Sorted inbox with important message identification based on global and user models

A server system receives a message associated with a user. The server extracts message signals from the message, the extracted message signals including a first plurality of message signals. The server generates an importance weight for each message signal of the first plurality of message signals using both a global importance prediction model and a user importance prediction model. The global importance prediction model is based on information associated with multiple users, and the user importance prediction model is based on information associated with the user. The server determines an importance score for the message based on the generated importance weights of the first plurality of message signals. The server sends message information for the message along with information regarding importance of the message for display at a client device, where the information regarding importance of the message is based at least on the determined importance score.

TECHNICAL FIELD

The disclosed embodiments relate generally to displaying messages, such as email, instant, and voicemail messages.

BACKGROUND

As the popularity of email communication has grown, so too has the number of email messages received and stored in user accounts. A user account typically includes all the messages sent to and from a respective email address or user name, excluding messages deleted from the account. However, some user accounts may be associated with a plurality of email addresses or user names, sometimes called aliases, which together may be considered to be a single logical email address or user name. The amount of received email can quickly overwhelm users—making it difficult to sift important messages from unimportant ones.

Additionally, many people now access and view their email on mobile devices, such as handheld computers or cell phones. Such mobile devices typically have small screens with even smaller message windows or interfaces for viewing messages. These interfaces often allow the user to view only a small number of messages at any given time, thereby requiring the user to interact more frequently with the interface to locate important messages, such as through scrolling through the messages. Such mobile devices may also employ network connectivity, which is sometimes charged by usage and is often slow. Users of these devices might wish to limit the messages they view to those of high importance when accessing message through this medium.

To deal with these problems, some message interfaces allow users to organize messages into folders or to apply user-defined labels to messages for easier identification. Additionally, in some email applications, users may order messages in a particular view in accordance with the value of single user-selected message header field, such as message delivery date, sender, or message title. However, these organizational techniques often fail to identify the messages that are most important to the user, leaving the user to scroll through many messages before locating the messages that he or she considers to be most important.

SUMMARY OF DISCLOSED EMBODIMENTS

In a server system having one or more processors and memory, and in a method performed by a server system having one or more processors and memory, the server system receives a message associated with a user and extracts message signals from the message. The extracted message signals include a first plurality of message signals. The server generates an importance weight for each message signal of the first plurality of message signals by determining a first weight for the respective message signal using a first importance prediction model, determining a second weight for the respective message signal using a second importance prediction model, and determining the importance weight of the respective message signal by combining the first weight and the second weight. The first importance prediction model is based on information associated with multiple users. The second importance prediction model is based on information associated with the user. The server determines an importance score for the message based on the generated importance weights of the first plurality of message signals. The server sends the message, along with information regarding importance of the message, to the user for display at a client device. The information regarding importance of the message is based at least on the determined importance score of the message.

In some embodiments, the extracted message signals include the first plurality of message signals and a second plurality of message signals. The server generates an importance weight for each message signal of the second plurality of message signals using the second importance prediction model, but not the first importance prediction model. The server determines the importance score for the message based on the generated importance weights of the first plurality of message signals and the generated importance weights of the second plurality of message signals.

In some embodiments, the server compares the importance score of the message with a threshold to determine importance of the message. In some embodiments, the server periodically updates the threshold using machine learning.

In some embodiments, the server automatically generates the importance weights for both the first and second importance prediction model without requiring the user to provide feedback data regarding importance of any message. Optionally, the server collects feedback data from the user regarding importance of one or more messages, and modifies the second importance prediction model using the feedback data. Optionally, the server periodically updates one or more of the importance weights in the first and second importance prediction models using machine-learning. Optionally, the server updates one or more of the importance weights in the first and second importance prediction models using a time-dependent decay function.

In some embodiments, the first and second importance prediction models both include a plurality of term-related weights, each for a distinct term-related message signal corresponding to presence or quantity of important terms in the message. Important terms include terms determined to be indicative of message importance.

In some embodiments, the message includes information identifying a set of message participants. The user has an associated social graph having a set of social graph members. The first and second importance prediction models both include a plurality of social graph-related weights, each concerning at least one of: presence of social graph members in the message participants, interactions of one or more social graph members with the message, interactions of one or more social graph members with information having a predefined relationship to the message.

At a client device with a display and in a method for displaying messages at the client device, the client device concurrently displays message information associated with a respective user by displaying first message information representing a first set of messages in a first area of the display, and displaying second message information representing a second set of messages in a second area of the display that is separate from the first area, where the first set of messages meet predefined message importance criteria, and each message in the first set of messages is excluded from the second set of messages.

In some embodiments, the displayed message information includes messages from a message account of the respective user. In some embodiments, the first message information is a first list of conversations, each conversation in the first list having at least one message meeting the predefined message importance criteria. Optionally, each conversation in the first list has at least one message that is unread by the user. Optionally, each of the second set of messages has been labeled with a predefined label by the user.

In some embodiments, the first message information is displayed in chronological order in the first area and the second message information is displayed in chronological order in the second area.

In some embodiments, the client device further concurrently displays a third set of messages in a third area that is separate from the first area and second area, wherein the first set of messages and second set of messages are excluded from the third set of messages.

In some embodiments, the client device further displays a respective expansion affordance in each of the first and second areas, and in response to user selection of the respective expansion affordance, expands the corresponding area and displays only the set of messages corresponding to the expanded area.

In some embodiments, each item represented by the first message information includes a predefined label to denote importance of the corresponding item, wherein each item represented by the first message information is a message or a conversation having one or more messages.

In some embodiments, the client device further displays a selectable importance marking affordance. In response to user selection of one or more items represented by the second message information and user selection of the selectable importance marking affordance, the client device marks the user selected items as important using the predefined label and moves the user selected items from one respective area of the display to another respective area of the display.

DESCRIPTION OF EMBODIMENTS

It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, so long as all occurrences of the first contact are renamed consistently and all occurrences of the second contact are renamed consistently. The first contact and the second contact are both contacts, but they are not the same contact.

