Anticipating and supporting email delays

Abstract

Systems and methods are provided for determining whether a user has deferred one or more emails. More specifically, the systems and methods can determine whether an email may have been deferred by the user, perform at least one action on the email determined to be likely deferred, determine a mode for providing instructions to the user to follow up on the email determined to be likely deferred, and cause instructions specific to the email determined to be likely deferred to be provided to the user. In some cases, the notification is based on a device associated with the user and / or can be included in at least one of: a task management application and / or a calendar application.

Description

Background Art

[0001] Email is one of the most popular online activities and remains a primary tool for communication and collaboration. An estimated 269 billion emails were sent and received daily in 2017, and studies show that information workers tend to spend up to 28% of their time reading and responding to emails. Email usage has evolved significantly beyond communication to encompass other areas such as task management and archiving. As a result, email is no longer just used for communication, and a phenomenon often referred to as "email overload" has become a widely studied area. Many researchers have studied the close relationship between people's tasks and their email practices; research has indicated that email activity tends to cluster in five main areas: flow, categorization, task management, archiving, and retrieval.

[0002] Email triage is the process of browsing unprocessed email and deciding what to do with it. As the number of unprocessed emails increases, email triage can quickly become a significant problem for users. During triage sessions, users often defer emails until a later time to manage the overflow. Email deferral is directly related to task management and occurs because people don't have enough time to take immediate action on emails, or they need to gather information before taking action on a specific email. Research indicates that users tend to defer responses to 37% of emails that require a response, and approximately 10% of messages receive a reply, reply-all, or forward action; 26% of these actions are taken at a later time (not immediately after first reading), indicating the importance of deferral.

[0003] The fact that a user defers an email does not mean that the message is unimportant. The deferred email may be very important and therefore require careful review and a carefully crafted reply. Alternatively, the email may not be important enough to warrant immediate attention. Deferral may also be the result of other factors unrelated to the message, such as the current user's workload and the device the user is currently using. For example, a common scenario for deferral involves the increased use of mobile devices for daily task management. Not only does categorization play a more important role on mobile devices, but users also need to complete categorization more quickly due to the fleeting, intermittent nature of mobile interactions. Studies of smartphone usage show that mobile users primarily identify which emails to delete and which to deal with immediately, and defer processing most messages until the user has access to a larger device. Summary of the Invention

[0004] Understanding email deferral characteristics, strategies, and motivations helps develop new experiences that enable people to perform tasks more efficiently. The ability to accurately predict whether an email is postponed has the potential to significantly improve the email experience. For example, an email system including a server and / or client can rely on a "defer and remind" model to postpone emails, and then remind users about emails they have postponed or even forgotten, thereby reducing the amount of effort users may need to spend to find emails again, and reducing the chances of users missing important emails to take action. According to examples of the present disclosure, the provided systems and methods are intended to predict when an email has been postponed, and then provide follow-up on the postponed emails. According to examples of the present disclosure, systems and methods are provided for postponing emails based on email and user characteristics. Qualitative and quantitative analysis can be utilized to determine the characteristics of emails postponed by one or more users and the most expected time for providing reminders to users to follow up or otherwise take action on the postponed emails. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0006] Figure 1 An example system for predicting when an email has been deferred and then providing follow-up on the deferred email is shown according to examples of the present disclosure.

[0007] Figure 2 Depicted are additional details of an email deferral prediction module and a deferred email follow-up module according to examples of the present disclosure.

[0008] Figure 3 Depicted are additional details of an email deferral and scheduled follow-up notification according to examples of the present disclosure.

[0009] Figure 4 Depicted are additional details of email deferral and reordered scheduled follow-up notifications according to examples of the present disclosure.

[0010] Figure 5 Details of a method for predicting when an email has been deferred and then providing a deferred email follow-up according to examples of the present disclosure.

[0011] Figure 6 is a block diagram illustrating the physical components of a computing device in which aspects of the present disclosure may be practiced.

[0012] Figure 7A and Figure 7B Details of a computing device are shown in which aspects of the disclosure may be practiced.

[0013] Figure 8 At least one aspect of the architecture of a system for processing data according to an example of the present disclosure is shown. DETAILED DESCRIPTION

[0014] In the following detailed description, reference is made to the accompanying drawings which form a part thereof, and specific embodiments or examples are shown therein by way of illustration. These aspects may be combined, other aspects may be utilized, and structural changes may be made without departing from the present disclosure. The embodiments may be practiced as methods, systems, or devices. Therefore, the embodiments may take the form of hardware implementations, complete software implementations, or implementations combining software and hardware aspects. Therefore, the following detailed description should not be considered restrictive, and the scope of the present disclosure is defined by the appended claims and their equivalents.

[0015] Figure 1 A system 100 for predicting when an email has been deferred and then providing follow-up on the deferred email, according to an example of the present disclosure, is depicted. An email may originate from one or more devices, such as, for example, a laptop computer 102, a desktop computer 104, or a mobile device 106. Emails A, B, and / or C (108, 110, and 112, respectively) may be sent to an email address of a first recipient (or first user). Emails A, B, and / or C (108, 110, and 112, respectively) may be transmitted over a network 116 and arrive at an email server 118, wherein the email server 118 may include a mail repository 120, or mail storage area, for storing emails for one or more users. As an example, the first user may have a user email repository 122, in which the first user's emails are stored or otherwise located. The user email repository 122 may additionally store, collaborate, and / or otherwise be available to the first user with capabilities related to email management, time management (such as appointments and / or time entries in a calendar), task management capabilities, and / or journaling capabilities. Example email servers 118 may include, but are not limited to, Microsoft Exchange. In some examples, email server 118 may be distributed across one or more servers and / or located at least partially in cloud storage and / or as part of a cloud computing offering.

