U.S. patent application number 12/240569 was filed with the patent office on 2010-04-01 for user perception of electronic messaging.
This patent application is currently assigned to MICROSOFT CORPORATION. Invention is credited to Mary P. Czerwinski, F. David Jones, Matthew B. MacLaurin, Dragos A. Manolescu, Henricus Johannes Maria Meijer, Raymond E. Ozzie, Matthew Jason Pope.
Application Number | 20100082751 12/240569 |
Document ID | / |
Family ID | 42058714 |
Filed Date | 2010-04-01 |
United States Patent
Application |
20100082751 |
Kind Code |
A1 |
Meijer; Henricus Johannes Maria ;
et al. |
April 1, 2010 |
USER PERCEPTION OF ELECTRONIC MESSAGING
Abstract
Determining user use context for electronic messaging and
disseminating a subset of the user use context to recipients and/or
senders of such electronic message is disclosed herein. By way of
example, the user use context can be based on a general context of
recipients, such as speed with which a message is disseminated or
consumed, number of child messages spawned, rate at which such
messages are spawned, and so on. Additionally, user use context can
also be based on individual context, by comparing individual
interaction to a message (e.g., time to read, time to delete,
number of child messages, etc.), with a baseline usage context
determined for the individual. The context can be disseminated to
recipients of the message or to the sender, to provide an overview
of perception of the electronic message.
Inventors: |
Meijer; Henricus Johannes
Maria; (Mercer Island, WA) ; Manolescu; Dragos
A.; (Kirkland, WA) ; Pope; Matthew Jason;
(Seattle, WA) ; MacLaurin; Matthew B.;
(Woodinville, WA) ; Jones; F. David; (Bellevue,
WA) ; Czerwinski; Mary P.; (Woodinville, WA) ;
Ozzie; Raymond E.; (Seattle, WA) |
Correspondence
Address: |
LEE & HAYES, PLLC
601 W. RIVERSIDE AVENUE, SUITE 1400
SPOKANE
WA
99201
US
|
Assignee: |
MICROSOFT CORPORATION
Redmond
WA
|
Family ID: |
42058714 |
Appl. No.: |
12/240569 |
Filed: |
September 29, 2008 |
Current U.S.
Class: |
709/206 |
Current CPC
Class: |
G06F 15/16 20130101;
H04L 43/08 20130101; G06Q 10/10 20130101; H04L 51/046 20130101;
H04L 51/32 20130101; G06Q 10/107 20130101; G06Q 10/0639
20130101 |
Class at
Publication: |
709/206 |
International
Class: |
G06F 15/16 20060101
G06F015/16 |
Claims
1. A computer-implemented method that facilitates e-mail handling,
comprising: analyzing user use context about an interaction with an
e-mail; and distributing information about the user use context to
other recipients of the e-mail, or a sender of the e-mail.
2. The method of claim 1, comprising aggregating user use context
of the e-mail across a plurality of recipients, and making
available a subset of the aggregated information to the other
recipients or the sender of the e-mail.
3. The method of claim 1, the user use context comprising whether
the message was deleted, responded to, tagged, forwarded, or
opened, or how long before the message was deleted, responded to,
tagged, forwarded or opened, or a device-determined location of the
user.
4. The method of claim 1, the user use context comprising a
biometric response of the user observed based on the
interaction.
5. The method of claim 2, comprising ranking the e-mail as a
function of the aggregated information, and providing ranking
information about the e-mail to the other recipients, or the
sender.
6. The method of claim 3, wherein the ranking comprises weighting
relative importance of a particular participant to another
participant or the sender.
7. The method of claim 1, further comprising aggregating the user
use context about the e-mail with user use context about a
disparate type of received electronic message to obtain a unified
user communication context.
8. The method of claim 1, further comprising employing the user use
context to compile a relative messaging predisposition for a
participant of the e-mail.
9. The method of claim 1, further comprising: employing language
processing to analyze content of the e-mail before the e-mail is
delivered and to classify a style or a sentiment of the content;
referencing user use context based at least on the determined style
or sentiment; and providing real-time feedback for the style,
content or sentiment of the e-mail based on the composition of the
e-mail and user use context of one or more recipients of the
e-mail.
10. The method of claim 1, further comprising: obtaining a
sentiment threshold and a threshold action pertaining to the
e-mail; monitoring a sentiment of content of the e-mail or
additional electronic messages pertaining to the e-mail; and
implementing the threshold action if the content sentiment of the
e-mail or additional messages exceeds the sentiment threshold.
11. A system embodied on computer readable hardware that
facilitates electronic message handling, comprising: a context
component that analyzes user context about an electronic message
exchanged between participants; and a distribution component that
disseminates information about the user context to a recipient or a
sender of the electronic message.
12. The system of claim 11, further comprising a data compilation
component that aggregates user use context of the electronic
message across a plurality of participants and makes available a
subset of the aggregated context to other participants of the
e-mail message.
13. The system of claim 11, further comprising a grading component
that ranks the electronic message as a function of aggregated user
context information across a plurality of participants and
distributes the ranking to one or more recipients or the
sender.
14. The system of claim 13, further comprising a relativity
component that modifies the ranking based on a significance of a
participant and the use context of the participant.
15. The system of claim 11, further comprising a messaging
unification component that aggregates user use context pertaining
to the message across a plurality of messaging systems.
16. The system of claim 11, further comprising a guidance component
that employs language processing to determine a style or sentiment
of content of the message, and references user use context and
makes available feedback, optionally as the message is compiled,
based on the composition of the message and a subset of the user
use context.
17. The system of claim 16, the guidance component suggests
modifying a style, content or sentiment of the message based on a
user use context of a participant.
18. The system of claim 11, further comprising a monitoring
component that compares a sentiment of the message, determined from
the user use context, with a sentiment threshold and implements an
action based on the comparison.
19. The system of claim 17, the monitoring component employs an
aggregated user use context of a plurality of participants of the
message to determine the sentiment.
20. A system that facilitates electronic message handling,
comprising: a context component that analyzes user use context
about a received electronic message; a grading component that ranks
the electronic message as a function of aggregated user context
information and distributes the ranking to a participant; a
relativity component that modifies the ranking based on a
significance of a participant and the use context of the
participant; and a distribution component that disseminates
information about the user use context to other participants of the
electronic message.
Description
BACKGROUND
[0001] E-mail and other electronic messaging systems have enabled a
technical revolution in business and personal communications, and
have provided a platform for social and organizational networking.
In recent years, use of electronic messaging, such as e-mail, short
messaging, text messaging, blogging, electronic forums, and so on,
has increased exponentially due to the inexpensive and near
instantaneous communication platform that electronic messaging
provides. Such platforms have rapidly decreased time required to
share and disseminate information, whether for a large,
multi-national organization, a network of friends or family
members, or remotely located small business partners.
[0002] The advent of electronic messaging, whether via fixed line
communications (e.g., computer and Internet) or mobile
communications (e.g., cellular phone), has led to diverse business
ventures supporting this technology. Initially, such ventures were
limited to large organizations with enough capital to support
initial infrastructure investments required for long range
electronic communication. For instance, the Internet was initially
a defense research project funded with military and university
funds. As commercial applications became apparent, private sector
ventures leveraged the initial structure to establish public and
private links to the initial architecture. The first forms of
electronic messaging over the Internet consisted of e-mail;
however, the versatile transport control protocol/Internet protocol
(TCP/IP) enabled other messaging architectures, such as short
message service (SMS), text messaging, to couple with the basic
communication infrastructure. As the World Wide Web expanded across
the Internet infrastructure and hypertext transport protocol (HTTP)
and other protocol web pages became a prevalent form of data
exchange, message forums, blogging and other forms of Web-based
electronic messaging became popular. One of the more sophisticated
recent advancements are the social networking sites that inter-link
individuals, or nodes, based on inter-personal relationships, or
ties. These sites provide a simple and powerful platform to share
information, communicate real-time or in delayed-time (e.g., via
posting on a forum), and so on.
[0003] Although more advanced electronic communication platforms
have developed over time, the original e-mail system has survived
as one of the most prevalent messaging systems, both in business
and private communication. One reason for this is the simplicity of
text-based communication coupled with the flexibility and feature
richness of modern applications operating on a standardized
operating system. For instance, various application files can be
attached or cut-n-pasted to e-mail, and bundled into data packets
transmitted by the e-mail application. Furthermore, e-mail can
quickly be disseminated to large numbers of individuals (e.g.,
individually or associated with a group-name), forwarded to more
individuals, responded to, and so on, resulting in mass
communication. A chain of e-mails, comprising an original or parent
message and child or forwarded and/or replied to messages, can
convey the history of a conversation between large numbers of
individuals. Accordingly, e-mail serves as a basis for most newer
technologies, often leading the way for newer electronic messaging
innovations, well over twenty years after its' inception.
