U.S. patent application number 14/850595 was filed with the patent office on 2017-03-16 for mobile device session analyzer.
The applicant listed for this patent is Alec Lazarescu, Matthew Shapiro. Invention is credited to Alec Lazarescu, Matthew Shapiro.
Application Number | 20170078156 14/850595 |
Document ID | / |
Family ID | 58237401 |
Filed Date | 2017-03-16 |
United States Patent
Application |
20170078156 |
Kind Code |
A1 |
Lazarescu; Alec ; et
al. |
March 16, 2017 |
MOBILE DEVICE SESSION ANALYZER
Abstract
Techniques described herein relate to determining performance
conditions for mobile device execution sessions based on analyses
of session context data and performance data associated with
content resource execution sessions. Mobile devices may receive and
execute content resources via input/output subsystems, during which
feedback data by the mobile devices. Mobile devices also may be
configured to detect and/or determine context data associated with
specific content resource execution sessions, using device sensors
and other techniques. Session analyzers may receive and analyze
content resource execution session data received from one or more
mobile devices, and determine user performance conditions for
content resource execution sessions.
Inventors: |
Lazarescu; Alec; (Fanwood,
NJ) ; Shapiro; Matthew; (Aurora, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Lazarescu; Alec
Shapiro; Matthew |
Fanwood
Aurora |
NJ
CO |
US
US |
|
|
Family ID: |
58237401 |
Appl. No.: |
14/850595 |
Filed: |
September 10, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/142 20130101;
H04L 41/5009 20130101; H04W 24/02 20130101; H04L 67/303 20130101;
H04L 67/32 20130101; H04L 43/04 20130101; H04L 67/306 20130101;
H04W 4/027 20130101; H04L 67/22 20130101; H04W 24/08 20130101; H04W
4/029 20180201; H04L 67/26 20130101 |
International
Class: |
H04L 12/24 20060101
H04L012/24; H04L 29/06 20060101 H04L029/06; H04L 29/08 20060101
H04L029/08; H04W 88/02 20060101 H04W088/02 |
Claims
1. A mobile device session analyzer system comprising: one or more
mobile communication devices, each mobile communication device
comprising: a processing unit comprising one or more processors;
one or more movement detection sensors, said movement detection
sensors comprising at least one of an accelerometer, a speedometer,
a compass, a gyroscope, or a digital positioning system; an
input/output (I/O) subsystem configured to receive input data via
one or more input devices connected to or integral with the mobile
communication device; and memory coupled with and readable by the
processing unit and storing therein a set of instructions which,
when executed by the processing unit, causes the mobile
communication device to: receive instructions via the I/O subsystem
to execute one or more content resources; in response to the
instructions, execute the content resources on the mobile
communication device during a content resource execution session;
receive user interaction data via the I/O subsystem during the
content resource execution session; determine context data
associated with content resource execution session; and transmit
data corresponding to the user interaction data received during the
content resource execution session and the context data associated
with the content resource execution session to one or more computer
servers, wherein the context data determined and transmitted to the
to one or more computer servers includes movement data detected by
the one or more movement detection sensors, the movement data
identifying movements of the mobile communication device during the
content resource execution session; and a computer server
comprising: a processing unit comprising one or more processors;
and memory coupled with and readable by the processing unit and
storing therein a set of instructions which, when executed by the
processing unit, causes the computer server to: receive data from
one or more mobile communication devices, the data corresponding to
a plurality of content resource execution sessions associated with
a first user; based on the received data, determine one or more
performance measurements for each of the plurality of content
resource execution sessions; determine context data for each of the
plurality of content resource execution sessions based on the
received data, wherein each determined context data comprises a
value or range of values for one or more session context variables;
analyze the performance measurements and the context data for the
plurality of content resource execution sessions; determine a set
of performance conditions for the first user, based on the analysis
of the performance measurements and the context data for the
content resource execution sessions, each said performance
condition comprising a value or range of values for one or more of
the session context variables, wherein each said performance
condition is associated with an expected performance level of the
first user during content resource execution sessions, said
performance conditions including at least one performance condition
corresponding to a pattern of device movement associated with an
expected performance level of the first user during content
resource execution sessions; perform monitoring of additional
context data received from a first mobile communication device
associated with the first user, the additional context data
including device movement data received from the first mobile
communication device; determine that a current context data of the
first mobile communication device matches one or more performance
conditions associated with the first user, including determining
that the device movement data received from the first mobile
communication device matches the pattern of device movement
associated with the expected performance level of the first user;
and transmit a notification to the first mobile communication
device, said notification indicating that the current context data
of the first mobile communication device matches the determined
performance conditions for the first user.
2-5. (canceled)
6. The mobile device session analyzer system of claim 1, wherein
one or more of the mobile communication devices includes an
electronic positioning system, wherein the context data transmitted
by the mobile communication devices includes location data
associated with one or more content resource execution sessions,
and wherein determining the performance conditions for the first
user comprises determining at least one performance condition
corresponding to a location or range of locations associated with
an expected performance level of the first user during content
resource execution sessions.
7. The mobile device session analyzer system of claim 1, wherein
one or more of the mobile communication devices includes a timing
system, wherein the context data transmitted by the mobile
communication devices includes at least one of session time data or
session length data associated with one or more content resource
execution sessions, and wherein determining the performance
conditions for the first user comprises determining at least one
performance condition corresponding to a session time or a session
length associated with an expected performance level of the first
user during content resource execution sessions.
8. The mobile device session analyzer system of claim 1, wherein
the context data transmitted by the mobile communication devices
includes data indicating a set of physical device characteristics
corresponding to capabilities of one or more mobile communication
devices on which one or more content resource execution sessions
were performed, and wherein determining the performance conditions
for the first user comprises determining at least one performance
condition corresponding to a device characteristic and capability
or set of device characteristics and capabilities associated with
an expected performance level of the first user during content
resource execution sessions.
9. The mobile device session analyzer system of claim 1, wherein
one or more of the mobile communication devices further includes a
plurality of health and activity sensors configured to collect user
biostatistics data, and wherein the context data transmitted by the
mobile communication devices further includes biostatistics data of
the first user associated, collected via the health and activity
sensors of the one or more mobile communication devices, within one
or more content resource execution sessions, and wherein
determining the performance conditions for the first user further
comprises determining at least one performance condition
corresponding to a set of user biostatistics data associated with
an expected performance level of the first user during content
resource execution sessions.
10. (canceled)
11. A method comprising: receiving, by a session analyzer server,
data from one or more mobile client devices, the data corresponding
to a plurality of content resource execution sessions associated
with a first user; determining, by the session analyzer server,
based on the received data, one or more performance measurements
for each of the plurality of content resource execution sessions;
determining, by the session analyzer server, context data for each
of the plurality of content resource execution sessions based on
the received data, wherein each determined context data comprises a
value or range of values for one or more session context variables;
analyzing, by the session analyzer server, the performance
measurements and the context data for the plurality of content
resource execution sessions; determining, by the session analyzer
server, a set of performance conditions for the first user, based
on the analysis of the performance measurements and the context
data for the content resource execution sessions, each said
performance condition comprising a value or range of values for one
or more of the session context variables, wherein each said
performance condition is associated with an expected performance
level of the first user during content resource execution sessions;
determining, by the session analyzer server, a subset of the
determined performance conditions associated with a first mobile
client device associated with the first user; transmitting, by the
session analyzer server, the determined subset of performance
conditions to the first mobile device associated with the first
user, wherein each performance condition transmitted to the first
mobile device includes a session context variable and a
corresponding value or range of values for the session context
variable; performing monitoring of additional context data received
from the first mobile client device associated with the first user;
determining that a current context data of the first mobile client
device matches one or more performance conditions associated with
the first user; and transmitting, by the session analyzer server, a
notification to the first mobile client device, said notification
indicating that the current context data of the first mobile client
device matches the determined performance conditions for the first
user.
12. (canceled)
13. The method of claim 11, wherein determining the performance
measurements for a first session of the plurality of content
resource execution sessions comprises: analyzing user feedback data
received via an I/O subsystem of a mobile client device in response
to the content resources executed during the first session.
14. The method of claim 11, wherein determining the performance
measurements for the plurality of content resource execution
sessions comprises: calculating a rate at which the first user
returns to previously executed content during the one or more
content resource execution sessions.
15. The method of claim 11, wherein determining the performance
measurements for a first session of the plurality of content
resource execution sessions comprises: comparing a set of user
inputs received via an I/O subsystem of a mobile client device
during the first session, to a predetermined set of preferred user
inputs; and based on the comparison, calculating one or more scores
for the first user for the first session.
16. The method of claim 11, wherein determining the performance
conditions for the first user comprises: analyzing location data
received from one or more mobile client devices associated with one
or more content resource execution sessions of the first user; and
determining at least one performance condition corresponding to a
location or range of locations associated with an expected
performance level of the first user during content resource
execution sessions, wherein transmitting the determined subset of
performance conditions comprises transmitting the location or range
of locations associated with the expected performance level of the
first user, to the first mobile device.
17. The method of claim 11, wherein determining the performance
conditions for the first user comprises: analyzing session timing
data received from one or more mobile client devices associated
with one or more content resource execution sessions of the first
user; and determining at least one performance condition
corresponding to a session time or a session length associated with
an expected performance level of the first user during content
resource execution sessions, wherein transmitting the determined
subset of performance conditions comprises transmitting the session
time or session length associated with the expected performance
level of the first user, to the first mobile device.
18. The method of claim 11, wherein determining the performance
conditions for the first user comprises: analyzing device
capability data received from one or more mobile client devices
associated with one or more content resource execution sessions of
the first user, the device capability data indicating a set of
physical device characteristics corresponding to capabilities of
the one or more mobile client devices; and determining at least one
performance condition corresponding to a device characteristic and
capability or set of device characteristics and capabilities
associated with an expected performance level of the first user
during content resource execution sessions.
19. The method of claim 11, wherein determining the performance
conditions for the first user comprises: analyzing user
biostatistics data received from one or more mobile client devices
associated with one or more content resource execution sessions of
the first user, the analyzed user biostatistics data collected via
a plurality of health and activity sensors of the one or more
mobile client devices, including at least one of health data for
the first user, sleep data for the first user, and diet data for
the first user; and determining at least one performance condition
corresponding to a set of user biostatistics data associated with
an expected performance level of the first user during content
resource execution sessions, the at least one performance condition
including at least one of a user health data condition, a user
sleep data condition, or a user diet data condition, wherein
transmitting the determined subset of performance conditions
comprises transmitting the set of user biostatistics data
associated with the expected performance level of the first user,
to the first mobile device.
20. The method of claim 11, wherein analyzing the performance
measurements and the context data, and determining the performance
conditions for the first user, comprises: analyzing one or more
time intervals between a plurality of related content resource
execution sessions; and determining a time interval or range of
time intervals between successive related content resource
execution sessions that is associated with an expected performance
level of the first user for the related content resource execution
sessions.
21. The method of claim 11, wherein determining the performance
conditions for the first user comprises: analyzing device movement
data received from one or more mobile client devices associated
with one or more content resource execution sessions of the first
user, the device movement data including data collected by at least
one of an accelerometer, a speedometer, a compass, a gyroscope, or
a digital positioning system of the one or more mobile client
devices; and determining at least one performance condition
corresponding to a device movement pattern associated with an
expected performance level of the first user during content
resource execution sessions, wherein transmitting the determined
subset of performance conditions comprises transmitting the device
movement pattern associated with the expected performance level of
the first user, to the first mobile device.
22. The method of claim 11, wherein the notification transmitted to
the first mobile client device is transmitted in response to a
request from the first mobile client device indicating that first
user intends to initiate a new content resource execution session
on the first mobile client device, the notification including one
or more suggestions to the first user for executing certain content
resources based on the current context data of the first mobile
client device.
23. The mobile device session analyzer system of claim 1, wherein
the determined set of performance conditions for the first user
include at least one of: a performance condition corresponding to a
walking pattern of device movement; a performance condition
corresponding to a jogging or running pattern of device movement; a
performance condition corresponding to an automobile travel pattern
of device movement; a performance condition corresponding to a
train travel pattern of device movement; or a performance condition
corresponding to an airplane travel pattern of device movement.
24. The method of claim 11, wherein determining the performance
conditions for the first user comprises: analyzing network speed
data received from one or more mobile client devices associated
with one or more content resource execution sessions of the first
user; and determining at least one performance condition
corresponding to a network speed associated with an expected
performance level of the first user during content resource
execution sessions, wherein transmitting the determined subset of
performance conditions comprises transmitting the network speed
associated with the expected performance level of the first user,
to the first mobile device.
25. The method of claim 11, wherein performing monitoring of
additional context data received from the first mobile client
device associated with the first user comprises: receiving, from
the first mobile client device, periodic transmissions of device
context data.
26. The method of claim 25, wherein the periodic transmissions of
device context data received from the first mobile client device
include data identifying a current user of the first mobile client
device and a current network of the first mobile client device.
Description
BACKGROUND
[0001] Effective performance and interactions during execution
sessions on mobile devices is often challenging. Different mobile
devices may have unique sets of device capabilities and
characteristics. Additionally, multiple mobile devices may be
related and used to perform similar tasks and execute related sets
of content resources. Certain mobile device may be personal and
individual devices, while others may be shared devices. Moreover,
the computing conditions and physical environments in which mobile
devices are used to perform tasks and execute resources may be
constantly changing. For example, mobile devices may be used at
different times and places, and in different network conditions,
physical environments, and the like, which may result in varying
and unpredictable performance during different content resource
execution sessions.
