U.S. patent application number 17/694327 was filed with the patent office on 2022-09-01 for system and method for providing crowd sourced metrics for network content broadcasters.
The applicant listed for this patent is TuneIn, Inc.. Invention is credited to Kristen George, Thomas Hutchings.
Application Number | 20220277324 17/694327 |
Document ID | / |
Family ID | 1000006337197 |
Filed Date | 2022-09-01 |
United States Patent
Application |
20220277324 |
Kind Code |
A1 |
Hutchings; Thomas ; et
al. |
September 1, 2022 |
SYSTEM AND METHOD FOR PROVIDING CROWD SOURCED METRICS FOR NETWORK
CONTENT BROADCASTERS
Abstract
A system and method for providing crowd sourced metrics for
broadcast content providers. For determinations of such metrics,
user consumption of broadcast content in multiple streams by
multiple content providers may be monitored and user consumption
information regarding the broadcast content may be obtained. One or
more content consumption metrics may be determined to quantify
individual user consumption of the broadcast content. Audience
metrics may be determined, for a content provider, to inform about
users that are available to consume broadcast content provided by
the content provider. Events within the broadcast content may be
determined and event information regarding individual user
consumption of the broadcast content at the event may be obtained.
Event metrics may be determined based on the obtained event
information to inform about consumption of the broadcast content at
the event by users.
Inventors: |
Hutchings; Thomas; (Palo
Alto, CA) ; George; Kristen; (Palo Alto, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
TuneIn, Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
1000006337197 |
Appl. No.: |
17/694327 |
Filed: |
March 14, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14215046 |
Mar 16, 2014 |
11308503 |
|
|
17694327 |
|
|
|
|
61799673 |
Mar 15, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0201
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A computer-implemented method, comprising: dynamically obtaining
broadcast content consumption information during playback of a
broadcast stream associated with broadcast content, wherein the
broadcast content consumption information corresponds to a user at
an event that is associated with the broadcast content; identifying
event content consumption information for a plurality of events
taking place during a plurality of broadcast streams, wherein
identifying the event content consumption information includes
using metadata detection, ID tag detection, header detection, voice
recognition, image analysis, motion detection, or signal detection;
dynamically determining event metrics over at least a segment of
the broadcast stream based on the broadcast content consumption
information, wherein an event metric includes an indication of a
duration of time of the broadcast event in which a display of a
device of the user is on; determining a commonality among one or
more additional users, wherein the one or more additional users are
associated with the broadcast content, and wherein the one or more
additional users are likely to interact with additional broadcast
content over a future period of time; executing a trained
machine-learning model, the trained machine-learning model
generates predictions of future event metrics for one or more
events associated with additional broadcast content over the future
period of time, wherein the trained machine-learning model uses
scaling, aggregation, regression, standard deviation,
summarization, categorization, or probability testing on the
broadcast content consumption information to generate the
predictions, wherein the future event metrics are based on the
event metrics, and wherein the future event metrics are associated
with the commonality among the user and the one or more additional
users; and facilitating a transmission that includes the future
event metrics, wherein when the future event metrics are received
at a content provider, the future event metrics are used by the
content provider to continually optimize quantified consumption of
the additional broadcast content by the user and the one or more
additional users.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 14/215,046 filed Mar. 16, 2014, which claims
the benefit of U.S. Provisional Patent Application 61/799,673,
filed Mar. 15, 2013, which are hereby incorporated by reference in
their entireties for all purposes.
BACKGROUND
[0002] This disclosure relates to providing network content
broadcasters crowd sourced metrics.
[0003] Systems that provide content providers with analysis of
consumption of broadcast content are known. Typically, such
analysis is restricted to taking what is known about participants
that agreed to be monitored (usually a limited sample, such as
Nielsen households), and then extrapolating this information to the
potential audience as a whole. Similarly, any information
describing the audience (e.g., demographics, other content
consumed, etc.) is also extrapolated from a relatively limited
audience sample.
BRIEF SUMMARY
[0004] One aspect of the disclosure relates to determining audience
metrics for content providers that stream broadcast content over
the Internet. Broadcast media, in contrast to on-demand content,
may be "pushed" to users. That is, the start and stop times of
broadcast media may be determined, or programmed, by a content
provider hosting a content stream from which users can choose to
consume content at the times programmed by the content provider.
On-demand content may include content that is made available to the
user, in its entirety, upon request. For example, a set of content
available on demand can be accessed at the discretion of the user
from start to finish (e.g., at the time they choose). The audience
metrics may be determined based on content consumption information
of one or more users that are available to consume broadcast
content provided by a content provider. The determined audience
metrics may indicate descriptions and/or predictions about the
available users: for example, without limitations, a description of
top 3 genres consumed by the available users within the last hour,
a prediction indicating an amount of the available users that will
consume a set of broadcast content should it be streamed by the
content provider in the next 5 minutes, and/or other metrics. In
some examples, the audience metrics may be determined to account
for time-shifting during the user consumption of the broadcast
content. Such audience metrics may provide the content providers
rich information about the available users and thus guidance for
the content providers to determine broadcast content to be
streamed.
[0005] Another aspect of the disclosure relates to determining
event metrics for content providers about events that took place
within broadcast content provided by the content providers. The
determined event metrics may quantify user consumption of the
broadcast content at the events, which may include, but without
limitations, streaming of a particular song, songs by a particular
artist, appearance of a guest, host and/or any other events that
took place within the broadcast content provided by the content
providers. One example of such even metrics may indicate a
prediction about an amount of users that stopped consuming the
broadcast content at an event (e.g., upon or during a streaming of
a particular song). In some examples, the event metrics may be
determined to account for time-shifting during the user consumption
of the broadcast content. Such event metrics may provide the
content providers rich information about user consumption of
broadcast content at events of interest and thus enable the content
providers to determine one or more events that may or may not be
streamed within their broadcast content.
[0006] A system configured to provide crowd sourced metrics for
broadcast content providers may include a server. The server may
operate in a client/server architecture with one or more client
computing platforms. The client computing platforms may be
associated with the users of the virtual space. The server may be
configured to execute one or more of a content consumption module,
a user module, an available user determination module, a content
consumption metrics a content selection module, an audience metrics
module, an event determination module, an event information module,
an event metrics module, and/or other modules.
[0007] The content consumption module may be configured to obtain
user consumption information by monitoring individual user
consumption of broadcast content streamed over the Internet in
multiple streams from multiple content providers. This may involve
monitoring user consumption of broadcast content at any given time.
The obtained user consumption information may comprise segment
information indicating description about the broadcast content
consumed by the user, segment(s) of the broadcast content consumed
by a user, time-shift information indicating time-shift(s) of the
user consumption of the broadcast content, context information
indicating a context in which the user consumed the broadcast
content, geolocation information indicating a location from which
the user consumed the broadcast content, device information about a
user device to which the broadcast content is streamed, user status
information, and/or any other information regarding user
consumption of the broadcast content. In some implementations, some
or all of the obtained user consumption information may be stored
in a database to facilitate historical views of individual user
consumption of broadcast content over one or more periods of time.