[0016] Emails, such as 108, 110, and / or 112, may initially be received at an email server 118 and then processed according to organizational email rules 124. For example, organizational email rules 124 may determine what additional email processing, if any, may occur on emails 108, 110, and / or 112. In one example, the additional processing may include archiving, security filtering, and the like. Additionally, user-specific email rules 138 may determine whether a particular action is to be taken based on one or more characteristics of the email. For example, if an email is received from a particular sender, user-specific email rules 138 may cause the email to be forwarded to another email address, be marked in some manner to distinguish such email from other emails, and / or be directed to one or more user-created folders. According to examples of the present disclosure, based on one or more characteristics of the email, the email server 118 may generate a deferral prediction 140 at the email deferral prediction module 126 regarding whether the email is likely to be deferred and / or whether the user is likely to revisit such email at a later point in time. For example, the deferral prediction 140 may be based on, but is not limited to, the amount of time and / or effort required for a user to respond to an email, the sender of the email, the sender's relationship to the user, the sender's perceived importance, the sender's location, the user's perceived responsiveness, the number of recipients included in the email, the email's urgency (such as whether it is marked as important, urgent, or otherwise), the user's estimated workload, and / or the estimated impact of the workload created by the email. According to an example of the present disclosure, the email deferral prediction module 126 may generate a prediction indicator and associate the prediction indicator with a particular email. For example, for an email determined to be "likely to be deferred," the email deferral prediction module 126 may associate a "likely to be deferred" prediction with the email. For example, data associated with the email, such as metadata, may be modified to indicate that the email is likely to be deferred. Additionally or alternatively, the email's status may be configured to indicate "likely to be deferred." Furthermore, the email deferral prediction module 126 may further determine a likelihood associated with the user responding to the email or otherwise taking action based on the email; that is, there is a high likelihood that the user will take some action with the email, regardless of whether the email is deferred. For example, an email may be deferred but may not require any specific action to be taken. In some cases, an email may be deferred but may also require a specific action to occur, such as a reply, reply all, forward action, reading the email, opening an attachment, following a link, etc., and / or performing a different task. It should be understood that the email deferral prediction module 126 can make a deferral and / or response prediction before and / or after the user has viewed the email.In some cases, the email may be presented to the user before the action is deferred; in other cases, the email may be routed to another location, marked with a label, or otherwise indicated as deferred before the user has viewed the email.

[0017] The email server 118 may also include a deferred email follow-up module 128. The deferred email follow-up module 128 may determine the optimal manner, mode, time, or other means for a user to follow up on a deferred email. For example, the deferred email follow-up module 128 may cause an appointment and / or reminder to be created on a calendar application, where the appointment or reminder is set or otherwise configured based on an estimated user workload. As another example, a reminder may be generated and added to a calendar application at 3:00 PM to follow up on a specific deferred email. In some cases, a reminder may be generated and added to a calendar application at 3:00 PM to follow up on multiple deferred emails. In some cases, the deferred email follow-up module 128 may cause an entry to be made in a task manager, journal, or other application that a user may utilize to organize tasks and / or time. In some examples, the deferred email follow-up module 128 may interact with a digital virtual assistant, such as Microsoft Cortana; the digital virtual assistant may associate a reminder with the deferred email and may determine the optimal or other optimal mode and manner for reminding the user to follow up and / or take action on the deferred email. While the email deferral prediction module 126 and the deferred email follow-up module 128 have been discussed as being executed by the email server 118, such modules 126 / 128 may be executed and / or at least partially executed at the email client 130. That is, the email deferral prediction module 126 may process emails at a client application, such as the email client 130. Furthermore, the deferred email follow-up module 128 may reside at least partially at the email client 130. Furthermore, the calendar application 132, the task application 134, and / or the digital virtual assistant 136, which are depicted as residing at the email client 130, may reside at the email server 118, a cloud computing platform, or elsewhere. For example, the email client 130 may correspond to, or otherwise be implemented as, a web client so that a user can access emails, deferred emails, or other content via a web browser.

[0018] Figure 2Additional, non-limiting details of the email deferral prediction module 126 and the deferred email follow-up module 128 according to an example of the present disclosure are depicted. More specifically, the email deferral prediction module 126 may include a deferral processor 204 configured to generate a deferral prediction and / or a deferral action 208 based on one or more inputs. The deferral processor 204 may implement one or more machine learning algorithms to predict such email deferrals. User settings 212, such as configuration settings or explicit deferral actions, may be utilized by the deferral processor 204 to make the email deferral prediction 208. As another example, one or more email characteristics 216, such as, but not limited to, the length of the email, the text size of the email, whether the email includes attachments, the number of attachments, the identity of the sender, the number of recipients, the relationship of the sender and / or recipients to the user, the sender's time zone, and an urgency or importance flag, may be utilized by the deferral processor to make the email deferral prediction. As another example, an estimate 220 of the time and effort required to process the email may be utilized by the deferral processor 204 to make the email deferral prediction 208. As another example, one or more user characteristics 224, such as, but not limited to, how many emails the user receives on average per day, the user's location, the device the user is using (e.g., mobile, desktop, client, etc.), a deferral strategy previously utilized by the user, whether the user is currently in a meeting, and / or the user's physical characteristics (e.g., heart rate, amount of sleep, blood sugar level, alertness level, average context switch time), may be utilized by the deferral processor 204 to make the email deferral prediction 208. As another example, a user workload 228 based on, for example, but not limited to, appointments, tasks, time of day, project deadlines, the user's schedule, the schedules of other users, and / or the number of applications currently open on the user's computing device may be utilized by the deferral processor 204 to make the email deferral prediction 208. As another example, user actions 232, such as, but not limited to, whether the user viewed the email, the amount of time spent viewing the email, the number of email windows opened, the number of emails revisited, the folders to which the user submitted the email, whether the user submitted the email in a known non-defer email folder, whether the user submitted the email in a known defer email folder, and / or whether the user partially replied to the email (e.g., replied to or otherwise forwarded the email to another recipient or recipients of the email), can be utilized by the defer processor 204 to make the email defer prediction 208. In some examples, an email can be determined to be likely to be deferred when the email has not been read or otherwise viewed by the user. In some examples, an email can be determined to be likely to be deferred after the email has been marked as read or otherwise viewed by the user.

[0019] The deferral processor 204 can be trained using user-specific data and / or organization-specific data to make one or more email deferral predictions 208. For example, email logs can be utilized to obtain one or more feature sets utilized in classifying deferred emails. Such features and classifiers can be utilized to train a machine learning algorithm to make email deferral predictions 236 / 240. The machine learning model can be trained based on aggregated data from many users, personal data from a single user, or a combination of both, allowing the model to capture general behavioral patterns but also provide personalized predictions tailored to the user's individual behavioral patterns. In some cases, one or more neural network models can be utilized to make email deferral predictions 236 / 240.

