U.S. patent application number 13/890163 was filed with the patent office on 2013-09-19 for delivery forecast computing apparatus for display and streaming video advertising.
This patent application is currently assigned to FREEWHEEL MEDIA, INC.. The applicant listed for this patent is Michael Henry Evangelista, Jonathan Marc Heller, Jingchun Yu. Invention is credited to Michael Henry Evangelista, Jonathan Marc Heller, Jingchun Yu.
Application Number | 20130247084 13/890163 |
Document ID | / |
Family ID | 42039836 |
Filed Date | 2013-09-19 |
United States Patent
Application |
20130247084 |
Kind Code |
A1 |
Heller; Jonathan Marc ; et
al. |
September 19, 2013 |
Delivery Forecast Computing Apparatus for Display and Streaming
Video Advertising
Abstract
A computer-driven apparatus coupled to a network receives data
from metadata sources and consumers' display devices. From the
sources, the apparatus collects metadata concerning characteristics
of a given item of host video and a proposed class of
advertisements for web delivery proximate or embedded in the given
item. The apparatus continually monitors actual delivery of the
given item by receiving transmissions from consumers' display
devices. The apparatus develops initial forecasting inputs based on
historical data from similar host videos, and then adjusts the
forecasting inputs based on the actual deliveries. These
forecasting inputs are used to compute an interim supply of
advertising opportunities associated with the given item, which is
reduced by relevant factors to provide a net available supply
forecast. The apparatus provides a human-readable display of
information including the net available supply forecast.
Inventors: |
Heller; Jonathan Marc; (San
Francisco, CA) ; Evangelista; Michael Henry;
(Metuchen, NJ) ; Yu; Jingchun; (Palo Alto,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Heller; Jonathan Marc
Evangelista; Michael Henry
Yu; Jingchun |
San Francisco
Metuchen
Palo Alto |
NJ
CA |
CA
US
US |
|
|
Assignee: |
FREEWHEEL MEDIA, INC.
San Mateo
CA
|
Family ID: |
42039836 |
Appl. No.: |
13/890163 |
Filed: |
May 8, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13119223 |
Apr 11, 2011 |
|
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PCT/US2009/057100 |
Sep 16, 2009 |
|
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13890163 |
|
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61097219 |
Sep 16, 2008 |
|
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Current U.S.
Class: |
725/14 |
Current CPC
Class: |
H04N 21/812 20130101;
G06Q 30/0272 20130101; G06Q 30/02 20130101 |
Class at
Publication: |
725/14 |
International
Class: |
H04N 21/81 20060101
H04N021/81 |
Claims
1-16. (canceled)
17. A method for calculating a number of advertisement
opportunities forecasted to be available for displaying
advertisements by a device comprising: receiving machine-readable
messages transmitted by the device displaying an instance of a
video during a first time interval, wherein the machine-readable
message includes a time corresponding to a portion of the video
displayed; and aggregating as a forecasting input the advertising
opportunities proximate or embedded in an instance of the video to
be displayed during a second time interval, wherein the advertising
opportunities available for displaying advertisements are within
the time upon beginning display of the video during the second time
interval.
18. The method of claim 17 wherein display by the device of an
advertisement in an advertising opportunity proximate or embedded
in the video triggers transmission of the message, and wherein the
time corresponds to a portion of the video displayed through
display of the advertisement.
19. A method for calculating the number of advertising
opportunities forecasted to be available for displaying
advertisements of a proposed class of advertisements comprising a
computer performing the steps of: receiving as a first input a
predetermined profile of a number of instances of a video displayed
by devices during a first time interval that precedes a second time
interval; receiving as a second input a predetermined profile of a
number of advertising opportunities proximate or embedded in an
instance of the video to be displayed during the second time
interval, the profile including, for each advertising opportunity,
a duration of the video from the beginning of the video to the
display of an advertisement in the advertisement opportunity;
receiving as a third input machine-readable messages transmitted by
a device displaying an instance of the video during the first time
interval, wherein the machine-readable message includes an
identification of the video and a duration of the portion of the
video displayed; aggregating a portion of the advertising
opportunities received as the second input, wherein the portion
includes the opportunities within the received duration of the
video displayed from the third input; and computing as a supply
forecast for the second time interval the number of instances from
the first input multiplied by the number of advertising
opportunities from the second input reduced by the difference
between the supply forecast and the aggregated portion.