SUMMARY
[0004] The following presents a simplified summary in order to
provide a basic understanding of some aspects of the claimed
subject matter. This summary is not an extensive overview. It is
not intended to identify key/critical elements or to delineate the
scope of the claimed subject matter. Its sole purpose is to present
some concepts in a simplified form as a prelude to the more
detailed description that is presented later.
[0005] The subject disclosure provides for determining user use
context for electronic messaging and disseminating a subset of the
user use context to recipients and/or senders of such message. The
user use context can be based on global recipient context, such as
speed with which a message is disseminated and/or consumed by
recipients, number of child messages spawned, speed with which such
messages are spawned, and so on. Additionally, user use context can
also be based on individual context (or group-based recipient
context), based on individual interaction with the message (e.g.,
time to read, time to delete, number of child messages, etc.)
optionally weighted by moving statistics of the individual's use
context. The context can be disseminated to recipients of the
message or to the sender, and can provide an overview of perception
of the electronic message.
[0006] According to other aspects of the subject disclosure,
provided is a mechanism for aggregating user use context of
electronic messaging across a plurality of messaging platforms. A
user's use of and interaction with multiple communication
platforms, including e-mail, text messaging, mobile short messaging
and like text and media platforms, as well as voice-based platforms
such as phones, including mobile phones, voice over IP (VoIP)
phones, landline phones, and so on, can be analyzed. The use and
interaction can be analyzed to infer a baseline use context for the
user, aggregated across the multiple communication platforms, and
per platform. According to further aspects, upon receiving a
message, the user's use context can be analyzed against the
baseline context to provide a perception of the received message.
Such perception can be reported to the user and/or returned to the
initiator as substantive feedback.
[0007] In one or more additional aspects, disclosed is predictive
context analysis for electronic messaging. Message compilation can
be analyzed as a user enters text or other data into a message.
Additionally, a superset of user use context generated for
recipients of the message can be referenced to predict a
disposition of the recipients, optionally as a function of the
messaging platform in which the data is entered. Feedback can be
given based on a comparison of the data entered and the predicted
disposition of the recipients. Thus, as an example, if the data is
determined to be abrasive in context, and a recipient is
predisposed to respond negatively to abrasive messages, the
feedback can suggest changing the context of the data and, in some
aspects, point out particular portions of the data determined to be
abrasive.
[0008] According to still other aspects of the subject disclosure,
message monitoring is provided that can analyze disseminating
messages and determine context of such messages. The determined
context can be analyzed against one or more contextual thresholds
and predetermined actions can be taken if a contextual threshold is
exceeded. As examples, predetermined actions can include preventing
dissemination of the message and/or child messages spawned there
from, identifying additional recipients having a contextual
interest to the message and adding such recipients to the message,
and so on.
[0009] The following description and the annexed drawings set forth
in detail certain illustrative aspects of the claimed subject
matter. These aspects are indicative, however, of but a few of the
various ways in which the principles of the claimed subject matter
may be employed and the claimed subject matter is intended to
include all such aspects and their equivalents. Other advantages
and distinguishing features of the claimed subject matter will
become apparent from the following detailed description of the
claimed subject matter when considered in conjunction with the
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 depicts a block diagram of an example system that
provides user use context for electronic messaging according to
some aspects of the subject disclosure.
[0011] FIG. 2 illustrates a block diagram of a sample system that
monitors a message platform to generate a use context for users of
the platform.
[0012] FIG. 3 depicts a block diagram of an example system that
ranks messages according to recipient perception according to some
aspects of the subject disclosure.
[0013] FIG. 4 illustrates a block diagram of a sample system that
provides user perception across a unified communication platform
according to further aspects.
[0014] FIG. 5 depicts a block diagram of an example system that
provides predictive perception of a message based on recipient and
message compilation.
[0015] FIG. 6 illustrates a block diagram of an example system that
monitors message sentiment and can perform actions based on
sentiment thresholds.
[0016] FIG. 7 illustrates a flowchart of an example methodology for
providing user use context for electronic messaging according to
further aspects disclosed herein.
[0017] FIG. 8 depicts a flowchart of a sample methodology for
determining user perception of electronic messaging for a community
of users.
[0018] FIG. 9 illustrates a flowchart of an example methodology for
employing user context in monitoring and providing predictive
feedback for electronic messaging.
[0019] FIG. 10 depicts a block diagram of an example operating
environment suitable to process and store user use data according
to some disclosed aspects.
[0020] FIG. 11 depicts a block diagram of an example remote
communication environment providing remote messaging and data
analysis in other disclosed aspects.
DETAILED DESCRIPTION
[0021] The claimed subject matter is now described with reference
to the drawings, wherein like reference numerals are used to refer
to like elements throughout. In the following description, for
purposes of explanation, numerous specific details are set forth in
order to provide a thorough understanding of the claimed subject
matter. It may be evident, however, that the claimed subject matter
may be practiced without these specific details. In other
instances, well-known structures and devices are shown in block
diagram form in order to facilitate describing the claimed subject
matter.
[0022] As used in this application, the terms "component,"
"module," "system", "interface", "engine", or the like are
generally intended to refer to a computer-related entity, either
hardware, a combination of hardware and software, software, or
software in execution. For example, a component may be, but is not
limited to being, a process running on a processor, a processor, an
object, an executable, a thread of execution, a program, and/or a
computer. By way of illustration, both an application running on a
controller and the controller can be a component. One or more
components may reside within a process and/or thread of execution
and a component can be localized on one computer and/or distributed
between two or more computers. As another example, an interface can
include I/O components as well as associated processor,
application, and/or API components, and can be as simple as a
command line or a more complex Integrated Development Environment
(IDE).
[0023] One limitation of text-based messaging systems is the
inability to convey ideas, concepts, feelings and emotions as
efficiently as in-person communication. Thus, emotions and
conceptual ideas expressed in person via body language, vocal tone
or inflection, somatic gestures, facial expressions and other
information that provides context for in-person communication are
limited or unavailable with text based communications. Even vocal
communication (e.g., telephone) can capture only some of the
contextual conveyances available with in-person communication.
Accordingly, although electronic messaging allows for
near-instantaneous communication to most parts of the world at low
prices, determining a participant's context and perception is still
very archaic.
[0024] Although user context, sentiment, perception, and other
emotional responses or dispositions are difficult to convey via
text, some innovation has occurred to overcome these limitations.
In some cases, users of text-based messaging can simply indicate
their emotions and feelings explicitly in text. However,
individuals are often not practiced at articulating emotion and
perception, so misunderstanding is common, and compilation of such
a message is often slow and cumbersome. Other innovations include
emoticons, such as ASCII text based emoticons (e.g., text-based
symbol combinations resembling smiling faces, frowning faces,
frustration etc., to convey emotions), graphical emoticons (e.g.,
animated cartoon-like figures, often round to represent a face,
utilized to convey emotion), and the like. Although emoticons had
been more widespread and arguably more successful in conveying
emotion through a text-based medium than explicit articulation,
they are still very limited in form and, like explicit
articulation, must be input manually to a message.
[0025] In contrast to the manual examples of conveying sentiment
and emotion described above, the subject disclosure provides an
automated mechanism to determine and convey user context and user
perception of electronic messaging, such as e-mail. A user's
interactions with a messaging system, and with particular messages,
can be monitored to determine a baseline context for the user. In
some aspects, machine learning can be employed to optimize a
statistical model for the user context over time and over multiple
interactions with sent and received messages to arrive at an
optimized baseline context. Such a context can be continually
updated based on successive message interactions.
[0026] When an electronic message is received, a user's response to
the message can be measured with respect to the baseline/optimal
context to determine the user's perception of the particular
message. Such interactions can include whether the e-mail was
opened, time required to read the message (optionally normalized
based on an amount of text data in the message), whether or how
many times the message was forwarded, how many recipients the
message was forwarded to, whether the message was deleted or saved
in an inbox, whether the message was moved to a user-created
folder, whether a link to a URL or other network link was followed,
whether an attachment was opened, copied, cut-n-pasted, forwarded,
etc., and so forth. Likewise, when a sender compiles a message, a
manner of interacting with a messaging interface can be analyzed.
Speed with which a user enters data into the message, the content
of the message (e.g., analyzed via natural language processing, or
other forms of language processing), a number of recipients, a
number of people copied, and so on, can be utilized to enrich the
use context information maintained for a user.