[0020] For example, a likelihood value corresponding to the likelihood that an email is likely to be postponed can be analyzed based on a logistic regression using previous emails of a user and / or a group of users. As a non-limiting example, logistic regression performs a predictive modeling process based on emails having the same or similar characteristics as other emails that cause postponement or non-postponement, so as to calculate a likelihood value corresponding to the likelihood of postponement. Therefore, the aforementioned modeling process can include training a model (e.g., a logistic regression model) based on commonalities between emails. Such commonalities can include, but are not limited to, email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content. Thereafter, the trained model can analyze the email to predict the likelihood of postponement. The email postponement prediction module 126 can perform modeling using any of a variety of known modeling techniques. For example, according to various exemplary embodiments, the email postponement prediction module 126 can apply a statistically based machine learning model, such as a logistic regression model, to determine the likelihood of postponement. The regression coefficients of the regression model can be estimated using maximum likelihood or learned from the email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content using supervised learning techniques. Thus, once appropriate regression coefficients are determined, the features contained in the feature vector, e.g., data associated with each of the email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content, can be inserted into a logistic regression model to predict the probability of a postponed event occurring. In other words, a feature vector is provided that includes various email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content, and the feature vector can be applied to the logistic regression model to determine the probability that an email is likely to be postponed. The email postponement prediction module 126 can use various other modeling techniques as would be appreciated by those skilled in the art. For example, other modeling techniques may include other machine learning models, such as naive Bayes models, support vector machine (SVM) models, decision tree models, and neural network models, all of which are understood by those skilled in the art.

[0021] According to various embodiments described above, a feature vector comprising various email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content can be used for the purpose of training a model (for generating and refining a model and / or its coefficients) and using the trained model (for making predictions). For example, if the modeling module utilizes a logistic regression model (as described above), the regression coefficients of the logistic regression model can be learned from the feature vector comprising various email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content using supervised learning techniques. Thus, in one non-limiting example, the email postponement prediction module 126 can operate in a training mode by assembling the feature vector comprising various email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content into a plurality of feature vectors. For the purpose of training the system, the system usually needs positive examples of emails being postponed and negative examples of emails not being postponed. The feature vector can then be utilized to refine the regression coefficient for the logistic regression model. For example, statistical learning based on stochastic gradient descent (SGD) technology can be utilized to refine the regression coefficient for the logistic regression model. Afterwards, once the regression coefficient is determined, the postponement processor 204 can operate to perform inference on the feature vector representing the email based on the trained model (including the trained model coefficient). For example, as a non-limiting example, the postponement processor 204 can be configured to predict the possibility that the email will be postponed based on various email features 216, time and effort 2209, user features 224, user workload 228, user actions 232, user settings 212 and / or email subject and content compared with the contribution or weight of these data utilized to train the model. In some embodiments, if the probability that the email will be postponed is greater than a specific threshold (e.g., 0.6, 0.75, etc.), the postponement processor 204 can classify the special email as a postponed email.

[0022] According to various exemplary embodiments, the process of training or retraining the model based on the various email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content may be performed periodically at regular time intervals (e.g., once a month), or may be performed at irregular time intervals, random time intervals, continuously, etc. Because information about the email deferral prediction module 126 may change over time, it is understood that the model itself may change over time (based on the various email characteristics 216, time and effort 2209, user characteristics 224, user workload 228, user actions 232, user settings 212, and / or email subject and content used to train the model).

[0023] In some cases, the snooze processor 204 may create a snooze action 208, such as moving the email to a specific folder and / or location, hiding the email from the user's view, or otherwise performing a snooze action with or without the user's knowledge.

[0024] The deferred email follow-up module 128 may include a deferred email follow-up processor 244 configured to determine an optimal manner and / or pattern 246 for providing instructions to the user to follow up on the deferred email (e.g., a predicted follow-up time, an order for follow-up, and / or a location for follow-up). The deferred email follow-up processor 244 may implement one or more machine learning algorithms to determine the manner and / or pattern 246. For example, user settings 248 such as configuration settings or explicit reminder actions (e.g., "always create a calendar entry" or "add an item to a task list") may be utilized by the deferred email follow-up processor 244 to determine a pattern and / or manner for following up on the deferred email. As another example, one or more email characteristics 252, such as, but not limited to, the identity of the sender, the number of recipients, the relationship of the sender and / or recipients to the user, the sender's time zone, an urgency or importance flag, whether text indicates a "reply time," the subject of the email message, the content of the email message, and / or some other action that needs to occur before a specific time, can be utilized by the deferred email follow-up processor 244 to determine a mode and / or manner for following up on the deferred email. As another example, an estimated amount of time and effort required to process the email can be utilized by the deferred email follow-up processor 244 to determine a mode and / or manner for following up on the deferred email. As another example, one or more user characteristics 256, such as, but not limited to, how many emails the user receives on average per day, the user's location, the device the user is using (e.g., mobile, desktop, client, etc.), a deferral strategy previously used by the user, whether the user is currently in a meeting, and / or the user's physical characteristics (e.g., heart rate, amount of sleep, blood sugar level, alertness level, average context switch time), can be utilized by the deferred email follow-up processor 244 to determine a pattern and / or manner of following up on deferred emails. As another example, a user workload 260 based on, for example, but not limited to, appointments, tasks, time of day, project deadlines, the user's schedule, the schedules of other users, and / or a number of applications currently open on the user's computing device, can be utilized by the deferred email follow-up processor 244 to determine a pattern and / or manner of following up on deferred emails.As another example, user actions, such as, but not limited to, whether the user viewed the email, the amount of time spent viewing the email, the number of email windows opened, the number of emails revisited, the folder to which the user submitted the email, whether the user submitted the email in a known non-deferred email folder, whether the user submitted the email in a known deferred email folder, and / or whether the user partially replied to the email (e.g., replied to or otherwise forwarded the email to another recipient or recipients of the email), can be utilized by the deferred email follow-up processor 244 to determine a pattern and / or manner for following up on the deferred email. As another example, the deferral prediction 264 and / or the likelihood that the user will perform a strong action can be utilized by the deferred email follow-up processor 244 to determine a pattern and / or manner for following up on the deferred email.

