system

Abstract

The system according to the embodiment aims to automatically sort emails according to their importance. [Solution] A system according to an embodiment includes an analysis unit, a determination unit, and a sorting unit. The analysis unit analyzes the content of emails. The determination unit determines priority based on the content analyzed by the analysis unit. The sorting unit automatically sorts emails into folders based on the priority determined by the determination unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there is a risk that important emails may be overlooked among a large volume of emails.

[0005] The system according to the embodiment aims to automatically sort emails according to their importance. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a determination unit, and a sorting unit. The analysis unit analyzes the content of emails. The determination unit determines the priority based on the content analyzed by the analysis unit. The sorting unit automatically sorts emails into folders based on the priority determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically sort emails according to their importance. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An email management system according to an embodiment of the present invention utilizes AI to determine the importance of emails and automatically sort them. In this email management system, AI first analyzes the content of emails and determines their importance based on keywords and context. Next, emails are automatically sorted into folders based on the determined importance. For example, important emails are sorted into an "important" folder, and regular emails are sorted into a "regular" folder. This system allows users to efficiently manage their emails without overlooking important emails. Furthermore, as the AI ​​continues to learn, it becomes possible to sort emails with greater accuracy. For example, it can learn the characteristics of emails that users previously deemed important and reflect them in future sorting. Furthermore, analyzing the contents of email attachments improves the accuracy of importance determination. In this way, an AI-based email importance determination and automatic sorting system can significantly improve the efficiency of daily email management and prevent important information from being overlooked. This allows the email management system to efficiently manage emails without overlooking important emails.

[0029] An email management system according to an embodiment includes an analysis unit, a determination unit, and a sorting unit. The analysis unit analyzes the content of emails. The analysis unit analyzes the content of emails using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit extracts keywords from the body of an email and evaluates the importance of the keywords. The analysis unit can also analyze the context of the email to understand the content of the email. For example, the analysis unit can determine whether the content of the email is business-related or personal-related based on the context of the email. The determination unit determines the priority based on the content analyzed by the analysis unit. The determination unit, for example, learns the user's past email processing history to improve the accuracy of priority determination. For example, the determination unit can learn characteristics of emails that the user previously determined to be important and reflect this in future determinations. The determination unit can also determine the priority based on information such as the trustworthiness of the email sender and the time period during which the email was received. For example, if the determination unit determines that the sender is trustworthy, it sets a high priority for the email. The sorting unit automatically sorts emails into folders based on the priority determined by the determination unit. The sorting unit can sort emails into folders based on, for example, sorting criteria that can be customized by the user. For example, the sorting unit can sort emails into specific folders based on keywords or senders set by the user. The sorting unit can also analyze the contents of email attachments and sort emails into folders based on those contents. For example, if the attachment is an important document, the sorting unit can sort the email into an "important" folder. This allows the email management system according to the embodiment to efficiently manage emails without the user overlooking important emails.

[0030] The analysis unit can analyze the content of the email using a natural language processing algorithm. Natural language processing algorithms include morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit extracts keywords from the body of the email and evaluates the importance of the keywords. For example, the analysis unit can use morphological analysis to divide the body of the email into words and evaluate the importance of each word. The analysis unit can also analyze the grammatical structure of the email and understand the meaning of the sentence using grammatical analysis. For example, the analysis unit can use grammatical analysis to identify the subject, predicate, and object of the email and analyze the meaning of the sentence. Furthermore, the analysis unit can understand the context of the email and analyze the content of the email using semantic analysis. For example, the analysis unit can use semantic analysis to understand the context of the email and determine whether the content of the email is business-related or personal-related. This allows the use of natural language processing technology to more accurately analyze the content of the email. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the body of an email into the generation AI, which can then extract keywords and perform contextual analysis.

[0031] The determination unit can learn the user's past email processing history and improve the accuracy of priority determination. The determination unit can, for example, learn the characteristics of emails that the user previously determined to be important and reflect them in future determinations. For example, the determination unit can learn keywords and sender information of emails that the user previously determined to be important and reflect them in determining the priority of future emails. The determination unit can also learn the characteristics of emails to which the user previously responded quickly and determine the priority of future emails based on those characteristics. For example, the determination unit can learn keywords and sender information of emails to which the user previously responded quickly and reflect them in determining the priority of future emails. The determination unit can also learn the content of emails that the user previously determined to be important and determine the priority of future emails based on those content. For example, the determination unit can analyze the content of emails that the user previously determined to be important and determine the priority of future emails based on those content. Thus, by learning the user's past email processing history, the accuracy of priority determination is improved. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's past email processing history into the generation AI, thereby improving the accuracy of priority determination by the generation AI.

[0032] The sorting unit can sort emails into folders based on sorting criteria that can be set by the user. The sorting unit can sort emails into specific folders based on keywords or senders set by the user, for example. For example, the sorting unit can sort emails containing "important" keywords set by the user into an "important" folder. The sorting unit can also sort emails from senders set by the user into specific folders. For example, the sorting unit can sort emails from senders set by the user into a "business" folder. The sorting unit can also sort emails into folders based on email categories set by the user. For example, the sorting unit can sort emails in the "business" category set by the user into a "business" folder. This allows email management tailored to the user's needs by sorting emails into folders based on sorting criteria that can be customized by the user. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input sorting criteria set by the user into a generation AI and have the generation AI sort the emails.

[0033] The email management system includes an attachment analysis module that analyzes the contents of email attachments. The attachment analysis module analyzes the contents of email attachments. The attachment analysis module analyzes the contents of email attachments using, for example, image recognition or text analysis technology. For example, the attachment analysis module can convert the contents of an image file into text data using OCR technology. The attachment analysis module can also analyze the contents of a text file using natural language processing technology. For example, the attachment analysis module can analyze the contents of a text file and determine the importance of the email based on the contents. Furthermore, the attachment analysis module can analyze the contents of an image file using image recognition technology and determine the importance of the email based on the contents. For example, the attachment analysis module can analyze the contents of an image file and set the importance of the email to a high level if the contents are an important document. This improves the accuracy of determining the importance of the email by analyzing the contents of the attachments. Some or all of the above-described processing in the attachment analysis module may be performed using, for example, AI, or may be performed without AI. For example, the attachment analysis module can input the contents of the attachment into the generation AI, causing the generation AI to analyze the contents.

[0034] The analysis unit can analyze the contents of the attachment using image recognition and text analysis algorithms. Image recognition and text analysis algorithms include OCR and natural language processing technologies. For example, the analysis unit can convert the contents of an image file into text data using OCR technology. For example, the analysis unit can scan an image file and extract character information using OCR technology. The analysis unit can also analyze the contents of a text file using natural language processing technology. For example, the analysis unit can analyze the contents of a text file and determine the importance of an email based on the content. Furthermore, the analysis unit can analyze the contents of an image file using image recognition technology and determine the importance of an email based on the content. For example, the analysis unit can analyze the contents of an image file and, if the content is an important document, set the importance of the email to a high level. This allows for more accurate analysis of the contents of the attachment using image recognition and text analysis technologies. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the contents of the attachment into a generation AI and have the generation AI analyze the contents.