[0027] In at least one other example, user use context can include
information pertaining to user location, or type of messaging
interface/messaging device employed by the user. For instance, the
user context can be updated to indicate a user is currently
employing a mobile device to interface to a messaging platform, a
portable phone, a desktop computer, a home computer, a laptop, etc.
The use context information can optionally categorize the user's
context as a function of messaging interface/device, classifying
different contexts and user tendencies as a function of
interface/device currently employed.
[0028] Furthermore, device-determined position (which can include
position entered into a UI of the device by the user) can be
utilized as a component of user use context information, either in
conjunction with the type of messaging interface/messaging device,
or separate there from. Thus, the user's location can be tracked
and changes in such location updated. Use context data can further
be classified as a function of current user location--identifying
sentiments, predispositions, communication style, device
tendencies, and so on--that are pertinent to one or more locations
of the user. Thus, for instance, the use context can at a minimum
include a current user location as well as a current device which
the user is coupled to a messaging system with, and expose such
information to participants of the messaging system. In at least
some aspects, however, changes in user style, predisposition,
sentiment, messaging preferences etc., can be determined as a
function of location/device and exposed to other participants.
[0029] In some aspects of the subject disclosure, user use context
can be determined at least in part from biometric data. For
instance, a camera coupled with a computer can capture video data
of a user interacting with a messaging interface. The video data
can be sent or streamed to a computing device. A device application
can analyze video data of the user, including facial expressions
and changes thereof, changes in skin color, identify sweating,
nervous activity, pupil size/dilation, and so on, to obtain
biometric response data for the user. Infrared sensors can
determine body temperatures, to detect changes in body temperature.
Audio devices (e.g., microphones) can capture spoken words and
sounds emitted by a user while interacting with a messaging device
(e.g., the computer). Thus, where a user speaks a comment or makes
a particular sound, laughs, becomes nervous, begins sweating,
becomes relaxed, etc., a use context can be inferred. Biometric
data, as well as user interface interactions can be aggregated to
derive an overall use context of a user.
[0030] In a broader sense, a wide variety of interactions, some
limited to a subset of messaging architectures and some universally
applicable to any electronic messaging architecture, can be
utilized to determine user sentiment or perception of a message. As
an example, a user's interaction with a messaging device upon
compiling or receiving a message, as well as biometric responses
resulting from interacting with a message interface, can be
analyzed to determine a particular sentiment or perception of the a
message. It should be appreciated that, although only some examples
of such interactions are specifically articulated here, the subject
disclosure contemplates any such user interaction known to one of
skill in the art, or made known to one of such skill by way of the
context provided herein.
[0031] Upon determining user context (e.g., sentiment, perception)
of a particular message, a response can be distributed to the
sender of the message or to one or more participants (including,
e.g., the user). The user's context can be aggregated with a
plurality of other user contexts to determine a ranking for the
message. In some aspects, the ranking can be modified based on
importance or significance of a particular user. The ranking as
well as a recipient context can be summarized via one or more
keywords or other suitable tags attached to the message. In some
aspects, the message can be sent with such tags; in other aspects,
a message can be updated as recipient context information is
gathered and analyzed. Accordingly, the sender, recipients and/or
other participants can quickly judge the importance of a received
message, as well as sentiment of other recipients. Thus, the
subject disclosure provides a significant benefit, especially for
users having a very high degree of exposure to incoming messages.
As a particular example, such a user can visually filter importance
of a received e-mail message based on the sentiment of other
recipients that are tagged with the received message.
[0032] It should be appreciated that, as described herein, the
claimed subject matter may be implemented as a method, apparatus,
or article of manufacture using standard programming and/or
engineering techniques to produce software, firmware, hardware, or
any combination thereof to control a computer to implement the
disclosed subject matter. The term "article of manufacture" as used
herein is intended to encompass a computer program accessible from
any computer-readable device, carrier, or media. For example,
computer readable media can include but are not limited to magnetic
storage devices (e.g., hard disk, floppy disk, magnetic strips . .
. ), optical disks (e.g., compact disk (CD), digital versatile disk
(DVD) . . . ), smart cards, and flash memory devices (e.g., card,
stick, key drive . . . ). Additionally it should be appreciated
that a carrier wave can be employed to carry computer-readable
electronic data such as those used in transmitting and receiving
electronic mail or in accessing a network such as the Internet or a
local area network (LAN). The aforementioned carrier wave, in
conjunction with transmission or reception hardware and/or
software, can also provide control of a computer to implement the
disclosed subject matter. Of course, those skilled in the art will
recognize many modifications may be made to this configuration
without departing from the scope or spirit of the claimed subject
matter.
[0033] Moreover, the word "exemplary" is used herein to mean
serving as an example, instance, or illustration. Any aspect or
design described herein as "exemplary" is not necessarily to be
construed as preferred or advantageous over other aspects or
designs. Rather, use of the word exemplary is intended to present
concepts in a concrete fashion. As used in this application and the
amended claims, the term "or" is intended to mean an inclusive "or"
rather than an exclusive "or". That is, unless specified otherwise,
or clear from context, "X employs A or B" is intended to mean any
of the natural inclusive permutations. That is, if X employs A; X
employs B; or X employs both A and B, then "X employs A or B" is
satisfied under any of the foregoing instances. In addition, the
articles "a" and "an" as used in this application and the appended
claims should generally be construed to mean "one or more" unless
specified otherwise or clear from context to be directed to a
singular form.
[0034] As used herein, the terms to "infer" or "inference" refer
generally to the process of reasoning about or inferring states of
the system, environment, and/or user from a set of observations as
captured via events and/or data. Inference can be employed to
identify a specific context or action, or can generate a
probability distribution over states, for example. The inference
can be probabilistic-that is, the computation of a probability
distribution over states of interest based on a consideration of
data and events. Inference can also refer to techniques employed
for composing higher-level events from a set of events and/or data.
Such inference results in the construction of new events or actions
from a set of observed events and/or stored event data, whether or
not the events are correlated in close temporal proximity, and
whether the events and data come from one or several event and data
sources.
[0035] Turning to the drawings, FIG. 1 illustrates a block diagram
of an example system 100 that provides user use context for
electronic messaging. The system 100 can be employed to
automatically provide perception or sentiment of electronic
messages, such as e-mail. The perception/sentiment can be
distributed to participants (e.g., sender, recipient, copied or
forwarded participant, respondent, etc.) of the e-mail, providing
additional context to facilitate rapid or selective consumption,
and an overall enhanced user experience of electronic
messaging.
[0036] System 100 can include a message perception system 102 that
receives messaging usage data as data input, and determines a
context or manner in which a user employs a messaging system to
generate a user use context. The user use context can be optimized
over time, based on multiple user interactions with the messaging
system, to more accurately reflect a user's predisposition toward
the messaging system. The use context can be distributed by message
perception system 100 to the user or other users of the messaging
system to provide information about how a message is received.
[0037] The messaging usage data can comprise any suitable
quantitative or qualitative representation of a user's
interaction(s) with received/sent electronic messages, or with an
interface for the electronic messages (e.g., such as an e-mail user
interface). User interactions can include user interface
commands/inputs undertaken after receiving a message (e.g., delete,
forward, save, move to a user-defined folder, indicate as spam or
not spam, provide an explicit importance or quality rating--low,
medium, high etc.--for the message based on a user's impressions of
the message, tag the message, and so on), time after receiving the
message that such actions are undertaken, frequency with which a
particular action is undertaken (among multiple received messages),
and the like. Additionally, the interactions can include biometric
responses obtained through one or more biometric sensors coupled
with a messaging device or messaging interface. Such interaction
data is received at a context component 104 that determines user
context from such data. Context component 104 can couple to a
messaging platform (not depicted) and extract the data or query a
messaging platform configured to monitor and collect such data. As
an additional source of user context data, a user profile 112,
specifying what context data to distribute to a user and when, can
be accessed to obtain user preferences with respect to distribution
of use context information (see below). The distribution
preferences can also be employed by context component 104 to
determine overall user use context data.
[0038] Where multiple messages are analyzed, context component 104
can employ statistics to determine a user's average, median, most
likely, least likely, etc., response(s) to a received message, or
user interface command(s) employed with a sent message. A context
superset can be established for the user based on the
commands/inputs, time to implement such commands/inputs, and the
statistics generated over multiple user interactions. Context
component 104 can employ a subset of the context superset pertinent
to receiving a message, or sending a message, to establish one or
more baseline contexts representative of the user's use of the
messaging platform. Once the baseline user use context is
determined, at least a subset of the baseline user use context can
be output by message perception system 102 to provide a user's
baseline context for a particular messaging system.
