system

Abstract

The system according to this embodiment aims to improve operational efficiency by automating the management of received emails. [Solution] The system according to the embodiment comprises a classification unit, a summarization unit, a reply generation unit, and an analysis unit. The classification unit automatically classifies and organizes received emails. The summarization unit summarizes the content of the emails classified by the classification unit. The reply generation unit automatically generates an appropriate reply based on the content of the emails summarized by the summarization unit. The analysis unit analyzes the reply content generated by the reply generation unit and reports on communication patterns and trends.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time and labor to manage received mails and reply to them, resulting in a decrease in work efficiency.

[0005] The system according to the embodiment aims to automate the management of received mails and improve work efficiency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a classification unit, a summarization unit, a reply generation unit, and an analysis unit. The classification unit automatically classifies and organizes received emails. The summarization unit summarizes the content of the emails classified by the classification unit. The reply generation unit automatically generates appropriate replies based on the content of the emails summarized by the summarization unit. The analysis unit analyzes the replies generated by the reply generation unit and reports on communication patterns and trends. [Effects of the Invention]

[0007] The system according to this embodiment can automate the management of received emails and improve operational efficiency. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The email management system according to an embodiment of the present invention is a system that streamlines email management by utilizing generation AI. This email management system automatically classifies and organizes received emails and notifies the user based on priority. Next, as an email content summary, it summarizes long emails and provides the user with concise information. Furthermore, as an automatic reply generation, it automatically generates appropriate replies based on the content of received emails. Finally, as email analysis and reporting, it analyzes email exchanges and reports on communication patterns and trends. For example, the email management system automatically classifies and organizes received emails and notifies the user based on priority. For example, the email management system summarizes long emails and provides the user with concise information. For example, the email management system automatically generates appropriate replies based on the content of received emails. For example, the email management system analyzes email exchanges and reports on communication patterns and trends. As a result, the email management system can be expected to streamline email management, prevent important emails from being missed, reduce spam, shorten email checking time, allow for quick access to important information, improve usability in mobile environments, reduce response time, ensure consistent communication, improve communication quality, enhance team performance, and increase customer satisfaction.

[0029] The email management system according to this embodiment comprises a classification unit, a summarization unit, a reply generation unit, and an analysis unit. The classification unit automatically classifies and organizes received emails. The classification unit classifies emails based on, for example, the content of the email, the sender, the subject, etc. The classification unit can, for example, prioritize the display of important emails and automatically filter out spam emails. The classification unit can, for example, analyze the content of emails and classify them based on their importance. The summarization unit summarizes long emails and provides users with concise information. The summarization unit can, for example, analyze the content of emails, extract important information, and generate a summary. The summarization unit can, for example, generate summaries based on the length of the email and the importance of the information being summarized. The summarization unit can, for example, use a generation AI to summarize the content of emails. The reply generation unit automatically generates appropriate replies based on the content of received emails. The reply generation unit can, for example, analyze the content of emails and generate appropriate replies. The reply generation unit can, for example, determine the content, format, and timing of replies based on the content of emails. The reply generation unit can generate replies based on the content of emails, for example, using a generation AI. The analysis unit analyzes email exchanges and reports on communication patterns and trends. The analysis unit can, for example, analyze the content of emails and extract communication patterns and trends. The analysis unit can, for example, analyze the frequency of email exchanges and reply times to identify communication patterns. The analysis unit can, for example, use a generation AI to analyze email exchanges. As a result, the email management system according to this embodiment is expected to improve the efficiency of email management, prevent important emails from being missed, reduce spam emails, shorten the time required to check emails, enable quick access to important information, improve usability in mobile environments, shorten the time required for replying, ensure consistent communication, improve the quality of communication, improve team performance, and increase customer satisfaction.

[0030] The classification unit automatically categorizes and organizes incoming emails. For example, it categorizes emails based on their content, sender, subject, etc. Specifically, it uses natural language processing technology to analyze the email body and extract keywords and phrases to understand the email's content. Sender information is cross-referenced with past correspondence and contact lists to prioritize emails from important senders. For subjects, it detects specific keywords and phrases and evaluates their importance. The classification unit can, for example, prioritize important emails and automatically filter out spam. For spam filtering, it uses machine learning algorithms to learn the characteristics of spam emails and detect new spam with high accuracy. Furthermore, it can continuously improve filtering accuracy by incorporating user feedback. The classification unit can, for example, analyze email content and categorize emails based on their importance. The importance evaluation comprehensively considers factors such as the urgency and relevance of the email content, the importance of the sender, and the frequency of past correspondence. This allows users to efficiently manage their emails without missing important messages. The classification unit provides customizable classification rules based on user settings and preferences, enabling email management tailored to individual needs. For example, emails related to specific projects or clients can be automatically sorted into specific folders. This reduces the burden of email management for users and supports efficient email processing.

[0031] The summarization unit summarizes long emails and provides users with concise information. For example, the summarization unit analyzes the email content, extracts important information, and generates a summary. Specifically, it uses natural language processing techniques to analyze the email body and extract important keywords and phrases. This allows it to grasp the main points of the email and generate a concise summary. The summarization unit can generate summaries based on factors such as the length of the email and the importance of the information being summarized. For long emails, multiple summarization algorithms can be combined to prioritize the extraction of important information and improve the accuracy of the summary. The summarization unit can also use generative AI to summarize email content. Generative AI learns from large datasets, understands email content, and generates appropriate summaries. For example, the generative AI takes the email body as input, extracts important information, and outputs a concise summary. This reduces the time users spend reading long emails and allows them to quickly grasp important information. The summarization unit can continuously improve the accuracy of its summaries based on user feedback. For example, users can evaluate the content of the summaries, and the summarization algorithm can be improved based on that evaluation. Furthermore, the summarization section supports multiple languages ​​and can summarize emails in different languages. This allows the summarization section to function effectively even in a global business environment, improving user convenience.