[0035] When analyzing the contents of an email, the analysis unit can improve the accuracy of the analysis based on the trustworthiness of the email sender. For example, if the sender has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the email. For example, if the sender has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the email. Furthermore, if the sender is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar emails. For example, if the sender is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar emails. Furthermore, if the sender is determined to be spam, the analysis unit can simplify the analysis of the email and set the importance level low. For example, if the sender is determined to be spam, the analysis unit can simplify the analysis of the email and set the importance level low. This improves the accuracy of the analysis by taking the trustworthiness of the email sender into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the sender's reliability data into the generation AI and have the generation AI evaluate the reliability.

[0036] When analyzing the contents of an email, the analysis unit can determine the analysis priority based on the time the email was received. For example, if the reception time is during business hours, the analysis unit can set the analysis priority to high. For example, if the reception time is during business hours, the analysis unit can set the analysis priority to high. Furthermore, if the reception time is outside business hours, the analysis unit can set the analysis priority to low. For example, if the reception time is outside business hours, the analysis unit can set the analysis priority to low. Furthermore, if the reception time is immediately before a specific important event, the analysis unit can set the analysis priority to highest. For example, if the reception time is immediately before a specific important event, the analysis unit can set the analysis priority to highest. In this way, by determining the analysis priority based on the time zone in which the email was received, important emails can be analyzed quickly. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input reception time data to a generation AI and have the generation AI determine the analysis priority.

[0037] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on the job title and department information of the email recipient. For example, if the recipient is a manager, the analysis unit can prioritize analyzing emails of high importance. For example, if the recipient is a manager, the analysis unit can prioritize analyzing emails of high importance. Furthermore, if the recipient belongs to a specific department, the analysis unit can prioritize keywords related to the department when analyzing the email. For example, if the recipient belongs to a specific department, the analysis unit can prioritize keywords related to the department when analyzing the email. Furthermore, if the recipient is a new employee, the analysis unit can prioritize analyzing emails with basic content. For example, if the recipient is a new employee, the analysis unit can prioritize analyzing emails with basic content. This improves the accuracy of the analysis by taking into account the job title and department information of the email recipient. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the recipient's job title and department information into a generation AI, which can improve the accuracy of the analysis.

[0038] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on project information related to the email. For example, if the email is related to a specific project, the analysis unit can emphasize keywords of the project when analyzing the content. For example, if the email is related to a specific project, the analysis unit can emphasize keywords of the project when analyzing the content. Furthermore, if the email is related to multiple projects, the analysis unit can determine the priority of the analysis based on the importance of each project. For example, if the email is related to multiple projects, the analysis unit can determine the priority of the analysis based on the importance of each project. Furthermore, if the email is related to a new project, the analysis unit can perform the analysis by referring to information on similar past projects. For example, if the email is related to a new project, the analysis unit can perform the analysis by referring to information on similar past projects. This improves the accuracy of the analysis by referring to project information related to the email. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input project information into a generation AI, which can improve the accuracy of the analysis.

[0039] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on the response speed of past emails. The determination unit, for example, learns the characteristics of emails that were quickly replied to in the past and sets a high importance level. The determination unit, for example, can learn the characteristics of emails that were quickly replied to in the past and set a high importance level. The determination unit can also learn the characteristics of emails that received delayed replies in the past and set a low importance level. For example, the determination unit can learn the characteristics of emails that received delayed replies in the past and set a low importance level. Furthermore, the determination unit can predict and determine the importance of a current email based on past response speeds. For example, the determination unit can predict and determine the importance of a current email based on past response speeds. This improves the accuracy of the determination by taking into account the response speeds of past emails. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input past response speed data into a generation AI, which can improve the accuracy of the determination.

[0040] When determining the priority of an email, the determination unit can apply an algorithm that evaluates the urgency of the email content. For example, if the email content contains keywords such as "urgent" or "urgent," the determination unit can set the importance level to high. For example, if the email content contains keywords such as "urgent" or "urgent," the determination unit can set the importance level to high. The determination unit can also evaluate the urgency level to high if the email content is related to a specific deadline. For example, the determination unit can evaluate the urgency level to high if the email content is related to a specific deadline. Furthermore, the determination unit can evaluate the urgency level to high if the email content relates to an important decision. For example, the determination unit can evaluate the urgency level to high if the email content relates to an important decision. In this way, by evaluating the urgency of the email content, emails with high importance can be quickly determined. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the urgency evaluation algorithm into the generation AI and have the generation AI evaluate the urgency.

[0041] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on the email sender's past behavioral history. For example, if the sender has a history of sending important emails in the past, the determination unit can set the importance level high. For example, if the sender has a history of sending important emails in the past, the determination unit can set the importance level high. Furthermore, if the sender has a history of sending spam emails in the past, the determination unit can set the importance level low. For example, if the sender has a history of sending spam emails in the past, the determination unit can set the importance level low. Furthermore, the determination unit can predict and determine the importance of a current email based on the sender's past behavioral history. For example, the determination unit can predict and determine the importance of a current email based on the sender's past behavioral history. This improves the accuracy of the determination by taking the email sender's past behavioral history into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the sender's behavioral history data into a generation AI, which can improve the accuracy of the determination.

[0042] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on information about meetings and events related to the email. For example, if the email is related to a specific meeting, the determination unit can make the determination based on the importance of the meeting. For example, if the email is related to a specific meeting, the determination unit can make the determination based on the importance of the meeting. Furthermore, if the email is related to a specific event, the determination unit can make the determination based on the importance of the event. For example, if the email is related to a specific event, the determination unit can make the determination based on the importance of the event. Furthermore, if the email is related to multiple meetings or events, the determination unit can make the determination based on the importance of each meeting or event. For example, if the email is related to multiple meetings or events, the determination unit can make the determination based on the importance of each meeting or event. This improves the accuracy of the determination by referring to the information about meetings and events related to the email. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input meeting and event information into a generation AI, which can improve the accuracy of the determination.

[0043] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on the frequency of folder use. For example, the sorting unit prioritizes sorting of important emails into frequently used folders. For example, the sorting unit can prioritize sorting of important emails into frequently used folders. The sorting unit can also sort regular emails into infrequently used folders. For example, the sorting unit can sort regular emails into infrequently used folders. Furthermore, the sorting unit can determine the optimal destination for sorting based on the frequency of folder use. For example, the sorting unit can determine the optimal destination for sorting based on the frequency of folder use. This improves the accuracy of sorting by taking the frequency of folder use into consideration. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input folder use frequency data into a generation AI, which can improve the accuracy of sorting.

