U.S. patent application number 17/736230 was filed with the patent office on 2022-08-18 for context-based notification processing system.
The applicant listed for this patent is Citrix Systems, Inc.. Invention is credited to Jian Ding, Hengbo Wang, Daowen Wei.
Application Number | 20220261300 17/736230 |
Document ID | / |
Family ID | |
Filed Date | 2022-08-18 |
United States Patent
Application |
20220261300 |
Kind Code |
A1 |
Wei; Daowen ; et
al. |
August 18, 2022 |
CONTEXT-BASED NOTIFICATION PROCESSING SYSTEM
Abstract
In some implementations, a method may involve determining first
feature vectors for a plurality of data items accessed by a user of
one or more client devices, the first feature vectors representing
first contextual data about the one or more client devices at times
that respective data items of the plurality of data items were
accessed, the plurality of data items including a first data item.
A predictive model, configured to classify input feature vectors
into context types, may be used to determine that the first feature
vector for the first data item is classified as a first context
type. A second feature vector representing second contextual data
about a first client device operated by the user may be determined
and the predictive model may be used to determined that the second
feature vector is classified as the first context type. Based at
least in part on the first and second feature vectors being
classified as the first context type and the first and second data
items being of a first data item type, the first client device may
be caused to present the second data item.
Inventors: |
Wei; Daowen; (Nanjing,
CN) ; Ding; Jian; (Nanjing, CN) ; Wang;
Hengbo; (Nanjing, CN) |
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Applicant: |
Name |
City |
State |
Country |
Type |
Citrix Systems, Inc. |
Fort Lauderdale |
FL |
US |
|
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Appl. No.: |
17/736230 |
Filed: |
May 4, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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17022570 |
Sep 16, 2020 |
11360830 |
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17736230 |
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PCT/CN2020/110931 |
Aug 25, 2020 |
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17022570 |
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International
Class: |
G06F 9/54 20060101
G06F009/54; G06F 16/9035 20060101 G06F016/9035; G06F 16/906
20060101 G06F016/906; G06Q 10/10 20060101 G06Q010/10 |
Claims
1. A method, comprising: generating, by a computing system, at
least first and second notifications to be sent to a client device
operated by a user, the first and second notifications indicating,
respectively, first and second events of first and second
applications accessible by the user; receiving, by the computing
system from the client device, first data indicative of a current
context of the client device; and sending, by the computing system
and based at least in part on the first data, the first
notification, but not the second notification, to the client
device.
2. The method of claim 1, wherein generating the first and second
notifications further comprises: causing the first and second
notifications to include respective user interface elements
enabling the user to take corresponding actions with respect to the
first and second applications.
3. The method of claim 1, wherein the first data comprises at least
one of an identifier of the client device, a current time, a
network to which the client device is connected, or a location of
the client device.
4. The method of claim 1, further comprising: prior to receiving
the first data, receiving, by the computing system, second data
indicative of one or more other notifications accessed by the user
operating one or more client devices, and a context of the one or
more client devices when the one or more other notifications were
accessed by the user; wherein sending the first notification, but
not the second notification, to the client device is further based
at least in part on the second data.
5. The method of claim 1, further comprising: receiving, from the
client device, a request for a context-based view of an activity
feed of notifications, the request including the first data;
wherein sending the first notification to the client device is
performed in response to the request.
6. The method of claim 1, further comprising: retrieving, by the
computing system, second data from the first application using
first access credentials associated with the user; determining, by
the computing system, that the second data is indicative of the
first event; retrieving, by the computing system, third data from
the second application using second access credentials associated
with the user; and determining, by the computing system, that the
third data is indicative of the second event.
7. The method of claim 6, wherein: the computing system retrieves
the second data from first application via a first application
programming interface (API) of the first application; and the
computing system retrieves the third data from second application
via a second API of the second application.
8. A system, comprising: at least one processor; and at least one
computer-readable medium encoded with instructions which, when
executed by the at least one processor, cause the system to:
generate at least first and second notifications to be sent to a
client device operated by a user, the first and second
notifications indicating, respectively, first and second events of
first and second applications accessible by the user, receive, from
the client device, first data indicative of a current context of
the client device, and send, based at least in part on the first
data, the first notification, but not the second notification, to
the client device.
9. The system of claim 8, wherein the at least one
computer-readable medium is further encoded with additional
instructions which, when executed by the at least one processor,
further cause the system to: cause the first and second
notifications to include respective user interface elements
enabling the user to take corresponding actions with respect to the
first and second applications.
10. The system of claim 8, wherein the first data comprises at
least one of an identifier of the client device, a current time, a
network to which the client device is connected, or a location of
the client device.
11. The system of claim 8, wherein the at least one
computer-readable medium is further encoded with additional
instructions which, when executed by the at least one processor,
further cause the system to: prior to receiving the first data,
receive second data indicative of one or more other notifications
accessed by the user operating one or more client devices, and a
context of the one or more client devices when the one or more
other notifications were accessed by the user; and send the first
notification, but not the second notification, to the client device
further based at least in part on the second data.
12. The system of claim 8, wherein the at least one
computer-readable medium is further encoded with additional
instructions which, when executed by the at least one processor,
further cause the system to: receive, from the client device, a
request for a context-based view of an activity feed of
notifications, the request including the first data; and send the
first notification to the client device in response to the
request.
13. The system of claim 8, wherein the at least one
computer-readable medium is further encoded with additional
instructions which, when executed by the at least one processor,
further cause the system to: retrieve second data from the first
application using first access credentials associated with the
user; determine that the second data is indicative of the first
event; retrieve third data from the second application using second
access credentials associated with the user; and determine that the
third data is indicative of the second event.
14. The system of claim 13, wherein the at least one
computer-readable medium is further encoded with additional
instructions which, when executed by the at least one processor,
further cause the system to: retrieve the second data from first
application via a first application programming interface (API) of
the first application; and retrieve the third data from second
application via a second API of the second application.
15. At least one non-transitory computer-readable medium encoded
with instructions which, when executed by at least one processor of
a system, cause the system to: generate at least first and second
notifications to be sent to a client device operated by a user, the
first and second notifications indicating, respectively, first and
second events of first and second applications accessible by the
user; receive, from the client device, first data indicative of a
current context of the client device; and send, based at least in
part on the first data, the first notification, but not the second
notification, to the client device.
16. The at least one non-transitory computer-readable medium of
claim 15, further encoded with additional instructions which, when
executed by the at least one processor, further cause the system
to: cause the first and second notifications to include respective
user interface elements enabling the user to take corresponding
actions with respect to the first and second applications.
17. The at least one non-transitory computer-readable medium of
claim 15, wherein the first data comprises at least one of an
identifier of the client device, a current time, a network to which
the client device is connected, or a location of the client
device.
18. The at least one non-transitory computer-readable medium of
claim 15, further encoded with additional instructions which, when
executed by the at least one processor, further cause the system
to: prior to receiving the first data, receive second data
indicative of one or more other notifications accessed by the user
operating one or more client devices, and a context of the one or
more client devices when the one or more other notifications were
accessed by the user; and send the first notification, but not the
second notification, to the client device further based at least in
part on the second data.
19. The at least one non-transitory computer-readable medium of
claim 15, further encoded with additional instructions which, when
executed by the at least one processor, further cause the system
to: retrieve second data from the first application using first
access credentials associated with the user; determine that the
second data is indicative of the first event; retrieve third data
from the second application using second access credentials
associated with the user; and determine that the third data is
indicative of the second event.
20. The at least one non-transitory computer-readable medium of
claim 19, further encoded with additional instructions which, when
executed by the at least one processor, further cause the system
to: retrieve the second data from first application via a first
application programming interface (API) of the first application;
and retrieve the third data from second application via a second
API of the second application.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of and claims the benefit
under 35 U.S.C. .sctn. 120 and 35 U.S.C. .sctn. 121 to U.S. patent
application Ser. No. 17/022,570, entitled CONTEXT-BASED
NOTIFICATION PROCESSING SYSTEM, filed Sep. 16, 2020, which is a
continuation of and claims the benefit under 35 U.S.C. .sctn. 120
and 35 U.S.C. .sctn. 365(c) to International Application
PCT/CN2020/110931, entitled CONTEXT-BASED NOTIFICATION PROCESSING
SYSTEM, with an international filing date of Aug. 25, 2020, the
entire contents of each which are incorporated herein by reference
for all purposes.
BACKGROUND
[0002] Various systems have been developed that allow client
devices to access applications and/or data files over a network.
Certain products offered by Citrix Systems, Inc., of Fort
Lauderdale, Fla., including the Citrix Workspace.TM. family of
products, provide such capabilities.
SUMMARY
[0003] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features, nor is it intended to limit the
scope of the claims included herewith.
[0004] In some of the disclosed embodiments, a method comprises
determining first feature vectors for a plurality of data items
accessed by a user of one or more client devices, the first feature
vectors representing first contextual data about the one or more
client devices at times that respective data items of the plurality
of data items were accessed, the plurality of data items including
a first data item; determining, using a predictive model configured
to classify input feature vectors into context types, that the
first feature vector for the first data item is classified as a
first context type; determining that the first data item is of a
first data item type; determining a second feature vector
representing second contextual data about a first client device
operated by the user; determining, using the predictive model, that
the second feature vector is classified as the first context type;
determining that a second data item is of the first data item type;
and causing, based at least in part on the first and second feature
vectors being classified as the first context type and the first
and second data items being of the first data item type, the first
client device to present the second data item.
[0005] In some disclosed embodiments a system comprises at least
one processor and at least one computer-readable medium encoded
with instructions which, when executed by the at least one
processor, cause the system to determine first feature vectors for
a plurality of data items accessed by a user of one or more client
devices, the first feature vectors representing first contextual
data about the one or more client devices at times that respective
data items of the plurality of data items were accessed, the
plurality of data items including a first data item, to determine,
using a predictive model configured to classify input feature
vectors into context types, that the first feature vector for the
first data item is classified as a first context type, to determine
that the first data item is of a first data item type, to determine
a second feature vector representing second contextual data about a
first client device operated by the user, to determine, using the
predictive model, that the second feature vector is classified as
the first context type, to determine that a second data item is of
the first data item type, and to cause, based at least in part on
the first and second feature vectors being classified as the first
context type and the first and second data items being of the first
data item type, the first client device to present the second
data.
[0006] In some disclose embodiments, a method comprises generating,
by a computing system, at least first and second notifications to
be sent to a client device operated by a user, the first and second
notifications indicating, respectively, first and second events of
first and second applications accessible by the user; receiving, by
the computing system from the client device, first data indicative
of a current context of the client device; and sending, by the
computing system and based at least in part on the first data, the
first notification, but not the second notification, to the client
device.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Objects, aspects, features, and advantages of embodiments
disclosed herein will become more fully apparent from the following
detailed description, the appended claims, and the accompanying
figures in which like reference numerals identify similar or
identical elements. Reference numerals that are introduced in the
specification in association with a figure may be repeated in one
or more subsequent figures without additional description in the
specification in order to provide context for other features, and
not every element may be labeled in every figure. The drawings are
not necessarily to scale, emphasis instead being placed upon
illustrating embodiments, principles and concepts. The drawings are
not intended to limit the scope of the claims included
herewith.
[0008] FIG. 1 shows a high-level example implementation of a
context-based notification processing system configured in
accordance with some aspects of the present disclosure;
[0009] FIG. 2 is a diagram of a network environment in which some
embodiments of the context-based notification processing system
disclosed herein may deployed;
[0010] FIG. 3 is a block diagram of a computing system that may be
used to implement one or more of the components of the computing
environment shown in FIG. 2 in accordance with some
embodiments;
[0011] FIG. 4 is a schematic block diagram of a cloud computing
environment in which various aspects of the disclosure may be
implemented;
[0012] FIG. 5A is a block diagram of an example system in which
resource management services may manage and streamline access by
clients to resource feeds (via one or more gateway services) and/or
software-as-a-service (SaaS) applications;
[0013] FIG. 5B is a block diagram showing an example implementation
of the system shown in FIG. 5A in which various resource management
services as well as a gateway service are located within a cloud
computing environment;
[0014] FIG. 5C is a block diagram similar to that shown in FIG. 5B
but in which the available resources are represented by a single
box labeled "systems of record," and further in which several
different services are included among the resource management
services;
[0015] FIG. 5D shows how a display screen may appear when an
intelligent activity feed feature of a multi-resource management
system, such as that shown in FIG. 5C, is employed;
[0016] FIG. 6 is a block diagram showing a more detailed example
implementation of the context-based notification processing system
shown in FIG. 1;
[0017] FIG. 7 shows an example routine that may be performed by the
notification access monitoring engine shown in FIG. 6;
[0018] FIG. 8 shows an example routine that may be performed by the
context determination engine shown in FIG. 6;
[0019] FIG. 9 shows an example routine that may be performed by the
notification data upload engine shown in FIG. 6;
[0020] FIG. 10 shows an example routine that may be performed by
the notification access monitoring service shown in FIG. 6;
[0021] FIG. 11 shows an example table that the notification access
monitoring service shown in FIG. 6 may populate with contextual
data for accessed notifications;
[0022] FIG. 12 shows an example routine that may be performed by
the context classifier training service shown in FIG. 6;
[0023] FIG. 13 shows an example technique that the context
classifier training service shown in FIG. 6 may use to train and/or
update a predictive model for use by the context-based notification
forecasting service and the context-based notification presentation
service;
[0024] FIG. 14 shows an example routine that may be performed by
the context-based notification forecasting service shown in FIG.
6;
[0025] FIG. 15 shows an example table that the context-based
notification forecasting service shown in FIG. 6 may populate with
user-specific context-based notification forecast scores;
[0026] FIG. 16 shows an example routine that may be performed by
the view determination engine shown in FIG. 6; and
[0027] FIG. 17 shows an example routine that may be performed by
the context-based notification presentation service shown in FIG.
6.
DETAILED DESCRIPTION
[0028] For purposes of reading the description of the various
embodiments below, the following descriptions of the sections of
the specification and their respective contents may be helpful:
[0029] Section A provides an introduction to example embodiments of
a context-based notification processing system;
[0030] Section B describes a network environment which may be
useful for practicing embodiments described herein;
[0031] Section C describes a computing system which may be useful
for practicing embodiments described herein;
[0032] Section D describes embodiments of systems and methods for
accessing computing resources using a cloud computing
environment;
[0033] Section E describes embodiments of systems and methods for
managing and streamlining access by clients to a variety of
resources;
[0034] Section F provides a more detailed description of example
embodiments of context-based notification processing system that
was introduced above in Section A;
[0035] Section G describes example implementations of methods,
systems/devices, and computer-readable media in accordance with the
present disclosure.