BRIEF SUMMARY
[0002] Various techniques (e.g., systems, methods, computer-program
products tangibly embodied in a non-transitory machine-readable
storage medium, etc.) are described herein for determining
performance conditions for mobile device execution sessions based
on analyses of session context data and performance data associated
with content resource execution sessions. Mobile devices may
receive and execute content resources via input/output subsystems,
during which feedback data by the mobile devices. Mobile devices
also may be configured to detect and/or determine context data
associated with specific content resource execution sessions, using
device sensors and other techniques. Session analyzers may receive
and analyze content resource execution session data received from
one or more mobile devices, and determine user performance
conditions for content resource execution sessions.
[0003] Additional techniques discussed herein relate to monitoring
one or more mobile devices after set of performance conditions have
been determined for the mobile devices and/or associated users.
Current context data for each mobile device may be tracked and
monitored, in order to provide notifications and resource
suggestions based on performance conditions associated with users,
mobile devices, and/or content resource types. Further techniques
described herein relate to determining performance conditions and
monitoring mobile device context data based on groups of related
content resource execution sessions.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a block diagram showing illustrating an example of
a content distribution network.
[0005] FIG. 2 is a block diagram illustrating a computer server and
computing environment within a content distribution network.
[0006] FIG. 3 is a block diagram illustrating an embodiment of one
or more data store servers within a content distribution
network.
[0007] FIG. 4 is a block diagram illustrating an embodiment of one
or more content management servers within a content distribution
network.
[0008] FIG. 5 is a block diagram illustrating the physical and
logical components of a special-purpose computer device within a
content distribution network.
[0009] FIG. 6 is a block diagram illustrating an example system
including a mobile device session analyzer and a plurality of
mobile devices, according to one or more embodiments of the
disclosure.
[0010] FIGS. 7A and 7B are illustrative data tables containing
example sets of mobile device data and session data, according to
one or more embodiments of the disclosure.
[0011] FIG. 8 is a block diagram illustrating another example
system including a mobile device session analyzer and a plurality
of mobile devices, according to one or more embodiments of the
disclosure.
[0012] FIG. 9 is a flow diagram illustrating an example process of
determining performance conditions corresponding to a user's
content resource execution sessions, according to one or more
embodiments of the disclosure.
[0013] FIGS. 10A and 10B are illustrative data tables containing
example performance condition data, according to one or more
embodiments of the disclosure.
[0014] FIG. 11 is a flow diagram illustrating an example process of
determining a set of content resources based on the current context
data of a mobile device, according to one or more embodiments of
the disclosure.
[0015] FIG. 12 is a flow diagram illustrating an example process of
determining content resources to present to a user based on the
current context data of a mobile device, according to one or more
embodiments of the disclosure.
[0016] FIG. 13 is an illustrative user interface of a mobile device
providing a notification to a user in response to evaluating the
performance conditions for the user based on the current context
data of the mobile device, according to one or more embodiments of
the disclosure.
[0017] FIG. 14 is an illustrative user interface of a mobile device
providing a set of content suggestions to a user based on the
current context data of the mobile device, according to one or more
embodiments of the disclosure.
[0018] FIG. 15 is a flow diagram illustrating an example process of
determining performance conditions corresponding to a user's group
of content resource execution sessions, according to one or more
embodiments of the disclosure.
[0019] FIG. 16 is an illustrative data table containing example
session group data, according to one or more embodiments of the
disclosure.
[0020] FIG. 17 is an illustrative user interface of a mobile device
providing a notification to a user in response to evaluating the
session group performance conditions for the user, according to one
or more embodiments of the disclosure.
[0021] In the appended figures, similar components and/or features
may have the same reference label. Further, various compo of the
same type may be distinguished by following the reference label by
a dash and a second label that distinguishes among the similar
components. If only the first reference label is used in the
specification, the description is applicable to any one of the
similar components having the same first reference label
irrespective of the second reference label.
DETAILED DESCRIPTION
[0022] The ensuing description provides illustrative embodiment(s)
only and is not intended to limit the scope, applicability or
configuration of the disclosure. Rather, the ensuing description of
the illustrative embodiment(s) will provide those skilled in the
art with an enabling description for implementing a preferred
exemplary embodiment. It is understood that various changes can be
made in the function and arrangement of elements without departing
from the spirit and scope as set forth in the appended claims.
[0023] With reference now to FIG. 1, a block diagram is shown
illustrating various components of a content distribution network
(CDN) 100 which implements and supports certain embodiments and
features described herein. Content distribution network 100 may
include one or more content management servers 102. As discussed
below in more detail, content management servers 102 may be any
desired type of server including, for example, a rack server, a
tower server, a miniature server, a blade server, a mini rack
server, a mobile server, an ultra-dense server, a super server, or
the like, and may include various hardware components, for example,
a motherboard, a processing units, memory systems, hard drives,
network interfaces, power supplies, etc. Content management server
102 may include one or more server farms, clusters, or any other
appropriate arrangement and/or combination or computer servers.
Content management server 102 may act according to stored
instructions located in a memory subsystem of the server 102, and
may run an operating system, including any commercially available
server operating system and/or any other operating systems
discussed herein.
[0024] The content distribution network 100 may include one or more
data store servers 104, such as database servers and file-based
storage systems. Data stores 104 may comprise stored data relevant
to the functions of the content distribution network 100.
Illustrative examples of data stores 104 that may be maintained in
certain embodiments of the content distribution network 100 are
described below in reference to FIG. 3. In some embodiments,
multiple data stores may reside on a single server 104, either
using the same storage components of server 104 or using different
physical storage components to assure data security and integrity
between data stores. In other embodiments, each data store may have
a separate dedicated data store server 104.
[0025] Content distribution network 100 also may include one or
more user devices 106 and/or supervisor devices 110. User devices
106 and supervisor devices 110 may display content received via the
content distribution network 100, and may support various types of
user interactions with the content. User devices 106 and supervisor
devices 110 may include mobile devices such as smartphones, tablet
computers, personal digital assistants, and wearable computing
devices. Such mobile devices may run a variety of mobile operating
systems, and may be enabled for Internet, e-mail, short message
service (SMS), Bluetooth.RTM., mobile radio-frequency
identification (M-RFID), and/or other communication protocols.
Other user devices 106 and supervisor devices 110 may be general
purpose personal computers or special-purpose computing devices
including, by way of example, personal computers, laptop computers,
workstation computers, projection devices, and interactive room
display systems. Additionally, user devices 106 and supervisor
devices 110 may be any other electronic devices, such as
thin-client computers, Internet-enabled gaming systems, business or
home appliances, and/or personal messaging devices, capable of
communicating over network(s) 120.
[0026] In different contexts of content distribution networks 100,
user devices 106 and supervisor devices 110 may correspond to
different types of specialized devices, for example, student
devices and teacher devices in an educational network, employee
devices and presentation devices in a company network, different
gaming devices in a gaming network, etc. In some embodiments, user
devices 106 and supervisor devices 110 may operate in the same
physical location 107, such as a classroom or conference room. In
such cases, the devices may contain components that support direct
communications with other nearby devices, such as a wireless
transceivers and wireless communications interfaces, Ethernet
sockets or other Local Area Network (LAN) interfaces, etc. In other
implementations, the user devices 106 and supervisor devices 110
need not be used at the same location 107, but may be used in
remote geographic locations in which each user device 106 and
supervisor device 110 may use security features and/or specialized
hardware (e.g., hardware-accelerated SSL and HTTPS, WS-Security,
firewalls, etc.) to communicate with the content management server
102 and/or other remotely located user devices 106. Additionally,
different user devices 106 and supervisor devices 110 may be
assigned different designated roles, such as presenter devices,
teacher devices, administrator devices, or the like, and in such
cases the different devices may be provided with additional
hardware and/or software components to provide content and support
user capabilities not available to the other devices.
[0027] The content distribution network 100 also may include a
privacy server 108 that maintains private user information at the
privacy server 108 while using applications or services hosted on
other servers. For example, the privacy server 108 may be used to
maintain private data of a user within one jurisdiction even though
the user is accessing an application hosted on a server (e.g., the
content management server 102) located outside the jurisdiction. In
such cases, the privacy server 108 may intercept communications
between a user device 106 or supervisor device 110 and other
devices that include private user information. The privacy server
108 may create a token or identifier that does not disclose the
private information and may use the token or identifier when
communicating with the other servers and systems, instead of using
the user's private information.
[0028] As illustrated in FIG. 1, the content management server 102
may be in communication with one or more additional servers, such
as a content server 112, a user data server 112, and/or an
administrator server 116. Each of these servers may include some or
all of the same physical and logical components as the content
management server(s) 102, and in some cases, the hardware and
software components of these servers 112-116 may be incorporated
into the content management server(s) 102, rather than being
implemented as separate computer servers.
[0029] Content server 112 may include hardware and software
components to generate, store, and maintain the content resources
for distribution to user devices 106 and other devices in the
network 100. For example, in content distribution networks 100 used
for professional training and educational purposes, content server
112 may include data stores of training materials, presentations,
interactive programs and simulations, course models, course
outlines, and various training interfaces that correspond to
different materials and/or different types of user devices 106. In
content distribution networks 100 used for media distribution,
interactive gaming, and the like, a content server 112 may include
media content files such as music, movies, television programming,
games, and advertisements.
[0030] User data server 114 may include hardware and software
components that store and process data for multiple users relating
to each user's activities and usage of the content distribution
network 100. For example, the content management server 102 may
record and track each user's system usage, including their user
device 106, content resources accessed, and interactions with other
user devices 106. This data may be stored and processed by the user
data server 114, to support user tracking and analysis features.
For instance, in the professional training and educational
contexts, the user data server 114 may store and analyze each
user's training materials viewed, presentations attended, courses
completed, interactions, evaluation results, and the like. The user
data server 114 may also include a repository for user-generated
material, such as evaluations and tests completed by users, and
documents and assignments prepared by users. In the context of
media distribution and interactive gaming, the user data server 114
may store and process resource access data for multiple users
(e.g., content titles accessed, access times, data usage amounts,
gaming histories, user devices and device types, etc.).
[0031] Administrator server 116 may include hardware and software
components to initiate various administrative functions at the
content management server 102 and other components within the
content distribution network 100. For example, the administrator
server 116 may monitor device status and performance for the
various servers, data stores, and/or user devices 106 in the
content distribution network 100. When necessary, the administrator
server 116 may add or remove devices from the network 100, and
perform device maintenance such as providing software updates to
the devices in the network 100. Various administrative tools on the
administrator server 116 may allow authorized users to set user
access permissions to various content resources, monitor resource
usage by users and devices 106, and perform analyses and generate
reports on specific network users and/or devices (e.g., resource
usage tracking reports, training evaluations, etc.).
[0032] The content distribution network 100 may include one or more
communication networks 120. Although only a single network 120 is
identified in FIG. 1, the content distribution network 100 may
include any number of different communication networks between any
of the computer servers and devices shown in FIG. 1 and/or other
devices described herein. Communication networks 120 may enable
communication between the various computing devices, servers, and
other components of the content distribution network 100. As
discussed below, various implementations of content distribution
networks 100 may employ different types of networks 120, for
example, computer networks, telecommunications networks, wireless
networks, and/or any combination of these and/or other
networks.
[0033] With reference to FIG. 2, an illustrative distributed
computing environment 200 is shown including a computer server 202,
four client computing devices 206, and other components that may
implement certain embodiments and features described herein. In
some embodiments, the server 202 may correspond to the content
management server 102 discussed above in FIG. 1, and the client
computing devices 206 may correspond to the user devices 106.
However, the computing environment 200 illustrated in FIG. 2 may
correspond to any other combination of devices and servers
configured to implement a client-server model or other distributed
computing architecture.
[0034] Client devices 206 may be configured to receive and execute
client applications over one or more networks 220. Such client
applications may be web browser based applications and/or
standalone software applications, such as mobile device
applications. Server 202 may be communicatively coupled with the
client devices 206 via one or more communication networks 220.
Client devices 206 may receive client applications from server 202
or from other application providers (e.g., public or private
application stores). Server 202 may be configured to run one or
more server software applications or services, for example,
web-based or cloud-based services, to support content distribution
and interaction with client devices 206. Users operating client
devices 206 may in turn utilize one or more client applications
(e.g., virtual client applications) to interact with server 202 to
utilize the services provided by these components.
[0035] Various different subsystems and/or components 204 may be
implemented on server 202. Users operating the client devices 206
may initiate one or more client applications to use services
provided by these subsystems and components. The subsystems and
components within the server 202 and client devices 206 may be
implemented in hardware, firmware, software, or combinations
thereof. Various different system configurations are possible in
different distributed computing systems 200 and content
distribution networks 100. The embodiment shown in FIG. 2 is thus
one example of a distributed computing system and is not intended
to be limiting.
[0036] Although exemplary computing environment 200 is shown with
four client computing devices 206, any number of client computing
devices may be supported. Other devices, such as specialized sensor
devices, etc., may interact with client devices 206 and/or server
202.
[0037] As shown in FIG. 2, various security and integration
components 208 may be used to send and manage communications
between the server 202 and user devices 206 over one or more
communication networks 220. The security and integration components
208 may include separate servers, such as web servers and/or
authentication servers, and/or specialized networking components,
such as firewalls, routers, gateways, load balancers, and the like.