[0025] Similar to the defer processor 204, the deferred email follow-up processor 244 can be trained using user-specific data and / or organization-specific data to make one or more determinations for providing instructions, notifications, or other content to the user to remind the user to follow up on the deferred email. For example, email logs, calendar logs, task logs, etc. can be used to obtain one or more feature sets used in classifying deferred emails, and further, the type of action and when the action occurred in the deferred email. Such features and classifiers can be used to train a machine learning algorithm to make determinations about the manner and / or pattern 246 used to indicate to the user that the user should follow up on the email. In some cases, one or more neural network models can be used to make pattern and / or manner predictions.

[0026] Figure 3A depiction of deferred emails with scheduled follow-up notifications according to an example of the present disclosure is provided. For example, a user's inbox 304 may include a deferred email folder 308, wherein the deferred email folder 308 may include emails associated with a deferral prediction made by the email deferral prediction module 1268. In some cases, the deferred emails may be further categorized according to one or more criteria, such as urgency, estimated time and workload, etc. The deferred emails may include an indication that such email may have been deferred and / or a reminder has been scheduled. The reminder may include, for example, a reminder time 312, indicating when the user may be reminded of the email. In some examples, the user's calendar 316 may indicate that the deferred email 320 needs to be reviewed and such a review time may be further scheduled in the user's calendar. In some cases, additional deferral information may be provided for each email, for example, one or more characteristics utilized by the email deferral prediction module to make such a prediction. In some cases, the order 324 in which the emails are to be revisited may be modified. In some cases, the indication of the date and / or time to remind the user to take certain actions or review the email may be modified. For example, Figure 3 It is shown that an order 324 may be established corresponding to C, A, and B. Such an order 324 may be established based on one or more of the inputs to the deferred email follow-up processor 244 as previously discussed. Figure 4 It is depicted that the order 404 in which instructions and / or reminders are provided to the user may be different. In this case, the order may be B, C, and A. This order 404 may be established based on one or more of the inputs to the deferred email follow-up module 128 as previously discussed. For example, the input to the deferred email follow-up module 128 may indicate that the user is using a mobile device instead of a desktop device (e.g., the user switched from a mobile device to a desktop device); the order 408 may change in response to the indication that the user is using a mobile device versus a desktop device.

[0027] Figure 5 Depicted are details of a method 500 for predicting when an email has been deferred and then providing follow-up on the deferred email, according to examples of the present disclosure. Figure 5 The general order of steps for method 500 is shown in FIG. Generally, method 500 begins at 504 and ends at 532. Method 500 may include more or fewer steps, or may be combined with Figure 5The method 500 may be performed as a set of computer-executable instructions executed by a computer system and encoded or stored on a computer-readable storage medium. In addition, the method 500 may be performed by gates or circuits associated with a processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a system on a chip (SOC), or other hardware device. Hereinafter, the method 500 will be referred to in conjunction with Figure 1-Figure 4 The described systems, components, modules, software, data structures, user interfaces, etc. are explained in detail.

[0028] The method begins at 504, where an email may be received. At 508, email deferral characteristics may be determined. For example, email characteristics, time and effort, and user characteristics may be determined and provided, for example, to the deferral processor 204. Similarly, user workload and / or user actions may be determined at 516 and provided to the deferral processor 204. At 520, the deferral processor 204 may determine the likelihood that the email will be deferred. Based on the likelihood that the email will be deferred, at 524, an action may be performed on the email. For example, actions such as moving, flagging, and / or associating the email with a deferral prediction may occur. Based on the deferral prediction, at 528, a method and mode for providing the user with instructions to follow up on the deferred email is determined. For example, a reminder within a calendar application may be set; a task may be added to a task list; and / or the email may be moved to a specific folder. Consequently, at 532, a follow-up instruction and / or notification may be generated to notify the user that the email is to be reviewed and / or addressed.

[0029] Figure 6-Figure 8 The and associated descriptions provide a discussion of various operating environments in which aspects of the present disclosure may be practiced. Figure 6-Figure 8 The devices and systems shown and discussed are for purposes of example and explanation, and are not limiting of the wide variety of computing device configurations that can be utilized to practice the aspects of the present disclosure described herein.

[0030] Figure 6 6 is a block diagram illustrating the physical components (e.g., hardware) of a computing device 600 in which aspects of the present disclosure may be practiced. The computing device components described below may be applicable to the computing devices described above. In a basic configuration, the computing device 600 may include at least one processing unit 602 and system memory 604. Depending on the configuration and type of the computing device, the system memory 604 may include, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories.

[0031] System memory 604 may include an operating system 605 and one or more program modules 606 suitable for running software applications 620, such as one or more components supported by the system described herein. As an example, system memory 604 may store an email deferral prediction module 623 and / or a deferral email follow-up module 624. For example, operating system 605 may be suitable for controlling the operation of computing device 600.

[0032] Furthermore, embodiments of the present disclosure may be practiced in conjunction with graphics libraries, other operating systems, or any other applications and are not limited to any particular application or system. Figure 6 608. The computing device 600 may have additional features or functionality. For example, the computing device 600 may also include additional data storage devices (removable and / or non-removable) such as, for example, magnetic disks, optical disks, or tapes. Such additional storage devices may be used to store data in a manner similar to that described in the preceding text. Figure 6 , which is illustrated by removable storage device 609 and non-removable storage device 610.

[0033] As described above, a number of program modules and data files may be stored in system memory 604. When executed on at least one processing unit 602, program modules 606 (e.g., applications 620) may perform processes including, but not limited to, the various aspects described herein. Other program modules used in accordance with various aspects of the present disclosure may include email and contact applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-assisted applications, and the like.