[0039] According to some aspects of the subject disclosure, message
perception system 102 can employ machine learning and optimization
108 to more accurately match the use context to a user's actual use
of a messaging platform over multiple messaging instances.
Furthermore, the optimization 108 can update the use context over
time to accommodate for changes in a user's interaction with a
messaging system. In order to infer user context having a highest
probability of matching a user's actual use of the messaging
system, machine learning and optimization component 108 can utilize
a set of models (e.g., user interface model, user use history
models, user biometric response models, use statistics model, etc.)
in connection with determining or inferring user predisposition
toward the messaging system in general and received messages in
particular. The models can be based on a plurality of information
(e.g., usage history, user profile information, message profile
information, message system profile information, etc.).
Optimization routines associated with machine learning and
optimization component 108 can harness a model that is trained from
previously collected data, a model that is based on a prior model
that is updated with new data, via model mixture or data mixing
methodology, or simply one that is trained with seed data, and
thereafter tuned in real-time by training with actual field data
based on parameters modified as a result of error correction
instances.
[0040] In addition, machine learning and optimization component 108
can employ machine learning and reasoning techniques in connection
with making determinations or inferences regarding optimization
decisions, such as matching context of users (e.g., for one or more
messaging systems) across a plurality of user use contexts. For
example, machine learning and optimization component 108 can employ
a probabilistic-based or statistical-based approach in connection
with identifying and/or updating a baseline user use context for a
plurality of users. Inferences can be based in part upon explicit
training of classifier(s) (not shown), or implicit training based
at least upon one or more monitored results, and the like.
[0041] Machine learning and optimization component 108 can also
employ one of numerous methodologies for learning from data and
then drawing inferences from the models so constructed (e.g.,
Hidden Markov Models (HMMs) and related prototypical dependency
models, more general probabilistic graphical models, such as
Bayesian networks, e.g., created by structure search using a
Bayesian model score or approximation, linear classifiers, such as
support vector machines (SVMs), non-linear classifiers, such as
methods referred to as "neural network" methodologies, fuzzy logic
methodologies, and other approaches that perform data fusion, etc.)
in accordance with implementing various aspects described herein.
Methodologies employed by optimization module 708 can also include
mechanisms for the capture of logical relationships such as theorem
provers or heuristic rule-based expert systems. Inferences derived
from such learned or manually constructed models can be employed in
other optimization techniques, such as linear and non-linear
programming, that seek to maximize probabilities of error. For
example, maximizing an overall accuracy of user use context data
and a user's interactions with a messaging system can be achieved
through such optimization techniques.
[0042] A subset of user use context data determined by message
perception system 102 can be distributed among participants of a
messaging system by distribution component 106. The distribution
component 106 can reference a profile data store 110 to obtain user
profile information 112 to determine what context data can be
provided to a particular message participant, and when.
Furthermore, as statistical models evolve, distribution component
106 can provide updated use context data, per a user's profile 112.
Thus, for example, users can opt in or opt out of the use context
distribution. In some aspects, users can specify particular
messages, message platforms (e.g., e-mail, short message service,
text message service, voice service--for instance employing speech
to text translation--and so on) or particular senders/recipients
about which use context data is to be distributed. Alternatively,
or in addition, users can specify in the user profile 112 what type
of collected data is to be transmitted to the user (e.g., average
use statistics, user perceptions based on particular use versus
baseline use). Accordingly, distribution of context data can be
customized according to user desires.
[0043] In some aspects of the subject disclosure, use context data
can be explicitly provided to a message participant. As examples,
the use context data can be provided on user request (e.g., via a
user interface command, query, dialogue box, a command to a message
control platform, and so forth), as part of a periodic exposure to
determined mood patterns of one or more other participants, as part
of a an automatic or partially-automatic (e.g., based partially on
user request or user profile) determination of message context
compared with participant predisposition, or the like. Thus, for
instance, an e-mail interface can have a dialogue box with
dispositions of various e-mail participants selected by a user (or,
e.g., based on identified message recipients). In such a case, a
participant can attempt to compile a message considering a
recipient's mood, such as typical mood for a time of day,
concurrent mood determined from recent messaging interactions
and/or biometric data, and so on. In another example, the use
context could inform the participant that a recipient typically
reads e-mail during the evening, and the message can be delayed
until a time that the message is more likely to be read (e.g., or
delayed so that the message is delivered at a time the recipient is
likely to be reading e-mail). It should be appreciated that various
other practical applications are possible based on exposing
determined participant use context to other message participants.
Although it is not feasible to articulate all such practical
applications, those applications known to one of skill in the art,
or made known to one of such skill by way of the context provided
herein are contemplated as part of the subject disclosure.
[0044] FIG. 2 illustrates a block diagram of a sample system 200
that monitors a message platform to generate a use context for
users of the platform. It should be appreciated that system 200 can
be centrally located, querying different user applications for user
use data, or distributed throughout user systems (e.g., comprising
a client application downloaded and installed onto a user's message
device, computer, etc.) to collect the user use data. User use
context data can then be stored and referenced to determine
information about a particular interaction with the message
platform or response to a particular received message, such as user
perception of the received message. Accordingly, system 200 can
employ user use context to automatically infer emotion, reaction,
sentiment, etc., information toward a particular instance of
electronic communication.
[0045] System 200 comprises a context component 202 that interfaces
with a messaging platform 204 employed by a plurality of users to
send and receive electronic information. Context component 202
interfaces to the messaging platform 204 to collect data pertaining
to users' interactions with a messaging system (e.g., e-mail, short
message service, text messaging, instance messaging, or in some
aspects, a voice-to-text translator coupled with a voice
communication platform--such as telephone, circuit-switched mobile
voice, VoIP, and so forth). Context component 202 can query the
message platform to obtain user context information for each of the
plurality of users based on individual user interactions with the
platform 204, as described above with respect to FIG. 1, supra.
Based on such information, context component 202 can generate a
user use context for each of the users, and supply the user use
contexts to compilation component 206.
[0046] The compilation component 206 can store the individual user
use contexts in a use context file 210 in database 210. Stored
context information can be referenced by context component 202 in
determining a particular user's perception of a received instance
of communication. That perception can be forwarded to a
distribution component 212 that submits the perception to a
recipient(s) or sender of the message, or updates tag information
associated with a delivered message, via the messaging platform
204.
[0047] In some instances, the perception can be based on data
included in the message (e.g., by employing natural language
processing--not depicted) compared with a subset of a user's use
context determined at least in part on the user's response to
receiving similar data, or data sharing a similar context. In other
instances, the perception can be based on responses of other
message recipients. Such responses can optionally be weighted as a
function of recipient importance with respect to a particular
participant (e.g., established in the particular participant's user
profile, or determined by context component 202).
[0048] Where suitable (e.g., to determine other recipient
perception/importance), messaging platform 204 can delay delivery
of a message to a particular recipient (e.g., per that
participant's user profile) until a message perception is
determined. Thus, an executive of an organization that receives a
substantially large amount of messages can have a message delayed
until perception information of other recipients of the message is
determined. Thus, the executive can save time in consuming message
data based on other recipients' perceptions. As another example,
message delivery or receipt (e.g., when it is displayed as having
been received for a user) can be based on a user's current context.
In some aspects, a participant can specify a desired
sender/recipient mood for delivery or display of a message; in
other aspects, the current mood versus desired mood can be
automatically determined. For instance, if a sender is determined
to be in an angry mood (e.g., based on user use context such as
high blood pressure, body temperature and rapid eye movement,
determined from biometric sensors, and rapid data input, high
keyboard pressure when typing, and so on) a message can be queued
for a predetermined period (or, e.g., until the sender's mood
changes or passes a threshold). Once the period expires, or the
user's mood changes, the user can be queried to confirm that the
message should be delivered. As another example, if a user is
asking a favor of a recipient, delivery of the message can be
delayed until the recipient is in a happy mood (e.g., determined
from laughter, low blood pressure, low body temperature, relaxed
posture and so forth).
[0049] It should be appreciated that context component 202 can
determine perception information automatically based on user
interaction with the message, as described herein, or based on
manual input. For instance, context component 202 can implicitly
determine context as described herein. In addition, context
component 202 can couple or modify the implicit determination with
explicit perception information (e.g., an importance ranking)
provided by a user receiving a message. Accordingly, system 200 can
provide a significant advantage over systems that require a
recipient to tag a message and forward the tagged message in order
to deliver context information to other users.