In some cases, the security and integration components 208 may
correspond to a set of dedicated hardware and/or software operating
at the same physical location and under the control of same
entities as server 202. For example, components 208 may include one
or more dedicated web servers and network hardware in a datacenter
or a cloud infrastructure. In other examples, the security and
integration components 208 may correspond to separate hardware and
software components which may be operated at a separate physical
location and/or by a separate entity.
[0038] Security and integration components 208 may implement
various security features for data transmission and storage, such
as authenticating users and restricting access to unknown or
unauthorized users. In various implementations, security and
integration components 208 may provide, for example, a file-based
integration scheme or a service-based integration scheme for
transmitting data between the various devices in the content
distribution network 100. Security and integration components 208
also may use secure data transmission protocols and/or encryption
for data transfers, for example, File Transfer Protocol (FTP),
Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy
(PGP) encryption.
[0039] In some embodiments, one or more web services may be
implemented within the security and integration components 208
and/or elsewhere within the content distribution network 100. Such
web services, including cross-domain and/or cross-platform web
services, may be developed for enterprise use in accordance with
various web service standards, such as RESTful web services (i.e.,
services based on the Representation State Transfer (REST)
architectural style and constraints), and/or web services designed
in accordance with the Web Service Interoperability (WS-I)
guidelines. Some web services may use the Secure Sockets Layer
(SSL) or Transport Layer Security (TLS) protocol to provide secure
connections between the server 202 and user devices 206. SSL or TLS
may use HTTP or HTTPS to provide authentication and
confidentiality. In other examples, web services may be implemented
using REST over HTTPS with the OAuth open standard for
authentication, or using the WS-Security standard which provides
for secure SOAP messages using XML encryption. In other examples,
the security and integration components 208 may include specialized
hardware for providing secure web services. For example, security
and integration components 208 may include secure network
appliances having built-in features such as hardware-accelerated
SSL and HTTPS, WS-Security, and firewalls. Such specialized
hardware may be installed and configured in front of any web
servers, so that any external devices may communicate directly with
the specialized hardware.
[0040] Communication network(s) 220 may be any type of network
familiar to those skilled in the art that can support data
communications using any of a variety of commercially-available
protocols, including without limitation, TCP/IP (transmission
control protocol/Internet protocol), SNA (systems network
architecture), IPX (Internet packet exchange), Secure Sockets Layer
(SSL) or Transport Layer Security (TLS) protocols, Hyper Text
Transfer Protocol (HTTP) and Secure Hyper Text Transfer Protocol
(HTTPS), Bluetooth.RTM., Near Field Communication (NFC), and the
like. Merely by way of example, network(s) 220 may be local area
networks (LAN), such as one based on Ethernet, Token-Ring and/or
the like. Network(s) 220 also may be wide-area networks, such as
the Internet. Networks 220 may include telecommunication networks
such as a public switched telephone networks (PSTNs), or virtual
networks such as an intranet or an extranet. Infrared and wireless
networks (e.g., using the Institute of Electrical and Electronics
(IEEE) 802.11 protocol suite or other wireless protocols) also may
be included in networks 220.
[0041] Computing environment 200 also may include one or more data
stores 210 and/or back-end servers 212. In certain examples, the
data stores 210 may correspond to data store server(s) 104
discussed above in FIG. 1, and back-end servers 212 may correspond
to the various back-end servers 112-116. Data stores 210 and
servers 212 may reside in the same datacenter or may operate at a
remote location from server 202. In some cases, one or more data
stores 210 may reside on a non-transitory storage medium within the
server 202. Other data stores 210 and back-end servers 212 may be
remote from server 202 and configured to communicate with server
202 via one or more networks 220. In certain embodiments, data
stores 210 and back-end servers 212 may reside in a storage-area
network (SAN), or may use storage-as-a-service (STaaS)
architectural model.
[0042] With reference to FIG. 3, an illustrative set of data stores
and/or data store servers is shown, corresponding to the data store
servers 104 of the content distribution network 100 discussed above
in FIG. 1. One or more individual data stores 301-309 may reside in
storage on a single computer server 104 (or a single server farm or
cluster) under the control of a single entity, or may reside on
separate servers operated by different entities and/or at remote
locations. In some embodiments, data stores 301-309 may be accessed
by the content management server 102 and/or other devices and
servers within the network 100 (e.g., user devices 106, supervisor
devices 110, administrator servers 116, etc.). Access to one or
more of the data stores 301-309 may be limited or denied based on
the processes, user credentials, and/or devices attempting to
interact with the data store.
[0043] The paragraphs below describe examples of specific data
stores that may be implemented within some embodiments of a content
distribution network 100. It should be understood that the below
descriptions of data stores 301-309, including their functionality
and types of data stored therein, are illustrative and
non-limiting. Data stores server architecture, design, and the
execution of specific data stores 301-309 may depend on the
context, size, and functional requirements of a content
distribution network 100. For example, in content distribution
systems 100 used for professional training and educational
purposes, separate databases or file-based storage systems may be
implemented in data store server(s) 104 to store trainee and/or
student data, trainer and/or professor data, training module data
and content descriptions, training results, evaluation data, and
the like. In contrast, in content distribution systems 100 used for
media distribution from content providers to subscribers, separate
data stores may be implemented in data stores server(s) 104 to
store listings of available content titles and descriptions,
content title usage statistics, subscriber profiles, account data,
payment data, network usage statistics, etc.
[0044] A user profile data store 301 may include information
relating to the end users within the content distribution network
100. This information may include user characteristics such as the
user names, access credentials (e.g., logins and passwords), user
preferences, and information relating to any previous user
interactions within the content distribution network 100 (e.g.,
requested content, posted content, content modules completed,
training scores or evaluations, other associated users, etc.).
[0045] An accounts data store 302 may generate and store account
data for different users in various roles within the content
distribution network 100. For example, accounts may be created in
an accounts data store 302 for individual end users, supervisors,
administrator users, and entities such as companies or educational
institutions. Account data may include account types, current
account status, account characteristics, and any parameters,
limits, restrictions associated with the accounts.
[0046] A content library data store 303 may include information
describing the individual content items (or content resources)
available via the content distribution network 100. In some
embodiments, the library data store 303 may include metadata,
properties, and other characteristics associated with the content
resources stored in the content server 112. Such data may identify
one or more aspects or content attributes of the associated content
resources, for example, subject matter, access level, or skill
level of the content resources, license attributes of the content
resources (e.g., any limitations and/or restrictions on the
licensable use and/or distribution of the content resource), price
attributes of the content resources (e.g., a price and/or price
structure for determining a payment amount for use or distribution
of the content resource), rating attributes for the content
resources (e.g., data indicating the evaluation or effectiveness of
the content resource), and the like. In some embodiments, the
library data store 303 may be configured to allow updating of
content metadata or properties, and to allow the addition and/or
removal of information relating to the content resources. For
example, content relationships may be implemented as graph
structures, which may be stored in the library data store 303 or in
an additional store for use by selection algorithms along with the
other metadata.
[0047] A pricing data store 304 may include pricing information
and/or pricing structures for determining payment amounts for
providing access to the content distribution network 100 and/or the
individual content resources within the network 100. In some cases,
pricing may be determined based on a user's access to the content
distribution network 100, for example, a time-based subscription
fee, or pricing based on network usage and. In other cases, pricing
may be tied to specific content resources. Certain content
resources may have associated pricing information, whereas other
pricing determinations may be based on the resources accessed, the
profiles and/or accounts of the user, and the desired level of
access (e.g., duration of access, network speed, etc.).
Additionally, the pricing data store 304 may include information
relating to compilation pricing for groups of content resources,
such as group prices and/or price structures for groupings of
resources.
[0048] A license data store 305 may include information relating to
licenses and/or licensing of the content resources within the
content distribution network 100. For example, the license data
store 305 may identify licenses and licensing terms for individual
content resources and/or compilations of content resources in the
content server 112, the rights holders for the content resources,
and/or common or large-scale right holder information such as
contact information for rights holders of content not included in
the content server 112.
[0049] A content access data store 306 may include access rights
and security information for the content distribution network 100
and specific content resources. For example, the content access
data store 306 may include login information (e.g., user
identifiers, logins, passwords, etc.) that can be verified during
user login attempts to the network 100. The content access data
store 306 also may be used to store assigned user roles and/or user
levels of access. For example, a user's access level may correspond
to the sets of content resources and/or the client or server
applications that the user is permitted to access. Certain users
may be permitted or denied access to certain applications and
resources based on their subscription level, training program,
course/grade level, etc. Certain users may have supervisory access
over one or more end users, allowing the supervisor to access all
or portions of the end user's content, activities, evaluations,
etc. Additionally, certain users may have administrative access
over some users and/or some applications in the content management
network 100, allowing such users to add and remove user accounts,
modify user access permissions, perform maintenance updates on
software and servers, etc.
[0050] A source data store 307 may include information relating to
the source of the content resources available via the content
distribution network. For example, a source data store 307 may
identify the authors and originating devices of content resources,
previous pieces of data and/or groups of data originating from the
same authors or originating devices, and the like.
[0051] An evaluation data store 308 may include information used to
direct the evaluation of users and content resources in the content
management network 100. In some embodiments, the evaluation data
store 308 may contain, for example, the analysis criteria and the
analysis guidelines for evaluating users (e.g., trainees/students,
gaming users, media content consumers, etc.) and/or for evaluating
the content resources in the network 100. The evaluation data store
308 also may include information relating to evaluation processing
tasks, for example, the identification of users and user devices
106 that have received certain content resources or accessed
certain applications, the status of evaluations or evaluation
histories for content resources, users, or applications, and the
like. Evaluation criteria may be stored in the evaluation data
store 308 including data and/or instructions in the form of one or
several electronic rubrics or scoring guides for use in the
evaluation of the content, users, or applications. The evaluation
data store 308 also may include past evaluations and/or evaluation
analyses for users, content, and applications, including relative
rankings, characterizations, explanations, and the like.
[0052] In addition to the illustrative data stores described above,
data store server(s) 104 (e.g., database servers, file-based
storage servers, etc.) may include one or more external data
aggregators 309. External data aggregators 309 may include
third-party data sources accessible to the content management
network 100, but not maintained by the content management network
100. External data aggregators 309 may include any electronic
information source relating to the users, content resources, or
applications of the content distribution network 100. For example,
external data aggregators 309 may be third-party data stores
containing demographic data, education related data, consumer sales
data, health related data, and the like. Illustrative external data
aggregators 309 may include, for example, social networking web
servers, public records data stores, learning management systems,
educational institution servers, business servers, consumer sales
data stores, medical record data stores, etc. Data retrieved from
various external data aggregators 309 may be used to verify and
update user account information, suggest user content, and perform
user and content evaluations.
[0053] With reference now to FIG. 4, a block diagram is shown
illustrating an embodiment of one or more content management
servers 102 within a content distribution network 100. As discussed
above, content management server(s) 102 may include various server
hardware and software components that manage the content resources
within the content distribution network 100 and provide interactive
and adaptive content to users on various user devices 106. For
example, content management server(s) 102 may provide instructions
to and receive information from the other devices within the
content distribution network 100, in order to manage and transmit
content resources, user data, and server or client applications
executing within the network 100.
[0054] A content management server 102 may include a content
customization system 402. The content customization system 402 may
be implemented using dedicated hardware within the content
distribution network 100 (e.g., a content customization server
402), or using designated hardware and software resources within a
shared content management server 102. In some embodiments, the
content customization system 402 may adjust the selection and
adaptive capabilities of content resources to match the needs and
desires of the users receiving the content. For example, the
content customization system 402 may query various data stores and
servers 104 to retrieve user information, such as user preferences
and characteristics (e.g., from a user profile data store 301),
user access restrictions to content recourses (e.g., from a content
access data store 306), previous user results and content
evaluations (e.g., from an evaluation data store 308), and the
like. Based on the retrieved information from data stores 104 and
other data sources, the content customization system 402 may modify
content resources for individual users.
[0055] A content management server 102 also may include a user
management system 404. The user management system 404 may be
implemented using dedicated hardware within the content
distribution network 100 (e.g., a user management server 404), or
using designated hardware and software resources within a shared
content management server 102. In some embodiments, the user
management system 404 may monitor the progress of users through
various types of content resources and groups, such as media
compilations, courses or curriculums in training or educational
contexts, interactive gaming environments, and the like. For
example, the user management system 404 may query one or more
databases and/or data store servers 104 to retrieve user data such
as associated content compilations or programs, content completion
status, user goals, results, and the like.
[0056] A content management server 102 also may include an
evaluation system 406. The evaluation system 406 may be implemented
using dedicated hardware within the content distribution network
100 (e.g., an evaluation server 406), or using designated hardware
and software resources within a shared content management server
102. The evaluation system 406 may be configured to receive and
analyze information from user devices 106. For example, various
ratings of content resources submitted by users may be compiled and
analyzed, and then stored in a data store (e.g., a content library
data store 303 and/or evaluation data store 308) associated with
the content. In some embodiments, the evaluation server 406 may
analyze the information to determine the effectiveness or
appropriateness of content resources with, for example, a subject
matter, an age group, a skill level, or the like. In some
embodiments, the evaluation system 406 may provide updates to the
content customization system 402 or the user management system 404,
with the attributes of one or more content resources or groups of
resources within the network 100. The evaluation system 406 also
may receive and analyze user evaluation data from user devices 106,
supervisor devices 110, and administrator servers 116, etc. For
instance, evaluation system 406 may receive, aggregate, and analyze
user evaluation data for different types of users (e.g., end users,
supervisors, administrators, etc.) in different contexts (e.g.,
media consumer ratings, trainee or student comprehension levels,
teacher effectiveness levels, gamer skill levels, etc.).