[0032] The reply generation unit automatically generates appropriate replies based on the content of received emails. For example, the reply generation unit analyzes the content of an email and generates an appropriate reply. Specifically, it uses natural language processing technology to analyze the email body and extract the information necessary for the reply. This allows it to understand the content of the reply and generate an appropriate reply. For example, the reply generation unit can determine the content, format, and timing of the reply based on the content of the email. The content of the reply is generated based on the email body, past exchanges, and user settings. The reply format is selected according to the situation, such as business email or casual email. The timing of the reply is determined based on the urgency and importance of the email. For example, the reply generation unit can use a generation AI to generate replies based on the content of an email. The generation AI learns from large datasets, understands the content of emails, and generates appropriate replies. For example, the generation AI takes the email body as input and outputs an appropriate reply. This allows users to reduce the time spent replying and maintain consistent communication. The reply generation unit can continuously improve the accuracy of replies based on user feedback. For example, users can rate replies, and the reply algorithm can be improved based on those ratings. Furthermore, the reply generation unit supports multiple languages, enabling it to generate appropriate replies to emails in different languages. This allows the reply generation unit to function effectively in a global business environment, improving user convenience.

[0033] The analytics department analyzes email exchanges and reports on communication patterns and trends. For example, the analytics department analyzes the content of emails and extracts communication patterns and trends. Specifically, it uses natural language processing technology to analyze the body of emails and extract keywords and phrases to understand the content of the communication. This allows it to grasp the frequency and trends of exchanges on specific themes and topics. For example, the analytics department can analyze the frequency and response time of email exchanges to identify communication patterns. For example, it can analyze the frequency and response time of email exchanges related to a specific project or client to evaluate the efficiency and effectiveness of communication. For example, the analytics department can use generative AI to analyze email exchanges. Generative AI learns from large datasets, understands the content of emails, and extracts communication patterns and trends. For example, generative AI takes the body of an email as input and outputs the frequency and trends of exchanges on specific themes and topics. This allows users to gain insights to improve the quality and efficiency of their communication. The analytics department can continuously improve the accuracy of its analysis based on user feedback. For example, users can evaluate the analysis results, and the analysis algorithm can be improved based on that evaluation. Furthermore, the analysis unit supports multiple languages ​​and can analyze email exchanges in different languages. This allows the analysis unit to function effectively even in a global business environment, improving user convenience.

[0034] The classification unit can automatically classify and organize incoming emails and notify the user based on priority. For example, the classification unit can automatically classify incoming emails and display important emails preferentially. For example, the classification unit can automatically filter spam emails. For example, the classification unit can analyze the content of emails and classify them based on importance. This prevents users from missing important emails by notifying them based on the priority of incoming emails. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input incoming emails into AI, which can then analyze the content of the emails and perform the classification.

[0035] The summarization unit can summarize long emails and provide users with concise information. For example, the summarization unit can analyze the content of an email, extract important information, and generate a summary. For example, the summarization unit can generate a summary based on the length of the email and the importance of the information to be summarized. For example, the summarization unit can use a generation AI to summarize the content of an email. This allows for the provision of concise information to users and reduces the time spent checking emails by summarizing long emails. Some or all of the above-described processes in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input a long email into an AI, which can then analyze the content of the email and generate a summary.

[0036] The reply generation unit can automatically generate an appropriate reply based on the content of the received email. For example, the reply generation unit can analyze the content of the email and generate an appropriate reply. For example, the reply generation unit can determine the content, format, and timing of the reply based on the content of the email. For example, the reply generation unit can generate a reply based on the content of the email using a generation AI. This reduces the time required for reply work by automatically generating an appropriate reply based on the content of the received email. Some or all of the above-described processes in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the received email into the AI, which can then analyze the content of the email and generate a reply.

[0037] The analysis department can analyze email exchanges and report on communication patterns and trends. For example, the analysis department can analyze the content of emails and extract communication patterns and trends. For example, the analysis department can analyze the frequency and response time of email exchanges to identify communication patterns. For example, the analysis department can use generative AI to analyze email exchanges. This allows for the improvement of communication quality by analyzing email exchanges and reporting on communication patterns and trends. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input email exchanges into AI, which can analyze the content of the emails and extract communication patterns and trends.

[0038] The classification unit can analyze the classification history of past emails and select the optimal classification algorithm. For example, the classification unit can analyze the characteristics of emails that users have previously marked as important and automatically classify emails with similar characteristics as important emails. For example, the classification unit can analyze the characteristics of emails that users have previously marked as spam and automatically classify emails with similar characteristics as spam emails. For example, the classification unit can analyze the characteristics of emails that users have previously moved to a specific folder and automatically classify emails with similar characteristics into the same folder. By analyzing the classification history of past emails, the system can select the optimal classification algorithm and improve the accuracy of email management. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the classification history of past emails into AI, which can then select the optimal classification algorithm.

[0039] The classification unit can automatically filter spam emails and important emails based on their content. For example, the classification unit can analyze keywords in the subject line and body of an email and automatically filter out spam emails. For example, the classification unit can analyze the sender address of an email and classify emails from trusted senders as important emails. For example, the classification unit can analyze the content of an email using natural language processing technology and prioritize displaying emails that contain important information. This improves the efficiency of email management by automatically filtering out spam emails and important emails based on their content. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of an email into an AI, which can then filter out spam emails and important emails.

[0040] The classification unit can prioritize the classification of emails based on the user's geographical location information. For example, if the user is in a specific region, the classification unit will prioritize displaying emails related to that region. For example, if the user is traveling, the classification unit will prioritize displaying emails related to their travel destination. For example, if the user is at home, the classification unit will prioritize displaying emails related to their home. This allows for email management tailored to the user's situation by prioritizing the classification of emails based on the user's geographical location information. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the user's geographical location information into AI, which can then classify the emails that are most relevant.

[0041] The classification unit can analyze a user's social media activity when classifying emails and classify relevant emails. For example, if a user posts about a specific topic on social media, the classification unit will prioritize displaying emails related to that topic. For example, if a user participates in a specific event on social media, the classification unit will prioritize displaying emails related to that event. For example, if a user belongs to a specific group on social media, the classification unit will prioritize displaying emails related to that group. This allows for email management tailored to the user's situation by analyzing the user's social media activity and classifying relevant emails. Some or all of the above processing in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the user's social media activity data into AI, and the AI ​​can classify relevant emails.

[0042] The summarization unit can adjust the level of detail in the summary based on the importance of the email during summary generation. For example, the summarization unit provides a detailed summary for high-importance emails. For example, the summarization unit provides a concise summary for low-importance emails. For example, the summarization unit provides a summary with a moderate level of detail for emails of moderate importance. In this way, by adjusting the level of detail in the summary based on the importance of the email, important emails are summarized in detail. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input the importance of the email into the AI, and the AI ​​can adjust the level of detail in the summary.