In some implementations, some or all of the obtained user
consumption information may be stored in memory storage transiently
for fast access by other modules of the system--for example, by the
available user determination module.
[0008] A user module may be configured to manage user accounts
associated with individual users. Individual user information may
be included in the user accounts. The individual user information
may comprise content consumption metrics quantifying individual
user consumption of broadcast content monitored by the content
consumption module.
[0009] The available user determination module may be configured to
determine users that are available to consume broadcast content
streamed by the content providers. The availability of a user for
such broadcast content may be determined based on user parameters,
such as, but not limited to, the user online/offline status, the
user device screen status, a duration of screen on or off in a
period (e.g., within last 10 minutes, 20 minutes, last half an
hour, last hour, etc.), the user selections of broadcast content, a
language spoken by the user, a level of device usage by the user
and/or any other user parameters. In some implementations, these
user parameters may be readily determined from the individual user
information such as the user consumption information determined by
the content consumption module. In some implementations, the
determinations of the available users may involve comparing the
obtained user parameters with one or more thresholds. In some
implementations, the available user determination module may be
configured to determine a group of available users. For example, it
may be determined by the available user determination module that
there is a group of users within an age group of 20-40 year old
available to consume the broadcast content.
[0010] Content consumption metrics module may be configured to
determine one or more content consumption metrics quantifying
individual user consumption of broadcast content based on the user
consumption information obtained by the content consumption module.
The determined consumption metrics may describe individual user
consumption of broadcast content in a past (e.g., within the last
hour, last 24 hours, last month, last year, and so on), and/or
predict individual user consumption of broadcast content in a
future (e.g., within the next 5, 10, 15, 20, 30, hour, two hours,
and so on).
[0011] In some examples, the content consumption metrics for a user
may include metrics indicating, for example, but not limited to,
top songs, artists, genres, hosts, guests, personalities and/or
other entities that the user has consumed in the past. Other
examples of content consumption metrics for a user may include
metrics indicating a context in which the user consumed the
broadcast content(e.g., indicating that the user consumed the
broadcast content in a stream that pushed the broadcast content to
the user, or that the user determined the broadcast content first
and then selected the stream), average duration of a song, talk
show, news and/or sports events the user has consumed in a period,
number of songs, talk shows, news programs, and/or sports events
the user consumed in the past, a consumption pattern of the user in
the past, , duration of the user device screen on time/off time
during the consumption, number of times the user switch the screen
from on to off and vice versa, device usage level during the
consumption, locations from which the user consumed the broadcast
content, and/or any other descriptive content consumption
quantifying individual content consumption.
[0012] In some examples, the content consumption metrics for a user
may include metrics indicating a likelihood that the user will or
will not consume broadcast content in a stream within a future
period, an estimated duration that the user will stay in the stream
to consume the broadcast content within the future period, a
likelihood that the user will switch from a current stream to
another stream within the future period, a likelihood that a user
will turn on or turn off the user screen device during the
streaming of the broadcast content, and/or any other predictive
consumption metrics for the user. In some examples, the content
consumption metrics may be determined based on information
regarding broadcast content selections as obtained by the content
consumption module, user preferences, and/or user device usage.
[0013] The content selection module may be configured to obtain
information regarding broadcast content selections made by a user.
Such user selection information may indicate consumption of
specific broadcast content in a stream by a user within a period.
In some examples, the user content selection information may be
obtained by analyzing user consumption information obtained by the
content consumption module, such as the segment information,
context information, user device information and/or other
information included in the user consumption information.
[0014] The audience metrics module may be configured to determine
audience metrics for a content provider. The determined audience
metrics may provide the content provider information about
available users to consume broadcast content by the content
provider, as determined by the available user determination module,
and may enable the content provider to determine a set of broadcast
content to be streamed in a stream in order to increase
audience-ship for that stream. Such audience metrics may be
determined based on content consumption metrics determined for the
individual available users. The determinations may involve
statistics methods such as scaling, aggregation, standard
deviation, summarizing, categorizing, regression, standard
deviation, neural networks, machine learning, and/or any other
statistical methods to determine audience metrics.
[0015] One example of the audience metrics is a metric that
indicates, for a content provider, a prediction about an amount of
available users will consume a set of broadcast content should the
content provider stream the set of broadcast content in a future
period (e.g., the next 1, 3, 5, 10, 20, 30 minutes, hour, day,
month, year, and so on). Other examples of the audience metrics may
include metrics indicating an amount of available user that will
switch from other streams to a stream provided by the content
provider within a future period, a total duration of on-time of the
device screens associated with the available users within the
future period, an amount of the available users that will switch
off of the stream provided by the content provider within the
future period, a total duration that the available users will stay
in the stream within the future period should the content provider
stream a set of broadcast content with in the future period, and/or
any other audience metrics.
[0016] The event determination module may be configured to
determine events within broadcast content streamed over the
Internet by content providers. Such events may include, but not
limited to, streaming of a song, streaming of songs of a genre,
streaming of songs by an artist, an appearance of a guest, an
appearance of a host, or a commercial break, and/or any other
events that may take place within the broadcast content. In some
examples, the content provider of the broadcast content may provide
metadata, ID tags, headers, signals, and/or any other identity
information about the broadcast content. In these examples, the
events may be determined by detecting such identity information
provided in the streaming by the content provider. In some other
examples, the event determination may involve recognition methods
for determining events within the broadcast content, such as, but
not limited to, voice recognition, image analysis, motion
detection, signal detection, and/or any other methods that may be
used to automatically recognize the identities of events within the
broadcast content.
[0017] The event information module may be configured to obtain
event consumption information indicating user consumption of
broadcast content at the events determined by the event
determination module. The determined event consumption information
may indicate individual user consumption of the broadcast content
at (i.e., upon or during) the determined events, such as., but not
limited to, user stream status, user device screen status, user
geolocation, user activities on the user device, and/or any other
information for individual users at the determined events. In some
examples, for such determinations, the event determination module
may be configured to obtain content consumption metrics for
individual users at the events. In those examples, the event
information determinations may involve corresponding the determined
events to the obtained user content consumption metrics according
to common time occurrences. In some examples, event information
module may be configured to dynamically obtain content consumption
information for individual users at the determined events within
the broadcast content.
[0018] The event metrics module may be configured to determine
event metrics for the events based on the event information
obtained by event information module. The determined event metrics
may quantify user consumption of the broadcast content in which the
event took place on an event level. Examples of the determined
event metrics may include, metrics indicate an amount of users that
joined or left the stream at the event or at specific segments of
the event, an amount of users that shared and/or liked the event on
social media, emailed about the event, and/or other user actives
related to the event during the streaming of the broadcast content
in which the event took place, a total duration of on-time of
screens of devices associated users that stayed in the stream at
the event, an amount of users that moved to a commercial
establishment at the event (e.g., during a talk show in which the
commercial establishment is mentioned), an amount of users that ran
an application on their devices at the event (e.g., an event of a
talk show in which the application is mentioned), and/or any other
event metrics. Such event metrics may be provided to the content
provider that streamed the events to guide the content provider to
make a decision of those events in future streaming of broadcast
content.