[0057] A content management server 102 also may include a content
delivery system 408. The content delivery system 408 may be
implemented using dedicated hardware within the content
distribution network 100 (e.g., a content delivery server 408), or
using designated hardware and software resources within a shared
content management server 102. The content delivery system 408 may
receive content resources from the content customization system 402
and/or from the user management system 404, and provide the
resources to user devices 106. The content delivery system 408 may
determine the appropriate presentation format for the content
resources based on the user characteristics and preferences, and/or
the device capabilities of user devices 106. If needed, the content
delivery system 408 may convert the content resources to the
appropriate presentation format and/or compress the content before
transmission. In some embodiments, the content delivery system 408
may also determine the appropriate transmission media and
communication protocols for transmission of the content
resources.
[0058] In some embodiments, the content delivery system 408 may
include specialized security and integration hardware 410, along
with corresponding software components to implement the appropriate
security features content transmission and storage, to provide the
supported network and client access models, and to support the
performance and scalability requirements of the network 100. The
security and integration layer 410 may include some or all of the
security and integration components 208 discussed above in FIG. 2,
and may control the transmission of content resources and other
data, as well as the receipt of requests and content interactions,
to and from the user devices 106, supervisor devices 110,
administrative servers 116, and other devices in the network
100.
[0059] With reference now to FIG. 5, a block diagram of an
illustrative computer system is shown. The system 500 may
correspond to any of the computing devices or servers of the
content distribution network 100 described above, or any other
computing devices described herein. In this example, computer
system 500 includes processing units 504 that communicate with a
number of peripheral subsystems via a bus subsystem 502. These
peripheral subsystems include, for example, a storage subsystem
510, an I/O subsystem 526, and a communications subsystem 532.
[0060] Bus subsystem 502 provides a mechanism for letting the
various components and subsystems of computer system 500
communicate with each other as intended. Although bus subsystem 502
is shown schematically as a single bus, alternative embodiments of
the bus subsystem may utilize multiple buses. Bus subsystem 502 may
be any of several types of bus structures including a memory bus or
memory controller, a peripheral bus, and a local bus using any of a
variety of bus architectures. Such architectures may include, for
example, an Industry Standard Architecture (ISA) bus, Micro Channel
Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics
Standards Association (VESA) local bus, and Peripheral Component
Interconnect (PCI) bus, which can be implemented as a Mezzanine bus
manufactured to the IEEE P1386.1 standard.
[0061] Processing unit 504, which may be implemented as one or more
integrated circuits (e.g., a conventional microprocessor or
microcontroller), controls the operation of computer system 500.
One or more processors, including single core and/or multicore
processors, may be included in processing unit 504. As shown in the
figure, processing unit 504 may be implemented as one or more
independent processing units 506 and/or 508 with single or
multicore processors and processor caches included in each
processing unit. In other embodiments, processing unit 504 may also
be implemented as a quad-core processing unit or larger multicore
designs (e.g., hexa-core processors, octo-core processors, ten-core
processors, or greater.
[0062] Processing unit 504 may execute a variety of software
processes embodied in program code, and may maintain multiple
concurrently executing programs or processes. At any given time,
some or all of the program code to be executed can be resident in
processor(s) 504 and/or in storage subsystem 510. In some
embodiments, computer system 500 may include one or more
specialized processors, such as digital signal processors (DSPs),
outboard processors, graphics processors, application-specific
processors, and/or the like.
[0063] I/O subsystem 526 may include device controllers 528 for one
or more user interface input devices and/or user interface output
devices 530. User interface input and output devices 530 may be
integral with the computer system 500 (e.g., integrated audio/video
systems, and/or touchscreen displays), or may be separate
peripheral devices which are attachable/detachable from the
computer system 500.
[0064] Input devices 530 may include a keyboard, pointing devices
such as a mouse or trackball, a touchpad or touch screen
incorporated into a display, a scroll wheel, a click wheel, a dial,
a button, a switch, a keypad, audio input devices with voice
command recognition systems, microphones, and other types of input
devices. Input devices 530 may also include three dimensional (3D)
mice, joysticks or pointing sticks, gamepads and graphic tablets,
and audio/visual devices such as speakers, digital cameras, digital
camcorders, portable media players, webcams, image scanners,
fingerprint scanners, barcode reader 3D scanners, 3D printers,
laser rangefinders, and eye gaze tracking devices. Additional input
devices 530 may include, for example, motion sensing and/or gesture
recognition devices that enable users to control and interact with
an input device through a natural user interface using gestures and
spoken commands, eye gesture recognition devices that detect eye
activity from users and transform the eye gestures as input into an
input device, voice recognition sensing devices that enable users
to interact with voice recognition systems through voice commands,
medical imaging input devices, MIDI keyboards, digital musical
instruments, and the like.
[0065] Output devices 530 may include one or more display
subsystems, indicator lights, or non-visual displays such as audio
output devices, etc. Display subsystems may include, for example,
cathode ray tube (CRT) displays, flat-panel devices, such as those
using a liquid crystal display (LCD) or plasma display,
light-emitting diode (LED) displays, projection devices, touch
screens, and the like. In general, use of the term "output device"
is intended to include all possible types of devices and mechanisms
for outputting information from computer system 500 to a user or
other computer. For example, output devices 530 may include,
without limitation, a variety of display devices that visually
convey text, graphics and audio/video information such as monitors,
printers, speakers, headphones, automotive navigation systems,
plotters, voice output devices, and modems.
[0066] Computer system 500 may comprise one or more storage
subsystems 510, comprising hardware and software components used
for storing data and program instructions, such as system memory
518 and computer-readable storage media 516. The system memory 518
and/or computer-readable storage media 516 may store program
instructions that are loadable and executable on processing units
504, as well as data generated during the execution of these
programs.
[0067] Depending on the configuration and type of computer system
500, system memory 318 may be stored in volatile memory (such as
random access memory (RAM) 512) and/or in non-volatile storage
drives 514 (such as read-only memory (ROM), flash memory, etc.) The
RAM 512 may contain data and/or program modules that are
immediately accessible to and/or presently being operated and
executed by processing units 504. In some implementations, system
memory 518 may include multiple different types of memory, such as
static random access memory (SRAM) or dynamic random access memory
(DRAM). In some implementations, a basic input/output system
(BIOS), containing the basic routines that help to transfer
information between elements within computer system 500, such as
during start-up, may typically be stored in the non-volatile
storage drives 514. By way of example, and not limitation, system
memory 518 may include application programs 520, such as client
applications, Web browsers, mid-tier applications, server
applications, etc., program data 522, and an operating system
524.
[0068] Storage subsystem 510 also may provide one or more tangible
computer-readable storage media 516 for storing the basic
programming and data constructs that provide the functionality of
some embodiments. Software (programs, code modules, instructions)
that when executed by a processor provide the functionality
described herein may be stored in storage subsystem 510. These
software modules or instructions may be executed by processing
units 504. Storage subsystem 510 may also provide a repository for
storing data used in accordance with the present invention.
[0069] Storage subsystem 300 may also include a computer-readable
storage media reader that can further be connected to
computer-readable storage media 516. Together and, optionally, in
combination with system memory 518, computer-readable storage media
516 may comprehensively represent remote, local, fixed, and/or
removable storage devices plus storage media for temporarily and/or
more permanently containing, storing, transmitting, and retrieving
computer-readable information.
[0070] Computer-readable storage media 516 containing program code,
or portions of program code, may include any appropriate media
known or used in the art, including storage media and communication
media, such as but not limited to, volatile and non-volatile,
removable and non-removable media implemented in any method or
technology for storage and/or transmission of information. This can
include tangible computer-readable storage media such as RAM, ROM,
electronically erasable programmable ROM (EEPROM), flash memory or
other memory technology, CD-ROM, digital versatile disk (DVD), or
other optical storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or other tangible
computer readable media. This can also include nontangible
computer-readable media, such as data signals, data transmissions,
or any other medium which can be used to transmit the desired
information and which can be accessed by computer system 500.
[0071] By way of example, computer-readable storage media 516 may
include a hard disk drive that reads from or writes to
non-removable, nonvolatile magnetic media, a magnetic disk drive
that reads from or writes to a removable, nonvolatile magnetic
disk, and an optical disk drive that reads from or writes to a
removable, nonvolatile optical disk such as a CD ROM, DVD, and
Blu-Ray.RTM. disk, or other optical media. Computer-readable
storage media 516 may include, but is not limited to, Zip.RTM.
drives, flash memory cards, universal serial bus (USB) flash
drives, secure digital (SD) cards, DVD disks, digital video tape,
and the like. Computer-readable storage media 516 may also include,
solid-state drives (SSD) based on non-volatile memory such as
flash-memory based SSDs, enterprise flash drives, solid state ROM,
and the like, SSDs based on volatile memory such as solid state
RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM
(MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and
flash memory based SSDs. The disk drives and their associated
computer-readable media may provide non-volatile storage of
computer-readable instructions, data structures, program modules,
and other data for computer system 500.
[0072] Communications subsystem 532 may provide a communication
interface from computer system 500 and external computing devices
via one or more communication networks, including local area
networks (LANs), wide area networks (WANs) (e.g., the Internet),
and various wireless telecommunications networks. As illustrated in
FIG. 5, the communications subsystem 532 may include, for example,
one or more network interface controllers (NICs) 534, such as
Ethernet cards, Asynchronous Transfer Mode NICs, Token Ring NICs,
and the like, as well as one or more wireless communications
interfaces 536, such as wireless network interface controllers
(WNICs), wireless network adapters, and the like. Additionally
and/or alternatively, the communications subsystem 532 may include
one or more modems (telephone, satellite, cable, ISDN), synchronous
or asynchronous digital subscriber line (DSL) units, FireWire.RTM.
interfaces, USB.RTM. interfaces, and the like. Communications
subsystem 536 also may include radio frequency (RF) transceiver
components for accessing wireless voice and/or data networks (e.g.,
using cellular telephone technology, advanced data network
technology, such as 3G, 4G or EDGE (enhanced data rates for global
evolution), WiFi (IEEE 802.11 family standards, or other mobile
communication technologies, or any combination thereof), global
positioning system (GPS) receiver components, and/or other
components.
[0073] The various physical components of the communications
subsystem 532 may be detachable components coupled to the computer
system 500 via a computer network, a FireWire.RTM. bus, or the
like, and/or may be physically integrated onto a motherboard of the
computer system 500. Communications subsystem 532 also may be
implemented in whole or in part by software.
[0074] In some embodiments, communications subsystem 532 may also
receive input communication in the form of structured and/or
unstructured data feeds, event streams, event updates, and the
like, on behalf of one or more users who may use or access computer
system 500. For example, communications subsystem 532 may be
configured to receive data feeds in real-time from users of social
networks and/or other communication services, web feeds such as
Rich Site Summary (RSS) feeds, and/or real-time updates from one or
more third party information sources (e.g., data aggregators 309).
Additionally, communications subsystem 532 may be configured to
receive data in the form of continuous data streams, which may
include event streams of real-time events and/or event updates
(e.g., sensor data applications, financial tickers, network
performance measuring tools, clickstream analysis tools, automobile
traffic monitoring, etc.). Communications subsystem 532 may output
such structured and/or unstructured data feeds, event streams,
event updates, and the like to one or more data stores 104 that may
be in communication with one or more streaming data source
computers coupled to computer system 500.
[0075] Due to the ever-changing nature of computers and networks,
the description of computer system 500 depicted in the figure is
intended only as a specific example. Many other configurations
having more or fewer components than the system depicted in the
figure are possible. For example, customized hardware might also be
used and/or particular elements might be implemented in hardware,
firmware, software, or a combination. Further, connection to other
computing devices, such as network input/output devices, may be
employed. Based on the disclosure and teachings provided herein, a
person of ordinary skill in the art will appreciate other ways
and/or methods to implement the various embodiments.
[0076] With reference now to FIG. 6, a block diagram is shown
illustrating an example of a mobile device session analyzer system
600. As shown in this example, a mobile device session analyzer
system 600 may include one or more mobile devices 610 configured to
communicate with a mobile device session analyzer 620. As discussed
below, mobile devices 610 may be configured to receive and execute
content resources via their respective input/output (I/O)
subsystems 611. As used herein, a content resource execution
session may refer to a continuous time period during which one or
more content resources are executed on a mobile device 610. During
such content resource execution sessions, a user of the mobile
device 610 may consume (e.g., watch, read, play, etc.) the content
resources, and may provide feedback relating to the content
resources via the I/O subsystem 611. Mobile devices 610 may be
configured to receive such user feedback and other relevant
performance data associated with content resource execution
sessions. The user feedback data and other performance data
associated with content resource execution sessions may be received
and/or determined before, during, or after the associated execution
sessions. Mobile devices 610 also may be configured to detect
and/or determine (e.g., using device sensors 612 and/or CPU 613)
context data associated with specific content resource execution
sessions. Such context data may include, for example, device
location, session time, session length, network data, movement and
noise data, user biostatistics data, data received from other
mobile device applications, and the like. Mobile devices 610 may
store the performance data and context data associated with content
resource execution sessions, for example, in local data stores 614,
and also may transmit such data to one or more session analyzers
620. As discussed in more detail below, session analyzers 620 may
include server components and/or processing engines, along with
specialized data storage, networking and hardware components,
configured to receive and analyze content resource execution
session data from the mobile devices 610, and to determine
performance conditions for associated users.