[0044] When sorting emails into folders, the sorting unit can apply different sorting algorithms based on the category of the email content. For example, if the email content is business-related, the sorting unit sorts the email into a business folder. For example, if the email content is business-related, the sorting unit can sort the email into a business folder. Furthermore, if the email content is private, the sorting unit can sort the email into a private folder. For example, if the email content is private, the sorting unit can sort the email into a private folder. Furthermore, if the email content is determined to be spam, the sorting unit can sort the email into a spam folder. For example, if the email content is determined to be spam, the sorting unit can sort the email into a spam folder. This enables sorting according to the category of the email content. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input email content category data into a generation AI, which can apply a sorting algorithm.

[0045] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on task information related to the emails. For example, if an email is related to a specific task, the sorting unit sorts the email into the task's folder. For example, if an email is related to a specific task, the sorting unit can sort the email into the task's folder. Furthermore, if an email is related to multiple tasks, the sorting unit can sort the email based on the importance of each task. For example, if an email is related to multiple tasks, the sorting unit can sort the email based on the importance of each task. Furthermore, if an email is related to a new task, the sorting unit can sort the email by referring to information about past similar tasks. For example, if an email is related to a new task, the sorting unit can sort the email by referring to information about past similar tasks. This improves the accuracy of sorting by referring to the task information related to the email. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input task information into a generation AI, which can improve the accuracy of sorting.

[0046] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on the job title and department information of the email sender. For example, if the sender is a manager, the sorting unit sorts the email into a folder with a high level of importance. For example, if the sender is a manager, the sorting unit can sort the email into a folder with a high level of importance. Furthermore, if the sender belongs to a specific department, the sorting unit can sort the email into a folder related to that department. For example, if the sender belongs to a specific department, the sorting unit can sort the email into a folder related to that department. Furthermore, if the sender is a new employee, the sorting unit can sort the email into a folder with basic content. For example, if the sender is a new employee, the sorting unit can sort the email into a folder with basic content. This improves the accuracy of sorting by taking into account the job title and department information of the email sender. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting department can input the sender's job title and department information into the generation AI, allowing the generation AI to improve sorting accuracy.

[0047] When analyzing the contents of an attachment, the analysis unit can apply different analysis algorithms based on the file type. For example, in the case of a text file, the analysis unit can analyze the contents using natural language processing technology. For example, in the case of a text file, the analysis unit can analyze the contents using natural language processing technology. Furthermore, in the case of an image file, the analysis unit can analyze the contents using image recognition technology. For example, in the case of an image file, the analysis unit can analyze the contents using image recognition technology. Furthermore, in the case of a PDF file, the analysis unit can analyze the contents using text extraction technology. For example, in the case of a PDF file, the analysis unit can analyze the contents using text extraction technology. This enables optimal analysis depending on the file type. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input file type data to a generation AI, which can apply an analysis algorithm.

[0048] When analyzing the contents of an attachment, the analysis unit can improve the accuracy of the analysis based on the trustworthiness of the file creator. For example, if the creator has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the file. For example, if the creator has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the file. Furthermore, if the creator is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar files. For example, if the creator is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar files. Furthermore, if the creator is determined to be spam, the analysis unit can simplify the analysis of the file and set a low importance level. For example, if the creator is determined to be spam, the analysis unit can simplify the analysis of the file and set a low importance level. This improves the accuracy of the analysis by taking the trustworthiness of the file creator into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the creator's trustworthiness data into a generation AI and have the generation AI evaluate the trustworthiness.

[0049] When analyzing the contents of an attachment, the analysis unit can improve the accuracy of the analysis based on project information related to the file. For example, if the attachment is related to a specific project, the analysis unit can emphasize keywords for that project when performing the analysis. For example, if the attachment is related to a specific project, the analysis unit can emphasize keywords for that project when performing the analysis. Furthermore, if the attachment is related to multiple projects, the analysis unit can determine the analysis priority based on the importance of each project. For example, if the attachment is related to multiple projects, the analysis unit can determine the analysis priority based on the importance of each project. Furthermore, if the attachment is related to a new project, the analysis unit can perform the analysis by referring to information about similar past projects. For example, if the attachment is related to a new project, the analysis unit can perform the analysis by referring to information about similar past projects. This improves the accuracy of the analysis by referring to the project information related to the file. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input project information into a generation AI, which can improve the accuracy of the analysis.

[0050] When analyzing the contents of an attachment, the analysis unit can improve the accuracy of the analysis based on the file's creation date and time. For example, if the file's creation date and time is recent, the analysis unit can set a high analysis priority for the file as it is the most recent information. For example, if the file's creation date and time is recent, the analysis unit can set a high analysis priority for the file as it is the most recent information. Furthermore, if the file's creation date and time is old, the analysis unit can set a low analysis priority for the file as it is older information. For example, if the file's creation date and time is just before a specific event or project, the analysis unit can set a high analysis priority for the file as it is older information. Furthermore, if the file's creation date and time is just before a specific event or project, the analysis unit can set a high analysis priority for the file as it is related to the event or project. For example, if the file's creation date and time is just before a specific event or project, the analysis unit can set a high analysis priority for the file as it is related to the event or project. This improves the accuracy of the analysis by taking the file's creation date and time into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input creation date and time data into a generation AI, which can improve the accuracy of the analysis.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on the past behavioral history of the email recipient. For example, the analysis unit can learn the characteristics of emails that the recipient has previously deemed important and reflect this in future analyses. The analysis unit can also learn the characteristics of emails that the recipient has previously replied to quickly and analyze future emails based on these characteristics. Furthermore, the analysis unit can learn the characteristics of emails that the recipient has previously deemed spam and simplify the analysis of spam emails based on these characteristics. This improves the accuracy of the analysis by taking into account the recipient's past behavioral history.

[0053] When determining the priority of an email, the determination unit can apply different determination algorithms based on the category of the email content. For example, if the email content is business-related, the determination unit can set a high priority for sorting it into a business folder. If the email content is private, the determination unit can set a low priority for sorting it into a private folder. Furthermore, if the email content is determined to be spam, the determination unit can set the lowest priority for sorting it into a spam folder. This makes it possible to determine the priority according to the category of the email content.

[0054] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on the conference and event information related to the email. For example, if the email is related to a specific conference, the sorting unit can sort it into the conference folder. Also, if the email is related to a specific event, the sorting unit can sort it into the event folder. Furthermore, if the email is related to multiple conferences or events, the sorting unit can sort it based on the importance of each conference or event. This improves the accuracy of sorting by referring to the conference and event information related to the email.