[0019] In some examples, the event metrics module may be configured
to determine event metrics based on event information for
individual users as obtained by the event information module using
methods, such as, but not limited to, scaling, aggregating,
summarizing, probability testing, neural networks, machine learning
and/or any other methods that may be used to determine event
metrics based on event information for individual users. In some
examples, the event metrics module may be configured to determine
event metrics for a group of users, as determined by the user
module, at the events.
[0020] These and other features, and characteristics of the present
technology, as well as the methods of operation and functions of
the related elements of structure and the combination of parts and
economies of manufacture, will become more apparent upon
consideration of the following description and the appended claims
with reference to the accompanying drawings, all of which form a
part of this specification, wherein like reference numerals
designate corresponding parts in the various figures. It is to be
expressly understood, however, that the drawings are for the
purpose of illustration and description only and are not intended
as a definition of the limits of the invention. As used in the
specification and in the claims, the singular form of "a", "an",
and "the" include plural referents unless the context clearly
dictates otherwise.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] FIG. 1 illustrates one example of a system configured to
provide crowd sourced metrics for broadcast content providers.
[0022] FIGS. 2A-2B illustrate various exemplary audience
metrics.
[0023] FIG. 3 illustrates various exemplary event metrics.
[0024] FIG. 4 illustrates one exemplary method of providing crowd
sourced metrics for broadcast content providers in accordance with
the disclosure.
[0025] FIG. 5 illustrates another exemplary method of providing
crowd sourced metrics for broadcast content providers in accordance
with the disclosure.
DETAILED DESCRIPTION
[0026] FIG. 1 illustrates one example of a system configured to
provide crowd sourced metrics for broadcast content providers.
Content such as songs, talk shows, news programs, sports events
(e.g., live games) and/or any other content may be broadcasted to
one or more users. In some examples, content providers may provide
one or more sets of such broadcast content to the users over the
Internet through streaming. A set of broadcast content may include
one or more of songs, talk shows, news programs, sports events,
and/or any content in an order by which the set of broadcast
content may be consumed by the users temporally. Information
regarding user consumption of broadcast content may be obtained at
any given time. One or more content consumption metrics may be
determined, based on the obtained user consumption information, to
quantify individual user consumption of the broadcast content. One
or more audience metrics may be determined based on content
consumption metrics for users available to consume broadcast
content. In one example, the determined audience metrics may
indicate a predication of an amount of users that will consume a
set of broadcast content should the content provider stream the set
of broadcast content.
[0027] Event information regarding user consumption of broadcast
content at events that took place within broadcast content may be
obtained. Examples of such events include, but not limited to,
streaming of a particular song, songs by a particular artist,
appearance of a guest, host and/or any other events. Event metrics
quantifying user consumption of broadcast content at such events
may be determined based on the obtained event information. In one
example, the determined event metrics may indicate an amount of
users that joined or left a stream at an event within broadcast
content. As such, the audience metrics and the event metrics may
provide content providers rich information about user consumption
of broadcast content streamed by the content providers. This may
guide the content providers to determine broadcast content to be
streamed to the users.
[0028] In some implementations, system 100 may include a server
102. The server 102 may be configured to communicate with one or
more client computing platforms 104 according to a client/server
architecture. The users may access system 100 and/or the virtual
space via client computing platforms 104. Server 102 may be
configured to execute one or more computer program modules. The one
or more computer program modules may include one or more of a
content consumption module 106, a user module 108, an available
user determination module 110, a content consumption metrics module
112, a content selection module 114, an audience metrics module
116, an event determination module 118, an event information module
120, an event metrics module 122, and/or other modules.
[0029] The content consumption module 106 may be configured to
obtain user consumption information by monitoring individual user
consumption of broadcast content streamed over the Internet in
multiple streams from multiple content providers. Content such as
songs, talk shows, news programs, sports events (e.g., live games)
and/or any other content may be broadcast to one or more users via
Internet streaming by content providers. A user may consume such
broadcast content by, e.g., listening to, re-broadcasting, and/or
viewing the broadcast content. Information regarding individual
user consumption of broadcast content may be obtained by monitoring
the user consumption of the broadcast content at any given time.
Such monitoring may include monitoring the user consumption of
broadcast content in a stream, which may involve monitoring a
location, device usage, stream selection(s), broadcast content
selection(s), and/or any other information regarding the user
during streaming of broadcast content by the content provider. In a
non-limiting example for illustration only, 10 streams (e.g.,
stations) of broadcast content (e.g., music shows, talk shows,
sports games, news and/or other content), at a time T, may be
provided by multiple content providers; individual user activities
related to those 10 streams of broadcast content, at the time T,
such as switching to a stream, leaving a stream, switching to a
stream from another stream, staying in a stream for a period of
time, turning on or off screens of a device to which broadcast
content is streamed, and/or any other user activities may be
monitored. Such monitored individual user consumption of the
broadcast content may be analyzed to obtain user consumption
information regarding the broadcast content.
[0030] The obtained user consumption information may comprise, for
example, but not limited to, segment information indicating
segment(s) of the broadcast content consumed by the user. The
segment information may be obtained, for example, by determining
segment(s) within the broadcast content during which the user
consumed the broadcast content, the locations of those segments
(e.g., as measured by a starting time and/or an end time within a
song, talk show, news program, and/or any other broadcast content),
duration of those segments, and/or any other segment information
regarding segments of the broadcast content consumed by the
user.
[0031] The obtained user consumption information may comprise
description about the broadcast content consumed by the user. In
some examples, the streaming of broadcast content consumed by the
user may provide identification information about the broadcast
content, such as, but not limited to titles (e.g., song title, talk
show name, etc.), the language of the broadcast content, performing
artists, hosts of a talk show, guests of the talk show, a genre of
the broadcast content, and/or any other information describing the
broadcast content being consumed by the user. Such identification
information about the broadcast content consumed by the user may be
obtained from the streaming of the broadcast content, e.g., via
metadata, headers, or tags embedded in the streaming. In some
examples, the description about the broadcast content consumed by
the user may be determined by content recognition methods, such as,
but not limited to, voice recognition, image analysis, signal
detection, motion detection, and/or any other content recognition
methods.
[0032] The obtained user consumption information may comprise
time-shift information indicating one or more time-shifts during
the user consumption of the broadcast content. Such time-shift
information may be obtained, for example, by determining one or
more time periods during which the user consumed the broadcast
content. For instance, it may be determined that the user paused a
streaming of a song for 3 minutes, resumed the streaming, paused
the streaming for another 2 minutes, and resumed the streaming
until the end of the song. In another instance, it may be
determined that the song was streamed to the user device and stored
on the user device at time T; and the user played back the song 2
hours after the time T.
[0033] The obtained user consumption information may comprise
context information indicating a context in which the user consumes
the broadcast content. Such context information may be obtained,
for example, by analyzing user's consumption of other broadcast
content leading to the consumption of the current broadcast
content. For instance, the context information may indicate that
the user was listening to a news program, then a talk show, before
switching to a stream broadcasting a song having political symbols.