FIG. 1Ais a block diagram of a distributed computer system100including client systems102and a server system106according to some embodiments. The server system106is connected to client computers102through one or more communication networks108.

Client computer102(sometimes called a “client system,” or “client device” or “client”) may be any computer or device through which a user of the client computer102can submit service requests to and receive messaging services or other services from the server system106. Examples of client computers102include, without limitation, desktop computers, laptop computers, tablet computers, mobile devices such as mobile phones, personal digital assistants, set-top boxes, or any combination of the above. A respective client computer102may contain one or more client applications104for submitting requests to the server system106. For example, client application104can be a web browser or other application that permits a user to search for, browse, and/or use information (e.g., web pages and web services) accessible through the communication network108.

The communication network(s)108can be any wired or wireless local area network (LAN) and/or wide area network (WAN), such as an intranet, an extranet, the Internet, or a combination of such networks. In some embodiments, the communication network108uses the HyperText Transport Protocol (HTTP) and the Transmission Control Protocol/Internet Protocol (TCP/IP) to transport information between different networks. The HTTP permits client devices to access various information items available on the Internet via the communication network108. The various embodiments, however, are not limited to the use of any particular protocol.

In some embodiments, the server system106includes a front end server112, a message signal extractor114, an importance weight generator116, an important message identifier118, importance prediction models122, a user account database124, a message database126, and a search engine120for searching message database126.

The front end server112is configured to receive data from a client computer102. In some embodiments the data is a message, HTTP request, Ajax request, or other communication. The HTTP request or Ajax request may include a search query (e.g., “label:inbox”) for processing by the search engine120.

In some embodiments, a message associated with a particular user is received by the server system106, where user data regarding the particular user is stored in the user account database124. The received message is stored in message database126. In implementations that organize messages into conversations (sometimes called threads or message threads), the message is stored in message database126either as a new conversation, or as a new message in an existing conversation.

When a message is received by the server system106, the message is sent from the front end server112to the message signal extractor114, which extracts one or more message signals from the message. For each message signal of at least a subset of the extracted message signals, an importance weight is generated by importance weight generator116using importance prediction models122. Based on the message signals and their generated importance weights, important message identifier118determines an importance score for the message. In some embodiments, the importance score of the message is used by important message identifier118for comparison with a threshold to determine importance of the message.

FIG. 1Bis a block diagram illustrating a process of generating importance scores for messages according to some embodiments. Importance prediction models122include a first importance prediction model132and a second importance prediction model134. Both the first importance prediction model132and the second importance prediction model134include a plurality of weights, each corresponding to respective message signals. The first importance prediction model132is based on information associated with multiple users. The first importance prediction model132is also called the global importance prediction model. The second importance prediction model134is based on information associated with a respective user (i.e., a single user). The second importance prediction model134is also called the user importance prediction model.

In some embodiments, the extracted message signals include a first plurality of message signals128. For each message signal of the first plurality of message signals128, the importance weight generator116generates an importance weight for the respective message signal by determining a first weight for the respective message signal using the first importance prediction model132, determining a second weight for the respective message signal using the second importance prediction model134, and combining the first weight and the second weight to determine the importance weight of the respective message signal. In some embodiments, the combining includes adding the first and second weight. Furthermore, in some implementations, the importance weight generator116performs table lookup or database lookup operations to obtain the first weight and second weight. As discussed below in more detail with reference toFIG. 5A, the importance weights in the various importance prediction models may be generated and updated using machine learning techniques. Optionally, in addition to a global importance prediction model and a user importance prediction model, the server system also uses a group importance prediction model, for a respective plurality of users (also called a group of users) that include the user for whom message services are being performed. In such implementations, weights from all three importance prediction models are determined and applied to corresponding extracted message signals so as to generate a combined importance score for a respective message.

It is noted that respective weights in the importance prediction models can have both positive and negative values. Weights with positive values are typically associated with message signals that are predictive of message importance. Weights with negative values are typically associated with message signals (e.g., a signal identifying that the message was automatically generated, or a signal that indicates the message includes one or more words on a predefined black list) that are associated with unimportant messages.

In some embodiments, the extracted message signals include a second plurality of message signals130. For each message signal of the second plurality of message signals130, importance weight generator116generates an importance weight for the respective message signal using second importance prediction model134but not first importance prediction model132. Thus, the second plurality of message signals130are message signals for which user-specific weights are generated, but for which global model weights are not generated.

In some embodiments, important message identifier118determines importance score136for the message based on the generated importance weights of the first plurality of message signals128. Alternatively, important message identifier118determines the importance score136for the message based on the generated importance weights of both the first plurality of message signals128and the second plurality of message signals130.

Attention is now directed back toFIG. 1A. Once the importance score of the message has been determined by important message identifier118, the message and information regarding importance of the message are stored in the message database126. In some embodiments, information regarding importance of the message is the importance score of the message. In some embodiments, information regarding importance of the message are labels denoting importance. For example, in one implementation messages with these labels (which denote message importance) have an importance score above a threshold. Alternatively, conversations having at least one message with an importance score above the threshold are assigned a label denoting importance.

Search engine120communicates with message database126to retrieve the message along with information regarding importance of the message, and sends the message and information regarding importance of the message to front end server112. Front end server112sends the message along with information regarding importance of the message to the user for display at a client device102. Alternatively, message information corresponding to the message (e.g., the subject line of the message, information identifying the sender of the message, etc.) is sent by front end server112, along with information regarding importance of the message to client device102. In yet another alternative, message information corresponding to the conversation that includes the message (e.g., a subject line of a message in the conversation and/or a snippet of text from the conversation, information identifying the sender of the message, etc.) is sent by front end server112, along with information regarding importance of the conversation to client device102.

While the modules and data structures used to identify important messages have been illustrated above in server system106, it should be understood that, in accordance with other embodiments, analogous modules and data structures which are also used to identify important messages are located at client computer102instead of, or in addition to, the modules and data structures shown in server system106above.