[0050] In addition to the foregoing, compilation component 206 can
aggregate user use context data among a plurality of users of the
messaging platform 204. For instance, individual user use contexts
provided by context component 202 can be aggregated into an
aggregated file at database 208. The aggregated context data can be
employed to generate user-independent data (e.g., by averaging user
use context data of the plurality of users), which can be employed
as a baseline context to determine user perception of a message for
an unknown user (e.g., a new user with no or little previous
context information collected).
[0051] As an alternative to the foregoing, aggregated context
information can be employed, to provide context information as a
function of one or more categories of users, determined by
categories of user context data. For instance, users determined to
become emotionally affected by tone, content, sentiment, etc., of
an electronic message (based on interaction with received messages
and an emotion-based model trained on user interactions exhibiting
strong emotion) can be categorized as having a sensitive
disposition to such messages, or a subset of such messages (e.g.,
based on the tone, content, sentiment). As another example, users
can be categorized as high use, medium use, low use, etc., users of
the messaging platform 204 in comparison with the aggregated use
data. By categorizing user personality, predisposition, usage,
etc., system 200 can provide predictive information for message
compilation (e.g., see FIG. 5, infra). Alternatively, or in
addition, system 200 can infer a category for new users and
generate user perception information based on interaction with a
received message, in conjunction with establishing a user use
context for the new user based on the interactions. The category
can be updated over subsequent interactions to optimize the user
category, or provide additional categories for the user based on
content/tone/sentiment of the message.
[0052] FIG. 3 depicts a block diagram of an example system 300 that
can rank electronic messages. Ranking can be based on user
perception determined from user context, optionally coupled with an
importance factor associated with a recipient of the message. The
ranking can then be provided to a sender or other recipients of the
message as a preview of contextual response to the message.
Accordingly, system 300 can import into electronic messaging some
aspects of in-person communication, determined from users'
perception of received messages in a context determined from user
interaction with a messaging system.
[0053] System 300 comprises a grading component 302 that can
establish a ranking for a particular message based on recipient
perception of the message. Such perception can be an aggregate of
individual user perceptions of the message, determined by comparing
each user's interaction with/reaction to the message and a user use
context 306 of each such user. Individual user contexts can be
maintained in separate records 310A, 310B, 310C and 310D. Thus, for
instance, a first record 310A can identify a first user, include a
user use context of such user, profile of the first user, message
pre-disposition, optionally determined as a function of one or more
messaging platforms, and so forth. Additionally, the records
310A-310D can specify a social and/or organizational context of
each user. In the case of a social context, social relationships or
ties can be specified for a user relative other users of the
messaging system, and stored in the records 310A-310D. Thus, for
instance, record 1 310A can specify familial relationships,
friendships, and other social networking contextual information for
other users of a messaging system. In the case of an organizational
context, the record 310A can specify business relationships, such
as peer, managerial, subordinate, division, team and like
relationships between members of the organization. Likewise, other
user records 310B-310D can store social and/or organizational
relationships of user 1, as well as other users (e.g., user 2, user
3, . . . , user N, where N is a positive integer).
[0054] Records 310A-310D can be maintained separately within the
database 304, as well as in an aggregated record (not depicted).
The aggregated record can combine user context of each record
310A-310D, as well as maintain a master social and organizational
network tree, describing relationships and ties for each user
context record 310A-310D stored at database 304. Thus, grading
component 302 can employ the user use context information to
determine user perception of the message, as discussed herein
(e.g., based on user interaction with a messaging interface,
biometric responses, and so on).
[0055] To rank a message, grading component 302 can compare user
perception(s) to a ranking scale, determined at least in part from
one or more perception thresholds, and provide a ranking for the
message. Additionally, a relativity component 312 can modify a
weight with which a particular user's perception of the message is
factored into the ranking. Thus, for instance, a weight of a user's
boss can be given higher rating than peers or subordinates.
Additionally, organization executives can be given higher weight
than the boss, and so on. Likewise, closer social relationships
(e.g., determined from a number or frequency of messaging
interactions, or context of message content) can be utilized to
establish different weights for social user relationships as well.
Such organizational and/or social relationships can be utilized as
seed data to train one or more models for optimizing perception
weights. The ranking(s) can therefore become adapted over time
based on subsequent messaging interactions, user feedback,
sender/recipient user profile, and the like. A message ranking can
be output by grading component 302 and submitted to a messaging
platform for delivery to the sender or a recipient(s) of a message,
optionally as a function of user profile delivery options.
[0056] FIG. 4 depicts a block diagram of a sample system 400 that
provides user use context across a plurality of electronic
messaging platforms 404A, 404B, 404C, 404D. Suitable messaging
platforms can include e-mail (404A), text messaging (404B), short
message service messaging (404C), a voice-text module (not
depicted) integrated with or overlaid onto a voice communication
system (404D), or other suitable messaging platforms, such as
blogging websites, message forums, and so on. System 400 can
comprise a unified communication server 402 that provides or
employs a common node or interconnection of nodes between the
messaging platforms 404A-404D to collect user use context
information across the various platforms 404A-404D. In some
aspects, the unified communication server 402 can distinguish the
multiple platforms (404A-404D) as a function of platform provider
(e.g., Microsoft, Lotus, T-Mobile, MSN messaging, etc.), product
name (e.g., Microsoft's Outlook.RTM., Lotus' Notes.RTM., T-Mobile's
short messaging service, and so on), product version (e.g., version
1.0, version 2.0) in lieu of or in addition to the type of
messaging platform, as discussed above. Accordingly, the unified
communication server 402 can be utilized to provide user use
context for various purposes, including benchmarking new messaging
platforms, new versions of such platforms, competitive analysis of
such platforms, and so on.
[0057] In some aspects, the unified communication server 402 can
reside on a user's computing device and collect usage context data
from multiple messaging applications operating on the device. In
other aspects, the server 402 can employ an intermediary network
from the user's computer to collect data from a plurality of the
user's devices coupled to the intermediary network. In such
aspects, the server 402 can query a monitoring component (not
depicted) that is installed on a device and configured to
communicate with the server 402 via the intermediary network (e.g.,
a client application local to a device). In still other aspects,
unified communication server 402 can reside on the intermediary
network and collect data from the plurality of devices as well as
the user's computing device. Other local and/or remote data
monitoring configurations, known in the art or made known to one of
skill in the art are also considered as part of the subject
disclosure (e.g., see FIG. 11, infra).
[0058] Data collected by the unified communication server 402 can
be forwarded to context component 406. As described herein, the
context component 406 can determine user use context from the
received data, in this case as a function of the cross-messaging
platform data. Thus, a user's use context for each of the
particular platforms 404A-404D can be determined, and stored in a
database 408 per platform 404A-404D and as a unified,
platform-independent usage context. Thus, in one instance, a user's
interaction with/response to a received message can be compared
with the context data associated with a platform over which the
message is received. A perception of the message can be determined
as described herein, which can be included in message ranking or
used to derive keywords/tags for the message, and so on. In another
instance, a platform-independent perception can be determined based
on the platform-independent usage context. Such a perception can be
useful in providing a generalized sentiment toward messaging, or
can be utilized as a normalization factor to determine degrees of
preference of the particular messaging systems 404A-404D for the
user. In such aspects of the subject disclosure, system 400 can
provide a very rich set of context information derived from a
user's interactions with various types of messaging platforms.
According to additional aspects, the cross-platform context data
can also be aggregated across a plurality of users (e.g., see FIGS.
2 and 3, supra) to provide a large superset of data for general
user context analysis. In addition, the superset of data can be
useful to normalize analysis per user, per platform, per user
perception, and so on.
[0059] FIG. 5 depicts a block diagram of an example system 500 that
provides predictive analysis and/or feedback for an electronic
message. A language processor 502 can analyze data input into the
message at a device user interface 504 as a user compiles the
message (e.g., by typing, cut-and-pasting, by speaking to a
microphone). A tone, sentiment or emotional perspective of the data
(referred collectively hereinafter as sentiment) can be analyzed to
provide a message sentiment. In some aspects, language processor
502 can employ machine learning and optimization 506 to adaptively
determine the sentiment. Such determination can be based on data
models and/or classifiers trained on use context data of a sender
of the message. Optionally, the models/classifiers can be trained
on a generic subset of user population, optionally having regional,
maturity, religious, racial, organizational, social, etc.,
similarities as the sender and/or recipients of the message. In the
latter case, language processor 502 can increase a likelihood that
the determined sentiment of the message matches sender and/or
recipient interpretations, based on slang, bias, or cultural
interpretations.