In another instance, the context information may indicate that the
user consumed a song in a stream that pushed the song to the user;
or the user consumed the song by determining the song first (e.g.,
through searching and/or browsing for the song), determining a
stream in which the song was streamed (e.g., through searching
and/or browsing stream information that indicates the song would be
streamed), and selecting the determined stream.
[0034] The obtained user consumption information may include
geolocation information indicating a location from which the user
consumes the broadcast content. In some implementations, such
geolocation location information regarding the users may be
obtained from client devices associated with the users, such as the
client devices 104 as illustrated. The geolocation information
obtained from the client devices 104 may specify the physical
locations of client devices 104. The geolocation information may
include one or more of Internet protocol address, MAC address, RFID
information, Wi-Fi connection location, Global Positioning System
coordinates, radio frequency triangulation information, information
entered to client device 104 by a user (e.g. , specifying the
location of client 104), and/or other information that may identify
a real world location. The content consumption module 106 may be
configured to obtain geolocation information of one or more of
client device 104 in a substantially ongoing manner (e.g., at a
sampling rate), at discrete intervals, responsive to user selection
or input, and/or according to other schemes. The content
consumption module 106 may be configured to obtain geolocation
information that has been transmitted wirelessly from client
devices 104. The content consumption module 106 may be configured
to obtain geolocation information that has been stored at client
devices 104 for transmission to user geolocation module 108 at a
later time (e.g., when docked to a computer). The content
consumption module 106 may be configured to manage storage of
geolocation of client devices 104. This may facilitate
determination of the geolocation of one of client device 104 at
some previous time.
[0035] The obtained user consumption information may comprise
device information about a user device to which the broadcast
content is streamed. Such device information may indicate, for
example, screen status of the use device (e.g., Is the screen
currently on or off? How long did the screen remain off or on
during the streaming? How many times did the screen status change,
from on to off or vice versa, during the streaming?), battery usage
of the device, processor load of the device, memory usage of the
device, other running applications on the device, and/or any other
device information.
[0036] The obtained user consumption information may comprise user
status information indicating individual user status during a
streaming of broadcast content. Such user status information may
indicate, for example, without limitations, user online/offline
status, user engagement status (e.g., working in other application
while consuming the broadcast content), state of the user device
(e.g., on or off) and/or any other user status information. Such
user status information may be obtained from a user device
associated with the user. For example, the power-on or off status
of the device may be obtained from a processor of the user device;
the user online or offline status may be determined through
connection information associated with the user device and
registered at system 100; and the user engagement information may
be determined through the session information associated with the
user and registered at system 100.
[0037] It should be understood that the above examples of user
consumption information regarding user consumption of broadcast
content are described for illustration purposes only and thus not
intended to be limiting. One of ordinary skill in the art will
recognize the user consumption information regarding user
consumption of broadcast content may comprise any other
information.
[0038] In any case, the user consumption information obtained by
the content consumption module 106 may be stored in the electronic
storage 126 to be accessed by other modules in the system 100,
e.g., the content consumption metrics module 112, available user
determination module 110, and the event metrics module 122. In some
examples, some or all of the obtained user consumption information
may be organized in a database and stored permanently in the
electronic storage 126. In some example examples, some or all of
the obtained user consumption information may be stored in the
electronic storage 126 transiently (e.g., the electronic storage
126 may include memory storage). In some implementations, the
obtained user consumption information such as the segment
information, context information, and/or description about the
broadcast content consumed by individual users may be stored in the
database permanently. These kinds of user consumption information
may be organized to be associated with corresponding users in one
or more time series to facilitate historical views of individual
users' consumption of broadcast content over one or more periods of
time. The obtained user consumption information such as the device
information and/or geolocation information, on the other hand, may
be stored in memory storage transiently for fast access by other
modules of system 100.
[0039] The user module 108 may be configured to manage user
accounts associated with individual users. Individual user
information may be included in the user accounts and may include
information stored by server 102, one or more of the client
computing platforms 104, and/or other storage locations. The user
information may comprise content consumption metrics quantifying
individual user consumption of broadcast content monitored by the
content consumption module 106. Other examples of user information
include information identifying users (e.g., a username or handle,
a number, an identifier, and/or other identifying information)
within the virtual space security login information (e.g., a login
code or password), virtual space account information, subscription
information, virtual currency account information (e.g., related to
currency held in credit for a user), relationship information
(e.g., information related to relationships between users in the
virtual space), virtual space usage information, demographic
information associated with users, interaction history among users
in the virtual space, information stated by users, purchase
information of users, browsing history of users, a client computing
platform identification associated with a user, a phone number
associated with a user, and/or other information related to
users.
[0040] The available user determination module 110 may be
configured to determine users that are available to consume
broadcast content streamed over the Internet by the content
providers. For such determinations, individual user information may
be accessed through the user module 108. One or more user
parameters may be obtained from the individual user information and
may be used to determine whether a user is available to consume
broadcast content provided by a content provider. Such an
availability of a user may be determined based on the user
parameters, such as, but not limited to, the user online/offline
status, but not limited to, the user online/offline status, the
user device screen status, a duration of screen on or off in a
period (e.g., within last 10 minutes, 20 minutes, last half an
hour, last hour, etc.), the user selections of broadcast content, a
language spoken by the user, a level of device usage by the user
and/or any other user parameters. In some implementations, these
user parameters may be readily determined from the individual user
information such as the user consumption information described
above. For example, the user consumption information may indicate
that a user U1 is online and is actively listening to alternative
rock songs in a stream S1 provided by a content provider P1. A set
of user parameters for user U1 may be obtained from this
consumption information of user U1: i.e. status: online, device:
on, screen: on selection: stream S1 by P1. In another example, the
user consumption information may indicate that a user U2 is online,
listening to country songs in a stream S2 provided by a content
provider P2 with screen off In that example a set of user
parameters may be determined from the user U2 consumption
information: i.e. status: online, device: on, device screen: off,
and selection: S2 by P2.
[0041] In some implementations, the determinations of the available
users may involve comparing the obtained user parameters with one
or more thresholds. For example, a threshold may be established
such that so long as the user status indicates the user is online,
the user is available to consume broadcast content provided by a
content provider. In another example, more than one threshold may
be established such that the obtained user parameters should breach
all of the thresholds for a determination that the user is
available to consume broadcast content. For example, the available
user determination module 110 may be configured such that a user is
determined as available to consume broadcast content in a stream
only when the user parameters indicate the user device screen is on
and the user is currently listening to broadcast content in a
stream provided by content provider P1. Accordingly, in the
examples above where users U1 and U2's consumption information is
determined, user U1 may be determined as available, but user U2 may
be determined as unavailable. It should be understood that the
above examples of determining individual user availability to
consume broadcast content to be provided a content provider are
described for illustration purposes only and thus not intended to
be limiting. One of ordinary skill in the art may recognize there
are various ways and/or standards for determining such user
availability.
[0042] In some implementations, the available user determination
module 110 may be configured to determine a group of available
users for broadcast content to be streamed. Such a group may be
determined based on, for example, without limitations, a common
geolocation, a common language spoken, one or more common
preferences shared by, a common income level, a common age group,
one or more songs, artists, genres, listened most as indicated by
the user consumption information, and/or any other common
characteristics of the group of available users. For example, the
available user determination module 110 may determine there is a
group of available users from China who are currently listening to
various songs in different streams; there is a group of available
users that are within an age group of 20-40 year old; there is a
group of available users who have listened an artist 80% of time
when they are online; and so on.
