropriate responses while streamlining information processing.

[0457] 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.

[0458] In this invention, the server includes information processing means for analyzing received information and automatically generating a response based on its content; evaluation means for evaluating the priority and urgency of the information based on the analysis results; and notification means for generating individual notifications for information evaluated as high priority or high urgency and sending them to the terminal. This enables a rapid response according to the priority of the information, allowing the user to effectively manage important information and make quick decisions.

[0459] "Information processing means" refers to technology that has the function of analyzing received information and automatically generating appropriate response proposals based on its content.

[0460] An "evaluation tool" is a technology that has the function of objectively evaluating the importance and urgency of information based on the content of the analyzed information.

[0461] A "notification method" is a technology that generates individual warnings or notifications for information of high priority or urgency, and quickly transmits that information to the user's terminal.

[0462] "Integration means" refers to technology that has the function of obtaining schedule information from an external management system and generating appropriate response proposals based on that information.

[0463] "Connection means" refers to a technology that presents multiple generated response options to the user's device, allowing the user to make the appropriate selection from among them.

[0464] "Communication method" refers to the technology that finalizes the response selected or modified by the user and sends the information as the final response.

[0465] "Recording means" refers to technology that has the function of storing analyzed and evaluated information in a database or similar system for future reference and analysis.

[0466] This invention relates to a system that efficiently processes received information and supports user information management. This system mainly consists of three components: a server, a terminal, and a user.

[0467] The server is the core of information reception and analyzes received messages using information processing tools. This analysis utilizes natural language processing techniques, such as generative models widely used in AI. The server categorizes and evaluates importance, and based on the results, notifies terminals of high-priority information. The evaluation tool automatically determines the priority of information, and notifications are sent immediately as needed.

[0468] A terminal is a device that presents information sent from a server to the user. Using a connection method, multiple response options are presented to the user. The user can select the most appropriate response through the terminal's interface. This process speeds up daily information processing.

[0469] The user is the one who uses their device to check information and select a response. The response selected by the user is sent to the recipient via the server through a communication method. For example, if a user receives an urgent meeting invitation, the server analyzes the content and suggests possible dates and times in conjunction with the user's schedule. This allows the user to respond quickly and manage their schedule efficiently.

[0470] As a concrete example, consider inputting the following prompt into the AI ​​model.

[0471] "You have received a new meeting invitation email. The title is 'Project Progress Meeting'. Please suggest possible dates and times that take into account the user's availability."

[0472] Thus, this system enables efficient and effective information processing in complex information environments, providing support to enable users to take quick and appropriate actions.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The server receives an electronic message. The received message is the input data. The server initiates natural language processing and uses a generative AI model to analyze the message content, determining its category and importance. The output is the message's category information and importance rating.

[0476] Step 2:

[0477] The server uses evaluation tools based on the analysis results to assess the priority and urgency of each message. The category information and importance ratings obtained from step 1 are used as input. Based on this assessment, the server determines which messages have high priority or urgency. The output is the priority and urgency evaluation data for each message.

[0478] Step 3:

[0479] The server generates individual notifications using notification methods for high-priority or high-urgency messages determined in step 2. This notification data is sent to the user's terminal. The input is priority and urgency evaluation data, and the output is notification information. Notifications sent to the terminal are immediately presented to the user.

[0480] Step 4:

[0481] The terminal presents the user with multiple response options received from the server. This includes user schedule information obtained by the server using a communication mechanism. Input consists of response option data from the server and user schedule information. The terminal uses this information and a connection mechanism to present options to the user. Output is the response option selected by the user.

[0482] Step 5:

[0483] The user selects the most suitable response from the options presented on the terminal. This selection is made by clicking on a specific response. The selected response is sent back to the server via a communication method. The input is the response selected by the user, and the output is the selection data sent to the server.

[0484] Step 6:

[0485] The server generates a final reply based on the user's selected reply. This final reply is configured to convey information to the sender. The server transmits it via a transmission method. The input is the selected reply data, and the output is the final reply message.

[0486] (Application Example 1)

[0487] 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."

[0488] In today's world, the sheer volume of electronic messages is increasing, requiring users to expend considerable effort managing them. Similarly, managing financial data has become increasingly complex, demanding efficient assessment of importance and appropriate action. However, traditional systems have struggled to integrate and manage this information effectively, enabling quick and appropriate responses.

[0489] 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.

[0490] In this invention, the server includes information processing means for analyzing received messages, evaluation means for assessing importance, notification means for generating and sending individual notifications, schedule adjustment means for adjusting schedules, and financial evaluation means for evaluating financial data. This enables efficient management of electronic messages and financial data, allowing users to make quick and appropriate decisions.

[0491] "Information processing means" refers to a device or program that has the function of analyzing a received electronic message and automatically generating a reply based on its content.

[0492] "Evaluation means" refers to a device or algorithm that has the function of evaluating the priority and urgency of electronic messages based on the analysis results.

[0493] A "notification means" is a device or program that has the function of generating individual notifications for electronic messages that are evaluated as having high priority or urgency, and sending them to the user's device.

[0494] A "schedule adjustment means" is a device or program that has the function of acquiring the user's schedule information from external schedule management application software and automatically generating a reply proposal that includes candidate dates and times.

[0495] "User interface means" refers to a device or program that provides a function to send multiple generated response options to a user device, allowing the user to review and select one.

[0496] A "financial evaluation tool" is a device or algorithm that has the function of evaluating highly important financial data based on the analysis of received information and automatically generating payment proposals based on that important financial data.

[0497] The system implementing this invention consists of a program that works in conjunction with several stages of information processing equipment. The overall process is centered around an information processing server and is carried out through interaction with terminal devices and users.

[0498] First, the server uses information processing tools to analyze received electronic messages with a natural language processing engine. Possible natural language processing engines used include common open-source libraries and cloud services (e.g., Google Cloud Natural Language API). The analyzed information is then evaluated to determine its importance and priority. This process utilizes machine learning models to optimize the process based on patterns from past messages.

[0499] Next, the server uses a notification mechanism to send individual push notifications to the user's device for messages deemed to be of high importance. This notification uses a cloud messaging service such as Firebase Cloud Messaging. A scheduling mechanism is also used to retrieve the user's schedule information from other external scheduling applications and automatically generate a reply proposal that includes suggested dates and times for the user to review and select.

[0500] This information is displayed on the device through a user interface, designed to allow users to operate it intuitively. The device-side interface can be adapted to a wide range of devices by using cross-platform development frameworks such as React Native.