[0077] In order to perform these features and other functionality
described herein, each of the components and sub-components
discussed in the example mobile device session analyzer system 600
may correspond to a single computer server or a complex computing
system including a combination of computing devices, storage
devices, network components, etc. Each of these components and
their respective subcomponents may be implemented in hardware,
software, or a combination thereof. Certain mobile devices 610 may
communicate directly with the session analyzer 620, while other
mobile devices 610 may communicate with the session analyzer 620
indirectly via one or more intermediary network components (e.g.,
routers, gateways, firewalls, etc.) or other devices (e.g., content
management servers 102, content servers 112, etc.). Although the
physical network components have not been shown in this figure so
as not to obscure the other elements depicted in the figure, it
should be understood that any of the network hardware components
and network architecture designs may be implemented in various
embodiments to support communication between the servers and
devices in the system 600. Additionally, different mobile devices
610 may use different networks and networks types to communicate
with the session analyzer 620, including one or more
telecommunications networks, cable networks, satellite networks,
cellular networks and other wireless networks, and computer-based
IP networks, and the like. Further, certain components within
mobile device session analyzer system 600 may include special
purpose hardware devices and/or special purpose software, such as
those included in I/O subsystems 611 and sensor systems 612 of the
mobile devices 610, as well as those within the processing engines
and data stores 621-624 of the session analyzer 620, discussed
below.
[0078] In some embodiments, a mobile device session analyzer system
600 may be integrated within, or configured to operate in
collaboration with, one or more content distribution networks 100.
For example, system 600 may be the same as, or may operate within
or in collaboration with, any of the content distribution network
(CDNs) 100 described above. Thus, specific examples of mobile
device session analyzer systems 600 may include, without
limitation, educational and professional training systems and
networks, interactive gaming systems and networks, media
distribution systems and networks, and enterprise application
systems and networks, websites and other Internet-based systems and
networks. In such cases, the mobile device session analyzer 620 may
be implemented within one or more content servers 112, content
management servers 102, and/or data store servers 104, and mobile
devices 610 may correspond to the user devices 106 and 110
described above in reference to CDN 100. Thus, within system 600
(which may also be referred to as CDN 600), mobile devices 610 may
request and receive content resources from the session analyzer
620, execute the content resources using the hardware and software
components of the user devices 610, and then transmit various user
interaction data back to the session analyzer 620. In other
examples, session analyzer 620 may be implemented using one or more
computer servers, and other specialized hardware and software
components, separately from other CDN components such as content
servers 112, content management servers 102, data store servers
104, and the like. In these examples, the session analyzer 620 may
be configured to communicate directly with mobile devices 610, or
indirectly through content management servers 102 and/or other
components and communications networks of the CDN 600.
[0079] As discussed below, system 600 and other embodiments
described herein may be used to execute content resources on user
devices 610, receive user inputs via user devices 610 during
content resources execution sessions, determine performance
measurements and/or corresponding user contexts associated with
content resources execution sessions, and analyze content resources
execution sessions in order to determine performance conditions for
users within the system 600. Content resources may refer to any
digital content, such as media content (e.g., music, movies,
television programming, audiobooks, advertisements, etc.), gaming
software, professional training and eLearning resources (e.g.,
courses, texts, lectures, interactive modules, tests and
evaluations, etc.), as well as websites and other web-based
content. Various types of content resources may be transmitted to,
stored on, and executed by mobile devices 610 as files, executable
software, services, and the like. Users may consume (e.g., view,
read, listen to, play, etc.) content resources via compatible
mobile devices 610 using the hardware and software resources on the
mobile device 610. In some embodiments, the content resources may
include interactive resources, such as interactive media, games, or
professional training or eLearning content modules, which may be
designed to elicit user responses or feedback before, during, or
after the execution of the content.
[0080] Mobile devices 610 may include laptop computers,
smartphones, tablet computers, and other various types of mobile
devices, each of which may include some or all of the hardware,
software, and networking components discussed above. Specifically,
a mobile device 610 may be any computing device with sufficient
memory for storing one or more content resources, and sufficient
processing and I/O subcomponents for executing the content
resources. As discussed above, such content resources may include
television programming, music, movies, lectures or materials in a
training course or eLearning system, and/or gaming content.
Accordingly, mobile devices 610 may include the necessary hardware
and software components to establish the network interfaces,
security and authentication capabilities, and content caching
capabilities to receive the content, store the content, and execute
the content to provide to users in real-time or at later time.
Moreover, in certain embodiments, a single mobile device 610 may
have different context data (e.g., location data, network
connectivity status data, movement and noise data, etc.) during
different content resource execution sessions. Laptop computers,
tablet computers, smart phones, smart watches, wearable computing
devices, and vehicle-based computing systems are all examples of
mobile devices 610 that may change locations and network
connectivity status, may detect movement and noise data, and may
have other changing context data during a single content resource
execution session or between multiple different execution
sessions.
[0081] In this example, mobile devices 610 each include an I/O
subsystem 611, one or more device sensors 612, a processing unit
613, a data store 614, and a network interface controller 615. As
discussed above, each mobile device 610 may be configured to
receive and execute content resources using their respective I/O
subsystems 611. Each I/O subsystem 611 may include hardware and
software components to support a specific set of output
capabilities (e.g., LCD display screen characteristics, screen
size, color display, video driver, speakers, audio driver, graphics
processor and drivers, etc.), and a specific set of input
capabilities (e.g., keyboard, mouse, touchscreen, voice control,
cameras, facial recognition, gesture recognition, etc.). Different
mobile devices 610 may support different input and output
capabilities within their I/O subsystems 611, and thus different
types of content resources may be compatible or incompatible with
certain mobile devices 610. For example, certain content resources
may require specific types of processors, graphics components, and
network components in order to be optimally executed on a mobile
device 610. Additionally or alternatively, various content
resources may require specific output capabilities (e.g., LCD
display screens, minimum screen sizes, color displays, video,
audio, graphics, etc.) and/or specific input capabilities (e.g.,
keyboard, mouse, touchscreen, voice control, gesture recognition,
etc.) in order to be optimally executed on a mobile device 610. In
some embodiments, users may establish user-specific preferences for
executing specific types of content resources on specific types of
user devices.
[0082] Each mobile device 610 also may include one or more device
sensors 612 configured to detect context data associated with
specific content resource execution sessions. Context data may
include any data describing the current (and/or previous) state of
the mobile device 610, the physical environment of the mobile
device 610, and/or the user of the mobile device 610, at the time
of a content resource execution session. For example, such context
data may include the location of the mobile device 610 and any
detected movement of the mobile device 610 during a content
resource execution session, including speed and overall distance
traveled, as well jiggling and vibrating movements indicative of
walking, jogging, running, or traveling in cars, busses, or trains,
etc. Accordingly, sensors 612 may include accelerometers,
speedometers, compasses, gyroscopes, and/or digital positioning
systems such as Global Position Satellite (GPS) receivers, in order
to detect the position and movement of the device 610 during an
execution session. Additional context data may include the
orientation of the mobile device 610, ambient noise and/or visual
background data detected by the device 610, and temperature and
lighting data within the physical environment of the mobile device
610 during an execution session. Thus, sensors 612 also may include
thermometers, barometers, light sensors, cameras, microphones, heat
sensors, motion sensors, and the like, in order to detect
background noises such as traffic, music, conversations, and other
potential distractions (e.g., other people nearby, animals, etc.),
as well as lighting and temperature factors that may affect
performance during an execution session.
[0083] In some embodiments, device sensors 612 may include user
health and activity sensors, such as heartrate and blood pressure
sensors, sleep monitors, and personal exercise/fitness sensors that
may detect and track the physical state and condition of the user
before and during a content resource execution session. For
instance, a mobile device 610 may be a personal and/or wearable
computing device configured to detect current health and activity
data of a user, and then transmit the biostatistics data for the
user to the session analyzer 620 and/or other mobile devices 610
within the system 600. As described below, such biostatistics data,
like other session context variables, may be stored and correlated
with user performance measurements during content resource
execution sessions to determine performance conditions for the
user.
[0084] It should be understood that the above examples of specific
sensors 612 are illustrative and non-limiting only, and that any
other mobile device sensor 612 may be activated during content
resource execution sessions to detect and record data that may be
used to determine user performance conditions based on correlations
between user performance measurements and sensor readings during
execution sessions. Additionally, as described below, certain types
of context data might not require specialized device sensors 612,
and thus such sensors 612 may be optional in some embodiments. For
example, the context data associated with a content resource
execution session may include session time, session length, and
current device capabilities and status data such as processor
status and availability, memory usage and availability, network
availability, etc. Additional context data may be received from
other applications executing on the mobile device 610 and/or other
related user devices 610. For instance, if a user recently or
concurrently activated and/or interacted with certain software
applications (e.g., a mobile phone application, music application,
gaming application, web browser, etc.) during a content resource
execution session, then the user's interactions may be stored as
context data which may correlate positively or negatively to the
user's performance during the execution session. In addition to
application launch states, additional context information may be
received from profiles built by applications granted security
permissions, as well as phone operating system-level services, from
various mobile device and mobile software providers (e.g., GOOGLE,
APPLE, MICROSOFT, etc.). Additionally, mobile assistant services
(e.g., SIRI, GOOGLE NOW, CORTANA, etc.) also may serve as a
potentially rich user profile context source.
[0085] After collecting various session data for one or more
content resource execution sessions, such as session context
variable data and user performance and/or user feedback data,
mobile devices 610 may store the session data in a local data store
614 or transmit the session data via a network interface 615 to a
mobile device session analyzer 620. As shown in FIG. 6, mobile
device session analyzer 620 may receive data relating to multiple
content resource execution sessions from multiple different mobile
devices 610. The session analyzer 620 may be configured to collect
and store mobile device data and session data in one or more data
stores 622 and 623. The session analyzer 620 may include one or
more processing engines 621 and 624 configured to analyze the
session data received from the mobile devices 610 and determine
performance conditions for users associated with the session data
(e.g., session analyzer engine 621), as well as monitoring the
context data of the mobile devices 610 against the user's
performance conditions to provide notifications that a user's
current context data matches the performance conditions determined
for the user (e.g., notification engine 624). The session analyzer
engine 621, notification engine 624, and data stores 622-623 may be
implemented as separate software (and/or storage) components within
a single computer server 620 in some examples, while in other
examples may be implemented as separate computer servers/systems
having separate dedicated processing units, storage devices, and/or
network components.
[0086] Referring now to FIGS. 7A and 7B, two tables are shown
including example mobile device data (FIG. 7A) and example content
resource execution session data (FIG. 7B) that may be illustrative
of certain embodiments of the disclosure. In this example, FIG. 7A
represents an example mobile device table 710 that may be stored in
a device data store 622 of a mobile device session analyzer 620.
Table 710 may store a list of device capabilities and/or
characteristics for one or more mobile devices 610 within a mobile
device session analyzer system 600. For example, table 710 (and/or
other tables 710 within a data store 622) may store any hardware
and software components and capabilities of the mobile devices 610
(processors, memory systems, network capabilities, drivers, client
applications, etc.), the input/output devices 611 and capabilities
(e.g., integrated and peripheral I/O devices, I/O software, I/O
capabilities, etc.), as well as any device sensors 612 and sensor
capabilities. In some examples, session analyzer 620 and/or other
components with a CDN 600 may perform a registration process for
new mobile devices 610, during which a mobile device 610 may
provide its device components, characteristics, and/or capabilities
to be enrolled within the CDN and/or session analyzer system 600.
In other examples, mobile devices 610 may transmit device
characteristics and capabilities data along with transmissions of
content resource execution session data, and the session analyzer
620 may extract the data and update table 710 with the received
device data. In either case, mobile devices 610 and session
analyzers 620 may be respectively configured to transmit and
receive/store updates to the device data for a mobile device 610.
For instance, updated device data may be transmitted by a mobile
device 610 in response to hardware or software upgrades to the
mobile device 610, or the installation or removal of a peripheral
I/O device 611, sensor 612, etc. The session analyzer 620 may
receive device data updates from mobile devices 610 and update
device data table 710 as needed.
[0087] FIG. 7B represents an example mobile device session table
720 that may be stored in a session data store 623 of a mobile
device session analyzer 620. Session data store 623 may include
various data tables 720 containing data relating to content
resource execution sessions performed by users via mobile devices
610. As shown in this example, mobile device session table 720
(and/or other tables 710 within data store 623) may store, for each
content resource execution session, a session identifier, a user
identifier (i.e., corresponding to the user that initiated the
content resource execution session), a device identifier (i.e.,
corresponding to the mobile device 610 on which the user content
resource session was executed), and one or more content resource
identifiers (i.e., corresponding to the specific content
resource(s) executed during the session). The table 720 also may
include one or more context data associated with each content
resource execution session, such as session time, session length,
session location, and/or any of the additional context data
discussed herein for content resource execution sessions. Finally,
example table 720 includes session performance data corresponding
to one or more performance measurements of the user associated with
the content resource execution sessions. Such performance
measurements, discussed in detail below, may be based on data such
as user feedback, user interactions, user consumption velocities
and remediation rates, etc.