20. The method of claim 19 wherein display by the device of an
advertisement in an advertising opportunity proximate or embedded
in the video triggers transmission of the message.
21. The method of claim 19, further comprising: receiving the
machine-readable messages transmitted during the second time
interval; adjusting the first input according to the number of
instances of the video displayed by devices during the second time
interval; adjusting the second input according to the number of
advertising opportunities proximate or embedded in an instance of
the video displayed during the second time interval; adjusting the
third input according to the duration of the portion of the video
displayed during the second time interval; aggregating a portion of
the advertising opportunities received as the second input, wherein
the portion includes the opportunities within the received duration
of the video displayed from the third input; and re-computing as
the supply forecast for the second time interval the number of
instances from the adjusted first input multiplied by the number of
advertising opportunities from the adjusted second input reduced by
the difference between the supply forecast and the aggregated
portion.
22. The method of claim 19, wherein the proposed class of
advertisements includes a duration for an advertisement, and
comprising the added step of reducing the supply forecast by the
aggregate of advertising opportunities in the supply forecast of
insufficient duration to fit the duration of an advertisement in
the proposed class.
23. The method of claim 19, comprising the added steps of: (a)
receiving commitments to place advertisements in the advertising
opportunities proximate or embedded in instances of the video
during a second time interval, (b) aggregating the commitments, and
(c) reducing the supply forecast by the aggregate of
commitments.
24. The method of claim 19, wherein the video includes a
permissible content rating and the proposed class of advertisements
includes a content rating for an advertisement, and comprising the
added step of reducing the supply forecast by the aggregate of
content ratings for advertisements in the proposed class that are
not within the permissible content rating of the video.
25. The method of claim 19, comprising the added step of reducing
the supply forecast by the aggregate of advertising opportunities
that do not have an associated legal right to display
advertisements in the proposed class.
26. The method of claim 19, wherein advertisements in the proposed
class includes a time window for display of the advertisement, and
comprising the added step of reducing the supply forecast by the
aggregate of advertising opportunities not within the time
window.
27. The method of claim 19, wherein the video includes distribution
points and advertisements in the proposed class of advertisements
includes adistribution point, and comprising the added step of
reducing the supply forecast by the aggregate of advertising
opportunities proximate or embedded in instances of the video to be
displayed at distribution points that are not within the
distribution point for advertisements in the proposed class.
28. The method of claim 19, wherein the advertising opportunities
proximate or embedded in the video have one or more constraints
including a machine-readable encoding format, blacklisting, content
rating, prohibition as to pre-rolls, and comprising the added step
of reducing the supply forecast by the aggregate of advertising
opportunities having a constraint that does not allow display of
the advertisements in the proposed class.
29. The method of claim 19, further comprising: before receiving
the first input, selecting the predetermined profile of the first
input from multiple profiles wherein an identified type of the
selected predetermined profile matches an indentified type of the
video.
30. The method of claim 20, further comprising: receiving the
machine-readable messages transmitted during the second time
interval; adjusting the first input according to the number of
instances of the video displayed by devices during the second time
interval; adjusting the second input according to the number of
advertising opportunities proximate or embedded in an instance of
the video displayed during the second time interval; adjusting the
third input according to the duration of the portion of the video
displayed during the second time interval; aggregating a portion of
the advertising opportunities received as the second input, wherein
the portion includes the opportunities within the received duration
of the video displayed from the third input; and re-computing as
the supply forecast for the second time interval the number of
instances from the adjusted first input multiplied by the number of
advertising opportunities from the adjusted second input reduced by
the difference between the supply forecast and the aggregated
portion.
31. The method of claim 20, wherein the proposed class of
advertisements includes a duration for an advertisement, and
comprising the added step of reducing the supply forecast by the
aggregate of advertising opportunities in the supply forecast of
insufficient duration to fit the duration of an advertisement in
the proposed class.
32. The method of claim 20, comprising the added steps of: (a)
receiving commitments to place advertisements in the advertising
opportunities proximate or embedded in instances of the video
during a second time interval, (b) aggregating the commitments, and
(c) reducing the supply forecast by the aggregate of
commitments.
33. The method of claim 20, wherein the video includes a
permissible content rating and the proposed class of advertisements
includes a content rating for an advertisement, and comprising the
added step of reducing the supply forecast by the aggregate of
content ratings for advertisements in the proposed class that are
not within the permissible content rating of the video.
