system

Abstract

The system according to the embodiment aims to efficiently process a large amount of emails and to provide appropriate responses and automate tasks. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, a processing unit, a response unit, a task unit, a quality unit, and a collection unit. The receiving unit receives email. The analysis unit analyzes the email received by the receiving unit. The processing unit processes the email based on the results of the analysis by the analysis unit. The response unit performs an automatic response based on the results of the analysis unit. The task unit automates simple tasks based on the results of the analysis unit. The quality unit performs quality control and feedback based on the results of the processing unit. The collection unit collects data based on the results of the quality 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] Conventional technologies have had the problem of making it difficult to efficiently process large volumes of email and automate appropriate responses and tasks.

[0005] The system according to the embodiment aims to efficiently process a large amount of emails and to provide appropriate responses and automate tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, a processing unit, a response unit, a task unit, a quality unit, and a collection unit. The receiving unit receives emails. The analysis unit analyzes the emails received by the receiving unit. The processing unit processes the emails based on the results of the analysis by the analysis unit. The response unit performs an automatic response based on the results of the analysis unit. The task unit automates simple tasks based on the results of the analysis unit. The quality unit performs quality control and feedback based on the results of the processing unit. The collection unit collects data based on the results of the quality unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently process a large volume of emails and provide appropriate responses and automate tasks. [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 processing system according to an embodiment of the present invention is a system that automatically processes a large volume of emails received daily. The email processing system receives emails, analyzes their contents, and automatically processes them appropriately. For example, emails that meet certain conditions are sorted into specific folders, and emails that do not meet certain conditions are deleted. The email processing system can also analyze existing emails and extract patterns and trends. Furthermore, the email processing system has an auto-response function that automatically replies to routine questions. It also automates simple tasks, such as automatically scheduling meetings and setting reminders. The email processing system also handles quality control and feedback, collecting data to improve processing accuracy. For example, the email processing system receives a large volume of emails received daily. For example, the email processing system analyzes the content of the received emails, sorts emails that meet certain conditions into specific folders, and deletes emails that do not meet certain conditions. The email processing system then automatically replies to routine questions. For example, the email processing system automatically performs simple tasks, such as automatically scheduling meetings and setting reminders. Next, the email processing system performs quality control and feedback to improve the accuracy of processing. For example, the email processing system collects data based on the results of quality control and feedback. This allows the email processing system to improve the efficiency and accuracy of email processing. As a result, the email processing system can automatically process the large amount of emails that arrive daily, improving the efficiency and accuracy of email processing. For example, the email processing system can improve the efficiency and accuracy of email processing by consistently automating everything from receiving emails to analyzing, processing, automatic responses, task automation, quality control, and data collection.

[0029] An email processing system according to an embodiment includes a receiving unit, an analyzing unit, a processing unit, a responding unit, a task unit, a quality unit, and a collecting unit. The receiving unit receives emails. The receiving unit can receive emails using protocols such as POP3, IMAP, and SMTP. The receiving unit can efficiently receive the large volume of emails that arrive daily. The analyzing unit analyzes the content of the emails received by the receiving unit. The analyzing unit can analyze the emails using, for example, natural language processing, keyword matching, and machine learning algorithms. The analyzing unit can analyze the content of the received emails, sort emails that meet certain conditions into specific folders, and delete emails that do not meet the certain conditions. The processing unit processes the emails based on the results of the analysis by the analyzing unit. The processing unit can, for example, classify, tag, and delete emails. The responding unit performs automatic responses based on the results of the analyzing unit. The responding unit can automatically reply to standard questions using, for example, standard phrases or template-based responses. The tasking unit automates simple tasks based on the results of the analyzing unit. The task unit can automatically perform simple tasks such as adjusting meeting schedules and setting reminders. The quality unit performs quality control and feedback based on the results of the processing unit. The quality unit can perform error checks and collect user feedback, for example. The collection unit collects data based on the results of the quality unit. The collection unit can collect log data and track user behavior, for example. As a result, the email processing system according to the embodiment can improve the efficiency and accuracy of email processing by consistently automating everything from receiving emails to analysis, processing, automatic responses, task automation, quality control, and data collection.

[0030] The receiving unit can receive multiple emails daily. The receiving unit can receive multiple emails daily using protocols such as POP3, IMAP, and SMTP. The receiving unit can receive emails, for example, according to the number of emails per day and the type of email to be received (spam, important email, etc.). This allows for efficient reception of large volumes of email. Some or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can use AI to select an email reception protocol and determine the optimal reception method.

[0031] The analysis unit can analyze the content of received emails, sort emails that meet certain conditions into specific folders, and delete emails that do not meet the certain conditions. The analysis unit can analyze the content of received emails using, for example, natural language processing, keyword matching, machine learning algorithms, etc. The analysis unit can analyze emails based on certain conditions, such as the frequency of keyword appearance or the sender's domain, and sort them into specific folders. The analysis unit can also delete emails that do not meet certain conditions. This improves the efficiency of email management by automatically sorting and deleting emails based on their content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can analyze the content of emails using AI and sort emails based on certain conditions.

[0032] The response unit can automatically reply to standard questions. The response unit can automatically reply to standard questions, for example, by using standard phrases or template-based responses. The response unit can automatically reply to standard questions, such as frequently asked questions or FAQs. This can reduce the user's effort by automatically replying to standard questions. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can use AI to generate an optimal response to a standard question and automatically reply.

[0033] The task unit can automatically perform simple tasks such as adjusting a meeting schedule or setting a reminder. For example, the task unit can automatically perform simple tasks such as adjusting a meeting schedule or setting a reminder. For example, the task unit can work with a calendar application to automatically adjust the date and time of a meeting. The task unit can also use a notification function to set reminders to remind the user of important tasks. This automates simple tasks, thereby reducing the user's workload. Some or all of the above-described processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to optimize and automatically perform schedule adjustment and reminder setting.

[0034] The quality department can perform quality control and feedback to improve processing accuracy. The quality department can perform quality control and feedback to improve processing accuracy, for example, by performing error checks and collecting user feedback. The quality department can perform quality control, for example, by detecting and correcting processing errors. The quality department can also collect feedback from users and use it to improve the system. In this way, quality control and feedback can improve system accuracy. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to perform error checks and analyze user feedback to optimize quality control.

[0035] The collection unit can collect data based on the results of quality control and feedback. The collection unit can collect data based on the results of quality control and feedback, for example, by collecting log data and tracking user behavior. The collection unit can, for example, collect system operation logs and provide data for evaluating processing accuracy and efficiency. The collection unit can also track user behavior and collect data to be used for improving the system. In this way, collecting data can be used to improve the system. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to analyze log data and track user behavior to optimize data collection.

[0036] The receiving unit can analyze the user's past email reception history and select the optimal reception method. For example, the receiving unit can analyze time periods during which the user frequently received emails in the past and concentrate email reception during those time periods. The receiving unit can also analyze the content of emails the user has received in the past and prioritize receiving important emails. Furthermore, the receiving unit can analyze the senders of emails the user has received in the past and prioritize receiving emails from specific senders. In this way, the optimal reception method can be provided by analyzing the past reception history. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can use AI to analyze the user's past email reception history and select the optimal reception method.

[0037] When receiving emails, the receiving unit can filter them based on the user's current projects and areas of interest. For example, the receiving unit can prioritize receiving emails related to projects the user is currently working on. The receiving unit can also prioritize receiving emails related to the user's areas of interest. Furthermore, the receiving unit can also prioritize receiving emails related to topics in which the user has previously shown interest. In this way, by filtering emails based on the user's projects and areas of interest, important emails can be received preferentially. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can use AI to analyze the user's current projects and areas of interest and perform filtering.

[0038] When receiving an email, the receiving unit can select the optimal receiving means depending on the user's input method. For example, if the user is using voice input, the receiving unit can prioritize receiving voice mail. Furthermore, if the user is using text input, the receiving unit can also prioritize receiving text mail. Furthermore, if the user is using image input, the receiving unit can also prioritize receiving image mail. This allows for selecting the optimal receiving means depending on the user's input method, thereby improving user convenience. Some or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can analyze the user's input method using AI and select the optimal receiving means.

[0039] When receiving emails, the receiving unit can prioritize receiving emails that are highly relevant based on the user's geographical location information. For example, when the user is in a specific area, the receiving unit can prioritize receiving emails related to that area. Furthermore, when the user is traveling, the receiving unit can prioritize receiving emails related to the user's travel destination. Furthermore, when the user is at home, the receiving unit can prioritize receiving emails related to the user's home. This allows for prioritized reception of emails that are highly relevant based on the user's geographical location information, thereby improving user convenience. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can analyze the user's geographical location information using AI and prioritize receiving emails that are highly relevant.