[0034] Furthermore, embodiments of the present disclosure may be practiced on circuits comprising discrete electronic components, packaged or integrated electronic chips containing logic gates, circuits utilizing a microprocessor, or on a single chip containing electronic components or a microprocessor. For example, embodiments of the present disclosure may be practiced via a system on a chip (SOC) wherein Figure 6 Each or more of the components shown in can be integrated onto a single integrated circuit. Such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") onto a chip substrate as a single integrated circuit. When operated via the SOC, the functions described herein regarding the ability to switch protocols for the client can be operated via dedicated logic integrated with other components of the computing device 600 on a single integrated circuit (chip). Embodiments of the present disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present disclosure may be implemented within a general-purpose computer or in any other circuit or system.

[0035] The computing device 600 may also have one or more input devices 612, such as a keyboard, a mouse, a pen, an audio or voice input device, a touch or slide input device, and the like. Output device(s) 614, such as a display, speakers, a printer, and the like, may also be included. The aforementioned devices are examples and other devices may be used. The computing device 600 may include one or more communication connections 616 that allow communication with other computing devices 650. Examples of suitable communication connections 616 include, but are not limited to, radio frequency (RF) transmitters, receivers, and / or transceiver circuits; universal serial bus (USB), parallel, and / or serial ports.

[0036] The term computer-readable storage media as used herein may include computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable storage media implemented in any method or technology for storing information (such as computer-readable instructions, data structures or program modules). System memory 1004, removable storage device 609, and non-removable storage device 610 are all examples of computer storage media (e.g., memory storage devices). Computer storage media may include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other article that can be used to store information and can be accessed by computing device 600. Any such computer storage media may be part of computing device 600. Computer storage media does not include carrier waves or other propagated or modulated data signals.

[0037] Communication media may be embodied as computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term "modulated data signal" may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0038] Figure 7A and 7B A computing device or mobile computing device 700 is shown, such as a mobile phone, a smart phone, a wearable computer (e.g., a smart watch), a tablet computer, a laptop computer, etc., with which aspects of the present disclosure can be practiced. In some aspects, the client (e.g., computing systems 104A-E) can be a mobile computing device. Figure 7A, illustrates one aspect of a mobile computing device 700 for implementing these aspects. In a basic configuration, the mobile computing device 700 is a handheld computer having both input and output elements. The mobile computing device 700 typically includes a display 705 and one or more input buttons 710 that allow a user to enter information into the mobile computing device 700. The display 705 of the mobile computing device 700 may also function as an input device (e.g., a touch screen display). If included, an optional side input element 715 allows further user input. The side input element 715 may be a rotary switch, a button, or any other type of manual input element. In alternative aspects, the mobile computing device 700 may incorporate more or fewer input elements. For example, in some aspects, the display 705 may not be a touch screen. In another alternative aspect, the mobile computing device 700 is a portable telephone system, such as a cellular phone. The mobile computing device 700 may also include an optional keypad 735. The optional keypad 735 may be a physical keypad or a "soft" keypad generated on the touch screen display. In various aspects, the output elements include a display 705 for displaying a graphical user interface (GUI), a visual indicator 9720 (e.g., a light emitting diode), and / or an audio transducer 725 (e.g., a speaker). In some aspects, the mobile computing device 700 incorporates a vibration transducer for providing tactile feedback to the user. In another aspect, the mobile computing device 700 incorporates input and / or output ports, such as an audio input (e.g., a microphone jack), an audio output (e.g., a headphone jack), and a video output (e.g., an HDMI port) for sending signals to or receiving signals from an external source.

[0039] Figure 7B 7 is a block diagram illustrating the architecture of one aspect of a computing device, server, or mobile computing device. That is, a computing device 700 can be incorporated into a system (e.g., architecture) 702 to implement some aspects. System 702 can be implemented as a "smartphone" capable of running one or more applications (e.g., a browser, email, calendar, contact manager, messaging client, game, and media client / player). In some aspects, system 702 is integrated into a computing device, such as an integrated personal digital assistant (PDA) and a wireless phone.

[0040] One or more application programs 766 can be loaded into memory 762 and run on or in association with operating system 764. Examples of application programs include a phone dialer, an email program, a personal information management (PIM) program, a word processing program, a spreadsheet program, an Internet browser program, a messaging program, and the like. System 702 also includes a non-volatile storage area 768 within memory 762. Non-volatile storage area 768 can be used to store persistent information that should not be lost when system 702 loses power. Application programs 766 can use and store information in non-volatile storage area 768, such as email or other messages used by email applications, and the like. A synchronization application (not shown) also resides on system 702 and is programmed to interact with a corresponding synchronization application resident on the host computer to synchronize information stored in non-volatile storage area 768 with corresponding information stored on the host computer. It should be understood that other applications may be loaded into memory 762 and executed on the mobile computing device 700 as described herein (eg, a search engine, an extractor module, a relevance ranking module, an answer scoring module, etc.).

[0041] System 702 has a power supply 770, which can be implemented as one or more batteries. Power supply 770 can also include an external power source, such as an AC adapter or a powered docking station to replenish or recharge the batteries.

[0042] System 702 may also include a radio interface layer 772 that performs the function of sending and receiving radio frequency communications. Radio interface layer 772 facilitates wireless connectivity between system 702 and the "outside world" via a communications carrier or service provider. Transmissions to and from radio interface layer 772 are controlled by operating system 764. In other words, communications received by radio interface layer 772 can be passed to application programs 766 via operating system 764, and vice versa.

[0043] The visual indicator 720 can be used to provide a visual notification, and / or the audio interface 774 can be used to generate an audible notification via the audio transducer 725. In the illustrated configuration, the visual indicator 720 is a light emitting diode (LED) and the audio transducer 725 is a speaker. These devices can be directly coupled to the power supply 770 so that when activated, they remain on for the duration specified by the notification mechanism, even if the processor 760 and other components can be turned off to save battery power. The LED can be programmed to remain lit indefinitely until the user takes action to indicate the power status of the device. The audio interface 774 is used to provide audible signals to the user and receive audible signals from the user. For example, in addition to being coupled to the audio transducer 725, the audio interface 774 can also be coupled to a microphone to receive audible input, such as to facilitate a telephone conversation. According to aspects of the present disclosure, the microphone can also be used as an audio sensor to facilitate the control of notifications, as will be described below. The system 702 may also include a video interface 976, which enables the operation of the onboard camera 730 to record still images, video streams, etc.