[0501] Finally, the financial evaluation tool analyzes the received financial data and generates a weighted payment plan. This process utilizes an AI model trained on the user's past payment history and uses the Stripe API to process payments.

[0502] As a concrete example, imagine a scenario where a user manages their monthly bills using this system on their smartphone. When the user receives a new bill, the server immediately analyzes it and suggests an appropriate payment plan. For example, the prompt might be, "Manage my monthly electricity bills and tell me the best payment plan."

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] The server first analyzes the received electronic message. In this step, information processing tools are used, and the message text is given as input. The server uses a natural language processing engine to analyze the message content, extracting keywords and classifying categories. This allows it to understand the subject and purpose of the message, and the analysis results are output.

[0506] Step 2:

[0507] The server uses an evaluation tool to assess the importance and priority of messages based on the analyzed information. The input is the analysis results, which are the output of step 1. Here, a machine learning model learns from past message data and calculates an importance score. This results in an output that determines which messages should be processed first.

[0508] Step 3:

[0509] The server generates individual notifications for high-priority messages using a notification mechanism and sends them to the terminal. The high-priority messages obtained in step 2 serve as input for this process. The server constructs a notification message and sends it to the user's terminal via a cloud messaging service. This notification triggers an action on the terminal.

[0510] Step 4:

[0511] The terminal displays notification messages received from the server to the user. The input is the notification sent in step 3. The terminal visually represents the notification content through the user interface and displays it in a format that is easy for the user to understand. This allows the user to quickly check the message.

[0512] Step 5:

[0513] The server uses a scheduling mechanism to retrieve user schedule information from an external scheduling application and automatically generates a reply proposal including suggested dates and times. The input requires the user's schedule data and analysis results. The server uses a scheduling API to check free time, calculates suggested dates and times, and presents them as output.

[0514] Step 6:

[0515] The terminal presents the user with suggested replies, including possible dates and times sent from the server, and supports their selection. Here, the output from step 5 serves as input. On the terminal, the suggested replies are presented in a way that the user can freely review and select from, and the selection result is returned via the user interface.

[0516] Step 7:

[0517] The server analyzes the received financial data using financial evaluation tools and automatically generates data importance and payment plan candidates. Financial data is used as input for this process. An AI model is used to generate an appropriate payment plan, and the results are output.

[0518] Step 8:

[0519] The user reviews the plan provided through the device and makes a final selection. Based on instructions from the server, the device displays the plan and prepares to return the user's selection to the server.

[0520] Step 9:

[0521] The server confirms the final response or payment based on the information selected by the user, and completes the process. Here, the selection from the terminal is the input, and the server completes the entire process by performing the necessary actions.

[0522] 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.

[0523] This invention relates to a system that recognizes the emotional state of a sender by utilizing natural language processing means for analyzing the content of an electronic message upon receipt, and an emotion engine. The server analyzes the body, subject, sender information, and emotional expressions within the received message to determine the message's category (e.g., urgent, scheduling, information sharing), priority, and urgency.

[0524] The server utilizes an emotion engine to evaluate the emotional tone extracted from a message and uses that to generate a response. For example, if an email from a business partner expresses dissatisfaction or concern, the server can suggest a respectful response that takes the emotional state into account.

[0525] By using a scheduling integration method, when scheduling adjustments are necessary, the server can retrieve the user's availability from an external scheduling application and automatically generate a response plan including suggested dates and times. Furthermore, the terminal presents these response plans to the user, providing an interface that allows the user to select the most suitable plan and edit it as needed.

[0526] For example, when a user receives an inquiry email from a customer, the server analyzes the email and uses an emotion engine to recognize the customer's emotions. If it determines that the customer is anxious, the server generates a reassuring reply and suggests it to the user. The user can review the suggested reply, select the appropriate one, or further modify it to finalize their response.

[0527] Ultimately, the server uses the sending method to send a reply email to the recipient based on the user's submitted response. Additionally, the analyzed sentiment information and response history are stored in a database via recording devices, which can be used for future reference and analysis.

[0528] With the above configuration, the present invention can improve the efficiency of electronic message management, reduce the burden on users, and support communication that takes emotions into consideration.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] When the server receives a new electronic message, it stores its contents in the mail database and starts the analysis process. The received message is processed by a natural language processing engine, which extracts information from the message body and subject.

[0532] Step 2:

[0533] The server uses natural language processing to determine the category, importance, and urgency of messages. Through analysis, it identifies keywords such as "urgent" and "meeting scheduling," and uses this information to set the message priority.

[0534] Step 3:

[0535] The server uses an emotion engine to analyze the message content and evaluate the sender's emotions. This includes emotional states such as joy, dissatisfaction, and anger. For example, a message with many expressions of gratitude would be recognized as "joyful."

[0536] Step 4:

[0537] The server generates the most appropriate response based on the assessed priority, urgency, and emotion. The tone and content of the response are adjusted to match the perceived emotion. For example, if "dissatisfaction" is detected, a polite response focused on problem resolution will be generated.

[0538] Step 5:

[0539] The server uses scheduling integration to retrieve user schedule information from external scheduling applications as needed. This makes it possible to suggest reply options, including possible dates and times.

[0540] Step 6:

[0541] The terminal displays multiple generated response options to the user, allowing the user to review and select an option through the interface. The user can also edit the response options as needed.

[0542] Step 7:

[0543] After the user selects or modifies a reply, the device sends that information to the server. The server then creates a final reply based on that information and sends it to the recipient via email through the sending method.

[0544] Step 8:

[0545] The server records all analysis and message sentiment, and stores it in a database for future reference. This information can be used to improve and analyze future interactions.

[0546] (Example 2)

[0547] 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."

[0548] In recent years, the surge in digital messaging has demanded that recipients efficiently manage a large volume of messages and respond appropriately. However, manually prioritizing messages and crafting responses tailored to their content is time-consuming and laborious, hindering efficient communication. Furthermore, responding with consideration for emotions is difficult, posing a significant challenge, especially in business communication. Solutions to these problems are needed.

[0549] 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.

[0550] In this invention, the server includes language processing means for analyzing received digital messages and generating reply candidates based on their content; analysis means for evaluating the priority and urgency of digital messages based on the analysis results; and notification means for generating individual information for digital messages evaluated as having high priority or urgency and transmitting it to the user device. This makes it possible to quickly and accurately determine the content of received messages and present appropriate reply suggestions to the user.