A. Introduction to Illustrative Embodiments of a Context-Based
Notification Processing System
[0036] An intelligent activity feed, such as that offered by the
Citrix Workspace.TM. family of products, provides significant
benefits, as it allows a user to respond to application-specific
events generated by disparate systems of record, without requiring
the user to switch context and separately launch the respective
applications to take actions with respect to the different events.
An example of a system capable of providing such an activity feed
is described in Section E below in connection with FIGS. 5A-D. In
such a system, a remote computing system may be responsible for
monitoring and interacting with various systems of record (e.g.,
SaaS applications, web applications, Windows applications, Linux
applications, desktops, file repositories and/or file sharing
systems, etc.) on behalf of a user operating a client device. As
Section E describes (in connection with FIGS. 5C and 5D), a user
524 may operate a client device 202 so as to interact with
"microapps" corresponding to particular functionalities of a
variety of systems of record 526, and such microapps may, in turn,
interact with the systems of record 526, e.g., via application
programming interfaces (APIs) of such systems, on behalf of the
user 524.
[0037] More specifically, and as described in more detail in
Section E, a microapp service 528 (shown in FIG. 5C) may
periodically request a sync with a data integration provider
service 530, so as to cause active data to be pulled from the
systems of record 526. In some implementations, for example, the
microapp service 528 may retrieve encrypted service account
credentials for the systems of record 526 from a credential wallet
service 532 and request a sync with the data integration provider
service 530. The data integration provider service 530 may then
decrypt the service account credentials and use those credentials
to retrieve data from the systems of record 526. The data
integration provider service 530 may then stream the retrieved data
to the microapp service 528. The microapp service 528 may store the
received systems of record data in the active data cache service
534 and also send raw events to an analytics service 536 for
processing. The analytics service 536 may create notifications
(e.g., targeted scored notifications) and send such notifications
to the notification service 538. The notification service 538 may
store the notifications in a database to be later served in an
activity feed and/or may send the notifications out immediately to
the client 202 as a push notification to the user 524.
[0038] FIG. 5D, which is also described in more detail in Section
E, shows how a display screen 540 presented by a resource access
application 522 (shown in FIG. 5C) may appear when an intelligent
activity feed feature is employed and a user 524 is logged on to
the system. As shown in FIG. 5D, an activity feed 544 may be
presented on the display screen 540 that includes a plurality of
notifications 546 about respective events that occurred within
various applications to which the user 524 has access rights. As
described below (in connection with FIG. 5D), in some
implementations, the notifications 546 may be sorted and/or
filtered in various ways to improve the accessibility of the
notifications 546 to the user 524. For example, as shown in FIG.
5D, in some implementations, the user may select a "date and time"
mode (see element 570) in which the notifications 546 may be sorted
by timestamps indicating when the notifications 546 were created.
Further, although not illustrated in FIG. 5D, in some
implementations, the user 524 may additionally or alternatively
select a "relevancy" mode (not illustrated), e.g., using the
element 570, in which the notifications may be sorted based on
relevancy scores assigned to them by the analytics service 536,
and/or may select an "application" mode (also not illustrated),
e.g., using the element 570, in which the notifications 546 may be
sorted by application type.
[0039] The inventors have recognized and appreciated that even
using the available filtering and/or sorting mechanisms, some users
may have difficulty locating the notifications that they actually
want to access to in a given contextual situation. For example,
some users may tend to complete certain tasks not requiring desk
space or other computing resources, such as responding to paid time
off (PTO) request notifications 546, using their smartphones and/or
while on a commuter train. Or some users may tend to review certain
"announcement" notifications 546, such as new hire announcements,
promotion announcements, announcements concerning company events,
etc., using their laptop computers and/or while on their lunch
breaks. Still other users may prefer to address notifications 546
relating to certain technical, business, or financial applications,
such as Jira, Confluence, or Salesforce, using their desktop
computers and/or while at their desks.
[0040] Offered is a system that can take a user's historical
behavior patterns with respect to accessing notifications 546 (or
other data items) in particular contextual situations into account
when determining how to present new notifications 546 (or other
data items) in a current contextual scenario. With reference to
FIGS. 5C and 5D, for example, in some implementations, an
additional option may be presented for the element 568 and/or the
element 570 that allows the user 524 to filter and/or sort
notifications based on the current "context" of the client device
202 the user 524 is operating. For example, in some
implementations, the user may choose "context-based" as a sorting
option using the element 570 shown in FIG. 5D.
[0041] An example implementation of a context-based notification
processing system 100 configured in accordance with some aspects of
the present disclosure is shown in FIG. 1. As shown, the system 100
may include one or more servers 204 (examples of which are
described below) as well as one or more databases or other storage
mediums 104 that are accessible by the server(s) 204. In some
implementations, as indicated by the arrow 106, the system 100 may
monitor a given user's interactions with the notifications 546 in
an activity feed 544 using one or more client devices 202 (examples
of which are also described below). For example, each time a user
clicks on or otherwise accesses a notification 546, the system 100
may create a record of that access in the storage medium(s) 104. In
some implementations, such records may include user identifiers
(IDs) as well as notification type IDs that indicate the types of
notifications 546 that were accessed, such as an expense report
approval request for Concur, a contract approval request for
Salesforce, etc. The system 100 may also determine and record
various pieces of contextual data concerning the client device(s)
202 at the time such notifications 546 are accessed. Examples of
such contextual data that may be so determined and recorded include
(A) a device ID identifying the particular client device 202 used
to access the notification 546, (B) the date and/or time the client
device 202 was used to access the notification 546, (C) a network
ID identifying the network to which the client device 202 was
connected at the time the notification 546 was accessed, (D) a
location (e.g., latitude and longitude) of the client device 202 at
the time it was used to access the notification 546.
[0042] As show in FIG. 1, once a sufficient amount of contextual
data has been accumulated for various notification access events by
a user 524, the system 100 may convert the contextual data for
respective notifications 546 into feature vectors 108 (e.g., using
one or more encoders--not shown in FIG. 1), with the contextual
data of each notification 546 being represented by a respective
multi-dimensional feature vector 108. The system 100 may then
provide those feature vectors 108 to a machine learning process
110. As illustrated, in some implementations, the machine learning
process 110 may perform an unsupervised machine learning technique
to identify clusters of data points in a multi-dimensional space.
The dimensions of a given feature vector 108 in the
multi-dimensional space may, for example, correspond to the
respective pieces of contextual data that were determined for a
particular notification access event.
[0043] As indicated by the arrow 112, the machine learning process
110 may be used to train a predictive model 114 to categorize
respective input feature vectors 116 into one of the clusters that
were identified using the clustering technique. Once it is properly
trained, the predictive model 114 may be used to assign labels,
referred to herein as "context tags," to the notification access
event records stored in the storage medium(s) 104. In particular,
for respective ones of the notification access event records, the
stored contextual information for the record may be converted into
a feature vector 116, e.g., using one or more encoders, that is
then provided to the predictive model 114 for classification into a
particular cluster. As illustrated in FIG. 1, the predictive model
114 may, for example, output context tags 118 corresponding to the
clusters into which it classifies the input feature vectors
116.
[0044] In some implementations, the system 100 may periodically
(e.g., once per day) evaluate at least some of the recorded
notification access event records, including the context tags 118
applied to them by the predictive model 114, to determine
"context-based notification forecast scores" for the possible
combinations of notification types and context tags that are
reflected in the evaluated data sets for respective users 524. In
some implementations, for example, the system 100 may use the
recorded notification access event records for a set period of time
(e.g., the last 20 days) to determine the context-based
notification forecast scores for respective users 524.
[0045] FIG. 1 shows an example table 120 that may be used to record
the determined context-based notification forecast scores for a
particular user 524. Although the illustrated example shows only
four possible combinations of notification type IDs and context
tags, it should be appreciated that, in practice, many more such
combinations are likely to occur in the evaluated data set. In some
implementations, the respective context-based notification forecast
scores may simply reflect, for the data set being considered, the
total number of notifications of a particular type (e.g., that have
a particular notification type ID) that have a particular context
tag. For example, an entry 122 in the table 120 may reflect that,
in the data set under consideration, context tag "A" was assigned
to a total of "23" records that included "Type A" as the
notification type ID. In other implementations, different weights
may be applied to different records when determining the
context-based notification forecast scores. For example, if records
for the last "X" days are being evaluated, lower weights may be
applied to older records, so that the more recent records influence
the context-based notification forecast scores more than the less
recent ones. In some implementations, for example, an exponential
moving average (e.g., a first-order infinite response filter that
applies weighting factors that decrease exponentially) may be
applied to weight the different records differently.
[0046] As indicated by an arrow 124 in FIG. 1, after the table 120
has been populated, the system 100 may receive current contextual
data from a client device 202 operated by the user 524 for whom the
table 120 was generated. In some implementations, for example, the
user 524 may have manipulated the user interface element 570 (shown
in FIG. 5D) to select "context-based" as the sorting option for the
activity feed 544, as discussed above. In response to detecting
such a selection, the client device 202 may gather the current
contextual data of the client device 202 and send it to the
server(s) 204 for processing along with a request for a
"context-based" view of the activity feed 544. Similar to the
contextual data that the system 100 accumulated during the
notification access monitoring process discussed above, examples of
current contextual data that may gathered and sent along with a
request for a context-based view of the activity feed 544 include
(A) a device ID identifying the client device 202 sending the
request, (B) the current date and/or time, (C) a network ID
identifying the network to which the client device 202 is currently
connected, and (D) a current location (e.g., latitude and
longitude) of the client device 202.
[0047] Upon receiving the current contextual data from the client
device 202 (e.g., per the arrow 124), the system 100 may encode the
contextual data into a context feature vector 116 and provide that
context feature vector 116 to the predictive model 114 for
determination of a context tag 118. After the context tag 118 has
been determined for the current contextual information, the table
120 may be consulted to determine, based on that determined context
tag 118, one or more notification types that are to be included in
the requested context-based view of the activity feed 544.
[0048] In some implementations, the notification types (e.g., as
indicated by the notification type IDs in the table 120) that (A)
have the same context tag as the current contextual data, and (B)
have higher than a threshold context-based notification forecast
score, may be selected as the notification types that are to be
included in the requested context-based view of the activity feed
544. For example, for the context-based notification forecast
scores shown in the table 120, if the threshold score was "2" and
the current contextual data was assigned context tag "A," then
"Type A" notifications but not "Type B" notifications would be
selected as the notification types that are to be included in the
requested context-based view. As another example, for the
context-based notification forecast scores shown in the table 120,
if the threshold score was "2" and the current contextual data was
assigned context tag "B," then both "Type A" notifications and
"Type B" notifications would be selected as the notification types
that are to be included in the requested context-based view.
[0049] After the pertinent notification type(s) for the user 524
requesting the context-based view of the activity feed 544 have
been determined, e.g., based on the entries in the table 120, the
system 100 may identify the notifications 546 of the determined
type(s) that are currently pending for the user 524, and may
construct an activity feed 544 that includes those notifications
546. In some implementations, the context-based notification
forecast scores may further be used, either by themselves or
together with other scores (e.g., relevance scores assigned by the
analytics service 536) to determine the order in which the
identified notifications 546 appear in the context-based view of
the activity feed 544. For example, the identified notifications
546 having notification type IDs with higher context-based
notification forecast scores may, in at least some circumstances,
be caused to appear earlier in the activity feed 544 than those
having notification type IDs with lower context-based notification
forecast scores.
[0050] Further, in some implementations, rather than presenting a
separate, context-based activity feed 544 that includes only
notifications 546 having notification type IDs that match
notification type IDs appearing in the table 120, the notification
type IDs in the table 120, and/or the context-based notification
forecast scores determined for those notification type IDs, may
additionally or alternatively be used to enhance the "relevance"
scores for some or all of the active notifications 546 in a user's
activity feed 544. In some implementations, for example, a
weighting value may be applied to relevance scores, e.g., as
determined by the analytics service 536 described below, based on
whether pending notifications 546 appear in the table 120 and/or
the context-based notification forecast scores that were determined
for those notification type IDs. Accordingly, the context-based
notification forecast scores may additionally or alternatively be
used to influence the order in which notifications 546 appear in a
user's activity feed 544 when the user selects the "relevance"
sorting option, e.g., via the user-interface element 570 shown in
FIG. 5D.
[0051] FIG. 1 further shows an example routine 128 that may be
executed by the server(s) 204 of the system to perform
context-based processing of data items in accordance with some
embodiments. As shown, at a step 130 of the routine 128, the
server(s) 204 may determine first feature vectors 108 (e.g., the
monitored context feature vectors 108) for a plurality of data
items (e.g., notifications 546) accessed by a user of one or more
client devices (e.g., the client device 202). As indicated, the
first feature vectors may represent contextual data (e.g., included
in one or more messages indicated by the arrow 106) about the one
or more client devices (e.g., the client device 202) at times that
respective data items (e.g., notifications 546) of the plurality of
data items (which include a first data item) were accessed.
[0052] At a step 132 of the routine 128, the server(s) 204 may use
a predictive model (e.g., the predictive model 114) to determine
that the first feature vector for the first data item (e.g., a
notification 546) is classified as a first context type (e.g., has
been assigned a particular context tag by the predictive model
114).
[0053] At a step 134 of the routine 128, the server(s) 204 may
determine that the first data item (e.g., a notification 546) is of
a first data item type, e.g., is a particular type of notification,
such as a PTO approval request, an expense report approval request,
a new hire announcement, etc.
[0054] At a step 136 of the routine 128, the server(s) 204 may
determine a second feature vector (e.g., a current context feature
vector 116) representing contextual information about a first
client device (e.g., the client device 202) operated by the user.
The second feature vector may, for example, have been determined
based on the current contextual data included in a message
indicated by the arrow 124.
[0055] At a step 138 of the routine 128, the server(s) 204 may
determine, using the predictive model (e.g., the predictive model
114), that the second feature vector is classified as the first
context type (e.g., has been assigned the same context tag as the
first feature vector).