34. The method of claim 20, comprising the added step of reducing
the supply forecast by the aggregate of advertising opportunities
that do not have an associated legal right to display
advertisements in the proposed class.
35. The method of claim 20, wherein advertisements in the proposed
class includes a time window for display of the advertisement, and
comprising the added step of reducing the supply forecast by the
aggregate of advertising opportunities not within the time
window.
36. The method of claim 20, wherein the video includes distribution
points and advertisements in the proposed class of advertisements
includes adistribution point, and comprising the added step of
reducing the supply forecast by the aggregate of advertising
opportunities proximate or embedded in instances of the video to be
displayed at distribution points that are not within the
distribution point for advertisements in the proposed class.
37. The method of claim 20, wherein the advertising opportunities
proximate or embedded in the video have one or more constraints
including a machine-readable encoding format, blacklisting, content
rating, prohibition as to pre-rolls, and comprising the added step
of reducing the supply forecast by the aggregate of advertising
opportunities having a constraint that does not allow display of
the advertisements in the proposed class.
38. The method of claim 20, further comprising: before receiving
the first input, selecting the predetermined profile of the first
input from multiple profiles wherein an identified type of the
selected predetermined profile matches an indentified type of the
video.
39. The method of claim 30, wherein the proposed class of
advertisements includes a duration for an advertisement, and
comprising the added step of reducing the supply forecast by the
aggregate of advertising opportunities in the supply forecast of
insufficient duration to fit the duration of an advertisement in
the proposed class.
40. The method of claim 39, comprising the added steps of: (a)
receiving commitments to place advertisements in the advertising
opportunities proximate or embedded in instances of the video
during a second time interval, (b) aggregating the commitments, and
(c) reducing the supply forecast by the aggregate of
commitments.
41. The method of claim 40, wherein the video includes a
permissible content rating and the proposed class of advertisements
includes a content rating for an advertisement, and comprising the
added step of reducing the supply forecast by the aggregate of
content ratings for advertisements in the proposed class that are
not within the permissible content rating of the video.
42. The method of claim 41, comprising the added step of reducing
the supply forecast by the aggregate of advertising opportunities
that do not have an associated legal right to display
advertisements in the proposed class.
43. The method of claim 42, wherein advertisements in the proposed
class includes a time window for display of the advertisement, and
comprising the added step of reducing the supply forecast by the
aggregate of advertising opportunities not within the time
window.
44. The method of claim 43, wherein the video includes distribution
points and advertisements in the proposed class of advertisements
includes a distribution point, and comprising the added step of
reducing the supply forecast by the aggregate of advertising
opportunities proximate or embedded in instances of the video to be
displayed at distribution points that are not within the
distribution point for advertisements in the proposed class.
45. The method of claim 44, wherein the advertising opportunities
proximate or embedded in the video have one or more constraints
including a machine-readable encoding format, blacklisting, content
rating, prohibition as to pre-rolls, and comprising the added step
of reducing the supply forecast by the aggregate of advertising
opportunities having a constraint that does not allow display of
the advertisements in the proposed class.
46. The method of claim 45, further comprising: before receiving
the first input, selecting the predetermined profile of the first
input from multiple profiles wherein an identified type of the
selected predetermined profile matches an identified type of the
video.
47. A system for calculating a number of advertisement
opportunities forecasted to be available for displaying
advertisements by a device comprising: A computer having at least a
storage device, an input/output, and a processor; the storage
device configured to receive through the input/output
machine-readable messages transmitted by the device displaying an
instance of a video during a first time interval, wherein the
machine-readable message includes a time corresponding to a portion
of the video displayed; and the processor configured to aggregate
as a forecasting input the advertising opportunities proximate or
embedded in an instance of the video to be displayed during a
second time interval, wherein the advertising opportunities
available for displaying advertisements are within the time upon
beginning display of the video during the second time interval.
48. The system of claim 47 wherein display by the device of an
advertisement in an advertising opportunity proximate or embedded
in the video triggers transmission of the message, and wherein the
time corresponds to a portion of the video displayed through
display of the advertisement.