[0055] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on the job title and department information of the email sender. For example, if the sender is a manager, the analysis unit can prioritize analyzing emails of high importance. Also, if the sender belongs to a specific department, the analysis unit can prioritize keywords related to that department in the analysis. Furthermore, if the sender is a new employee, the analysis unit can prioritize analyzing emails with basic content. This improves the accuracy of the analysis by taking into account the job title and department information of the email sender.

[0056] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on task information related to the email. For example, if the email is related to a specific task, the determination unit can make a determination based on the importance of that task. Also, if the email is related to multiple tasks, the determination unit can make a determination based on the importance of each task. Furthermore, if the email is related to a new task, the determination unit can make a determination by referring to information on similar past tasks. In this way, by referring to task information related to the email, the accuracy of the determination is improved.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The analysis unit analyzes the content of the email. The analysis unit analyzes the content of the email using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit extracts keywords from the body of the email and evaluates the importance of those keywords. The analysis unit can also analyze the context of the email to understand the content of the email. For example, the analysis unit can determine whether the content of the email is business-related or personal-related based on the context of the email. Step 2: The determination unit determines the priority based on the content analyzed by the analysis unit. The determination unit, for example, learns the user's past email processing history and improves the accuracy of priority determination. For example, the determination unit can learn the characteristics of emails that the user has previously judged to be important and reflect this in future determinations. The determination unit can also determine the priority by taking into account information such as the trustworthiness of the email sender and the time period during which the email was received. For example, if the determination unit determines that the sender is trustworthy, it sets the priority of the email high. Step 3: The sorting unit automatically sorts emails into folders based on the priority determined by the judgment unit. The sorting unit can sort emails into folders based on sorting criteria that can be customized by the user. For example, the sorting unit can sort emails into specific folders based on keywords or senders set by the user. The sorting unit can also analyze the contents of email attachments and sort emails into folders based on those contents. For example, the sorting unit can sort an email into an "important" folder if the attachment is an important document.

[0059] (Example 2) An email management system according to an embodiment of the present invention utilizes AI to determine the importance of emails and automatically sort them. In this email management system, AI first analyzes the content of emails and determines their importance based on keywords and context. Next, emails are automatically sorted into folders based on the determined importance. For example, important emails are sorted into an "important" folder, and regular emails are sorted into a "regular" folder. This system allows users to efficiently manage their emails without overlooking important emails. Furthermore, as the AI ​​continues to learn, it becomes possible to sort emails with greater accuracy. For example, it can learn the characteristics of emails that users previously deemed important and reflect them in future sorting. Furthermore, analyzing the contents of email attachments improves the accuracy of importance determination. In this way, an AI-based email importance determination and automatic sorting system can significantly improve the efficiency of daily email management and prevent important information from being overlooked. This allows the email management system to efficiently manage emails without overlooking important emails.

[0060] An email management system according to an embodiment includes an analysis unit, a determination unit, and a sorting unit. The analysis unit analyzes the content of emails. The analysis unit analyzes the content of emails using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit extracts keywords from the body of an email and evaluates the importance of the keywords. The analysis unit can also analyze the context of the email to understand the content of the email. For example, the analysis unit can determine whether the content of the email is business-related or personal-related based on the context of the email. The determination unit determines the priority based on the content analyzed by the analysis unit. The determination unit, for example, learns the user's past email processing history to improve the accuracy of priority determination. For example, the determination unit can learn characteristics of emails that the user previously determined to be important and reflect this in future determinations. The determination unit can also determine the priority based on information such as the trustworthiness of the email sender and the time period during which the email was received. For example, if the determination unit determines that the sender is trustworthy, it sets a high priority for the email. The sorting unit automatically sorts emails into folders based on the priority determined by the determination unit. The sorting unit can sort emails into folders based on, for example, sorting criteria that can be customized by the user. For example, the sorting unit can sort emails into specific folders based on keywords or senders set by the user. The sorting unit can also analyze the contents of email attachments and sort emails into folders based on those contents. For example, if the attachment is an important document, the sorting unit can sort the email into an "important" folder. This allows the email management system according to the embodiment to efficiently manage emails without the user overlooking important emails.

[0061] The analysis unit can analyze the content of the email using a natural language processing algorithm. Natural language processing algorithms include morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit extracts keywords from the body of the email and evaluates the importance of the keywords. For example, the analysis unit can use morphological analysis to divide the body of the email into words and evaluate the importance of each word. The analysis unit can also analyze the grammatical structure of the email and understand the meaning of the sentence using grammatical analysis. For example, the analysis unit can use grammatical analysis to identify the subject, predicate, and object of the email and analyze the meaning of the sentence. Furthermore, the analysis unit can understand the context of the email and analyze the content of the email using semantic analysis. For example, the analysis unit can use semantic analysis to understand the context of the email and determine whether the content of the email is business-related or personal-related. This allows the use of natural language processing technology to more accurately analyze the content of the email. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the body of an email into the generation AI, which can then extract keywords and perform contextual analysis.

[0062] The determination unit can learn the user's past email processing history and improve the accuracy of priority determination. The determination unit can, for example, learn the characteristics of emails that the user previously determined to be important and reflect them in future determinations. For example, the determination unit can learn keywords and sender information of emails that the user previously determined to be important and reflect them in determining the priority of future emails. The determination unit can also learn the characteristics of emails to which the user previously responded quickly and determine the priority of future emails based on those characteristics. For example, the determination unit can learn keywords and sender information of emails to which the user previously responded quickly and reflect them in determining the priority of future emails. The determination unit can also learn the content of emails that the user previously determined to be important and determine the priority of future emails based on those content. For example, the determination unit can analyze the content of emails that the user previously determined to be important and determine the priority of future emails based on those content. Thus, by learning the user's past email processing history, the accuracy of priority determination is improved. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the user's past email processing history into the generation AI, thereby improving the accuracy of priority determination by the generation AI.

[0063] The sorting unit can sort emails into folders based on sorting criteria that can be set by the user. The sorting unit can sort emails into specific folders based on keywords or senders set by the user, for example. For example, the sorting unit can sort emails containing "important" keywords set by the user into an "important" folder. The sorting unit can also sort emails from senders set by the user into specific folders. For example, the sorting unit can sort emails from senders set by the user into a "business" folder. The sorting unit can also sort emails into folders based on email categories set by the user. For example, the sorting unit can sort emails in the "business" category set by the user into a "business" folder. This allows email management tailored to the user's needs by sorting emails into folders based on sorting criteria that can be customized by the user. Some or all of the above-mentioned processing in the sorting unit may be performed using, or without, AI. For example, the sorting unit can input sorting criteria set by the user into a generation AI and have the generation AI sort the emails.