[0056] At a step 140 of the routine 128, the server(s) 204 may
determine that a second data item (e.g., a pending notification 546
for the user) is of the first data item type.
[0057] At a step 142 of the routine 128, the server(s) 204 may
cause the first client device (e.g., the client device 202) to
present the second data item (e.g., a notification). As indicated,
the presentation of the second data item may be based at least in
part on the first and second feature vectors being classified as
the first context type (e.g., being assigned the same context tag)
and the first and second data items being of the first data item
type.
[0058] Additional details and example implementations of
embodiments of the present disclosure are set forth below in
Section F, following a description of example systems and network
environments in which such embodiments may be deployed.
B. Network Environment
[0059] Referring to FIG. 2, an illustrative network environment 200
is depicted. As shown, the network environment 200 may include one
or more clients 202(1)-202(n) (also generally referred to as local
machine(s) 202 or client(s) 202) in communication with one or more
servers 204(1)-204(n) (also generally referred to as remote
machine(s) 204 or server(s) 204) via one or more networks
206(1)-206(n) (generally referred to as network(s) 206). In some
embodiments, a client 202 may communicate with a server 204 via one
or more appliances 208(1)-208(n) (generally referred to as
appliance(s) 208 or gateway(s) 208). In some embodiments, a client
202 may have the capacity to function as both a client node seeking
access to resources provided by a server 204 and as a server 204
providing access to hosted resources for other clients 202.
[0060] Although the embodiment shown in FIG. 2 shows one or more
networks 206 between the clients 202 and the servers 204, in other
embodiments, the clients 202 and the servers 204 may be on the same
network 206. When multiple networks 206 are employed, the various
networks 206 may be the same type of network or different types of
networks. For example, in some embodiments, the networks 206(1) and
206(n) may be private networks such as local area network (LANs) or
company Intranets, while the network 206(2) may be a public
network, such as a metropolitan area network (MAN), wide area
network (WAN), or the Internet. In other embodiments, one or both
of the network 206(1) and the network 206(n), as well as the
network 206(2), may be public networks. In yet other embodiments,
all three of the network 206(1), the network 206(2) and the network
206(n) may be private networks. The networks 206 may employ one or
more types of physical networks and/or network topologies, such as
wired and/or wireless networks, and may employ one or more
communication transport protocols, such as transmission control
protocol (TCP), internet protocol (IP), user datagram protocol
(UDP) or other similar protocols. In some embodiments, the
network(s) 206 may include one or more mobile telephone networks
that use various protocols to communicate among mobile devices. In
some embodiments, the network(s) 206 may include one or more
wireless local-area networks (WLANs). For short range
communications within a WLAN, clients 202 may communicate using
802.11, Bluetooth, and/or Near Field Communication (NFC).
[0061] As shown in FIG. 2, one or more appliances 208 may be
located at various points or in various communication paths of the
network environment 200. For example, the appliance 208(1) may be
deployed between the network 206(1) and the network 206(2), and the
appliance 208(n) may be deployed between the network 206(2) and the
network 206(n). In some embodiments, the appliances 208 may
communicate with one another and work in conjunction to, for
example, accelerate network traffic between the clients 202 and the
servers 204. In some embodiments, appliances 208 may act as a
gateway between two or more networks. In other embodiments, one or
more of the appliances 208 may instead be implemented in
conjunction with or as part of a single one of the clients 202 or
servers 204 to allow such device to connect directly to one of the
networks 206. In some embodiments, one of more appliances 208 may
operate as an application delivery controller (ADC) to provide one
or more of the clients 202 with access to business applications and
other data deployed in a datacenter, the cloud, or delivered as
Software as a Service (SaaS) across a range of client devices,
and/or provide other functionality such as load balancing, etc. In
some embodiments, one or more of the appliances 208 may be
implemented as network devices sold by Citrix Systems, Inc., of
Fort Lauderdale, Fla., such as Citrix Gateway.TM. or Citrix
ADC.TM..
[0062] A server 204 may be any server type such as, for example: a
file server; an application server; a web server; a proxy server;
an appliance; a network appliance; a gateway; an application
gateway; a gateway server; a virtualization server; a deployment
server; a Secure Sockets Layer Virtual Private Network (SSL VPN)
server; a firewall; a web server; a server executing an active
directory; a cloud server; or a server executing an application
acceleration program that provides firewall functionality,
application functionality, or load balancing functionality.
[0063] A server 204 may execute, operate or otherwise provide an
application that may be any one of the following: software; a
program; executable instructions; a virtual machine; a hypervisor;
a web browser; a web-based client; a client-server application; a
thin-client computing client; an ActiveX control; a Java applet;
software related to voice over internet protocol (VoIP)
communications like a soft IP telephone; an application for
streaming video and/or audio; an application for facilitating
real-time-data communications; a HTTP client; a FTP client; an
Oscar client; a Telnet client; or any other set of executable
instructions.
[0064] In some embodiments, a server 204 may execute a remote
presentation services program or other program that uses a
thin-client or a remote-display protocol to capture display output
generated by an application executing on a server 204 and transmit
the application display output to a client device 202.
[0065] In yet other embodiments, a server 204 may execute a virtual
machine providing, to a user of a client 202, access to a computing
environment. The client 202 may be a virtual machine. The virtual
machine may be managed by, for example, a hypervisor, a virtual
machine manager (VMM), or any other hardware virtualization
technique within the server 204.
[0066] As shown in FIG. 2, in some embodiments, groups of the
servers 204 may operate as one or more server farms 210. The
servers 204 of such server farms 210 may be logically grouped, and
may either be geographically co-located (e.g., on premises) or
geographically dispersed (e.g., cloud based) from the clients 202
and/or other servers 204. In some embodiments, two or more server
farms 210 may communicate with one another, e.g., via respective
appliances 208 connected to the network 206(2), to allow multiple
server-based processes to interact with one another.
[0067] As also shown in FIG. 2, in some embodiments, one or more of
the appliances 208 may include, be replaced by, or be in
communication with, one or more additional appliances, such as WAN
optimization appliances 212(1)-212(n), referred to generally as WAN
optimization appliance(s) 212. For example, WAN optimization
appliances 212 may accelerate, cache, compress or otherwise
optimize or improve performance, operation, flow control, or
quality of service of network traffic, such as traffic to and/or
from a WAN connection, such as optimizing Wide Area File Services
(WAFS), accelerating Server Message Block (SMB) or Common Internet
File System (CIFS). In some embodiments, one or more of the
appliances 212 may be a performance enhancing proxy or a WAN
optimization controller.
[0068] In some embodiments, one or more of the appliances 208, 212
may be implemented as products sold by Citrix Systems, Inc., of
Fort Lauderdale, Fla., such as Citrix SD-WAN.TM. or Citrix
Cloud.TM.. For example, in some implementations, one or more of the
appliances 208, 212 may be cloud connectors that enable
communications to be exchanged between resources within a cloud
computing environment and resources outside such an environment,
e.g., resources hosted within a data center of+ an
organization.
C. Computing Environment
[0069] FIG. 3 illustrates an example of a computing system 300 that
may be used to implement one or more of the respective components
(e.g., the clients 202, the servers 204, the appliances 208, 212)
within the network environment 200 shown in FIG. 2. As shown in
FIG. 3, the computing system 300 may include one or more processors
302, volatile memory 304 (e.g., RAM), non-volatile memory 306
(e.g., one or more hard disk drives (HDDs) or other magnetic or
optical storage media, one or more solid state drives (SSDs) such
as a flash drive or other solid state storage media, one or more
hybrid magnetic and solid state drives, and/or one or more virtual
storage volumes, such as a cloud storage, or a combination of such
physical storage volumes and virtual storage volumes or arrays
thereof), a user interface (UI) 308, one or more communications
interfaces 310, and a communication bus 312. The user interface 308
may include a graphical user interface (GUI) 314 (e.g., a
touchscreen, a display, etc.) and one or more input/output (I/O)
devices 316 (e.g., a mouse, a keyboard, etc.). The non-volatile
memory 306 may store an operating system 318, one or more
applications 320, and data 322 such that, for example, computer
instructions of the operating system 318 and/or applications 320
are executed by the processor(s) 302 out of the volatile memory
304. Data may be entered using an input device of the GUI 314 or
received from I/O device(s) 316. Various elements of the computing
system 300 may communicate via communication the bus 312. The
computing system 300 as shown in FIG. 3 is shown merely as an
example, as the clients 202, servers 204 and/or appliances 208 and
212 may be implemented by any computing or processing environment
and with any type of machine or set of machines that may have
suitable hardware and/or software capable of operating as described
herein.
[0070] The processor(s) 302 may be implemented by one or more
programmable processors executing one or more computer programs to
perform the functions of the system. As used herein, the term
"processor" describes an electronic circuit that performs a
function, an operation, or a sequence of operations. The function,
operation, or sequence of operations may be hard coded into the
electronic circuit or soft coded by way of instructions held in a
memory device. A "processor" may perform the function, operation,
or sequence of operations using digital values or using analog
signals. In some embodiments, the "processor" can be embodied in
one or more application specific integrated circuits (ASICs),
microprocessors, digital signal processors, microcontrollers, field
programmable gate arrays (FPGAs), programmable logic arrays (PLAs),
multi-core processors, or general-purpose computers with associated
memory. The "processor" may be analog, digital or mixed-signal. In
some embodiments, the "processor" may be one or more physical
processors or one or more "virtual" (e.g., remotely located or
"cloud") processors.
[0071] The communications interfaces 310 may include one or more
interfaces to enable the computing system 300 to access a computer
network such as a Local Area Network (LAN), a Wide Area Network
(WAN), a Personal Area Network (PAN), or the Internet through a
variety of wired and/or wireless connections, including cellular
connections.
[0072] As noted above, in some embodiments, one or more computing
systems 300 may execute an application on behalf of a user of a
client computing device (e.g., a client 202 shown in FIG. 2), may
execute a virtual machine, which provides an execution session
within which applications execute on behalf of a user or a client
computing device (e.g., a client 202 shown in FIG. 2), such as a
hosted desktop session, may execute a terminal services session to
provide a hosted desktop environment, or may provide access to a
computing environment including one or more of: one or more
applications, one or more desktop applications, and one or more
desktop sessions in which one or more applications may execute.
D. Systems and Methods for Delivering Shared Resources Using a
Cloud Computing Environment
[0073] Referring to FIG. 4, a cloud computing environment 400 is
depicted, which may also be referred to as a cloud environment,
cloud computing or cloud network. The cloud computing environment
400 can provide the delivery of shared computing services and/or
resources to multiple users or tenants. For example, the shared
resources and services can include, but are not limited to,
networks, network bandwidth, servers, processing, memory, storage,
applications, virtual machines, databases, software, hardware,
analytics, and intelligence.
[0074] In the cloud computing environment 400, one or more clients
202 (such as those described in connection with FIG. 2) are in
communication with a cloud network 404. The cloud network 404 may
include back-end platforms, e.g., servers, storage, server farms
and/or data centers. The clients 202 may correspond to a single
organization/tenant or multiple organizations/tenants. More
particularly, in one example implementation, the cloud computing
environment 400 may provide a private cloud serving a single
organization (e.g., enterprise cloud). In another example, the
cloud computing environment 400 may provide a community or public
cloud serving multiple organizations/tenants.
[0075] In some embodiments, a gateway appliance(s) or service may
be utilized to provide access to cloud computing resources and
virtual sessions. By way of example, Citrix Gateway, provided by
Citrix Systems, Inc., may be deployed on-premises or on public
clouds to provide users with secure access and single sign-on to
virtual, SaaS and web applications. Furthermore, to protect users
from web threats, a gateway such as Citrix Secure Web Gateway may
be used. Citrix Secure Web Gateway uses a cloud-based service and a
local cache to check for URL reputation and category.
[0076] In still further embodiments, the cloud computing
environment 400 may provide a hybrid cloud that is a combination of
a public cloud and one or more resources located outside such a
cloud, such as resources hosted within one or more data centers of
an organization. Public clouds may include public servers that are
maintained by third parties to the clients 202 or the
enterprise/tenant. The servers may be located off-site in remote
geographical locations or otherwise. In some implementations, one
or more cloud connectors may be used to facilitate the exchange of
communications between one more resources within the cloud
computing environment 400 and one or more resources outside of such
an environment.
[0077] The cloud computing environment 400 can provide resource
pooling to serve multiple users via clients 202 through a
multi-tenant environment or multi-tenant model with different
physical and virtual resources dynamically assigned and reassigned
responsive to different demands within the respective environment.
The multi-tenant environment can include a system or architecture
that can provide a single instance of software, an application or a
software application to serve multiple users. In some embodiments,
the cloud computing environment 400 can provide on-demand
self-service to unilaterally provision computing capabilities
(e.g., server time, network storage) across a network for multiple
clients 202. By way of example, provisioning services may be
provided through a system such as Citrix Provisioning Services
(Citrix PVS). Citrix PVS is a software-streaming technology that
delivers patches, updates, and other configuration information to
multiple virtual desktop endpoints through a shared desktop image.
The cloud computing environment 400 can provide an elasticity to
dynamically scale out or scale in response to different demands
from one or more clients 202. In some embodiments, the cloud
computing environment 400 may include or provide monitoring
services to monitor, control and/or generate reports corresponding
to the provided shared services and resources.
[0078] In some embodiments, the cloud computing environment 400 may
provide cloud-based delivery of different types of cloud computing
services, such as Software as a service (SaaS) 402, Platform as a
Service (PaaS) 404, Infrastructure as a Service (IaaS) 406, and
Desktop as a Service (DaaS) 408, for example. IaaS may refer to a
user renting the use of infrastructure resources that are needed
during a specified time period. IaaS providers may offer storage,
networking, servers or virtualization resources from large pools,
allowing the users to quickly scale up by accessing more resources
as needed. Examples of IaaS include AMAZON WEB SERVICES provided by
Amazon.com, Inc., of Seattle, Wash., RACKSPACE CLOUD provided by
Rackspace US, Inc., of San Antonio, Tex., Google Compute Engine
provided by Google Inc. of Mountain View, Calif., or RIGHTSCALE
provided by RightScale, Inc., of Santa Barbara, Calif.