[0044] The mobile computing device 700 implementing the system 702 may have additional features or functionality. For example, the mobile computing device 700 may also include additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage devices may be used to store data on the mobile computing device 700. Figure 7B In the figure, non-volatile storage area 968 is shown.

[0045] The data / information generated or captured by the mobile computing device 700 and stored via the system 702 can be stored locally on the mobile computing device 700, as described above, or the data can be stored on any number of storage media that the device can access via the radio interface layer 772 or via a wired connection between the mobile computing device 700 and a separate computing device associated with the mobile computing device 700, such as a server computer in a distributed computing network, such as the Internet. It should be understood that such data / information can be accessed via the mobile computing device 700 via the radio interface layer 772 or via a distributed computing network. Similarly, such data / information can be readily transferred between computing devices for storage and use according to well-known data / information transmission and storage means, including electronic mail and collaborative data / information sharing systems.

[0046] Figure 8One aspect of the architecture of a system for processing data received from a remote source at a computing system, such as a personal computer 804, a tablet computing device 806, or a mobile computing device 808, as described above, is shown. The content displayed at the server device 1202 can be stored in different communication channels or other storage types. For example, a directory service 822, a web portal 824, a mailbox service 826, an instant messaging repository 828, or a social networking site 830 can be used to store various documents.

[0047] Clients communicating with the server device 802 can employ the email deferral prediction module 821 and the deferred email follow-up module 823, and / or the email deferral prediction module 821 and the deferred email follow-up module 823 can be employed by the server device 802. The server device 802 can provide data to and from client computing devices such as a personal computer 804, a tablet computing device 806, and / or a mobile computing device 808 (e.g., a smartphone) via a network 815. For example, the computer system described above can be embodied in the personal computer 804, the tablet computing device 806, and / or the mobile computing device 808 (e.g., a smartphone). In addition to receiving graphics data that can be used for pre-processing at a graphics originating system or post-processing at a receiving computing system, any of these embodiments of the computing device can obtain content from the repository 816.

[0048] Figure 8 An exemplary mobile computing device 800 is shown that can perform one or more aspects disclosed herein. In addition, the aspects and functions described herein can be run on a distributed system (e.g., a cloud-based computing system), where application functions, memory, data storage and retrieval, and various processing functions can be run remotely from each other via a distributed computing network, such as the Internet or an intranet. Various types of user interfaces and information can be displayed via an onboard computing device display or via a remote display unit associated with one or more computing devices. For example, various types of user interfaces and information can be displayed and interacted with on a wall on which various types of user interfaces and information are projected. Interaction with a variety of computing systems that can implement embodiments of the present invention includes keystroke input, touch screen input, voice or other audio input, gesture input, where the associated computing device is equipped with detection (e.g., camera) capabilities for capturing and interpreting user gestures to control functions of the computing device, etc.

[0049] The phrases "at least one," "one or more," "or," and "and / or" are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions "at least one of A, B, and C," "at least one of A, B, or C," "one or more of A, B, and C," "one or more of A, B, or C," "A, B and / or C," and "A, B, or C" refers to A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B, and C together.

[0050] The term "a" or "an" entity refers to one or more of that entity. Thus, the terms "a" (or "an"), "one or more," and "at least one" can be used interchangeably herein. It should also be noted that the terms "comprising," "including," and "having" can be used interchangeably.

[0051] As used herein, the term "automatic" and its variations refer to any process or operation, typically continuous or semi-continuous, that is performed without substantial human input. However, a process or operation may be automatic if input is received before the process or operation is performed, even if the performance of the process or operation utilizes material or immaterial human input. Human input is considered material if it affects the manner in which the process or operation is performed. Human input that consents to the performance of the process or operation is not considered "material."

[0052] Any of the steps, functions, and operations discussed herein may be performed continuously and automatically.

[0053] The exemplary systems and methods of the present disclosure have been described with respect to computing devices. However, to avoid unnecessarily obscuring the present disclosure, the foregoing description omits many known structures and devices. This omission should not be construed as limiting. Specific details are set forth to provide an understanding of the present disclosure. However, it should be understood that the present disclosure can be practiced in a variety of ways beyond the specific details set forth herein.

[0054] Furthermore, while the exemplary aspects described herein illustrate collocation of various components of the system, certain components of the system may be remotely located in distant portions of a distributed network, such as a LAN and / or the Internet, or within a dedicated system. Thus, it should be understood that the components of the system may be combined into one or more devices, such as servers, communication devices, or collocated at specific nodes of a distributed network, such as an analog and / or digital telecommunications network, a packet-switched network, or a circuit-switched network. As will be appreciated from the foregoing description, and for computational efficiency reasons, the components of the system may be arranged at any location within the distributed network of components without affecting the operation of the system.

[0055] Furthermore, it should be understood that the various links connecting the elements may be wired or wireless links, or any combination thereof, or any other known or later developed element(s) capable of providing data to and / or transmitting data from the connected elements. These wired or wireless links may also be secure links and may be capable of transmitting encrypted information. For example, the transmission medium used as the link may be any suitable electrical signal carrier, including coaxial cable, copper wire, and optical fiber, and may take the form of sound waves or light waves, such as those generated during radio wave and infrared data communication.

[0056] While the flow charts have been discussed and illustrated with respect to a particular sequence of events, it should be understood that changes, additions, and omissions to this sequence can occur without materially affecting the configuration and operation of the disclosed aspects.

[0057] Various variations and modifications of the present disclosure may be used. It would be possible to provide some features of the present disclosure without providing others.

[0058] In another configuration, the systems and methods of the present disclosure can be implemented in conjunction with a special-purpose computer, a programmable microprocessor or microcontroller and (multiple) peripheral integrated circuit components, an ASIC or other integrated circuit, a digital signal processor, a hard-wired electronic or logic circuit, such as a discrete component circuit, a programmable logic device or gate array, such as a PLD, PLA, FPGA, PAL, a special-purpose computer, any comparable device, and the like. In general, any (multiple) devices or devices capable of implementing the methods described herein can be used to implement various aspects of the present disclosure. Exemplary hardware that can be used for the present disclosure includes computers, handheld devices, phones (e.g., cellular, Internet-enabled, digital, analog, hybrid, etc.) and other hardware known in the art. Some of these devices include processors (e.g., single or multiple microprocessors), memory, non-volatile memory, input devices, and output devices. In addition, alternative software implementations including, but not limited to, distributed processing or component / object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.