[0064] The email management system includes an attachment analysis module that analyzes the contents of email attachments. The attachment analysis module analyzes the contents of email attachments. The attachment analysis module analyzes the contents of email attachments using, for example, image recognition or text analysis technology. For example, the attachment analysis module can convert the contents of an image file into text data using OCR technology. The attachment analysis module can also analyze the contents of a text file using natural language processing technology. For example, the attachment analysis module can analyze the contents of a text file and determine the importance of the email based on the contents. Furthermore, the attachment analysis module can analyze the contents of an image file using image recognition technology and determine the importance of the email based on the contents. For example, the attachment analysis module can analyze the contents of an image file and set the importance of the email to a high level if the contents are an important document. This improves the accuracy of determining the importance of the email by analyzing the contents of the attachments. Some or all of the above-described processing in the attachment analysis module may be performed using, for example, AI, or may be performed without AI. For example, the attachment analysis module can input the contents of the attachment into the generation AI, causing the generation AI to analyze the contents.

[0065] The analysis unit can analyze the contents of the attachment using image recognition and text analysis algorithms. Image recognition and text analysis algorithms include OCR and natural language processing technologies. For example, the analysis unit can convert the contents of an image file into text data using OCR technology. For example, the analysis unit can scan an image file and extract character information using OCR technology. The analysis unit can also analyze the contents of a text file using natural language processing technology. For example, the analysis unit can analyze the contents of a text file and determine the importance of an email based on the content. Furthermore, the analysis unit can analyze the contents of an image file using image recognition technology and determine the importance of an email based on the content. For example, the analysis unit can analyze the contents of an image file and, if the content is an important document, set the importance of the email to a high level. This allows for more accurate analysis of the contents of the attachment using image recognition and text analysis technologies. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the contents of the attachment into a generation AI and have the generation AI analyze the contents.

[0066] The analysis unit can estimate the user's emotions and change the email analysis method based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit can focus on important keywords in the analysis to improve analysis accuracy. For example, when the user is feeling stressed, the analysis unit can focus on important keywords in the analysis. Furthermore, when the user is relaxed, the analysis unit can perform detailed context analysis to gain a deeper understanding of the email content. For example, when the user is relaxed, the analysis unit can perform detailed context analysis to gain a deeper understanding of the email content. Furthermore, when the user is in a hurry, the analysis unit can quickly perform analysis to prioritize extracting important information. For example, when the user is in a hurry, the analysis unit can quickly perform analysis to prioritize extracting important information. This enables more appropriate analysis by adjusting the email analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to a generation AI and have the generation AI estimate the emotion.

[0067] When analyzing the contents of an email, the analysis unit can improve the accuracy of the analysis based on the trustworthiness of the email sender. For example, if the sender has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the email. For example, if the sender has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the email. Furthermore, if the sender is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar emails. For example, if the sender is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar emails. Furthermore, if the sender is determined to be spam, the analysis unit can simplify the analysis of the email and set the importance level low. For example, if the sender is determined to be spam, the analysis unit can simplify the analysis of the email and set the importance level low. This improves the accuracy of the analysis by taking the trustworthiness of the email sender into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the sender's reliability data into the generation AI and have the generation AI evaluate the reliability.

[0068] When analyzing the contents of an email, the analysis unit can determine the analysis priority based on the time the email was received. For example, if the reception time is during business hours, the analysis unit can set the analysis priority to high. For example, if the reception time is during business hours, the analysis unit can set the analysis priority to high. Furthermore, if the reception time is outside business hours, the analysis unit can set the analysis priority to low. For example, if the reception time is outside business hours, the analysis unit can set the analysis priority to low. Furthermore, if the reception time is immediately before a specific important event, the analysis unit can set the analysis priority to highest. For example, if the reception time is immediately before a specific important event, the analysis unit can set the analysis priority to highest. In this way, by determining the analysis priority based on the time zone in which the email was received, important emails can be analyzed quickly. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input reception time data to a generation AI and have the generation AI determine the analysis priority.

[0069] The analysis unit can estimate the user's emotions and set a priority order for emails to be analyzed based on the estimated user emotions. For example, when the user is stressed, the analysis unit prioritizes analyzing important emails. For example, when the user is stressed, the analysis unit can prioritize analyzing important emails. Furthermore, when the user is relaxed, the analysis unit can analyze emails including regular emails. For example, when the user is relaxed, the analysis unit can analyze emails including regular emails. Furthermore, when the user is in a hurry, the analysis unit can prioritize analyzing emails with high urgency. For example, when the user is in a hurry, the analysis unit can prioritize analyzing emails with high urgency. Thus, by determining the priority order for emails to be analyzed based on the user's emotions, important emails can be analyzed with priority. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0070] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on the job title and department information of the email recipient. For example, if the recipient is a manager, the analysis unit can prioritize analyzing emails of high importance. For example, if the recipient is a manager, the analysis unit can prioritize analyzing emails of high importance. Furthermore, if the recipient belongs to a specific department, the analysis unit can prioritize keywords related to the department when analyzing the email. For example, if the recipient belongs to a specific department, the analysis unit can prioritize keywords related to the department when analyzing the email. Furthermore, if the recipient is a new employee, the analysis unit can prioritize analyzing emails with basic content. For example, if the recipient is a new employee, the analysis unit can prioritize analyzing emails with basic content. This improves the accuracy of the analysis by taking into account the job title and department information of the email recipient. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the recipient's job title and department information into a generation AI, which can improve the accuracy of the analysis.

[0071] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on project information related to the email. For example, if the email is related to a specific project, the analysis unit can emphasize keywords of the project when analyzing the content. For example, if the email is related to a specific project, the analysis unit can emphasize keywords of the project when analyzing the content. Furthermore, if the email is related to multiple projects, the analysis unit can determine the priority of the analysis based on the importance of each project. For example, if the email is related to multiple projects, the analysis unit can determine the priority of the analysis based on the importance of each project. Furthermore, if the email is related to a new project, the analysis unit can perform the analysis by referring to information on similar past projects. For example, if the email is related to a new project, the analysis unit can perform the analysis by referring to information on similar past projects. This improves the accuracy of the analysis by referring to project information related to the email. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input project information into a generation AI, which can improve the accuracy of the analysis.