FIG. 2is a block diagram illustrating a server system106in accordance with some embodiments. The server system106typically includes one or more processing units (CPU's)202for executing modules, programs and/or instructions stored in memory206and thereby performing processing operations; one or more network or other communications interfaces204; memory206; and one or more communication buses208for interconnecting these components. Communication buses208may include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Memory206includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory206may optionally include one or more storage devices remotely located from the CPU(s)202. Memory206, or alternately the non-volatile memory device(s) within memory206, comprises a non-transitory computer readable storage medium. In some embodiments, memory206or the computer readable storage medium of memory206stores the following programs, modules and data structures, or a subset thereof:an operating system210that includes procedures for handling various basic system services and for performing hardware dependent tasks;a network communication module212that is used for connecting the server computer106to other computers via the one or more communication network interfaces204(wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;a message signal extractor114, for extracting one or more message signals from the received message;an importance weight generator116, for generating importance weights for each message signal of at least a subset of the extracted message signals;importance prediction models122including first importance prediction model132and one or more second importance prediction models134(e.g., one for each distinct user for whom message importance services are to be provided), for storing a plurality of weights, each corresponding to a respective message signal;a user account database124, for storing user data, as discussed below with reference toFIG. 3B;a message database126, for storing messages and related information, as discussed below with reference toFIG. 3A;an important message identifier118, for generating importance scores for messages using an importance score generator218; in some embodiments, the important message identifier118also includes an importance threshold220(or in some implementations, more than one importance threshold), which is used for determining importance of messages.

Each of the above identified modules, applications or programs corresponds to a set of instructions, executable by the one or more processors of server system106, for performing a function described above. The above identified modules, applications or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory206may store a subset of the modules and data structures identified above. Furthermore, memory206may store additional modules and data structures not described above.

AlthoughFIG. 2shows a “server system,”FIG. 2is intended more as functional description of the various features which may be present in a set of servers than as a structural schematic of the embodiments described herein. In practice, and as recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some items shown separately inFIG. 2could be implemented on single servers and single items could be implemented by one or more servers. The actual number of servers used to implement a server system and how features are allocated among them will vary from one implementation to another, and may depend in part on the amount of data traffic that the system must handle during peak usage periods as well as during average usage periods.

FIG. 3Adepicts an exemplary data structure of a message record304in message database126(FIG. 1A) according to some embodiments. The message database includes stores messages (in message records304) for a plurality of user accounts such as Account1, Account2, . . . , and Account M. For a specific account such as Account2, the database stores a set302of message records304corresponding to a plurality of messages such as Message1, Message2, . . . , and Message N. For a specific message such as Message2, message data304-2includes header information306and message content320. In some embodiments, message data304-2includes the importance score of the message321. Optionally, message data304-2further includes a message identifier305that uniquely identifies the message, and/or a conversation identifier and message identifier that together uniquely identify the message. Optionally, message data304-2for a respective message includes feedback data328(e.g., one or more of: time elapsed between message receipt and reading, whether the user has replied or forwarded the message, whether the user has read or replied or forwarded the message more than once, whether the user has explicitly marked the message as being important, and whether the user has explicitly marked the message as not important).

In some embodiments, header information306includes information308identifying the senders and recipients of the message, the message subject310, one or more labels (if any) applied to the message312, one or more time stamps316, and other metadata318. In some embodiments, the labels applied to a respective message312include an importance label314(e.g., when the message importance score exceeds a threshold), which denotes importance of the message. The one or more time stamps316include information indicating the time when the message is received by the user account, and optionally include time information (which may be stored in the header306or elsewhere in the database302) that indicates the time(s) when the user read the message and the time(s) when the user replied to the message. Thus, the one or more time stamps316may be useful in calculating how quickly the user reads, responds to or otherwise interacts with the message. Optionally, other metadata318includes one of more values such as the number of times the message has been read, forwarded, and other metrics of interaction. The message content320contains the content of the message, e.g., text, images, and attachments. Those of ordinary skill in the art would recognize other ways to store the message information. For example, an attachment might be stored in another storage structure with a reference to it stored in the message record304.

FIG. 3Bdepicts an exemplary data structure of a user account record322in the user account database124(FIG. 1A) according to some embodiments. The user account record322includes a plurality of user accounts such as User Account1, User Account2, . . . , and User Account M. For a specific user account such as User Account2, user account record322-2includes a contact list324(or includes a pointer to contact list324) that includes a list of contacts associated with the user, and optionally includes one or more of: social graph data326, and important terms330. Optionally, the user data322-2also includes a user-specific importance prediction model332, which is described in greater detail below with respect toFIG. 3C.

Alternatively, user-specific importance prediction model332for a respective user or user account is stored in a separate database from user account database124. It is noted that a user may have multiple accounts, or multiple usernames for messaging, and that in some implementations a single user-specific importance prediction model332is used in conjunction with two or more of the usernames and/or accounts of the user.

In some embodiments, the user has an associated social graph that includes one or more social graph members. Each of the one or more social graph members has a calculated social graph weight based on the interactions between the user and the respective social graph member. Social graph data326includes information regarding the one or more social graph members. In some embodiments, information regarding the one or more social graph members includes the calculated social graph weights of the one or more social graph members. Optionally, if the user sends and/or receives messages sent to a group of social graph members, social graph data326also includes weights for that group of members of the social graph. Thus, social graph data326for a respective user optionally includes weights for a plurality of groups of social graph members with whom the user has communicated as a group.

In some embodiments, server106(FIG. 1A) collects feedback data from the user regarding importance of a respective message. For example, the user may explicitly mark a message as important, or not important. In another example, the speed with which a user opens a new message, or deletes a message without opening it, may be treated as feedback data. Optionally, the feedback data from the user is stored in the user account database124. Alternatively, the feedback data is stored in the message database126. Feedback data from the user is described in more detail below with reference toFIG. 5C.

Important terms330include terms determined to be indicative of message importance. In some embodiments, important terms330are specific to the user, and thus a respective user account322includes a set of user-specific important terms330. Important terms are described in more detail below with reference toFIG. 5A.