[0079] PaaS providers may offer functionality provided by IaaS,
including, e.g., storage, networking, servers or virtualization, as
well as additional resources such as, e.g., the operating system,
middleware, or runtime resources. Examples of PaaS include WINDOWS
AZURE provided by Microsoft Corporation of Redmond, Wash., Google
App Engine provided by Google Inc., and HEROKU provided by Heroku,
Inc. of San Francisco, Calif.
[0080] SaaS providers may offer the resources that PaaS provides,
including storage, networking, servers, virtualization, operating
system, middleware, or runtime resources. In some embodiments, SaaS
providers may offer additional resources including, e.g., data and
application resources. Examples of SaaS include GOOGLE APPS
provided by Google Inc., SALESFORCE provided by Salesforce.com Inc.
of San Francisco, Calif., or OFFICE 365 provided by Microsoft
Corporation. Examples of SaaS may also include data storage
providers, e.g. Citrix ShareFile from Citrix Systems, DROPBOX
provided by Dropbox, Inc. of San Francisco, Calif., Microsoft
SKYDRIVE provided by Microsoft Corporation, Google Drive provided
by Google Inc., or Apple ICLOUD provided by Apple Inc. of
Cupertino, Calif.
[0081] Similar to SaaS, DaaS (which is also known as hosted desktop
services) is a form of virtual desktop infrastructure (VDI) in
which virtual desktop sessions are typically delivered as a cloud
service along with the apps used on the virtual desktop. Citrix
Cloud from Citrix Systems is one example of a DaaS delivery
platform. DaaS delivery platforms may be hosted on a public cloud
computing infrastructure, such as AZURE CLOUD from Microsoft
Corporation of Redmond, Wash., or AMAZON WEB SERVICES provided by
Amazon.com, Inc., of Seattle, Wash., for example. In the case of
Citrix Cloud, Citrix Workspace app may be used as a single-entry
point for bringing apps, files and desktops together (whether
on-premises or in the cloud) to deliver a unified experience.
E. Systems and Methods for Managing and Streamlining Access by
Client Devices to a Variety of Resources
[0082] FIG. 5A is a block diagram of an example multi-resource
access system 500 in which one or more resource management services
502 may manage and streamline access by one or more clients 202 to
one or more resource feeds 504 (via one or more gateway services
506) and/or one or more software-as-a-service (SaaS) applications
508. In particular, the resource management service(s) 502 may
employ an identity provider 510 to authenticate the identity of a
user of a client 202 and, following authentication, identify one or
more resources the user is authorized to access. In response to the
user selecting one of the identified resources, the resource
management service(s) 502 may send appropriate access credentials
to the requesting client 202, and the client 202 may then use those
credentials to access the selected resource. For the resource
feed(s) 504, the client 202 may use the supplied credentials to
access the selected resource via a gateway service 506. For the
SaaS application(s) 508, the client 202 may use the credentials to
access the selected application directly.
[0083] The client(s) 202 may be any type of computing devices
capable of accessing the resource feed(s) 504 and/or the SaaS
application(s) 508, and may, for example, include a variety of
desktop or laptop computers, smartphones, tablets, etc. The
resource feed(s) 504 may include any of numerous resource types and
may be provided from any of numerous locations. In some
embodiments, for example, the resource feed(s) 504 may include one
or more systems or services for providing virtual applications
and/or desktops to the client(s) 202, one or more file repositories
and/or file sharing systems, one or more secure browser services,
one or more access control services for the SaaS applications 508,
one or more management services for local applications on the
client(s) 202, one or more internet enabled devices or sensors,
etc. The resource management service(s) 502, the resource feed(s)
504, the gateway service(s) 506, the SaaS application(s) 508, and
the identity provider 510 may be located within an on-premises data
center of an organization for which the multi-resource access
system 500 is deployed, within one or more cloud computing
environments, or elsewhere.
[0084] FIG. 5B is a block diagram showing an example implementation
of the multi-resource access system 500 shown in FIG. 5A in which
various resource management services 502 as well as a gateway
service 506 are located within a cloud computing environment 512.
The cloud computing environment may, for example, include Microsoft
Azure Cloud, Amazon Web Services, Google Cloud, or IBM Cloud. It
should be appreciated, however, that in other implementations, one
or more (or all) of the components of the resource management
services 502 and/or the gateway service 506 may alternatively be
located outside the cloud computing environment 512, such as within
a data center hosted by an organization.
[0085] For any of the illustrated components (other than the client
202) that are not based within the cloud computing environment 512,
cloud connectors (not shown in FIG. 5B) may be used to interface
those components with the cloud computing environment 512. Such
cloud connectors may, for example, run on Windows Server instances
and/or Linux Server instances hosted in resource locations and may
create a reverse proxy to route traffic between those resource
locations and the cloud computing environment 512. In the
illustrated example, the cloud-based resource management services
502 include a client interface service 514, an identity service
516, a resource feed service 518, and a single sign-on service 520.
As shown, in some embodiments, the client 202 may use a resource
access application 522 to communicate with the client interface
service 514 as well as to present a user interface on the client
202 that a user 524 can operate to access the resource feed(s) 504
and/or the SaaS application(s) 508. The resource access application
522 may either be installed on the client 202, or may be executed
by the client interface service 514 (or elsewhere in the
multi-resource access system 500) and accessed using a web browser
(not shown in FIG. 5B) on the client 202.
[0086] As explained in more detail below, in some embodiments, the
resource access application 522 and associated components may
provide the user 524 with a personalized, all-in-one interface
enabling instant and seamless access to all the user's SaaS and web
applications, files, virtual Windows applications, virtual Linux
applications, desktops, mobile applications, Citrix Virtual Apps
and Desktops.TM., local applications, and other data.
[0087] When the resource access application 522 is launched or
otherwise accessed by the user 524, the client interface service
514 may send a sign-on request to the identity service 516. In some
embodiments, the identity provider 510 may be located on the
premises of the organization for which the multi-resource access
system 500 is deployed. The identity provider 510 may, for example,
correspond to an on-premises Windows Active Directory. In such
embodiments, the identity provider 510 may be connected to the
cloud-based identity service 516 using a cloud connector (not shown
in FIG. 5B), as described above. Upon receiving a sign-on request,
the identity service 516 may cause the resource access application
522 (via the client interface service 514) to prompt the user 524
for the user's authentication credentials (e.g., username and
password). Upon receiving the user's authentication credentials,
the client interface service 514 may pass the credentials along to
the identity service 516, and the identity service 516 may, in
turn, forward them to the identity provider 510 for authentication,
for example, by comparing them against an Active Directory domain.
Once the identity service 516 receives confirmation from the
identity provider 510 that the user's identity has been properly
authenticated, the client interface service 514 may send a request
to the resource feed service 518 for a list of subscribed resources
for the user 524.
[0088] In other embodiments (not illustrated in FIG. 5B), the
identity provider 510 may be a cloud-based identity service, such
as a Microsoft Azure Active Directory. In such embodiments, upon
receiving a sign-on request from the client interface service 514,
the identity service 516 may, via the client interface service 514,
cause the client 202 to be redirected to the cloud-based identity
service to complete an authentication process. The cloud-based
identity service may then cause the client 202 to prompt the user
524 to enter the user's authentication credentials. Upon
determining the user's identity has been properly authenticated,
the cloud-based identity service may send a message to the resource
access application 522 indicating the authentication attempt was
successful, and the resource access application 522 may then inform
the client interface service 514 of the successfully
authentication. Once the identity service 516 receives confirmation
from the client interface service 514 that the user's identity has
been properly authenticated, the client interface service 514 may
send a request to the resource feed service 518 for a list of
subscribed resources for the user 524.
[0089] The resource feed service 518 may request identity tokens
for configured resources from the single sign-on service 520. The
resource feed service 518 may then pass the feed-specific identity
tokens it receives to the points of authentication for the
respective resource feeds 504. The resource feeds 504 may then
respond with lists of resources configured for the respective
identities. The resource feed service 518 may then aggregate all
items from the different feeds and forward them to the client
interface service 514, which may cause the resource access
application 522 to present a list of available resources on a user
interface of the client 202. The list of available resources may,
for example, be presented on the user interface of the client 202
as a set of selectable icons or other elements corresponding to
accessible resources. The resources so identified may, for example,
include one or more virtual applications and/or desktops (e.g.,
Citrix Virtual Apps and Desktops.TM., VMware Horizon, Microsoft
RDS, etc.), one or more file repositories and/or file sharing
systems (e.g., Sharefile.RTM., one or more secure browsers, one or
more internet enabled devices or sensors, one or more local
applications installed on the client 202, and/or one or more SaaS
applications 508 to which the user 524 has subscribed. The lists of
local applications and the SaaS applications 508 may, for example,
be supplied by resource feeds 504 for respective services that
manage which such applications are to be made available to the user
524 via the resource access application 522. Examples of SaaS
applications 508 that may be managed and accessed as described
herein include Microsoft Office 365 applications, SAP SaaS
applications, Workday applications, etc.
[0090] For resources other than local applications and the SaaS
application(s) 508, upon the user 524 selecting one of the listed
available resources, the resource access application 522 may cause
the client interface service 514 to forward a request for the
specified resource to the resource feed service 518. In response to
receiving such a request, the resource feed service 518 may request
an identity token for the corresponding feed from the single
sign-on service 520. The resource feed service 518 may then pass
the identity token received from the single sign-on service 520 to
the client interface service 514 where a launch ticket for the
resource may be generated and sent to the resource access
application 522. Upon receiving the launch ticket, the resource
access application 522 may initiate a secure session to the gateway
service 506 and present the launch ticket. When the gateway service
506 is presented with the launch ticket, it may initiate a secure
session to the appropriate resource feed and present the identity
token to that feed to seamlessly authenticate the user 524. Once
the session initializes, the client 202 may proceed to access the
selected resource.
[0091] When the user 524 selects a local application, the resource
access application 522 may cause the selected local application to
launch on the client 202. When the user 524 selects a SaaS
application 508, the resource access application 522 may cause the
client interface service 514 to request a one-time uniform resource
locator (URL) from the gateway service 506 as well a preferred
browser for use in accessing the SaaS application 508. After the
gateway service 506 returns the one-time URL and identifies the
preferred browser, the client interface service 514 may pass that
information along to the resource access application 522. The
client 202 may then launch the identified browser and initiate a
connection to the gateway service 506. The gateway service 506 may
then request an assertion from the single sign-on service 520. Upon
receiving the assertion, the gateway service 506 may cause the
identified browser on the client 202 to be redirected to the logon
page for identified SaaS application 508 and present the assertion.
The SaaS may then contact the gateway service 506 to validate the
assertion and authenticate the user 524. Once the user has been
authenticated, communication may occur directly between the
identified browser and the selected SaaS application 508, thus
allowing the user 524 to use the client 202 to access the selected
SaaS application 508.
[0092] In some embodiments, the preferred browser identified by the
gateway service 506 may be a specialized browser embedded in the
resource access application 522 (when the resource application is
installed on the client 202) or provided by one of the resource
feeds 504 (when the resource access application 522 is located
remotely), e.g., via a secure browser service. In such embodiments,
the SaaS applications 508 may incorporate enhanced security
policies to enforce one or more restrictions on the embedded
browser. Examples of such policies include (1) requiring use of the
specialized browser and disabling use of other local browsers, (2)
restricting clipboard access, e.g., by disabling cut/copy/paste
operations between the application and the clipboard, (3)
restricting printing, e.g., by disabling the ability to print from
within the browser, (3) restricting navigation, e.g., by disabling
the next and/or back browser buttons, (4) restricting downloads,
e.g., by disabling the ability to download from within the SaaS
application, and (5) displaying watermarks, e.g., by overlaying a
screen-based watermark showing the username and IP address
associated with the client 202 such that the watermark will appear
as displayed on the screen if the user tries to print or take a
screenshot. Further, in some embodiments, when a user selects a
hyperlink within a SaaS application, the specialized browser may
send the URL for the link to an access control service (e.g.,
implemented as one of the resource feed(s) 504) for assessment of
its security risk by a web filtering service. For approved URLs,
the specialized browser may be permitted to access the link. For
suspicious links, however, the web filtering service may have the
client interface service 514 send the link to a secure browser
service, which may start a new virtual browser session with the
client 202, and thus allow the user to access the potentially
harmful linked content in a safe environment.
[0093] In some embodiments, in addition to or in lieu of providing
the user 524 with a list of resources that are available to be
accessed individually, as described above, the user 524 may instead
be permitted to choose to access a streamlined feed of event
notifications and/or available actions that may be taken with
respect to events that are automatically detected with respect to
one or more of the resources. This streamlined resource activity
feed, which may be customized for individual users, may allow users
to monitor important activity involving all of their resources-SaaS
applications, web applications, Windows applications, Linux
applications, desktops, file repositories and/or file sharing
systems, and other data through a single interface, without needing
to switch context from one resource to another. Further, event
notifications in a resource activity feed may be accompanied by a
discrete set of user-interface elements, e.g., "approve," "deny,"
and "see more detail" buttons, allowing a user to take one or more
simple actions with respect to events right within the user's feed.
In some embodiments, such a streamlined, intelligent resource
activity feed may be enabled by one or more micro-applications, or
"microapps," that can interface with underlying associated
resources using APIs or the like. The responsive actions may be
user-initiated activities that are taken within the microapps and
that provide inputs to the underlying applications through the API
or other interface. The actions a user performs within the microapp
may, for example, be designed to address specific common problems
and use cases quickly and easily, adding to increased user
productivity (e.g., request personal time off, submit a help desk
ticket, etc.). In some embodiments, notifications from such
event-driven microapps may additionally or alternatively be pushed
to clients 202 to notify a user 524 of something that requires the
user's attention (e.g., approval of an expense report, new course
available for registration, etc.).
[0094] FIG. 5C is a block diagram similar to that shown in FIG. 5B
but in which the available resources (e.g., SaaS applications, web
applications, Windows applications, Linux applications, desktops,
file repositories and/or file sharing systems, and other data) are
represented by a single box 526 labeled "systems of record," and
further in which several different services are included within the
resource management services block 502. As explained below, the
services shown in FIG. 5C may enable the provision of a streamlined
resource activity feed and/or notification process for a client
202. In the example shown, in addition to the client interface
service 514 discussed above, the illustrated services include a
microapp service 528, a data integration provider service 530, a
credential wallet service 532, an active data cache service 534, an
analytics service 536, and a notification service 538. In various
embodiments, the services shown in FIG. 5C may be employed either
in addition to or instead of the different services shown in FIG.