[0040] When receiving an email, the receiving unit can analyze the user's social media activity and receive related emails. For example, the receiving unit can prioritize receiving emails from accounts the user follows on social media. The receiving unit can also analyze the content of the user's social media posts and prioritize receiving related emails. Furthermore, the receiving unit can also prioritize receiving related emails by referring to the activities of the user's friends on social media. This can improve user convenience by receiving related emails based on the user's social media activity. Some or all of the above-described processing in the receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the receiving unit can analyze the user's social media activity using AI and receive related emails.

[0041] The receiving unit can customize the reception method by reflecting the user's past feedback when receiving emails. For example, the receiving unit can prioritize receiving emails that the user previously rated as important. The receiving unit can also filter out emails that the user previously rated as unnecessary so as not to receive them. Furthermore, the receiving unit can customize and optimize the reception method based on the user's past feedback. In this way, the reception method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can analyze the user's past feedback using AI to customize the reception method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. The analysis unit can adjust the level of detail of the analysis based on, for example, the importance of the email. The analysis unit can perform a detailed analysis on, for example, important emails. The analysis unit can also perform a concise analysis on unimportant emails. Furthermore, the analysis unit can also perform a quick analysis on emails with a high level of urgency. In this way, by adjusting the level of detail of the analysis according to the importance of the email, detailed analysis can be performed on important emails. 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 evaluate the importance of the email using AI and adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of email. For example, the analysis unit can apply a business analysis algorithm to business email. The analysis unit can also apply a private analysis algorithm to private email. Furthermore, the analysis unit can apply a spam filtering analysis algorithm to spam email. This allows for applying an appropriate analysis algorithm depending on the category of email, thereby improving the accuracy of analysis. 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 classify email categories using AI and apply an appropriate analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results evaluated by the user in the past. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by reflecting the user's past feedback. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. 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 analyze the user's past analysis results using AI to improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the email was received. For example, the analysis unit can prioritize the analysis of recently received emails. The analysis unit can also prioritize the analysis of emails with a high level of urgency. Furthermore, the analysis unit can also prioritize the analysis of emails received within a time period specified by the user. In this way, by determining the priority of analysis based on the time when the email was received, emails with a high level of urgency 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 using AI. For example, the analysis unit can use AI to analyze the time when the email was received and determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of emails. For example, the analysis unit can prioritize analysis of highly relevant emails. The analysis unit can also postpone emails with low relevance. Furthermore, the analysis unit can prioritize analysis of emails related to highly relevant topics specified by the user. In this way, by adjusting the order of analysis based on the relevance of emails, highly relevant emails can be analyzed with priority. 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 evaluate the relevance of emails using AI and adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. 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 use AI to evaluate the user's level of expertise and adjust the use of technical terms in the analysis.

[0048] The processing unit can adjust the level of detail of the processing based on the importance of the email during processing. The processing unit can adjust the level of detail of the processing based on, for example, the importance of the email. The processing unit can, for example, perform detailed processing on important emails. The processing unit can also perform simple processing on unimportant emails. Furthermore, the processing unit can also perform quick processing on emails with a high level of urgency. In this way, by adjusting the level of detail of the processing according to the importance of the email, detailed processing can be performed on important emails. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can evaluate the importance of the email using AI and adjust the level of detail of the processing.

[0049] The processing unit can apply different processing algorithms depending on the category of email during processing. For example, the processing unit can apply a business processing algorithm to business email. The processing unit can also apply a private processing algorithm to private email. Furthermore, the processing unit can also apply a spam filtering processing algorithm to spam email. This allows for applying an appropriate processing algorithm depending on the category of email, thereby improving processing accuracy. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can classify email categories using AI and apply an appropriate processing algorithm.

[0050] During processing, the processing unit can improve the accuracy of the processing by referring to the user's past processing results. The processing unit can improve the accuracy of the processing by referring to, for example, processing results evaluated by the user in the past. The processing unit can also optimize the processing algorithm based on the user's past processing results. Furthermore, the processing unit can also improve the accuracy of the processing by reflecting the user's past feedback. In this way, the accuracy of the processing can be improved by referring to the user's past processing results. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can analyze the user's past processing results using AI to improve the accuracy of the processing.

[0051] During processing, the processing unit can adjust the order of processing based on the time of receipt of emails. For example, the processing unit can prioritize processing of recently received emails. The processing unit can also prioritize processing of emails with a high level of urgency. Furthermore, the processing unit can also prioritize processing of emails received during a time period specified by the user. In this way, by adjusting the order of processing based on the time of receipt of emails, emails with a high level of urgency can be processed quickly. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can analyze the time of receipt of emails using AI and adjust the order of processing.

[0052] During processing, the processing unit can adjust the processing order based on the relevance of the emails. For example, the processing unit can prioritize processing highly relevant emails. The processing unit can also postpone less relevant emails. Furthermore, the processing unit can prioritize processing emails related to highly relevant topics specified by the user. In this way, by adjusting the processing order based on the relevance of the emails, highly relevant emails can be prioritized for processing. Some or all of the above-described processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can evaluate the relevance of emails using AI and adjust the processing order.

[0053] During processing, the processing unit can adjust the processing method according to the user's level of expertise. For example, if the user has expertise, the processing unit can apply a specialized processing method. Furthermore, if the user does not have expertise, the processing unit can apply a simple and easy-to-understand processing method. Furthermore, the processing unit can adjust the processing method according to the user's level of expertise. In this way, by adjusting the processing method according to the user's level of expertise, it is possible to provide processing results that are easy for the user to understand. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can use AI to evaluate the user's level of expertise and adjust the processing method.

[0054] When responding, the response unit can adjust the level of detail of the response based on the importance of the email. The response unit can adjust the level of detail of the response based on, for example, the importance of the email. The response unit can provide a detailed response to, for example, an important email. The response unit can also provide a concise response to a non-important email. Furthermore, the response unit can also provide a quick response to an email with a high level of urgency. In this way, by adjusting the level of detail of the response according to the importance of the email, a detailed response can be provided to an important email. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can evaluate the importance of the email using AI and adjust the level of detail of the response.

[0055] When responding, the response unit can apply different response algorithms depending on the category of the email. For example, the response unit can apply a business-oriented response algorithm to business emails. The response unit can also apply a private-oriented response algorithm to private emails. Furthermore, the response unit can apply a spam filtering response algorithm to spam emails. This allows for applying an appropriate response algorithm depending on the category of the email, thereby improving the accuracy of the response. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can classify the category of the email using AI and apply an appropriate response algorithm.

[0056] When responding, the response unit can improve the accuracy of the response by referring to the user's past response results. The response unit can improve the accuracy of the response by referring to, for example, response results evaluated by the user in the past. The response unit can also optimize the response algorithm based on the user's past response results. Furthermore, the response unit can also improve the accuracy of the response by reflecting the user's past feedback. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can analyze the user's past response results using AI to improve the accuracy of the response.

[0057] When responding, the response unit can determine the priority of the response based on the time when the email was received. For example, the response unit can prioritize responses to recently received emails. The response unit can also prioritize responses to emails with a high level of urgency. Furthermore, the response unit can also prioritize responses to emails received within a time period specified by the user. In this way, by determining the priority of the response based on the time when the email was received, it is possible to respond quickly to emails with a high level of urgency. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can use AI to analyze the time when the email was received and determine the priority of the response.

[0058] When responding, the response unit can adjust the order of responses based on the relevance of the emails. For example, the response unit can prioritize responses to highly relevant emails. The response unit can also postpone responses to less relevant emails. Furthermore, the response unit can prioritize responses to emails related to highly relevant topics specified by the user. In this way, by adjusting the order of responses based on the relevance of the emails, it is possible to prioritize responses to highly relevant emails. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can evaluate the relevance of emails using AI and adjust the order of responses.

[0059] When responding, the response unit can adjust the use of technical terminology in the response depending on the user's level of expertise. For example, if the user has technical expertise, the response unit can provide a response that uses a lot of technical terminology. Also, if the user does not have technical expertise, the response unit can provide a concise and easy-to-understand response. Furthermore, the response unit can adjust the way the response is expressed depending on the user's level of expertise. In this way, by adjusting the use of technical terminology in the response depending on the user's level of expertise, it is possible to provide a response that is easy for the user to understand. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can use AI to evaluate the user's level of expertise and adjust the use of technical terminology in the response.