FIG. 3Cdepicts an exemplary data structure of importance prediction models122(FIG. 1A), according to some embodiments. Importance prediction models122includes a global importance prediction model132and a set of user importance prediction models134(FIG. 1B). User importance prediction models134include a plurality of user-specific importance prediction models, for a plurality of respective users. In this example, user-specific importance prediction models134include User Model1, User Model2, . . . , and User Model P. Both global model132and user-specific model such as User Model1include a plurality of weights, each corresponding to a respective message signal or a respective combination message signal. In some implementations, the message signals used include both individual message signals (each based on a single message signal) and one or more combination message signals (each based on two or more message signals). Combination message signals are described in more detail below with reference toFIG. 5A. In some embodiments, as shown inFIG. 3C, the plurality of weights and signal identifiers for their respective message signals are stored in records334,336,338,340,342in a look-up table in importance prediction model database122.

FIG. 4is a block diagram illustrating a client computer102in accordance with some embodiments. The client computer102typically includes one or more processing units (CPU's)402for executing modules, programs and/or instructions stored in memory406and thereby performing processing operations; one or more network or other communications interfaces404; memory406; and one or more communication buses408for interconnecting these components. Communication buses408may include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Client computer102optionally may include a user interface410comprising a display device and a keyboard, mouse, touch-sensitive surface or other input device. Memory406includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory406may optionally include one or more storage devices remotely located from the CPU(s)402. Memory406, or alternately the non-volatile memory device(s) within memory406, comprises a computer readable storage medium. In some embodiments, memory406or the computer readable storage medium of memory406stores the following programs, modules and data structures, or a subset thereof:an operating system412that includes procedures for handling various basic system services and for performing hardware dependent tasks;a network communication module414that is used for connecting the client computer102to other computers via the one or more communication network interfaces404(wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on;a client application416, for rendering messages to the user of the client and receiving input from the user (e.g., labeling a message as important or unimportant);optionally, a user account database418, for storing user data; andoptionally, a message database420, for storing messages and other communication received from a server system106.

Each of the above identified modules, applications or programs corresponds to a set of instructions, executable by the one or more processors of client computer102, for performing a function described above. The above identified modules, applications or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory406may store a subset of the modules and data structures identified above. Furthermore, memory406may store additional modules and data structures not described above.

FIG. 5A-5Care flowcharts representing a server method500for identifying important messages, in accordance with some embodiments. Server method500may be governed by instructions that are stored in a computer readable storage medium and that are executed by one or more processors of one or more servers (see server system106,FIG. 2). Each of the operations shown inFIG. 5may correspond to instructions stored in a computer memory or computer readable storage medium (e.g., memory206,FIG. 2). The computer readable storage medium may include a magnetic or optical disk storage device, solid state storage devices such as flash memory devices, or other non-volatile memory device or devices. The computer readable instructions stored on the computer readable storage medium are in source code, assembly language code, object code, or other instruction format that is interpreted by one or more processors.

In some embodiments, server106(FIGS. 1,2) automatically generates (502) the plurality of weights included in first and second importance prediction models (132,134,FIG. 1B) using data from message database126and user account database124, without requiring the user to provide explicit feedback regarding importance of any messages. Each of the plurality of weights in the first and second importance prediction models corresponds to a respective message signal. In some embodiments, a message signal in the first (global) importance prediction model is given a greater weight if the server determines the probability is high that a common user will act within a predetermined amount of time upon messages exhibiting the message signal. The term “exhibited” is defined as follows. A message signal extracted from a respective message is said to be exhibited by that message. For example, the server may find that a common user will more likely respond within 48 hours to a message where the user is the only recipient than to other messages where the user is not the only recipient. In this example, the message signal “User is the only recipient” is given a greater weight than the message signal “User is not the only recipient” in the global importance prediction model. In some embodiments, a message signal in the second (user) importance prediction model is given a greater weight if the server determines the probability is high that a specific user will act within a predetermined amount of time upon messages exhibiting the message signal. For example, the server may find that a specific user is likely respond within 48 hours to messages from a person named “John Doe,” and a corresponding message signal is given an commensurate weight. More generally, a weight is assigned to each message signal that is included in an importance prediction model that is commensurate with its predictive power, e.g., for predicting the likelihood that messages exhibiting the message signal will be opened by the user within a predefined period of time after the signal has been received in the user account.

In some embodiments, server106automatically generates and/or periodically updates (504) one or more of the importance weights in the first and second importance prediction models using machine learning. Machine learning includes a set of techniques, implemented using software tools and computer systems, that generate functions and predictive models (e.g., by determining weights to be applied to components of the functions or predictive models). Machine learning is well known to those skilled in the art and is therefore not described in detail in this document. In some embodiments, in order for the machine learning technique to adapt to changes in the activity in a user's messaging account (e.g., changes in the messages being received by a user, for example, a sudden influx of messages concerning events such as holidays, and/or changes in a user's behavior), the server creates a user profile that is independent of the importance prediction model for a respective user. The user profile tracks user behavior-related statistical data, such as the number of messages the user receives, reads or replies to per day, the percentage of messages read or replied to by the user per day and the number of messages marked as important by the user per day. When the user profile indicates that characteristics of the messages being received by the user deviate from those normally received by the user in the past, and/or indicates that the user's behavior with respect to received messages deviates from the user's normal behavior with respect to one or more statistical data by more than a predetermined amount, the machine learning technique will ignore any new user data in the learning process until either the deviation of received messages and/or user behavior ends, or the deviation of received messages and/or user behavior persists long enough so that it becomes the new normal with respect to received messages and/or user behavior.

Optionally, the server updates (506) one or more of the importance weights in the first and second importance prediction models using a time-dependent decay function to discount over time the importance of user actions with respect to messages having a respective message signal. Thus, the influence of user actions (e.g., opening, replying, deleting) on messages exhibiting a message signal are discounted by an amount that corresponds to how long ago the user actions occurred. For example, in these embodiments, a user action that occurred two weeks ago (or perhaps two months ago) is more heavily discounted than a similar user action that occurred one day ago.