[0551] A "digital message" is a document containing information that is sent and received electronically via the internet or other means.

[0552] "Language processing means" refers to technologies that analyze text within digital messages, understand and organize its content, and perform processing to generate reply candidates.

[0553] "Analysis means" refers to a system or technology for evaluating the content of a received message and determining its priority and urgency.

[0554] A "notification means" is a device or system that has the function of notifying user devices of information about messages deemed important or urgent.

[0555] A "management application program" refers to software or services for schedule management and scheduling, which enable integration with digital messaging.

[0556] "Integration means" refers to an interface or technology that enables information exchange with external systems and allows for processing and proposals based on received digital messages.

[0557] "Means of communication" refers to a device or system that has communication capabilities for sending a reply that the user has finally confirmed.

[0558] The invention will now be described in terms of embodiments. This system is composed of a combination of various technologies to efficiently receive, analyze, and generate replies to digital messages.

[0559] The server captures digital messages received via the internet and analyzes the message body, subject, and sender information. During this process, natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding are used to organize the message content into structured data.

[0560] The server then uses an emotion engine to evaluate the emotional tone of the message based on the analyzed data. The emotion engine used is a sentiment analysis tool such as Microsoft Azure Text Analytics, which scores the message as having positive, negative, or neutral sentiment.

[0561] Once sentiment assessment is complete, the server uses a generative AI model to generate the optimal response. Generative AI, such as OpenAI's GPT model, can be used, and prompts can be used to instruct the server to "consider the sentiment of the email and generate an appropriate response."

[0562] It can also integrate with external management application programs, for example, by using Google Calendar or Microsoft Outlook Calendar to retrieve schedule information. Based on this information, the server generates response suggestions that take into account the user's availability.

[0563] The generated multiple response options are presented to the user via the device. The device provides a user interface and is designed to allow the user to intuitively review, select, and modify the response options.

[0564] For example, if a user receives an email from a business partner requesting to schedule a meeting for next week, the server performs sentiment analysis and generates a reply in a friendly tone. It also checks the user's calendar and suggests possible dates and times. An example of a prompt used in this process is, "Generate a tone-conscious reply to schedule a meeting based on the digital message."

[0565] This system enables quick and appropriate responses to digital messages and reduces the administrative burden on users.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] The server receives digital messages via the internet. It takes the message body, subject, and sender information as input and preprocesses them. Specifically, it removes unnecessary spaces and special characters from the text and converts the message into a parseable format. As a result of this conversion process, the preprocessed text data is output.

[0569] Step 2:

[0570] The server analyzes the data by passing the pre-processed messages through natural language processing tools. The input is the output data from step 1, and keywords and phrases from the messages are extracted using natural language processing techniques. This outputs the message category (e.g., urgent, scheduling, information sharing) and priority. NLU tools are used for analysis.

[0571] Step 3:

[0572] The server uses an emotion engine to evaluate the emotional tone of the analyzed message. In this step, it receives the output from step 2 (keywords and phrases) as input and scores the sentiment based on it. As a result, the emotional state of the message (positive, negative, neutral) is output. This process uses a sentiment analysis tool.

[0573] Step 4:

[0574] The server utilizes a generative AI model to generate the optimal reply. Based on the category information and sentiment state obtained in steps 2 and 3, prompts are used to input the information into the AI ​​model. For example, a prompt such as "Consider the sentiment of the email and generate an appropriate reply" might be used. The output of this step is multiple reply options.

[0575] Step 5:

[0576] The server uses a scheduling mechanism to retrieve the user's availability from an external management application program. It then compares the user's schedule information with the response candidates generated in step 4 to create candidates that include suggested dates and times. The output in this step is a response proposal that reflects the schedule.

[0577] Step 6:

[0578] The terminal presents the user with multiple reply options sent from the server via the user interface. The user can review these as input and select or modify the best option. The selected or modified reply becomes the output of this step.

[0579] Step 7:

[0580] Finally, the server generates a reply that the user has confirmed and sends it as an email. The output from step 6 is used as input, and the transmission is performed using the communication protocol. Additionally, the analysis data and reply history are recorded and stored in a database for future reference. The output here consists of sent emails and recorded data.

[0581] (Application Example 2)

[0582] 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."

[0583] In email management, there is a problem in providing prompt and appropriate replies based on the content of received messages. Furthermore, there is a need to improve the quality of communication by providing replies that take the sender's emotions into consideration. This invention aims to solve these conventional problems by analyzing the emotional information contained in emails and generating appropriate reply proposals based on that analysis.

[0584] 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.

[0585] In this invention, the server includes a natural language processing means that analyzes received emails and automatically generates reply proposals based on their content; an evaluation means that evaluates the priority and urgency of electronic messages based on the analysis results; and an emotion recognition means that uses emotional properties extracted from the message to generate reply proposals appropriate to the emotional state. This enables the creation of efficient replies that take emotions into consideration.

[0586] "Email" refers to document data sent and received in digital format via the internet or other networks.

[0587] "Natural language processing means" refers to technologies or systems that enable computers to understand, analyze, and generate responses to human language.

[0588] "Evaluation means" refers to a method or device for determining the priority and urgency of related information based on analyzed data.

[0589] "Notification means" refers to a method or device for informing the user of important or urgent information based on evaluation results.

[0590] "Interface means" refers to a method or device for a user to review and select an appropriate response from multiple generated candidates.

[0591] "Schedule integration means" refers to a function that shares or retrieves a user's schedule with an external system and generates appropriate response suggestions.

[0592] "Emotion recognition means" refers to a function or process for analyzing emotional elements extracted from electronic messages and generating an appropriate response based on this analysis.

[0593] This invention is a system that automatically generates appropriate reply suggestions by using natural language processing technology on a server to analyze emails, identify the content of the message and the sender's emotional state, and then generate appropriate reply suggestions. An emotion engine is used for emotion recognition, evaluating the emotional properties extracted from the message. Based on the evaluated information, the priority and urgency of the email are also determined.

[0594] The server uses, for example, the Python programming language and leverages natural language processing libraries such as spaCy and transformers. This allows it to analyze email content, and the sentiment engine evaluates emotional properties to determine the tone of the message. Furthermore, the evaluation process generates suggested replies based on the sender's emotional state, enabling users to communicate in an emotionally sensitive manner without feeling stressed.