5B. Further, as noted above in connection with FIG. 5B, it should
be appreciated that, in other implementations, one or more (or all)
of the components of the resource management services 502 shown in
FIG. 5C may alternatively be located outside the cloud computing
environment 512, such as within a data center hosted by an
organization.
[0095] In some embodiments, a microapp may be a single use case
made available to users to streamline functionality from complex
enterprise applications. Microapps may, for example, utilize APIs
available within SaaS, web, or home-grown applications allowing
users to see content without needing a full launch of the
application or the need to switch context. Absent such microapps,
users would need to launch an application, navigate to the action
they need to perform, and then perform the action. Microapps may
streamline routine tasks for frequently performed actions and
provide users the ability to perform actions within the resource
access application 522 without having to launch the native
application. The system shown in FIG. 5C may, for example,
aggregate relevant notifications, tasks, and insights, and thereby
give the user 524 a dynamic productivity tool. In some embodiments,
the resource activity feed may be intelligently populated by
utilizing machine learning and artificial intelligence (AI)
algorithms. Further, in some implementations, microapps may be
configured within the cloud computing environment 512, thus giving
administrators a powerful tool to create more productive workflows,
without the need for additional infrastructure. Whether pushed to a
user or initiated by a user, microapps may provide short cuts that
simplify and streamline key tasks that would otherwise require
opening full enterprise applications. In some embodiments,
out-of-the-box templates may allow administrators with API account
permissions to build microapp solutions targeted for their needs.
Administrators may also, in some embodiments, be provided with the
tools they need to build custom microapps.
[0096] Referring to FIG. 5C, the systems of record 526 may
represent the applications and/or other resources the resource
management services 502 may interact with to create microapps.
These resources may be SaaS applications, legacy applications, or
homegrown applications, and can be hosted on-premises or within a
cloud computing environment. Connectors with out-of-the-box
templates for several applications may be provided and integration
with other applications may additionally or alternatively be
configured through a microapp page builder. Such a microapp page
builder may, for example, connect to legacy, on-premises, and SaaS
systems by creating streamlined user workflows via microapp
actions. The resource management services 502, and in particular
the data integration provider service 530, may, for example,
support REST API, JSON, OData-JSON, and 6ML. As explained in more
detail below, the data integration provider service 530 may also
write back to the systems of record, for example, using OAuth2 or a
service account.
[0097] In some embodiments, the microapp service 528 may be a
single-tenant service responsible for creating the microapps. The
microapp service 528 may send raw events, pulled from the systems
of record 526, to the analytics service 536 for processing. The
microapp service may, for example, periodically pull active data
from the systems of record 526.
[0098] In some embodiments, the active data cache service 534 may
be single-tenant and may store all configuration information and
microapp data. It may, for example, utilize a per-tenant database
encryption key and per-tenant database credentials.
[0099] In some embodiments, the credential wallet service 532 may
store encrypted service credentials for the systems of record 526
and user OAuth2 tokens.
[0100] In some embodiments, the data integration provider service
530 may interact with the systems of record 526 to decrypt end-user
credentials and write back actions to the systems of record 526
under the identity of the end-user. The write-back actions may, for
example, utilize a user's actual account to ensure all actions
performed are compliant with data policies of the application or
other resource being interacted with.
[0101] In some embodiments, the analytics service 536 may process
the raw events received from the microapps service 528 to create
targeted scored notifications and send such notifications to the
notification service 538.
[0102] Finally, in some embodiments, the notification service 538
may process any notifications it receives from the analytics
service 536. In some implementations, the notification service 538
may store the notifications in a database to be later served in an
activity feed. In other embodiments, the notification service 538
may additionally or alternatively send the notifications out
immediately to the client 202 as a push notification to the user
524.
[0103] In some embodiments, a process for synchronizing with the
systems of record 526 and generating notifications may operate as
follows. The microapp service 528 may retrieve encrypted service
account credentials for the systems of record 526 from the
credential wallet service 532 and request a sync with the data
integration provider service 530. The data integration provider
service 530 may then decrypt the service account credentials and
use those credentials to retrieve data from the systems of record
526. The data integration provider service 530 may then stream the
retrieved data to the microapp service 528. The microapp service
528 may store the received systems of record data in the active
data cache service 534 and also send raw events to the analytics
service 536. The analytics service 536 may create targeted scored
notifications and send such notifications to the notification
service 538. The notification service 538 may store the
notifications in a database to be later served in an activity feed
and/or may send the notifications out immediately to the client 202
as a push notification to the user 524.
[0104] In some embodiments, a process for processing a
user-initiated action via a microapp may operate as follows. The
client 202 may receive data from the microapp service 528 (via the
client interface service 514) to render information corresponding
to the microapp. The microapp service 528 may receive data from the
active data cache service 534 to support that rendering. The user
524 may invoke an action from the microapp, causing the resource
access application 522 to send an action request to the microapp
service 528 (via the client interface service 514). The microapp
service 528 may then retrieve from the credential wallet service
532 an encrypted Oauth2 token for the system of record for which
the action is to be invoked, and may send the action to the data
integration provider service 530 together with the encrypted OAuth2
token. The data integration provider service 530 may then decrypt
the OAuth2 token and write the action to the appropriate system of
record under the identity of the user 524. The data integration
provider service 530 may then read back changed data from the
written-to system of record and send that changed data to the
microapp service 528. The microapp service 528 may then update the
active data cache service 534 with the updated data and cause a
message to be sent to the resource access application 522 (via the
client interface service 514) notifying the user 524 that the
action was successfully completed.
[0105] In some embodiments, in addition to or in lieu of the
functionality described above, the resource management services 502
may provide users the ability to search for relevant information
across all files and applications. A simple keyword search may, for
example, be used to find application resources, SaaS applications,
desktops, files, etc. This functionality may enhance user
productivity and efficiency as application and data sprawl is
prevalent across all organizations.
[0106] In other embodiments, in addition to or in lieu of the
functionality described above, the resource management services 502
may enable virtual assistance functionality that allows users to
remain productive and take quick actions. Users may, for example,
interact with the "Virtual Assistant" and ask questions such as
"What is Bob Smith's phone number?" or "What absences are pending
my approval?" The resource management services 502 may, for
example, parse these requests and respond because they are
integrated with multiple systems on the back-end. In some
embodiments, users may be able to interact with the virtual
assistant through either the resource access application 522 or
directly from another resource, such as Microsoft Teams. This
feature may allow employees to work efficiently, stay organized,
and deliver only the specific information they're looking for.
[0107] FIG. 5D shows how a display screen 540 presented by a
resource access application 522 (shown in FIG. 5C) may appear when
an intelligent activity feed feature is employed and a user is
logged on to the system. Such a screen may be provided, for
example, when the user clicks on or otherwise selects a "home" user
interface element 542. As shown, an activity feed 544 may be
presented on the screen 540 that includes a plurality of
notifications 546 about respective events that occurred within
various applications to which the user has access rights. An
example implementation of a system capable of providing an activity
feed 544 like that shown is described above in connection with FIG.
5C. As explained above, a user's authentication credentials may be
used to gain access to various systems of record (e.g., SalesForce,
Ariba, Concur, RightSignature, etc.) with which the user has
accounts, and events that occur within such systems of record may
be evaluated to generate notifications 546 to the user concerning
actions that the user can take relating to such events. As shown in
FIG. 5D, in some implementations, the notifications 546 may include
a title 560 and a body 562, and may also include a logo 564 and/or
a name 566 of the system or record to which the notification 546
corresponds, thus helping the user understand the proper context
with which to decide how best to respond to the notification 546.
In some implementations, one or more filters may be used to control
the types, date ranges, etc., of the notifications 546 that are
presented in the activity feed 544. The filters that can be used
for this purpose may be revealed, for example, by clicking on or
otherwise selecting the "show filters" user interface element 568.
Further, in some embodiments, a user interface element 570 may
additionally or alternatively be employed to select a manner in
which the notifications 546 are sorted within the activity feed. In
some implementations, for example, the notifications 546 may be
sorted in accordance with the "date and time" they were created (as
shown for the element 570 in FIG. 5D), a "relevancy" mode (not
illustrated) may be selected (e.g., using the element 570) in which
the notifications may be sorted based on relevancy scores assigned
to them by the analytics service 536, and/or an "application" mode
(not illustrated) may be selected (e.g., using the element 570) in
which the notifications 546 may be sorted by application type.
[0108] When presented with such an activity feed 544, the user may
respond to the notifications 546 by clicking on or otherwise
selecting a corresponding action element 548 (e.g., "Approve,"
"Reject," "Open," "Like," "Submit," etc.), or else by dismissing
the notification, e.g., by clicking on or otherwise selecting a
"close" element 550. As explained in connection with FIG. 5C below,
the notifications 546 and corresponding action elements 548 may be
implemented, for example, using "microapps" that can read and/or
write data to systems of record using application programming
interface (API) functions or the like, rather than by performing
full launches of the applications for such systems of record. In
some implementations, a user may additionally or alternatively view
additional details concerning the event that triggered the
notification and/or may access additional functionality enabled by
the microapp corresponding to the notification 546 (e.g., in a
separate, pop-up window corresponding to the microapp) by clicking
on or otherwise selecting a portion of the notification 546 other
than one of the user-interface elements 548, 550. In some
embodiments, the user may additionally or alternatively be able to
select a user interface element either within the notification 546
or within a separate window corresponding to the microapp that
allows the user to launch the native application to which the
notification relates and respond to the event that prompted the
notification via that native application rather than via the
microapp. In addition to the event-driven actions accessible via
the action elements 548 in the notifications 546, a user may
alternatively initiate microapp actions by selecting a desired
action, e.g., via a drop-down menu accessible using the "action"
user-interface element 552 or by selecting a desired action from a
list 554 of recently and/or commonly used microapp actions. As
shown, additional resources may also be accessed through the screen
540 by clicking on or otherwise selecting one or more other user
interface elements that may be presented on the screen. For
example, in some embodiments, the user may also access files (e.g.,
via a Citrix ShareFile.TM. platform) by selecting a desired file,
e.g., via a drop-down menu accessible using the "files" user
interface element 556 or by selecting a desired file from a list
558 of recently and/or commonly used files. Further, in some
embodiments, one or more applications may additionally or
alternatively be accessible (e.g., via a Citrix Virtual Apps and
Desktops.TM. service) by clicking on or otherwise selecting an
"apps" user-interface element 572 to reveal a list of accessible
applications or by selecting a desired application from a list (not
shown in FIG. 5D but similar to the list 558) of recently and/or
commonly used applications. And still further, in some
implementations, one or more desktops may additionally or
alternatively be accessed (e.g., via a Citrix Virtual Apps and
Desktops.TM. service) by clicking on or otherwise selecting a
"desktops" user-interface element 574 to reveal a list of
accessible desktops or by or by selecting a desired desktop from a
list (not shown in FIG. 5D but similar to the list 558) of recently
and/or commonly used desktops.
[0109] The activity feed shown in FIG. 5D provides significant
benefits, as it allows a user to respond to application-specific
events generated by disparate systems of record without needing to
navigate to, launch, and interface with multiple different native
applications.
F. Detailed Description of Example Embodiments of the Context-Based
Notification Processing System Introduced in Section A
[0110] FIG. 6 shows example components that may be included in the
context-based notification processing system 100 such as that
introduced above in Section A. As shown, in some implementations,
some components of the system 100 may be embodied within the client
device(s) 202 and other components of the system 100 may be
embodied within the server(s) 204. In particular, as illustrated,
in some implementations, the client device(s) 202 may include a
notification data upload engine 602, a notification access
monitoring engine 604, a context determination engine 606, a view
determination engine 608, and one or more storage mediums 610. In
some implementations, the engines 602, 604, 606 and 608 may, for
example, be components of, or operate in conjunction with, the
resource access application 522 described above in connection with
FIGS. 5B and 5C. Further, as also illustrated in FIG. 6, in some
implementations, the server(s) 204 may include a notification
access monitoring service 612, a context classifier training
service 614, a context-based notification forecasting service 616,
and a context-based notification presentation service 618. In some
implementations, the services 612, 614, 616 and 618 may, for
example, be included amongst, or operate in conjunction with, the
resource management services 502 described above in connection with
FIGS. 5B and 5C.
[0111] In some implementations, the storage medium(s) 610 may be
encoded with instructions which, when executed by one or more
processors of the client device(s) 202, may cause the client
device(s) 202 to perform the functions of the engines 602, 604,
606, and 608 described herein. Similarly, in some implementations,
the storage medium(s) 104 may be encoded with instructions which,
when executed by one or more processors of the server(s) 204, may
cause the server(s) 204 to perform the functions of the services
612, 614, 616, and 618 described herein.
[0112] At a high-level, the notification access monitoring engine
604 may monitor a user's interactions with notifications 546 in an
activity feed 544 to identify instances in which the user 524
clicks on or otherwise accesses notifications 546, and may create
records of such access events in the storage medium(s) 610. As
described in more detail below, the notification access monitoring
engine 604 may additionally request current contextual data from
the context determination engine 606, and may record such
contextual data in the storage medium(s) 610 as part of those
created records. As noted in Section A, examples of such contextual
data that may be so determined and included in the records include
(A) a device ID identifying the particular client device 202 used
to access the notification 546, (B) the date and/or time the client
device 202 was used to access the notification 546, (C) a network
ID identifying the network to which the client device 202 was
connected at the time the notification 546 was accessed, (D) a
location (e.g., latitude and longitude) of the client device 202 at
the time it was used to access the notification 546. An example
routine 700 that may be performed by the notification access
monitoring engine 604 is described below in connection with FIG. 7.
An example routine 800 that may be performed by the context
determination engine 606 is described below in connection with FIG.
8.
[0113] The notification data upload engine 602 may be responsible
for uploading the new records created by the notification access
monitoring engine 604 from the storage medium(s) 610 to the
notification access monitoring service 612. As explained below, in
some implementations, such record uploads may be performed
periodically, e.g., once per day, at a time when the computational
load on the client device 202 is low. An example routine 900 that
may be performed by the notification data upload engine 602 is
described below in connection with FIG. 9.