[0060] When automating a task, the task unit can adjust the level of detail of the task based on the importance of the email. The task unit can adjust the level of detail of the task based on, for example, the importance of the email. The task unit can perform detailed automation for tasks related to important emails, for example. The task unit can also perform simple automation for tasks related to non-important emails. Furthermore, the task unit can also quickly automate tasks related to highly urgent emails. In this way, by adjusting the level of detail of the task according to the importance of the email, detailed automation can be performed for important tasks. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can evaluate the importance of an email using AI and adjust the level of detail of the task.

[0061] When automating a task, the task unit can apply different task automation algorithms depending on the category of the email. For example, the task unit can apply a business automation algorithm to a task related to business email. The task unit can also apply a private automation algorithm to a task related to private email. Furthermore, the task unit can also apply a spam filtering automation algorithm to a task related to spam email. In this way, by applying an appropriate task automation algorithm depending on the category of email, the accuracy of task automation can be improved. Some or all of the above-mentioned processing in the task unit may be performed using AI, for example, or may be performed without using AI. For example, the task unit can classify the category of email using AI and apply an appropriate task automation algorithm.

[0062] When automating a task, the task unit can improve the accuracy of the task by referring to the user's past task results. For example, the task unit can improve the accuracy of the task by referring to task results previously evaluated by the user. The task unit can also optimize the task automation algorithm based on the user's past task results. Furthermore, the task unit can improve the accuracy of the task by reflecting the user's past feedback. In this way, the accuracy of the task can be improved by referring to the user's past task results. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can analyze the user's past task results using AI to improve the accuracy of the task.

[0063] When automating tasks, the task unit can adjust the order of tasks based on the time when emails were received. For example, the task unit can prioritize automating tasks related to recently received emails. The task unit can also prioritize automating tasks related to highly urgent emails. Furthermore, the task unit can also prioritize automating tasks related to emails received within a time period specified by the user. In this way, by adjusting the order of tasks based on the time when emails were received, highly urgent tasks can be quickly automated. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to analyze the time when emails were received and adjust the order of tasks.

[0064] When automating tasks, the task unit can adjust the order of tasks based on the relevance of emails. For example, the task unit can prioritize automation of tasks related to highly relevant emails. The task unit can also postpone automation of tasks related to less relevant emails. Furthermore, the task unit can prioritize automation of tasks related to emails on a highly relevant topic specified by the user. In this way, by adjusting the order of tasks based on the relevance of emails, highly relevant tasks can be prioritized for automation. Some or all of the above-described processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to evaluate the relevance of emails and adjust the order of tasks.

[0065] When automating a task, the task unit can adjust the task method according to the user's level of expertise. For example, if the user has specialized knowledge, the task unit can apply a specialized task automation method. Also, if the user does not have specialized knowledge, the task unit can apply a simple and easy-to-understand task automation method. Furthermore, the task unit can adjust the task automation method according to the user's level of expertise. In this way, by adjusting the task method according to the user's level of expertise, task automation that is easy for the user to understand can be provided. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to evaluate the user's level of expertise and adjust the task method.

[0066] During quality control, the quality department can adjust the level of detail of the quality control based on the importance of the email. The quality department can adjust the level of detail of the quality control based on, for example, the importance of the email. The quality department can perform detailed quality control on important emails, for example. The quality department can also perform simple quality control on unimportant emails. Furthermore, the quality department can also perform quick quality control on emails with a high level of urgency. In this way, by adjusting the level of detail of the quality control according to the importance of the email, detailed quality control can be performed on important emails. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can evaluate the importance of the email using AI and adjust the level of detail of the quality control.

[0067] During quality control, the quality department can apply different quality control algorithms depending on the category of email. For example, the quality department can apply a business quality control algorithm to business email. The quality department can also apply a private quality control algorithm to private email. Furthermore, the quality department can apply a spam filtering quality control algorithm to spam email. This allows for applying an appropriate quality control algorithm depending on the category of email, thereby improving the accuracy of quality control. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can classify email categories using AI and apply an appropriate quality control algorithm.

[0068] During quality control, the quality department can improve the accuracy of quality control by referring to the user's past quality control results. For example, the quality department can improve the accuracy of quality control by referring to quality control results evaluated by the user in the past. The quality department can also optimize the quality control algorithm based on the user's past quality control results. Furthermore, the quality department can improve the accuracy of quality control by reflecting the user's past feedback. In this way, the accuracy of quality control can be improved by referring to the user's past quality control results. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to analyze the user's past quality control results and improve the accuracy of quality control.

[0069] During quality control, the quality department can adjust the order of quality control based on the time of receipt of emails. For example, the quality department can prioritize quality control for recently received emails. The quality department can also prioritize quality control for highly urgent emails. Furthermore, the quality department can also prioritize quality control for emails received during a time period specified by the user. In this way, by adjusting the order of quality control based on the time of receipt of emails, it is possible to quickly perform quality control on highly urgent emails. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to analyze the time of receipt of emails and adjust the order of quality control.

[0070] During quality control, the quality department can adjust the order of quality control based on the relevance of emails. For example, the quality department can prioritize quality control for highly relevant emails. The quality department can also postpone quality control for less relevant emails. Furthermore, the quality department can prioritize quality control for emails related to highly relevant topics specified by the user. In this way, by adjusting the order of quality control based on the relevance of emails, highly relevant emails can be prioritized in quality control. Some or all of the above-described processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to evaluate the relevance of emails and adjust the order of quality control.

[0071] During quality control, the quality department can adjust the quality control method according to the user's level of expertise. For example, if the user has expertise, the quality department can apply a specialized quality control method. Furthermore, if the user does not have expertise, the quality department can apply a simple and easy-to-understand quality control method. Furthermore, the quality department can adjust the quality control method according to the user's level of expertise. By adjusting the quality control method according to the user's level of expertise, it is possible to provide quality control that is easy for the user to understand. Some or all of the above-described processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to evaluate the user's level of expertise and adjust the quality control method.

[0072] The collection unit can adjust the level of detail of data collection based on the importance of emails when collecting data. The collection unit can adjust the level of detail of data collection based on, for example, the importance of emails. The collection unit can, for example, perform detailed data collection for important emails. The collection unit can also perform brief data collection for unimportant emails. Furthermore, the collection unit can also quickly collect data for emails with a high level of urgency. In this way, by adjusting the level of detail of data collection according to the importance of emails, important data can be collected in detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can evaluate the importance of emails using AI and adjust the level of detail of data collection.

[0073] When collecting data, the collection unit can apply different data collection algorithms depending on the category of email. For example, the collection unit can apply a business-oriented data collection algorithm to business emails. The collection unit can also apply a private-oriented data collection algorithm to private emails. Furthermore, the collection unit can apply a spam filtering data collection algorithm to spam emails. This allows for applying an appropriate data collection algorithm depending on the category of email, thereby improving the accuracy of data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can classify email categories using AI and apply an appropriate data collection algorithm.

[0074] When collecting data, the collection unit can improve the accuracy of the data collection by referring to the user's past data collection results. The collection unit can improve the accuracy of the data collection by referring to, for example, data collection results previously evaluated by the user. The collection unit can also optimize the data collection algorithm based on the user's past data collection results. Furthermore, the collection unit can improve the accuracy of the data collection by reflecting the user's past feedback. In this way, the accuracy of the data collection can be improved by referring to the user's past data collection results. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's past data collection results using AI to improve the accuracy of the data collection.

[0075] During data collection, the collection unit can adjust the order of data collection based on the time of receipt of emails. For example, the collection unit can prioritize data collection for recently received emails. The collection unit can also prioritize data collection for emails with a high level of urgency. Furthermore, the collection unit can also prioritize data collection for emails received within a time period specified by the user. By adjusting the order of data collection based on the time of receipt of emails, data with a high level of urgency can be collected quickly. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the time of receipt of emails using AI and adjust the order of data collection.

[0076] During data collection, the collection unit can adjust the order of data collection based on the relevance of emails. For example, the collection unit can prioritize data collection for highly relevant emails. The collection unit can also postpone data collection for less relevant emails. Furthermore, the collection unit can prioritize data collection for emails related to highly relevant topics specified by the user. In this way, by adjusting the order of data collection based on the relevance of emails, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can evaluate the relevance of emails using AI and adjust the order of data collection.