49. A system for calculating the number of advertising
opportunities forecasted to be available for displaying
advertisements of a proposed class of advertisements comprising: a
computer having at least a storage device, an input/output, and a
processor; the computer configured to receive as a first input a
predetermined profile of a number of instances of a video displayed
by devices during a first time interval that precedes a second time
interval; the computer configured to receive as a second input a
predetermined profile of a number of advertising opportunities
proximate or embedded in an instance of the video to be displayed
during the second time interval, the profile including, for each
advertising opportunity, a duration of the video from the beginning
of the video to the display of an advertisement in the
advertisement opportunity; the computer configured to receive as a
third input machine-readable messages transmitted by a device
displaying an instance of the video during the first time interval,
wherein the machine-readable message includes an identification of
the video and a duration of the portion of the video displayed; the
computer configured to aggregate a portion of the advertising
opportunities received as the second input, wherein the portion
includes the opportunities within the received duration of the
video displayed from the third input; and the computer configured
to compute as a supply forecast for the second time interval the
number of instances from the first input multiplied by the number
of advertising opportunities from the second input reduced by the
difference between the supply forecast and the aggregated
portion.
50. The system of claim 49 wherein display by the device of an
advertisement in an advertising opportunity proximate or embedded
in the video triggers transmission of the message.
51. The system of claim 49, further comprising: the computer
configured to receive the machine-readable messages transmitted
during the second time interval; the computer configured to adjust
the first input according to the number of instances of the video
displayed by devices during the second time interval; the computer
configured to adjust the second input according to the number of
advertising opportunities proximate or embedded in an instance of
the video displayed during the second time interval; the computer
configured to adjust the third input according to the duration of
the portion of the video displayed during the second time interval;
the computer configured to aggregate a portion of the advertising
opportunities received as the second input, wherein the portion
includes the opportunities within the received duration of the
video displayed from the third input; and the computer configured
to re-compute as the supply forecast for the second time interval
the number of instances from the adjusted first input multiplied by
the number of advertising opportunities from the adjusted second
input reduced by the difference between the supply forecast and the
aggregated portion.
52. The system of claim 49, wherein the proposed class of
advertisements includes a duration for an advertisement, and the
computer is configured to reduce the supply forecast by the
aggregate of advertising opportunities in the supply forecast of
insufficient duration to fit the duration of an advertisement in
the proposed class.
53. The system of claim 49, comprising the computer configured to
perform the added steps of: (a) receiving commitments to place
advertisements in the advertising opportunities proximate or
embedded in instances of the video during a second time interval,
(b) aggregating the commitments, and (c) reducing the supply
forecast by the aggregate of commitments.
54. The system of claim 49, wherein the video includes a
permissible content rating and the proposed class of advertisements
includes a content rating for an advertisement, and comprising the
computer configured to reduce the supply forecast by the aggregate
of content ratings for advertisements in the proposed class that
are not within the permissible content rating of the video.
55. The system of claim 49, comprising the computer configured to
reduce the supply forecast by the aggregate of advertising
opportunities that do not have an associated legal right to display
advertisements in the proposed class.
56. The system of claim 49, wherein advertisements in the proposed
class includes a time window for display of the advertisement, and
comprising the computer configured to reduce the supply forecast by
the aggregate of advertising opportunities not within the time
window.
57. The system of claim 49, wherein the video includes distribution
points and advertisements in the proposed class of advertisements
includes a distribution point, and comprising the computer
configured to reduce the supply forecast by the aggregate of
advertising opportunities proximate or embedded in instances of the
video to be displayed at distribution points that are not within
the distribution point for advertisements in the proposed
class.
58. The system of claim 49, wherein the advertising opportunities
proximate or embedded in the video have one or more constraints
including a machine-readable encoding format, blacklisting, content
rating, prohibition as to pre-rolls, and comprising the computer
configured to reduce the supply forecast by the aggregate of
advertising opportunities having a constraint that does not allow
display of the advertisements in the proposed class.
59. The system of claim 49, further comprising: before receiving
the first input, the computer configured to select the
predetermined profile of the first input from multiple profiles
wherein an identified type of the selected predetermined profile
matches an indentified type of the video.
60. The system of claim 50, further comprising: the computer
configured to receive the machine-readable messages transmitted
during the second time interval; the computer configured to adjust
the first input according to the number of instances of the video
displayed by devices during the second time interval; the computer
configured to adjust the second input according to the number of
advertising opportunities proximate or embedded in an instance of
the video displayed during the second time interval; the computer
configured to adjust the third input according to the duration of
the portion of the video displayed during the second time interval;
the computer configured to aggregate a portion of the advertising
opportunities received as the second input, wherein the portion
includes the opportunities within the received duration of the
video displayed from the third input; and the computer configured
to re-compute as the supply forecast for the second time interval
the number of instances from the adjusted first input multiplied by
the number of advertising opportunities from the adjusted second
input reduced by the difference between the supply forecast and the
aggregated portion.