[0114] The notification access monitoring service 612 may receive
the records, including the contextual data determined by the
context determination engine 606, that are uploaded from the
notification data upload engine 602, and may write those records to
the storage medium(s) 104, e.g., as rows in one or more tables. An
example routine 1000 that may be performed by the notification
access monitoring service 612 is described below in connection with
FIG. 10. An example table 1100 that may be populated with data for
respective notification access events, including empty fields for
context tags 118 that are to be subsequently determined by the
context-based notification forecasting service 616 (as explained
below), is described below in connection with FIG. 11.
[0115] The context classifier training service 614 may be
responsible for training and/or updating the predictive model 114
that is used by the context-based notification forecasting service
616 and the context-based notification presentation service 618, as
explained below. An example routine 1200 that may be performed by
the context classifier training service 614 is described below in
connection with FIG. 12. Example techniques that may be used to
train the predictive model 114 using a collection of contextual
data samples, as well as to use the predictive model 114 to
determine a context tag 118 for a given contextual data sample, are
described below in connection with FIG. 13.
[0116] The context-based notification forecasting service 616 may
be responsible for calculating context-based notification forecast
scores that can subsequently be used by the context-based
notification presentation service 618 to determine the types of
notifications that are to be included in a context-based view of an
activity feed 544 generated for a client device 202, based on the
current contextual situation of that client device 202. For
example, as explained below, in some implementations, the
context-based notification forecasting service 616 may periodically
(e.g., once per day): (A) select a subset of the data in the table
1100 that is to be used for notification forecasting purposes
(e.g., data from the past twenty days), (B) use the predictive
model 114 to update the context tags 118 for the respective
contextual data samples, and (C) use the selected/updated records
to calculate context-based notification forecast scores for the
respective context tag/notification type ID combinations in the
table 1100. An example routine 1400 that may be performed by the
context-based notification forecasting service 616 is described
below in connection with FIG. 14. An example table 1500 populated
with context-based notification forecast scores (determined by the
context-based notification forecasting service 616) for a given
user (i.e., the user 524 with user ID "U1") is described below in
connection with FIG. 15.
[0117] The view determination engine 608 of the client device(s)
202 and the context-based notification presentation service 618 of
the server(s) 204 may operate together to present a user 524 of a
client device 202 with a context-based view of an activity feed
544. In particular, in some implementations, the view determination
engine 608 may determine that a context-based view of the activity
feed 544 has been requested (e.g., by detecting selection of a
"context-based" option via a user interface element 568, 570). In
response to making such a determination, the view determination
engine 608 may acquire current contextual data (e.g., from the
context determination engine 606) and may send a request for such a
view to the context-based notification presentation service 618,
together with the determined contextual data.
[0118] Upon receiving the request for a context-based view of the
activity feed 544 and the current contextual data from the client
device 202, the context-based notification presentation service 618
may use the predictive model 114 to determine a context tag 118 for
the current contextual data. For example, the context-based
notification presentation service 618 may encode the received
contextual data into a feature vector 116 and then feed that
feature vector 116 to the predictive model 114 to as to yield a
context tag 118 based on the current contextual data.
Alternatively, in some implementations, the predictive model 114,
when generated and/or updated, may be provided to the client
device(s) 202, so as to enable the client device(s) 202 to instead
determine the context tags 118 for respective contextual data
samples. In any event, once context-based notification presentation
service 618 has the context tag 118 based on the current contextual
data, the context-based notification presentation service 618 may
reference the table 1500 to identify the types of pending
notifications 546 that are to be included in the context-based
activity feed 544, as requested. In some implementations, for
example, the notification types (e.g., as indicated by the
notification type IDs in the table 1500) that (A) have the same
context tag 118 as the current contextual data, and (B) have higher
than a threshold context-based notification forecast score, may be
selected as the notification types that are to be included in the
requested context-based view of the activity feed 544. For example,
for the context-based notification forecast scores shown in the
table 1500, if the threshold score was "2" and the current
contextual data was assigned context tag "C," then type "NT3"
notifications 546, but not types "NT1," "NT2" or NT4" notifications
546, would be selected as the notification types that are to be
included in the requested context-based view of the activity feed
544 for the user 524 with user ID "U1." As another example, for the
context-based notification forecast scores shown in the table 1500,
if the threshold score was "3" and the current contextual data was
assigned context tag "B," then both type "NT1" and type "NT3"
notifications, but not types "NT2" or "NT4" notifications, would be
selected as the notification types that are to be included in the
requested context-based view of the activity feed 544 for the user
524 with user ID "U1."
[0119] After the context-based notification presentation service
618 has determined pertinent notification type(s) for the user 524
requesting the context-based view of the activity feed 544, e.g.,
based on the entries in the table 1500, the context-based
notification presentation service 618 may identify the
notifications 546 of the determined type(s) that are currently
pending for the user 524, and may construct an activity feed 544
that includes those notifications 546. The context-based
notification presentation service 618 may then send that activity
feed 544 to the client device 202 for presentation as a
context-based view of the activity feed 544. In some
implementations, the context-based notification forecast scores
(e.g., in the table 1500) may further be used, either by themselves
or together with other scores (e.g., relevance scores assigned by
the analytics service 536) to determine the order in which the
identified notifications 546 appear in the context-based view of
the activity feed 544. For example, the identified notifications
546 having notification type IDs with higher context-based
notification forecast scores in the table 1500 may, in at least
some circumstances, be caused to appear earlier in the activity
feed 544 than those having notification type IDs with lower
context-based notification forecast scores. An example routine 1600
that may be performed by the view determination engine 608 is
described below in connection with FIG. 16. An example routine 1700
that may be performed by the context-based notification
presentation service 618 is described below in connection with FIG.
17.
[0120] As noted above, FIG. 7 shows an example routine 700 that may
be performed by the notification access monitoring engine 604 shown
in FIG. 6. As shown, the routine 700 may begin at a decision step
702, at which the notification access monitoring engine 604 may
determine whether a user has clicked on or otherwise accessed a
notification 546 in an activity feed 544. As indicated, the routine
700 may proceed to a step 704 when such a notification access event
is detected.
[0121] At a step 704 of the routine 700, the notification access
monitoring engine 604 may determine a notification ID for the
accessed notification 546. In some implementations, for example,
different notification IDs may be assigned to respective
notifications 546 created by the analytics service 536 (described
above in connection with FIG. 5C) so as to allow notifications 546
to be distinguished from one another within the system 100. Such
notification IDs may, for example, be included in metadata that
accompanies the notifications 546 that are sent to client devices
202.
[0122] At a step 706 of the routine 700, the notification access
monitoring engine 604 may determine a notification type ID for the
accessed notification 546. The determined notification type ID may
indicate the type of notification 546 that was accessed. In some
implementations, the notification type IDs may be names that are
given to particular types of notifications, such as "PTO approval
request," "expense report approval request," "new hire
announcement," etc. In other implementations, the notification type
IDs may be identification numbers that are assigned to identify
different types of notifications 546. As with the notification IDs,
different notification type IDs may be assigned to respective
notifications 546 created by the analytics service 536 (described
above in connection with FIG. 5C) so as to allow different types of
notifications 546 to be distinguished from one another within the
system 100. In some implementations, such notification type IDs may
likewise be included in metadata that accompanies the notifications
546 that are sent to client devices 202.
[0123] At a step 708, the notification access monitoring engine 604
may request the context determination engine 606 to determine
contextual data about the client device 202 at the time the
notification 546 was accessed. An example routine 800 that may be
employed by the context determination engine 606, as well as
examples of contextual data that be determined by that engine, are
described below in connection with FIG. 8.
[0124] At a decision step 710, the notification access monitoring
engine 604 may determine whether the requested contextual data has
been received from the context determination engine 606. As
indicated, the routine 700 may proceed to a step 712 when the
requested contextual data has been received.
[0125] At the step 712 of the routine 700, the notification access
monitoring engine 604 may store a record locally on the client
device 202, e.g., in the storage medium(s) 610 shown in FIG. 6, for
the detected notification access event. As indicated, such a record
may include the notification ID, the notification type ID, and the
contextual data received from the context determination engine
606.
[0126] As noted previously, FIG. 8 shows an example routine 800
that may be performed by the context determination engine 606 shown
in FIG. 6 to determine contextual data concerning the client device
202 at a particular time, such as when an access event is detected
by the notification access monitoring engine 604.
[0127] At a decision step 802 of the routine 800, the context
determination engine 606 may determine whether a request for
contextual data has been received from another component, such as
the notification access monitoring engine 604 (as described above)
or the view determination engine 608 (as described below). As
indicated, the routine 800 may proceed to a step 804 when such a
request is received.
[0128] At the step 804, the context determination engine 606 may
determine a user ID for the user who is currently operating the
client device 202. For example, in some implementations, the user
ID may be the user name that the user 524 entered to gain access to
resource access application 522. In other implementations, the user
ID may be an identification number, separate from such a user name,
that is assigned to identify a particular user 524 of the system
100. Since the system 100 may perform context-based notification
processing on a user-by-user basis, determining user IDs may allow
the system 100 to attribute particular notification access events
to specific users 524.
[0129] At the step 806 of the routine 800, the context
determination engine 606 may determine a device ID of the client
device 202 on which the notification access event was detected. As
some users 524 access notifications 546 using multiple different
client devices 202, e.g., a smartphone, a laptop computer, a
desktop computer, etc., the device ID may be used to differentiate
amongst access events by different types of client devices 202.
[0130] At the step 808, the context determination engine 606 may
determine the current date and/or time, e.g., by recording a value
of a calendar and/or clock maintained by the client device 202.
[0131] At the step 810, the context determination engine 606 may
determine a network ID of the network, if any, to which the client
device 202 is currently connected. In some implementations, the
network IDs may include the names and/or identifiers of specific
networks to which client devices 202 are connected. In other
implementations, the network IDs may additionally or alternatively
indicate particular types of networks, such as 3G, 4G, 5G, wired
local area network (LAN), wireless LAN, etc., to which such devices
are connected.
[0132] At the step 812 of the routine 800, the context
determination engine 606 may determine the current location of the
client device 202. For example, the client device 202 may obtain
the current coordinates (e.g., latitude and longitude) from a
global positioning system (GPS) chip or other location
determination device or system.
[0133] At the step 814, the context determination engine 606 may
send the contextual data gathered per the steps 804, 806, 808, 810
and 812 to the component that requested it, e.g., the notification
access monitoring engine 604 (as described above) or the view
determination engine 608 (as described below).
[0134] FIG. 9 shows an example routine 900 that may be performed by
the notification data upload engine 602 shown in FIG. 6. As shown,
the routine 900 may begin at a decision step 902, at which the
notification data upload engine 602 may determine whether a
particular period of time, e.g., twenty-four hours, has elapsed
since it last uploaded context-specific notification access event
records to the notification access monitoring service 612 (shown in
FIG. 6). As indicated, the routine 900 may proceed to a decision
step 904 when more than the threshold period of time has
elapsed.
[0135] At the decision step 904, the notification data upload
engine 602 may evaluate the current load on the client device 202,
such as by determining processing capacity and/or available network
bandwidth of the client device 202. As indicated, in some
implementations, the notification data upload engine 602 may wait
until the load is low, e.g., below a threshold, before proceeding
to a step 906, at which it may send the new access event records it
has accumulated (since the last time the routine 900 was performed
by the client device 202) to the notification access monitoring
service 612.
[0136] FIG. 10 shows an example routine 1000 that may be performed
by the notification access monitoring service 612 shown in FIG. 6.
As shown, the routine 1000 may begin at a decision step 1002, at
which the notification access monitoring service 612 may determine
whether any new notification access event records have been
received from the notification data upload engine 602 of client
device 202. As indicated, the routine 1000 may proceed to a step
1004, upon receipt of one or more such new notification access
event records. At the step 1004, the notification access monitoring
service 612 may upload the newly-received notification access event
records to the storage medium(s) 104, e.g., to a database table, as
described below.
[0137] FIG. 11 shows an example table 1100 that the notification
access monitoring service 612 may populate with notification access
event records that are received from notification data upload
engine(s) 602 of one or more client devices 202. As shown, the
table 1100 may correlate the notification access event records by
user ID (per "user ID" entries 1102). In illustrated example,
different rows of the table 1100 represent respective notification
access event records. Further, as indicated, some implementations,
the notification access event records, may include "notification
ID" entries 1104, "notification type ID" entries 1106, "device ID"
entries 1108, "time" entries 1110, "network ID" entries 1112, and
"location" entries 1114. The table entries 1108, 1110, 1112, and
1114 may, for example, be populated with corresponding items of the
contextual data (received from the notification data upload engine
602) that were determined by the context determination engine 606
for the notification access events detected by the notification
access monitoring engine 604 of the client device 202. In some
implementations, the right-most entries in the table 1100 (i.e.,
the "context tag" entries 1116) may be determined and populated by
the context-based notification forecasting service 616 (e.g., using
the predictive model 114 to evaluate the contextual data for the
record), as explained in more detail below.
[0138] FIG. 12 shows an example routine 1200 that may be performed
by the context classifier training service 614 shown in FIG. 6. As
shown, the routine 1200 may begin at a decision step 1202, at which
the context classifier training service 614 may determine whether a
particular period of time, e.g., twenty days, has elapsed since it
last re-trained a predictive model 114 for a user 524 using
contextual data of accumulated notification access event records
for that user 524. As indicated, the routine 1200 may proceed to a
step 1204 when it determines that the period of time has
elapsed.
[0139] At the step 1204, the context classifier training service
614 may select a subset of the accumulated notification access
event records to use for re-training the user's predictive model
114. In some implementations, for example, the context classifier
training service 614 may select the user's notification access
event records (e.g., as stored in the table 1100) for the prior
twenty days for such purpose. At a step 1206, the context
classifier training service 614 may use the records selected at the
step 1204 to retrain the predictive model 114 for the user 524.
[0140] FIG. 13 shows an example technique that the context
classifier training service 614 may use to train and/or update a
user's predictive model 114, as well as the manner in which the
trained predictive model 114 may subsequently be used (e.g., by the
context-based notification forecasting service 616 and/or the
context-based notification presentation service 618) to determine
context tags 118 for particular sets of contextual data.