[0043] The summarization unit can apply different summarization algorithms depending on the email category when generating summaries. For example, for business emails, the summarization unit provides a summary that highlights key points. For private emails, the summarization unit provides a summary that includes emotional elements. For spam emails, the summarization unit provides a concise summary. By applying different summarization algorithms depending on the email category, appropriate summaries are provided for each category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the email category into the AI, and the AI ​​can apply different summarization algorithms.

[0044] The summarization unit can determine the priority of summaries based on when the emails were sent. For example, the summarization unit will prioritize summarizing recently sent emails. For example, it will postpone summarizing older emails. For example, it will prioritize emails sent within a specific time period. This ensures that the most recent emails are summarized first by determining the priority of summaries based on when the emails were sent. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the email sending dates into the AI, which can then determine the priority of summaries.

[0045] The summarization unit can adjust the order of summaries based on the relevance of the emails during summary generation. For example, the summarization unit prioritizes generating summaries for highly relevant emails. For example, it postpones generating summaries for less relevant emails. For example, for emails related to a specific topic, the summarization unit generates summaries in an order appropriate to that topic. In this way, by adjusting the order of summaries based on the relevance of the emails, highly relevant emails are prioritized for summarization. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can input the relevance of the emails into the AI, and the AI ​​can adjust the order of the summaries.

[0046] The reply generation unit can adjust the level of detail in the reply based on the content of the email when generating a reply. For example, the reply generation unit provides a detailed reply for important emails. For example, the reply generation unit provides a concise reply for unimportant emails. For example, the reply generation unit provides a reply with an appropriate level of detail for emails of moderate importance. In this way, by adjusting the level of detail in the reply based on the content of the email, a detailed reply is provided for important emails. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the content of the email into AI, and the AI ​​can adjust the level of detail in the reply.

[0047] The reply generation unit can apply different reply algorithms depending on the email category when generating a reply. For example, the reply generation unit provides a formal reply for business emails, a casual reply for private emails, and a concise reply for spam emails. By applying different reply algorithms depending on the email category, appropriate replies are provided according to the category. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the email category into the AI, and the AI ​​can apply different reply algorithms.

[0048] The reply generation unit can determine the priority of replies based on when the email was sent. For example, the reply generation unit will prioritize replies to recently sent emails. For example, the reply generation unit will postpone replies to older emails. For example, the reply generation unit will prioritize replies to emails sent within a specific time period according to that time. In this way, by determining the priority of replies based on when the email was sent, the most recent emails will receive priority replies. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the email sending time into the AI, and the AI ​​can determine the priority of replies.

[0049] The reply generation unit can adjust the order of replies based on the relevance of the emails when generating replies. For example, the reply generation unit will prioritize generating replies to highly relevant emails. For example, the reply generation unit will postpone generating replies to less relevant emails. For example, the reply generation unit will generate replies to emails related to a specific topic in an order appropriate to that topic. In this way, by adjusting the order of replies based on the relevance of the emails, highly relevant emails will be given priority in receiving replies. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or not using AI. For example, the reply generation unit can input the relevance of the emails into the AI, and the AI ​​can adjust the order of replies.

[0050] The analysis unit can predict current communication patterns by referring to past email data during analysis. For example, the analysis unit can analyze communication patterns with people the user has frequently interacted with in the past and predict current patterns. For example, the analysis unit can analyze emails the user has exchanged in the past on a specific topic and predict patterns related to the current topic. For example, the analysis unit can analyze emails the user has exchanged in the past during a specific time period and predict patterns related to the current time period. This allows for improvement of the quality of current communication by predicting current communication patterns by referring to past email data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past email data into AI, which can then predict current communication patterns.

[0051] The analysis department can apply different analysis methods to each email category during analysis. For example, for business emails, the analysis department will perform an analysis that highlights important points. For personal emails, the analysis department will perform an analysis that includes emotional elements. For spam emails, the analysis department will perform a concise analysis. By applying different analysis methods to each email category, appropriate analysis is provided for each category. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the email categories into the AI, and the AI ​​can apply different analysis methods.

[0052] The analysis unit can analyze changes in communication patterns based on the timing of email transmissions during the analysis process. For example, the analysis unit can analyze the communication patterns of emails sent during a specific time period, according to that time period. For example, the analysis unit can analyze the communication patterns of emails sent on a specific day of the week, according to that day of the week. For example, the analysis unit can analyze the communication patterns of emails sent within a specific period, according to that period. By analyzing changes in communication patterns based on the timing of email transmissions, the quality of communication can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of email transmissions into the AI, which can then analyze changes in communication patterns.

[0053] The analysis department can analyze communication trends by referring to relevant market data for email during analysis. For example, the analysis department can analyze current communication trends based on relevant market data. For example, the analysis department can predict future communication trends based on relevant market data. For example, the analysis department can analyze communication trends in a specific industry based on relevant market data. This allows for improvement in the quality of communication by analyzing communication trends by referring to relevant market data for email. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant market data into AI, and the AI ​​can analyze communication trends.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The email management system can also be equipped with a speech recognition unit. The speech recognition unit can classify, summarize, and generate replies to emails based on voice commands from the user. For example, if the user says, "Show me important emails," the speech recognition unit can analyze the command and instruct the classification unit to display the important emails. Furthermore, if the user says, "Summarize this email," the speech recognition unit can analyze the command and instruct the summarization unit to generate a summary of the email. Also, if the user says, "Reply to this email," the speech recognition unit can analyze the command and instruct the reply generation unit to generate a reply. As a result, by using speech recognition, users can manage their emails without using their hands, improving convenience.

[0056] The email management system can also include a translation function. This function can automatically translate the content of incoming emails and display them to the user. For example, it can translate an email written in English into Japanese and display it. The translation function can also analyze the content of an email and translate it into the appropriate language. Furthermore, it can use generative AI to translate the content of an email. This allows for the understanding of emails written in different languages, facilitating smoother international communication.

[0057] The email management system can also include a schedule integration unit. This unit can integrate with the user's schedule information and adjust email priorities accordingly. For example, if a user is in a meeting, only important emails can be notified, while other emails are delayed. The schedule integration unit can also retrieve the user's calendar information and adjust email priorities based on their current schedule. This enables email management tailored to the user's schedule, preventing important emails from being missed.

