system

Abstract

We provide the system. [Solution] Means for analyzing messages, A means for automatically generating a response based on the analyzed content, A means for presenting the generated response to a terminal device, A method for presenting responses along with suggested dates and times in conjunction with the scheduling function, A means of analyzing public notices and assessing their urgency, Means of providing special warnings according to the urgency, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The reply work associated with receiving an email requires a great deal of time and effort from the user. Such work is often not performed efficiently, especially when scheduling or dealing with urgent matters. As a result, there is a problem that resources for concentrating on important tasks are reduced. The object of the present invention is to solve the problem of assisting in the efficient creation of replies to such emails.

Means for Solving the Problems

[0005] This invention provides a system that analyzes received emails, automatically generates a proposed reply based on its content, and presents it to the user's terminal. The system, in conjunction with a scheduling application, includes a function to generate a proposed reply that includes appropriate suggested dates and times in the case of emails related to scheduling. Furthermore, it can learn the user's past reply style and generate more precise reply proposals based on that data. In addition, by having a function to set a special flag based on the urgency of the email and notify the user accordingly, the user can immediately recognize and respond to important emails, significantly reducing the effort required to reply to emails.

[0006] "Incoming email" refers to email messages received by a user's system or account via a communication network.

[0007] "Means of analysis" refers to the process or technique for understanding the content of a received email and extracting its meaning.

[0008] "Means for automatically generating reply proposals" refers to algorithms or software that automatically create appropriate reply content based on the results of email analysis.

[0009] A "user terminal" is a computing device that displays generated information and can be operated by the user.

[0010] A "scheduling application" is software used to manage dates and times and record appointments.

[0011] "Suggested dates and times" refer to the multiple possible dates and times suggested when scheduling an appointment.

[0012] "User's past reply style" refers to the trends in the format and expression of emails that each user has sent in the past.

[0013] "Urgency" is a measure used to determine whether the content of an email requires a prompt response. [Brief explanation of the drawing]

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

Best Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system for streamlining email reply processes, specifically using AI technology to analyze the content of received emails and assist users in responding quickly. The system functions as follows:

[0036] First, when the server receives an email, it passes it to an AI agent for analysis. In this analysis, the AI ​​agent uses natural language processing to grasp the subject and important information of the email and assess its urgency.

[0037] Next, the AI ​​agent automatically generates a response based on the analysis results. In particular, for emails regarding scheduling, the AI ​​integrates with a scheduling application to retrieve available times from the user's calendar and creates a response that includes multiple possible dates and times. At this time, the AI ​​agent refers to the user's past response history and style and strives to provide a response at a level of writing that suits the user.

[0038] The generated response is presented to the user by the device. The user can review it and edit the content as needed. Furthermore, if the email is of high urgency, the AI ​​agent will set a special flag, and the device will provide the user with a notification prompting immediate action.

[0039] This system will allow users to save a significant amount of time and focus on more important tasks. For example, consider a business person receiving numerous meeting scheduling requests via email. This system will streamline scheduling and reduce the effort required to respond to each email individually. This overall process is expected to significantly improve the efficiency of email management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server receives a new email. Once the email is received, the server sends it to the AI ​​agent. The AI ​​agent prepares to analyze the contents of the received email.

[0043] Step 2:

[0044] The AI ​​agent analyzes incoming emails using natural language processing techniques to identify the subject, important information, and urgency of the email. Based on the analysis, it categorizes the email (e.g., inquiry, scheduling, emergency response, etc.).

[0045] Step 3:

[0046] The AI ​​agent automatically generates response suggestions based on the email category. Specifically for scheduling emails, it integrates with scheduling applications, accesses the user's calendar to find available times, and creates response suggestions including multiple possible dates and times. This process takes into account the user's past response history and style.

[0047] Step 4:

[0048] The AI ​​agent generates and sends suggested replies to the device. The device then displays a list of these replies to the user. For urgent emails, a special flag is set, and the device notifies the user that immediate action is required.

[0049] Step 5:

[0050] The user reviews the suggested reply and edits it as needed. After editing, the user confirms their chosen reply and completes the preparation for sending.

[0051] Step 6:

[0052] The device sends the user's confirmed reply to the server. The server then forwards this reply to the sender via email. Furthermore, the server records the content of this transmission and stores it as training data for the AI, which will be used to improve the accuracy of future analyses.

[0053] (Example 1)

[0054] 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."

[0055] In recent years, with the increase in electronic communication, responding quickly and appropriately to individual electronic messages has become a crucial issue for users. However, manual replies are time-consuming and labor-intensive, placing a significant burden on busy users. Furthermore, it is difficult to immediately prioritize emails and respond appropriately. To address this challenge, there is a need for a system that efficiently and automatically generates response proposals, allowing users to quickly review and respond to them.

[0056] 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.

[0057] In this invention, the server includes means for analyzing received information, means for automatically generating a response proposal based on the analyzed information, means for presenting the generated response proposal to a user device, and means for evaluating the importance and urgency of the information using natural language processing technology. This enables an automated response system that can quickly analyze electronic messages and provide appropriate responses without overloading the user.

[0058] "Received information" refers to data received by a device through a communication network, and specifically includes electronic messages and notifications.

[0059] "Means of analysis" refers to technical components that process received information, understand its content, and identify keywords and themes.

[0060] "Draft response" refers to the proposed response content that is automatically generated based on the analysis results.

[0061] "Means of automatic generation" refers to a process or structure that mechanically creates response proposals using a predetermined algorithm.

[0062] A "user device" refers to a terminal or device that a user directly uses and on which a message is displayed.

[0063] "Means of presentation" refers to a mechanism that allows the user to visually or audibly confirm the generated response proposals.

[0064] "Timetable application software" refers to programs used for schedule management and scheduling, and specifically includes calendar applications.

[0065] "Natural language processing technology" refers to the technology used to process and analyze human language and convert it into a form that machines can understand.

[0066] "Means for evaluating importance and urgency" refers to a function that evaluates information based on specific criteria in order to determine its value and urgency.

[0067] This invention is a system aimed at improving the efficiency of electronic communication, in which the server, terminal, and user elements work together. Specifically, the server analyzes received information using natural language processing technology and generates a response based on the results.

[0068] The server uses common natural language processing libraries to leverage natural language processing techniques in information analysis. This technique allows the server to identify the subject and importance of electronic messages and assess their urgency as needed.

[0069] Next, an AI model is used to automatically generate response suggestions based on the analysis results. Here, the user's past response formats and styles are considered to generate appropriate sentences. A machine learning algorithm is incorporated for this purpose.

[0070] The generated draft response is then presented to the user by the terminal. The terminal provides an interface that allows the user to review the response and edit it as needed. This makes it easy for the user to review the draft response and finalize the message to be sent.

[0071] Furthermore, the server marks information it deems highly urgent during the analysis phase. The terminal then uses this information to send a notification prompting the user to take immediate action. For example, if an invitation email for a very important meeting arrives, the terminal uses its instant notification function to display an alarm to the user.

[0072] As a concrete example, consider a situation where a user receives numerous meeting requests. By using this system, the user can automatically include available time slots in their reply proposals through a scheduling application, reducing the effort required for manual responses.

[0073] An example of a prompt for a generative AI model is, "Analyze this new email and generate a suggested reply." This prompt allows the server to process messages efficiently and provide users with quick and accurate responses.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server receives electronic messages. The input is an unprocessed electronic message that has arrived via the network, and the output is text data in a parseable format. At this stage, the server accesses a database, records the received message, and prepares the basic data for use in subsequent parsing processes.

[0077] Step 2:

[0078] The server analyzes text data in a parsable format using natural language processing techniques. The input is the text data obtained in the previous step, and the output is the analysis results, including the message's subject, keywords, importance, and urgency. This analysis uses machine learning libraries to understand the content structure of the message and identify particularly important information.

[0079] Step 3:

[0080] The server automatically generates a response based on the analysis results. The input is the information obtained in the analysis step, and the output is a specific response proposal. In this step, a generative AI model is used to generate an appropriate, contextually relevant automation message in text format, referencing the user's past response style.

[0081] Step 4:

[0082] The terminal presents the generated response proposal to the user. The input is the response proposal sent from the server, and the output is a display of the response proposal in a format that is easy for the user to understand. The terminal displays a user interface and provides the user with options to edit the proposal or use it as is.

[0083] Step 5:

[0084] The server issues notifications based on the urgency of each message. The input is the urgency flag set during analysis, and the output is the emergency notification presented to the user on the terminal. In this step, actions such as beeping or displaying a notification screen are performed under specific conditions to prompt the user to take a quick action.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern society, individuals are increasingly receiving numerous government notices and messages, and are required to respond to them quickly and appropriately. However, managing and responding to these messages is time-consuming and labor-intensive, and there is a particular challenge in responding quickly to urgent notices. Therefore, there is a need to develop systems that streamline citizens' lives and make effective use of resources.

[0088] 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.

[0089] In this invention, the server includes means for analyzing messages, means for automatically generating responses based on the analyzed content, means for presenting the generated responses to terminal devices, means for presenting responses along with candidate dates and times in cooperation with a scheduling function, means for analyzing notifications for citizens and evaluating their urgency, and means for providing special warnings according to the urgency. This makes it possible to respond efficiently and quickly to the large number of notifications received by citizens, thereby improving their quality of life.

[0090] "Means of analyzing messages" refer to techniques for analyzing the content of received messages and identifying and understanding their subject matter and important information.

[0091] "Means for automatically generating responses" refers to technologies that generate appropriate response sentences on behalf of the user based on the analyzed message content.

[0092] "Means for presenting the generated response to a terminal device" refers to technology for displaying automatically generated response text on a terminal so that the user can review and edit it.

[0093] "A means of presenting responses along with suggested dates and times in conjunction with the scheduling function" refers to a technology that, in conjunction with the calendar function, presents users with a selection of suggested dates and times for responding.

[0094] "Methods for analyzing notifications for citizens and assessing their urgency" refers to technologies for analyzing notifications from the government, determining their urgency, and setting priorities.

[0095] "Means of providing special warnings according to urgency" refers to technology that issues warnings to users according to the urgency of the notification, prompting them to take a swift action.

[0096] This invention relates to a system for efficiently processing administrative notices and messages received by citizens. The central operation of the system takes place on a server. Upon receiving a message, the server first uses means to analyze its content and understand it. In this process, natural language processing technology is utilized to identify the subject and important information of the message. Specifically, language processing is performed using Python and spaCy.

[0097] Next, the server automatically generates a response using the OpenAI® GPT model based on the analyzed information. This is a crucial step in providing a quick and appropriate response on behalf of the user. This automatically generated response is presented to the user by the terminal device, and the user can review it and edit it as needed.

[0098] Furthermore, the system integrates with a scheduling function and provides suggested dates and times as needed. This makes it easy to select an appropriate response time for a message. In addition, it analyzes notifications for citizens and, based on the evaluation of their urgency, displays a special warning on the terminal if the urgency is high. This process allows users to respond without delay even in cases requiring a quick response.

[0099] For example, if the server receives a notification from the city regarding a new public project, it will generate a response message to promptly submit opinions on the project and provide appropriate feedback. An example of a prompt message in this case would be: "Read the email and create an automated response based on the following information: the user's past response style, inquiries about the city's construction project, and whether they support or oppose it. If it is urgent, use language that reflects that."

[0100] This invention aims to streamline information processing in citizens' daily lives, thereby freeing up time to focus on more important activities.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server receives an electronic message. At this point, the input is the electronic message received from the mail server, and the output is its raw data. The server then prepares this data for analysis.

[0104] Step 2:

[0105] The server analyzes received messages using spaCy, a natural language processing tool. The input is the raw data obtained in step 1, and the output is the analysis result that identifies the message's subject and important information. This process structures the message content, making it easier to process in the next step.