[0141] As FIG. 13 illustrates, the contextual data from the
selected notification access event records may be used as training
data 1302 for the machine learning process 110. More specifically,
in some implementations, for each of the selected notification
access event records, one or more encoders 1304 may encode the
various pieces of contextual data from that record into a feature
vector 1306a, 1306b, 1306n, so that a total of "n" feature vectors
are generated for a set of "n" selected notification access event
records. In some implementations, the different pieces of
contextual data (e.g., the device ID, the time, the network ID, the
location, etc.) may be identified as separate "features" of a
feature vector 1306, such that, for each feature vector 1306, the
respective feature values represent different dimensions in a
multi-dimensional space. Accordingly, each of the feature vectors
1306a, 1306b, 1306n, etc., may represent a single point in the
multi-dimensional space.
[0142] As shown in FIG. 13, the feature vectors 1306a, 1306b, 1306n
may be provided to the machine learning process 110, and the
results of the machine learning process 110 may, in turn, be used
train the predictive model 114. In some implementations, the
machine learning process 110 may employ an unsupervised learning
process to train the predictive model 114. For example, in some
implementations, the machine learning process 110 may use a
clustering process to identify a set of "clusters" within the
multi-dimensional space for the feature vectors 1306. Examples of
suitable data clustering processes included K-means clustering and
density-based spatial clustering of applications with noise
(DBSCAN). Any of a number of other data clustering techniques, such
as mean-shift clustering, expectation-maximum (EM) clustering using
Gaussian mixture models (GMM), and k-nearest neighbor (KNN)
classification, may additionally or alternatively be employed in
some implementations.
[0143] After identifying clusters of data points within the
multi-dimensional feature space, the machine learning process 110
may train the predictive model 114 to classify a given feature
vector 1306x into one of the clusters the machine learning process
110 identified. As explained below, in some implementations, a set
of contextual data (e.g., either from a notification access record
in the table 1100 or from a request for a context-based view of the
notification feed 544 received from the view determination engine
608) may be provided as new data 1308 to one or more encoders 1310
(which may be the same as, or operate in the same manner as, the
encoder(s) 1304). As shown in FIG. 13, the encoder(s) 1310 may
encode the received contextual data into the feature vector 1306x,
and may provide the feature vector 1306x to the predictive model
114 for evaluation. As indicated, the predictive model may then
output a cluster ID 1312 identifying the previously-identified
cluster into which it has classified the contextual data (i.e., the
new data 1308). As explained in more detail below, the cluster ID
1312 output by the predictive model 114 may be used either as the
context tag 118 that is written to the table 1100 (e.g., a "context
tag" entry 1116) or as the context tag 118 that is used by the
context-based notification presentation service 618 to identify
contextually-relevant notifications 546, as explained below.
[0144] FIG. 14 shows an example routine 1400 that may be performed
by the context-based notification forecasting service 616 shown in
FIG. 6. As shown, the routine 1400 may begin at a decision step
1402, at which the context-based notification forecasting service
616 may determine whether a particular period of time, e.g.,
twenty-four hours, has elapsed since it last updated the
context-based notification forecast scores in the table 1500 (shown
in FIG. 15). As indicated, the routine 1400 may proceed to a step
1404 when it determines that the period of time has elapsed.
[0145] At a step 1404, the context-based notification forecasting
service 616 may determine the notification access event records
(e.g., from the table 1100) that are to be used to determine the
context-based notification forecast stores for the table 1500. In
some implementations, for example, the context-based notification
forecasting service 616 may identify the notification access event
records in the table 1100 that were generated less than a threshold
period of time (e.g., 20 days) in the past. The "time" entries 1110
in the table 1100 may, for example, be used for that purpose. In
some implementations, the threshold time period used to select
notification access event records at the step 1404 may be the same
as the threshold time period that is used to determine (at the
decision step 1202 of the routine 1200--shown in FIG. 12) whether
to update the predictive model 114. In other implementations,
different threshold time periods may be used for those two
purposes.
[0146] At a step 1406, the context-based notification forecasting
service 616 may determine and/or update the context tags 118 in the
table 1100 (i.e., the "context tag" entries 1116) for the
notification access event records selected at the step 1406. With
reference to FIGS. 11 and 13, to update a given context tag 118 for
a notification access event record, the encoder(s) 1310 may be used
to encode the stored contextual data for the record, e.g., the
device ID, the time, the network ID, and the location, into a
feature vector 1306x, and may provide that feature vector 1306x to
the predictive model 114 for processing. As explained above,
because of how the predictive model 114 was trained (using the
machine learning process 110), the predictive model 114 may output
a cluster ID 1312 that may be used as the (new or updated) context
tag 118 for the notification access event access record under
consideration.
[0147] At a step 1408, the context-based notification forecasting
service 616 may generate and/or update the table 1500 of
context-based notification forecast scores using the notification
access event records that were selected at the step 1404 and for
which the context tags 118 were updated at the step 1406. In the
illustrated example, the table 1500 shows a set of determined
context-based notification forecast scores for the user "U1" (as
indicated by the "user ID" entries 1502). Although the example
shown includes only twelve possible combinations of notification
type IDs and context tags 118, it should be appreciated that, in
practice, many more such combinations are likely to occur in the
evaluated data set. As noted previously in Section A, in some
implementations, the respective context-based notification forecast
scores may simply reflect, for the data set being considered, the
total number of notifications access event records for a particular
type of notification 546 (as indicated by the "notification type
ID" entries 1106) that include a particular context tag 118 (as
indicated by the "context tag" entries 1116). For example, an entry
1512 in the table 1500 may reflect that, in the notification access
event records under consideration, the context tag "C" was assigned
to a total of "37" notification access event records that included
"NT3" as the "notification type ID" entry 1106. In other
implementations, different weights may be applied to different
notification access event records when determining the
context-based notification forecast scores in the table 1500. For
example, if records for the last "X" days are being evaluated,
lower weights may be applied to older records, so that the more
recent records influence the context-based notification forecast
scores more than the less recent ones. In some implementations, for
example, an exponential moving average (e.g., a first-order
infinite response filter that applies weighting factors that
decrease exponentially) may be applied to weight the different
notification access event records differently.
[0148] FIG. 16 shows an example routine 1600 that may be performed
by the view determination engine 608 shown in FIG. 6. As noted
previously, the view determination engine 608 may be located on a
client device 202, e.g., as a component of the resource access
application 522 shown in FIG. 5C. As shown, the routine 1600 may
begin at a decision step 1602, at which the view determination
engine 608 may determine whether a user request for a context-based
view of notifications 546, e.g., in the notification feed 544 shown
in FIG. 5D, has been detected. As discussed above, in some
implementations, a user 524 of a resource access application 522
(see FIG. 5C) on a client device 202 may, for example, be presented
with an option to request such a context-based view, e.g., via a
user-interface element 568, 570 on the display screen 540. As
indicated, the routine 1600 may proceed to a step 1604 when it
determines that such a context-based view of the activity feed 544
has been requested.
[0149] At the step 1604, the view determination engine 608 may
request the current contextual data from the context determination
engine 606 (shown in FIG. 6). The manner in which the context
determination engine 606 may determine such contextual data, as
well as examples of the contextual data that may be so determined,
are described above in connection with FIG. 8. In some
implementations, the items of contextual data that the context
determination engine 606 determines in response to requests from
the view determination engine 608 may be the same as those that are
determined in response to requests by the notification access
monitoring engine 604, as described above. For example, similar to
the contextual data that the context determination engine 606
accumulated during the notification access monitoring process
discussed above, examples of contextual data that may gathered by
the context determination engine 606 in response to the request per
the step 1604 include (A) a device ID identifying the client device
202 sending the request, (B) the current date and/or time, (C) a
network ID identifying the network to which the client device 202
is currently connected, and (D) a current location (e.g., latitude
and longitude) of the client device 202.
[0150] Per a decision step 1606, the routine 1600 may proceed to a
step 1608 when the contextual data has been received from the
context determination engine 606 in response to the request sent at
the step 1604.
[0151] At the step 1608 of the routine 1600, the view determination
engine 608 may send a request for a context-based view of the
activity feed 544 to the context-based notification presentation
service 618 (shown in FIG. 6). As indicated, that request may
include the current contextual data that was received from the
context determination engine (per the decision step 1606).
[0152] At a decision step 1610, the view determination engine 610
may determine whether a set of notifications 546 to be included in
the requested context-based view of the activity feed 544 have been
received from the context-based notification presentation service
618. As indicated, the routine 1600 may proceed to a step 1612 when
the requested set of notifications 546 has been received.
[0153] At the step 1612 of the routine 1600, the view determination
engine 608 may cause the client device 202 to present a
context-based view of the activity feed 544 that includes the
notifications 546 that were received from the context-based
notification presentation service 618, as described below.
[0154] FIG. 17 shows an example routine 1700 that may be performed
by the context-based notification presentation service 618 shown in
FIG. 6. As shown, the routine 1700 may begin at a decision step
1602, at which the context-based notification presentation service
618 may determine whether a request for a context-based view of an
activity feed 544 has been received from a client device 202. As
indicated, the routine 1700 may proceed to a step 1704 when the
context-based notification presentation service 618 receives such a
request. As noted above, such requests for context-based activity
feed views may include the current contextual data that was
determined by the context determination engine(s) 606 of the
requesting client device(s) 202.
[0155] At the step 1704 of the routine 1700, the context-based
notification presentation service 618 may use the predictive model
114 (shown in FIG. 13) to determine a context tag 118 for the
contextual data that was included in the request. As explained
above in connection with FIG. 13, the context-based notification
presentation service 618 may, for example, encode the received
contextual data into a context feature vector 116 and may provide
that context feature vector 116 to the predictive model 114 for
determination of a context tag 118.
[0156] At a step 1706 of the routine 1700, the context-based
notification presentation service 618 may evaluate the entries in
the table 1500 to determine the types of notifications 546 (e.g.,
notifications having particular notification type IDs) that are to
be included in the requested context-based view of the activity
feed 544. In particular, in some implementations, the context-based
notification presentation service 618 may identify the notification
type IDs (per the "notification type ID" entries 1504) for which
context-based notification forecast scores in the table are (A)
associated with the same context tag as the context tag 118
determined at the step 1704, and (B) greater than a threshold
value. For example, for the context-based notification forecast
scores shown in the table 1500, if the threshold score was "2" and
the current contextual data was assigned context tag "C," then type
"NT3" notifications 546, but not types "NT1," "NT2" or NT4"
notifications 546, would be selected as the notification types that
are to be included in the requested context-based notification view
for the user 524 with user ID "U1." As another example, for the
context-based notification forecast scores shown in the table 1500,
if the threshold score was "3" and the current contextual data was
assigned context tag "B," then both type "NT1" and type "NT3"
notifications, but not types "NT2" or "NT4" notifications, would be
selected as the notification types that are to be included in the
requested context-based notification view for the user 524 with
user ID "U1."
[0157] At a step 1708 of the routine 1700, the context-based
notification presentation service 618 may determine the pending
notifications for the requesting user 524 that have notification
type IDs that match any of the notification type IDs determined at
the step 1706. In some implementations, the context-based
notification presentation service 618 may, for example, be a
component of, or operate in conjunction with, the notification
service 538 described above in connection with FIG. 5C. As such,
the context-based notification presentation service 618 may have
access to records indicating the notification type IDs of the
pending notifications 546 for respective users. The context-based
notification presentation service 618 may thus select those pending
notifications 546 having matching notification type IDs for
inclusion in the context-based view of the activity feed 544 that
is to be presented to the requesting user 524.
[0158] At a step 1710, after the context-based notification
presentation service 618 has selected the pending notifications 546
that are to be included in the request context-based view, the
context-based notification presentation service 618 may construct a
notification feed 544 that includes those notifications 546, and
may send that notification feed 544 to the client device 202 that
requested it. In some implementations, the context-based
notification forecast scores may further be used, either by
themselves or together with other scores (e.g., relevance scores
assigned by the analytics service 536) to determine the order in
which the identified notifications 546 appear in the context-based
view of the activity feed 544. For example, the identified
notifications 546 having notification type IDs with higher
context-based notification forecast scores may, in at least some
circumstances, be caused to appear earlier in the activity feed 544
than those having notification type IDs with lower context-based
notification forecast scores.
[0159] Further, in some implementations, rather than presenting a
separate, context-based view of an activity feed 544 that includes
only notifications 546 having notification type IDs that match
notification type IDs appearing in the table 1500, the notification
type IDs appearing in the table 1500, and/or the context-based
notification forecast scores determined for those notification type
IDs, may additionally or alternatively be used to enhance the
"relevance" scores for other of the active notifications 546 in a
user's activity feed 544. In some implementations, for example, a
weighting value may be applied to relevance scores, e.g., as
determined by the analytics service 536 described below, based on
whether pending notifications 546 appear in the table 1500 and/or
the context-based notification forecast scores that were determined
for those notification type IDs. Accordingly, the context-based
notification forecast scores may additionally or alternatively be
used to influence the order in which notifications 546 appear in a
user's activity feed 544 when the user selects the "relevance"
sorting option, e.g., via the user-interface element 570 shown in
FIG. 5D.
G. Example Implementations of Methods, Systems, and
Computer-Readable Media in Accordance with the Present
Disclosure
[0160] The following paragraphs (M1) through (M12) describe
examples of methods that may be implemented in accordance with the
present disclosure.
[0161] (M1) A method may be performed that involves determining
first feature vectors for a plurality of data items accessed by a
user of one or more client devices, the first feature vectors
representing first contextual data about the one or more client
devices at times that respective data items of the plurality of
data items were accessed, the plurality of data items including a
first data item; determining, using a predictive model configured
to classify input feature vectors into context types, that the
first feature vector for the first data item is classified as a
first context type; determining that the first data item is of a
first data item type; determining a second feature vector
representing second contextual data about a first client device
operated by the user; determining, using the predictive model, that
the second feature vector is classified as the first context type;
determining that a second data item is of the first data item type;
and causing, based at least in part on the first and second feature
vectors being classified as the first context type and the first
and second data items being of the first data item type, the first
client device to present the second data item.
[0162] (M2) A method may be performed as described in paragraph
(M1), and may further involve generating, using at least a first
group of the first feature vectors and a clustering process, the
predictive model.