[0058] The email management system can also include a feedback section. This feedback section can collect user feedback and use it to improve the system's accuracy. For example, if a user provides feedback stating "This email is not important," the feedback section can analyze that information and reflect it in the classification section. Furthermore, if a user provides feedback stating "This summary is insufficient," the feedback section can analyze that information and reflect it in the summarization section. Also, if a user provides feedback stating "This reply is inappropriate," the feedback section can analyze that information and reflect it in the reply generation section. This allows the system's accuracy to be improved by leveraging user feedback.

[0059] The email management system can also include a security unit. This unit can assess the security risks of incoming emails and issue warnings to users. For example, it can detect emails that may contain phishing or malware and display warnings to users. The security unit can also analyze email content and sender information to assess security risks. This allows users to identify potentially risky emails in advance and take appropriate measures.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The classification unit automatically categorizes and organizes incoming emails. The classification unit categorizes emails based on factors such as content, sender, and subject. The classification unit can prioritize displaying important emails and automatically filter out spam. Step 2: The summarization unit summarizes long emails and provides users with concise information. The summarization unit analyzes the content of the email, extracts important information, and generates a summary. The summarization unit can summarize the content of the email using generation AI. Step 3: The reply generation unit automatically generates an appropriate reply based on the content of the received email. The reply generation unit analyzes the content of the email and generates an appropriate reply. The reply generation unit can generate a reply based on the content of the email using generation AI. Step 4: The analysis department analyzes email exchanges and reports on communication patterns and trends. The analysis department analyzes the content of emails and extracts communication patterns and trends. The analysis department can use generative AI to analyze email exchanges.

[0062] (Example of form 2) The email management system according to an embodiment of the present invention is a system that streamlines email management by utilizing generation AI. This email management system automatically classifies and organizes received emails and notifies the user based on priority. Next, as an email content summary, it summarizes long emails and provides the user with concise information. Furthermore, as an automatic reply generation, it automatically generates appropriate replies based on the content of received emails. Finally, as email analysis and reporting, it analyzes email exchanges and reports on communication patterns and trends. For example, the email management system automatically classifies and organizes received emails and notifies the user based on priority. For example, the email management system summarizes long emails and provides the user with concise information. For example, the email management system automatically generates appropriate replies based on the content of received emails. For example, the email management system analyzes email exchanges and reports on communication patterns and trends. As a result, the email management system can be expected to streamline email management, prevent important emails from being missed, reduce spam, shorten email checking time, allow for quick access to important information, improve usability in mobile environments, reduce response time, ensure consistent communication, improve communication quality, enhance team performance, and increase customer satisfaction.

[0063] The email management system according to this embodiment comprises a classification unit, a summarization unit, a reply generation unit, and an analysis unit. The classification unit automatically classifies and organizes received emails. The classification unit classifies emails based on, for example, the content of the email, the sender, the subject, etc. The classification unit can, for example, prioritize the display of important emails and automatically filter out spam emails. The classification unit can, for example, analyze the content of emails and classify them based on their importance. The summarization unit summarizes long emails and provides users with concise information. The summarization unit can, for example, analyze the content of emails, extract important information, and generate a summary. The summarization unit can, for example, generate summaries based on the length of the email and the importance of the information being summarized. The summarization unit can, for example, use a generation AI to summarize the content of emails. The reply generation unit automatically generates appropriate replies based on the content of received emails. The reply generation unit can, for example, analyze the content of emails and generate appropriate replies. The reply generation unit can, for example, determine the content, format, and timing of replies based on the content of emails. The reply generation unit can generate replies based on the content of emails, for example, using a generation AI. The analysis unit analyzes email exchanges and reports on communication patterns and trends. The analysis unit can, for example, analyze the content of emails and extract communication patterns and trends. The analysis unit can, for example, analyze the frequency of email exchanges and reply times to identify communication patterns. The analysis unit can, for example, use a generation AI to analyze email exchanges. As a result, the email management system according to this embodiment is expected to improve the efficiency of email management, prevent important emails from being missed, reduce spam emails, shorten the time required to check emails, enable quick access to important information, improve usability in mobile environments, shorten the time required for replying, ensure consistent communication, improve the quality of communication, improve team performance, and increase customer satisfaction.

[0064] The classification unit automatically categorizes and organizes incoming emails. For example, it categorizes emails based on their content, sender, subject, etc. Specifically, it uses natural language processing technology to analyze the email body and extract keywords and phrases to understand the email's content. Sender information is cross-referenced with past correspondence and contact lists to prioritize emails from important senders. For subjects, it detects specific keywords and phrases and evaluates their importance. The classification unit can, for example, prioritize important emails and automatically filter out spam. For spam filtering, it uses machine learning algorithms to learn the characteristics of spam emails and detect new spam with high accuracy. Furthermore, it can continuously improve filtering accuracy by incorporating user feedback. The classification unit can, for example, analyze email content and categorize emails based on their importance. The importance evaluation comprehensively considers factors such as the urgency and relevance of the email content, the importance of the sender, and the frequency of past correspondence. This allows users to efficiently manage their emails without missing important messages. The classification unit provides customizable classification rules based on user settings and preferences, enabling email management tailored to individual needs. For example, emails related to specific projects or clients can be automatically sorted into specific folders. This reduces the burden of email management for users and supports efficient email processing.

[0065] The summarization unit summarizes long emails and provides users with concise information. For example, the summarization unit analyzes the email content, extracts important information, and generates a summary. Specifically, it uses natural language processing techniques to analyze the email body and extract important keywords and phrases. This allows it to grasp the main points of the email and generate a concise summary. The summarization unit can generate summaries based on factors such as the length of the email and the importance of the information being summarized. For long emails, multiple summarization algorithms can be combined to prioritize the extraction of important information and improve the accuracy of the summary. The summarization unit can also use generative AI to summarize email content. Generative AI learns from large datasets, understands email content, and generates appropriate summaries. For example, the generative AI takes the email body as input, extracts important information, and outputs a concise summary. This reduces the time users spend reading long emails and allows them to quickly grasp important information. The summarization unit can continuously improve the accuracy of its summaries based on user feedback. For example, users can evaluate the content of the summaries, and the summarization algorithm can be improved based on that evaluation. Furthermore, the summarization section supports multiple languages ​​and can summarize emails in different languages. This allows the summarization section to function effectively even in a global business environment, improving user convenience.