[0077] During data collection, the collection unit can adjust the data collection method according to the user's level of expertise. For example, if the user has expertise, the collection unit can apply a specialized data collection method. Furthermore, if the user does not have expertise, the collection unit can apply a simple and easy-to-understand data collection method. Furthermore, the collection unit can adjust the data collection method according to the user's level of expertise. By adjusting the data collection method according to the user's level of expertise, data collection that is easy for the user to understand can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to evaluate the user's level of expertise and adjust the data collection method.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The processing unit can automatically select a reply template based on the content of the email. For example, a business template can be used for business emails, and a private template can be used for private emails. The processing unit can also apply a spam filtering template to spam emails. Furthermore, the processing unit can also select the most appropriate template by referring to templates used by the user in the past. This allows the efficiency and accuracy of replies to be improved by selecting an appropriate reply template according to the content of the email.

[0080] The task unit can analyze the user's past task completion history and propose an optimal task schedule. For example, if the user has tended to complete tasks during a specific time period in the past, the task can be scheduled for that time period. Also, if the user has prioritized completing a specific task in the past, the task can be scheduled with that priority. Furthermore, the required time for a task can be predicted by referring to the time it took the user to complete a task in the past. In this way, the optimal task schedule can be proposed by referring to the user's past task completion history.

[0081] The collection unit can optimize the data collection method based on the user's past data collection results. For example, the collection unit can select the optimal data collection method by referring to data collection methods that the user has evaluated in the past. The collection unit can also select the optimal tool by referring to data collection tools that the user has used in the past. Furthermore, the collection unit can suggest the optimal data collection method by referring to the types of data that the user has collected in the past. In this way, the data collection method can be optimized by referring to the user's past data collection results.

[0082] The processing unit can automatically assign tags based on the content of emails. For example, it can assign a "business" tag to business emails and a "private" tag to private emails. It can also assign a "spam" tag to spam emails. It can also assign the most appropriate tag by referring to tags previously assigned by the user. This allows for efficient email management by automatically assigning appropriate tags according to the content of emails.

[0083] The task section can analyze the user's current project status and suggest optimal task priorities. For example, it can prioritize tasks related to the project the user is currently working on. It can also suggest optimal task priorities by referring to the status of projects the user has completed in the past. It can also analyze the issues the user is facing in the current project and prioritize tasks related to those issues. In this way, it is possible to suggest optimal task priorities by analyzing the user's current project status.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The receiving unit receives email. The receiving unit can receive email using protocols such as POP3, IMAP, and SMTP. The receiving unit can also efficiently receive the large amount of email that arrives every day. Step 2: The analysis unit analyzes the content of the email received by the receiving unit. The analysis unit can analyze the email using, for example, natural language processing, keyword matching, machine learning algorithms, etc. The analysis unit can also analyze the content of the received email, sort emails that meet certain conditions into specific folders, and delete emails that do not meet certain conditions. Step 3: The processing unit processes the email based on the results of the analysis by the analysis unit. For example, the processing unit can classify, tag, or delete the email. Step 4: The response unit generates an automatic response based on the results of the analysis unit. The response unit can automatically respond to standard questions using, for example, standard phrases, template-based responses, etc. Step 5: The task unit automates simple tasks based on the results of the analysis unit. For example, the task unit can automate simple tasks such as adjusting meeting schedules and setting reminders. Step 6: The quality department performs quality control and feedback based on the results of the processing department. The quality department can, for example, perform error checks, collect user feedback, etc. Step 7: The collection unit collects data based on the results of the quality unit. The collection unit can, for example, collect log data, track user behavior, etc.

[0086] (Example 2) An email processing system according to an embodiment of the present invention is a system that automatically processes a large volume of emails received daily. The email processing system receives emails, analyzes their contents, and automatically processes them appropriately. For example, emails that meet certain conditions are sorted into specific folders, and emails that do not meet certain conditions are deleted. The email processing system can also analyze existing emails and extract patterns and trends. Furthermore, the email processing system has an auto-response function that automatically replies to routine questions. It also automates simple tasks, such as automatically scheduling meetings and setting reminders. The email processing system also handles quality control and feedback, collecting data to improve processing accuracy. For example, the email processing system receives a large volume of emails received daily. For example, the email processing system analyzes the content of the received emails, sorts emails that meet certain conditions into specific folders, and deletes emails that do not meet certain conditions. The email processing system then automatically replies to routine questions. For example, the email processing system automatically performs simple tasks, such as automatically scheduling meetings and setting reminders. Next, the email processing system performs quality control and feedback to improve the accuracy of processing. For example, the email processing system collects data based on the results of quality control and feedback. This allows the email processing system to improve the efficiency and accuracy of email processing. As a result, the email processing system can automatically process the large amount of emails that arrive daily, improving the efficiency and accuracy of email processing. For example, the email processing system can improve the efficiency and accuracy of email processing by consistently automating everything from receiving emails to analyzing, processing, automatic responses, task automation, quality control, and data collection.

[0087] An email processing system according to an embodiment includes a receiving unit, an analyzing unit, a processing unit, a responding unit, a task unit, a quality unit, and a collecting unit. The receiving unit receives emails. The receiving unit can receive emails using protocols such as POP3, IMAP, and SMTP. The receiving unit can efficiently receive the large volume of emails that arrive daily. The analyzing unit analyzes the content of the emails received by the receiving unit. The analyzing unit can analyze the emails using, for example, natural language processing, keyword matching, and machine learning algorithms. The analyzing unit can analyze the content of the received emails, sort emails that meet certain conditions into specific folders, and delete emails that do not meet the certain conditions. The processing unit processes the emails based on the results of the analysis by the analyzing unit. The processing unit can, for example, classify, tag, and delete emails. The responding unit performs automatic responses based on the results of the analyzing unit. The responding unit can automatically reply to standard questions using, for example, standard phrases or template-based responses. The tasking unit automates simple tasks based on the results of the analyzing unit. The task unit can automatically perform simple tasks such as adjusting meeting schedules and setting reminders. The quality unit performs quality control and feedback based on the results of the processing unit. The quality unit can perform error checks and collect user feedback, for example. The collection unit collects data based on the results of the quality unit. The collection unit can collect log data and track user behavior, for example. As a result, the email processing system according to the embodiment can improve the efficiency and accuracy of email processing by consistently automating everything from receiving emails to analysis, processing, automatic responses, task automation, quality control, and data collection.

[0088] The receiving unit can receive multiple emails daily. The receiving unit can receive multiple emails daily using protocols such as POP3, IMAP, and SMTP. The receiving unit can receive emails, for example, according to the number of emails per day and the type of email to be received (spam, important email, etc.). This allows for efficient reception of large volumes of email. Some or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can use AI to select an email reception protocol and determine the optimal reception method.

[0089] The analysis unit can analyze the content of received emails, sort emails that meet certain conditions into specific folders, and delete emails that do not meet the certain conditions. The analysis unit can analyze the content of received emails using, for example, natural language processing, keyword matching, machine learning algorithms, etc. The analysis unit can analyze emails based on certain conditions, such as the frequency of keyword appearance or the sender's domain, and sort them into specific folders. The analysis unit can also delete emails that do not meet certain conditions. This improves the efficiency of email management by automatically sorting and deleting emails based on their content. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can analyze the content of emails using AI and sort emails based on certain conditions.

[0090] The response unit can automatically reply to standard questions. The response unit can automatically reply to standard questions, for example, by using standard phrases or template-based responses. The response unit can automatically reply to standard questions, such as frequently asked questions or FAQs. This can reduce the user's effort by automatically replying to standard questions. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can use AI to generate an optimal response to a standard question and automatically reply.

[0091] The task unit can automatically perform simple tasks such as adjusting a meeting schedule or setting a reminder. For example, the task unit can automatically perform simple tasks such as adjusting a meeting schedule or setting a reminder. For example, the task unit can work with a calendar application to automatically adjust the date and time of a meeting. The task unit can also use a notification function to set reminders to remind the user of important tasks. This automates simple tasks, thereby reducing the user's workload. Some or all of the above-described processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to optimize and automatically perform schedule adjustment and reminder setting.

[0092] The quality department can perform quality control and feedback to improve processing accuracy. The quality department can perform quality control and feedback to improve processing accuracy, for example, by performing error checks and collecting user feedback. The quality department can perform quality control, for example, by detecting and correcting processing errors. The quality department can also collect feedback from users and use it to improve the system. In this way, quality control and feedback can improve system accuracy. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to perform error checks and analyze user feedback to optimize quality control.