[0088] Referring now to FIG. 8, an example diagram is shown
illustrating a sequence of data transmissions from mobile devices
810 to a mobile device session analyzer 820, where each data
transmission may include data relating to a content resource
execution session performed by a user on the mobile device 810. The
architecture and hardware/software components of system 800 may be
similar (or identical) to those in system 600, discussed above. For
example, each mobile device 810 may correspond to a mobile device
610, and session analyzer 820 may correspond to session analyzer
620. However, FIG. 8 illustrates specific examples of different
mobile devices 810 associated with a user, and specific examples of
transmitting execution session data from the mobile devices 810. In
this case, a user may use five different mobile devices 810 at
various different times to execute content resources: a smartphone
810a, a smartwatch 810b, a tablet computer 810c, a vehicle-based
computer system 810d, and a laptop computer 810e. As discussed
above, each mobile device 810 may have different device
characteristics and capabilities, which may be stored by the
individual devices 810 and/or within a data store of the session
analyzer 820.
[0089] FIG. 8 also illustrates a sequence of data transmissions
(1-9) from the individual mobile devices 810 to the mobile device
session analyzer server 820. Each data transmission (1-9) may
contain information describing one or more content resource
execution sessions performed by a user on the mobile device 810.
The data transmitted from mobile devices 810 during data
transmissions (1-9) may include similar data to that discussed
above in reference to FIG. 7B, for example, user identifiers,
device identifiers, content resource identifiers, context data in
the form of session context variable and value pairs (e.g., session
time and value, session length and value, session location and
value, network type and value, network speed and value, etc.),
and/or session performance data indicating performance measurements
of the user during the content resource execution sessions. As
illustrated in this example, the context data for each mobile
device 810 may change over time, as the mobile devices 810 may
change locations, users, networks, etc. Thus, each data
transmission (1-9) may include a unique combination of device data,
user data, time data, location data, and/or network data, as well
as additional unique context data and session performance data.
[0090] Referring now to FIG. 9, a flow diagram is shown
illustrating a process of determining one or more performance
conditions associated with a user, based on a user's content
resource execution sessions. As described below, the steps in this
process may be performed by one or more components in the mobile
device session analyzer systems 600 described above, such as a
mobile device 610 and/or a session analyzer server 620. Prior to
the steps of FIG. 9, discussed below, a mobile device 610 may
request and receive a set of content resources, for example, from a
content management server 102 or content server 112 (either of
which may be implemented within a session analyzer server 620), or
other content repository. Content resources may include any digital
content that may be executed on a user device 610, such as media
content (e.g., music, movies, television programming, audiobooks,
advertisements, etc.), gaming software, professional training and
eLearning resources (e.g., online courses, texts, video lectures,
interactive software modules, tests and evaluations, etc.), as well
as websites and other web-based content. Different types of content
resources may be transmitted to, stored on, and executed by mobile
devices 610 as files, executable software, services, and the
like.
[0091] During the execution of one or more content resources by a
user, which may be referred to as a content resources execution
session, the user may consume (e.g., view, read, listen to, play,
etc.) and interact with the content resources using the hardware
and software components of their mobile devices 610 (e.g., I/O
subsystems 611). As discussed above, different mobile devices 610
may have different I/O subsystems 611 and other hardware, software,
and network differences that support different types of resource
consumption and resource interaction behaviors by users.
[0092] Additionally, during a content resource execution session, a
mobile device 610 may collect and store various data relating to
the execution session. For example, the mobile device 610 may store
basic session data, such as user that requested and executed the
resources, the specific resources executed, the session time, etc.
The mobile device 610 also may detect and store various context
data during execution sessions, including the location of the
mobile device 610 and other physical environment data (e.g.,
movement data, noise data, lighting data, temperature data, etc.),
user biostatistics data (e.g., heartrate, blood pressure, sleep
data, diet data, exercise/fitness data, etc.), data received from
other applications on the mobile device 610 or other user devices
610 (e.g., phone application data, music application data, gaming
data, web browsing data, etc.), as well as other sensor data and
any additional context data discussed herein.
[0093] The mobile device 610 also may collect and store user
feedback and other user interaction data during execution sessions.
User feedback and user interaction data may be received via the I/O
subsystem 611 of the mobile device 610 executing the content, or in
other cases may be received via the input and output components of
a different user device. For example, in some embodiments, content
may be executed/played on one device (e.g., a television, classroom
or conference room projector, audio system, etc.), and the user may
interact with the content and provides feedback via a different
device (e.g., a smartphone, personal tablet or laptop, etc.). The
user interaction data collected and stored by the mobile device 610
during an execution session may include user selections of content
resources (e.g., starting, stopping, pausing, skipping forward or
back, replaying sections, etc.), user responses to
decision/navigation points within interactive user content
resources, user behaviors while consuming content resources (e.g.,
text inputs, audio responses, gestures, facial expressions, body
language, etc.), scores of user interactions (e.g., user scores of
game levels, eLearning modules, tests or evaluations, etc.), and/or
the user's answers to explicit requests for feedback or evaluations
of content resources. Such user interaction data may be referred to
as clickstream processing in some cases. Additional user
interaction data may include data metrics relating to a user's
interaction with the content, such as amount of time taken for the
user to make a selection, the number of selections/attempts the
user makes at a given navigation point, and any aids, tools, or
help that the user used when interacting with the content.
[0094] In some embodiments, some or all of the data collected and
stored by mobile devices 610 during content resource execution
sessions, including session data, context data, and/or user
interaction data, etc., may be transmitted by the mobile devices
610 to the session analyzer 620 during or after the execution
sessions, as shown in FIG. 8. In such embodiments, the session
analyzer 620 may receive content resource execution session data
from multiple devices 610, and may perform the steps of FIG. 9 on
the aggregated data from multiple devices 610. In other
embodiments, the mobile devices 610 may perform certain steps and
analyses, such as user performance measurements, evaluations of
context data to identify potential distractions and other relevant
factors, before transmitting data to the session analyzer 620. In
still other examples, each mobile device 610 may perform each of
the steps and FIG. 9 individually, without any communication with a
session analyzer server 620. The mobile device session analyzer 620
may be optional in such cases, and mobile devices 610 may
communicate directly with each other, or need not communicate at
all and each mobile device 610 may determine its own
device-specific performance conditions for the user.
[0095] In step 901, data relating to one or more content resource
execution sessions may be received, for example, by a mobile device
session analyzer 620 from one or more mobile devices 610. As
discussed above, the data received in step 901 may include session
data (e.g., user identifier, resource identifiers, execution times,
etc.), various types of context data (e.g., location data, physical
environment data, user biostatistics data, data received from other
applications, etc.), user interaction data received by the mobile
device 610, and any other data collected in connection with an
execution of content resources on the mobile device 610.
[0096] In step 902, one or more user performance measurements may
be determined for the content resource execution session. In some
embodiments, a mobile device 610 and/or mobile device session
analyzer 620 may calculate user performance with respect to content
resource execution sessions based on the user interaction data
received in step 901. Several different types and techniques of
performance measurements may be determined in different embodiments
and/or based on different types of content resources. In some
cases, a performance measurement may correspond to a user score or
evaluation. For example, gaming software, professional training or
eLearning software, and other content resources may calculate user
scores for game levels, comprehension levels of eLearning modules,
automated online tests, etc. User scores for interactive content
resources may be calculated during the execution of the resources
(e.g., for an interactive game) or after the completion of the
execution session (e.g., for a training/eLearning module), and in
some cases the evaluation/scoring process may be performed at a
different mobile device 610 then the device executing the
resources.
[0097] Other types of performance measurement that may be
determined in step 902 may include express feedback from the user
regarding the content resources, such as user content ratings,
discussion posts, content evaluations, and the like. For instance,
certain CDNs 100 and/or specific content resources may solicit user
ratings during or after execution of the content (e.g., movie and
television program ratings, game reviews, training/eLearning module
ratings, course ratings, instructor ratings, etc.). Additional user
feedback that may be collected for performance measurements may
include the amount of time the user played/executed the resources,
a record of the user decisions and other interactions during the
execution of the resources, and audio and/or visual cues (e.g.,
voice feedback, facial expressions, gestures, body language, etc.)
which may be collected and stored by the mobile device 610 during
the execution of the content resources.
[0098] Additionally, in some embodiments, the velocity and/or the
remediation rate of a user during one or more content resource
execution sessions may be determined and used as a user performance
measurement in step 902. Consumption velocity may refer to the
speed at which a user consumes content resources, and remediation
rate may refer to the rate at which the user returns to previously
consumed/executed content during the execution session. To
illustrate, a first user who quickly consumes and completes one or
more media resources, game levels, eLearning modules, and the like,
may be assigned a high consumption velocity for the content
resource execution session, whereas a second user who proceeds
slowly through the content resources (e.g., frequently pausing,
repeating portions or sections multiple times, selecting all
possible choices or answers in an interactive resource, etc.) may
be assigned a low consumption velocity. However, if the first user
(or another user) returns frequently to previously viewed/executed
resources, then the first user may be assigned a high remediation
rate, whereas if the second user (or another user) rarely returns
to a content resource after completing it, then the second user may
be assigned a low remediation rate. Consumption velocities and/or
remediation rates for a user during one or more execution sessions
may be calculated based on the overall times required to complete
resources, the numbers of user decisions/interactions during
execution sessions, and/or numbers or rates of resource
consumption, etc.
[0099] In step 903, one or more items of context data may be
determined for the content resource execution session, based on the
data received in step 901. In some embodiments, context data may be
collected and determined by one or more mobile devices 610, a
mobile device session analyzer 620, or a combination of mobile
devices 610 and session analyzers 620. The context data determined
in step 903 may include any of the various types of context data
discussed above, for example, device location data, device movement
data, data relating to the physical environment of the device 610
(e.g., noise, lighting, other distractions), user biostatistics
data, data received from other software applications relating to
mobile device 610 or the user.
[0100] In some examples, the mobile device 610 may detect certain
raw context data using device sensors 612, such as location data,
movement data, noise data, image data, user biostatistics data, and
the like, and then transmit the raw context data to the session
analyzer 620 for analysis and classification. For instance, the
mobile device 610 may collect and transmit raw time data
corresponding to a content resource execution session (e.g., start
time, stop time, user interaction times), and the session analyzer
620 may use the raw time data to classify the execution session
into one or more time ranges (e.g., morning, afternoon, evening,
etc.). Similarly, the mobile device 610 may collect and transmit
raw location data for an execution session, and the session
analyzer 620 may analyze the raw execution data to determine a
location or location (e.g., home, work, school, library, vehicle,
coffee shop, etc.). The session analyzer 620 also may analyze
location and/or movement data (e.g., accelerometer readings,
gyroscope readings, etc.) to determine user activity context data
(e.g., jogging, driving, bus, train, or air travel, etc.). As
another example, the mobile device 610 may collect and transmit raw
noise data, light data, and/or image data, and the session analyzer
620 may analyze the data to identify distraction context data
(e.g., ambient noise levels, music, background conversations,
high/low lighting conditions, etc.).
[0101] As illustrated in these examples, the mobile device session
analyzer 620 may receive and analyze raw context data from mobile
devices 610, and then group and/or classify the context data into
ranges in order to characterize an execution session as having a
particular context. In some cases, the session analyzer 620 may
determine context data pairs, each pair consisting of a session
context variable (e.g., time, location, movement pattern, noise
level, distractions, network connectivity, etc.) and a value or
value range for the session context variable. Examples of values or
value ranges for a time session context variable may include, for
instance, 7:30 am, 2:00 pm-4:30 pm, weekday morning, Sunday
afternoon, after school in summertime, holiday weekend, etc.
Examples of values or value ranges for an ambient noise session
context variable may include, for instance, silence, quiet
background music, background conversation, loud music, traffic
noise, pet noise, etc. Additionally illustrative examples of
session context variables and corresponding values may be found in
the first two columns of table 1010 in FIG. 10A.
[0102] In some cases, the session analyzer 620 may determine
certain types of context data for a mobile device 610 without
receiving any raw context data or other context information from
the mobile device 610. For example, the relevant context data for
an execution session may include the capabilities and
characteristics of the mobile device 610, which may be stored in a
device data store 622 (or other storage system) and need not be
transmitted by the mobile device 610. Additionally, the session
analyzer 620 may use some context data received from a mobile
device 610, and then access one or more external servers to
determine additional context data. For instance, location data
received from a mobile device may be used by the session analyzer
620 to access external data sources to determine characteristics
about the location (e.g., public or private, type of business,
etc.), along with current weather conditions at the location,
traffic and road conditions at the location, and the like, so that
the mobile device 610 need not transmit this additional context
data as well.
[0103] Also, for a content resource execution session performed on
one mobile device 610a, the session analyzer 620 may receive or
retrieve context data from other mobile devices 610b-610d
associated with the same user. For example, if a user executes a
set of content resources on a first mobile device 610a that does
not include certain device sensors 611 (e.g., a GPS receiver,
movement sensors, microphones, cameras, etc.), then another mobile
device 610b located near the first device 610a may be activated to
detect, store, and transmit the relevant context data (e.g.,
location data, movement data, audio data, image/video data, etc.).