[0066] The reply generation unit automatically generates appropriate replies based on the content of received emails. For example, the reply generation unit analyzes the content of an email and generates an appropriate reply. Specifically, it uses natural language processing technology to analyze the email body and extract the information necessary for the reply. This allows it to understand the content of the reply and generate an appropriate reply. For example, the reply generation unit can determine the content, format, and timing of the reply based on the content of the email. The content of the reply is generated based on the email body, past exchanges, and user settings. The reply format is selected according to the situation, such as business email or casual email. The timing of the reply is determined based on the urgency and importance of the email. For example, the reply generation unit can use a generation AI to generate replies based on the content of an email. The generation AI learns from large datasets, understands the content of emails, and generates appropriate replies. For example, the generation AI takes the email body as input and outputs an appropriate reply. This allows users to reduce the time spent replying and maintain consistent communication. The reply generation unit can continuously improve the accuracy of replies based on user feedback. For example, users can rate replies, and the reply algorithm can be improved based on those ratings. Furthermore, the reply generation unit supports multiple languages, enabling it to generate appropriate replies to emails in different languages. This allows the reply generation unit to function effectively in a global business environment, improving user convenience.

[0067] The analytics department analyzes email exchanges and reports on communication patterns and trends. For example, the analytics department analyzes the content of emails and extracts communication patterns and trends. Specifically, it uses natural language processing technology to analyze the body of emails and extract keywords and phrases to understand the content of the communication. This allows it to grasp the frequency and trends of exchanges on specific themes and topics. For example, the analytics department can analyze the frequency and response time of email exchanges to identify communication patterns. For example, it can analyze the frequency and response time of email exchanges related to a specific project or client to evaluate the efficiency and effectiveness of communication. For example, the analytics department can use generative AI to analyze email exchanges. Generative AI learns from large datasets, understands the content of emails, and extracts communication patterns and trends. For example, generative AI takes the body of an email as input and outputs the frequency and trends of exchanges on specific themes and topics. This allows users to gain insights to improve the quality and efficiency of their communication. The analytics department can continuously improve the accuracy of its analysis based on user feedback. For example, users can evaluate the analysis results, and the analysis algorithm can be improved based on that evaluation. Furthermore, the analysis unit supports multiple languages ​​and can analyze email exchanges in different languages. This allows the analysis unit to function effectively even in a global business environment, improving user convenience.

[0068] The classification unit can automatically classify and organize incoming emails and notify the user based on priority. For example, the classification unit can automatically classify incoming emails and display important emails preferentially. For example, the classification unit can automatically filter spam emails. For example, the classification unit can analyze the content of emails and classify them based on importance. This prevents users from missing important emails by notifying them based on the priority of incoming emails. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input incoming emails into AI, which can then analyze the content of the emails and perform the classification.

[0069] The summarization unit can summarize long emails and provide users with concise information. For example, the summarization unit can analyze the content of an email, extract important information, and generate a summary. For example, the summarization unit can generate a summary based on the length of the email and the importance of the information to be summarized. For example, the summarization unit can use a generation AI to summarize the content of an email. This allows for the provision of concise information to users and reduces the time spent checking emails by summarizing long emails. Some or all of the above-described processes in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input a long email into an AI, which can then analyze the content of the email and generate a summary.

[0070] The reply generation unit can automatically generate an appropriate reply based on the content of the received email. For example, the reply generation unit can analyze the content of the email and generate an appropriate reply. For example, the reply generation unit can determine the content, format, and timing of the reply based on the content of the email. For example, the reply generation unit can generate a reply based on the content of the email using a generation AI. This reduces the time required for reply work by automatically generating an appropriate reply based on the content of the received email. Some or all of the above-described processes in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the received email into the AI, which can then analyze the content of the email and generate a reply.

[0071] The analysis department can analyze email exchanges and report on communication patterns and trends. For example, the analysis department can analyze the content of emails and extract communication patterns and trends. For example, the analysis department can analyze the frequency and response time of email exchanges to identify communication patterns. For example, the analysis department can use generative AI to analyze email exchanges. This allows for the improvement of communication quality by analyzing email exchanges and reporting on communication patterns and trends. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input email exchanges into AI, which can analyze the content of the emails and extract communication patterns and trends.

[0072] The classification unit can estimate the user's emotions and adjust the email classification criteria based on the estimated emotions. For example, if the user is stressed, the classification unit will prioritize important emails and automatically filter out spam. If the user is relaxed, the classification unit will display all emails equally and allow the user to classify them themselves. If the user is in a hurry, the classification unit will prioritize urgent emails and postpone other emails. This allows for situation-appropriate email management by adjusting the email classification criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into an AI, which can estimate emotions and adjust the classification criteria.

[0073] The classification unit can analyze the classification history of past emails and select the optimal classification algorithm. For example, the classification unit can analyze the characteristics of emails that users have previously marked as important and automatically classify emails with similar characteristics as important emails. For example, the classification unit can analyze the characteristics of emails that users have previously marked as spam and automatically classify emails with similar characteristics as spam emails. For example, the classification unit can analyze the characteristics of emails that users have previously moved to a specific folder and automatically classify emails with similar characteristics into the same folder. By analyzing the classification history of past emails, the system can select the optimal classification algorithm and improve the accuracy of email management. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the classification history of past emails into AI, which can then select the optimal classification algorithm.

[0074] The classification unit can automatically filter spam emails and important emails based on their content. For example, the classification unit can analyze keywords in the subject line and body of an email and automatically filter out spam emails. For example, the classification unit can analyze the sender address of an email and classify emails from trusted senders as important emails. For example, the classification unit can analyze the content of an email using natural language processing technology and prioritize displaying emails that contain important information. This improves the efficiency of email management by automatically filtering out spam emails and important emails based on their content. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the content of an email into an AI, which can then filter out spam emails and important emails.

[0075] The classification unit can estimate the user's emotions and determine the priority of classified emails based on the estimated emotions. For example, if the user is stressed, the classification unit will display important emails with the highest priority and automatically filter out spam. If the user is relaxed, the classification unit will display all emails equally and allow the user to classify them themselves. If the user is in a hurry, the classification unit will display urgent emails with the highest priority and postpone other emails. This enables email management tailored to the user's situation by determining the priority of classified emails based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of emails.