[0106] Step 3:

[0107] The server uses the analysis results to automatically generate a response using OpenAI GPT. The input here is the analysis results from step 2, and the output is a response that can be presented to the user. As an example of a generated prompt, "Include the user's past response style, inquiries about the city's construction project, and opinions for / against it," and formulate an appropriate response.

[0108] Step 4:

[0109] The server sends the generated response to the terminal device. The terminal receives this input, presents it to the user, and provides an interface for the user to review and edit the output. This allows the user to review the response and make corrections as needed.

[0110] Step 5:

[0111] Once the user confirms and completes the response, the terminal sends that response back to the server. Based on this input, the server interacts with the scheduling application and generates output that provides the user with information, including additional suggested dates and times as needed.

[0112] Step 6:

[0113] When the server receives a notification for citizens, it assesses its urgency. The raw data of the notification received in Step 1 is processed, and the output is a priority rating based on urgency. If it is deemed highly urgent, it is marked and proceeds to the next step.

[0114] Step 7:

[0115] In cases of high urgency, the device provides the user with a special warning. The input is the urgency assessment defined in step 6, and the output is a warning notification prompting the user to take specific action. This step enables support for quick and appropriate action on important notifications.

[0116] 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.

[0117] This invention is a system that automatically generates reply suggestions based on the content of received emails. In particular, by combining it with an emotion engine that analyzes the user's emotions, it enables more appropriate reply suggestions. This system is implemented according to the following procedure.

[0118] First, when the server receives an email, it passes the email data to an AI agent. The AI ​​agent is equipped with natural language processing capabilities and analyzes the context and important information within the email. Furthermore, an emotion engine evaluates the sender's emotions based on the text data extracted from the email. This evaluation helps to understand the tone and intensity of the emotions expressed in the email.

[0119] Next, the AI ​​agent automatically generates a response based on the email analysis results and the sentiment engine's evaluation. During the generation process, it considers the sentiment analysis results from the sentiment engine and adjusts the tone and style of the response. For example, if the sender is irritated, it prioritizes generating a response using a polite and calm tone. Furthermore, it utilizes the user's past response style and history as training data to provide more personalized responses.

[0120] The generated response suggestions are presented to the user by the terminal. The user reviews the suggested responses, makes adjustments and edits as needed, and then makes the best choice. Furthermore, if the email is of high urgency, the server sets a special flag, and the terminal notifies the user to encourage a prompt response.

[0121] As a concrete example, consider a situation where a customer support representative receives a complaint email. This system can accurately analyze the sender's emotions and provide an appropriate response that takes into account the need for a calm response. As a result, the representative can communicate with the customer efficiently and effectively. In this way, this system improves the efficiency and appropriateness of email responses, significantly reducing the workload of the user.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server receives a new email. The received email is sent to an AI agent for analysis.

[0125] Step 2:

[0126] The AI ​​agent first uses natural language processing technology to analyze the email text and extract the email's subject and keywords. Based on this information, it then classifies the content of the email.

[0127] Step 3:

[0128] The emotion engine within the AI ​​agent recognizes emotions from the email text. Specifically, it calculates emotion scores for words and evaluates the sender's emotional tone (e.g., joy, anger, sadness, etc.).

[0129] Step 4:

[0130] The AI ​​agent automatically generates a response based on the analysis results and sentiment analysis. During this process, it adjusts the tone of the response to reflect the results of the sentiment engine. For example, if the sender's emotions are negative, it will use polite and considerate language.

[0131] Step 5:

[0132] The AI ​​agent sends several generated response options to the device. The device then presents these to the user, showing the reasoning behind each recommendation and the available options.

[0133] Step 6:

[0134] The user reviews the suggested reply options and edits or selects as appropriate. Once the user confirms their reply, the information is sent from the device to the server, and an email is sent as the official reply.

[0135] Step 7:

[0136] The server records the content of the sent emails and stores it as training data for the AI ​​agent. This data will be used to improve the accuracy of future analyses.

[0137] (Example 2)

[0138] 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".

[0139] In recent years, email communication has been steadily increasing, and businesses in particular face the need to process a large volume of emails. However, responding quickly and appropriately to every email is a significant burden. Furthermore, accurately understanding the sender's sentiment and responding with the appropriate tone is not easy. There is a need for a system that enables efficient and effective email replies.

[0140] 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.

[0141] In this invention, the server includes means for analyzing an incoming email, means for automatically generating a reply based on the analyzed content, means for evaluating the sender's emotions, means for adjusting the tone and style of the reply based on the evaluated emotions, and means for presenting the generated reply to the user's terminal. This enables prompt, emotionally appropriate email replies.

[0142] "Incoming email" refers to new electronic messages that arrive in a user's account via a communication network.

[0143] "Analysis" refers to the process of understanding the content of an email using natural language processing technology and extracting important information and context.

[0144] A "draft reply" is a text message automatically generated as a response to an email received.

[0145] "Emotional assessment" is the process of analyzing the sender's emotional state from the content of an email and identifying that emotion.

[0146] "Adjusting tone and style" refers to the act of selecting an appropriate tone and writing style in a draft email reply, taking into consideration the sender's feelings.

[0147] A "terminal" is an electronic device used to present generated response suggestions to the user.

[0148] This system is designed to efficiently and effectively process emails received by the server. Upon receiving an email, the server hands over the email data to an AI agent. The AI ​​agent uses natural language processing techniques to analyze the email content in detail. This natural language processing utilizes software such as Google® Cloud Natural Language API and spaCy. The analyzed text is then processed to extract important information.

[0149] Next, the AI ​​agent uses an emotion engine to evaluate the sender's emotions. Tools such as Hume AI and IBM Watson® Tone Analyzer are used for this evaluation. The emotion engine quantifies the type and intensity of emotions contained in the transmitted text and provides this information to the AI ​​agent.

[0150] Subsequently, the AI ​​agent automatically generates a response using a generative AI model. By using prompts, the model can suggest the most appropriate response. This generation process takes into account the sender's sentiment assessment and adjusts the tone and style of the response. For example, if the sender is feeling dissatisfied, the AI ​​agent will generate a response that emphasizes a calm tone.

[0151] The generated response is presented to the user by the terminal. This allows the user to review the suggested response and edit it as needed. If the email content is urgent, the server flags the email, and the terminal notifies the user, prompting a quick response.

[0152] Examples of specific prompt messages include the following:

[0153] "Analyze the sentiment of the sender in the following email and suggest an appropriate reply. Email content: 'I am dissatisfied because your product is malfunctioning. Please address this promptly.'"

[0154] Thus, this invention improves the efficiency and quality of email replies, thereby reducing the burden on users.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server receives emails. The server detects new emails that have reached user accounts via the network and extracts their content as data. The input is newly received emails, and the output is the email data to be analyzed.

[0158] Step 2:

[0159] The server passes the email data to the AI ​​agent. The AI ​​agent receives the text data of the email and begins natural language processing. The input is the email data, and the output is text data for analysis.

[0160] Step 3:

[0161] An AI agent analyzes the content of emails. The AI ​​agent uses a natural language processing library to extract context and keywords from the text. The input is the text data of the email, and the output is the important information and keywords extracted through the analysis.

[0162] Step 4:

[0163] The emotion engine evaluates the sender's emotions. Based on the analysis results provided by the AI ​​agent, the emotion engine numerically evaluates the sender's emotional state. The input is the analyzed information, and the output is the sender's emotion evaluation data.

[0164] Step 5:

[0165] The AI ​​agent generates response suggestions. Using sentiment evaluation data and analysis information, the AI ​​agent inputs a prompt sentence into a generation AI model and obtains an appropriate response suggestion. The input is sentiment evaluation data and analysis information, and the output is the generated response suggestion.

[0166] Step 6:

[0167] The terminal presents the user with a generated response. The terminal displays the response in the user interface, allowing the user to review it. The input is the generated response, and the output is the presentation to the user.

[0168] Step 7:

[0169] The user reviews and edits the proposed reply. The user reviews the displayed reply and makes any necessary changes based on their own judgment. The input is the proposed reply, and the output is the content of the final email that will be sent.

[0170] (Application Example 2)

[0171] 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".

[0172] There is a growing demand for faster customer service and improved customer satisfaction in electronic payment services. However, communicating with customers via email makes it difficult to properly understand their emotions and context, and to create personalized responses. Furthermore, efficient processing is required because urgent issues need to be addressed immediately.

[0173] 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.

[0174] In this invention, the server includes means for analyzing information, means for automatically generating response proposals based on the analyzed information, and means for adjusting the tone of the response proposals based on sentiment analysis. This enables the rapid and appropriate generation of responses based on customer sentiment.

[0175] "Information" refers to data that is analyzed to generate responses, such as emails and customer inquiries.

[0176] "Analyzing" refers to the process of understanding the context and emotions behind information and extracting key points.

[0177] A "draft response" is a document that automatically generates a reply to the customer based on the analyzed information.

[0178] A "display device" refers to a terminal or device used by a user to review, adjust, or select response options.

[0179] "Emotional analysis" refers to the technology of reading and analyzing human emotions from information, which influences the tone of the response.

[0180] "Adjusting the tone" means changing the overall atmosphere and style of a response based on the emotions derived from the information.

[0181] An "information management application" is software that manages the history of customer communication, such as emails, and uses it to generate response proposals.

[0182] "Identification information" refers to specific flags or tags used to indicate the priority or urgency of a response.

[0183] The system that realizes this invention is primarily implemented using a server, a display device, natural language processing technology, and sentiment analysis technology. The server stores the received emails related to electronic payments and begins processing the information. Google Natural Language API and similar text analysis tools are used for the analysis. This analysis means extracts the context and important information of the email and deeply evaluates the sender's emotions. For sentiment analysis technology, sentiment detection engines such as Affectiva API are used.

[0184] As a result of the analysis, the server automatically generates a response. OpenAI's GPT model is used to generate the response, creating text in an appropriate tone and style based on key information and sentiment analysis results. The generated response is presented to the user via a display device. The display device is primarily a smartphone or computer, designed to allow the user to review, adjust, and edit the response.

[0185] Furthermore, by linking with the information management system, various customer interaction histories are accumulated as learning data. Based on this history, response suggestions are customized to match the user's past response style. In addition, the server evaluates the priority of emails, assigns identification information, and notifies the display device, enabling a rapid response to urgent issues.

[0186] As a concrete example, consider a case where a customer sends a complaint email stating that their purchase could not be completed due to a payment error. The server analyzes the context and detects the customer's frustration through sentiment analysis. Based on this, the system generates a proposed response such as, "We sincerely apologize for the inconvenience this has caused, and we are working diligently to confirm your order and resolve the issue," and presents it to the user.

[0187] Examples of prompts for a generative AI model are as follows:

[0188] "Content of the received email: 'My payment was successful, but the purchase hasn't been completed.' User sentiment: Frustrated. Based on the above, please suggest an appropriate reply."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server stores received emails in a database. The input is the content of the email, and the output is the stored email data. This data is used for subsequent analysis.

[0192] Step 2:

[0193] The server uses Google's Natural Language API to analyze the content of emails. The input is stored email data, and the output is extracted context and important information. This deepens the understanding of the email content.

[0194] Step 3:

[0195] The server uses the Affectiva API's sentiment analysis technology to evaluate the sender's emotions from the analyzed context. The input is the analyzed contextual information, and the output is the type and intensity of the emotion. This allows for tone-setting of the response.

[0196] Step 4:

[0197] The server automatically generates response suggestions using OpenAI's GPT model. The input consists of context, key information, and sentiment analysis results, while the output is the generated response suggestion. Appropriate tone and style are considered to prepare the response for the user.

[0198] Step 5:

[0199] The server sends the generated draft response to the terminal and displays it to the user. The input is the generated draft response, and the output is the draft response displayed on the terminal. The user then reviews the draft response and makes adjustments and edits as needed.