[0043] Content consumption metrics module 112 may be configured to
determine one or more content consumption metrics quantifying
individual user consumption of broadcast content based on the user
consumption information obtained by the content consumption module
106. The determined consumption metrics may include metrics that
describe individual user consumption of broadcast content in a past
(e.g., within the last hour, last 24 hours, last month, last year,
and so on), and may include metrics that predict individual user
consumption of broadcast content in a future (e.g., within the next
5, 10, 15, 20, 30, hour, two hours, and so on). The descriptive
content consumption metrics for a user may include metrics
indicating, for example, but not limited to, top songs, artists,
genres, hosts, guests, personalities and/or other entities that the
user has consumed in the past. For instance, such a descriptive
metric may indicate top 10 songs in all broadcast content that have
been consumed by the user in the last month; top songs by 3 artists
that have been consumed by the user in the last 24 hours; and so
on. In another example, the determined descriptive content
consumption metrics may indicate a context in which the user
consumed the broadcast content. E.g., a metric value such as
"tuning" may be determined in cases where the obtained user
consumption information indicates that the user consumed the
broadcast content in a stream that pushed the broadcast content to
the user; and a "listening" metric value may be determined in cases
where the obtained user consumption information indicates that the
user determined the broadcast content first and then selected the
stream.
[0044] Other examples of descriptive content consumption metrics
for a user include metrics indicating average duration of a song,
talk show, news and/or sports events the user has consumed in a
period, number of songs, talk shows, news programs, and/or sports
events the user consumed in the past, a consumption pattern of the
user in the past, duration of the user device screen on time/off
time during the consumption, number of times the user switch the
screen from on to off and vice versa, device usage level during the
consumption, locations from which the user consumed the broadcast
content, and/or any other descriptive content consumption
quantifying individual content consumption.
[0045] The descriptive content consumption metrics may be
determined from the user consumption information obtained by the
content consumption module 106. In some examples, for such
determinations, the content consumption metrics module 112 may be
configured to employ statistics methods such as categorizing,
summarizing, tabulating, distribution analysis, univariate analysis
and/or any other statistics methods that may be used to quantify
individual consumption of the broadcast content. For example, a
number of songs, artists listened, name of the songs, artists,
order of the songs listened, genre of the songs listened, total
duration of the songs listening, average duration of individual
song listening, segments in the songs listened, user device screen
status, user device usage level during song listening and/or other
content consumption metrics quantifying an individual user
consumption of broadcast content in the past hour may be determined
from the segment information and user device information included
in the user consumption information as determined by the content
consumption module 106. In another example, descriptive content
consumption metrics such as, top songs, artists, genre, etc.,
consumed by the user during the user's lifetime registered with the
system 100 may be determined from the user consumption information
to facilitate historical views of user consumption of broadcast
content.
[0046] The determined content consumption metrics may include
metrics that predict individual user consumption of broadcast
content in a future. Such predictive content consumption metrics
for a user may be determined from the consumption information
obtained by the content consumption module 106. Examples of
predictive content consumption metrics for a user may include a
likelihood that the user will or will not consume broadcast content
in a stream within a future period, an estimated duration that the
user will stay in the stream to consume the broadcast content
within the future period, a likelihood that the user will switch
from a current stream to another stream within the future period, a
likelihood that a user will turn on or turn off the user screen
device during the streaming of the broadcast content , and/or any
other predictive consumption metrics for the user.
[0047] In some examples, for such determinations, the content
consumption metrics module 112 may be configured to employ
statistics methods such as regression analysis (e.g., linear
regression, discrete regression, logistical regression, probit
regression, and/or any other regression analyses), time series
analysis (e.g., autocorrelation, trend estimation, seasonal
variation, and/or any other time series analyses), survival or
duration analysis, machine learning techniques, neural networks,
radial basis functions, support vector machines, geospatial
predictive modeling, and/or any other statistics methods that may
be used to analyze current and/or historical content consumption by
an individual user to predict a future content consumption by the
individual user.
[0048] The determined predictive content consumption metrics may
include, for example, a metric indicating a likelihood that an
individual user will consume or will not consume a set of broadcast
content to be streamed by a content provider. Such a likelihood may
be determined, for instance, based on information regarding
broadcast content selections made by the user in the last hour as
obtained by the content consumption module 106, user preferences,
and/or user device usage. The information about content selections
made by the user in the last hour may reveal a consumption pattern
of the user in the last hour. By way of a non-limiting example to
illustrate information about content selections by the user, the
user, in one example, may have switched to a stream S1 when a 90s
rock song R1 by an artist X was streamed, stayed in the stream S1
until the end of song R1's streaming and switched to a news program
when a country song C1 was steamed in the S1 following song R1,
switched back to S1 when another 90s rock song R2 also artist X was
streamed, stayed in the stream S1 until the end of song R1's
streaming and switched to the news program again when a hip-pop son
H1 was steamed in the S1 following song R2, and so on. Based on
such content selections by the user in the last hour, a consumption
pattern about the user's consumption of broadcast content in the
last hour may be determined and may indicate that the user switched
to stream S1 when 90s songs by artist X were streamed and switched
off of stream S1 when other songs were streamed. Based on this
information and other user consumption information such as, the
current user device usage and user preferences (e.g., 90s rock
songs specified as favorite genre by the user), a likelihood e.g.,
there is more than 50% of chance that the user will switch to the
rock song stream provided by the content provider P1 in the next 5
minutes should the content provider P1 stream a 90s hit rock song
in the next 5 minutes may be quantified using, for example, time
series analysis.
[0049] In some examples, in determining the content consumption
metrics, the content consumption metrics module 112 may be
configured to account for time-shift(s) of the user consumption of
the broadcast content determined by the content consumption module
106. For example, in a case where the content consumption module
106 determines that the user consumed the song in a time period or
time periods later than the time period in which the song was
streamed by the content provider, the content consumption metrics
module 112 determines the content consumption metrics based on user
consumption of the song during the later time period(s) in which
the user actually consumed the song.
[0050] Content selection module 114 may be configured to obtain
information regarding broadcast content selections made by a user.
Such user selection information may indicate consumption of
specific broadcast content in a stream by a user within a period.
In a non-limiting example as an illustration only, user content
selection information for a user U may indicate that the user
consumed a classical rock song at a 15th minute mark prior to a
current time in stream S1, an alternative rock song at a 11th
minute mark prior to the current time in stream S1, a country song
at 7th minute mark prior to the current time in stream S2, another
classical rock song at a 6th minute mark prior to the current time
in stream S1, a heavy metal rock song at a 3rd minute mark prior to
the current time in stream S1 and until the current time.