[0060] The determined message sentiment can be provided to a
guidance component 508, which references the message sentiment
against user use context 510 data stored in a database 512. By
comparing the message sentiment and user use context 510, guidance
component 508 can infer a response to the message. Feedback can be
provided to the sender based on the response, and optionally based
on inferred user intent determined from a user use context of the
sender.
[0061] As an illustrative example of the foregoing, a sender, while
angry with a peer's project results, types an e-mail message to the
peer (specified in a To: line) on a laptop computer (504), while
out of the office. Language processor 502 determines the message
sentiment is abrasive, angry, etc., and provides this determination
to guidance component 508. The guidance component 508 determines
that the recipient commonly sends and receives messages compiled in
a slang or abrasive tone. Accordingly, guidance component 508
determines the message sentiment is appropriate or tolerable for
the recipient, and provides neutral or no feedback to the sender.
On the other hand, if the guidance component 508 determines that
the recipient is sensitive to abrasive/angry context, guidance
component 508 can provide feedback (e.g., a red flag, a warning
sign, an illuminated red light, a bawling emoticon vigorously
shedding tears, etc.) suggesting the message be altered based on
the recipient's sensitivities. The latter feedback can optionally
be conditioned on a use context of the sender, as well. For
instance, if the sender typically sends abrasive messages even to
sensitive individuals (particularly while out of the office),
guidance component 508 can assume that the sender's intent is met
by the abrasive sentiment of the message, and provide neutral or
even positive feedback (e.g., a green light, thumbs up, a
devilishly grinning smiley face, a brawny emoticon kicking sand in
another's face, and so on), for the abrasive message.
[0062] Where the message includes recipients (e.g., on a "To:"
line, "Copy:" line, etc.), guidance component 506 can extract
user-specific context data from the use context 508. However, where
recipient information is not yet provided, guidance component 506
can infer a potential recipient(s), based on prior messaging
activity, or individuals socially related to the user, or having a
business/enterprise relationship with the user. Alternatively,
guidance component 506 can employ user-aggregated data, as
described herein (e.g., see FIG. 4, supra) to provide generalized,
user-independent feedback. Thus, prior to a sender indicating
recipients of the message, guidance component 508 can provide
feedback based on general user context data 510, or context data
(510) derived from individuals sharing a social and/or enterprise
network with the sender. Once one or more recipients are
identified, guidance component 508 can tailor the feedback to the
recipients and update the user interface 504 based thereon.
Feedback, as described herein, can include a variety of user
interface alerts, data outputs (e.g., a pop-up box or window),
graphical rendering (e.g., pictures, signs, etc.), animated
graphics (e.g., animated emoticons performing various actions), and
so on, suitable to suggest a course of action to a sender (e.g.,
message is good, message should be modified, message is neutral,
and so forth).
[0063] In some aspects, language processor 502 can indicate
portions of data input by the sender that are predominant factors
in determining the overall sentiment of the message. Thus, text can
be highlighted, underlined, or rendered in some other suitable
manner to distinguish the indicated portions from other input data.
In such a manner, system 500 can enable predictive-feedback based
on predicted message sentiment and recipient response, optionally
modified as a function of inferred user intent. Accordingly, system
500 can be a powerful tool to bridge the current gaps between
electronic messaging and in-person communication.
[0064] FIG. 6 illustrates a block diagram of an example system 600
that monitors message sentiment and performs predetermined actions
based thereon. Actions can be determined by comparing one or more
sentiment thresholds versus the message sentiment. In addition,
system 600 can monitor child messages that spawn off of a parent
message (e.g., forwarded messages, replied messages, etc.) and
determine an overall sentiment of a chain of messages as the
messages are sent or distributed. Determination is based on message
content and can be compared to predispositions of participants of
the message chain, determined from user use contexts of such
participants. In practice, system 600 can be utilized to stop
propagation of unproductive, offensive, or like messages, or
promote productive and encouraging messages, or like
applications.
[0065] System 600 can comprise a monitoring component 602 that
monitors data included in messages sent via a messaging platform(s)
604. Messages can be monitored individually, or monitored in
combination with child messages spawned off of a message parent. A
sentiment component 606 can analyze the data and determine message
sentiment, as described herein (e.g., see FIG. 5, supra). Sentiment
for individual parent messages is determined, and the sentiment can
then be modified for a chain of messages based on the content of
children messages tied to the parent message. In some aspects,
monitoring component 602 can employ a machine learning and
optimization 608 to determine and update the sentiment, based on
data models or trained classifiers, as described herein or known in
the art.
[0066] Monitoring component 602 can determine participants of a
message or message chain and query a user predisposition component
610 to obtain dispositions of the participants. Participant
dispositions can be determined from user use context information
614 stored in a database 612 coupled with the user predisposition
component 610.
[0067] In addition to the foregoing, monitoring component 602 can
compare participant dispositions with one or more disposition
thresholds received by monitoring component 602. Such disposition
thresholds can be entered by a network manager, office manager,
company executive, and so on, as a mechanism to define an
acceptable content or sentiment range for electronic messages. By
employing such thresholds, monitoring component 602 can determine
whether one or more threshold actions should be taken (e.g., stop
propagation of a message or chain of messages, including subsequent
message spawns, add participants to a message chain where subject
matter or message sentiment are pertinent or appropriate for such
participants, and so on). Actions are sent to the messaging
platform 604, which can implement the actions from a messaging
control server (not depicted). Thus, based on the disposition
thresholds and threshold actions, system 600 can facilitate
automated management of electronic messaging to promote desired
sentiments and prevent undesired sentiments in a social or
enterprise network.
[0068] The aforementioned systems have been described with respect
to interaction between several components. It should be appreciated
that such systems and components can include those components or
sub-components specified therein, some of the specified components
or sub-components, and/or additional components. For example, a
system could include user interface component 504, language
processor 502, guidance component 508, databases 512 and 612,
monitoring component 602 and user predisposition component 610, or
a different combination of these and other components.
Sub-components could also be implemented as components
communicatively coupled to other components rather than included
within parent components. Additionally, it should be noted that one
or more components could be combined into a single component
providing aggregate functionality. For instance, monitoring
component 602 can include user predisposition component 610, or
vice versa, to facilitate monitoring sentiment of electronic
messages and obtaining user predisposition by way of a single
component. The components may also interact with one or more other
components not specifically described herein but known by those of
skill in the art.
[0069] Furthermore, as will be appreciated, various portions of the
disclosed systems above and methods below may include or consist of
artificial intelligence or knowledge or rule based components,
sub-components, processes, means, methodologies, or mechanisms
(e.g., support vector machines, neural networks, expert systems,
Bayesian belief networks, fuzzy logic, data fusion engines,
classifiers . . . ). Such components, inter alia, and in addition
to that already described herein, can automate certain mechanisms
or processes performed thereby to make portions of the systems and
methods more adaptive as well as efficient and intelligent.
[0070] In view of the exemplary systems described supra,
methodologies that may be implemented in accordance with the
disclosed subject matter will be better appreciated with reference
to the flow charts of FIGS. 7-9. While for purposes of simplicity
of explanation, the methodologies are shown and described as a
series of blocks, it is to be understood and appreciated that the
claimed subject matter is not limited by the order of the blocks,
as some blocks may occur in different orders and/or concurrently
with other blocks from what is depicted and described herein.
Moreover, not all illustrated blocks may be required to implement
the methodologies described hereinafter. Additionally, it should be
further appreciated that the methodologies disclosed hereinafter
and throughout this specification are capable of being stored on an
article of manufacture to facilitate transporting and transferring
such methodologies to computers. The term article of manufacture,
as used, is intended to encompass a computer program accessible
from any computer-readable device, device in conjunction with a
carrier, or media.
[0071] FIG. 7 illustrates a flowchart of an example methodology 700
for providing user use context for electronic messaging according
to further aspects disclosed herein. At 702, method 700 can analyze
user use context for a received message. The user use context can
be based on a user's interaction with or response to the received
message via a messaging interface (e.g., a user interface). In
addition, the user use context can be determined based on content
of the received message, and a tone or sentiment of such content.
User interaction/response can comprise user interface actions
relating to the received message, such as opening, forwarding,
deleting, copying, replying to, saving, or tagging the message, or
like user interface actions, or a combination thereof.
Additionally, the user use context can be optimized over time,
based on usage data models or trained classifiers employing user
action data analyzed over multiple received messages. Statistical
models can be constructed, based on time to perform an action, a
number of actions performed, frequency of one or more actions, and
so on. Such models can be utilized to maintain a dynamic use
context for the user, optimized over time and reflecting changes in
the user's interactions with such messages. Additionally, a user's
disposition toward a particular message can be inferred based on
interaction/response to a particular message as compared with the
statistical models.