[0200] Step 6:

[0201] The server works in conjunction with an information management application to store past response history as training data. Input consists of response proposals viewed on the terminal and user adjustment history, while output is updated training data. This allows for more personalized response generation in the future.

[0202] Step 7:

[0203] The server assesses the urgency of the email and, if necessary, adds identifying information before sending a notification to the terminal. The input is the received email and its parsed information, and the output is a notification with urgency identification information. This encourages users to respond quickly.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention is a system for streamlining email reply processes, specifically using AI technology to analyze the content of received emails and assist users in responding quickly. The system functions as follows:

[0221] First, when the server receives an email, it passes it to an AI agent for analysis. In this analysis, the AI ​​agent uses natural language processing to grasp the subject and important information of the email and assess its urgency.

[0222] Next, the AI ​​agent automatically generates a response based on the analysis results. In particular, for emails regarding scheduling, the AI ​​integrates with a scheduling application to retrieve available times from the user's calendar and creates a response that includes multiple possible dates and times. At this time, the AI ​​agent refers to the user's past response history and style and strives to provide a response at a level of writing that suits the user.

[0223] The generated response is presented to the user by the device. The user can review it and edit the content as needed. Furthermore, if the email is of high urgency, the AI ​​agent will set a special flag, and the device will provide the user with a notification prompting immediate action.

[0224] This system will allow users to save a significant amount of time and focus on more important tasks. For example, consider a business person receiving numerous meeting scheduling requests via email. This system will streamline scheduling and reduce the effort required to respond to each email individually. This overall process is expected to significantly improve the efficiency of email management.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server receives a new email. Once the email is received, the server sends it to the AI ​​agent. The AI ​​agent prepares to analyze the contents of the received email.

[0228] Step 2:

[0229] The AI ​​agent analyzes incoming emails using natural language processing techniques to identify the subject, important information, and urgency of the email. Based on the analysis, it categorizes the email (e.g., inquiry, scheduling, emergency response, etc.).

[0230] Step 3:

[0231] The AI ​​agent automatically generates response suggestions based on the email category. Specifically for scheduling emails, it integrates with scheduling applications, accesses the user's calendar to find available times, and creates response suggestions including multiple possible dates and times. This process takes into account the user's past response history and style.

[0232] Step 4:

[0233] The AI ​​agent generates and sends suggested replies to the device. The device displays a list of suggested replies to the user. For urgent emails, a special flag is set, and the device notifies the user that immediate action is required.

[0234] Step 5:

[0235] The user reviews the suggested reply and edits it as needed. After editing, the user confirms their chosen reply and completes the preparation for sending.

[0236] Step 6:

[0237] The device sends the user's confirmed reply to the server. The server then forwards this reply to the sender via email. Furthermore, the server records the content of this transmission and stores it as training data for the AI, which will be used to improve the accuracy of future analyses.

[0238] (Example 1)

[0239] 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."

[0240] In recent years, with the increase in electronic communication, responding quickly and appropriately to individual electronic messages has become a crucial issue for users. However, manual replies are time-consuming and labor-intensive, placing a significant burden on busy users. Furthermore, it is difficult to immediately prioritize emails and respond appropriately. To address this challenge, there is a need for a system that efficiently and automatically generates response proposals, allowing users to quickly review and respond to them.

[0241] 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.

[0242] In this invention, the server includes means for analyzing received information, means for automatically generating a response proposal based on the analyzed information, means for presenting the generated response proposal to a user device, and means for evaluating the importance and urgency of the information using natural language processing technology. This enables an automated response system that can quickly analyze electronic messages and provide appropriate responses without overloading the user.

[0243] "Received information" refers to data received by a device through a communication network, and specifically includes electronic messages and notifications.

[0244] "Means of analysis" refers to technical components that process received information, understand its content, and identify keywords and themes.

[0245] "Draft response" refers to the proposed response content that is automatically generated based on the analysis results.

[0246] "Means of automatic generation" refers to a process or structure that mechanically creates response proposals using a predetermined algorithm.

[0247] A "user device" refers to a terminal or device that a user directly uses and on which a message is displayed.

[0248] "Means of presentation" refers to a mechanism that allows the user to visually or audibly confirm the generated response proposals.

[0249] "Timetable application software" refers to programs used for schedule management and scheduling, and specifically includes calendar applications.

[0250] "Natural language processing technology" refers to the technology used to process and analyze human language and convert it into a form that machines can understand.

[0251] "Means for evaluating importance and urgency" refers to a function that evaluates information based on specific criteria in order to determine its value and urgency.

[0252] This invention is a system aimed at improving the efficiency of electronic communication, in which the server, terminal, and user elements work together. Specifically, the server analyzes received information using natural language processing technology and generates a response based on the results.

[0253] The server uses common natural language processing libraries to leverage natural language processing techniques in information analysis. This technique allows the server to identify the subject and importance of electronic messages and assess their urgency as needed.

[0254] Next, an AI model is used to automatically generate response suggestions based on the analysis results. Here, the user's past response formats and styles are considered to generate appropriate sentences. A machine learning algorithm is incorporated for this purpose.

[0255] The generated draft response is then presented to the user by the terminal. The terminal provides an interface that allows the user to review the response and edit it as needed. This makes it easy for the user to review the draft response and finalize the message to be sent.

[0256] Furthermore, the server marks information it deems highly urgent during the analysis phase. The terminal then uses this information to send a notification prompting the user to take immediate action. For example, if an invitation email for a very important meeting arrives, the terminal uses its instant notification function to display an alarm to the user.

[0257] As a concrete example, consider a situation where a user receives numerous meeting requests. By using this system, the user can automatically include available time slots in their reply proposals through a scheduling application, reducing the effort required for manual responses.

[0258] An example of a prompt for a generative AI model is, "Analyze this new email and generate a suggested reply." This prompt allows the server to process messages efficiently and provide users with quick and accurate responses.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server receives electronic messages. The input is an unprocessed electronic message that has arrived via the network, and the output is text data in a parseable format. At this stage, the server accesses a database, records the received message, and prepares the basic data for use in subsequent parsing processes.

[0262] Step 2:

[0263] The server analyzes text data in a parsable format using natural language processing techniques. The input is the text data obtained in the previous step, and the output is the analysis results, including the message's subject, keywords, importance, and urgency. This analysis uses machine learning libraries to understand the content structure of the message and identify particularly important information.

[0264] Step 3:

[0265] The server automatically generates a response based on the analysis results. The input is the information obtained in the analysis step, and the output is a specific response proposal. In this step, a generative AI model is used to generate an appropriate, contextually relevant automation message in text format, referencing the user's past response style.

[0266] Step 4:

[0267] The terminal presents the generated response proposal to the user. The input is the response proposal sent from the server, and the output is a display of the response proposal in a format that is easy for the user to understand. The terminal displays a user interface and provides the user with options to edit the proposal or use it as is.

[0268] Step 5:

[0269] The server issues notifications based on the urgency of each message. The input is the urgency flag set during analysis, and the output is the emergency notification presented to the user on the terminal. In this step, actions such as beeping or displaying a notification screen are performed under specific conditions to prompt the user to take a quick action.

[0270] (Application Example 1)

[0271] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In modern society, individuals are increasingly receiving numerous government notices and messages, and are required to respond to them quickly and appropriately. However, managing and responding to these messages is time-consuming and labor-intensive, and there is a particular challenge in responding quickly to urgent notices. Therefore, there is a need to develop systems that streamline citizens' lives and make effective use of resources.

[0273] 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.

[0274] In this invention, the server includes means for analyzing messages, means for automatically generating responses based on the analyzed content, means for presenting the generated responses to terminal devices, means for presenting responses along with candidate dates and times in cooperation with a scheduling function, means for analyzing notifications for citizens and evaluating their urgency, and means for providing special warnings according to the urgency. This makes it possible to respond efficiently and quickly to the large number of notifications received by citizens, thereby improving their quality of life.

[0275] "Means of message analysis" refers to techniques for analyzing the content of a received message, identifying its subject and important information, and understanding it.

[0276] "Means for automatically generating responses" refers to technologies that generate appropriate response sentences on behalf of the user based on the analyzed message content.

[0277] "Means for presenting the generated response to a terminal device" refers to technology for displaying automatically generated response text on a terminal so that the user can review and edit it.

[0278] "A means of presenting responses along with suggested dates and times in conjunction with the scheduling function" refers to a technology that, in conjunction with the calendar function, presents users with a selection of suggested dates and times for responding.

[0279] "Methods for analyzing notifications for citizens and assessing their urgency" refers to technologies for analyzing notifications from the government, determining their urgency, and setting priorities.

[0280] "Means of providing special warnings according to urgency" refers to technology that issues warnings to users according to the urgency of the notification, prompting them to take a swift action.

[0281] This invention relates to a system for efficiently processing administrative notifications and messages received by citizens. The central operation of the system is performed on a server. When the server first receives a message, it understands the content using means for analyzing the content. At this time, natural language processing technology is utilized to identify the theme and important information of the message. Specifically, language processing is performed using Python and spaCy.

[0282] Next, based on the analyzed information, the server automatically generates a response using OpenAI's GPT model. This is an important step for quickly and appropriately responding on behalf of the user. This automatically generated response is presented to the user by the terminal device, and the user can confirm it and edit it as needed.

[0283] In addition, the system cooperates with the schedule management function and provides candidate dates and times as needed. Thereby, it is possible to easily select an appropriate response date and time for the message. Furthermore, notifications for citizens are analyzed, and according to the result of evaluating their urgency, if the urgency is high, a special warning is displayed on the terminal. Through this process, users can respond promptly even in cases where quick response is required without delay.

[0284] As a specific example, for instance, when receiving a notice from the city regarding a new public project, the server generates a response text for quickly sending opinions on the project and provides appropriate feedback. As an example of the prompt text at this time, there is "Read the content of the email and create an auto-reply draft based on the following information: the user's past reply style, inquiries regarding the city's construction project, including赞成 / 反对 opinions. If the urgency is high, use words that reflect it."

[0285] The purpose of this invention is to streamline information processing in citizens' daily lives and ensure time for concentrating on more important activities.

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The server receives an electronic message. At this point, the input is the electronic message received from the mail server, and the output is the raw data. The server prepares to analyze this data.

[0289] Step 2:

[0290] The server analyzes the received message using spaCy, a natural language processing tool. The input is the raw data obtained in Step 1, and the output is the analysis result that identifies the theme and important information of the message. This process structures the content of the message and facilitates the processing in the next step.

[0291] Step 3:

[0292] The server uses the analysis result to automatically generate a response message using OpenAI GPT. Here, the input is the analysis result of Step 2, and the output is the response message that can be presented to the user. As an example of the generation prompt, use "Include the user's past reply style, inquiries about the city's construction project, and赞成 / 反对 (approval / disapproval) opinions." to formulate an appropriate response.

[0293] Step 4:

[0294] The server sends the generated response message to the terminal device. The terminal receives this input, presents it to the user, and provides an interface for the user to confirm and edit as output. This allows the user to confirm the response message and make corrections if necessary.

[0295] Step 5:

[0296] Once the user confirms and completes the response, the terminal sends that response back to the server. Based on this input, the server interacts with the scheduling application and generates output that provides the user with information, including additional suggested dates and times as needed.

[0297] Step 6:

[0298] When the server receives a notification for citizens, it assesses its urgency. The raw data of the notification received in Step 1 is processed, and the output is a priority rating based on urgency. If it is deemed highly urgent, it is marked and proceeds to the next step.

[0299] Step 7:

[0300] In cases of high urgency, the device provides the user with a special warning. The input is the urgency assessment defined in step 6, and the output is a warning notification prompting the user to take specific action. This step enables support for quick and appropriate action on important notifications.

[0301] 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.