[0595] The terminal presents the user with multiple generated response options and provides an interface to support selection and editing. Through this interface, the user can review the presented response options and modify them as needed. The interface is designed with ease of use in mind, allowing for intuitive operation.

[0596] For example, if a user receives an email from a customer stating, "I am concerned about the recent delay in payment," the server analyzes the email and, using its sentiment engine, determines it to be "dissatisfied." Based on this, the server automatically generates a proposed reply offering an apology and prompt action, and presents it to the user. After the user reviews the proposed reply, they can press the send button, and the reply will be automatically sent.

[0597] By using a generative AI model, the accuracy of message analysis and sentiment evaluation is improved, enabling appropriate responses in a variety of communication scenarios. An example of a prompt is the instruction, "Customer message: I am concerned about the recent payment delay. Please generate a response." This prompt prompts the server to start the process of generating a response that takes sentiment and content into consideration.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The server receives the email data as input and first analyzes the text using natural language processing techniques. For example, it uses the spaCy library to break down the email content into morphemes and extract topics and keywords. This analysis result forms the basis for the next processing step.

[0601] Step 2:

[0602] The server uses an evaluation tool with the analyzed data as input to determine the priority and urgency of the message. In this step, for example, urgency is quantified based on keyword frequency and context, and the results are output as data.

[0603] Step 3:

[0604] The server uses emotion recognition tools to perform sentiment analysis. It evaluates the emotional tone from the input message text and classifies the emotion into categories such as "positive" or "negative" using, for example, a sentiment analysis model like Transformers. The output is a label of the emotional state.

[0605] Step 4:

[0606] The server takes the obtained emotional state and evaluation data as input and automatically generates appropriate response suggestions via a response suggestion generation mechanism. Using a generation AI model, it constructs response sentences that take emotions into consideration. The output is multiple generated response suggestions.

[0607] Step 5:

[0608] The device receives multiple generated response options and presents them to the user through a user interface. This interface provides the user with the ability to review, select, or further edit the response options.

[0609] Step 6:

[0610] The user uses the terminal interface to review the suggested reply and edit it as needed. They then finalize the reply and prepare to send it.

[0611] Step 7:

[0612] The server takes the user's confirmed reply as input and sends the reply email to the recipient via the sending method. Once the sending is complete, the result is recorded in the log.

[0613] Step 8:

[0614] The server stores the analyzed emails, their sentiment status, priority data, and reply history in a data storage unit. The stored data serves as a source of information for future reference and analysis.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention provides a system for efficiently managing received electronic messages and reducing the burden on users. When a server receives an electronic message, it uses natural language processing to analyze its content and determine the message's category and importance. Based on this analysis, individual notifications are generated for messages with high priority or urgency and sent to the user via the terminal.

[0633] In addition, the server automatically evaluates priority using evaluation tools and uses this information to generate appropriate response suggestions. For example, when a meeting invitation email is received, the server can use scheduling integration tools to retrieve the user's availability from an external scheduling application and automatically generate a response suggestion including possible dates and times.

[0634] The terminal presents the user with multiple response options sent from the server and supports confirmation and selection via an interface. In this way, the user can quickly provide an appropriate response.

[0635] For example, if a user receives an urgent email from a business partner, the server immediately analyzes its contents and assesses its urgency. If it contains important information, a push notification is sent to the device, allowing the user to access the email immediately. Furthermore, if scheduling adjustments are necessary, suggested dates and times reflecting the user's availability are displayed, enabling them to respond immediately.

[0636] Ultimately, the server confirms the reply selected by the user and sends the reply email to the recipient using the appropriate sending method. This entire system flow allows users to streamline their daily email processing and make better use of their time. This system supports businesses in managing large volumes of email and promotes fast and accurate communication.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] When the server receives a new electronic message, it first stores the message in its mail database. Then, it adds it to a queue as a message to be analyzed.

[0640] Step 2:

[0641] The server uses a natural language processing engine to analyze the body, subject, and sender information of received messages. This allows it to identify the message category (e.g., urgent, scheduling, information sharing) and extract the necessary information.

[0642] Step 3:

[0643] The server executes evaluation measures based on the analysis results to determine the priority and urgency of the message. This is done based on the detection of specific keywords and patterns, as well as the message sender.

[0644] Step 4:

[0645] Based on the evaluation results, the server sends individual notifications to user terminals for messages deemed to be of high urgency. This allows users to quickly become aware of important messages.

[0646] Step 5:

[0647] The server uses scheduling integration to retrieve user calendar information from external scheduling applications as needed. This generates candidate dates and times, which are then included in the reply suggestions.

[0648] Step 6:

[0649] Based on the analyzed information, the server generates several appropriate response options and sends them to the user's terminal. These response options may include a suggested schedule, if necessary.

[0650] Step 7:

[0651] The terminal displays the user with suggested replies received from the server and provides an interface for selection and editing. The user uses this interface to select the best reply and modify its content as needed.

[0652] Step 8:

[0653] Once the user confirms their reply, the device sends that information to the server, which then uses its sending method to send the final reply email to the recipient. The server then logs this reply.

[0654] (Example 1)

[0655] 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".

[0656] In today's information-saturated world, particularly through email, the sheer volume of incoming information presents users with the risk of overlooking important details and the problem of spending excessive time processing information. Furthermore, manual reply processes are inefficient, and this inefficiency is particularly evident in business settings where quick responses are crucial. The challenge lies in solving these problems and supporting appropriate responses while streamlining information processing.

[0657] 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.

[0658] In this invention, the server includes information processing means for analyzing received information and automatically generating a response based on its content; evaluation means for evaluating the priority and urgency of the information based on the analysis results; and notification means for generating individual notifications for information evaluated as high priority or high urgency and sending them to the terminal. This enables a rapid response according to the priority of the information, allowing the user to effectively manage important information and make quick decisions.

[0659] "Information processing means" refers to technology that has the function of analyzing received information and automatically generating appropriate response proposals based on its content.

[0660] An "evaluation tool" is a technology that has the function of objectively evaluating the importance and urgency of information based on the content of the analyzed information.

[0661] A "notification method" is a technology that generates individual warnings or notifications for information of high priority or urgency, and quickly transmits that information to the user's terminal.

[0662] "Integration means" refers to technology that has the function of obtaining schedule information from an external management system and generating appropriate response proposals based on that information.

[0663] "Connection means" refers to a technology that presents multiple generated response options to the user's device, allowing the user to make the appropriate selection from among them.