[0076] The classification unit can prioritize the classification of emails based on the user's geographical location information. For example, if the user is in a specific region, the classification unit will prioritize displaying emails related to that region. For example, if the user is traveling, the classification unit will prioritize displaying emails related to their travel destination. For example, if the user is at home, the classification unit will prioritize displaying emails related to their home. This allows for email management tailored to the user's situation by prioritizing the classification of emails based on the user's geographical location information. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the user's geographical location information into AI, which can then classify the emails that are most relevant.

[0077] The classification unit can analyze a user's social media activity when classifying emails and classify relevant emails. For example, if a user posts about a specific topic on social media, the classification unit will prioritize displaying emails related to that topic. For example, if a user participates in a specific event on social media, the classification unit will prioritize displaying emails related to that event. For example, if a user belongs to a specific group on social media, the classification unit will prioritize displaying emails related to that group. This allows for email management tailored to the user's situation by analyzing the user's social media activity and classifying relevant emails. Some or all of the above processing in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the user's social media activity data into AI, and the AI ​​can classify relevant emails.

[0078] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the summarization unit provides a concise and to-the-point summary. For example, if the user is relaxed, the summarization unit provides a summary that includes detailed information. For example, if the user is in a hurry, the summarization unit provides a summary that includes only the most important information. In this way, by adjusting the way the summary is presented based on the user's emotions, a summary tailored to the user's situation is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input user emotion data into an AI, which can estimate the emotions and adjust the way the summary is presented.

[0079] The summarization unit can adjust the level of detail in the summary based on the importance of the email during summary generation. For example, the summarization unit provides a detailed summary for high-importance emails. For example, the summarization unit provides a concise summary for low-importance emails. For example, the summarization unit provides a summary with a moderate level of detail for emails of moderate importance. In this way, by adjusting the level of detail in the summary based on the importance of the email, important emails are summarized in detail. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input the importance of the email into the AI, and the AI ​​can adjust the level of detail in the summary.

[0080] The summarization unit can apply different summarization algorithms depending on the email category when generating summaries. For example, for business emails, the summarization unit provides a summary that highlights key points. For private emails, the summarization unit provides a summary that includes emotional elements. For spam emails, the summarization unit provides a concise summary. By applying different summarization algorithms depending on the email category, appropriate summaries are provided for each category. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the email category into the AI, and the AI ​​can apply different summarization algorithms.

[0081] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is stressed, the summarization unit provides a short, concise summary. For example, if the user is relaxed, the summarization unit provides a longer summary containing more detailed information. For example, if the user is in a hurry, the summarization unit provides a short summary containing only the most important information. By adjusting the length of the summary based on the user's emotions, a summary tailored to the user's situation is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input user emotion data into an AI, which can estimate the emotions and adjust the length of the summary.

[0082] The summarization unit can determine the priority of summaries based on when the emails were sent. For example, the summarization unit will prioritize summarizing recently sent emails. For example, it will postpone summarizing older emails. For example, it will prioritize emails sent within a specific time period. This ensures that the most recent emails are summarized first by determining the priority of summaries based on when the emails were sent. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input the email sending dates into the AI, which can then determine the priority of summaries.

[0083] The summarization unit can adjust the order of summaries based on the relevance of the emails during summary generation. For example, the summarization unit prioritizes generating summaries for highly relevant emails. For example, it postpones generating summaries for less relevant emails. For example, for emails related to a specific topic, the summarization unit generates summaries in an order appropriate to that topic. In this way, by adjusting the order of summaries based on the relevance of the emails, highly relevant emails are prioritized for summarization. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not. For example, the summarization unit can input the relevance of the emails into the AI, and the AI ​​can adjust the order of the summaries.

[0084] The reply generation unit can estimate the user's emotions and adjust the way the reply is expressed based on the estimated emotions. For example, if the user is stressed, the reply generation unit will provide a concise and to-the-point reply. For example, if the user is relaxed, the reply generation unit will provide a reply that includes detailed information. For example, if the user is in a hurry, the reply generation unit will provide a reply that includes only the most important information. In this way, by adjusting the way the reply is expressed based on the user's emotions, a reply appropriate to the user's situation is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or not using AI. For example, the reply generation unit can input user emotion data into an AI, which can estimate the emotions and adjust the way the reply is expressed.

[0085] The reply generation unit can adjust the level of detail in the reply based on the content of the email when generating a reply. For example, the reply generation unit provides a detailed reply for important emails. For example, the reply generation unit provides a concise reply for unimportant emails. For example, the reply generation unit provides a reply with an appropriate level of detail for emails of moderate importance. In this way, by adjusting the level of detail in the reply based on the content of the email, a detailed reply is provided for important emails. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the content of the email into AI, and the AI ​​can adjust the level of detail in the reply.

[0086] The reply generation unit can apply different reply algorithms depending on the email category when generating a reply. For example, the reply generation unit provides a formal reply for business emails, a casual reply for private emails, and a concise reply for spam emails. By applying different reply algorithms depending on the email category, appropriate replies are provided according to the category. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the email category into the AI, and the AI ​​can apply different reply algorithms.

[0087] The reply generation unit can estimate the user's emotions and adjust the length of the reply based on the estimated emotions. For example, if the user is stressed, the reply generation unit will provide a short, to-the-point reply. For example, if the user is relaxed, the reply generation unit will provide a longer reply that includes detailed information. For example, if the user is in a hurry, the reply generation unit will provide a short reply that includes only the most important information. In this way, by adjusting the length of the reply based on the user's emotions, a reply appropriate to the user's situation is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or not using AI. For example, the reply generation unit can input user emotion data into an AI, which can estimate the emotion and adjust the length of the reply.

[0088] The reply generation unit can determine the priority of replies based on when the email was sent. For example, the reply generation unit will prioritize replies to recently sent emails. For example, the reply generation unit will postpone replies to older emails. For example, the reply generation unit will prioritize replies to emails sent within a specific time period according to that time. In this way, by determining the priority of replies based on when the email was sent, the most recent emails will receive priority replies. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or without AI. For example, the reply generation unit can input the email sending time into the AI, and the AI ​​can determine the priority of replies.