In such cases, the other mobile device 610b may transmit the
context data to the first mobile device 610a, or directly to the
session analyzer 620. Additionally, certain software applications
currently running (or recently ran) on the user's other devices
610b may provide relevant context data for an execution session on
the first mobile device 610a. For instance, if a user is executing
a set of content resources on a first mobile device 610a, while at
the same time using the telephone application, music player, gaming
applications, and/or web browser on the user's other devices
610b-610d, then these devices may provide the relevant application
information to the session analyzer 620 as well. In some
embodiments, the session analyzer 620 may be configured to request
and/or receive context data from any other user devices 610
positioned near the mobile device 610a during the same time window
as an execution session.
[0104] As shown in FIG. 9, steps 901-903 may be repeated multiple
times before the performance conditions for a user are calculated
in step 904. In some cases, a minimum threshold of content resource
execution session data, including both user performance data and
session context data, may be required before performance conditions
can be calculated with statistical reliability. Additionally, even
after a set of performance conditions is initially calculated for a
user in step 904, the user may continue to perform content resource
execution sessions on the user's mobile devices 610. In such cases,
steps 901-903 may continue to be performed for any newly received
execution session data, and step 904 may be performed again to
update the user's performance conditions based on the newly
received data.
[0105] In step 904, one or more performance conditions may be
determined for a user, based on the content resource execution
session data received and analyzed in steps 901-903. As used
herein, a performance condition for a user may refer to a
statistical correlation between a value or range of values of a
session context variable, and a predicted effect on the user's
performance during content resource execution sessions. For
example, a performance condition for a user may correlate a
particular time of day to a higher or lower level of performance by
the user during content resource execution sessions. Other example
performance conditions may correlate specific locations (or types
of locations), movement data, background noise (e.g., types of
noise and/or noise levels), temperatures, etc., to higher or lower
levels of performance by the user during execution sessions.
[0106] Referring briefly to FIGS. 10A and 10B, two example tables
are shown including illustrative performance condition data. FIG.
10A is an example performance condition table 1010 listing a set of
illustrative performance conditions for a user. In this example,
each performance condition listed in table 1010 includes a session
context variable, a value (or range of values) for the session
context variable, and a corresponding predicted performance effect
for the user. In this example, the user's performance during
content resource execution sessions may be calculated on a point
scale from 0 to 100. The Performance +/- column in table 1010 lists
the predicted performance increase or decrease for the user, when
the context of an execution session is within the identified
value/range for the identified context variable. For instance, when
the user in this example executes content resources on weekday
mornings the user's predicted performance will be 4.5 points higher
than it otherwise would, and when the user executes the content
resources at home the user's predicted performance will be 8.6
points higher than it otherwise would. However, when the user
executes content resources in an environment with ambient noise
ranging between 60-65 dB, the user's predicted performance will be
6.7 points lower than it otherwise would, and when the user slept
only 4-6 hours the previous night the user's predicted performance
will be 2.0 points lower than it otherwise would, and so on. It
should be understood that the examples of performance conditions
shown in FIG. 10A are illustrative only and non-limiting, and in
various embodiments performance conditions may be based on any
context data type, factor, and/or value or range discussed
herein.
[0107] Certain performance conditions determined in step 904 may be
applicable to individual users, while other performance conditions
may be applicable to groups of users, such as a group of family
members, roommates, coworkers, or all users within a CDN 600.
Additionally, certain performance conditions determined in step 904
may be applicable to specific CDNs 600 and/or specific types of
resources within CDNs 600. For example, as shown in FIG. 10B,
performance condition table 1010 may include performance conditions
determined based on an analysis of the content resource execution
sessions of a single user ("User A") within a single gaming
application ("Game ABC") of an online gaming CDN 600. As this
example illustrates, a single user may have different sets of
performance conditions applicable to different types of CDN (e.g.,
media distribution CDNs, professional training/eLearning CDNs,
gaming CDNs, etc.) and for different content resource types within
one or more CDNs 600.
[0108] In order to determine the performance conditions in step
904, the session analyzer 620 (or mobile device 610) may execute
statistical analysis techniques to identify a session context
variable, isolate the variable and the value or range of values for
the variable, and calculate the performance effect of the variable
and value/range. For example, the session analyzer 620 may use
regression analyses to control for the performance effects of other
session context variables, thereby isolating a single variable and
single value/range for the variable, while effectively holding
constant the other possible variables affecting user performance
during the execution sessions. After removing the potential
performance effects caused by other session context variables via
regression analysis, the effect caused by one isolated variable may
be calculated to determine the performance +/- of that variable.
Similar regressions may be used to isolate analyze the performance
effect caused by all session context variables, and performance
effect calculations may be done for one or more different values,
groups, or ranges for each variable. Such calculations also may
take into account (e.g., control for) the difficulty of the content
resources executed during a session, based on performance data from
other users executing the same resources and/or the performance of
the user executing other similar resources.
[0109] In step 905, the performance conditions determined in step
904 may be transmitted to one or more user devices. In some cases,
reports including a listing of performance conditions may be
generated and transmitted to various client devices, as well as
instructor/administrator devices associated with the users. Thus,
users, system administrators, instructors or teachers, and/or
managers may receive performance condition data for users, allowing
users of each role to adjust future behaviors relating to the
presentation or consumption of content resources. For example, a
distributor or presenter of online gaming content, certain media
resources, or a professional training/eLearning resources may
require users to use mobile devices having specific device
capabilities, or to execute resources at certain times/places,
etc., based on the determined performance conditions. Similarly,
users consuming resources may alter consumption patterns based on
the determined performance conditions. Additionally, as discussed
below in reference to FIGS. 11-14, after a set of performance
conditions have been determined for a user, the current context of
the user and the user's devices 610 may be monitored in order to
provide notifications and suggestions to the user for executing
certain content resources at certain times. In some such
embodiments, pairs of session context variables and values/ranges
may be transmitted to individual mobile devices 610, so that the
mobile devices 610 may periodically test the received pairs of
session context variables and values/ranges against the current
context data of the user, device, and environment.
[0110] Referring now to FIG. 11, a flow diagram is shown
illustrating an example process of determining a set of content
resources based on the current context data of a mobile device. As
described below, the steps in this process may be performed by one
or more components in the mobile device session analyzer systems
600 described above, such as a session analyzer server 620 and/or
mobile device 610. Steps 1101-1105 may be performed after the
determination of performance conditions for one or more users,
based on analyses of previous content resource execution sessions
by the users. As noted above, certain performance conditions may be
user specific, CDN specific, resource type-specific, and/or device
specific, while other performance conditions may apply to multiple
users, CDNs, resource types, and/or devices. Accordingly, the
performance conditions discussed in steps 1101-1105 may include
both user-, CDN-, resource type-, and device-specific performance
conditions, as well as performance conditions applying to multiple
users, CDNs, resource types, and devices.
[0111] In step 1101, the session analyzer 620 may receive and/or
detect a current context data of a mobile device 610. For example,
the session analyzer 620 may receive periodic transmissions of
context data from a mobile device 610a. The session analyzer 620
may determine and/or retrieve additional context data for the
mobile device 610a using other techniques described above, such as
retrieving context data from local data stores 622, other mobile
devices 610, and/or external data sources.
[0112] In step 1102, the session analyzer 620 may compare the
current context data of the mobile device 610a to the previously
determined performance conditions associated with the user and/or
the mobile device 610a. For instance, if a user has one or more
performance conditions based on time, location, device movement,
and background noise, then the session analyzer 620 in step 1102
may compare the session context variable values for these
performance conditions to the current context data of the user's
mobile devices 610. In some cases, the session analyzer 620 may sum
(or otherwise combine) the anticipated performance effect for
multiple different performance conditions. Referring again to
example table 101, if the current context data received in step
1101 indicates that the current time is Tuesday at 10:00 am (+4.5),
the user is currently travelling in his/her vehicle (-1.5), and
current using his/her smartphone (-4.3), then the session analyzer
620 may sum these matching performance conditions to determine an
anticipated net negative effect (4.5-1.5-4.3=-1.3) on user
performance for the current context. As another example, if the
current context data received in step 1101 indicates that the
current time is Thursday at 8:00 am (+4.5), the user is currently
at home (+8.6), and is current using a touchscreen device (+7.3),
then the session analyzer 620 may sum these matching performance
conditions to determine an anticipated net positive effect
(4.5+8.6+7.3=20.4) on user performance for the current context.
[0113] In step 1103, if the session analyzer 620 determines that
overall current context data of the user and/or mobile device 610a
corresponds to an anticipated net positive effect on user
performance (1103: Yes), then the session analyzer 620 may
determine a set of content resources (step 1104) and transmit a
notification to the user's mobile device 610 (step 1105), as
discussed below. To the contrary, if the session analyzer 620
determines that overall current context data of the user and/or
mobile device 610a corresponds to an anticipated net negative
effect on user performance, or to an insufficiently net positive
effect that does not reach a minimum threshold (1103: No), then the
session analyzer 620 would not transmit a notification to the
user's mobile device 610 but would return to step 1101 to await
updated context data from the mobile device 610.
[0114] In step 1104, after determining that the current context
data of the user and/or mobile device 610a corresponds to an
anticipated net positive effect on user performance, then the
session analyzer 620 may determine one or more content resources to
suggest to the user. In some cases, the session analyzer 620 may
select the next content resource(s) in a sequence of content
resources being executed by the user, such as television program
episodes, sequential game levels, sequential eLearning modules,
etc. Additionally, as noted above, certain performance conditions
may be CDN-specific and/or resource type-specific performance
conditions. Therefore, the determination of content resources in
step 1104 may be performed by analyzing the subset of performance
conditions relating to the specific CDN and/or resource type. For
instance, referring again to FIGS. 10A-10B, a calculation of an
anticipated net positive effect on user performance in this example
may be applicable only for User A, and only for Game ABC on the
gaming CDN. Thus, steps 1103 and 1104 may include determining a
highest anticipated net positive effect on user performance among
different CDNs and/or resource types, based on the same set of
current context data.
[0115] In step 1105, the session analyzer 620 may transmit a
notification to the user's mobile device 610a indicating that the
current context of the mobile device 610a is correlated with high
user performance. For example, email, text message, or
application-based notifications may be sent in real-time or near
real-time to the user's mobile device 610a and/or other nearby
mobile devices 610. Such transmissions may indicate to the user
that the current context data (e.g., user state, device state,
physical environment, etc.) corresponds to general high-performance
conditions for the user, or may include one or more suggestions of
the specific content resources determined step 1104, which may have
the highest anticipated performance conditions for the user.
[0116] Referring briefly to FIG. 13, an example of a user interface
screen 1310 is shown notifying the user that the current context
data for the user and/or mobile device 1300 is correlated with a
high level of user performance for executing content resources on
the mobile device 1300. The notification 1310 also includes two
suggestions of specific content resources 1311 and 1312 that
correspond to the highest anticipated user performance conditions
for the current context data.
[0117] Referring now to FIG. 12, another flow diagram is shown
illustrating an example process of determining a set of content
resources based on the current context data of a mobile device. The
process shown in FIG. 12 may be similar to the process described
above in FIG. 11 in several respects. For example, the steps in
this process may be performed by one or more components in the
mobile device session analyzer systems 600 described above, such as
a session analyzer server 620 and/or mobile device 610.
Additionally, steps 1201-1205 may be performed after the
determination of performance conditions for one or more users,
based on analyses of previous content resource execution sessions
by the users. However, the example process shown in FIG. 12 may be
initiated by the user, for example, by instantiating and
interacting with a CDN client application on a mobile device
610.
[0118] In step 1201, a request may be received by a session
analyzer 620 from a mobile device indicating that a user intends to
initiate a content resource execution session on the mobile device
610. For example, a user may instantiate and/or activate a CDN
client application on a mobile device 610, such as a web browser,
media player, gaming application, professional training or
eLearning client, or any other CDN client application. In some
cases, the user may expressly request a suggestion of one or more
content resources for the user's current execution session on the
mobile device 610.
[0119] In step 1202, in response to the user-initiated request, the
session analyzer 620 may retrieve and determine the current context
data of the mobile device 610. Step 1202 may be similar or
identical to step 1101 discussed above. For example, the session
analyzer 620 may request and receive a transmission of context data
from a mobile device 610. The session analyzer 620 also may
retrieve additional context data for the mobile device 610 using
the other techniques discussed above, such as retrieving context
data from local data stores 622, other mobile devices 610, and/or
external data sources.
[0120] In step 1203, the session analyzer 620 may compare the
current context data of the mobile device 610 to the previously
determined performance conditions associated with the user and/or
the mobile device 610. Step 1203 may be similar or identical to
step 1102 discussed above. For instance, if a user has one or more
performance conditions based on time, location, device movement,
and background noise, then the session analyzer 620 in step 1203
may compare the session context variable values for these
performance conditions to the current context data of the user's
mobile device 610. The session analyzer 620 also may sum (or
otherwise combine) the anticipated performance effect for multiple
different performance conditions, as discussed above.
[0121] In step 1204, the session analyzer 620 may determine one or
more sets of content resources to suggest to the user. Step 1204
may be similar or identical to step 1104 discussed above. For
example, the session analyzer 620 may select the next content
resource(s) in a sequence of content resources being executed by
the user, such as television program episodes, sequential game
levels, sequential eLearning modules, etc. In some cases, the
session analyzer 620 be performed by analyzing the subset of
performance conditions relating to multiple different CDNs and/or
resource types, in order to determine the highest anticipated net
positive effect on user performance among different the CDNs and/or
resource types, based on the same set of current context data, as
discussed above.