[0302] This invention is a system that automatically generates reply suggestions based on the content of received emails. In particular, by combining it with an emotion engine that analyzes the user's emotions, it enables more appropriate reply suggestions. This system is implemented according to the following procedure.

[0303] First, when the server receives an email, it passes the email data to an AI agent. The AI ​​agent is equipped with natural language processing capabilities and analyzes the context and important information within the email. Furthermore, an emotion engine evaluates the sender's emotions based on the text data extracted from the email. This evaluation helps to understand the tone and intensity of the emotions expressed in the email.

[0304] Next, based on the analysis results of the email and the evaluation of the sentiment engine, the AI agent automatically generates a reply draft. In the generation process, the sentiment analysis results by the sentiment engine are considered to adjust the tone and style of the text in the reply draft. For example, when the sender is angry, a reply using a polite and calm tone is preferentially generated. Furthermore, the past reply styles and histories of the user are utilized as learning data to provide more personalized replies.

[0305] The generated reply draft is presented to the user by the terminal. The user checks the presented reply draft, makes adjustments and edits if necessary, and then makes an optimal selection. Also, when the email has a high urgency, the server sets a special flag, and the terminal notifies the user of it to prompt a quick response.

[0306] As a specific example, consider the situation where a customer support staff receives a claim email. This system can accurately analyze the sentiment of the sender and provide an appropriate reply draft considering that a calm response is required. As a result, the staff can communicate with the customer efficiently and effectively. Thus, this system realizes the efficiency and properness of email replies and greatly reduces the work burden of the user.

[0307] The following describes the processing flow.

[0308] Step 1:

[0309] The server receives a new email. The received email is sent to the AI agent for analysis.

[0310] Step 2:

[0311] First, the AI agent analyzes the text of the email using natural language processing technology, extracts the subject and keywords of the email. Based on this information, the content classification of the email is performed.

[0312] Step 3:

[0313] The emotion engine within the AI ​​agent recognizes emotions from the email text. Specifically, it calculates emotion scores for words and evaluates the sender's emotional tone (e.g., joy, anger, sadness, etc.).

[0314] Step 4:

[0315] The AI ​​agent automatically generates a response based on the analysis results and sentiment analysis. During this process, it adjusts the tone of the response to reflect the results of the sentiment engine. For example, if the sender's emotions are negative, it will use polite and considerate language.

[0316] Step 5:

[0317] The AI ​​agent sends several generated response options to the device. The device then presents these to the user, showing the reasoning behind each recommendation and the available options.

[0318] Step 6:

[0319] The user reviews the suggested reply options and edits or selects as appropriate. Once the user confirms their reply, the information is sent from the device to the server, and an email is sent as the official reply.

[0320] Step 7:

[0321] The server records the content of the sent emails and saves them as training data for the AI ​​agent. This data will be used to improve the accuracy of future analyses.

[0322] (Example 2)

[0323] 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".

[0324] In recent years, email communication has been steadily increasing, and businesses in particular face the need to process a large volume of emails. However, responding quickly and appropriately to every email is a significant burden. Furthermore, accurately understanding the sender's sentiment and responding with the appropriate tone is not easy. There is a need for a system that enables efficient and effective email replies.

[0325] 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.

[0326] In this invention, the server includes means for analyzing an incoming email, means for automatically generating a reply based on the analyzed content, means for evaluating the sender's emotions, means for adjusting the tone and style of the reply based on the evaluated emotions, and means for presenting the generated reply to the user's terminal. This enables prompt, emotionally appropriate email replies.

[0327] "Incoming email" refers to new electronic messages that arrive in a user's account via a communication network.

[0328] "Analysis" refers to the process of understanding the content of an email using natural language processing technology and extracting important information and context.

[0329] A "draft reply" is a text message automatically generated as a response to an email received.

[0330] "Emotional assessment" is the process of analyzing the sender's emotional state from the content of an email and identifying that emotion.

[0331] "Adjusting tone and style" refers to the act of selecting an appropriate tone and writing style in a draft email reply, taking into consideration the sender's feelings.

[0332] A "terminal" is an electronic device used to present generated response suggestions to the user.

[0333] This system is designed to efficiently and effectively process emails received by the server. Upon receiving an email, the server hands over the email data to an AI agent. The AI ​​agent uses natural language processing techniques to analyze the email content in detail. This natural language processing utilizes software such as Google Cloud Natural Language API and spaCy. The analyzed text is then processed to extract important information.

[0334] Next, the AI ​​agent uses an emotion engine to evaluate the sender's emotions. Tools such as Hume AI and IBM Watson Tone Analyzer are used for this evaluation. The emotion engine quantifies the type and intensity of emotions contained in the transmitted text and provides this information to the AI ​​agent.

[0335] Subsequently, the AI ​​agent automatically generates a response using a generative AI model. By using prompts, the model can suggest the most appropriate response. This generation process takes into account the sender's sentiment assessment and adjusts the tone and style of the response. For example, if the sender is feeling dissatisfied, the AI ​​agent will generate a response that emphasizes a calm tone.

[0336] The generated response is presented to the user by the terminal. This allows the user to review the suggested response and edit it as needed. If the email content is urgent, the server flags the email, and the terminal notifies the user, prompting a quick response.

[0337] Examples of specific prompt messages include the following:

[0338] "Analyze the sentiment of the sender in the following email and suggest an appropriate reply. Email content: 'I am dissatisfied because your product is malfunctioning. Please address this promptly.'"

[0339] Thus, this invention improves the efficiency and quality of email replies, thereby reducing the burden on users.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The server receives emails. The server detects new emails that have reached user accounts via the network and extracts their content as data. The input is newly received emails, and the output is the email data to be analyzed.

[0343] Step 2:

[0344] The server passes the email data to the AI ​​agent. The AI ​​agent receives the text data of the email and begins natural language processing. The input is the email data, and the output is text data for analysis.

[0345] Step 3:

[0346] An AI agent analyzes the content of emails. The AI ​​agent uses a natural language processing library to extract context and keywords from the text. The input is the text data of the email, and the output is the important information and keywords extracted through the analysis.

[0347] Step 4:

[0348] The emotion engine evaluates the sender's emotions. Based on the analysis results provided by the AI ​​agent, the emotion engine numerically evaluates the sender's emotional state. The input is the analyzed information, and the output is the sender's emotion evaluation data.

[0349] Step 5:

[0350] The AI ​​agent generates response suggestions. Using sentiment evaluation data and analysis information, the AI ​​agent inputs a prompt sentence into a generation AI model and obtains an appropriate response suggestion. The input is sentiment evaluation data and analysis information, and the output is the generated response suggestion.

[0351] Step 6:

[0352] The terminal presents the user with a generated response. The terminal displays the response in the user interface, allowing the user to review it. The input is the generated response, and the output is the presentation to the user.

[0353] Step 7:

[0354] The user reviews and edits the proposed reply. The user reviews the displayed reply and makes any necessary changes based on their own judgment. The input is the proposed reply, and the output is the content of the final email that will be sent.

[0355] (Application Example 2)

[0356] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0357] There is a growing demand for faster customer service and improved customer satisfaction in electronic payment services. However, communicating with customers via email makes it difficult to properly understand their emotions and context, and to create personalized responses. Furthermore, efficient processing is required because urgent issues need to be addressed immediately.

[0358] 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.

[0359] In this invention, the server includes means for analyzing information, means for automatically generating response proposals based on the analyzed information, and means for adjusting the tone of the response proposals based on sentiment analysis. This enables the rapid and appropriate generation of responses based on customer sentiment.

[0360] "Information" refers to data that is analyzed to generate responses, such as emails and customer inquiries.

[0361] "Analyzing" refers to the process of understanding the context and emotions behind information and extracting key points.

[0362] A "draft response" is a document that automatically generates a reply to the customer based on the analyzed information.

[0363] A "display device" refers to a terminal or device used by a user to review, adjust, or select response options.

[0364] "Emotional analysis" refers to the technology of reading and analyzing human emotions from information, which influences the tone of the response.

[0365] "Adjusting the tone" means changing the overall atmosphere and style of a response based on the emotions derived from the information.

[0366] An "information management application" is software that manages the history of customer communication, such as emails, and uses it to generate response proposals.

[0367] "Identification information" refers to specific flags or tags used to indicate the priority or urgency of a response.

[0368] The system that realizes this invention is primarily implemented using a server, a display device, natural language processing technology, and sentiment analysis technology. The server stores the received emails related to electronic payments and begins processing the information. Google Natural Language API and similar text analysis tools are used for the analysis. This analysis means extracts the context and important information of the email and deeply evaluates the sender's emotions. For sentiment analysis technology, sentiment detection engines such as Affectiva API are used.

[0369] As a result of the analysis, the server automatically generates a response. OpenAI's GPT model is used to generate the response, creating text in an appropriate tone and style based on key information and sentiment analysis results. The generated response is presented to the user via a display device. The display device is primarily a smartphone or computer, designed to allow the user to review, adjust, and edit the response.

[0370] Furthermore, by linking with the information management system, various customer interaction histories are accumulated as learning data. Based on this history, response suggestions are customized to match the user's past response style. In addition, the server evaluates the priority of emails, assigns identification information, and notifies the display device, enabling a rapid response to urgent issues.

[0371] As a concrete example, consider a case where a customer sends a complaint email stating that their purchase could not be completed due to a payment error. The server analyzes the context and detects the customer's frustration through sentiment analysis. Based on this, the system generates a proposed response such as, "We sincerely apologize for the inconvenience this has caused, and we are working diligently to confirm your order and resolve the issue," and presents it to the user.

[0372] Examples of prompts for a generative AI model are as follows:

[0373] "Content of the received email: 'My payment was successful, but the purchase hasn't been completed.' User sentiment: Frustrated. Based on the above, please suggest an appropriate reply."

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

[0375] Step 1:

[0376] The server stores received emails in a database. The input is the content of the email, and the output is the stored email data. This data is used for subsequent analysis.

[0377] Step 2:

[0378] The server uses Google's Natural Language API to analyze the content of emails. The input is stored email data, and the output is extracted context and important information. This deepens the understanding of the email content.

[0379] Step 3:

[0380] The server uses the Affectiva API's sentiment analysis technology to evaluate the sender's emotions from the analyzed context. The input is the analyzed contextual information, and the output is the type and intensity of the emotion. This allows for tone-setting of the response.

[0381] Step 4:

[0382] The server automatically generates response suggestions using OpenAI's GPT model. The input consists of context, key information, and sentiment analysis results, while the output is the generated response suggestion. Appropriate tone and style are considered to prepare the response for the user.

[0383] Step 5:

[0384] The server sends the generated draft response to the terminal and displays it to the user. The input is the generated draft response, and the output is the draft response displayed on the terminal. The user then reviews the draft response and makes adjustments and edits as needed.

[0385] Step 6:

[0386] The server works in conjunction with an information management application to store past response history as training data. Input consists of response proposals viewed on the terminal and user adjustment history, while output is updated training data. This allows for more personalized response generation in the future.

[0387] Step 7:

[0388] The server assesses the urgency of the email and, if necessary, adds identifying information before sending a notification to the terminal. The input is the received email and its parsed information, and the output is a notification with urgency identification information. This encourages users to respond quickly.

[0389] 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.

[0390] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0391] 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.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] This invention is a system for streamlining email reply processes, specifically using AI technology to analyze the content of received emails and assist users in responding quickly. The system functions as follows:

[0406] First, when the server receives an email, it passes it to an AI agent for analysis. In this analysis, the AI ​​agent uses natural language processing to grasp the subject and important information of the email and assess its urgency.

[0407] Next, the AI ​​agent automatically generates a response based on the analysis results. In particular, for emails regarding scheduling, the AI ​​integrates with a scheduling application to retrieve available times from the user's calendar and creates a response that includes multiple possible dates and times. At this time, the AI ​​agent refers to the user's past response history and style and strives to provide a response at a level of writing that suits the user.