[0072] The determination unit can estimate the user's emotions and adjust the priority determination criteria based on the estimated user emotions. For example, when the user is stressed, the determination unit can prioritize emails with high importance. For example, when the user is stressed, the determination unit can prioritize emails with high importance. Furthermore, when the user is relaxed, the determination unit can also make a determination including regular emails. For example, when the user is relaxed, the determination unit can also make a determination including regular emails. Furthermore, when the user is in a hurry, the determination unit can prioritize emails with high urgency. For example, when the user is in a hurry, the determination unit can prioritize emails with high urgency. This allows for more appropriate determination by adjusting the priority determination criteria based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0073] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on the response speed of past emails. The determination unit, for example, learns the characteristics of emails that were quickly replied to in the past and sets a high importance level. The determination unit, for example, can learn the characteristics of emails that were quickly replied to in the past and set a high importance level. The determination unit can also learn the characteristics of emails that received delayed replies in the past and set a low importance level. For example, the determination unit can learn the characteristics of emails that received delayed replies in the past and set a low importance level. Furthermore, the determination unit can predict and determine the importance of a current email based on past response speeds. For example, the determination unit can predict and determine the importance of a current email based on past response speeds. This improves the accuracy of the determination by taking into account the response speeds of past emails. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input past response speed data into a generation AI, which can improve the accuracy of the determination.

[0074] When determining the priority of an email, the determination unit can apply an algorithm that evaluates the urgency of the email content. For example, if the email content contains keywords such as "urgent" or "urgent," the determination unit can set the importance level to high. For example, if the email content contains keywords such as "urgent" or "urgent," the determination unit can set the importance level to high. The determination unit can also evaluate the urgency level to high if the email content is related to a specific deadline. For example, the determination unit can evaluate the urgency level to high if the email content is related to a specific deadline. Furthermore, the determination unit can evaluate the urgency level to high if the email content relates to an important decision. For example, the determination unit can evaluate the urgency level to high if the email content relates to an important decision. In this way, by evaluating the urgency of the email content, emails with high importance can be quickly determined. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the urgency evaluation algorithm into the generation AI and have the generation AI evaluate the urgency.

[0075] The determination unit can estimate the user's emotions and adjust the order in which the priority determination results are displayed based on the estimated user's emotions. For example, when the user is stressed, the determination unit can display emails with a high level of importance first. For example, when the user is stressed, the determination unit can display emails with a high level of importance first. Furthermore, when the user is relaxed, the determination unit can adjust the order to include regular emails. For example, when the user is relaxed, the determination unit can adjust the order to include regular emails. Furthermore, when the user is in a hurry, the determination unit can display emails with a high level of urgency first. For example, when the user is in a hurry, the determination unit can display emails with a high level of urgency first. Thus, by adjusting the order in which the priority determination results are displayed based on the user's emotions, important emails can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0076] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on the email sender's past behavioral history. For example, if the sender has a history of sending important emails in the past, the determination unit can set the importance level high. For example, if the sender has a history of sending important emails in the past, the determination unit can set the importance level high. Furthermore, if the sender has a history of sending spam emails in the past, the determination unit can set the importance level low. For example, if the sender has a history of sending spam emails in the past, the determination unit can set the importance level low. Furthermore, the determination unit can predict and determine the importance of a current email based on the sender's past behavioral history. For example, the determination unit can predict and determine the importance of a current email based on the sender's past behavioral history. This improves the accuracy of the determination by taking the email sender's past behavioral history into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the sender's behavioral history data into a generation AI, which can improve the accuracy of the determination.

[0077] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on information about meetings and events related to the email. For example, if the email is related to a specific meeting, the determination unit can make the determination based on the importance of the meeting. For example, if the email is related to a specific meeting, the determination unit can make the determination based on the importance of the meeting. Furthermore, if the email is related to a specific event, the determination unit can make the determination based on the importance of the event. For example, if the email is related to a specific event, the determination unit can make the determination based on the importance of the event. Furthermore, if the email is related to multiple meetings or events, the determination unit can make the determination based on the importance of each meeting or event. For example, if the email is related to multiple meetings or events, the determination unit can make the determination based on the importance of each meeting or event. This improves the accuracy of the determination by referring to the information about meetings and events related to the email. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input meeting and event information into a generation AI, which can improve the accuracy of the determination.

[0078] The sorting unit can estimate the user's emotions and change the sorting criteria based on the estimated user emotions. For example, when the user is feeling stressed, the sorting unit can prioritize sorting emails of high importance. For example, when the user is feeling stressed, the sorting unit can prioritize sorting emails of high importance. Furthermore, when the user is relaxed, the sorting unit can also sort emails of normal importance. For example, when the user is relaxed, the sorting unit can also sort emails of normal importance. Furthermore, when the user is in a hurry, the sorting unit can prioritize sorting emails of high urgency. For example, when the user is in a hurry, the sorting unit can prioritize sorting emails of high urgency. This allows for more appropriate sorting by adjusting the sorting criteria based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit may input user emotion data into a generation AI and have the generation AI estimate the emotion.

[0079] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on the frequency of folder use. For example, the sorting unit prioritizes sorting of important emails into frequently used folders. For example, the sorting unit can prioritize sorting of important emails into frequently used folders. The sorting unit can also sort regular emails into infrequently used folders. For example, the sorting unit can sort regular emails into infrequently used folders. Furthermore, the sorting unit can determine the optimal destination for sorting based on the frequency of folder use. For example, the sorting unit can determine the optimal destination for sorting based on the frequency of folder use. This improves the accuracy of sorting by taking the frequency of folder use into consideration. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input folder use frequency data into a generation AI, which can improve the accuracy of sorting.

[0080] When sorting emails into folders, the sorting unit can apply different sorting algorithms based on the category of the email content. For example, if the email content is business-related, the sorting unit sorts the email into a business folder. For example, if the email content is business-related, the sorting unit can sort the email into a business folder. Furthermore, if the email content is private, the sorting unit can sort the email into a private folder. For example, if the email content is private, the sorting unit can sort the email into a private folder. Furthermore, if the email content is determined to be spam, the sorting unit can sort the email into a spam folder. For example, if the email content is determined to be spam, the sorting unit can sort the email into a spam folder. This enables sorting according to the category of the email content. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input email content category data into a generation AI, which can apply a sorting algorithm.

[0081] The sorting unit can estimate the user's emotions and set sorting priorities based on the estimated user emotions. For example, when the user is feeling stressed, the sorting unit prioritizes sorting of emails with high importance. For example, when the user is feeling stressed, the sorting unit can prioritize sorting of emails with high importance. Furthermore, when the user is relaxed, the sorting unit can also sort emails with normal importance. For example, when the user is relaxed, the sorting unit can also sort emails with normal importance. Furthermore, when the user is in a hurry, the sorting unit can prioritize sorting of emails with high urgency. For example, when the user is in a hurry, the sorting unit can prioritize sorting of emails with high urgency. In this way, by determining the sorting priorities based on the user's emotions, important emails can be prioritized for sorting. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit may input user emotion data into a generation AI and have the generation AI estimate the emotion.