61. The system of claim 50, wherein the proposed class of
advertisements includes a duration for an advertisement, and
comprising the computer configured to reduce the supply forecast by
the aggregate of advertising opportunities in the supply forecast
of insufficient duration to fit the duration of an advertisement in
the proposed class.
62. The system of claim 50, comprising the computer configured to:
(a) receive commitments to place advertisements in the advertising
opportunities proximate or embedded in instances of the video
during a second time interval, (b) aggregate the commitments, and
(c) reduce the supply forecast by the aggregate of commitments.
63. The system of claim 50, wherein the video includes a
permissible content rating and the proposed class of advertisements
includes a content rating for an advertisement, and comprising the
computer configured to reduce the supply forecast by the aggregate
of content ratings for advertisements in the proposed class that
are not within the permissible content rating of the video.
64. The system of claim 50, comprising the computer configured to
reduce the supply forecast by the aggregate of advertising
opportunities that do not have an associated legal right to display
advertisements in the proposed class.
65. The system of claim 50, wherein advertisements in the proposed
class includes a time window for display of the advertisement, and
comprising the computer configured to reduce the supply forecast by
the aggregate of advertising opportunities not within the time
window.
66. The system of claim 50, wherein the video includes distribution
points and advertisements in the proposed class of advertisements
includes a distribution point, and comprising the computer
configured to reduce the supply forecast by the aggregate of
advertising opportunities proximate or embedded in instances of the
video to be displayed at distribution points that are not within
the distribution point for advertisements in the proposed
class.
67. The system of claim 50, wherein the advertising opportunities
proximate or embedded in the video have one or more constraints
including a machine-readable encoding format, blacklisting, content
rating, prohibition as to pre-rolls, and comprising the computer
configured to reduce the supply forecast by the aggregate of
advertising opportunities having a constraint that does not allow
display of the advertisements in the proposed class.
68. The system of claim 50, further comprising: before receiving
the first input, the computer configured to select the
predetermined profile of the first input from multiple profiles
wherein an identified type of the selected predetermined profile
matches an indentified type of the video.
69. The system of claim 50, wherein the proposed class of
advertisements includes a duration for an advertisement, and
comprising the computer configured to reduce the supply forecast by
the aggregate of advertising opportunities in the supply forecast
of insufficient duration to fit the duration of an advertisement in
the proposed class.
70. The system of claim 69, comprising the computer configured to:
(a) receive commitments to place advertisements in the advertising
opportunities proximate or embedded in instances of the video
during a second time interval, (b) aggregate the commitments, and
(c) reduce the supply forecast by the aggregate of commitments.
71. The system of claim 70, wherein the video includes a
permissible content rating and the proposed class of advertisements
includes a content rating for an advertisement, and comprising the
computer configured to reduce the supply forecast by the aggregate
of content ratings for advertisements in the proposed class that
are not within the permissible content rating of the video.
72. The system of claim 71, comprising the computer configured to
reduce the supply forecast by the aggregate of advertising
opportunities that do not have an associated legal right to display
advertisements in the proposed class.
73. The system of claim 72, wherein advertisements in the proposed
class includes a time window for display of the advertisement, and
comprising the computer configured to reduce the supply forecast by
the aggregate of advertising opportunities not within the time
window.
74. The system of claim 73, wherein the video includes distribution
points and advertisements in the proposed class of advertisements
includes a distribution point, and comprising the computer
configured to reduce the supply forecast by the aggregate of
advertising opportunities proximate or embedded in instances of the
video to be displayed at distribution points that are not within
the distribution point for advertisements in the proposed
class.
75. The system of claim 74, wherein the advertising opportunities
proximate or embedded in the video have one or more constraints
including a machine-readable encoding format, blacklisting, content
rating, prohibition as to pre-rolls, and comprising the computer
configured to reduce the supply forecast by the aggregate of
advertising opportunities having a constraint that does not allow
display of the advertisements in the proposed class.