[0093] The collection unit can collect data based on the results of quality control and feedback. The collection unit can collect data based on the results of quality control and feedback, for example, by collecting log data and tracking user behavior. The collection unit can, for example, collect system operation logs and provide data for evaluating processing accuracy and efficiency. The collection unit can also track user behavior and collect data to be used for improving the system. In this way, collecting data can be used to improve the system. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to analyze log data and track user behavior to optimize data collection.

[0094] The receiving unit can estimate the user's emotions and adjust the timing of receiving emails based on the estimated user emotions. The receiving unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is stressed, the receiving unit can adjust the system so that only important emails are received immediately and other emails are received later. The receiving unit can also adjust the system so that all emails are received immediately when the user is relaxed. Furthermore, when the user is busy, the receiving unit can adjust the system so that non-urgent emails are received later. This allows the user's stress to be reduced by adjusting the timing of email reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 receiving unit may be performed using, for example, AI, or without AI. For example, the receiving unit can estimate the user's emotions using AI and adjust the timing of email reception based on the estimated emotions.

[0095] The receiving unit can analyze the user's past email reception history and select the optimal reception method. For example, the receiving unit can analyze time periods during which the user frequently received emails in the past and concentrate email reception during those time periods. The receiving unit can also analyze the content of emails the user has received in the past and prioritize receiving important emails. Furthermore, the receiving unit can analyze the senders of emails the user has received in the past and prioritize receiving emails from specific senders. In this way, the optimal reception method can be provided by analyzing the past reception history. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can use AI to analyze the user's past email reception history and select the optimal reception method.

[0096] When receiving emails, the receiving unit can filter them based on the user's current projects and areas of interest. For example, the receiving unit can prioritize receiving emails related to projects the user is currently working on. The receiving unit can also prioritize receiving emails related to the user's areas of interest. Furthermore, the receiving unit can also prioritize receiving emails related to topics in which the user has previously shown interest. In this way, by filtering emails based on the user's projects and areas of interest, important emails can be received preferentially. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can use AI to analyze the user's current projects and areas of interest and perform filtering.

[0097] When receiving an email, the receiving unit can select the optimal receiving means depending on the user's input method. For example, if the user is using voice input, the receiving unit can prioritize receiving voice mail. Furthermore, if the user is using text input, the receiving unit can also prioritize receiving text mail. Furthermore, if the user is using image input, the receiving unit can also prioritize receiving image mail. This allows for selecting the optimal receiving means depending on the user's input method, thereby improving user convenience. Some or all of the above-mentioned processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can analyze the user's input method using AI and select the optimal receiving means.

[0098] The receiving unit can estimate the user's emotions and determine the priority of emails to be received based on the estimated user emotions. The receiving unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is feeling stressed, the receiving unit can prioritize receiving only important emails. Furthermore, when the user is relaxed, the receiving unit can receive all emails equally. Furthermore, when the user is busy, the receiving unit can adjust the priority so that non-urgent emails are received later. In this way, by determining the priority of emails according to the user's emotions, important emails can be received preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can estimate the user's emotions using AI and determine the priority of emails based on the estimated emotions.

[0099] When receiving emails, the receiving unit can prioritize receiving emails that are highly relevant based on the user's geographical location information. For example, when the user is in a specific area, the receiving unit can prioritize receiving emails related to that area. Furthermore, when the user is traveling, the receiving unit can prioritize receiving emails related to the user's travel destination. Furthermore, when the user is at home, the receiving unit can prioritize receiving emails related to the user's home. This allows for prioritized reception of emails that are highly relevant based on the user's geographical location information, thereby improving user convenience. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can analyze the user's geographical location information using AI and prioritize receiving emails that are highly relevant.

[0100] When receiving an email, the receiving unit can analyze the user's social media activity and receive related emails. For example, the receiving unit can prioritize receiving emails from accounts the user follows on social media. The receiving unit can also analyze the content of the user's social media posts and prioritize receiving related emails. Furthermore, the receiving unit can also prioritize receiving related emails by referring to the activities of the user's friends on social media. This can improve user convenience by receiving related emails based on the user's social media activity. Some or all of the above-described processing in the receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the receiving unit can analyze the user's social media activity using AI and receive related emails.

[0101] The receiving unit can customize the reception method by reflecting the user's past feedback when receiving emails. For example, the receiving unit can prioritize receiving emails that the user previously rated as important. The receiving unit can also filter out emails that the user previously rated as unnecessary so as not to receive them. Furthermore, the receiving unit can customize and optimize the reception method based on the user's past feedback. In this way, the reception method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can analyze the user's past feedback using AI to customize the reception method.

[0102] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, if the user is stressed, the analysis unit can provide a concise and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is busy, the analysis unit can provide an analysis result that emphasizes only the important points. This allows the analysis result to be easily understood by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved 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, AI, or without AI. For example, the analysis unit can estimate the user's emotions using AI and adjust the presentation method of the analysis based on the estimated emotions.

[0103] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. The analysis unit can adjust the level of detail of the analysis based on, for example, the importance of the email. The analysis unit can perform a detailed analysis on, for example, important emails. The analysis unit can also perform a concise analysis on unimportant emails. Furthermore, the analysis unit can also perform a quick analysis on emails with a high level of urgency. In this way, by adjusting the level of detail of the analysis according to the importance of the email, detailed analysis can be performed on important emails. 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 evaluate the importance of the email using AI and adjust the level of detail of the analysis.

[0104] During analysis, the analysis unit can apply different analysis algorithms depending on the category of email. For example, the analysis unit can apply a business analysis algorithm to business email. The analysis unit can also apply a private analysis algorithm to private email. Furthermore, the analysis unit can apply a spam filtering analysis algorithm to spam email. This allows for applying an appropriate analysis algorithm depending on the category of email, thereby improving the accuracy of analysis. 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 classify email categories using AI and apply an appropriate analysis algorithm.

[0105] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results evaluated by the user in the past. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. Furthermore, the analysis unit can improve the accuracy of the analysis by reflecting the user's past feedback. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. 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 analyze the user's past analysis results using AI to improve the accuracy of the analysis.

[0106] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is busy, the analysis unit can provide an analysis result that emphasizes only the important points. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing an analysis result of an appropriate length for the user. Emotion estimation is achieved 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the user's emotions using AI and adjust the length of the analysis based on the estimated emotions.

[0107] During analysis, the analysis unit can determine the priority of analysis based on the time when the email was received. For example, the analysis unit can prioritize the analysis of recently received emails. The analysis unit can also prioritize the analysis of emails with a high level of urgency. Furthermore, the analysis unit can also prioritize the analysis of emails received within a time period specified by the user. In this way, by determining the priority of analysis based on the time when the email was received, emails with a high level of urgency 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 using AI. For example, the analysis unit can use AI to analyze the time when the email was received and determine the priority of analysis.

[0108] During analysis, the analysis unit can adjust the order of analysis based on the relevance of emails. For example, the analysis unit can prioritize analysis of highly relevant emails. The analysis unit can also postpone emails with low relevance. Furthermore, the analysis unit can prioritize analysis of emails related to highly relevant topics specified by the user. In this way, by adjusting the order of analysis based on the relevance of emails, highly relevant emails can be analyzed with priority. 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 evaluate the relevance of emails using AI and adjust the order of analysis.

[0109] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. 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 use AI to evaluate the user's level of expertise and adjust the use of technical terms in the analysis.

[0110] The processing unit can estimate the user's emotion and adjust the processing method based on the estimated user's emotion. The processing unit can estimate the user's emotion using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, the processing unit can perform simple and quick processing when the user is stressed. The processing unit can also perform detailed processing when the user is relaxed. Furthermore, the processing unit can process only important points when the user is busy. This allows the processing method to be adjusted according to the user's emotion, thereby providing an appropriate processing method for the user. 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 processing unit may be performed using, for example, AI, or without AI. For example, the processing unit can estimate the user's emotion using AI and adjust the processing method based on the estimated emotion.

[0111] The processing unit can adjust the level of detail of the processing based on the importance of the email during processing. The processing unit can adjust the level of detail of the processing based on, for example, the importance of the email. The processing unit can, for example, perform detailed processing on important emails. The processing unit can also perform simple processing on unimportant emails. Furthermore, the processing unit can also perform quick processing on emails with a high level of urgency. In this way, by adjusting the level of detail of the processing according to the importance of the email, detailed processing can be performed on important emails. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can evaluate the importance of the email using AI and adjust the level of detail of the processing.