[0059] In yet another configuration, the disclosed methods can be readily implemented in conjunction with software using object or object-oriented software development environments that provide portable source code that can be used on a variety of computer or workstation platforms. Alternatively, the disclosed systems can be implemented partially or fully in hardware using standard logic circuits or VLSI designs. Whether software or hardware is used to implement a system according to the present disclosure depends on the speed and / or efficiency requirements of the system, the specific functionality, and the specific software or hardware systems or microprocessor or microcomputer systems used.

[0060] According to at least one example, a system for providing an indication to a user based on a determination of whether an email is likely to be deferred is provided. The system may include a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to determine whether an email is likely to be deferred by the user, perform at least one action on the email determined to be likely to be deferred, determine a mode for providing an indication to the user to follow up on the email determined to be likely to be deferred, and cause an indication specific to the email determined to be likely to be deferred to be provided to the user.

[0061] At least one aspect of the above examples may include that the mode for providing an instruction to a user to follow up on an email determined to be likely to be deferred includes at least one of: configuring a task, configuring a reminder, configuring a calendar entry, and configuring a notification window. At least one aspect of at least one of the above examples and / or aspects may include that the mode for providing an instruction to a user to follow up on an email determined to be likely to be deferred is based on a device associated with the user. At least one aspect of at least one of the above examples and / or aspects may include that the at least one action includes at least one of: moving the email from a first folder to a second folder, associating a deferred status with the email, and determining a likelihood that the user will reply to the email. At least one aspect of at least one of the above examples and / or aspects may include that the instructions cause the processor to determine that the email is likely to be deferred by the user based on at least one of: characteristics of the email, one or more recipients of the email, the sender of the email, the workload and / or effort associated with the email, a deferred action taken by the user, or a current workload associated with the user. At least one aspect of at least one of the above examples and / or aspects may include that determining whether the email is likely to be deferred is performed using a machine learning model trained to determine whether an email is likely to be deferred by the user. At least one aspect of at least one of the above examples and / or aspects may include: wherein the machine learning model is trained using feature vectors derived from emails associated with a plurality of users.

[0062] According to at least one example, a method is provided. The method may include receiving an email, determining that the received email may be deferred by a user, performing at least one action on the email determined to be deferred, determining a mode for providing an instruction to the user to follow up on the email determined to be deferred, and causing an instruction specific to the email determined to be deferred to be provided to the user. At least one aspect of at least one of the above examples and / or aspects may include determining that the received email may be deferred by the user when the email is in an unread state. At least one aspect of at least one of the above examples and / or aspects may include wherein the mode for providing the instruction to the user to follow up on the email determined to be deferred includes at least one of: configuring a task, configuring a reminder, configuring a calendar entry, and configuring a notification window. At least one aspect of at least one of the above examples and / or aspects may include wherein the mode for providing the instruction to the user to follow up on the email determined to be deferred is based on a device associated with the user. At least one aspect of at least one of the above examples and / or aspects may include scheduling a review time in the user's calendar, wherein the review time is based on the email determined to be deferred. At least one aspect of at least one of the above examples and / or aspects may include determining that an email is likely to be postponed using a machine learning model that is trained to determine whether an email is likely to be postponed by a user. At least one aspect of at least one of the above examples and / or aspects may include determining that an email is likely to be postponed based on at least one of: characteristics of the email, one or more recipients of the email, the sender of the email, the workload and / or effort associated with the email, a postponement action taken by the user, or a current workload associated with the user. At least one aspect of at least one of the above examples and / or aspects may include determining that multiple emails are likely to be postponed by the user, and causing an indication specific to each of the multiple emails to be provided to the user in an order different from the order in which the multiple emails were received. At least one aspect of at least one of the above examples and / or aspects may include: wherein the machine learning model is trained using a feature vector derived from emails associated with multiple users.

[0063] According to at least one example, a method is provided. The method may include receiving a plurality of emails, determining that one or more of the plurality of emails may be postponed by a user, performing at least one action on the one or more of the plurality of emails, determining, for each of the one or more emails, a mode for providing an instruction to the user to follow up on the emails, and causing, for each of the one or more emails, an email-specific instruction to be provided to the user. At least one aspect of at least one of the above examples and / or aspects may include determining that an email of the one or more emails is postponed when the email is unread. At least one aspect of at least one of the above examples and / or aspects may include determining a priority status associated with each of the one or more emails, and sorting the instruction specific to each of the one or more emails according to the priority status. At least one aspect of at least one of the above examples and / or aspects may include adding the one or more emails to at least one of a task management application and / or a calendar application. At least one aspect of at least one of the above examples and / or aspects may include associating the instruction specific to each of the one or more emails with a time entry in the calendar application, and the time entry changing depending on the user's location. At least one aspect of at least one of the above examples and / or aspects may include: wherein determining whether an email is likely to be deferred is performed using a machine learning model, the machine learning model being trained to determine whether an email is likely to be deferred by a user. At least one aspect of at least one of the above examples and / or aspects may include the machine learning model being trained using feature vectors derived from emails associated with a plurality of users.

[0064] According to at least one example, a machine learning model trained to perform one or more aspects of the above examples and / or aspects may be provided. According to at least one example, a machine learning model trained to determine whether an email is likely to be postponed is provided. The machine learning model may determine whether an email is likely to be postponed by a user. The machine learning model may perform at least one action on an email determined to be likely to be postponed. The machine learning model may determine a pattern for providing an instruction to the user to follow up on an email determined to be likely to be postponed. The machine learning model may cause instructions specific to the email determined to be likely to be postponed to be provided to the user.

[0065] In yet another configuration, the disclosed methods may be implemented in part as software that may be stored on a storage medium and executed on a programmed general-purpose computer in cooperation with a controller and memory, a dedicated computer, a microprocessor, etc. In these cases, the disclosed systems and methods may be implemented as a program embedded on a personal computer, such as an applet, or CGI scripts, as resources residing on a server or computer workstation, as routines embedded in a dedicated measurement system, system component, etc. The system may also be implemented by physically incorporating the system and / or method into a software and / or hardware system.