[0408] The generated response is presented to the user by the device. The user can review it and edit the content as needed. Furthermore, if the email is of high urgency, the AI ​​agent will set a special flag, and the device will provide the user with a notification prompting immediate action.

[0409] This system will allow users to save a significant amount of time and focus on more important tasks. For example, consider a business person receiving numerous meeting scheduling requests via email. This system will streamline scheduling and reduce the effort required to respond to each email individually. This overall process is expected to significantly improve the efficiency of email management.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The server receives a new email. Once the email is received, the server sends it to the AI ​​agent. The AI ​​agent prepares to analyze the contents of the received email.

[0413] Step 2:

[0414] The AI ​​agent analyzes incoming emails using natural language processing techniques to identify the subject, important information, and urgency of the email. Based on the analysis, it categorizes the email (e.g., inquiry, scheduling, emergency response, etc.).

[0415] Step 3:

[0416] The AI ​​agent automatically generates response suggestions based on the email category. Specifically for scheduling emails, it integrates with scheduling applications, accesses the user's calendar to find available times, and creates response suggestions including multiple possible dates and times. This process takes into account the user's past response history and style.

[0417] Step 4:

[0418] The AI ​​agent generates and sends suggested replies to the device. The device displays a list of suggested replies to the user. For urgent emails, a special flag is set, and the device notifies the user that immediate action is required.

[0419] Step 5:

[0420] The user reviews the suggested reply and edits it as needed. After editing, the user confirms their chosen reply and completes the preparation for sending.

[0421] Step 6:

[0422] The device sends the user's confirmed reply to the server. The server then forwards this reply to the sender via email. Furthermore, the server records the content of this transmission and stores it as training data for the AI, which will be used to improve the accuracy of future analyses.

[0423] (Example 1)

[0424] 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."

[0425] In recent years, with the increase in electronic communication, responding quickly and appropriately to individual electronic messages has become a crucial issue for users. However, manual replies are time-consuming and labor-intensive, placing a significant burden on busy users. Furthermore, it is difficult to immediately prioritize emails and respond appropriately. To address this challenge, there is a need for a system that efficiently and automatically generates response proposals, allowing users to quickly review and respond to them.

[0426] 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.

[0427] In this invention, the server includes means for analyzing received information, means for automatically generating a response proposal based on the analyzed information, means for presenting the generated response proposal to a user device, and means for evaluating the importance and urgency of the information using natural language processing technology. This enables an automated response system that can quickly analyze electronic messages and provide appropriate responses without overloading the user.

[0428] "Received information" refers to data received by a device through a communication network, and specifically includes electronic messages and notifications.

[0429] "Means of analysis" refers to technical components that process received information, understand its content, and identify keywords and themes.

[0430] "Draft response" refers to the proposed response content that is automatically generated based on the analysis results.

[0431] "Means of automatic generation" refers to a process or structure that mechanically creates response proposals using a predetermined algorithm.

[0432] A "user device" refers to a terminal or device that a user directly uses and on which a message is displayed.

[0433] "Means of presentation" refers to a mechanism that allows the user to visually or audibly confirm the generated response proposals.

[0434] "Timetable application software" refers to programs used for schedule management and scheduling, and specifically includes calendar applications.

[0435] "Natural language processing technology" refers to the technology used to process and analyze human language and convert it into a form that machines can understand.

[0436] "Means for evaluating importance and urgency" refers to a function that evaluates information based on specific criteria in order to determine its value and urgency.

[0437] This invention is a system aimed at improving the efficiency of electronic communication, in which the server, terminal, and user elements work together. Specifically, the server analyzes received information using natural language processing technology and generates a response based on the results.

[0438] The server uses common natural language processing libraries to leverage natural language processing techniques in information analysis. This technique allows the server to identify the subject and importance of electronic messages and assess their urgency as needed.

[0439] Next, an AI model is used to automatically generate response suggestions based on the analysis results. Here, the user's past response formats and styles are considered to generate appropriate sentences. A machine learning algorithm is incorporated for this purpose.

[0440] The generated draft response is then presented to the user by the terminal. The terminal provides an interface that allows the user to review the response and edit it as needed. This makes it easy for the user to review the draft response and finalize the message to be sent.

[0441] Furthermore, the server marks information it deems highly urgent during the analysis phase. The terminal then uses this information to send a notification prompting the user to take immediate action. For example, if an invitation email for a very important meeting arrives, the terminal uses its instant notification function to display an alarm to the user.

[0442] As a concrete example, consider a situation where a user receives numerous meeting requests. By using this system, the user can automatically include available time slots in their reply proposals through a scheduling application, reducing the effort required for manual responses.

[0443] An example of a prompt for a generative AI model is, "Analyze this new email and generate a suggested reply." This prompt allows the server to process messages efficiently and provide users with quick and accurate responses.

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

[0445] Step 1:

[0446] The server receives electronic messages. The input is an unprocessed electronic message that has arrived via the network, and the output is text data in a parseable format. At this stage, the server accesses a database, records the received message, and prepares the basic data for use in subsequent parsing processes.

[0447] Step 2:

[0448] The server analyzes text data in a parsable format using natural language processing techniques. The input is the text data obtained in the previous step, and the output is the analysis results, including the message's subject, keywords, importance, and urgency. This analysis uses machine learning libraries to understand the content structure of the message and identify particularly important information.

[0449] Step 3:

[0450] The server automatically generates a response based on the analysis results. The input is the information obtained in the analysis step, and the output is a specific response proposal. In this step, a generative AI model is used to generate an appropriate, contextually relevant automation message in text format, referencing the user's past response style.

[0451] Step 4:

[0452] The terminal presents the generated response proposal to the user. The input is the response proposal sent from the server, and the output is a display of the response proposal in a format that is easy for the user to understand. The terminal displays a user interface and provides the user with options to edit the proposal or use it as is.

[0453] Step 5:

[0454] The server issues notifications based on the urgency of each message. The input is the urgency flag set during analysis, and the output is the emergency notification presented to the user on the terminal. In this step, actions such as beeping or displaying a notification screen are performed under specific conditions to prompt the user to take a quick action.

[0455] (Application Example 1)

[0456] 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."

[0457] In modern society, individuals are increasingly receiving numerous government notices and messages, and are required to respond to them quickly and appropriately. However, managing and responding to these messages is time-consuming and labor-intensive, and there is a particular challenge in responding quickly to urgent notices. Therefore, there is a need to develop systems that streamline citizens' lives and make effective use of resources.

[0458] 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.

[0459] In this invention, the server includes means for analyzing messages, means for automatically generating responses based on the analyzed content, means for presenting the generated responses to terminal devices, means for presenting responses along with candidate dates and times in cooperation with a scheduling function, means for analyzing notifications for citizens and evaluating their urgency, and means for providing special warnings according to the urgency. This makes it possible to respond efficiently and quickly to the large number of notifications received by citizens, thereby improving their quality of life.

[0460] "Means of message analysis" refers to techniques for analyzing the content of a received message, identifying its subject and important information, and understanding it.

[0461] "Means for automatically generating responses" refers to technologies that generate appropriate response sentences on behalf of the user based on the analyzed message content.

[0462] "Means for presenting the generated response to a terminal device" refers to technology for displaying automatically generated response text on a terminal so that the user can review and edit it.

[0463] "A means of presenting responses along with suggested dates and times in conjunction with the scheduling function" refers to a technology that, in conjunction with the calendar function, presents users with a selection of suggested dates and times for responding.

[0464] "Methods for analyzing notifications for citizens and assessing their urgency" refers to technologies for analyzing notifications from the government, determining their urgency, and setting priorities.

[0465] "Means of providing special warnings according to urgency" refers to technology that issues warnings to users according to the urgency of the notification, prompting them to take a swift action.

[0466] This invention relates to a system for efficiently processing administrative notices and messages received by citizens. The central operation of the system takes place on a server. Upon receiving a message, the server first uses means to analyze its content and understand it. In this process, natural language processing technology is utilized to identify the subject and important information of the message. Specifically, language processing is performed using Python and spaCy.

[0467] Next, the server automatically generates a response using OpenAI's GPT model based on the analyzed information. This is a crucial step in providing a quick and appropriate response on behalf of the user. This automatically generated response is presented to the user by the terminal device, and the user can review and edit it as needed.

[0468] Furthermore, the system integrates with a scheduling function and provides suggested dates and times as needed. This makes it easy to select an appropriate response time for a message. In addition, it analyzes notifications for citizens and, based on the evaluation of their urgency, displays a special warning on the terminal if the urgency is high. This process allows users to respond without delay even in cases requiring a quick response.

[0469] For example, if the server receives a notification from the city regarding a new public project, it will generate a response message to promptly submit opinions on the project and provide appropriate feedback. An example of a prompt message in this case would be: "Read the email and create an automated response based on the following information: the user's past response style, inquiries about the city's construction project, and whether they support or oppose it. If it is urgent, use language that reflects that."

[0470] This invention aims to streamline information processing in citizens' daily lives, thereby freeing up time to focus on more important activities.

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The server receives an electronic message. At this point, the input is the electronic message received from the mail server, and the output is its raw data. The server prepares this data for analysis.

[0474] Step 2:

[0475] The server analyzes received messages using spaCy, a natural language processing tool. The input is the raw data obtained in step 1, and the output is the analysis result that identifies the message's subject and important information. This process structures the message content, making it easier to process in the next step.

[0476] Step 3:

[0477] The server uses the analysis results to automatically generate a response using OpenAI GPT. The input here is the analysis results from step 2, and the output is a response that can be presented to the user. As an example of a generated prompt, "Include the user's past response style, inquiries about the city's construction project, and opinions for / against it," and formulate an appropriate response.

[0478] Step 4:

[0479] The server sends the generated response to the terminal device. The terminal receives this input, presents it to the user, and provides an interface for the user to review and edit the output. This allows the user to review the response and make corrections as needed.

[0480] Step 5:

[0481] Once the user confirms and completes the response, the terminal sends that response back to the server. Based on this input, the server interacts with the scheduling application and generates output that provides the user with information, including additional suggested dates and times as needed.

[0482] Step 6:

[0483] When the server receives a notification for citizens, it assesses its urgency. The raw data of the notification received in Step 1 is processed, and the output is a priority rating based on urgency. If it is deemed highly urgent, it is marked and proceeds to the next step.

[0484] Step 7:

[0485] In cases of high urgency, the device provides the user with a special warning. The input is the urgency assessment defined in step 6, and the output is a warning notification prompting the user to take specific action. This step enables support for quick and appropriate action on important notifications.

[0486] 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.

[0487] This invention is a system that automatically generates reply suggestions based on the content of received emails. In particular, by combining it with an emotion engine that analyzes the user's emotions, it enables more appropriate reply suggestions. This system is implemented according to the following procedure.

[0488] First, when the server receives an email, it passes the email data to an AI agent. The AI ​​agent is equipped with natural language processing capabilities and analyzes the context and important information within the email. Furthermore, an emotion engine evaluates the sender's emotions based on the text data extracted from the email. This evaluation helps to understand the tone and intensity of the emotions expressed in the email.

[0489] Next, the AI ​​agent automatically generates a response based on the email analysis results and the sentiment engine's evaluation. During the generation process, it considers the sentiment analysis results from the sentiment engine and adjusts the tone and style of the response. For example, if the sender is irritated, it prioritizes generating a response using a polite and calm tone. Furthermore, it utilizes the user's past response style and history as training data to provide more personalized responses.

[0490] The generated response suggestions are presented to the user by the terminal. The user reviews the suggested responses, makes adjustments and edits as needed, and then makes the best choice. Furthermore, if the email is of high urgency, the server sets a special flag, and the terminal notifies the user to encourage a prompt response.