In some embodiments, server106receives (508) a message associated with a user. The received message includes (510) information identifying message participants. Message participants include people listed in one or more of the To, From, and Cc fields of the message, and possibly in other fields as well (e.g., a Reply-To field, if provided). In some embodiments, message participants also include people listed in the To, From, Cc, . . . fields of other messages that are in the same conversation as the received message. In some embodiments, the user has (510) an associated social graph (326,FIG. 3B). In some circumstances, the user's social graph data326(FIG. 3B) includes information regarding one or more of the message participants.

In some embodiments, server106extracts (512) one or more message signals from the message. In some embodiments, some of the message signals are extracted from the message header. Examples of message signals extracted from the message header include: a signal identifying whether the user is the only recipient of the message, a signal identifying whether the message is sent by the user, a signal identifying whether the subject of the message contains one or more words conveying importance (e.g., “important,” “please read,” “urgent,” “action needed,” “confidential” and “reminder”), a signal identifying whether the subject of the message contains the user's display name, one or more signals identifying the labels (if any) applied to the message, a signal identifying whether the message is addressed to the user using the Bcc field; a signal indicating if the message was sent to a list to which the user has subscribed; one or more signals indicating whether the message was sent from one or more particular domains (e.g., domains identified in a user profile as domains from which the user appears to receive important messages (or unimportant messages), based on prior user actions with respect to such messages); and one or more signals indicating whether the received message was sent during a correspond time-of-day range (e.g., 8 AM-5 PM, 5:01 PM-11 PM, or 11:01 PM-7:59 AM). The example given here are non-exhaustive; many other messages signals may be generated based on information extracted from the message.

In some embodiments, some of the message signals are extracted from the message body, i.e., content of the message. Examples of message signals extracted from the message content include: a signal identifying whether the content contains one or more words conveying importance, a signal identifying whether the content contains the user's display name.

In some embodiments, when generating an importance score for a received message, message signals are extracted from the context of the received message (also herein called the current message) within a thread that includes at least one earlier message. Examples of message signals extracted from the context of the message within a thread include: a signal indicating whether the thread is initiated by the user, a signal identifying whether the message is the first message in the thread, a signal identifying whether the user has replied to a message in the thread, and one or more time related signals, such as signals identifying how long an earlier message in the thread (i.e., a message received prior to the current message) has remained unread by the user, and/or how quickly the user first read or responded to an earlier message in the thread.

In some embodiments, the extracted message signals include (516) a plurality of term-related message signals. A term-related message signal corresponds to presence or quantity of important terms in the message, where important terms include terms determined to be indicative of message importance. Examples of important terms include “important,” “please read,” “urgent,” “action needed,” “confidential,” “reminder,” and the name of the respective user. Some important terms apply to multiple users, and some important terms only apply to a specific user. In some embodiments, the number of important terms (e.g., terms found in one or more lists of important terms) in a message (e.g., in the message body and/or message subject) are counted. In some embodiments, the count of important terms in a message includes important terms in the subject of the first message in a conversation but not in the subjects of other messages in the conversation, to avoid repetitively counting the same term. Optionally, the extracted message signals include multiple signals, each of which corresponds to a particular count or range of counts of important terms in the received message. For example, the extracted message signals may include: one important term; 2 important terms; 3 to 4 important terms; 5 or more important terms; and, one or more important terms in message subject. Optionally, the important term list for a respective user is updated periodically, or from time to time, based on recently received messages or based on messages received since the last time the important term list was generated or updated.

In some embodiments, the extracted message signals include (518) a plurality of social graph-related message signals. Examples of social graph-related message signals include: a signal identifying the percentage of messages the user reads from the sender of the message, a signal identifying the percentage of messages the user reads that have at least one of the same recipients as the received message, a signal identifying the percentage of messages that are read out of the total messages sent to exactly the same group of recipients, a signal identifying whether the sender of the message has a corresponding social graph weight associated with the user, a signal identifying the social graph weight of the sender. Social graph weight is described above with reference toFIG. 3B.

In some embodiments, the extracted message signals include (514) a first plurality of message signals. Optionally, each of the first plurality of message signals has corresponding importance weights in both the global importance prediction model and the user importance prediction model. For a respective message signal in the first plurality of message signals, its first (global) weight in the global importance prediction model is a baseline value of the importance weight of the message signal, while its second (user) weight in the user importance prediction model represents how much the user model deviates from the global model, in other words, the difference between the user model and the global model. For example, a respective message signal in the first plurality of message signals has a first weight equal to 0.5 in the global importance prediction model and a second weight equal to −0.2 in the user importance prediction model, where the difference between the global model and the user model for this respective message signal is quantitatively expressed as −0.2.

In some embodiments, the first plurality of message signals include (519) one or more combination message signals, where a combination message signal includes combination of two or more other message signals. In some embodiments, two or more message signals are combined using a logic function (e.g. AND, OR, XOR . . . ) to generate a combination message signal. Weights are generated for the generated combination message signal and used to help determine message importance. For example, the server extracts a first message signal “This message was from a very important person (VIP): True or False,” and a second message signal “The message was sent by an automated system: True or False.” The two message signals can be combined into one combination message signal using a logic AND function; for example, the combination message signal is determined to be True only when “the message is sent by a VIP” AND “the message is NOT sent by an automated system.” It is advantageous to use combination message signals in cases where the combination message signal has been determined (e.g., through the use of machine learning) to have greater message importance predictive power than the combination's constituent message signals in isolation.

In some embodiments, server106generates (520) an importance weight for each message signal of the first plurality of message signals. For a respective message signal (522), server106determines (524) a first weight for the respective message signal using the first (global) importance prediction model. The server also determines (526) a second weight for the respective message signal using the second (user) importance prediction model.

In some embodiments, both the first and second importance prediction models include (528) a plurality of term-related weights, each corresponding to a term-related message signal. Term-related message signals are described in more detail above with reference toFIG. 5A.