[0066] If described, the present disclosure is not limited to standards and protocols. Other similar standards and protocols not mentioned herein exist and are considered to be included in the present disclosure. In addition, the standards and protocols mentioned herein, as well as other similar standards and protocols not mentioned herein, are periodically replaced by faster or more efficient equivalents having substantially the same functions. Such replacement standards and protocols having the same functions are considered to be equivalents included in the present disclosure.

[0067] In various configurations and aspects, the present disclosure includes components, methods, processes, systems and / or apparatus substantially as depicted and described herein, including various combinations, subcombinations, and subsets thereof. After understanding the present disclosure, those skilled in the art will understand how to make and use the systems and methods of the present disclosure. The present disclosure, in various configurations and aspects, includes providing devices and processes in the absence of items not depicted and / or described herein or in various configurations or aspects herein, including in the absence of such items that may have been used in previous devices or processes, e.g., to improve performance, ease of implementation, and / or reduce implementation costs.

Claims

1. A system for processing e-mails, comprising: processor; as well as a memory comprising instructions that, when executed by the processor, cause the processor to implement a trained defer processor and a trained email follow-up processor: The deferral processor is configured to: Determining whether a received email is likely to be deferred by the user; as well as performing at least one deferral action on the email determined to be likely to be deferred, the deferral processor having been trained based on analysis of previous emails by the user and / or a group of users; and wherein the email follow-up processor is configured to: determining a mode for providing an instruction to the user to follow up on the email determined to be likely to be postponed, and determining a reminder time for the instruction; as well as Causing an indication specific to the email determined to be likely to be deferred to be provided to the user at the determined reminder time, the email follow-up processor having been trained by obtaining, from one or more of an email log, a calendar log, and a task log, an action type and when the action occurred for a previously deferred email.

2. The system of claim 1, wherein the mode for providing instructions to the user to follow up on the email determined to be likely to be postponed comprises at least one of: configuring a task, configuring a reminder, configuring a calendar entry, and configuring a notification window. 3 . The system of claim 2 , wherein the mode for providing instructions to the user to follow up on the email determined to be likely to be postponed is based on a device associated with the user.

4. The system of claim 1 , wherein the at least one defer action comprises at least one of: moving the email from a first folder to a second folder, associating a defer status with the email, and determining a likelihood that the user will reply to the email.

5. The system of claim 1 , wherein the instructions cause the processor to determine that the email is likely to be postponed by the user based on at least one of: characteristics of the email, one or more users of the email, an email sender, an amount of work and / or effort associated with the email, a postponement action taken by the user, or a current workload associated with the user.

6. The system of claim 1 , wherein determining whether an email is likely to be postponed is performed using a machine learning model trained to determine whether an email is likely to be postponed by the user.

7. The system of claim 6, wherein the machine learning model is trained using feature vectors derived from emails associated with a plurality of users.

8. A method for processing e-mail, comprising: Receive emails; determining, by a trained deferral processor, whether a received email is likely to be deferred by a user; performing at least one deferral action on the email determined to be likely to be deferred, the deferral processor having been trained based on analysis of previous emails by the user and / or a group of users; determining, by a trained email follow-up processor, a pattern for providing an instruction to the user to follow up on the email determined to be likely to be deferred; determining a reminder time for the instruction; as well as Causing an indication specific to the email determined to be likely to be deferred to be provided to the user at the determined reminder time, the email follow-up processor having been trained by obtaining, from one or more of an email log, a calendar log, and a task log, an action type and when the action occurred for a previously deferred email.

9. The method according to claim 8, further comprising: When the received email has an unread status, it is determined that the email may be postponed by the user.

10. The method of claim 9, wherein the mode for providing instructions to the user to follow up on the email determined to be likely to be postponed comprises at least one of: configuring a task, configuring a reminder, configuring a calendar entry, and configuring a notification window.

11. The method of claim 9, wherein the mode for providing instructions to the user to follow up on the email determined to be likely to be deferred is based on a device associated with the user.

12. The method of claim 9, further comprising scheduling a review time in the user's calendar, wherein the review time is based on the determined email that is likely to be postponed.

13. The method of claim 9, wherein the deferral processor is trained using regression coefficients based on at least the workload of the user, the time and effort spent by the user on the email, and characteristics of the user.

14. The method of claim 9, wherein determining that the email may be postponed is based on at least one of: characteristics of the email, one or more users of the email, a sender of the email, an amount of work and / or effort associated with the email, a postponement action taken by the user, or a current workload associated with the user.

15. The method according to claim 9, further comprising: determining that a plurality of emails may be deferred by the user; as well as The instructions specific to each of the plurality of emails are caused to be provided to the user in an order different from the order in which the plurality of emails were received.

16. A method for processing electronic mail, comprising: Receive multiple emails; determining, by a trained defer processor, using a trained machine learning model that one or more of the plurality of emails is likely to be deferred by a user, the machine learning model being trained based on an analysis of prior emails by the user and / or a group of users; performing at least one deferral action on the one or more emails among the plurality of emails that are determined to be likely to be deferred; For each of the one or more emails determined to be potentially postponed: determining, by a trained email follow-up processor, a pattern for providing instructions to the user to follow up on the email, and determining a reminder time for the instructions; as well as For each of the one or more emails determined to be potentially postponed: Causing an instruction specific to the email to be provided to the user at the determined reminder time, the email follow-up processor having been trained by obtaining an action type and when the action occurred on a previously deferred email from one or more of an email log, a calendar log, and a task log. 17 . The method of claim 16 , further comprising determining that an email of the one or more emails is to be postponed when the email has not been read.

18. The method according to claim 16, further comprising: determining a priority status associated with each of the received plurality of emails; as well as The indications specific to each of the one or more emails are ordered according to the priority status.

19. The method of claim 16, wherein the received plurality of emails are added to at least one of a task management application and / or a calendar application.

20. The method of claim 16, wherein the indication specific to each email message in the one or more emails is associated with a time entry in a calendar application, and wherein the time entry changes depending on a user's location.

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