[0072] At 704, method 700 can distribute a subset of the user use
context information to a sender of the received message, to the
recipient, or to other recipients of the message. Such distribution
can be helpful to the sender, by indicating recipient perception(s)
of the electronic message. Thus, the sender can employ the
distributed information as a learning tool with which to improve
electronic communications with recipients in subsequent messages.
As described, method 700 can provide a powerful tool for
incorporating user contextual information, heretofore available
only through direct inter-personal communication, into electronic
communication. Such a result can greatly enhance the power and
effectiveness of electronic communication, magnifying the practical
benefits thereof.
[0073] FIG. 8 depicts a flowchart of an example methodology 800 for
providing aggregated user use context for electronic messaging. At
802, method 800 can analyze user use context for a received
electronic message, as described herein. At 804, method 800 can
aggregate analyzed use context among a plurality of users. For
instance, individual user use context can be determined based on
individual interactions with/responses to a messaging system. The
individual context can then be combined and stored in a pooled data
file, for instance. At 806, method 800 can aggregate analyzed use
context across multiple message platforms. Such platforms can
include e-mail, text messaging, short message service, website
forum posts, website blogging, voice-to-text or voice-to-data
communication on a computer, VoIP device, or telephone, and so
on.
[0074] According to some aspects of the subject disclosure, the
aggregated individual context and aggregated multi-platform context
can optionally be further aggregated into a user context superset,
spanning multiple users and multiple communication platforms.
Furthermore, context data can be monitored and collected for users
and various platforms over time. Such data can be aggregated as a
function of time, providing a high-level measure of
inter-relatedness, tone, health and sentiment. In addition, user
use patterns and changes in such patterns can be determined over
time. For instance, if a user typically has a different mood in the
morning as opposed to the evening, patterns in user message
perception can be identified throughout the day and referenced in a
use context file for that user. Likewise, if a user prefers e-mail
in the morning and short message service, or another messaging
platform in the evening, the use context file can maintain such
information as well. Accordingly, time-based variations in usage
can provide useful information for determining disposition of
users.
[0075] In addition, an aggregated superset of use context data can
be categorized as a function of various categories of interest.
Such categories can include per user context, per region context
(e.g., based on user(s) location(s)), per message system context
(including, e.g., different messaging platforms, different
messaging products, different product versions, and so forth), per
social network contexts, per enterprise contexts, per
group/team/organization contexts, and the like. The data superset
can be utilized to normalize individual or group use context across
a rich variety of user contexts.
[0076] At 808, method 800 can rank a received message based on a
subset of aggregated contextual information. Ranking can comprise a
value on a quantitative scale, a degree on a qualitative scale, or
the like. Additionally, at 810, method 800 can derive keywords
relevant to the aggregated use context for the message. The
keywords can be obtained from a pool of such keywords, selected to
be representative of recipient response(s) to the message,
optionally normalized over the aggregated contextual data. At 812,
method 800 can tag the message or spawns of the message with the
keyword, and distribute the tags to a message sender/recipient, or
deliver the message bundled with the tags. As described, method 800
provides a useful mechanism to include user contextual feedback for
electronic messaging.
[0077] FIG. 9 illustrates a flowchart of an example methodology 900
for employing user context in monitoring and providing predictive
feedback for electronic messaging. At 902, method 900 can monitor
user compilation of an electronic message, and, at 904, identify
recipients of the electronic message. At 906, method 900 can access
an aggregated use context of the identified participants. At 908,
method 900 can determine electronic messaging predispositions of
the identified participants, based on individual user data within
the aggregated use context, or as a general disposition based on
the aggregated context.
[0078] At 910, method 900 can compare content of the electronic
message with the user predispositions. At 912, method 900 can
provide feedback based on the foregoing comparison. The feedback
can be distributed to the sender of the electronic message, to
recipients of such message, or to a managing entity responsible for
monitoring electronic messaging employed by the sender and
participants.
[0079] At 914, method 900 can obtain one or more disposition
thresholds and, at 916, one or more threshold actions matched to
the disposition thresholds. At 918, method 900 can monitor use
context of recipients of the message. The monitoring can comprise,
for instance, determining a sentiment of the electronic message,
based on the message content, compared with the predispositions of
the recipients. At 920, method 900 can monitor content of message
spawns comprising a chain of messages related to the electronic
message. At 922, method 900 can determine an overall disposition of
the messaging chain. Furthermore, at 924, method 900 can compare
the overall disposition of the messaging chain, and/or a
disposition of the electronic message, to the disposition
thresholds. At 926, method 900 can implement one or more actions
based on the comparison. For instance, if the disposition of the
message chain exceeds an allowable sentiment threshold, the message
chain can be prevented from further dissemination. Alternatively,
or in addition, if the message chain meets an interest threshold
pertinent to a group of users, the message can be distributed to
additional recipients not included in the message chain. At 928, a
provider of the disposition thresholds and threshold actions can be
updated based on the results of the foregoing comparison, and based
on any threshold actions taken by method 900. Thus, method 900 can
act as an electronic monitor or chaperone for a messaging platform,
determining message sentiment and taking predetermined actions
based thereon.
[0080] Referring now to FIG. 10, there is illustrated a block
diagram of an exemplary computer system operable to execute the
disclosed architecture. In order to provide additional context for
various aspects of the claimed subject matter, FIG. 10 and the
following discussion are intended to provide a brief, general
description of a suitable computing environment 1000 in which the
various aspects of the claimed subject matter can be implemented.
Additionally, while the claimed subject matter described above can
be suitable for application in the general context of
computer-executable instructions that can run on one or more
computers, those skilled in the art will recognize that the claimed
subject matter also can be implemented in combination with other
program modules and/or as a combination of hardware and
software.
[0081] Generally, program modules include routines, programs,
components, data structures, etc., that perform particular tasks or
implement particular abstract data types. Moreover, those skilled
in the art will appreciate that the inventive methods can be
practiced with other computer system configurations, including
single-processor or multiprocessor computer systems, minicomputers,
mainframe computers, as well as personal computers, hand-held
computing devices, microprocessor-based or programmable consumer
electronics, and the like, each of which can be operatively coupled
to one or more associated devices.
[0082] The illustrated aspects of the claimed subject matter can
also be practiced in distributed computing environments where
certain tasks are performed by remote processing devices that are
linked through a communications network. In a distributed computing
environment, program modules can be located in both local and
remote memory storage devices.
[0083] A computer typically includes a variety of computer-readable
media. Computer-readable media can be any available media that can
be accessed by the computer and includes both volatile and
nonvolatile media, removable and non-removable media. By way of
example, and not limitation, computer-readable media can comprise
computer storage media and communication media. Computer storage
media can include both volatile and nonvolatile, removable and
non-removable media implemented in any method or technology for
storage of information such as computer-readable instructions, data
structures, program modules or other data. Computer storage media
includes, but is not limited to, RAM, ROM, EEPROM, flash memory or
other memory technology, CD-ROM, digital versatile disk (DVD) or
other optical disk storage, magnetic cassettes, magnetic tape,
magnetic disk storage or other magnetic storage devices, or any
other medium which can be used to store the desired information and
which can be accessed by the computer.
[0084] Communication media typically embodies 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" means a signal that has one or more of its
characteristics set or changed in such a manner as to encode
information in the signal. By way of example, and not limitation,
communication media includes wired media such as a wired network or
direct-wired connection, and wireless media such as acoustic, RF,
infrared and other wireless media. Combinations of the any of the
above should also be included within the scope of computer-readable
media.
[0085] Continuing to reference FIG. 10, the exemplary environment
1000 for implementing various aspects of the claimed subject matter
includes a computer 1002, the computer 1002 including a processing
unit 1004, a system memory 1006 and a system bus 1008. The system
bus 1008 couples to system components including, but not limited
to, the system memory 1006 to the processing unit 1004. The
processing unit 1004 can be any of various commercially available
processors. Dual microprocessors and other multi-processor
architectures can also be employed as the processing unit 1004.
[0086] The system bus 1008 can be any of several types of bus
structure that can further interconnect to a memory bus (with or
without a memory controller), a peripheral bus, and a local bus
using any of a variety of commercially available bus architectures.