[0051] In some examples, such user content selection information
may be obtained by analyzing user consumption information obtained
by the content consumption module 106, such as the segment
information, context information, user device information and/or
other information included in the user consumption information. For
example, the segment information may provide the content selection
module 114 information about segments of user consumption within a
period, e.g., during 15th to 7th minute prior to the current time
the user stayed in S1, during the 6th minute prior to the current
time the user switched to S2 from S1, and during the 5th minute
until the current time the user switched back to S1 from S1. Based
on this segment information, user selections of broadcast content
during the past 15th minute may be determined by juxtaposing stream
information of S1 and S2 during the past 15th minute, and the
obtained segment information about the user.
[0052] It should be understood that the above examples of user
selections are described for illustration purposes only and thus
are not intended to be limiting. One of ordinary skill in the art
will recognize that the information regarding user content
selections may include a variety of information about user content
selections, such as, but not limited to, artists, genre, talk show,
hosts, guests, sports events selected by the user, specific
segments within the selected broadcast content during which the
user consume the selected broadcast content (e.g., closing argument
section in a political talk show featuring debates) and/or any
other information indicating any other aspects related to user
content selections.
[0053] Audience metrics module 116 may be configured to determine
audience metrics for a content provider. The determined audience
metrics may provide the content provider information about
available users to consume broadcast content by the content
provider, as determined by the available user determination module
110, and may enable the content provider to determine a set of
broadcast content to be streamed in a stream in order to increase
audience- ship for that stream. To so facilitate the content
provider, the audience metrics may be determined by the audience
metrics module 116 to quantify content consumption by the available
users in a past and/or provide predictions about content
consumption by the available users in a future. Such audience
metrics may be determined based on content consumption metrics
determined for the individual available users. In some examples
where the content consumption metrics are determined to account for
time-shifting during the individual available user consumption of
the broadcast content, the audience metrics determined based on
such content consumption metrics account for the time-shifting
accordingly.
[0054] In some examples, the audience metrics module 116 may employ
statistics methods such as scaling, aggregation, standard
deviation, summarizing, categorizing and/or any other statistics
methods to determine descriptive audience metrics based on
descriptive content consumption metrics for individual available
users. In a non-limiting example as an illustration of such
audience metrics only, 40 users may be determined by the available
user determination module 110 as available to consume broadcast
content provided by a content provider P1. In that example, content
consumption metrics for those 40 available users may be obtained by
the content consumption metrics module 112. The obtained content
consumption metrics may indicate that 30 of the 40 available users
has consumed rock songs as their number one genre in the past month
and 5 of the 40 available users, although did not consume rock
songs as their number one genre in the past, has specified that
rock as one of their favorite genre in user preferences. Based on
such descriptive content consumption metrics for the available
users, the audience metrics module 116 may determine that the
number one genre favored by the 40 available users is rock.
[0055] In some examples, the audience metrics module 116 may employ
methods such as scaling, regression, standard deviation, neural
networks, machine learning, and/or any other methods that may
determine predictions about content consumption by the available
users in a future period to determine predictive audience metrics.
One example such predictive audience metrics is a metric that
indicates, for a content provider, a prediction about an amount of
available users will consume a set of broadcast content should the
content provider stream the set of broadcast content in a future
period (e.g., the next 1, 3, 5, 10, 20, 30 minutes, hour, day,
month, year, and so on). Such a predictive audience metric may be
determined by the audience metrics module 116, for example, using
an averaging method. For instance, the likelihood associated with
individual available users that they will consume a set of
broadcast content may be aggregated and then averaged to obtain an
average likelihood. This average likelihood may be then multiplied
by the number of available users to obtain an estimated amount of
user that will consume the set broadcast content within the further
period. Other examples of the predictive audience metrics may
include metrics indicating an amount of available user that will
switch from other streams to a stream provided by the content
provider within a future period, a total duration of on-time of the
device screens associated with the available users within the
future period, an amount of the available users will switch off of
the stream provided by the content provider within the future
period, a total duration that the available users will stay in the
stream within the future period should the content provider stream
a set of broadcast content with in the future period, and/or any
other predictive audience metrics. In some examples, the audience
metrics module 116 may be configured to determine audience metrics
for a group of available users grouped by, for example, but not
limited to, a common geolocation, a common language spoken, a
common age group, a common income level, a common demographics, a
common education background, a common occupation, and/or any other
common user characteristics.
[0056] Event determination module 118 may be configured to
determine events within broadcast content streamed over the
Internet by content providers. One or more events may take place
within the broadcast content by content providers. Such events may
include, but not limited to, streaming of a song, streaming of
songs of a genre, streaming of songs by an artist, an appearance of
a guest, an appearance of a host, or a commercial break, and/or any
other events that may be streamed with the broadcast content. As
such, the determined events may include events within the broadcast
content that have durations (e.g., streaming of a song, talk shown,
commercial break), and/or events with the broadcast content that
took place at discrete point (e.g., an appearance of a guest in a
talk show at 7 minute 45 second mark in the show, a mentioning of a
product in a talk show at 11 minute 32 second mark and so on). In
some examples, the content provider of the broadcast content may
provide metadata, ID tags, headers, signals, and/or any other
identity information to identify events within the broadcast
content, such as, but not limited to, titles of the broadcast
content (e.g., song titles, talk show titles) being streamed at a
given time, performing artists of the broadcast content, talk show
hosts, guests appearance in the broadcast content (e.g., host H is
appearing in this segment of the broadcast content, guest G is
appearing in that segment of the broadcast content), indication
that a product is being mentioned in the broadcast content,
indication of commercial breaks being streamed in the broadcast
content, and/or any other events that may be streamed within the
broadcast content.
[0057] In these examples, the event determination module 118 may be
configured to determine, e.g., through programing rules, events by
matching the broadcast content identification information received
from the content provider with a list of events of interest. In
some other examples, the event determination module 118 may be
configured to automatically recognize the identities of events
within the broadcast content through, for example, voice
recognition, image analysis, motion detection, signal detection,
and/or any other methods that may be used to automatically
recognize the identities of events within the broadcast
content.
[0058] Event information module 120 may be configured to obtain
event consumption information indicating user consumption of
broadcast content at the events determined by the event
determination module. The determined event information may indicate
individual user consumption of the broadcast content at (i.e., upon
or during) the determined events by the event determination module
118. In some examples, for such determinations, the event
determination module 118 may be configured to obtain content
consumption metrics for individual users at the events. In some
examples, such determinations may involve corresponding the
determined events to the user content consumption according to the
common time occurrence. For example, for a determined event--e.g.,
a streaming of a song, the start time and end time of the streaming
of the song in a stream may be obtained by the event determination
module 118; and user consumption metrics quantifying individual
user consumption of the broadcast content in the stream during the
starting time and end time of the streaming of the song may be
determined: such as, user stream status (i.e., did the user leave
the stream or join the stream), user device screen status (i.e.,
did the user turn on the screen or turn off the screen), user
geolocation information (e.g., where was the user in the real
world, and did the user start moving to another location e.g., a
restaurant advertised in a commercial break), user activities on
the user device (e.g., did the user open an application mentioned
in talk show), and/or any other metrics for individual users
obtained at the determined the event. In some examples where the
content consumption metrics are determined to account for
time-shifting during the individual available user consumption of
the broadcast content, the event information determined based on
such content consumption metrics account for the time-shifting
accordingly.