[0112] The processing unit can apply different processing algorithms depending on the category of email during processing. For example, the processing unit can apply a business processing algorithm to business email. The processing unit can also apply a private processing algorithm to private email. Furthermore, the processing unit can also apply a spam filtering processing algorithm to spam email. This allows for applying an appropriate processing algorithm depending on the category of email, thereby improving processing accuracy. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can classify email categories using AI and apply an appropriate processing algorithm.

[0113] During processing, the processing unit can improve the accuracy of the processing by referring to the user's past processing results. The processing unit can improve the accuracy of the processing by referring to, for example, processing results evaluated by the user in the past. The processing unit can also optimize the processing algorithm based on the user's past processing results. Furthermore, the processing unit can also improve the accuracy of the processing by reflecting the user's past feedback. In this way, the accuracy of the processing can be improved by referring to the user's past processing results. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can analyze the user's past processing results using AI to improve the accuracy of the processing.

[0114] The processing unit can estimate the user's emotions and determine processing priorities based on the estimated user emotions. The processing unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is stressed, the processing unit can prioritize processing only important emails. Furthermore, when the user is relaxed, the processing unit can process all emails equally. Furthermore, when the user is busy, the processing unit can adjust the priority so that non-urgent emails are processed later. In this way, by determining processing priorities according to the user's emotions, important emails can be processed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, 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 processing unit may be performed using, for example, AI, or without AI. For example, the processing unit can estimate the user's emotions using AI and determine processing priorities based on the estimated emotions.

[0115] During processing, the processing unit can adjust the order of processing based on the time of receipt of emails. For example, the processing unit can prioritize processing of recently received emails. The processing unit can also prioritize processing of emails with a high level of urgency. Furthermore, the processing unit can also prioritize processing of emails received during a time period specified by the user. In this way, by adjusting the order of processing based on the time of receipt of emails, emails with a high level of urgency can be processed quickly. Some or all of the above-described processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can analyze the time of receipt of emails using AI and adjust the order of processing.

[0116] During processing, the processing unit can adjust the processing order based on the relevance of the emails. For example, the processing unit can prioritize processing highly relevant emails. The processing unit can also postpone less relevant emails. Furthermore, the processing unit can prioritize processing emails related to highly relevant topics specified by the user. In this way, by adjusting the processing order based on the relevance of the emails, highly relevant emails can be prioritized for processing. Some or all of the above-described processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can evaluate the relevance of emails using AI and adjust the processing order.

[0117] During processing, the processing unit can adjust the processing method according to the user's level of expertise. For example, if the user has expertise, the processing unit can apply a specialized processing method. Furthermore, if the user does not have expertise, the processing unit can apply a simple and easy-to-understand processing method. Furthermore, the processing unit can adjust the processing method according to the user's level of expertise. In this way, by adjusting the processing method according to the user's level of expertise, it is possible to provide processing results that are easy for the user to understand. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can use AI to evaluate the user's level of expertise and adjust the processing method.

[0118] The response unit can estimate the user's emotion and adjust the way a response is expressed based on the estimated user's emotion. The response unit can estimate the user's emotion using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, if the user is stressed, the response unit can provide a concise and to-the-point response. Alternatively, if the user is relaxed, the response unit can provide a detailed response. Furthermore, if the user is busy, the response unit can provide a response that emphasizes only the important points. This allows the response to be adjusted according to the user's emotion, thereby providing an appropriate response for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 response unit may be performed using, for example, AI, or without AI. For example, the response unit can estimate the user's emotion using AI and adjust the way a response is expressed based on the estimated emotion.

[0119] When responding, the response unit can adjust the level of detail of the response based on the importance of the email. The response unit can adjust the level of detail of the response based on, for example, the importance of the email. The response unit can provide a detailed response to, for example, an important email. The response unit can also provide a concise response to a non-important email. Furthermore, the response unit can also provide a quick response to an email with a high level of urgency. In this way, by adjusting the level of detail of the response according to the importance of the email, a detailed response can be provided to an important email. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can evaluate the importance of the email using AI and adjust the level of detail of the response.

[0120] When responding, the response unit can apply different response algorithms depending on the category of the email. For example, the response unit can apply a business-oriented response algorithm to business emails. The response unit can also apply a private-oriented response algorithm to private emails. Furthermore, the response unit can apply a spam filtering response algorithm to spam emails. This allows for applying an appropriate response algorithm depending on the category of the email, thereby improving the accuracy of the response. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can classify the category of the email using AI and apply an appropriate response algorithm.

[0121] When responding, the response unit can improve the accuracy of the response by referring to the user's past response results. The response unit can improve the accuracy of the response by referring to, for example, response results evaluated by the user in the past. The response unit can also optimize the response algorithm based on the user's past response results. Furthermore, the response unit can also improve the accuracy of the response by reflecting the user's past feedback. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can analyze the user's past response results using AI to improve the accuracy of the response.

[0122] The response unit can estimate the user's emotion and adjust the length of the response based on the estimated user's emotion. The response unit can estimate the user's emotion using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, if the user is stressed, the response unit can provide a short and to-the-point response. For example, if the user is relaxed, the response unit can provide a detailed response. Furthermore, if the user is busy, the response unit can provide a response that emphasizes only the important points. This allows the response length to be adjusted according to the user's emotion, thereby providing a response of appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 response unit may be performed using, for example, AI, or without AI. For example, the response unit can estimate the user's emotion using AI and adjust the length of the response based on the estimated emotion.

[0123] When responding, the response unit can determine the priority of the response based on the time when the email was received. For example, the response unit can prioritize responses to recently received emails. The response unit can also prioritize responses to emails with a high level of urgency. Furthermore, the response unit can also prioritize responses to emails received within a time period specified by the user. In this way, by determining the priority of the response based on the time when the email was received, it is possible to respond quickly to emails with a high level of urgency. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can use AI to analyze the time when the email was received and determine the priority of the response.

[0124] When responding, the response unit can adjust the order of responses based on the relevance of the emails. For example, the response unit can prioritize responses to highly relevant emails. The response unit can also postpone responses to less relevant emails. Furthermore, the response unit can prioritize responses to emails related to highly relevant topics specified by the user. In this way, by adjusting the order of responses based on the relevance of the emails, it is possible to prioritize responses to highly relevant emails. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can evaluate the relevance of emails using AI and adjust the order of responses.

[0125] When responding, the response unit can adjust the use of technical terminology in the response depending on the user's level of expertise. For example, if the user has technical expertise, the response unit can provide a response that uses a lot of technical terminology. Also, if the user does not have technical expertise, the response unit can provide a concise and easy-to-understand response. Furthermore, the response unit can adjust the way the response is expressed depending on the user's level of expertise. In this way, by adjusting the use of technical terminology in the response depending on the user's level of expertise, it is possible to provide a response that is easy for the user to understand. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can use AI to evaluate the user's level of expertise and adjust the use of technical terminology in the response.

[0126] The task unit can estimate the user's emotions and adjust the task automation method based on the estimated user emotions. The task unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is stressed, the task unit can perform simple and quick task automation. Furthermore, when the user is relaxed, the task unit can perform detailed task automation. Furthermore, when the user is busy, the task unit can automate only important tasks. This allows the task automation method to be adjusted according to the user's emotions, thereby providing appropriate task automation for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 task unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the task unit can estimate the user's emotions using an AI and adjust the task automation method based on the estimated emotions.

[0127] When automating a task, the task unit can adjust the level of detail of the task based on the importance of the email. The task unit can adjust the level of detail of the task based on, for example, the importance of the email. The task unit can perform detailed automation for tasks related to important emails, for example. The task unit can also perform simple automation for tasks related to non-important emails. Furthermore, the task unit can also quickly automate tasks related to highly urgent emails. In this way, by adjusting the level of detail of the task according to the importance of the email, detailed automation can be performed for important tasks. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can evaluate the importance of an email using AI and adjust the level of detail of the task.

[0128] When automating a task, the task unit can apply different task automation algorithms depending on the category of the email. For example, the task unit can apply a business automation algorithm to a task related to business email. The task unit can also apply a private automation algorithm to a task related to private email. Furthermore, the task unit can also apply a spam filtering automation algorithm to a task related to spam email. In this way, by applying an appropriate task automation algorithm depending on the category of email, the accuracy of task automation can be improved. Some or all of the above-mentioned processing in the task unit may be performed using AI, for example, or may be performed without using AI. For example, the task unit can classify the category of email using AI and apply an appropriate task automation algorithm.