[0664] "Communication method" refers to the technology that finalizes the response selected or modified by the user and sends the information as the final response.

[0665] "Recording means" refers to technology that has the function of storing analyzed and evaluated information in a database or similar system for future reference and analysis.

[0666] This invention relates to a system that efficiently processes received information and supports user information management. This system mainly consists of three components: a server, a terminal, and a user.

[0667] The server is the core of information reception and analyzes received messages using information processing tools. This analysis utilizes natural language processing techniques, such as generative models widely used in AI. The server categorizes and evaluates importance, and based on the results, notifies terminals of high-priority information. The evaluation tool automatically determines the priority of information, and notifications are sent immediately as needed.

[0668] A terminal is a device that presents information sent from a server to the user. Using a connection method, multiple response options are presented to the user. The user can select the most appropriate response through the terminal's interface. This process speeds up daily information processing.

[0669] The user is the one who uses their device to check information and select a response. The response selected by the user is sent to the recipient via the server through a communication method. For example, if a user receives an urgent meeting invitation, the server analyzes the content and suggests possible dates and times in conjunction with the user's schedule. This allows the user to respond quickly and manage their schedule efficiently.

[0670] As a concrete example, consider inputting the following prompt into the AI ​​model.

[0671] "You have received a new meeting invitation email. The title is 'Project Progress Meeting'. Please suggest possible dates and times that take into account the user's availability."

[0672] Thus, this system enables efficient and effective information processing in complex information environments, providing support to enable users to take quick and appropriate actions.

[0673] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0674] Step 1:

[0675] The server receives an electronic message. The received message is the input data. The server initiates natural language processing and uses a generative AI model to analyze the message content, determining its category and importance. The output is the message's category information and importance rating.

[0676] Step 2:

[0677] The server uses evaluation tools based on the analysis results to assess the priority and urgency of each message. The category information and importance ratings obtained from step 1 are used as input. Based on this assessment, the server determines which messages have high priority or urgency. The output is the priority and urgency evaluation data for each message.

[0678] Step 3:

[0679] The server generates individual notifications using notification methods for high-priority or high-urgency messages determined in step 2. This notification data is sent to the user's terminal. The input is priority and urgency evaluation data, and the output is notification information. Notifications sent to the terminal are immediately presented to the user.

[0680] Step 4:

[0681] The terminal presents the user with multiple response options received from the server. This includes user schedule information obtained by the server using a communication mechanism. Input consists of response option data from the server and user schedule information. The terminal uses this information and a connection mechanism to present options to the user. Output is the response option selected by the user.

[0682] Step 5:

[0683] The user selects the most suitable response from the options presented on the terminal. This selection is made by clicking on a specific response. The selected response is sent back to the server via a communication method. The input is the response selected by the user, and the output is the selection data sent to the server.

[0684] Step 6:

[0685] The server generates a final reply based on the user's selected reply. This final reply is configured to convey information to the sender. The server transmits it via a transmission method. The input is the selected reply data, and the output is the final reply message.

[0686] (Application Example 1)

[0687] 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".

[0688] In today's world, the sheer volume of electronic messages is increasing, requiring users to expend considerable effort managing them. Similarly, managing financial data has become increasingly complex, demanding efficient assessment of importance and appropriate action. However, traditional systems have struggled to integrate and manage this information effectively, enabling quick and appropriate responses.

[0689] 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.

[0690] In this invention, the server includes information processing means for analyzing received messages, evaluation means for assessing importance, notification means for generating and sending individual notifications, schedule adjustment means for adjusting schedules, and financial evaluation means for evaluating financial data. This enables efficient management of electronic messages and financial data, allowing users to make quick and appropriate decisions.

[0691] "Information processing means" refers to a device or program that has the function of analyzing a received electronic message and automatically generating a reply based on its content.

[0692] "Evaluation means" refers to a device or algorithm that has the function of evaluating the priority and urgency of electronic messages based on the analysis results.

[0693] A "notification means" is a device or program that has the function of generating individual notifications for electronic messages that are evaluated as having high priority or urgency, and sending them to the user's device.

[0694] A "schedule adjustment means" is a device or program that has the function of acquiring the user's schedule information from external schedule management application software and automatically generating a reply proposal that includes candidate dates and times.

[0695] "User interface means" refers to a device or program that provides a function to send multiple generated response options to a user device, allowing the user to review and select one.

[0696] A "financial evaluation tool" is a device or algorithm that has the function of evaluating highly important financial data based on the analysis of received information and automatically generating payment proposals based on that important financial data.

[0697] The system implementing this invention consists of a program that works in conjunction with several stages of information processing equipment. The overall process is centered around an information processing server and is carried out through interaction with terminal devices and users.

[0698] First, the server uses information processing tools to analyze received electronic messages with a natural language processing engine. Possible natural language processing engines used include common open-source libraries and cloud services (e.g., Google Cloud Natural Language API). The analyzed information is then evaluated to determine its importance and priority. This process utilizes machine learning models to optimize the process based on patterns from past messages.

[0699] Next, the server uses a notification mechanism to send individual push notifications to the user's device for messages deemed to be of high importance. This notification uses a cloud messaging service such as Firebase Cloud Messaging. A scheduling mechanism is also used to retrieve the user's schedule information from other external scheduling applications and automatically generate a reply proposal that includes suggested dates and times for the user to review and select.

[0700] This information is displayed on the device through a user interface, designed to allow users to operate it intuitively. The device-side interface can be adapted to a wide range of devices by using cross-platform development frameworks such as React Native.

[0701] Finally, the financial evaluation tool analyzes the received financial data and generates a weighted payment plan. This process utilizes an AI model trained on the user's past payment history and uses the Stripe API to process payments.

[0702] As a concrete example, imagine a scenario where a user manages their monthly bills using this system on their smartphone. When the user receives a new bill, the server immediately analyzes it and suggests an appropriate payment plan. For example, the prompt might be, "Manage my monthly electricity bills and tell me the best payment plan."

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] The server first analyzes the received electronic message. In this step, information processing tools are used, and the message text is given as input. The server uses a natural language processing engine to analyze the message content, extracting keywords and classifying categories. This allows it to understand the subject and purpose of the message, and the analysis results are output.

[0706] Step 2:

[0707] The server uses an evaluation tool to assess the importance and priority of messages based on the analyzed information. The input is the analysis results, which are the output of step 1. Here, a machine learning model learns from past message data and calculates an importance score. This results in an output that determines which messages should be processed first.