[0059] In some other examples, event information module 120 may be
configured to dynamically obtain content consumption information
for individual users at the determined events within the broadcast
content. For example, at a determination that a streaming of a song
is taking place within the broadcast content of a stream, the event
information module 120 may obtain content consumption metrics for
those users who are in the stream, such as, user stream status
(i.e., did the user leave the stream or join the stream), user
device screen status (i.e., did the user turn on the screen or turn
off the screen), user's geolocation information (e.g., where was
the user in the real world, and did the user start moving to
another location e.g., a restaurant advertised in a commercial
break), user's activities on the user device (e.g., did the user
open an application mentioned in talk show), and/or any other
consumption metrics for individual users during the starting time
and end time of the streaming of the song.
[0060] The event metrics module 122 may be configured determine
event metrics for the events based on the event information
obtained by event information module 120. The determined event
metrics may quantify user consumption of the broadcast content in
which the event took place on an event level. Examples of the
determined event metrics may include, metrics indicate an amount of
users that joined or left the stream at the event or at specific
segments of the event, an amount of users that shared and/or liked
the event on social media, emailed about the event, a total
duration of on-time of screens of devices associated users that
stayed in the stream at the event, an amount of users that moved to
a commercial establishment at the event (e.g., an event of a talk
show in which the commercial establishment is mentioned), an amount
of users that ran an application on their devices at the event
(e.g., an event of a talk show in which the application is
mentioned), and/or any other event metrics. Such event metrics may
be provided to the content provider that streamed the events such
that the content provider may be enabled to make a decision of
those events in future streaming of broadcast content.
[0061] In some examples, the event metrics module 122 may be
configured to determine event metrics based on event information
for individual users as obtained by the event information module
120 using methods, such as, but not limited to, scaling,
aggregating, summarizing, probability testing, neural networks,
machine learning and/or any other methods that may be used to
determine event metrics based on event information for individual
users. For example, in a case where the obtained event information
indicates that 40 individual users turned on their device screens
for periods corresponding to each of the 40 individual users during
a streaming of a song (e.g., those users may have turned on their
screens to obtain song information), a total duration of the device
screen on-time for those 40 users may be determined by aggregating
individual periods of the device screen on-time corresponding to
the 40 individual users.
[0062] In some examples, the event metrics module 122 module may be
configured to determine event metrics for a group of users at the
events. Such a group may be determined by the user module 108,
based on, for example, without limitations, a common geolocation, a
common language spoken, one or more common preferences shared by, a
common income level, a common age group, one or more songs,
artists, genres, listened most as indicated by the user consumption
information, and/or any other common characteristics of the group
of available users. For example, the event metrics 122 may
determine event metrics for a group of users from China who are at
the events; may, simultaneously or alternatively, determine event
metrics for a group of users who are within an age group of 20-40
year old; and so on.
[0063] The server 102, client computing platforms 104, and/or
external resources 120 may be operatively linked via one or more
electronic communication links. For example, such electronic
communication links may be established, at least in part, via a
network such as the Internet and/or other networks. It will be
appreciated that this is not intended to be limiting, and that the
scope of this disclosure includes implementations in which servers
102, client computing platforms 104, and/or external resources 120
may be operatively linked via some other communication media.
[0064] A given client computing platform 104 may include one or
more processors configured to execute computer program modules. The
computer program modules may be configured to enable an expert or
user associated with the given client computing platform 104 to
interface with system 100 and/or external resources 118, and/or
provide other functionality attributed herein to client computing
platforms 104. By way of non-limiting example, the given client
computing platform 104 may include one or more of a desktop
computer, a laptop computer, a handheld computer, a tablet
computing platform, a NetBook, a Smartphone, a gaming console,
and/or other computing platforms.
[0065] Server 102 may include electronic storage 120, one or more
processors 122, and/or other components. Server 102 may include
communication lines, or ports to enable the exchange of information
with a network and/or other computing platforms. Illustration of
server 102 in FIG. 1 is not intended to be limiting. Server 102 may
include a plurality of hardware, software, and/or firmware
components operating together to provide the functionality
attributed herein to server 102. For example, server 102 may be
implemented by a cloud of computing platforms operating together as
server 102.
[0066] Electronic storage 126 may comprise non-transitory storage
media that electronically stores information. The electronic
storage media of electronic storage 126 may include one or both of
system storage that is provided integrally (i.e., substantially
non-removable) with server 102 and/or removable storage that is
removably connectable to server 102 via, for example, a port (e.g.,
a USB port, a firewire port, etc.) or a drive (e.g., a disk drive,
etc.). Electronic storage 126 may include one or more of optically
readable storage media (e.g., optical disks, etc.), magnetically
readable storage media (e.g., magnetic tape, magnetic hard drive,
floppy drive, etc.), electrical charge-based storage media (e.g.,
EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive,
etc.), and/or other electronically readable storage media.
Electronic storage 126 may include one or more virtual storage
resources (e.g., cloud storage, a virtual private network, and/or
other virtual storage resources). Electronic storage 122 may store
software algorithms, information determined by processor 128,
information received from server 102, information received from
client computing platforms 104, and/or other information that
enables server 102 to function as described herein.
[0067] Processor(s) 128 is configured to provide information
processing capabilities in server 102. As such, processor 128 may
include one or more of a digital processor, an analog processor, a
digital circuit designed to process information, an analog circuit
designed to process information, a state machine, and/or other
mechanisms for electronically processing information. Although
processor 128 is shown in FIG. 1 as a single entity, this is for
illustrative purposes only. In some implementations, processor 128
may include a plurality of processing units. These processing units
may be physically located within the same device, or processor 128
may represent processing functionality of a plurality of devices
operating in coordination. The processor 128 may be configured to
execute modules 106, 108, 110, 112, 114, 116, 118, 120, 122 and/or
other modules. Processor 128 may be configured to execute modules
106, 108, 110, 112, 114, 116, 118, 120, 122 and/or other modules by
software; hardware; firmware; some combination of software,
hardware, and/or firmware; and/or other mechanisms for configuring
processing capabilities on processor 128. As used herein, the term
"module" may refer to any component or set of components that
perform the functionality attributed to the module. This may
include one or more physical processors during execution of
processor readable instructions, the processor readable
instructions, circuitry, hardware, storage media, or any other
components.
[0068] It should be appreciated that although modules 106, 108,
110, 112, 114, 116, 118, 120, 122 are illustrated in FIG. 1 as
being implemented within a single processing unit, in
implementations in which processor 128 includes multiple processing
units, one or more of modules 106, 108, 110, 112, 114, 116, 118,
120, 122 may be implemented remotely from the other modules. The
description of the functionality provided by the different modules
106, 108, 110, 112, 114, 116, 118, 120, 122 described below is for
illustrative purposes, and is not intended to be limiting, as any
of modules 106, 108, 110, 112, 114, 116, 118, 120, 122 may provide
more or less functionality than is described. For example, one or
more of modules 106, 108, 110, 112, 114, 116, 118, 120, 122 may be
eliminated, and some or all of its functionality may be provided by
other ones of modules 106, 108, 110, 112, 114, 116, 118, 120, 122.