[0129] When automating a task, the task unit can improve the accuracy of the task by referring to the user's past task results. For example, the task unit can improve the accuracy of the task by referring to task results previously evaluated by the user. The task unit can also optimize the task automation algorithm based on the user's past task results. Furthermore, the task unit can improve the accuracy of the task by reflecting the user's past feedback. In this way, the accuracy of the task can be improved by referring to the user's past task results. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can analyze the user's past task results using AI to improve the accuracy of the task.

[0130] The task unit can estimate the user's emotions and prioritize tasks based on the estimated user emotions. The task unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is feeling stressed, the task unit can prioritize automating only important tasks. Furthermore, when the user is relaxed, the task unit can automate all tasks equally. Furthermore, when the user is busy, the task unit can adjust the priority so that non-urgent tasks are automated later. In this way, by determining the priority of tasks according to the user's emotions, important tasks can be prioritized and automated. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, 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 task unit may be performed using, for example, an AI. For example, the task unit can estimate the user's emotions using an AI and prioritize tasks based on the estimated emotions.

[0131] When automating tasks, the task unit can adjust the order of tasks based on the time when emails were received. For example, the task unit can prioritize automating tasks related to recently received emails. The task unit can also prioritize automating tasks related to highly urgent emails. Furthermore, the task unit can also prioritize automating tasks related to emails received within a time period specified by the user. In this way, by adjusting the order of tasks based on the time when emails were received, highly urgent tasks can be quickly automated. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to analyze the time when emails were received and adjust the order of tasks.

[0132] When automating tasks, the task unit can adjust the order of tasks based on the relevance of emails. For example, the task unit can prioritize automation of tasks related to highly relevant emails. The task unit can also postpone automation of tasks related to less relevant emails. Furthermore, the task unit can prioritize automation of tasks related to emails on a highly relevant topic specified by the user. In this way, by adjusting the order of tasks based on the relevance of emails, highly relevant tasks can be prioritized for automation. Some or all of the above-described processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to evaluate the relevance of emails and adjust the order of tasks.

[0133] When automating a task, the task unit can adjust the task method according to the user's level of expertise. For example, if the user has specialized knowledge, the task unit can apply a specialized task automation method. Also, if the user does not have specialized knowledge, the task unit can apply a simple and easy-to-understand task automation method. Furthermore, the task unit can adjust the task automation method according to the user's level of expertise. In this way, by adjusting the task method according to the user's level of expertise, task automation that is easy for the user to understand can be provided. Some or all of the above-mentioned processing in the task unit may be performed using, for example, AI, or may be performed without using AI. For example, the task unit can use AI to evaluate the user's level of expertise and adjust the task method.

[0134] The quality unit can estimate a user's emotions and adjust the quality control method based on the estimated user emotions. The quality unit can estimate a user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, if a user is feeling stressed, the quality unit can perform concise and quick quality control. The quality unit can also perform detailed quality control if the user is relaxed. Furthermore, if a user is busy, the quality unit can perform quality control for only important points. This allows the quality control method to be adjusted according to the user's emotions, thereby providing appropriate quality control for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, for example, 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 quality unit may be performed using, for example, AI, or without AI. For example, the quality unit can estimate a user's emotions using AI and adjust the quality control method based on the estimated emotions.

[0135] During quality control, the quality department can adjust the level of detail of the quality control based on the importance of the email. The quality department can adjust the level of detail of the quality control based on, for example, the importance of the email. The quality department can perform detailed quality control on important emails, for example. The quality department can also perform simple quality control on unimportant emails. Furthermore, the quality department can also perform quick quality control on emails with a high level of urgency. In this way, by adjusting the level of detail of the quality control according to the importance of the email, detailed quality control can be performed on important emails. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can evaluate the importance of the email using AI and adjust the level of detail of the quality control.

[0136] During quality control, the quality department can apply different quality control algorithms depending on the category of email. For example, the quality department can apply a business quality control algorithm to business email. The quality department can also apply a private quality control algorithm to private email. Furthermore, the quality department can apply a spam filtering quality control algorithm to spam email. This allows for applying an appropriate quality control algorithm depending on the category of email, thereby improving the accuracy of quality control. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can classify email categories using AI and apply an appropriate quality control algorithm.

[0137] During quality control, the quality department can improve the accuracy of quality control by referring to the user's past quality control results. For example, the quality department can improve the accuracy of quality control by referring to quality control results evaluated by the user in the past. The quality department can also optimize the quality control algorithm based on the user's past quality control results. Furthermore, the quality department can improve the accuracy of quality control by reflecting the user's past feedback. In this way, the accuracy of quality control can be improved by referring to the user's past quality control results. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to analyze the user's past quality control results and improve the accuracy of quality control.

[0138] The quality unit can estimate a user's emotions and determine quality control priorities based on the estimated user emotions. The quality unit can estimate a user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, if a user is feeling stressed, the quality unit can prioritize managing only important quality control items. Alternatively, if a user is relaxed, the quality unit can manage all quality control items equally. Furthermore, if a user is busy, the quality unit can adjust the priority so that non-urgent quality control items are managed later. This allows important quality control items to be prioritized by determining quality control priorities 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 quality unit can be performed using, for example, AI, or without AI. For example, the quality unit can estimate a user's emotions using AI and determine quality control priorities based on the estimated emotions.

[0139] During quality control, the quality department can adjust the order of quality control based on the time of receipt of emails. For example, the quality department can prioritize quality control for recently received emails. The quality department can also prioritize quality control for highly urgent emails. Furthermore, the quality department can also prioritize quality control for emails received during a time period specified by the user. In this way, by adjusting the order of quality control based on the time of receipt of emails, it is possible to quickly perform quality control on highly urgent emails. Some or all of the above-mentioned processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to analyze the time of receipt of emails and adjust the order of quality control.

[0140] During quality control, the quality department can adjust the order of quality control based on the relevance of emails. For example, the quality department can prioritize quality control for highly relevant emails. The quality department can also postpone quality control for less relevant emails. Furthermore, the quality department can prioritize quality control for emails related to highly relevant topics specified by the user. In this way, by adjusting the order of quality control based on the relevance of emails, highly relevant emails can be prioritized in quality control. Some or all of the above-described processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to evaluate the relevance of emails and adjust the order of quality control.

[0141] During quality control, the quality department can adjust the quality control method according to the user's level of expertise. For example, if the user has expertise, the quality department can apply a specialized quality control method. Furthermore, if the user does not have expertise, the quality department can apply a simple and easy-to-understand quality control method. Furthermore, the quality department can adjust the quality control method according to the user's level of expertise. By adjusting the quality control method according to the user's level of expertise, it is possible to provide quality control that is easy for the user to understand. Some or all of the above-described processing in the quality department may be performed using, for example, AI, or may be performed without using AI. For example, the quality department can use AI to evaluate the user's level of expertise and adjust the quality control method.

[0142] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is stressed, the collection unit can perform concise and quick data collection. Furthermore, when the user is relaxed, the collection unit can perform detailed data collection. Furthermore, when the user is busy, the collection unit can collect data only on important points. This allows the data collection method to be adjusted according to the user's emotions, thereby providing appropriate data collection for the user. Emotion estimation is achieved 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 collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can estimate the user's emotions using AI and adjust the data collection method based on the estimated emotions.

[0143] The collection unit can adjust the level of detail of data collection based on the importance of emails when collecting data. The collection unit can adjust the level of detail of data collection based on, for example, the importance of emails. The collection unit can, for example, perform detailed data collection for important emails. The collection unit can also perform brief data collection for unimportant emails. Furthermore, the collection unit can also quickly collect data for emails with a high level of urgency. In this way, by adjusting the level of detail of data collection according to the importance of emails, important data can be collected in detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can evaluate the importance of emails using AI and adjust the level of detail of data collection.

[0144] When collecting data, the collection unit can apply different data collection algorithms depending on the category of email. For example, the collection unit can apply a business-oriented data collection algorithm to business emails. The collection unit can also apply a private-oriented data collection algorithm to private emails. Furthermore, the collection unit can apply a spam filtering data collection algorithm to spam emails. This allows for applying an appropriate data collection algorithm depending on the category of email, thereby improving the accuracy of data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can classify email categories using AI and apply an appropriate data collection algorithm.

[0145] When collecting data, the collection unit can improve the accuracy of the data collection by referring to the user's past data collection results. The collection unit can improve the accuracy of the data collection by referring to, for example, data collection results previously evaluated by the user. The collection unit can also optimize the data collection algorithm based on the user's past data collection results. Furthermore, the collection unit can improve the accuracy of the data collection by reflecting the user's past feedback. In this way, the accuracy of the data collection can be improved by referring to the user's past data collection results. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the user's past data collection results using AI to improve the accuracy of the data collection.