[0163] (M3) A method may be performed as described in paragraph
(M1) or paragraph (M2), and may further involve determining that
the first feature vectors for at least a threshold number of the
first data items of the first type have been classified as the
first context type; wherein causing the first client device to
present the second data item may be further based at least in part
on the threshold number of the first data items of the first type
having been classified as the first context type.
[0164] (M4) A method may be performed as described in any of
paragraphs (M1) through (M3), and may further involve detecting
events of one or more applications; and generating the first and
second data items as first and second notifications, respectively,
concerning the detected events.
[0165] (M5) A method may be performed as described in paragraph
(M4), wherein generating the second data item may further comprise
causing the second notification to include at least one user
interface element enabling the user to take an action with respect
to an application to which the second notification relates.
[0166] (M6) A method may be performed as described in any of
paragraphs (M1) through (M5), and may further involve receiving,
from the first client device, a request for a context-based view of
an activity feed of notifications, the request including the second
contextual data; wherein causing the first client device to present
the second data item is performed in response to the request.
[0167] (M7) A method may be performed as described in any of
paragraphs (M1) through (M6), wherein the second contextual data
may comprise at least one of an identifier of the first client
device, a current time, a network to which the first client device
is connected, or a location of the first client device.
[0168] (M8) A method may be performed as described in any of
paragraphs (M1) through (M7), and may further involve receiving the
first contextual data from the one or more client devices, wherein
the first contextual data may comprise one or more identifiers of
the one or more client devices, current times at which the
plurality of data items were accessed by the user, one or more
networks to which the one or more client devices were connected
when the plurality of data items were accessed by the user, or
locations of the one or more client devices when the plurality of
data items were accessed by the user.
[0169] (M9) A method may be performed as described in any of
paragraphs (M1) through (M8), and may further involve determining a
relevance score indicative of a predicted relevance of the second
data item to the user, wherein relevance score is based at least in
part on the first and second feature vectors being classified as
the first context type and the first and second data items being of
the first data item type; and determining to cause the first client
device to present the second data item based at least in part on
the relevance score.
[0170] (M10) A method may be performed that involves generating, by
a computing system, at least first and second notifications to be
sent to a client device operated by a user, the first and second
notifications indicating, respectively, first and second events of
first and second applications accessible by the user; receiving, by
the computing system from the client device, first data indicative
of a current context of the client device; and sending, by the
computing system and based at least in part on the first data, the
first notification, but not the second notification, to the client
device.
[0171] (M11) A method may be performed as described in paragraph
(M10), wherein generating the first and second notifications may
further involve causing the first and second notifications to
include respective user interface elements enabling the user to
take corresponding actions with respect to the first and second
applications.
[0172] (M12) A method may be performed as described in paragraph
(M10) or paragraph (M11), wherein the first data may comprise at
least one of an identifier of the client device, a current time, a
network to which the client device is connected, or a location of
the client device.
[0173] The following paragraphs (S1) through (S12) describe
examples of systems and devices that may be implemented in
accordance with the present disclosure.
[0174] (S1) A system may comprise at least one processor, and at
least one computer-readable medium encoded with instructions which,
when executed by the at least one processor, cause the system to
determine first feature vectors for a plurality of data items
accessed by a user of one or more client devices, the first feature
vectors representing first contextual data about the one or more
client devices at times that respective data items of the plurality
of data items were accessed, the plurality of data items including
a first data item, to determine, using a predictive model
configured to classify input feature vectors into context types,
that the first feature vector for the first data item is classified
as a first context type, to determine that the first data item is
of a first data item type, to determine a second feature vector
representing second contextual data about a first client device
operated by the user, to determine, using the predictive model,
that the second feature vector is classified as the first context
type, to determine that a second data item is of the first data
item type, and to cause, based at least in part on the first and
second feature vectors being classified as the first context type
and the first and second data items being of the first data item
type, the first client device to present the second data item.
[0175] (S2) A system may be configured as described in paragraph
(S1), wherein the at least one computer-readable medium may be
encoded with additional instructions which, when executed by the at
least one processor, may further cause the system to generate,
using at least a first group of the first feature vectors and a
clustering process, the predictive model.
[0176] (S3) A system may be configured as described in paragraph
(S1) or paragraph (S2), wherein the at least one computer-readable
medium may be encoded with additional instructions which, when
executed by the at least one processor, may further cause the
system to determine that the first feature vectors for at least a
threshold number of the first data items of the first type have
been classified as the first context type, and to cause the first
client device to present the second data item further based at
least in part on the threshold number of the first data items of
the first type having been classified as the first context
type.
[0177] (S4) A system may be configured as described in any of
paragraphs (S1) through (S3), wherein the at least one
computer-readable medium may be encoded with additional
instructions which, when executed by the at least one processor,
may further cause the system to detect events of one or more
applications, and to generate the first and second data items as
first and second notifications, respectively, concerning the
detected events.
[0178] (S5) A system may be configured as described in paragraph
(S4), wherein the at least one computer-readable medium may be
encoded with additional instructions which, when executed by the at
least one processor, may further cause the system to generate the
second data item at least part by causing the second notification
to include at least one user interface element enabling the user to
take an action with respect to an application to which the second
notification relates.
[0179] (S6) A system may be configured as described in any of
paragraphs (S1) through (S5), wherein the at least one
computer-readable medium may be encoded with additional
instructions which, when executed by the at least one processor,
may further cause the system to receive, from the first client
device, a request for a context-based view of an activity feed of
notifications, the request including the second contextual data,
and to cause the first client device to present the second data
item in response to the request.
[0180] (S7) A system may be configured as described in any of
paragraphs (S1) through (S6), wherein the second contextual data
may comprise at least one of an identifier of the first client
device, a current time, a network to which the first client device
is connected, or a location of the first client device.
[0181] (S8) A system may be configured as described in any of
paragraphs (S1) through (S7), wherein the at least one
computer-readable medium may be encoded with additional
instructions which, when executed by the at least one processor,
may further cause the system to receive the first contextual data
from the one or more client devices, wherein the first contextual
data may comprise one or more identifiers of the one or more client
devices, current times at which the plurality of data items were
accessed by the user, one or more networks to which the one or more
client devices were connected when the plurality of data items were
accessed by the user, or locations of the one or more client
devices when the plurality of data items were accessed by the
user.
[0182] (S9) A system may be configured as described in any of
paragraphs (S1) through (S8), wherein the at least one
computer-readable medium may be encoded with additional
instructions which, when executed by the at least one processor,
may further cause the system to determine a relevance score
indicative of a predicted relevance of the second data item to the
user, wherein relevance score may be based at least in part on the
first and second feature vectors being classified as the first
context type and the first and second data items being of the first
data item type, and to determine to cause the first client device
to present the second data item based at least in part on the
relevance score.
[0183] (S10) A system may comprise at least one processor, and at
least one computer-readable medium encoded with instructions which,
when executed by the at least one processor, cause the system to
generate at least first and second notifications to be sent to a
client device operated by a user, the first and second
notifications indicating, respectively, first and second events of
first and second applications accessible by the user, to receive,
from the client device, first data indicative of a current context
of the client device, and to send, based at least in part on the
first data, the first notification, but not the second
notification, to the client device.
[0184] (S11) A system may be configured as described in paragraph
(S10), wherein the at least one computer-readable medium may be
encoded with additional instructions which, when executed by the at
least one processor, may further cause the system to generate the
first and second notifications at least in part by causing the
first and second notifications to include respective user interface
elements enabling the user to take corresponding actions with
respect to the first and second applications.
[0185] (S12) A system may be configured as described in paragraph
(S10) or paragraph (S11), wherein the first data may comprise at
least one of an identifier of the client device, a current time, a
network to which the client device is connected, or a location of
the client device.
[0186] The following paragraphs (CRM1) through (CRM12) describe
examples of computer-readable media that may be implemented in
accordance with the present disclosure.
[0187] (CRM1) At least one non-transitory computer-readable medium
may be encoded with instructions which, when executed by the at
least one processor of a computing system, cause the computing
system to determine first feature vectors for a plurality of data
items accessed by a user of one or more client devices, the first
feature vectors representing first contextual data about the one or
more client devices at times that respective data items of the
plurality of data items were accessed, the plurality of data items
including a first data item, to determine, using a predictive model
configured to classify input feature vectors into context types,
that the first feature vector for the first data item is classified
as a first context type, to determine that the first data item is
of a first data item type, to determine a second feature vector
representing second contextual data about a first client device
operated by the user, to determine, using the predictive model,
that the second feature vector is classified as the first context
type, to determine that a second data item is of the first data
item type, and to cause, based at least in part on the first and
second feature vectors being classified as the first context type
and the first and second data items being of the first data item
type, the first client device to present the second data item.
[0188] (CRM2) At least one non-transitory computer-readable medium
may be configured as described in paragraph (CRM1), and may be
encoded with additional instructions which, when executed by the at
least one processor, further cause the computing system to
generate, using at least a first group of the first feature vectors
and a clustering process, the predictive model.
[0189] (CRM3) At least one non-transitory computer-readable medium
may be configured as described in paragraph (CRM1) or paragraph
(CRM2), and may be encoded with additional instructions which, when
executed by the at least one processor, further cause the computing
system to determine that the first feature vectors for at least a
threshold number of the first data items of the first type have
been classified as the first context type, and to cause the first
client device to present the second data item further based at
least in part on the threshold number of the first data items of
the first type having been classified as the first context
type.
[0190] (CRM4) At least one non-transitory computer-readable medium
may be configured as described in any of paragraphs (CRM1) through
(CRM3), and may be encoded with additional instructions which, when
executed by the at least one processor, further cause the computing
system to detect events of one or more applications, and to
generate the first and second data items as first and second
notifications, respectively, concerning the detected events.
[0191] (CRM5) At least one non-transitory computer-readable medium
may be configured as described in paragraph (CRM4), and may be
encoded with additional instructions which, when executed by the at
least one processor, further cause the computing system to generate
the second data item at least part by causing the second
notification to include at least one user interface element
enabling the user to take an action with respect to an application
to which the second notification relates.
[0192] (CRM6) At least one non-transitory computer-readable medium
may be configured as described in any of paragraphs (CRM1) through
(CRM5), and may be encoded with additional instructions which, when
executed by the at least one processor, further cause the computing
system to receive, from the first client device, a request for a
context-based view of an activity feed of notifications, the
request including the second contextual data, and to cause the
first client device to present the second data item in response to
the request.
[0193] (CRM7) At least one non-transitory computer-readable medium
may be configured as described in any of paragraphs (CRM1) through
(CRM6), wherein the second contextual data may comprise at least
one of an identifier of the first client device, a current time, a
network to which the first client device is connected, or a
location of the first client device.
[0194] (CRM8) At least one non-transitory computer-readable medium
may be configured as described in any of paragraphs (CRM1) through
(CRM7), and may be encoded with additional instructions which, when
executed by the at least one processor, further cause the computing
system to receive the first contextual data from the one or more
client devices, wherein the first contextual data may comprise one
or more identifiers of the one or more client devices, current
times at which the plurality of data items were accessed by the
user, one or more networks to which the one or more client devices
were connected when the plurality of data items were accessed by
the user, or locations of the one or more client devices when the
plurality of data items were accessed by the user.
[0195] (CRM9) At least one non-transitory computer-readable medium
may be configured as described in any of paragraphs (CRM1) through
(CRM8), and may be encoded with additional instructions which, when
executed by the at least one processor, further cause the computing
system to determine a relevance score indicative of a predicted
relevance of the second data item to the user, wherein relevance
score may be based at least in part on the first and second feature
vectors being classified as the first context type and the first
and second data items being of the first data item type, and to
determine to cause the first client device to present the second
data item based at least in part on the relevance score.
[0196] (CRM10) At least one non-transitory computer-readable medium
may be encoded with instructions which, when executed by the at
least one processor of a computing system, cause the computing
system to generate at least first and second notifications to be
sent to a client device operated by a user, the first and second
notifications indicating, respectively, first and second events of
first and second applications accessible by the user, to receive,
from the client device, first data indicative of a current context
of the client device, and to send, based at least in part on the
first data, the first notification, but not the second
notification, to the client device.
[0197] (CRM11) At least one non-transitory computer-readable medium
may be configured as described in paragraph (CRM10), and may be
encoded with additional instructions which, when executed by the at
least one processor, further cause the computing system to generate
the first and second notifications at least in part by causing the
first and second notifications to include respective user interface
elements enabling the user to take corresponding actions with
respect to the first and second applications.
[0198] (CRM12) At least one non-transitory computer-readable medium
may be configured as described in paragraph (CRM10) or paragraph
(CRM11), wherein the first data may comprise at least one of an
identifier of the client device, a current time, a network to which
the client device is connected, or a location of the client
device.
[0199] Having thus described several aspects of at least one
embodiment, it is to be appreciated that various alterations,
modifications, and improvements will readily occur to those skilled
in the art. Such alterations, modifications, and improvements are
intended to be part of this disclosure, and are intended to be
within the spirit and scope of the disclosure. Accordingly, the
foregoing description and drawings are by way of example only.
[0200] Various aspects of the present disclosure may be used alone,
in combination, or in a variety of arrangements not specifically
discussed in the embodiments described in the foregoing and is
therefore not limited in this application to the details and
arrangement of components set forth in the foregoing description or
illustrated in the drawings. For example, aspects described in one
embodiment may be combined in any manner with aspects described in
other embodiments.
[0201] Also, the disclosed aspects may be embodied as a method, of
which an example has been provided. The acts performed as part of
the method may be ordered in any suitable way. Accordingly,
embodiments may be constructed in which acts are performed in an
order different than illustrated, which may include performing some
acts simultaneously, even though shown as sequential acts in
illustrative embodiments.
[0202] Use of ordinal terms such as "first," "second," "third,"
etc. in the claims to modify a claim element does not by itself
connote any priority, precedence or order of one claim element over
another or the temporal order in which acts of a method are
performed, but are used merely as labels to distinguish one claimed
element having a certain name from another element having a same
name (but for use of the ordinal term) to distinguish the claim
elements.
[0203] Also, the phraseology and terminology used herein is used
for the purpose of description and should not be regarded as
limiting. The use of "including," "comprising," or "having,"
"containing," "involving," and variations thereof herein, is meant
to encompass the items listed thereafter and equivalents thereof as
well as additional items.
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