As another example, processor 120 may be configured to execute one
or more additional modules that may perform some or all of the
functionality attributed below to one of modules 106, 108, 110,
112, 114, 116, 118, 120, 122.
[0069] FIG. 2A-2B illustrates various exemplary audience metrics.
As shown in FIG. 2A, several songs 206 by various artists were
played in a 28 minute period ending at a current time T. The dots
204 demonstrate a number of users at various time points during
that 28 minute period listened a song played at the time point. For
example, at one time point, 2341 listeners listened to a by
Radiohead, as shown. As shown, audience metrics 202 may be
determined, e.g., by the audience metrics module 116, for these
listeners: e.g., at that time point, an audience metric describing
user gender may indicate that 58% of the 2341 listeners were male
and another audience metric describing user geolocation may
indicate that 79% of those 2341 listeners were listening in the
United States. As also shown, at the current time T, the audience
metrics, e.g., determined by audience metrics module 116, may
indicate that 69% of the 2456 listeners are male and 85% of the
2456 listeners are listening in the United States.
[0070] FIG. 2B illustrates another example of determined audience
metrics 202, e.g., by the audience metrics module 116. In this
example, the audience metrics 202 may be determined for a set of
broadcast content 208 (i.e., upcoming songs by various artists as
shown) that will be streamed immediately after the current time T.
As illustrated, the audience metrics may be determined to indicate
that 180 current listeners will leave but 560 new listeners will
join during the streaming of the set of upcoming songs. The
determined audience metrics in this example may also indicate that
80% of the listeners that will be listening to the upcoming set of
songs 208 are male and 92% will be listening in the United
States.
[0071] FIG. 3 illustrates various exemplary event metrics. As
shown, several events 302, i.e. streaming of various songs, may be
determined. For these events 302, various event metrics that
describe user consumption of the broadcast content may be
determined, as shown. For example, for the streaming of Rolling in
the Deep by Adelle during a 24 hour of broadcast content (e.g.,
Today's broadcast, Yesterday's broadcast as shown), various even
metrics 304 may be determined to indicate, such as, 174 times the
song was tweeted, 62 users shared the song on the Facebook, 18
users emailed about the song, 1042 users liked the song on the
Facebook and 10 users skipped the song during the streaming of the
song, as illustrated.
[0072] FIG. 4 illustrates a exemplary method 400 of providing crowd
sourced metrics for broadcast content providers. The operations of
method 400 presented below are intended to be illustrative. In some
embodiments, method 400 may be accomplished with one or more
additional operations not described, and/or without one or more of
the operations discussed. Additionally, the order in which the
operations of method 400 are illustrated in FIG. 4 and described
below is not intended to be limiting.
[0073] In some embodiments, method 400 may be implemented in one or
more processing devices (e.g., a digital processor, an analog
processor, a digital circuit designed to process information, an
analog circuit designed to process information, a state machine,
and/or other mechanisms for electronically processing information).
The one or more processing devices may include one or more devices
executing some or all of the operations of method 400 in response
to instructions stored electronically on an electronic storage
medium. The one or more processing devices may include one or more
devices configured through hardware, firmware, and/or software to
be specifically designed for execution of one or more of the
operations of method 400.
[0074] At operation 402, user consumption of broadcast content
provided in multiple streams by multiple content providers may be
monitored. In some implementations, operation 402 may be performed
by a content consumption module the same as or similar to content
consumption module 106 (shown in FIG. 1 and described herein).
[0075] At operation 404, user consumption information regarding the
broadcast content may be obtained. In some examples, the obtained
user consumption information may be stored in a database to
facilitate historical views of user consumption of the broadcast
content. In some implementations, operation 404 may be performed by
a content consumption module the same as or similar to content
consumption module 106 (shown in FIG. 1 and described herein).
[0076] At operation 406, one or more users may be determined as
available users to consume broadcast content to be streamed by a
content provider. In some implementations, operation 406 may be
performed by an available user determination module the same as or
similar to available user determination module 110 (shown in FIG. 1
and described herein).
[0077] At operation 408, content consumption metrics for individual
available users may be determined based on their content
consumption information, as obtained in operation 404. In some
implementations, operation 408 may be performed by a content
consumption metrics module the same as or similar to the content
consumption metrics module 112 (shown in FIG. 1 and described
herein).
[0078] At operation 410, audience metrics may be determined based
on the content consumption metrics determined in operation 408. In
some implementations, operation 410 may be performed by an audience
metrics module the same as or similar to the audience metrics
module 116 (shown in FIG. 1 and described herein).
[0079] FIG. 5 illustrates an exemplary method 500 of providing
crowd sourced metrics for broadcast content providers. The
operations of method 500 presented below are intended to be
illustrative. In some embodiments, method 500 may be accomplished
with one or more additional operations not described, and/or
without one or more of the operations discussed. Additionally, the
order in which the operations of method 500 are illustrated in FIG.
5 and described below is not intended to be limiting.
[0080] In some embodiments, method 500 may be implemented in one or
more processing devices (e.g., a digital processor, an analog
processor, a digital circuit designed to process information, an
analog circuit designed to process information, a state machine,
and/or other mechanisms for electronically processing information).
The one or more processing devices may include one or more devices
executing some or all of the operations of method 500 in response
to instructions stored electronically on an electronic storage
medium. The one or more processing devices may include one or more
devices configured through hardware, firmware, and/or software to
be specifically designed for execution of one or more of the
operations of method 500.
[0081] At operation 502, user consumption of broadcast content
provided in multiple streams by multiple content providers may be
monitored. In some implementations, operation 502 may be performed
by a content consumption module the same as or similar to content
consumption module 106 (shown in FIG. 1 and described herein).
[0082] At operation 504, user consumption information regarding the
broadcast content may be obtained. In some examples, the obtained
user consumption information may be stored in a database to
facilitate historical views of user consumption of the broadcast
content. In some implementations, operation 504 may be performed by
a content consumption module the same as or similar to content
consumption module 106 (shown in FIG. 1 and described herein).
[0083] At operation 506, events within broadcast content may be
determined. In some implementations, operation 506 may be performed
by an event determination module 118 the same as or similar to
audience metrics module 116 (shown in FIG. 1 and described
herein).
[0084] At operation 508, event information for individual available
users may be determined based on their content consumption
information, as obtained in operation 504. In some implementations,
operation 508 may be performed by an event information module 120
the same as or similar to the event information module 120 (shown
in FIG. 1 and described herein).
[0085] At operation 510, event metrics may be determined based on
the event information determined in operation 508. In some
implementations, operation 510 may be performed by an event metrics
module the same as or similar to the event metrics module 122
(shown in FIG. 1 and described herein).
[0086] Although the present technology has been described in detail
for the purpose of illustration based on what is currently
considered to be the most practical and preferred implementations,
it is to be understood that such detail is solely for that purpose
and that the technology is not limited to the disclosed
implementations, but, on the contrary, is intended to cover
modifications and equivalent arrangements that are within the
spirit and scope of the appended claims. For example, it is to be
understood that the present technology contemplates that, to the
extent possible, one or more features of any implementation can be
combined with one or more features of any other implementation.
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