[0082] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on task information related to the emails. For example, if an email is related to a specific task, the sorting unit sorts the email into the task's folder. For example, if an email is related to a specific task, the sorting unit can sort the email into the task's folder. Furthermore, if an email is related to multiple tasks, the sorting unit can sort the email based on the importance of each task. For example, if an email is related to multiple tasks, the sorting unit can sort the email based on the importance of each task. Furthermore, if an email is related to a new task, the sorting unit can sort the email by referring to information about past similar tasks. For example, if an email is related to a new task, the sorting unit can sort the email by referring to information about past similar tasks. This improves the accuracy of sorting by referring to the task information related to the email. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting unit can input task information into a generation AI, which can improve the accuracy of sorting.

[0083] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on the job title and department information of the email sender. For example, if the sender is a manager, the sorting unit sorts the email into a folder with a high level of importance. For example, if the sender is a manager, the sorting unit can sort the email into a folder with a high level of importance. Furthermore, if the sender belongs to a specific department, the sorting unit can sort the email into a folder related to that department. For example, if the sender belongs to a specific department, the sorting unit can sort the email into a folder related to that department. Furthermore, if the sender is a new employee, the sorting unit can sort the email into a folder with basic content. For example, if the sender is a new employee, the sorting unit can sort the email into a folder with basic content. This improves the accuracy of sorting by taking into account the job title and department information of the email sender. Some or all of the above-mentioned processing in the sorting unit may be performed using, for example, AI, or may be performed without using AI. For example, the sorting department can input the sender's job title and department information into the generation AI, allowing the generation AI to improve sorting accuracy.

[0084] The analysis unit can estimate the user's emotions and change the analysis method for the attachment based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can focus on important keywords when performing the analysis. For example, if the user is feeling stressed, the analysis unit can focus on important keywords when performing the analysis. Furthermore, if the user is relaxed, the analysis unit can perform detailed contextual analysis to gain a deeper understanding of the contents of the attachment. For example, if the user is relaxed, the analysis unit can perform detailed contextual analysis to gain a deeper understanding of the contents of the attachment. Furthermore, if the user is in a hurry, the analysis unit can quickly perform the analysis and prioritize extracting important information. For example, if the user is in a hurry, the analysis unit can quickly perform the analysis and prioritize extracting important information. This enables more appropriate analysis by adjusting the analysis method for the attachment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to a generation AI and have the generation AI estimate the emotion.

[0085] When analyzing the contents of an attachment, the analysis unit can apply different analysis algorithms based on the file type. For example, in the case of a text file, the analysis unit can analyze the contents using natural language processing technology. For example, in the case of a text file, the analysis unit can analyze the contents using natural language processing technology. Furthermore, in the case of an image file, the analysis unit can analyze the contents using image recognition technology. For example, in the case of an image file, the analysis unit can analyze the contents using image recognition technology. Furthermore, in the case of a PDF file, the analysis unit can analyze the contents using text extraction technology. For example, in the case of a PDF file, the analysis unit can analyze the contents using text extraction technology. This enables optimal analysis depending on the file type. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input file type data to a generation AI, which can apply an analysis algorithm.

[0086] When analyzing the contents of an attachment, the analysis unit can improve the accuracy of the analysis based on the trustworthiness of the file creator. For example, if the creator has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the file. For example, if the creator has been determined to be trustworthy in the past, the analysis unit can set a high trustworthiness level in the analysis of the file. Furthermore, if the creator is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar files. For example, if the creator is new, the analysis unit can perform the analysis by referring to the trustworthiness of past similar files. Furthermore, if the creator is determined to be spam, the analysis unit can simplify the analysis of the file and set a low importance level. For example, if the creator is determined to be spam, the analysis unit can simplify the analysis of the file and set a low importance level. This improves the accuracy of the analysis by taking the trustworthiness of the file creator into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the creator's trustworthiness data into a generation AI and have the generation AI evaluate the trustworthiness.

[0087] The analysis unit can estimate the user's emotions and set a priority order for the attachments to be analyzed based on the estimated user emotions. For example, when the user is stressed, the analysis unit can prioritize analyzing important attachments. For example, when the user is stressed, the analysis unit can prioritize analyzing important attachments. Furthermore, when the user is relaxed, the analysis unit can analyze not only normal attachments but also normal attachments. For example, when the user is relaxed, the analysis unit can analyze not only normal attachments. Furthermore, when the user is in a hurry, the analysis unit can prioritize analyzing attachments with high urgency. For example, when the user is in a hurry, the analysis unit can prioritize analyzing attachments with high urgency. Thus, by determining the priority order for the attachments to be analyzed based on the user's emotions, important attachments can be prioritized for analysis. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion.

[0088] When analyzing the contents of an attachment, the analysis unit can improve the accuracy of the analysis based on project information related to the file. For example, if the attachment is related to a specific project, the analysis unit can emphasize keywords for that project when performing the analysis. For example, if the attachment is related to a specific project, the analysis unit can emphasize keywords for that project when performing the analysis. Furthermore, if the attachment is related to multiple projects, the analysis unit can determine the analysis priority based on the importance of each project. For example, if the attachment is related to multiple projects, the analysis unit can determine the analysis priority based on the importance of each project. Furthermore, if the attachment is related to a new project, the analysis unit can perform the analysis by referring to information about similar past projects. For example, if the attachment is related to a new project, the analysis unit can perform the analysis by referring to information about similar past projects. This improves the accuracy of the analysis by referring to the project information related to the file. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input project information into a generation AI, which can improve the accuracy of the analysis.

[0089] When analyzing the contents of an attachment, the analysis unit can improve the accuracy of the analysis based on the file's creation date and time. For example, if the file's creation date and time is recent, the analysis unit can set a high analysis priority for the file as it is the most recent information. For example, if the file's creation date and time is recent, the analysis unit can set a high analysis priority for the file as it is the most recent information. Furthermore, if the file's creation date and time is old, the analysis unit can set a low analysis priority for the file as it is older information. For example, if the file's creation date and time is just before a specific event or project, the analysis unit can set a high analysis priority for the file as it is older information. Furthermore, if the file's creation date and time is just before a specific event or project, the analysis unit can set a high analysis priority for the file as it is related to the event or project. For example, if the file's creation date and time is just before a specific event or project, the analysis unit can set a high analysis priority for the file as it is related to the event or project. This improves the accuracy of the analysis by taking the file's creation date and time into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input creation date and time data into a generation AI, which can improve the accuracy of the analysis. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, determination unit, sorting unit, and attachment analysis module is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the content of emails. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the priority based on the analyzed content. The sorting unit is realized, for example, by the control unit 46A of the smart device 14 and sorts emails into folders based on the determined priority. The attachment analysis module is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of email attachments. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, determination unit, sorting unit, and attachment analysis module is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the content of emails. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the priority based on the analyzed content. The sorting unit is realized, for example, by the control unit 46A of the smart glasses 214 and sorts emails into folders based on the determined priority. The attachment analysis module is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of email attachments. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, determination unit, sorting unit, and attachment analysis module is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the content of emails. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the priority based on the analyzed content. The sorting unit is realized, for example, by the control unit 46A of the headset type terminal 314 and sorts emails into folders based on the determined priority. The attachment analysis module is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of email attachments. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, determination unit, sorting unit, and attachment analysis module is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the content of emails. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the priority based on the analyzed content. The sorting unit is realized, for example, by the control unit 46A of the robot 414 and sorts emails into folders based on the determined priority. The attachment analysis module is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of email attachments.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on the past behavioral history of the email recipient. For example, the analysis unit can learn the characteristics of emails that the recipient has previously deemed important and reflect this in future analyses. The analysis unit can also learn the characteristics of emails that the recipient has previously replied to quickly and analyze future emails based on these characteristics. Furthermore, the analysis unit can learn the characteristics of emails that the recipient has previously deemed spam and simplify the analysis of spam emails based on these characteristics. This improves the accuracy of the analysis by taking into account the recipient's past behavioral history.