[0708] Step 3:

[0709] The server generates individual notifications for high-priority messages using a notification mechanism and sends them to the terminal. The high-priority messages obtained in step 2 serve as input for this process. The server constructs a notification message and sends it to the user's terminal via a cloud messaging service. This notification triggers an action on the terminal.

[0710] Step 4:

[0711] The terminal displays notification messages received from the server to the user. The input is the notification sent in step 3. The terminal visually represents the notification content through the user interface and displays it in a format that is easy for the user to understand. This allows the user to quickly check the message.

[0712] Step 5:

[0713] The server uses a scheduling mechanism to retrieve user schedule information from an external scheduling application and automatically generates a reply proposal including suggested dates and times. The input requires the user's schedule data and analysis results. The server uses a scheduling API to check free time, calculates suggested dates and times, and presents them as output.

[0714] Step 6:

[0715] The terminal presents the user with suggested replies, including possible dates and times sent from the server, and supports their selection. Here, the output from step 5 serves as input. On the terminal, the suggested replies are presented in a way that the user can freely review and select from, and the selection result is returned via the user interface.

[0716] Step 7:

[0717] The server analyzes the received financial data using financial evaluation tools and automatically generates data importance and payment plan candidates. Financial data is used as input for this process. An AI model is used to generate an appropriate payment plan, and the results are output.

[0718] Step 8:

[0719] The user reviews the plan provided through the device and makes a final selection. Based on instructions from the server, the device displays the plan and prepares to return the user's selection to the server.

[0720] Step 9:

[0721] The server confirms the final response or payment based on the information selected by the user, and completes the process. Here, the selection from the terminal is the input, and the server completes the entire process by performing the necessary actions.

[0722] 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.

[0723] This invention relates to a system that recognizes the emotional state of a sender by utilizing natural language processing means for analyzing the content of an electronic message upon receipt, and an emotion engine. The server analyzes the body, subject, sender information, and emotional expressions within the received message to determine the message's category (e.g., urgent, scheduling, information sharing), priority, and urgency.

[0724] The server utilizes an emotion engine to evaluate the emotional tone extracted from a message and uses that to generate a response. For example, if an email from a business partner expresses dissatisfaction or concern, the server can suggest a respectful response that takes the emotional state into account.

[0725] By using a scheduling integration method, when scheduling adjustments are necessary, the server can retrieve the user's availability from an external scheduling application and automatically generate a response plan including suggested dates and times. Furthermore, the terminal presents these response plans to the user, providing an interface that allows the user to select the most suitable plan and edit it as needed.

[0726] For example, when a user receives an inquiry email from a customer, the server analyzes the email and uses an emotion engine to recognize the customer's emotions. If it determines that the customer is anxious, the server generates a reassuring reply and suggests it to the user. The user can review the suggested reply, select the appropriate one, or further modify it to finalize their response.

[0727] Ultimately, the server uses the sending method to send a reply email to the recipient based on the user's submitted response. Additionally, the analyzed sentiment information and response history are stored in a database via recording devices, which can be used for future reference and analysis.

[0728] With the above configuration, the present invention can improve the efficiency of electronic message management, reduce the burden on users, and support communication that takes emotions into consideration.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] When the server receives a new electronic message, it stores its contents in the mail database and starts the analysis process. The received message is processed by a natural language processing engine, which extracts information from the message body and subject.

[0732] Step 2:

[0733] The server uses natural language processing to determine the category, importance, and urgency of messages. Through analysis, it identifies keywords such as "urgent" and "meeting scheduling," and uses this information to set the message priority.

[0734] Step 3:

[0735] The server uses an emotion engine to analyze the message content and evaluate the sender's emotions. This includes emotional states such as joy, dissatisfaction, and anger. For example, a message with many expressions of gratitude would be recognized as "joyful."

[0736] Step 4:

[0737] The server generates the most appropriate response based on the assessed priority, urgency, and emotion. The tone and content of the response are adjusted to match the perceived emotion. For example, if "dissatisfaction" is detected, a polite response focused on problem resolution will be generated.

[0738] Step 5:

[0739] The server uses scheduling integration to retrieve user schedule information from external scheduling applications as needed. This makes it possible to suggest reply options, including possible dates and times.

[0740] Step 6:

[0741] The terminal displays multiple generated response options to the user, allowing the user to review and select an option through the interface. The user can also edit the response options as needed.

[0742] Step 7:

[0743] After the user selects or modifies a reply, the device sends that information to the server. The server then creates a final reply based on that information and sends it to the recipient via email through the sending method.

[0744] Step 8:

[0745] The server records all analysis and message sentiment, and stores it in a database for future reference. This information can be used to improve and analyze future interactions.

[0746] (Example 2)

[0747] 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".

[0748] In recent years, the surge in digital messaging has demanded that recipients efficiently manage a large volume of messages and respond appropriately. However, manually prioritizing messages and crafting responses tailored to their content is time-consuming and laborious, hindering efficient communication. Furthermore, responding with consideration for emotions is difficult, posing a significant challenge, especially in business communication. Solutions to these problems are needed.

[0749] 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.

[0750] In this invention, the server includes language processing means for analyzing received digital messages and generating reply candidates based on their content; analysis means for evaluating the priority and urgency of digital messages based on the analysis results; and notification means for generating individual information for digital messages evaluated as having high priority or urgency and transmitting it to the user device. This makes it possible to quickly and accurately determine the content of received messages and present appropriate reply suggestions to the user.

[0751] A "digital message" is a document containing information that is sent and received electronically via the internet or other means.

[0752] "Language processing means" refers to technologies that analyze text within digital messages, understand and organize its content, and perform processing to generate reply candidates.

[0753] "Analysis means" refers to a system or technology for evaluating the content of a received message and determining its priority and urgency.

[0754] A "notification means" is a device or system that has the function of notifying user devices of information about messages deemed important or urgent.

[0755] A "management application program" refers to software or services for schedule management and scheduling, which enable integration with digital messaging.

[0756] "Integration means" refers to an interface or technology that enables information exchange with external systems and allows for processing and proposals based on received digital messages.

[0757] "Means of communication" refers to a device or system that has communication capabilities for sending a reply that the user has finally confirmed.

[0758] The invention will now be described in terms of embodiments. This system is composed of a combination of various technologies to efficiently receive, analyze, and generate replies to digital messages.

[0759] The server captures digital messages received via the internet and analyzes the message body, subject, and sender information. During this process, natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding are used to organize the message content into structured data.