[0122] In step 1205, the session analyzer 620 may transmit the
suggestions of the specific content resources determined step 1204
to the user's mobile device 610 for presentation to the user. Step
1205 may be similar or identical to step 1105 discussed above. In
various examples, the session analyzer 620 may transmit the
determined content resource suggestions via email, text message, or
application-based notifications, and the suggestions may be ranked
in some cases in order of the highest anticipated net positive
effect on user performance.
[0123] Referring briefly to FIG. 14, another example of a user
interface screen 1410 is shown on a mobile device 1400, providing
multiple content suggestions in response to the user-initiated
initiation that the user may wish to perform a content resource
execution session. In this case, three content resource suggestions
1411-41413 have been determined based on the current context data
of the mobile device 1400, including the current context state of
the mobile device, the user, and the physical environment of the
user and mobile device.
[0124] As discussed above, the steps of FIGS. 11 and 12 may be
performed by a session analyzer server 620 or other central
computing system, based on context data and user input data
received from mobile device 610. However, in certain embodiments,
some or all of the steps of FIGS. 11 and 12 may be performed by
individual mobile devices 610 instead of, or in addition to,
session analyzer 620. For example, a mobile device 610a may
determine performance conditions individually and/or may receive
transmissions of additional performance conditions for the user
from the session analyzer 620 and/or other mobile devices 610.
Using these locally stored performance conditions, the mobile
device 610a may continuously detect and evaluate the current
context data of the mobile device 610a, and may generate user
notifications and/or content suggestions using similar techniques
to those discussed above.
[0125] Referring now to FIG. 15, another flow diagram is shown
illustrating a process of determining one or more performance
conditions associated with a user, based on a user's content
resource execution sessions. The process shown in FIG. 15 may be
similar to the process described above in FIG. 9 in several
respects. For example, data from the user's content resource
execution sessions, including session context data and session
performance data, may be analyzed to determine performance
conditions for the user. Similarly, the steps in this process may
be performed by one or more components in the mobile device session
analyzer systems 600 described above, such as a session analyzer
server 620 and/or mobile device 610. However, the steps of FIG. 15,
discussed below, describe groups of related execution sessions
performed by a user, rather than individual execution sessions. For
example, a user's overall performance for a group of content
resource execution sessions may be analyzed based on session group
characteristics instead of (or in addition to) the context data of
individual execution sessions.
[0126] In step 1501, data relating to one or more content resource
execution sessions may be received, for example, by a mobile device
session analyzer 620 from one or more mobile devices 610. Step 1501
or may be similar or identical to step 901 discussed above. For
example, the data received in step 1501 may include session data
(e.g., user identifier, resource identifiers, execution session
start time, execution session stop time, etc.), as well as various
types of context data, user interaction data received by the mobile
device 610, and any other data collected in connection with an
execution of content resources on the mobile device 610. As shown
in FIG. 15, step 1501 may execute multiple times, and thus data for
multiple different execution sessions may be received, before steps
1502-1506 are performed to analyze and determine performance
conditions based on the execution session data.
[0127] In step 1502, after receiving data describing multiple
content resource execution sessions performed by a user on one or
more mobile devices 610, the received data may be sorted and
grouped into related execution sessions for analysis purposes. For
example, the received execution session data may be sorted to group
execution sessions performed by the user within the same CDN 100
(or 600), such as a specific media distribution CDN, a gaming CDN,
a professional training or eLearning CDN, and the like.
Additionally, within a specific CDN 100 or 600, the execution
session data may be sorted and/or grouped by resource content type.
For instance, within a gaming CDN 600, the received execution
session data may be grouped into sessions by the specific game
and/or game levels played. For a professional training or eLearning
CDN 600, the received execution session data may be grouped into
sessions by the specific training/learning modules, subjects,
courses, etc.
[0128] In step 1503, one or more performance measurements may be
determined for the user, for each group of content resource
execution sessions determined in step 1502. Thus, step 1503 may
include similar or identical techniques to those discussed above in
step 902. However, rather than determining performance measurements
for individual execution sessions by a user, the performance
determination in step 1503 may apply to the groups of execution
sessions determined in step 1502. In some cases, performance
measurements for session groups may correspond to a user scores or
evaluations. For example, gaming software may calculate overall
user scores for games or game levels completed over multiple
execution session by the user. Similarly, professional training or
eLearning software may calculate overall user scores based on
automated online tests or other evaluations completed at the end of
an training/eLearning module, course, semester, curriculum,
etc.
[0129] Additionally, performance measurements of session groups may
be based on express feedback from the user regarding the group of
content resources, such as user content ratings, discussion posts,
content evaluations, and the like, for groups of execution sessions
(e.g., training/eLearning courses, games, television programs,
etc.). As discussed above, such express user feedback also may
include factors such as the amount of time the user played/executed
the content resources within the group, the user decisions and
other interactions during the execution sessions for the group of
resources, and audio and/or visual cues collected and stored by the
mobile device 610 during the group of execution sessions. In some
embodiments, the performance measurements determined in step 1503
also may include overall consumption velocities and/or remediation
rates for the user for the group of execution sessions, rather than
for individual execution sessions as discussed above in step
902.
[0130] In step 1504, one or more session group characteristics may
be determined for each group of content resource execution sessions
determined in step 1502. Such session group characteristics may
include characteristics that apply to groups of execution sessions,
rather than to individual execution sessions. For example, session
analyzer server 620 and/or mobile devices 610 may determine the
number of content resource execution sessions in the group, the
average length of time of each content resource execution session,
and the average amount of time between the content resource
execution sessions in the group.
[0131] Referring briefly to FIG. 16, an example data table 1600 is
shown containing illustrative session group data. In this example,
a number of different session groups have been defined in the first
two columns of table 1600, each session group correspond to a
specific user and specific CDN/resource type. For each session
group, table 1600 includes the number of execution sessions
performed by the user within the session group, the average length
of each session in the group, and the average amount of time
between each session in the group. It should be understood that the
session group characteristics used in this example are illustrative
only and non-limiting. For instance, instead of (or in addition to)
average session length and average time between sessions,
additional session group characteristics may include the standard
deviations and variances of the session length and time between
sessions, as well as other statistical measurements determined for
each group of execution sessions. Additionally, for each session
group in example table 1600, a performance measure is shown for
session group. These performance measurements may correspond to the
one or more of the group performance measurements determined in
step 1503.
[0132] In step 1505, the session analyzer server 620 and/or mobile
devices 610 may determine (or update) sets of user performance
conditions for one or more groups of content resource execution
sessions. Thus, step 1505 may include similar or identical
techniques to those discussed above in step 904. However, rather
than determining correlations between context data and user
performance in individual execution sessions, step 1505 may include
determining statistical correlations between session group
characteristics (step 1504) and the user's performance data for the
overall session group (step 1503). For example, a performance
condition for a session group may correlate a particular pattern
between the number, length, and frequency of execution sessions,
and a higher or lower level of performance by the user for the
session group as a whole. For instance, session analyzer 620 or
mobile device 610 may determine that a user scores consistently
better on training/eLearning module tests when the user studies in
short frequent study sessions, as compared to longer and less
frequent study sessions. As another example, the session analyzer
620 or mobile device 610 may determine that a user has a
consistently lower remediation rate when the user reviews
training/course materials at a frequency of 2 to 7 days, and a
significantly higher remediation rate when the user reviews
training/course materials at a frequency of greater than 7
days.
[0133] In some embodiments, the performance conditions for session
groups determined in step 1505 may be similar to those determined
for individual execution sessions, discussed above in step 904. For
example, a session analyzer 620 or mobile device 610 may determine
pairs of session group characteristics (or combinations of session
group characteristics), along with corresponding predicted
performance effect for the user (e.g., a Performance +/- on a
session group performance point scale). As with performance
conditions for individual execution sessions, certain session group
performance conditions determined in step 1505 may apply to
individual users, while other performance conditions may be
applicable to groups of users. Additionally, certain session group
performance conditions may be applicable to specific CDNs 600
and/or specific types of resources within CDNs 600, while others
may apply to one or more users across multiple different CDNs
and/or resource types.
[0134] In order to determine the session group performance
conditions in step 1505, the session analyzer 620 or mobile device
610 may execute statistical analysis techniques to identify a
session group characteristic (or combination of characteristics),
isolate the characteristic(s), and calculate the performance effect
of the characteristics. For example, the session analyzer 620 may
use regression analyses to control for the performance effects of
other session context variables and other session group
characteristics, thereby isolating a single session group
characteristic (or combination of characteristics), while
effectively holding constant all other variables affecting user
performance during the session group. After removing the potential
performance effects caused by other session group characteristic,
and individual session context variables, via regression analysis,
the effect caused by one isolated group characteristic(s) may be
calculated to determine the performance +/- of those
characteristics. Similar regressions may be used to isolate analyze
the performance effect caused by all session group characteristics.
Such calculations also may take into account (e.g., control for)
the difficulty of the content resources executed during the session
group, based on performance data from other users executing the
same resource group and/or the performance of the user executing
other similar resource groups.
[0135] In step 1506, session group performance conditions
determined in step 1505 may be transmitted to one or more user
devices. Step 1506 may be similar or identical to step 905
discussed above. For example, reports of session group performance
conditions may be generated and transmitted to client devices, as
well as instructor/administrator devices associated with the users.
Additionally, as discussed above in reference to FIGS. 11-14, after
a set of session group performance conditions have been determined
for a user, the current context of the user and the user's devices
610 may be monitored in order to provide notifications and
suggestions to the user for executing certain content resources at
certain times based on the session group performance data.
[0136] For example, referring briefly to FIG. 17, an example of a
user interface screen 1701 is shown on a mobile device 1700. In
this example, a notification and content suggestion 1711 is
provided to the user based on an analysis of the user's session
group performance conditions with respect to an eLearning course
module. Specifically, in this example, a session analyzer 620
and/or mobile device 610 has determined a suggested pattern of
study session times, frequencies, and lengths for a user, based on
the user's session group performance conditions.
[0137] A number of variations and modifications of the disclosed
embodiments can also be used. Specific details are given in the
above description to provide a thorough understanding of the
embodiments. However, it is understood that the embodiments may be
practiced without these specific details. For example, well-known
circuits, processes, algorithms, structures, and techniques may be
shown without unnecessary detail in order to avoid obscuring the
embodiments.
[0138] Implementation of the techniques, blocks, steps and means
described above may be done in various ways. For example, these
techniques, blocks, steps and means may be implemented in hardware,
software, or a combination thereof. For a hardware implementation,
the processing units may be implemented within one or more
application specific integrated circuits (ASICs), digital signal
processors (DSPs), digital signal processing devices (DSPDs),
programmable logic devices (PLDs), field programmable gate arrays
(FPGAs), processors, controllers, micro-controllers,
microprocessors, other electronic units designed to perform the
functions described above, and/or a combination thereof.
[0139] Also, it is noted that the embodiments may be described as a
process which is depicted as a flowchart, a flow diagram, a swim
diagram, a data flow diagram, a structure diagram, or a block
diagram. Although a depiction may describe the operations as a
sequential process, many of the operations can be performed in
parallel or concurrently. In addition, the order of the operations
may be re-arranged. A process is terminated when its operations are
completed, but could have additional steps not included in the
figure. A process may correspond to a method, a function, a
procedure, a subroutine, a subprogram, etc. When a process
corresponds to a function, its termination corresponds to a return
of the function to the calling function or the main function.
[0140] Furthermore, embodiments may be implemented by hardware,
software, scripting languages, firmware, middleware, microcode,
hardware description languages, and/or any combination thereof.
When implemented in software, firmware, middleware, scripting
language, and/or microcode, the program code or code segments to
perform the necessary tasks may be stored in a machine readable
medium such as a storage medium. A code segment or
machine-executable instruction may represent a procedure, a
function, a subprogram, a program, a routine, a subroutine, a
module, a software package, a script, a class, or any combination
of instructions, data structures, and/or program statements. A code
segment may be coupled to another code segment or a hardware
circuit by passing and/or receiving information, data, arguments,
parameters, and/or memory contents. Information, arguments,
parameters, data, etc. may be passed, forwarded, or transmitted via
any suitable means including memory sharing, message passing, token
passing, network transmission, etc.
[0141] For a firmware and/or software implementation, the
methodologies may be implemented with modules (e.g., procedures,
functions, and so on) that perform the functions described herein.
Any machine-readable medium tangibly embodying instructions may be
used in implementing the methodologies described herein. For
example, software codes may be stored in a memory. Memory may be
implemented within the processor or external to the processor. As
used herein the term "memory" refers to any type of long term,
short term, volatile, nonvolatile, or other storage medium and is
not to be limited to any particular type of memory or number of
memories, or type of media upon which memory is stored.
[0142] Moreover, as disclosed herein, the term "storage medium" may
represent one or more memories for storing data, including read
only memory (ROM), random access memory (RAM), magnetic RAM, core
memory, magnetic disk storage mediums, optical storage mediums,
flash memory devices and/or other machine readable mediums for
storing information. The term "machine-readable medium" includes,
but is not limited to portable or fixed storage devices, optical
storage devices, and/or various other storage mediums capable of
storing that contain or carry instruction(s) and/or data.
[0143] While the principles of the disclosure have been described
above in connection with specific apparatuses and methods, it is to
be clearly understood that this description is made only by way of
example and not as limitation on the scope of the disclosure.
* * * * *