In some embodiments, both the first and second importance prediction models include (530) a plurality of social graph-related weights, each corresponding to a social graph-related message signal. Social graph-related message signals are described in more detail above with reference toFIG. 5A.

In some embodiments, server106determines (532) the importance weight of a respective message signal by combining the first and the second weights. In some embodiments, the importance weight of the respective message signal is determined (534) by adding the first weight and the second weight.

Operations522to534, for determining the importance weight for a respective message signal, are repeated (536) for each message signal in the first plurality of message signals. In some embodiments, upon determining (538) that an importance weight has been generated for each and every message signal in the first plurality of message signals, server106determines (552) an importance score for the message based on the generated importance weights of the first plurality of message signals. In some embodiments, the importance score of the message is determined by adding up the importance weights of each message signal in the first plurality of message signals.

In some embodiments, the extracted message signals further include (540) a second plurality of message signals. In some embodiments, each of the second plurality of message signals has corresponding importance weights in only the user importance prediction model but not the global importance prediction model. In these embodiments, the server generates (542) an importance weight for each message signal of the second plurality of message signals. For a respective message signal (544), server106determines (546) the importance weight for the respective message signal using the second (user) importance prediction model but not the first (global) importance prediction model. These operations are repeated so as to determine the importance weight for each respective message signal in the second plurality of message signals (548).

Upon determining (550) that an importance weight has been generated for each and every message signal in the first and second pluralities of message signals, server106determines (554) an importance score for the message based on the generated importance weights of the first and second pluralities of message signals. In some embodiments, the importance score of the message is determined by adding up the importance weights of each message signal in the first and second pluralities of message signals. In some embodiments, the server determines importance scores for the message and a plurality of other messages using the method described above and then orders the message and the plurality of other messages based on their importance scores.

In some embodiments, after determining an importance score for the message, server106compares (556) the importance score of the message with a threshold to determine importance of the message. In some embodiments, messages with importance scores greater than the threshold are determined to be important. In some embodiments, messages with importance scores less than the threshold are determined to be unimportant. In some embodiments, the threshold is predetermined by the server. In some embodiments, the server periodically updates (558) the threshold using machine learning.

In some embodiments, the server compares the importance score of the message with multiple thresholds to determine a level of importance for the message. For example, the server compares the importance score of the message with two thresholds T1and T2, where T1is less than T2. If the importance score of the message is greater than T2, the message is determined to be “Very Important;” if the importance score of the message is less than T2but greater than T1, the message is determined to be “Important;” if the importance score of the message is less than T1, the message is determined to be “Unimportant.” By comparing the importance score of multiple received messages with the multiple thresholds, each of the received messages is assigned an importance level and the messages are divided into tiers or levels of importance.

In some embodiments, the server sends (560) the message (or message information concerning the message) along with information regarding importance of the message to the user for display at a client device. In some embodiments, information regarding importance of the message includes instructions for displaying the message in a sub-region of a display window where the sub-region is used to display important messages, as described in greater detail below with reference toFIG. 6A, which depicts a “Sorted Inbox” user interface of a messaging application. In some embodiments, information regarding importance of the message includes instructions for displaying a predefined label to denote importance of the message, as described in greater detail below with reference toFIG. 6A. As noted above, instead of sending the message itself along with the importance information, the server may send information corresponding to the message (e.g., the subject line of the message, information identifying the sender of the message, information identifying a subject of the conversation to which the message belong, etc.) along with the message importance information.

In some embodiment, after the server sends the message (or message information) along with information regarding importance of the message to the user for display, the user can optionally provide feedback data regarding importance of the message or any other messages, for example by marking one or more of the messages as “important” or “unimportant,” as illustrated inFIGS. 6E-6F. In some embodiments, server106collects (562) the optional feedback data from the user regarding importance of the message or any other messages, and incorporates the optional user feedback data in generating and/or updating (562) importance weights for the second (user) importance prediction model. In another embodiment, server106collects (562) the optional feedback data from the user regarding importance of the message or any other messages, and incorporates the optional user feedback data in generating and/or updating importance weights for both the first (global) and second (user) importance prediction models, but the user feedback data is given lower weight for updating the global model than for updating the user model.

FIG. 6Ais a schematic screenshot of a “Sorted Inbox” user interface600of a messaging application in which lists of conversations are displayed in two non-overlapping areas of the user interface600, according to some embodiments. At the top left corner of the screenshot, there is a “Compose Mail” link602. The user clicks on link602to start writing a new message. Nearby link602, there are a textbox604and a “Search Mail” button606. After the user submits one or more query terms through textbox604and clicks on the “Search Mail” button606, the system generates a new display listing messages or conversations matching the query terms. Nearby textbox604are two importance marking affordances (sometimes called buttons, or user interface buttons), “Important”608and “Unimportant”610. The user selects one of the two affordances to mark a message or conversation as important or unimportant, which will be described in more detail below with reference toFIGS. 6E-6F. Next to the “Important” and “Unimportant” affordances are two pull-down lists: “add label” list612and “more actions” list614. Right below the “Compose Mail” link602, there are a series of group boxes, each box corresponding to a system-defined category, represented by a respective system-defined label such as “Sorted Inbox,” “Inbox,” “Starred,” “Sent Mail,” “Drafts,” “All Mail,” “Spam,” “Trash,” “Important,” etc. In some embodiments, the set of system-defined categories may be different, including a subset of these categories and/or additional categories. Below “Categories” group boxes616, there are another series of group boxes, each box corresponding to a user-defined category represented by a respective user-defined label such as Label1, Label2. . . , etc. Note that since the current screenshot displays only conversations belonging to the “Sorted Inbox” category, the corresponding link in group boxes616has been highlighted.