[0491] As a concrete example, consider a situation where a customer support representative receives a complaint email. This system can accurately analyze the sender's emotions and provide an appropriate response that takes into account the need for a calm response. As a result, the representative can communicate with the customer efficiently and effectively. In this way, this system improves the efficiency and appropriateness of email responses, significantly reducing the workload of the user.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The server receives a new email. The received email is sent to an AI agent for analysis.

[0495] Step 2:

[0496] The AI ​​agent first uses natural language processing technology to analyze the email text and extract the email's subject and keywords. Based on this information, it then classifies the content of the email.

[0497] Step 3:

[0498] The emotion engine within the AI ​​agent recognizes emotions from the email text. Specifically, it calculates emotion scores for words and evaluates the sender's emotional tone (e.g., joy, anger, sadness, etc.).

[0499] Step 4:

[0500] The AI ​​agent automatically generates a response based on the analysis results and sentiment analysis. During this process, it adjusts the tone of the response to reflect the results of the sentiment engine. For example, if the sender's emotions are negative, it will use polite and considerate language.

[0501] Step 5:

[0502] The AI ​​agent sends several generated response options to the device. The device then presents these to the user, showing the reasons for each recommendation and the available options.

[0503] Step 6:

[0504] The user reviews the suggested reply options and edits or selects as appropriate. Once the user confirms their reply, the information is sent from the device to the server, and an email is sent as the official reply.

[0505] Step 7:

[0506] The server records the content of the sent emails and stores it as training data for the AI ​​agent. This data will be used to improve the accuracy of future analyses.

[0507] (Example 2)

[0508] 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."

[0509] In recent years, email communication has been steadily increasing, and businesses in particular face the need to process a large volume of emails. However, responding quickly and appropriately to every email is a significant burden. Furthermore, accurately understanding the sender's sentiment and responding with the appropriate tone is not easy. There is a need for a system that enables efficient and effective email replies.

[0510] 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.

[0511] In this invention, the server includes means for analyzing an incoming email, means for automatically generating a reply based on the analyzed content, means for evaluating the sender's emotions, means for adjusting the tone and style of the reply based on the evaluated emotions, and means for presenting the generated reply to the user's terminal. This enables prompt, emotionally appropriate email replies.

[0512] "Incoming email" refers to new electronic messages that arrive in a user's account via a communication network.

[0513] "Analysis" refers to the process of understanding the content of an email using natural language processing technology and extracting important information and context.

[0514] A "draft reply" is a text message automatically generated as a response to an email received.

[0515] "Emotional assessment" is the process of analyzing the sender's emotional state from the content of an email and identifying that emotion.

[0516] "Adjusting tone and style" refers to the act of selecting an appropriate tone and writing style in a draft email reply, taking into consideration the sender's feelings.

[0517] A "terminal" is an electronic device used to present generated response suggestions to the user.

[0518] This system is designed to efficiently and effectively process emails received by the server. Upon receiving an email, the server hands over the email data to an AI agent. The AI ​​agent uses natural language processing techniques to analyze the email content in detail. This natural language processing utilizes software such as Google Cloud Natural Language API and spaCy. The analyzed text is then processed to extract important information.

[0519] Next, the AI ​​agent uses an emotion engine to evaluate the sender's emotions. Tools such as Hume AI and IBM Watson Tone Analyzer are used for this evaluation. The emotion engine quantifies the type and intensity of emotions contained in the transmitted text and provides this information to the AI ​​agent.

[0520] Subsequently, the AI ​​agent automatically generates a response using a generative AI model. By using prompts, the model can suggest the most appropriate response. This generation process takes into account the sender's sentiment assessment and adjusts the tone and style of the response. For example, if the sender is feeling dissatisfied, the AI ​​agent will generate a response that emphasizes a calm tone.

[0521] The generated response is presented to the user by the terminal. This allows the user to review the suggested response and edit it as needed. If the email content is urgent, the server flags the email, and the terminal notifies the user, prompting a quick response.

[0522] Examples of specific prompt messages include the following:

[0523] "Analyze the sentiment of the sender in the following email and suggest an appropriate reply. Email content: 'I am dissatisfied because your product is malfunctioning. Please address this promptly.'"

[0524] Thus, this invention improves the efficiency and quality of email replies, thereby reducing the burden on users.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] The server receives emails. The server detects new emails that have reached user accounts via the network and extracts their content as data. The input is newly received emails, and the output is the email data to be analyzed.

[0528] Step 2:

[0529] The server passes the email data to the AI ​​agent. The AI ​​agent receives the text data of the email and begins natural language processing. The input is the email data, and the output is text data for analysis.

[0530] Step 3:

[0531] An AI agent analyzes the content of emails. The AI ​​agent uses a natural language processing library to extract context and keywords from the text. The input is the text data of the email, and the output is the important information and keywords extracted through the analysis.

[0532] Step 4:

[0533] The emotion engine evaluates the sender's emotions. Based on the analysis results provided by the AI ​​agent, the emotion engine numerically evaluates the sender's emotional state. The input is the analyzed information, and the output is the sender's emotion evaluation data.

[0534] Step 5:

[0535] The AI ​​agent generates response suggestions. Using sentiment evaluation data and analysis information, the AI ​​agent inputs a prompt sentence into a generation AI model and obtains an appropriate response suggestion. The input is sentiment evaluation data and analysis information, and the output is the generated response suggestion.

[0536] Step 6:

[0537] The terminal presents the user with a generated response. The terminal displays the response in the user interface, allowing the user to review it. The input is the generated response, and the output is the presentation to the user.

[0538] Step 7:

[0539] The user reviews and edits the proposed reply. The user reviews the displayed reply and makes any necessary changes based on their own judgment. The input is the proposed reply, and the output is the content of the final email that will be sent.

[0540] (Application Example 2)

[0541] 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."

[0542] There is a growing demand for faster customer service and improved customer satisfaction in electronic payment services. However, communicating with customers via email makes it difficult to properly understand their emotions and context, and to create personalized responses. Furthermore, efficient processing is required because urgent issues need to be addressed immediately.

[0543] 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.

[0544] In this invention, the server includes means for analyzing information, means for automatically generating response proposals based on the analyzed information, and means for adjusting the tone of the response proposals based on sentiment analysis. This enables the rapid and appropriate generation of responses based on customer sentiment.

[0545] "Information" refers to data that is analyzed to generate responses, such as emails and customer inquiries.

[0546] "Analyzing" refers to the process of understanding the context and emotions behind information and extracting key points.

[0547] A "draft response" is a document that automatically generates a reply to the customer based on the analyzed information.

[0548] A "display device" refers to a terminal or device used by a user to review, adjust, or select response options.

[0549] "Emotional analysis" refers to the technology of reading and analyzing human emotions from information, which influences the tone of the response.

[0550] "Adjusting the tone" means changing the overall tone and style of a response based on the emotions derived from the information.

[0551] An "information management application" is software that manages the history of customer communication, such as emails, and uses it to generate response proposals.

[0552] "Identification information" refers to specific flags or tags used to indicate the priority or urgency of a response.

[0553] The system that realizes this invention is primarily implemented using a server, a display device, natural language processing technology, and sentiment analysis technology. The server stores the received emails related to electronic payments and begins processing the information. Google Natural Language API and similar text analysis tools are used for the analysis. This analysis means extracts the context and important information of the email and deeply evaluates the sender's emotions. For sentiment analysis technology, sentiment detection engines such as Affectiva API are used.

[0554] As a result of the analysis, the server automatically generates a response. OpenAI's GPT model is used to generate the response, creating text in an appropriate tone and style based on key information and sentiment analysis results. The generated response is presented to the user via a display device. The display device is primarily a smartphone or computer, designed to allow the user to review, adjust, and edit the response.

[0555] Furthermore, by linking with the information management system, various customer interaction histories are accumulated as learning data. Based on this history, response suggestions are customized to match the user's past response style. In addition, the server evaluates the priority of emails, assigns identification information, and notifies the display device, enabling a rapid response to urgent issues.

[0556] As a concrete example, consider a case where a customer sends a complaint email stating that their purchase could not be completed due to a payment error. The server analyzes the context and detects the customer's frustration through sentiment analysis. Based on this, the system generates a proposed response such as, "We sincerely apologize for the inconvenience this has caused, and we are working diligently to confirm your order and resolve the issue," and presents it to the user.

[0557] Examples of prompts for a generative AI model are as follows:

[0558] "Content of the received email: 'My payment was successful, but the purchase hasn't been completed.' User sentiment: Frustrated. Based on the above, please suggest an appropriate reply."

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The server stores received emails in a database. The input is the content of the email, and the output is the stored email data. This data is used for subsequent analysis.

[0562] Step 2:

[0563] The server uses Google's Natural Language API to analyze the content of emails. The input is stored email data, and the output is extracted context and important information. This deepens the understanding of the email content.

[0564] Step 3:

[0565] The server uses the Affectiva API's sentiment analysis technology to evaluate the sender's emotions from the analyzed context. The input is the analyzed contextual information, and the output is the type and intensity of the emotion. This allows for tone-setting of the response.

[0566] Step 4:

[0567] The server automatically generates response suggestions using OpenAI's GPT model. The input consists of context, key information, and sentiment analysis results, while the output is the generated response suggestion. Appropriate tone and style are considered to prepare the response for the user.

[0568] Step 5:

[0569] The server sends the generated draft response to the terminal and displays it to the user. The input is the generated draft response, and the output is the draft response displayed on the terminal. The user then reviews the draft response and makes adjustments and edits as needed.

[0570] Step 6:

[0571] The server works in conjunction with an information management application to store past response history as training data. Input consists of response proposals viewed on the terminal and user adjustment history, while output is updated training data. This allows for more personalized response generation in the future.

[0572] Step 7:

[0573] The server assesses the urgency of the email and, if necessary, adds identifying information before sending a notification to the terminal. The input is the received email and its parsed information, and the output is a notification with urgency identification information. This encourages users to respond quickly.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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".

[0591] This invention is a system for streamlining email reply processes, specifically using AI technology to analyze the content of received emails and assist users in responding quickly. The system functions as follows:

[0592] First, when the server receives an email, it passes it to an AI agent for analysis. In this analysis, the AI ​​agent uses natural language processing to grasp the subject and important information of the email and assess its urgency.

[0593] Next, the AI ​​agent automatically generates a response based on the analysis results. In particular, for emails regarding scheduling, the AI ​​integrates with a scheduling application to retrieve available times from the user's calendar and creates a response that includes multiple possible dates and times. At this time, the AI ​​agent refers to the user's past response history and style and strives to provide a response at a level of writing that suits the user.

[0594] The generated response is presented to the user by the device. The user can review it and edit the content as needed. Furthermore, if the email is of high urgency, the AI ​​agent will set a special flag, and the device will provide the user with a notification prompting immediate action.

[0595] This system will allow users to save a significant amount of time and focus on more important tasks. For example, consider a business person receiving numerous meeting scheduling requests via email. This system will streamline scheduling and reduce the effort required to respond to each email individually. This overall process is expected to significantly improve the efficiency of email management.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The server receives a new email. Once the email is received, the server sends it to the AI ​​agent. The AI ​​agent prepares to analyze the contents of the received email.

[0599] Step 2:

[0600] The AI ​​agent analyzes incoming emails using natural language processing techniques to identify the subject, important information, and urgency of the email. Based on the analysis, it categorizes the email (e.g., inquiry, scheduling, emergency response, etc.).

[0601] Step 3:

[0602] The AI ​​agent automatically generates response suggestions based on the email category. Specifically for scheduling emails, it integrates with scheduling applications, accesses the user's calendar to find available times, and creates response suggestions including multiple possible dates and times. This process takes into account the user's past response history and style.

[0603] Step 4:

[0604] The AI ​​agent generates and sends suggested replies to the device. The device displays a list of suggested replies to the user. For urgent emails, a special flag is set, and the device notifies the user that immediate action is required.