[0146] The collection unit can estimate the user's emotions and determine the priority of data collection based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, facial expression recognition, voice analysis, text analysis, etc. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. The collection unit can also collect all data equally when the user is relaxed. Furthermore, when the user is busy, the collection unit can adjust the priority so that non-urgent data is collected later. In this way, by determining the priority of data collection according to the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can estimate the user's emotions using AI and determine the priority of data collection based on the estimated emotions.

[0147] During data collection, the collection unit can adjust the order of data collection based on the time of receipt of emails. For example, the collection unit can prioritize data collection for recently received emails. The collection unit can also prioritize data collection for emails with a high level of urgency. Furthermore, the collection unit can also prioritize data collection for emails received within a time period specified by the user. By adjusting the order of data collection based on the time of receipt of emails, data with a high level of urgency can be collected quickly. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the time of receipt of emails using AI and adjust the order of data collection.

[0148] During data collection, the collection unit can adjust the order of data collection based on the relevance of emails. For example, the collection unit can prioritize data collection for highly relevant emails. The collection unit can also postpone data collection for less relevant emails. Furthermore, the collection unit can prioritize data collection for emails related to highly relevant topics specified by the user. In this way, by adjusting the order of data collection based on the relevance of emails, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can evaluate the relevance of emails using AI and adjust the order of data collection.

[0149] During data collection, the collection unit can adjust the data collection method according to the user's level of expertise. For example, if the user has expertise, the collection unit can apply a specialized data collection method. Furthermore, if the user does not have expertise, the collection unit can apply a simple and easy-to-understand data collection method. Furthermore, the collection unit can adjust the data collection method according to the user's level of expertise. By adjusting the data collection method according to the user's level of expertise, data collection that is easy for the user to understand can be provided. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to evaluate the user's level of expertise and adjust the data collection method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, processing unit, response unit, task unit, quality unit, and collection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the smart device 14 or the communication I / F 26 of the data processing device 12. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The response unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The task unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The quality unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, processing unit, response unit, task unit, quality unit, and collection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The response unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The task unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The quality unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, processing unit, response unit, task unit, quality unit, and collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The response unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The task unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The quality unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, processing unit, response unit, task unit, quality unit, and collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the receiving unit is realized by the communication I / F 44 of the robot 414 or the communication I / F 26 of the data processing device 12. The analyzing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The response unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The task unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The quality unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analyzing only important emails and postpone other emails. Also, if the user is relaxed, it can analyze all emails equally. Furthermore, if the user is busy, it can adjust the priority so that non-urgent emails are analyzed later. In this way, by determining the analysis priority according to the user's emotions, it is possible to analyze important emails quickly.

[0152] The processing unit can automatically select a reply template based on the content of the email. For example, a business template can be used for business emails, and a private template can be used for private emails. The processing unit can also apply a spam filtering template to spam emails. Furthermore, the processing unit can also select the most appropriate template by referring to templates used by the user in the past. This allows the efficiency and accuracy of replies to be improved by selecting an appropriate reply template according to the content of the email.

[0153] The response unit can estimate the user's emotions and adjust the tone of the response based on the estimated user's emotions. For example, if the user is feeling stressed, the response unit can respond in a calm tone. If the user is relaxed, the response unit can respond in a friendly tone. Furthermore, if the user is busy, the response unit can respond in a concise and to-the-point tone. In this way, by adjusting the tone of the response according to the user's emotions, it is possible to provide a response that is appropriate for the user.

[0154] The task unit can analyze the user's past task completion history and propose an optimal task schedule. For example, if the user has tended to complete tasks during a specific time period in the past, the task can be scheduled for that time period. Also, if the user has prioritized completing a specific task in the past, the task can be scheduled with that priority. Furthermore, the required time for a task can be predicted by referring to the time it took the user to complete a task in the past. In this way, the optimal task schedule can be proposed by referring to the user's past task completion history.

[0155] The quality department can estimate the user's emotions and adjust the quality control feedback method based on the estimated user's emotions. For example, if the user is feeling stressed, concise and positive feedback can be provided. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is busy, feedback that emphasizes only the important points can be provided. In this way, by adjusting the quality control feedback method according to the user's emotions, it is possible to provide appropriate feedback to the user.

[0156] The collection unit can optimize the data collection method based on the user's past data collection results. For example, the collection unit can select the optimal data collection method by referring to data collection methods that the user has evaluated in the past. The collection unit can also select the optimal tool by referring to data collection tools that the user has used in the past. Furthermore, the collection unit can suggest the optimal data collection method by referring to the types of data that the user has collected in the past. In this way, the data collection method can be optimized by referring to the user's past data collection results.

[0157] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, it can display a concise analysis result that focuses on the main points. If the user is relaxed, it can display a detailed analysis result. Furthermore, if the user is busy, it can display an analysis result that emphasizes only the important points. In this way, by adjusting the display method of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0158] The processing unit can automatically assign tags based on the content of emails. For example, it can assign a "business" tag to business emails and a "private" tag to private emails. It can also assign a "spam" tag to spam emails. It can also assign the most appropriate tag by referring to tags previously assigned by the user. This allows for efficient email management by automatically assigning appropriate tags according to the content of emails.

[0159] The response unit can estimate the user's emotions and adjust the timing of the response based on the estimated user emotions. For example, if the user is feeling stressed, the response unit can respond quickly. If the user is relaxed, the response unit can respond at a normal timing. Furthermore, if the user is busy, the response unit can adjust the timing so that a non-urgent response is made later. In this way, by adjusting the timing of the response according to the user's emotions, the response can be provided at a timing appropriate for the user.

[0160] The task section can analyze the user's current project status and suggest optimal task priorities. For example, it can prioritize tasks related to the project the user is currently working on. It can also suggest optimal task priorities by referring to the status of projects the user has completed in the past. It can also analyze the issues the user is facing in the current project and prioritize tasks related to those issues. In this way, it is possible to suggest optimal task priorities by analyzing the user's current project status.

[0161] The processing flow of the second embodiment will be briefly explained below.

[0162] Step 1: The receiving unit receives email. The receiving unit can receive email using protocols such as POP3, IMAP, and SMTP. The receiving unit can also efficiently receive the large amount of email that arrives every day. Step 2: The analysis unit analyzes the content of the email received by the receiving unit. The analysis unit can analyze the email using, for example, natural language processing, keyword matching, machine learning algorithms, etc. The analysis unit can also analyze the content of the received email, sort emails that meet certain conditions into specific folders, and delete emails that do not meet certain conditions. Step 3: The processing unit processes the email based on the results of the analysis by the analysis unit. For example, the processing unit can classify, tag, or delete the email. Step 4: The response unit generates an automatic response based on the results of the analysis unit. The response unit can automatically respond to standard questions using, for example, standard phrases, template-based responses, etc. Step 5: The task unit automates simple tasks based on the results of the analysis unit. For example, the task unit can automate simple tasks such as adjusting meeting schedules and setting reminders. Step 6: The quality department performs quality control and feedback based on the results of the processing department. The quality department can, for example, perform error checks, collect user feedback, etc. Step 7: The collection unit collects data based on the results of the quality unit. The collection unit can, for example, collect log data, track user behavior, etc.

[0163] 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.

[0164] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0168] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0214] 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.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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."

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Explanation of symbols]

[0235] 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. a receiving unit for receiving emails; an analysis unit that analyzes the email received by the receiving unit; a processing unit that processes emails based on the results of the analysis by the analysis unit; a response unit that automatically responds based on the results of the analysis unit; a task unit that automates simple tasks based on the results of the analysis unit; a quality control unit that performs quality control and feedback based on the results of the processing unit; a collection unit that collects data based on the results of the quality unit; A system characterized by:

2. The receiving unit Receive multiple emails daily 2. The system of claim 1.

3. The analysis unit Analyze the content of received emails, sort emails that meet certain conditions into specific folders, and delete emails that do not meet certain conditions.

2. The system of claim 1.

4. The response unit Automate responses to common questions 2. The system of claim 1.

5. The task unit Automate simple tasks like scheduling meetings or setting reminders 2. The system of claim 1.

6. The quality department Provide quality control and feedback to improve processing accuracy 2. The system of claim 1.

7. The collecting unit Collect data based on quality control and feedback results 2. The system of claim 1.

8. The receiving unit Estimate the user's emotions and adjust the timing of receiving emails based on the estimated user emotions 2. The system of claim 1.

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