In some embodiments, the “Sorted Inbox” category includes messages and conversations organized and displayed according to importance of the respective message or conversation, as described in more detail below with reference toFIGS. 6A-6F. In some embodiments, the “Starred” category includes messages and conversations in which the user plans to be actively involved, e.g., by sending messages to other participants, which is similar to a “To Do” list. Thus, in some embodiment, a message may be flagged with a predefined label such as a star640, so that a user can search for starred messages. In some embodiments, the “Important” category616-1includes messages and conversations that have been determined to meet predefined message importance criteria. The predefined message importance criteria are described in more detail above with reference toFIG. 5C.

Next to “Categories” group box616and below the row of buttons608,610and drop down boxes612,614there is message area of the user interface, which is used to display message information (e.g., a list of conversations matching a query, or the messages in a conversation), representing a set of messages. Typically, the message information is for messages to and from the message account of a respective user. Typically, the displayed message information includes or concerns messages in which the respective user is a participant (e.g., specified in the To, Cc, or Bcc field of each message). In some embodiments, the displayed messages are (or include) email messages. Optionally, the displayed message information includes or concerns two or more types of messages, such as two or more of: email messages, chat messages, SMS messages, voice messages, and video messages.

In some embodiments, the message area concurrently displays message information for a first set of messages632in a first area of the user interface, and message information for a second set of messages in a second area of the user interface that is separate from the first area. As shown inFIG. 6A, in some embodiments, the first area in the message area includes a heading618(e.g., “Important”) to identify the messages displayed in the first area. Heading618is displayed at the top left corner of the first area. Optionally, heading618is also a link618that, when selected by a user, provides the user with options for determining which messages are to be displayed in the first area of the user interface, and more generally for configuring that area of the user interface. Similarly, the heading for each additional message area (e.g., heading636above the second area and heading644above the third area) is also a link that, when selected by a user, provides the user with options for determining which messages are to be displayed in that area of the user interface, and more generally for configuring that area of the user interface.

Below heading618is a list of messages or conversations632each occupying one row in the first area. Each conversation/message displayed in the first area meets predefined message importance criteria, and each conversation displayed in the first area has at least one message meeting the predefined message importance criteria. The predefined message importance criteria are described in more detail above with reference toFIG. 5C. It should be noted that it is advantageous to display only messages that have been determined to meet the predefined importance criteria in the first area because it enables the user to quickly and conveniently locate messages that have been determined to be important, without having to browse multiple pages of messages to locate important messages on each page.

In some embodiments, each row in the first area includes a checkbox622, and information for one conversation, including: a sender list624, a recipient status indicator626, a message/conversation topic and snippet of the message/conversation628and a date/time value630(e.g., the date/time of receipt of the last message in the listed conversation). Additional information about each of these fields can be found in U.S. Pat. No. 7,584,426 (see, for example, description ofFIG. 3B), which is hereby incorporated by reference in its entirety. In some embodiments, the set of fields displayed may be different, including a subset of the aforementioned fields and/or additional fields. In some embodiments, checkbox622is checked by the user if the user decides to move the corresponding message or conversation from one system-defined category, e.g., “Inbox”, to another one, e.g., “Trash”, or if the user decides to attach a user-defined label to the corresponding conversation, or if the user decides to move the corresponding message or conversation from one area of the display to another one. At the top right corner of the first area, there is a “View All” link620(also called an expansion affordance), which is described in greater detail below with reference toFIGS. 6C-6D. Optionally, the “Sorted Inbox” user interface600of the messaging application also includes a collapse affordance621for collapsing the first area and thereby removing items previously listed in the first area from being displayed the user interface. Optionally, each of the distinct message areas of the “Sorted Inbox” user interface600includes a respective collapse affordance (e.g., collapse affordances621and639inFIG. 6A, and621,639and647inFIG. 6B) for collapsing the corresponding area of the user interface and thereby removing items previously listed in the respective area from being displayed the user interface.

In some embodiments, the list of messages and conversations632is displayed in chronological order in the first area. In some embodiments, messages that have been received most recently are displayed above messages that have been received earlier. It is advantageous to display the list of important messages632in chronological order because chronological order makes the most intuitive sense to many users; some users may be confused if conversations or messages are not displayed in chronological order.

In some embodiments, messages and conversations that have not been read by the user are highlighted. In some embodiments, all messages in the list of important messages632are unread by the user, and all conversations in list632have at least one message that is unread by the user. It is advantageous to display only unread messages that have been determined to meet the predefined importance criteria in the first area because it enables the user to quickly and conveniently locate unread messages that have been determined to be important, which in many cases are among the most important messages to the user. Optionally, the user is given the option of displaying important conversations/messages in the list632, without regard to whether the user has read all the messages in the list632.

In some embodiments, the message area of the display window inFIG. 6Afurther includes a second area with a heading636(e.g., “Starred”) displayed at the top left corner of the second area. Below heading636is a list of conversations642each occupying one row in the second area. Each conversation in list642has at least one message flagged by the user with the predefined label. Alternatively, in embodiments that show a list of messages instead of a list of conversations, each message in list642would be a message flagged by the user with the predefined label. In some embodiments, a star symbol640is displayed next to the conversation/message checkbox for each conversation/message in list642. At the top right corner of the second area, there is a “View Starred” link638(also called an expansion affordance), which is described in greater detail below with reference toFIGS. 6C-6D. In some embodiments, the list of messages and conversations642is displayed in chronological order in the second area. In some embodiments, each of the conversations/messages in list632of the first area is excluded from list642of the second area. In some embodiments, no conversation/message in list642meets the predefined message importance criteria. As applied to lists of conversations, this means that no conversation in list642contains a message that meets the predefined message importance criteria.