[0092] When determining the priority of an email, the determination unit can apply different determination algorithms based on the category of the email content. For example, if the email content is business-related, the determination unit can set a high priority for sorting it into a business folder. If the email content is private, the determination unit can set a low priority for sorting it into a private folder. Furthermore, if the email content is determined to be spam, the determination unit can set the lowest priority for sorting it into a spam folder. This makes it possible to determine the priority according to the category of the email content.

[0093] When sorting emails into folders, the sorting unit can improve the accuracy of sorting based on the conference and event information related to the email. For example, if the email is related to a specific conference, the sorting unit can sort it into the conference folder. Also, if the email is related to a specific event, the sorting unit can sort it into the event folder. Furthermore, if the email is related to multiple conferences or events, the sorting unit can sort it based on the importance of each conference or event. This improves the accuracy of sorting by referring to the conference and event information related to the email.

[0094] When analyzing the content of an email, the analysis unit can improve the accuracy of the analysis based on the job title and department information of the email sender. For example, if the sender is a manager, the analysis unit can prioritize analyzing emails of high importance. Also, if the sender belongs to a specific department, the analysis unit can prioritize keywords related to that department in the analysis. Furthermore, if the sender is a new employee, the analysis unit can prioritize analyzing emails with basic content. This improves the accuracy of the analysis by taking into account the job title and department information of the email sender.

[0095] When determining the priority of an email, the determination unit can improve the accuracy of the determination based on task information related to the email. For example, if the email is related to a specific task, the determination unit can make a determination based on the importance of that task. Also, if the email is related to multiple tasks, the determination unit can make a determination based on the importance of each task. Furthermore, if the email is related to a new task, the determination unit can make a determination by referring to information on similar past tasks. In this way, by referring to task information related to the email, the accuracy of the determination is improved.

[0096] The analysis unit can estimate the user's emotions and set the priority of emails to be analyzed based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing important emails. Also, if the user is relaxed, the analysis unit can analyze emails including regular emails. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing emails with a high level of urgency. Thus, by determining the priority of emails to be analyzed based on the user's emotions, important emails can be prioritized for analysis.

[0097] The determination unit can estimate the user's emotions and adjust the priority determination criteria based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit can prioritize emails with high importance. Also, if the user is relaxed, the determination unit can also determine emails with normal importance. Furthermore, if the user is in a hurry, the determination unit can prioritize emails with high urgency. This allows for more appropriate determination by adjusting the priority determination criteria based on the user's emotions.

[0098] The sorting unit can estimate the user's emotions and change the sorting criteria based on the estimated user emotions. For example, if the user is feeling stressed, the sorting unit can prioritize sorting emails of high importance. Also, if the user is relaxed, the sorting unit can sort emails including regular emails. Furthermore, if the user is in a hurry, the sorting unit can prioritize sorting emails of high urgency. This allows for more appropriate sorting by adjusting the sorting criteria based on the user's emotions.

[0099] The analysis unit can estimate the user's emotions and change the attachment analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can focus on important keywords in the analysis. If the user is feeling relaxed, the analysis unit can perform detailed contextual analysis to gain a deeper understanding of the contents of the attachment. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze the attachment and prioritize extracting important information. This allows for more appropriate analysis by adjusting the attachment analysis method based on the user's emotions.

[0100] The analysis unit can estimate the user's emotions and change the email analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can focus on important keywords in the analysis to improve the accuracy of the analysis. If the user is relaxed, the analysis unit can perform detailed contextual analysis to gain a deeper understanding of the email content. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze the email and prioritize extracting important information. This allows for more appropriate analysis by adjusting the email analysis method based on the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The analysis unit analyzes the content of the email. The analysis unit analyzes the content of the email using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit extracts keywords from the body of the email and evaluates the importance of those keywords. The analysis unit can also analyze the context of the email to understand the content of the email. For example, the analysis unit can determine whether the content of the email is business-related or personal-related based on the context of the email. Step 2: The determination unit determines the priority based on the content analyzed by the analysis unit. The determination unit, for example, learns the user's past email processing history and improves the accuracy of priority determination. For example, the determination unit can learn the characteristics of emails that the user has previously judged to be important and reflect this in future determinations. The determination unit can also determine the priority by taking into account information such as the trustworthiness of the email sender and the time period during which the email was received. For example, if the determination unit determines that the sender is trustworthy, it sets the priority of the email high. Step 3: The sorting unit automatically sorts emails into folders based on the priority determined by the judgment unit. The sorting unit can sort emails into folders based on sorting criteria that can be customized by the user. For example, the sorting unit can sort emails into specific folders based on keywords or senders set by the user. The sorting unit can also analyze the contents of email attachments and sort emails into folders based on those contents. For example, the sorting unit can sort an email into an "important" folder if the attachment is an important document.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of the email; a determination unit that determines a priority based on the content analyzed by the analysis unit; a sorting unit that automatically sorts emails into folders based on the priority determined by the determining unit; Equipped with A system characterized by:

2. The analysis unit Analyze email content using natural language processing algorithms 2. The system of claim 1.

3. The determination unit Learn from users' past email processing history to improve the accuracy of priority determination 2. The system of claim 1.

4. The sorting unit includes: Sorts emails into folders based on user-configurable criteria 2. The system of claim 1.

5. Equipped with an attachment analysis module that analyzes the contents of email attachments 2. The system of claim 1.

6. The analysis unit Analyzes the contents of attachments using image recognition and text analysis algorithms 6. The system of claim 5.

7. The analysis unit Infer user sentiment and change email analysis method based on the estimated user sentiment 2. The system of claim 1.

8. The analysis unit When analyzing email content, improve the accuracy of analysis based on the trustworthiness of the email sender.

2. The system of claim 1.

9. The analysis unit When analyzing email content, determine the priority of analysis based on the time of receipt of the email.

2. The system of claim 1.

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