[0089] The reply generation unit can adjust the order of replies based on the relevance of the emails when generating replies. For example, the reply generation unit will prioritize generating replies to highly relevant emails. For example, the reply generation unit will postpone generating replies to less relevant emails. For example, the reply generation unit will generate replies to emails related to a specific topic in an order appropriate to that topic. In this way, by adjusting the order of replies based on the relevance of the emails, highly relevant emails will be given priority in receiving replies. Some or all of the above processing in the reply generation unit may be performed using AI, for example, or not using AI. For example, the reply generation unit can input the relevance of the emails into the AI, and the AI ​​can adjust the order of replies.

[0090] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is relaxed, the analysis unit provides analysis results that include detailed information. For example, if the user is in a hurry, the analysis unit provides analysis results that include only the most important information. In this way, by adjusting how the analysis results are displayed based on the user's emotions, analysis results tailored to the user's situation are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can estimate the emotions and adjust how the analysis results are displayed.

[0091] The analysis unit can predict current communication patterns by referring to past email data during analysis. For example, the analysis unit can analyze communication patterns with people the user has frequently interacted with in the past and predict current patterns. For example, the analysis unit can analyze emails the user has exchanged in the past on a specific topic and predict patterns related to the current topic. For example, the analysis unit can analyze emails the user has exchanged in the past during a specific time period and predict patterns related to the current time period. This allows for improvement of the quality of current communication by predicting current communication patterns by referring to past email data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past email data into AI, which can then predict current communication patterns.

[0092] The analysis department can apply different analysis methods to each email category during analysis. For example, for business emails, the analysis department will perform an analysis that highlights important points. For personal emails, the analysis department will perform an analysis that includes emotional elements. For spam emails, the analysis department will perform a concise analysis. By applying different analysis methods to each email category, appropriate analysis is provided for each category. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the email categories into the AI, and the AI ​​can apply different analysis methods.

[0093] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will display the most important analysis results with the highest priority. For example, if the user is relaxed, the analysis unit will display all analysis results equally. For example, if the user is in a hurry, the analysis unit will display only the most important analysis results. In this way, by adjusting the importance of the analysis results based on the user's emotions, important analysis results that are appropriate to the user's situation are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can estimate emotions and adjust the importance of the analysis results.

[0094] The analysis unit can analyze changes in communication patterns based on the timing of email transmissions during the analysis process. For example, the analysis unit can analyze the communication patterns of emails sent during a specific time period, according to that time period. For example, the analysis unit can analyze the communication patterns of emails sent on a specific day of the week, according to that day of the week. For example, the analysis unit can analyze the communication patterns of emails sent within a specific period, according to that period. By analyzing changes in communication patterns based on the timing of email transmissions, the quality of communication can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of email transmissions into the AI, which can then analyze changes in communication patterns.

[0095] The analysis department can analyze communication trends by referring to relevant market data for email during analysis. For example, the analysis department can analyze current communication trends based on relevant market data. For example, the analysis department can predict future communication trends based on relevant market data. For example, the analysis department can analyze communication trends in a specific industry based on relevant market data. This allows for improvement in the quality of communication by analyzing communication trends by referring to relevant market data for email. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant market data into AI, and the AI ​​can analyze communication trends.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The email management system can also be equipped with a speech recognition unit. The speech recognition unit can classify, summarize, and generate replies to emails based on voice commands from the user. For example, if the user says, "Show me important emails," the speech recognition unit can analyze the command and instruct the classification unit to display the important emails. Furthermore, if the user says, "Summarize this email," the speech recognition unit can analyze the command and instruct the summarization unit to generate a summary of the email. Also, if the user says, "Reply to this email," the speech recognition unit can analyze the command and instruct the reply generation unit to generate a reply. As a result, by using speech recognition, users can manage their emails without using their hands, improving convenience.

[0098] The email management system can also include a translation function. This function can automatically translate the content of incoming emails and display them to the user. For example, it can translate an email written in English into Japanese and display it. The translation function can also analyze the content of an email and translate it into the appropriate language. Furthermore, it can use generative AI to translate the content of an email. This allows for the understanding of emails written in different languages, facilitating smoother international communication.

[0099] The email management system can also include a schedule integration unit. This unit can integrate with the user's schedule information and adjust email priorities accordingly. For example, if a user is in a meeting, only important emails can be notified, while other emails are delayed. The schedule integration unit can also retrieve the user's calendar information and adjust email priorities based on their current schedule. This enables email management tailored to the user's schedule, preventing important emails from being missed.

[0100] The email management system can also include a feedback section. This feedback section can collect user feedback and use it to improve the system's accuracy. For example, if a user provides feedback stating "This email is not important," the feedback section can analyze that information and reflect it in the classification section. Furthermore, if a user provides feedback stating "This summary is insufficient," the feedback section can analyze that information and reflect it in the summarization section. Also, if a user provides feedback stating "This reply is inappropriate," the feedback section can analyze that information and reflect it in the reply generation section. This allows the system's accuracy to be improved by leveraging user feedback.

[0101] The email management system can also include a security unit. This unit can assess the security risks of incoming emails and issue warnings to users. For example, it can detect emails that may contain phishing or malware and display warnings to users. The security unit can also analyze email content and sender information to assess security risks. This allows users to identify potentially risky emails in advance and take appropriate measures.

[0102] The email management system can also include an emotion estimation unit. This unit can estimate the user's emotions and adjust email priorities based on those emotions. For example, if a user is stressed, only important emails can be notified, while other emails are prioritized later. The emotion estimation unit can estimate emotions from, for example, the user's facial expressions or voice. This enables email management tailored to the user's emotions, reducing the user's burden.

[0103] The email management system may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is feeling stressed, a concise and to-the-point summary can be provided. The emotion estimation unit can, for example, estimate emotions from the user's facial expressions or voice. This allows for the provision of summaries tailored to the user's emotions, reducing the user's burden.

[0104] The email management system may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the way the reply is expressed based on the estimated emotions. For example, if the user is feeling stressed, it can provide a concise and to-the-point reply. The emotion estimation unit can, for example, estimate emotions from the user's facial expressions or voice. This allows for replies tailored to the user's emotions, reducing the user's burden.

[0105] The email management system can also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, it can provide a concise and to-the-point analysis result. The emotion estimation unit can estimate emotions from, for example, the user's facial expressions or voice. This allows for the provision of analysis results tailored to the user's emotions, reducing the user's burden.