As shown inFIG. 6B, in some embodiments, the message area of the message application's user interface further includes a third area for displaying a third set of messages. In most, if not all, embodiments, messages in the first set of messages and messages in the second set of messages are excluded from the third set of messages displayed in the third area. In this example, the third area includes a heading644(e.g., “Everything else”) displayed at the top left corner of the third area. Below heading644is a list650of conversations or messages, each occupying one row in the third area. At the top right corner of the third area, there is a “View Inbox” link646(also called an expansion affordance) as described in greater detail below with reference toFIGS. 6C-6D. In some embodiments, the list650of conversations/messages is displayed in chronological order in the third area. In some embodiments, each of the conversations/messages in list632of the first area and list642of the second area is excluded from list650of the third area. In yet other embodiments, the user interface includes four distinct message areas (not shown), each for displaying a distinct set of conversations/messages. Optionally, the user can configure the user interface to determine the content of each message area. Optionally, the user can configure one of these areas to including only messages having a particular user-defined label (e.g., messages having the user-defined label “vacation”). Stated another say, the client device selects, for display in the respective area of the user interface, only messages (or conversations having at least one message) labeled with the particular user-defined label.

Though not shown, in some embodiments, each of the first, second and third areas inFIG. 6Bincludes a clickable drop-down header that allows a user to customize the way messages are displayed in a respective area and/or apply actions to all visible messages displayed in a respective area. In some embodiments, such a clickable drop-down header is attached to link “Important”618, link “Starred”636and link “Everything Else”644respectively. The clickable drop-down header includes options that enable a user to customize the size of a respective area, the category of a respective area, etc. Optionally, the clickable drop-down header includes options that enable the user to select or deselect all visible messages in a respective area, or apply other actions to all visible messages in a respective area.

FIG. 6Cis a schematic screenshot of a “Sorted Inbox” user interface of a messaging application, depicting how a user expands the first area of the messaging application user interface by selecting the “View All” link (also called an expansion affordance), according to some embodiments. If a user wants to view only the messages and conversations in list632of the first area, the user can click (652) on the “View All” link located at the top right corner of the first area to expand the first area. The user interface schematically depicted inFIG. 6Cwill then change to the user interface schematically depicted inFIG. 6D, which depicts the first area in its expanded state. As shown inFIG. 6D, after the user clicks on the “View All” link to expand the first area, only conversations/messages in list632are displayed in the message area of the display window. In some embodiments, list632includes more conversations/messages than the maximum number of messages that can be displayed in the first area inFIG. 6C, but after the user expands the first area by clicking on the “View All” link, additional conversations/messages from list632that are not displayed in the first area inFIG. 6Ccan be displayed in the message area inFIG. 6D. In analogous manner, the user can click on the “View Starred” link638(FIG. 6A) to expand the second area, or click on the “View Inbox” link646(FIG. 6B) to expand the third area.

Though not shown, in some embodiments, each of the first, second and third areas inFIG. 6Bincludes a collapse affordance621,639,647(also called an display area collapse link) that allows a user to collapse a respective display area of the user interface. In some embodiments, a respective collapse affordance is displayed above and near the left margin of the corresponding display area. When a user clicks on the collapse affordance of a respective area, the respective area is collapsed. In some embodiments, the respective area in its collapsed state only shows a header with the title of the respective area and a count of messages included in the respective area.

FIG. 6Eis a schematic screenshot of a messaging application user interface, depicting how a user can mark an item displayed in the second area as important by selecting the item and clicking on the “Important” button (also called an importance marking affordance), according to some embodiments. If a user wants to mark a message or conversation in the second area (e.g., conversation660inFIG. 6E) as important, the user can select (670) the checkbox of conversation660and click (672) the button “Important.” The screenshot inFIG. 6Ewill then change to the screenshot inFIG. 6F, where conversation660is moved from the second area to the first area and is included in the list632of the first area. Accordingly, in this example, conversation662is moved from the second message in list642(as shown inFIG. 6E) to the first message in list642(as shown inFIG. 6F).

FIG. 7Ais a flow chart representing a method700for displaying (702) messages associated with a respective user, according to some embodiments. A client system (e.g., client system102,FIGS. 1A and 4) displays (704) first message information representing a first set of messages in a first area of the display and displays (706) second message information representing a second set of messages in a second area of the display that is separate from the first area (see, for example, the areas occupied by lists632and642,FIG. 6A). In some embodiments, client system102further displays (708) a third set of messages in a third area that is separate from the first and second areas (see, for example, the areas occupied by lists632,642and650,FIG. 6B). In some embodiments, both the first message information and the second message information are displayed (710) by client system102in chronological order in their respective display areas. Details of method700are described above with reference toFIGS. 6A-6F.

FIG. 7Bis a flow chart illustrating the operation of an expansion affordance, according to some embodiments. Client system102system displays (712) a respective expansion affordance (e.g.,620,638,FIG. 6A) in each of the first and second areas. In some embodiments, client system102receives (714) a user selection of the expansion affordance for the first area, and expands (716) the first area and displays only the first message information. In another embodiment, client system102receives (714) a user selection of the expansion affordance for the second area, and expands (718) the second area and displays only the second message information. Details of the method are described above with reference toFIGS. 6C-6D.

FIG. 7Cis a flow chart illustrating the operation of an importance marking affordance, according to some embodiments. The system displays (720) a selectable importance marking affordance. For example,FIG. 6Ashows two selectable importance marking affordances, the Important608and Not Important610marking affordances. In some embodiments, the system receives (722) a user selection of one or more items represented by the second message information and receives (724) a user selection of the selectable importance marking affordance. The system then marks (726) the user selected items as important using a predefined label. Details of the method are described above with reference toFIGS. 6E-6F. The user action of marking selected items as important, or as not important, provides user-generated feedback data regarding importance of one or more user-selected messages. Optionally, the server (106,FIG. 2) collects feedback data from the user regarding importance of one or more messages, and modifies the second importance prediction model using the feedback data. Optionally, the server periodically (or from time to time) updates one or more of the importance weights in the first and second importance prediction models using machine-learning that is based, at least in part, on the feedback data. Optionally, the server updates one or more of the importance weights in the first and second importance prediction models using a time-dependent decay function that is applied to the feedback data and optionally to information concerning other user actions on messages. Optionally, the server also updates one or more importance thresholds based, at least in part, on the feedback data, so as to adjust the volume of conversations/messages classified as important and displayed in the message display area (e.g., seeFIG. 6A) for important conversations/messages.