[0760] The server then uses an emotion engine to evaluate the emotional tone of the message based on the analyzed data. The emotion engine used is a sentiment analysis tool such as Microsoft Azure Text Analytics, which scores the message as having positive, negative, or neutral sentiment.

[0761] Once sentiment assessment is complete, the server uses a generative AI model to generate the optimal response. Generative AI, such as OpenAI's GPT model, can be used, and prompts can be used to instruct the server to "consider the sentiment of the email and generate an appropriate response."

[0762] It can also integrate with external management application programs, for example, by using Google Calendar or Microsoft Outlook Calendar to retrieve schedule information. Based on this information, the server generates response suggestions that take into account the user's availability.

[0763] The generated multiple response options are presented to the user via the device. The device provides a user interface and is designed to allow the user to intuitively review, select, and modify the response options.

[0764] For example, if a user receives an email from a business partner requesting to schedule a meeting for next week, the server performs sentiment analysis and generates a reply in a friendly tone. It also checks the user's calendar and suggests possible dates and times. An example of a prompt used in this process is, "Generate a tone-conscious reply to schedule a meeting based on the digital message."

[0765] This system enables quick and appropriate responses to digital messages and reduces the administrative burden on users.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] The server receives digital messages via the internet. It takes the message body, subject, and sender information as input and preprocesses them. Specifically, it removes unnecessary spaces and special characters from the text and converts the message into a parseable format. As a result of this conversion process, the preprocessed text data is output.

[0769] Step 2:

[0770] The server analyzes the data by passing the pre-processed messages through natural language processing tools. The input is the output data from step 1, and keywords and phrases from the messages are extracted using natural language processing techniques. This outputs the message category (e.g., urgent, scheduling, information sharing) and priority. NLU tools are used for analysis.

[0771] Step 3:

[0772] The server uses an emotion engine to evaluate the emotional tone of the analyzed message. In this step, it receives the output from step 2 (keywords and phrases) as input and scores the sentiment based on it. As a result, the emotional state of the message (positive, negative, neutral) is output. This process uses a sentiment analysis tool.

[0773] Step 4:

[0774] The server utilizes a generative AI model to generate the optimal reply. Based on the category information and sentiment state obtained in steps 2 and 3, prompts are used to input the information into the AI ​​model. For example, a prompt such as "Consider the sentiment of the email and generate an appropriate reply" might be used. The output of this step is multiple reply options.

[0775] Step 5:

[0776] The server uses a scheduling mechanism to retrieve the user's availability from an external management application program. It then compares the user's schedule information with the response candidates generated in step 4 to create candidates that include suggested dates and times. The output in this step is a response proposal that reflects the schedule.

[0777] Step 6:

[0778] The terminal presents the user with multiple reply options sent from the server via the user interface. The user can review these as input and select or modify the best option. The selected or modified reply becomes the output of this step.

[0779] Step 7:

[0780] Finally, the server generates a reply that the user has confirmed and sends it as an email. The output from step 6 is used as input, and the transmission is performed using the communication protocol. Additionally, the analysis data and reply history are recorded and stored in a database for future reference. The output here consists of sent emails and recorded data.

[0781] (Application Example 2)

[0782] 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".

[0783] In email management, there is a problem in providing prompt and appropriate replies based on the content of received messages. Furthermore, there is a need to improve the quality of communication by providing replies that take the sender's emotions into consideration. This invention aims to solve these conventional problems by analyzing the emotional information contained in emails and generating appropriate reply proposals based on that analysis.

[0784] 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.

[0785] In this invention, the server includes a natural language processing means that analyzes received emails and automatically generates reply proposals based on their content; an evaluation means that evaluates the priority and urgency of electronic messages based on the analysis results; and an emotion recognition means that uses emotional properties extracted from the message to generate reply proposals appropriate to the emotional state. This enables the creation of efficient replies that take emotions into consideration.

[0786] "Email" refers to document data sent and received in digital format via the internet or other networks.

[0787] "Natural language processing means" refers to technologies or systems that enable computers to understand, analyze, and generate responses to human language.

[0788] "Evaluation means" refers to a method or device for determining the priority and urgency of related information based on analyzed data.

[0789] "Notification means" refers to a method or device for informing the user of important or urgent information based on evaluation results.

[0790] "Interface means" refers to a method or device for a user to review and select an appropriate response from multiple generated candidates.

[0791] "Schedule integration means" refers to a function that shares or retrieves a user's schedule with an external system and generates appropriate response suggestions.

[0792] "Emotion recognition means" refers to a function or process for analyzing emotional elements extracted from electronic messages and generating an appropriate response based on this analysis.

[0793] This invention is a system that automatically generates appropriate reply suggestions by using natural language processing technology on a server to analyze emails, identify the content of the message and the sender's emotional state, and then generate appropriate reply suggestions. An emotion engine is used for emotion recognition, evaluating the emotional properties extracted from the message. Based on the evaluated information, the priority and urgency of the email are also determined.

[0794] The server uses, for example, the Python programming language and leverages natural language processing libraries such as spaCy and transformers. This allows it to analyze email content, and the sentiment engine evaluates emotional properties to determine the tone of the message. Furthermore, the evaluation process generates suggested replies based on the sender's emotional state, enabling users to communicate in an emotionally sensitive manner without feeling stressed.

[0795] The terminal presents the user with multiple generated response options and provides an interface to support selection and editing. Through this interface, the user can review the presented response options and modify them as needed. The interface is designed with ease of use in mind, allowing for intuitive operation.

[0796] For example, if a user receives an email from a customer stating, "I am concerned about the recent delay in payment," the server analyzes the email and, using its sentiment engine, determines it to be "dissatisfied." Based on this, the server automatically generates a proposed reply offering an apology and prompt action, and presents it to the user. After the user reviews the proposed reply, they can press the send button, and the reply will be automatically sent.

[0797] By using a generative AI model, the accuracy of message analysis and sentiment evaluation is improved, enabling appropriate responses in a variety of communication scenarios. An example of a prompt is the instruction, "Customer message: I am concerned about the recent payment delay. Please generate a response." This prompt prompts the server to start the process of generating a response that takes sentiment and content into consideration.

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] The server receives the email data as input and first analyzes the text using natural language processing techniques. For example, it uses the spaCy library to break down the email content into morphemes and extract topics and keywords. This analysis result forms the basis for the next processing step.