[0605] Step 5:

[0606] The user reviews the suggested reply and edits it as needed. After editing, the user confirms their chosen reply and completes the preparation for sending.

[0607] Step 6:

[0608] The device sends the user's confirmed reply to the server. The server then forwards this reply to the sender via email. Furthermore, the server records the content of this transmission and stores it as training data for the AI, which will be used to improve the accuracy of future analyses.

[0609] (Example 1)

[0610] 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".

[0611] In recent years, with the increase in electronic communication, responding quickly and appropriately to individual electronic messages has become a crucial issue for users. However, manual replies are time-consuming and labor-intensive, placing a significant burden on busy users. Furthermore, it is difficult to immediately prioritize emails and respond appropriately. To address this challenge, there is a need for a system that efficiently and automatically generates response proposals, allowing users to quickly review and respond to them.

[0612] 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.

[0613] In this invention, the server includes means for analyzing received information, means for automatically generating a response proposal based on the analyzed information, means for presenting the generated response proposal to a user device, and means for evaluating the importance and urgency of the information using natural language processing technology. This enables an automated response system that can quickly analyze electronic messages and provide appropriate responses without overloading the user.

[0614] "Received information" refers to data received by a device through a communication network, and specifically includes electronic messages and notifications.

[0615] "Means of analysis" refers to technical components that process received information, understand its content, and identify keywords and themes.

[0616] "Draft response" refers to the proposed response content that is automatically generated based on the analysis results.

[0617] "Means of automatic generation" refers to a process or structure that mechanically creates response proposals using a predetermined algorithm.

[0618] A "user device" refers to a terminal or device that a user directly uses and on which a message is displayed.

[0619] "Means of presentation" refers to a mechanism that allows the user to visually or audibly confirm the generated response proposals.

[0620] "Timetable application software" refers to programs used for schedule management and scheduling, and specifically includes calendar applications.

[0621] "Natural language processing technology" refers to the technology used to process and analyze human language and convert it into a form that machines can understand.

[0622] "Means for evaluating importance and urgency" refers to a function that evaluates information based on specific criteria in order to determine its value and urgency.

[0623] This invention is a system aimed at improving the efficiency of electronic communication, in which the server, terminal, and user elements work together. Specifically, the server analyzes received information using natural language processing technology and generates a response based on the results.

[0624] The server uses common natural language processing libraries to leverage natural language processing techniques in information analysis. This technique allows the server to identify the subject and importance of electronic messages and assess their urgency as needed.

[0625] Next, an AI model is used to automatically generate response suggestions based on the analysis results. Here, the user's past response formats and styles are considered to generate appropriate sentences. A machine learning algorithm is incorporated for this purpose.

[0626] The generated draft response is then presented to the user by the terminal. The terminal provides an interface that allows the user to review the response and edit it as needed. This makes it easy for the user to review the draft response and finalize the message to be sent.

[0627] Furthermore, the server marks information it deems highly urgent during the analysis phase. The terminal then uses this information to send a notification prompting the user to take immediate action. For example, if an invitation email for a very important meeting arrives, the terminal uses its instant notification function to display an alarm to the user.

[0628] As a concrete example, consider a situation where a user receives numerous meeting requests. By using this system, the user can automatically include available time slots in their reply proposals through a scheduling application, reducing the effort required for manual responses.

[0629] An example of a prompt for a generative AI model is, "Analyze this new email and generate a suggested reply." This prompt allows the server to process messages efficiently and provide users with quick and accurate responses.

[0630] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0631] Step 1:

[0632] The server receives electronic messages. The input is an unprocessed electronic message that has arrived via the network, and the output is text data in a parseable format. At this stage, the server accesses a database, records the received message, and prepares the basic data for use in subsequent parsing processes.

[0633] Step 2:

[0634] The server analyzes text data in a parsable format using natural language processing techniques. The input is the text data obtained in the previous step, and the output is the analysis results, including the message's subject, keywords, importance, and urgency. This analysis uses machine learning libraries to understand the content structure of the message and identify particularly important information.

[0635] Step 3:

[0636] The server automatically generates a response based on the analysis results. The input is the information obtained in the analysis step, and the output is a specific response proposal. In this step, a generative AI model is used to generate an appropriate, contextually relevant automation message in text format, referencing the user's past response style.

[0637] Step 4:

[0638] The terminal presents the generated response proposal to the user. The input is the response proposal sent from the server, and the output is a display of the response proposal in a format that is easy for the user to understand. The terminal displays a user interface and provides the user with options to edit the proposal or use it as is.

[0639] Step 5:

[0640] The server issues notifications based on the urgency of each message. The input is the urgency flag set during analysis, and the output is the emergency notification presented to the user on the terminal. In this step, actions such as beeping or displaying a notification screen are performed under specific conditions to prompt the user to take a quick action.

[0641] (Application Example 1)

[0642] 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".

[0643] In modern society, individuals are increasingly receiving numerous government notices and messages, and are required to respond to them quickly and appropriately. However, managing and responding to these messages is time-consuming and labor-intensive, and there is a particular challenge in responding quickly to urgent notices. Therefore, there is a need to develop systems that streamline citizens' lives and make effective use of resources.

[0644] 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.

[0645] In this invention, the server includes means for analyzing messages, means for automatically generating responses based on the analyzed content, means for presenting the generated responses to terminal devices, means for presenting responses along with candidate dates and times in cooperation with a scheduling function, means for analyzing notifications for citizens and evaluating their urgency, and means for providing special warnings according to the urgency. This makes it possible to respond efficiently and quickly to the large number of notifications received by citizens, thereby improving their quality of life.

[0646] "Means of message analysis" refers to techniques for analyzing the content of a received message, identifying its subject and important information, and understanding it.

[0647] "Means for automatically generating responses" refers to technologies that generate appropriate response sentences on behalf of the user based on the analyzed message content.

[0648] "Means for presenting the generated response to a terminal device" refers to technology for displaying automatically generated response text on a terminal so that the user can review and edit it.

[0649] "A means of presenting responses along with suggested dates and times in conjunction with the scheduling function" refers to a technology that, in conjunction with the calendar function, presents users with a selection of suggested dates and times for responding.

[0650] "Methods for analyzing notifications for citizens and assessing their urgency" refers to technologies for analyzing notifications from the government, determining their urgency, and setting priorities.

[0651] "Means of providing special warnings according to urgency" refers to technology that issues warnings to users according to the urgency of the notification, prompting them to take a swift action.

[0652] This invention relates to a system for efficiently processing administrative notices and messages received by citizens. The central operation of the system takes place on a server. Upon receiving a message, the server first uses means to analyze its content and understand it. In this process, natural language processing technology is utilized to identify the subject and important information of the message. Specifically, language processing is performed using Python and spaCy.

[0653] Next, the server automatically generates a response using OpenAI's GPT model based on the analyzed information. This is a crucial step in providing a quick and appropriate response on behalf of the user. This automatically generated response is presented to the user by the terminal device, and the user can review and edit it as needed.

[0654] Furthermore, the system integrates with a scheduling function and provides suggested dates and times as needed. This makes it easy to select an appropriate response time for a message. In addition, it analyzes notifications for citizens and, based on the evaluation of their urgency, displays a special warning on the terminal if the urgency is high. This process allows users to respond without delay even in cases requiring a quick response.

[0655] For example, if the server receives a notification from the city regarding a new public project, it will generate a response message to promptly submit opinions on the project and provide appropriate feedback. An example of a prompt message in this case would be: "Read the email and create an automated response based on the following information: the user's past response style, inquiries about the city's construction project, and whether they support or oppose it. If it is urgent, use language that reflects that."

[0656] This invention aims to streamline information processing in citizens' daily lives, thereby freeing up time to focus on more important activities.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The server receives an electronic message. At this point, the input is the electronic message received from the mail server, and the output is its raw data. The server prepares this data for analysis.

[0660] Step 2:

[0661] The server analyzes received messages using spaCy, a natural language processing tool. The input is the raw data obtained in step 1, and the output is the analysis result that identifies the message's subject and important information. This process structures the message content, making it easier to process in the next step.

[0662] Step 3:

[0663] The server uses the analysis results to automatically generate a response using OpenAI GPT. The input here is the analysis results from step 2, and the output is a response that can be presented to the user. As an example of a generated prompt, "Include the user's past response style, inquiries about the city's construction project, and opinions for / against it," and formulate an appropriate response.

[0664] Step 4:

[0665] The server sends the generated response to the terminal device. The terminal receives this input, presents it to the user, and provides an interface for the user to review and edit the output. This allows the user to review the response and make corrections as needed.

[0666] Step 5:

[0667] Once the user confirms and completes the response, the terminal sends that response back to the server. Based on this input, the server interacts with the scheduling application and generates output that provides the user with information, including additional suggested dates and times as needed.

[0668] Step 6:

[0669] When the server receives a notification for citizens, it assesses its urgency. The raw data of the notification received in Step 1 is processed, and the output is a priority rating based on urgency. If it is deemed highly urgent, it is marked and proceeds to the next step.

[0670] Step 7:

[0671] In cases of high urgency, the device provides the user with a special warning. The input is the urgency assessment defined in step 6, and the output is a warning notification prompting the user to take specific action. This step enables support for quick and appropriate action on important notifications.

[0672] 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.

[0673] This invention is a system that automatically generates reply suggestions based on the content of received emails. In particular, by combining it with an emotion engine that analyzes the user's emotions, it enables more appropriate reply suggestions. This system is implemented according to the following procedure.

[0674] First, when the server receives an email, it passes the email data to an AI agent. The AI ​​agent is equipped with natural language processing capabilities and analyzes the context and important information within the email. Furthermore, an emotion engine evaluates the sender's emotions based on the text data extracted from the email. This evaluation helps to understand the tone and intensity of the emotions expressed in the email.

[0675] Next, the AI ​​agent automatically generates a response based on the email analysis results and the sentiment engine's evaluation. During the generation process, it considers the sentiment analysis results from the sentiment engine and adjusts the tone and style of the response. For example, if the sender is irritated, it prioritizes generating a response using a polite and calm tone. Furthermore, it utilizes the user's past response style and history as training data to provide more personalized responses.

[0676] The generated response suggestions are presented to the user by the terminal. The user reviews the suggested responses, makes adjustments and edits as needed, and then makes the best choice. Furthermore, if the email is of high urgency, the server sets a special flag, and the terminal notifies the user to encourage a prompt response.

[0677] As a concrete example, consider a situation where a customer support representative receives a complaint email. This system can accurately analyze the sender's emotions and provide an appropriate response that takes into account the need for a calm response. As a result, the representative can communicate with the customer efficiently and effectively. In this way, this system improves the efficiency and appropriateness of email responses, significantly reducing the workload of the user.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The server receives a new email. The received email is sent to an AI agent for analysis.

[0681] Step 2:

[0682] The AI ​​agent first uses natural language processing technology to analyze the email text and extract the email's subject and keywords. Based on this information, it then classifies the content of the email.

[0683] Step 3:

[0684] The emotion engine within the AI ​​agent recognizes emotions from the email text. Specifically, it calculates emotion scores for words and evaluates the sender's emotional tone (e.g., joy, anger, sadness, etc.).

[0685] Step 4:

[0686] The AI ​​agent automatically generates a response based on the analysis results and sentiment analysis. During this process, it adjusts the tone of the response to reflect the results of the sentiment engine. For example, if the sender's emotions are negative, it will use polite and considerate language.

[0687] Step 5:

[0688] The AI ​​agent sends several generated response options to the device. The device then presents these to the user, showing the reasons for each recommendation and the available options.

[0689] Step 6:

[0690] The user reviews the suggested reply options and edits or selects as appropriate. Once the user confirms their reply, the information is sent from the device to the server, and an email is sent as the official reply.