[0106] The email management system can also include an emotion estimation unit. This unit can estimate the user's emotions and adjust email priorities based on those emotions. For example, if a user is stressed, only important emails can be notified, while other emails are prioritized later. The emotion estimation unit can estimate emotions from, for example, the user's facial expressions or voice. This enables email management tailored to the user's emotions, reducing the user's burden.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The classification unit automatically categorizes and organizes incoming emails. The classification unit categorizes emails based on factors such as content, sender, and subject. The classification unit can prioritize displaying important emails and automatically filter out spam. Step 2: The summarization unit summarizes long emails and provides users with concise information. The summarization unit analyzes the content of the email, extracts important information, and generates a summary. The summarization unit can summarize the content of the email using generation AI. Step 3: The reply generation unit automatically generates an appropriate reply based on the content of the received email. The reply generation unit analyzes the content of the email and generates an appropriate reply. The reply generation unit can generate a reply based on the content of the email using generation AI. Step 4: The analysis department analyzes email exchanges and reports on communication patterns and trends. The analysis department analyzes the content of emails and extracts communication patterns and trends. The analysis department can use generative AI to analyze email exchanges.

[0109] 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the classification unit, summarization unit, reply generation unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the classification unit is implemented by the control unit 46A of the smart device 14 and automatically classifies and organizes received emails. The summarization unit is implemented by the specific processing unit 290 of the data processing device 12 and summarizes long emails to provide the user with concise information. The reply generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates an appropriate reply based on the content of the received email. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes email exchanges and reports communication patterns and trends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0116] 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.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0118] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] 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.

[0120] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the classification unit, summarization unit, reply generation unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the smart glasses 214 and automatically classifies and organizes received emails. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes long emails to provide the user with concise information. The reply generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates an appropriate reply based on the content of the received email. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes email exchanges and reports communication patterns and trends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0134] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] 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.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the classification unit, summarization unit, reply generation unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the headset terminal 314 and automatically classifies and organizes received emails. The summarization unit is implemented by the specific processing unit 290 of the data processing unit 12 and summarizes long emails to provide the user with concise information. The reply generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates an appropriate reply based on the content of the received email. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes email exchanges and reports communication patterns and trends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0150] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] 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.

[0152] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] 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.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the classification unit, summarization unit, reply generation unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the robot 414 and automatically classifies and organizes received emails. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and summarizes long emails to provide the user with concise information. The reply generation unit is implemented by, for example, the control unit 46A of the robot 414 and automatically generates an appropriate reply based on the content of the received email. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes email exchanges and reports communication patterns and trends. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0162] 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.

[0163] Figure 9 shows the 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.

[0164] 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.

[0165] 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.

[0166] 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, and motorcycles, 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 based, for example, 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.

[0167] 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."

[0168] 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.

[0169] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] 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 other things 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.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A classification unit that automatically sorts and organizes incoming emails, A summarization unit that summarizes the content of emails classified by the classification unit, A reply generation unit that automatically generates an appropriate reply based on the content of the email summarized by the summarization unit, The system includes an analysis unit that analyzes the reply content generated by the reply generation unit and reports on communication patterns and trends. A system characterized by the following features. (Note 2) The aforementioned classification unit is Automatically classifies and organizes incoming emails and notifies the user based on priority. The system described in Appendix 1, characterized by the features described herein. (Note 3) The summary section above is, Summarize lengthy emails and provide users with concise information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reply generation unit, Automatically generate an appropriate reply based on the content of the received email. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze email exchanges and report on communication patterns and trends. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned classification unit is It estimates the user's sentiment and adjusts email classification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned classification unit is Analyze past email classification history to select the optimal classification algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned classification unit is Automatically filters out spam and important emails based on their content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned classification unit is It estimates the user's emotions and prioritizes emails categorized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned classification unit is When classifying emails, the system prioritizes classifying highly relevant emails based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned classification unit is When classifying emails, analyze users' social media activity and categorize relevant emails. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the email. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, When generating summaries, prioritize summaries based on when the emails were sent. The system described in Appendix 1, characterized by the features described herein. (Note 17) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the emails. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reply generation unit, It estimates the user's emotions and adjusts the way it expresses its replies based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reply generation unit, When generating a reply, adjust the level of detail in the reply based on the content of the email. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reply generation unit, When generating replies, apply different reply algorithms depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reply generation unit, It estimates the user's emotions and adjusts the length of the reply based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reply generation unit, When generating a reply, the system prioritizes replies based on when the email was sent. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reply generation unit, When generating replies, the order of replies is adjusted based on the relevance of the emails. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During analysis, past email data is referenced to predict current communication patterns. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, different analytical methods are applied to each email category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is It estimates the user's emotions and adjusts the importance of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is During the analysis, we analyze changes in communication patterns based on when emails were sent. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, we refer to relevant market data on email to analyze communication trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A classification unit that automatically sorts and organizes incoming emails, A summarization unit that summarizes the content of emails classified by the classification unit, A reply generation unit that automatically generates an appropriate reply based on the content of the email summarized by the summarization unit, The system includes an analysis unit that analyzes the reply content generated by the reply generation unit and reports on communication patterns and trends. A system characterized by the following features.

2. The aforementioned classification unit is Automatically categorizes and organizes incoming emails and notifies users based on priority. The system according to feature 1.

3. The summary section above is, Summarize lengthy emails and provide users with concise information. The system according to feature 1.

4. The aforementioned reply generation unit, Automatically generate an appropriate reply based on the content of the received email. The system according to feature 1.

5. The aforementioned analysis unit is Analyze email exchanges and report on communication patterns and trends. The system according to feature 1.

6. The aforementioned classification unit is It estimates the user's sentiment and adjusts email classification criteria based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned classification unit is Analyze past email classification history to select the optimal classification algorithm. The system according to feature 1.

8. The aforementioned classification unit is Automatically filters out spam and important emails based on their content. The system according to feature 1.

9. The aforementioned classification unit is It estimates the user's emotions and prioritizes emails categorized based on those estimated emotions. The system according to feature 1.

10. The aforementioned classification unit is When classifying emails, the system prioritizes classifying highly relevant emails based on the user's geographical location. The system according to feature 1.

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