[0801] Step 2:

[0802] The server uses an evaluation tool with the analyzed data as input to determine the priority and urgency of the message. In this step, for example, urgency is quantified based on keyword frequency and context, and the results are output as data.

[0803] Step 3:

[0804] The server uses emotion recognition tools to perform sentiment analysis. It evaluates the emotional tone from the input message text and classifies the emotion into categories such as "positive" or "negative" using, for example, a sentiment analysis model like Transformers. The output is a label of the emotional state.

[0805] Step 4:

[0806] The server takes the obtained emotional state and evaluation data as input and automatically generates appropriate response suggestions via a response suggestion generation mechanism. Using a generation AI model, it constructs response sentences that take emotions into consideration. The output is multiple generated response suggestions.

[0807] Step 5:

[0808] The device receives multiple generated response options and presents them to the user through a user interface. This interface provides the user with the ability to review, select, or further edit the response options.

[0809] Step 6:

[0810] The user uses the terminal interface to review the suggested reply and edit it as needed. They then finalize the reply and prepare to send it.

[0811] Step 7:

[0812] The server takes the user's confirmed reply as input and sends the reply email to the recipient via the sending method. Once the sending is complete, the result is recorded in the log.

[0813] Step 8:

[0814] The server stores the analyzed emails, their sentiment status, priority data, and reply history in a data storage unit. The stored data serves as a source of information for future reference and analysis.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] A natural language processing means that analyzes received electronic messages and automatically generates reply proposals based on their content,

[0839] An evaluation method for evaluating the priority and urgency of electronic messages based on the analysis results,

[0840] A notification means that generates individual notifications and sends them to the user's terminal for electronic messages that are assessed as high priority or urgent,

[0841] A scheduling integration method that retrieves user schedule information from an external scheduling application and automatically generates a reply proposal including suggested dates and times,

[0842] An interface means that sends multiple generated response options to the user's terminal, allowing the user to review and select one;

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising a sending means for generating and sending a final reply based on a reply proposal selected or modified by the user.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising recording means for recording analyzed and evaluated electronic messages and storing them in a database for future reference and analysis.

[0848] "Example 1"

[0849] (Claim 1)

[0850] An information processing means that analyzes received information and automatically generates a response based on its content,

[0851] An evaluation method for assessing the priority and urgency of information based on the analysis results,

[0852] A notification system that generates and sends individual notifications to terminals for information assessed as high priority or urgency,

[0853] A collaborative mechanism that retrieves schedule information from an external management system and automatically generates a reply proposal including suggested dates and times,

[0854] A connection means that sends multiple generated response options to the terminal, allowing for confirmation and selection,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising a means for generating and sending a final reply based on selected or modified reply proposals.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising recording means for recording analyzed and evaluated information and storing it in storage means for future reference and analysis.

[0860] "Application Example 1"

[0861] (Claim 1)

[0862] An information processing means that analyzes received electronic messages and automatically generates a reply based on their content,

[0863] An evaluation method for evaluating the priority and urgency of electronic messages based on the analysis results,

[0864] A notification means that generates individual notifications for electronic messages assessed as high priority or urgency and transmits them to the user's device.

[0865] A scheduling adjustment method that obtains user schedule information from external scheduling application software and automatically generates a reply proposal including candidate dates and times,

[0866] A user interface means that transmits multiple generated response options to the user device, allowing the user to review and select one;

[0867] A financial evaluation tool that evaluates highly important financial data based on the analysis of received information and automatically generates proposed payment options based on that important financial data,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, further comprising a transmission means for generating and transmitting a final reply or payment based on a reply proposal and payment proposal selected or modified by the user.

[0871] (Claim 3)

[0872] The system according to claim 1, further comprising recording means for recording analyzed and evaluated electronic messages and financial data and storing them in a data management device for future reference and analysis.

[0873] "Example 2 of combining an emotion engine"

[0874] (Claim 1)

[0875] A language processing means that analyzes a received digital message and generates reply candidates based on its content,

[0876] An analytical means for evaluating the priority and urgency of digital messages based on the analysis results,

[0877] A notification means that generates individual information for digital messages that are assessed as having high priority or urgency and transmits it to the user device,

[0878] A means of collaboration that obtains user schedule information from an external management application program and generates reply candidates including suggested dates and times,

[0879] A presentation means that sends multiple generated reply candidates to the user device and provides a means for the user to confirm and select one,

[0880] A means for analyzing the emotional state of a received message using an analysis device with an emotion evaluation function, and for creating emotionally sensitive reply candidates based on the received message,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, further comprising a means for generating and sending a final reply based on reply candidates selected or modified by the user.

[0884] (Claim 3)

[0885] The system according to claim 1, further comprising recording means for storing analyzed and evaluated digital messages and saving them to multiple storage devices for future reference and analysis.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] A natural language processing tool that analyzes received emails and automatically generates reply proposals based on their content,

[0889] An evaluation method for evaluating the priority and urgency of electronic messages based on the analysis results,

[0890] A notification means for generating individual notifications and transmitting them to an information processing device for electronic messages that have been assessed as having high priority or urgency,

[0891] A scheduling integration method that obtains user schedule information from an external scheduling management program and automatically generates a reply proposal including suggested dates and times,

[0892] An emotion recognition means that uses emotional properties extracted from a message to generate response suggestions appropriate to the emotional state,

[0893] An interface means that sends multiple generated response options to the user's terminal, allowing the user to review and select one;

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising a transmission means for generating and sending a final reply based on a reply draft selected or modified by the user.

[0897] (Claim 3)

[0898] The system according to claim 1, further comprising recording means for recording analyzed and evaluated electronic messages and storing them in a data storage unit for future reference and analysis. [Explanation of Symbols]

[0899] 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 natural language processing means that analyzes received electronic messages and automatically generates reply proposals based on their content, An evaluation method for evaluating the priority and urgency of electronic messages based on the analysis results, A notification means that generates individual notifications and sends them to the user's terminal for electronic messages that are assessed as high priority or urgent, A scheduling integration method that retrieves user schedule information from an external scheduling application and automatically generates a reply proposal including suggested dates and times, An interface means that sends multiple generated response options to the user's terminal, allowing the user to review and select one; A system that includes this.

2. The system according to claim 1, further comprising a transmission means for generating and sending a final reply based on a reply proposal selected or modified by the user.

3. The system according to claim 1, further comprising recording means for recording analyzed and evaluated electronic messages and storing them in a database for future reference and analysis.

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