The system memory 1006 includes read-only memory (ROM) 1010 and
random access memory (RAM) 1012. A basic input/output system (BIOS)
is stored in a non-volatile memory 1010 such as ROM, EPROM, EEPROM,
which BIOS contains the basic routines that help to transfer
information between elements within the computer 1002, such as
during start-up. The RAM 1012 can also include a high-speed RAM
such as static RAM for caching data.
[0087] The computer 1002 further includes an internal hard disk
drive (HDD) 1014A (e.g., EIDE, SATA), which internal hard disk
drive 1014A can also be configured for external use (1014B) in a
suitable chassis (not shown), a magnetic floppy disk drive (FDD)
1016, (e.g., to read from or write to a removable diskette 1018)
and an optical disk drive 1020, (e.g., reading a CD-ROM disk 1022
or, to read from or write to other high capacity optical media such
as the DVD). The hard disk drive 1014, magnetic disk drive 1016 and
optical disk drive 1020 can be connected to the system bus 1008 by
a hard disk drive interface 1024, a magnetic disk drive interface
1026 and an optical drive interface 1028, respectively. The
interface 1024 for external drive implementations includes at least
one or both of Universal Serial Bus (USB) and IEEE1394 interface
technologies. Other external drive connection technologies are
within contemplation of the subject matter claimed herein.
[0088] The drives and their associated computer-readable media
provide nonvolatile storage of data, data structures,
computer-executable instructions, and so forth. For the computer
1002, the drives and media accommodate the storage of any data in a
suitable digital format. Although the description of
computer-readable media above refers to a HDD, a removable magnetic
diskette, and a removable optical media such as a CD or DVD, it
should be appreciated by those skilled in the art that other types
of media which are readable by a computer, such as zip drives,
magnetic cassettes, flash memory cards, cartridges, and the like,
can also be used in the exemplary operating environment, and
further, that any such media can contain computer-executable
instructions for performing the methods of the claimed subject
matter.
[0089] A number of program modules can be stored in the drives and
RAM 1012, including an operating system 1030, one or more
application programs 1032, other program modules 1034 and program
data 1036. All or portions of the operating system, applications,
modules, and/or data can also be cached in the RAM 1012. It is
appreciated that the claimed subject matter can be implemented with
various commercially available operating systems or combinations of
operating systems.
[0090] A user can enter commands and information into the computer
1002 through one or more wired/wireless input devices, e.g., a
keyboard 1038 and a pointing device, such as a mouse 1040. Other
input devices (not shown) can include a microphone, an IR remote
control, a joystick, a game pad, a stylus pen, touch screen, or the
like. These and other input devices are often connected to the
processing unit 1004 through an input device interface 1042 that is
coupled to the system bus 1008, but can be connected by other
interfaces, such as a parallel port, an IEEE1394 serial port, a
game port, a USB port, an IR interface, etc.
[0091] A monitor 1044 or other type of display device is also
connected to the system bus 1008 via an interface, such as a video
adapter 1046. In addition to the monitor 1044, a computer typically
includes other peripheral output devices (not shown), such as
speakers, printers, etc.
[0092] The computer 1002 can operate in a networked environment
using logical connections via wired and/or wireless communications
to one or more remote computers, such as a remote computer(s) 1048.
The remote computer(s) 1048 can be a workstation, a server
computer, a router, a personal computer, portable computer,
microprocessor-based entertainment appliance, a peer device or
other common network node, and typically includes many or all of
the elements described relative to the computer 1002, although, for
purposes of brevity, only a memory/storage device 1050 is
illustrated. The logical connections depicted include
wired/wireless connectivity to a local area network (LAN) 1052
and/or larger networks, e.g., a wide area network (WAN) 1054. Such
LAN and WAN networking environments are commonplace in offices and
companies, and facilitate enterprise-wide computer networks, such
as intranets, all of which can connect to a global communications
network, e.g., the Internet.
[0093] When used in a LAN networking environment, the computer 1002
is connected to the local network 1052 through a wired and/or
wireless communication network interface or adapter 1056. The
adapter 1056 can facilitate wired or wireless communication to the
LAN 1052, which can also include a wireless access point disposed
thereon for communicating with the wireless adapter 1056.
[0094] When used in a WAN networking environment, the computer 1002
can include a modem 1058, or is connected to a communications
server on the WAN 1054, or has other means for establishing
communications over the WAN 1054, such as by way of the Internet.
The modem 1058, which can be internal or external and a wired or
wireless device, is connected to the system bus 1008 via the serial
port interface 1042. In a networked environment, program modules
depicted relative to the computer 1002, or portions thereof, can be
stored in the remote memory/storage device 1050. It will be
appreciated that the network connections shown are exemplary and
other means of establishing a communications link between the
computers can be used.
[0095] The computer 1002 is operable to communicate with any
wireless devices or entities operatively disposed in wireless
communication, e.g., a printer, scanner, desktop and/or portable
computer, portable data assistant, communications satellite, any
piece of equipment or location associated with a wirelessly
detectable tag (e.g., a kiosk, news stand, restroom), and
telephone. This includes at least Wi-Fi and Bluetooth.TM. wireless
technologies. Thus, the communication can be a predefined structure
as with a conventional network or simply an ad hoc communication
between at least two devices.
[0096] Wi-Fi, or Wireless Fidelity, allows connection to the
Internet from a couch at home, a bed in a hotel room, or a
conference room at work, without wires. Wi-Fi is a wireless
technology similar to that used in a cell phone that enables such
devices, e.g., computers, to send and receive data indoors and out;
anywhere within the range of a base station. Wi-Fi networks use
radio technologies called IEEE802.11 (a, b, g, n, etc.) to provide
secure, reliable, fast wireless connectivity. A Wi-Fi network can
be used to connect computers to each other, to the Internet, and to
wired networks (which use IEEE802.3 or Ethernet). Wi-Fi networks
operate in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps
(802.11a) or 54 Mbps (802.11b) data rate, for example, or with
products that contain both bands (dual band), so the networks can
provide real-world performance similar to the basic 10BaseT wired
Ethernet networks used in many offices.
[0097] Referring now to FIG. 11, there is illustrated a schematic
block diagram of an exemplary computer compilation system operable
to execute the disclosed architecture. The system 1100 includes one
or more client(s) 1102. The client(s) 1102 can be hardware and/or
software (e.g., threads, processes, computing devices). The
client(s) 1102 can house cookie(s) and/or associated contextual
information by employing the claimed subject matter, for
example.
[0098] The system 1100 also includes one or more server(s) 1104.
The server(s) 1104 can also be hardware and/or software (e.g.,
threads, processes, computing devices). The servers 1104 can house
threads to perform transformations by employing the claimed subject
matter, for example. One possible communication between a client
1102 and a server 1104 can be in the form of a data packet adapted
to be transmitted between two or more computer processes. The data
packet can include a cookie and/or associated contextual
information, for example. The system 1100 includes a communication
framework 1106 (e.g., a global communication network such as the
Internet) that can be employed to facilitate communications between
the client(s) 1102 and the server(s) 1104.
[0099] Communications can be facilitated via a wired (including
optical fiber) and/or wireless technology. The client(s) 1102 are
operatively connected to one or more client data store(s) 1108 that
can be employed to store information local to the client(s) 1102
(e.g., cookie(s) and/or associated contextual information).
Similarly, the server(s) 1104 are operatively connected to one or
more server data store(s) 1110 that can be employed to store
information local to the servers 1104.
[0100] What has been described above includes examples of the
various embodiments. It is, of course, not possible to describe
every conceivable combination of components or methodologies for
purposes of describing the embodiments, but one of ordinary skill
in the art can recognize that many further combinations and
permutations are possible. Accordingly, the detailed description is
intended to embrace all such alterations, modifications, and
variations that fall within the spirit and scope of the appended
claims.
[0101] In particular and in regard to the various functions
performed by the above described components, devices, circuits,
systems and the like, the terms (including a reference to a
"means") used to describe such components are intended to
correspond, unless otherwise indicated, to any component which
performs the specified function of the described component (e.g., a
functional equivalent), even though not structurally equivalent to
the disclosed structure, which performs the function in the herein
illustrated exemplary aspects of the embodiments. In this regard,
it will also be recognized that the embodiments include a system as
well as a computer-readable medium having computer-executable
instructions for performing the acts and/or events of the various
methods.
[0102] In addition, while a particular feature may have been
disclosed with respect to only one of several implementations, such
feature can be combined with one or more other features of the
other implementations as may be desired and advantageous for any
given or particular application. Furthermore, to the extent that
the terms "includes," and "including" and variants thereof are used
in either the detailed description or the claims, these terms are
intended to be inclusive in a manner similar to the term
"comprising."
* * * * *