[0691] Step 7:

[0692] The server records the content of the sent emails and stores it as training data for the AI ​​agent. This data will be used to improve the accuracy of future analyses.

[0693] (Example 2)

[0694] 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".

[0695] In recent years, email communication has been steadily increasing, and businesses in particular face the need to process a large volume of emails. However, responding quickly and appropriately to every email is a significant burden. Furthermore, accurately understanding the sender's sentiment and responding with the appropriate tone is not easy. There is a need for a system that enables efficient and effective email replies.

[0696] 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.

[0697] In this invention, the server includes means for analyzing an incoming email, means for automatically generating a reply based on the analyzed content, means for evaluating the sender's emotions, means for adjusting the tone and style of the reply based on the evaluated emotions, and means for presenting the generated reply to the user's terminal. This enables prompt, emotionally appropriate email replies.

[0698] "Incoming email" refers to new electronic messages that arrive in a user's account via a communication network.

[0699] "Analysis" refers to the process of understanding the content of an email using natural language processing technology and extracting important information and context.

[0700] A "draft reply" is a text message automatically generated as a response to an email received.

[0701] "Emotional assessment" is the process of analyzing the sender's emotional state from the content of an email and identifying that emotion.

[0702] "Adjusting tone and style" refers to the act of selecting an appropriate tone and writing style in a draft email reply, taking into consideration the sender's feelings.

[0703] A "terminal" is an electronic device used to present generated response suggestions to the user.

[0704] This system is designed to efficiently and effectively process emails received by the server. Upon receiving an email, the server hands over the email data to an AI agent. The AI ​​agent uses natural language processing techniques to analyze the email content in detail. This natural language processing utilizes software such as Google Cloud Natural Language API and spaCy. The analyzed text is then processed to extract important information.

[0705] Next, the AI ​​agent uses an emotion engine to evaluate the sender's emotions. Tools such as Hume AI and IBM Watson Tone Analyzer are used for this evaluation. The emotion engine quantifies the type and intensity of emotions contained in the transmitted text and provides this information to the AI ​​agent.

[0706] Subsequently, the AI ​​agent automatically generates a response using a generative AI model. By using prompts, the model can suggest the most appropriate response. This generation process takes into account the sender's sentiment assessment and adjusts the tone and style of the response. For example, if the sender is feeling dissatisfied, the AI ​​agent will generate a response that emphasizes a calm tone.

[0707] The generated response is presented to the user by the terminal. This allows the user to review the suggested response and edit it as needed. If the email content is urgent, the server flags the email, and the terminal notifies the user, prompting a quick response.

[0708] Examples of specific prompt messages include the following:

[0709] "Analyze the sentiment of the sender in the following email and suggest an appropriate reply. Email content: 'I am dissatisfied because your product is malfunctioning. Please address this promptly.'"

[0710] Thus, this invention improves the efficiency and quality of email replies, thereby reducing the burden on users.

[0711] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0712] Step 1:

[0713] The server receives emails. The server detects new emails that have reached user accounts via the network and extracts their content as data. The input is newly received emails, and the output is the email data to be analyzed.

[0714] Step 2:

[0715] The server passes the email data to the AI ​​agent. The AI ​​agent receives the text data of the email and begins natural language processing. The input is the email data, and the output is text data for analysis.

[0716] Step 3:

[0717] An AI agent analyzes the content of emails. The AI ​​agent uses a natural language processing library to extract context and keywords from the text. The input is the text data of the email, and the output is the important information and keywords extracted through the analysis.

[0718] Step 4:

[0719] The emotion engine evaluates the sender's emotions. Based on the analysis results provided by the AI ​​agent, the emotion engine numerically evaluates the sender's emotional state. The input is the analyzed information, and the output is the sender's emotion evaluation data.

[0720] Step 5:

[0721] The AI ​​agent generates response suggestions. Using sentiment evaluation data and analysis information, the AI ​​agent inputs a prompt sentence into a generation AI model and obtains an appropriate response suggestion. The input is sentiment evaluation data and analysis information, and the output is the generated response suggestion.

[0722] Step 6:

[0723] The terminal presents the user with a generated response. The terminal displays the response in the user interface, allowing the user to review it. The input is the generated response, and the output is the presentation to the user.

[0724] Step 7:

[0725] The user reviews and edits the proposed reply. The user reviews the displayed reply and makes any necessary changes based on their own judgment. The input is the proposed reply, and the output is the content of the final email that will be sent.

[0726] (Application Example 2)

[0727] 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".

[0728] There is a growing demand for faster customer service and improved customer satisfaction in electronic payment services. However, communicating with customers via email makes it difficult to properly understand their emotions and context, and to create personalized responses. Furthermore, efficient processing is required because urgent issues need to be addressed immediately.

[0729] 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.

[0730] In this invention, the server includes means for analyzing information, means for automatically generating response proposals based on the analyzed information, and means for adjusting the tone of the response proposals based on sentiment analysis. This enables the rapid and appropriate generation of responses based on customer sentiment.

[0731] "Information" refers to data that is analyzed to generate responses, such as emails and customer inquiries.

[0732] "Analyzing" refers to the process of understanding the context and emotions behind information and extracting key points.

[0733] A "draft response" is a document that automatically generates a reply to the customer based on the analyzed information.

[0734] A "display device" refers to a terminal or device used by a user to review, adjust, or select response options.

[0735] "Emotional analysis" refers to the technology of reading and analyzing human emotions from information, which influences the tone of the response.

[0736] "Adjusting the tone" means changing the overall tone and style of a response based on the emotions derived from the information.

[0737] An "information management application" is software that manages the history of customer communication, such as emails, and uses it to generate response proposals.

[0738] "Identification information" refers to specific flags or tags used to indicate the priority or urgency of a response.

[0739] The system that realizes this invention is primarily implemented using a server, a display device, natural language processing technology, and sentiment analysis technology. The server stores the received emails related to electronic payments and begins processing the information. Google Natural Language API and similar text analysis tools are used for the analysis. This analysis means extracts the context and important information of the email and deeply evaluates the sender's emotions. For sentiment analysis technology, sentiment detection engines such as Affectiva API are used.

[0740] As a result of the analysis, the server automatically generates a response. OpenAI's GPT model is used to generate the response, creating text in an appropriate tone and style based on key information and sentiment analysis results. The generated response is presented to the user via a display device. The display device is primarily a smartphone or computer, designed to allow the user to review, adjust, and edit the response.

[0741] Furthermore, by linking with the information management system, various customer interaction histories are accumulated as learning data. Based on this history, response suggestions are customized to match the user's past response style. In addition, the server evaluates the priority of emails, assigns identification information, and notifies the display device, enabling a rapid response to urgent issues.

[0742] As a concrete example, consider a case where a customer sends a complaint email stating that their purchase could not be completed due to a payment error. The server analyzes the context and detects the customer's frustration through sentiment analysis. Based on this, the system generates a proposed response such as, "We sincerely apologize for the inconvenience this has caused, and we are working diligently to confirm your order and resolve the issue," and presents it to the user.

[0743] Examples of prompts for a generative AI model are as follows:

[0744] "Content of the received email: 'My payment was successful, but the purchase hasn't been completed.' User sentiment: Frustrated. Based on the above, please suggest an appropriate reply."

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The server stores received emails in a database. The input is the content of the email, and the output is the stored email data. This data is used for subsequent analysis.

[0748] Step 2:

[0749] The server uses Google's Natural Language API to analyze the content of emails. The input is stored email data, and the output is extracted context and important information. This deepens the understanding of the email content.

[0750] Step 3:

[0751] The server uses the Affectiva API's sentiment analysis technology to evaluate the sender's emotions from the analyzed context. The input is the analyzed contextual information, and the output is the type and intensity of the emotion. This allows for tone-setting of the response.

[0752] Step 4:

[0753] The server automatically generates response suggestions using OpenAI's GPT model. The input consists of context, key information, and sentiment analysis results, while the output is the generated response suggestion. Appropriate tone and style are considered to prepare the response for the user.

[0754] Step 5:

[0755] The server sends the generated draft response to the terminal and displays it to the user. The input is the generated draft response, and the output is the draft response displayed on the terminal. The user then reviews the draft response and makes adjustments and edits as needed.

[0756] Step 6:

[0757] The server works in conjunction with an information management application to store past response history as training data. Input consists of response proposals viewed on the terminal and user adjustment history, while output is updated training data. This allows for more personalized response generation in the future.

[0758] Step 7:

[0759] The server assesses the urgency of the email and, if necessary, adds identifying information before sending a notification to the terminal. The input is the received email and its parsed information, and the output is a notification with urgency identification information. This encourages users to respond quickly.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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."

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0781] The following is further disclosed regarding the embodiments described above.

[0782] (Claim 1)

[0783] A means of analyzing received emails,

[0784] A means for automatically generating response proposals based on the analyzed content,

[0785] A means of presenting the generated reply draft to the user's terminal,

[0786] A method for presenting response suggestions along with proposed dates and times in conjunction with a scheduling application,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, which learns the user's past reply style and reflects it in generating reply suggestions.

[0790] (Claim 3)

[0791] The system according to claim 1, which sets a special flag based on the urgency and notifies the user.

[0792] "Example 1"

[0793] (Claim 1)

[0794] A means of analyzing received information,

[0795] A means for automatically generating response proposals based on analyzed information,

[0796] A means for presenting the generated response proposal to the user device,

[0797] A means of presenting response options along with candidate times in conjunction with timetable application software,

[0798] A means for evaluating the importance and urgency of information using natural language processing technology,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, which learns the user's past response format and reflects it in generating response suggestions.

[0802] (Claim 3)

[0803] The system according to claim 1, which specially marks and notifies the user based on the degree of urgency.

[0804] "Application Example 1"

[0805] (Claim 1)

[0806] Means for analyzing messages,

[0807] A means for automatically generating a response based on the analyzed content,

[0808] A means for presenting the generated response to a terminal device,

[0809] A method for presenting responses along with suggested dates and times in conjunction with the scheduling function,

[0810] A means of analyzing public notices and assessing their urgency,

[0811] Means of providing special warnings according to the urgency,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, which learns the user's past response style and reflects it in response generation.

[0815] (Claim 3)

[0816] The system according to claim 1, which flags notifications based on their urgency and prompts users to take action.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] A means of analyzing received emails,

[0820] A means for automatically generating response proposals based on the analyzed content,

[0821] A means of evaluating the sender's emotions,

[0822] A means of adjusting the tone and style of the response based on the evaluated emotions,

[0823] A means of presenting the generated reply draft to the user's terminal,

[0824] A method for presenting response suggestions along with proposed dates and times in conjunction with a scheduling application,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, which learns the user's past reply style and reflects it in generating reply suggestions.

[0828] (Claim 3)

[0829] The system according to claim 1, which sets a special flag based on the urgency and notifies the user.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] Means for analyzing information,

[0833] A means for automatically generating response proposals based on analyzed information,

[0834] A means for presenting the generated response proposal on a display device,

[0835] A means of adjusting the tone of response proposals based on emotion analysis,

[0836] A means of presenting proposed responses in conjunction with an information management application,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which learns past response styles and reflects them in the generation of response proposals.

[0840] (Claim 3)

[0841] The system according to claim 1, which assigns special identification information based on priority and notifies the user. [Explanation of Symbols]

[0842] 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. Means for analyzing messages, A means for automatically generating a response based on the analyzed content, A means for presenting the generated response to a terminal device, A method for presenting responses along with suggested dates and times in conjunction with the scheduling function, A means of analyzing public notices and assessing their urgency, Means of providing special warnings according to the urgency, A system that includes this.

2. The system according to claim 1, which learns the user's past response style and reflects it in response generation.

3. The system according to claim 1, which flags notifications based on their urgency and prompts users to take action.

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