76. The system of claim 75, further comprising: before receiving
the first input, the computer configured to select the
predetermined profile of the first input from multiple profiles
wherein an identified type of the selected predetermined profile
matches an indentified type of the video.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a U.S. National Phase entry of
International Patent Application No. PCT/US2009/057100, filed Sep.
16, 2009 and claims priority to U.S. Patent Application No.
61/097,219, filed on Sep. 16, 2008 in the name of Heller and
entitled SYSTEM AND METHOD FOR FORECASTING THE AGGREGATE VOLUME OF
FUTURE EVENTS FOR NUMEROUS ITEMS OF DIGITAL CONTENT AVAILABLE FOR
CONSUMPTION FROM NUMEROUS DIFFERENT POINTS OF ACCESS. The entirety
of the foregoing applications are incorporated herein by
reference.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The invention relates to digital data processing machines.
More particularly, the invention concerns a digital data processing
machine configured to gather machine-readable data relevant a given
item of host video content and a proposed class of advertising
opportunities provided by the host video, and to generate a
forecast as to future viewing of advertisements of the proposed
class, provide a human-readable output based on the forecast.
[0004] 2. Description of the Related Art
[0005] The internet has become a major means of distributing
digital content. Much of this content is advertising supported. A
fundamental need of any advertising sales business is the ability
to forecast how many times how many people will view their content
and how much resulting advertising inventory is available for
sale.
[0006] Traditionally the advertising properties on the web are
destinations where users come to repeatedly access content. This
creates a historical trend of content consumption that can be used
to project, or forecast, future content consumption. This is the
method that is traditionally used to forecast future events like
future available ad inventory.
[0007] With the advent of video as a major type of content consumed
online, a fundamental change has occurred in user consumption
patterns. This change renders historical trend projection
inaccurate as a means of forecasting future events. With some forms
of digital content, such as video, the individual item of content
is what draws the consumer and creates advertising inventory. It is
no longer true that the destination where the content is on display
draws the audience only but now the item of content itself can draw
the audience. As an example, consumers will go to watch a
particular episode newly released of their favorite show as opposed
to going to a particular destination regularly just to see what
content is there that day, such as on a news site. So, an
individual video has its own identity and audience draw whereas a
web page or article may not.
[0008] This creates a fundamental change in the patterns of content
consumption for these types of content which in turn require a new
and unique method of forecasting future events based on such
content consumption.
[0009] With content items with their own consumer identity, such as
video, forecasting based solely off of past trends does not
incorporate the key drivers of consumption and will not be an
accurate forecast. As discovered by the inventors, this is because
volume of consumption and the resultant number of events to
forecast, for aggregations of content with individual identity, has
many driving factors that are not reflected in past behavior. These
driving factors are explained further below.
[0010] Because existing forecasting methods for events driven by
digital content consumption are not aware of such drivers, they are
not accurate forecasts for content where the item itself is the
draw and it is syndicated across numerous partners.
[0011] Another shortcoming of existing forecasting methods is that
they fail to adequately translate forecasted content consumption
into advertising inventory available for sale. For a unit of
advertising inventory to be available for sale, it must be
physically capable of displaying the desired advertisement itself.
For example, if someone wants to put an ad in slot A, the ad must
physically fit into slot A to be a truly useable available spot. If
the slot does not fit the ad, then the ad is not truly available.
With digital content on display in many syndicated locations, there
is great variability into what types of advertisements are allowed
to show in different such locations. As discovered by the inventors
then, the existing forecasting methods lack adequate practical use
because they are not fully aware of such constraints, both physical
and by business term, and show only those available ad inventory
units that are capable of actually displaying the advertisement in
question.
SUMMARY OF THE INVENTION
[0012] A computer-driven apparatus coupled to a network receives
data from metadata sources and consumers' display devices. From the
sources, the apparatus collects metadata concerning characteristics
of a given item of host video and a proposed class of
advertisements for web delivery proximate or embedded in the given
item. The apparatus continually monitors actual delivery of the
given item by receiving transmissions from consumers' display
devices. The apparatus develops initial forecasting inputs based on
historical data from similar host videos, and then adjusts the
forecasting inputs based on the actual deliveries. These
forecasting inputs are used to compute an interim supply of
advertising opportunities associated with the given item, which is
reduced by relevant factors to provide a net available supply
forecast. The apparatus provides a human-readable display of
information including the net available supply forecast.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a block diagram of the components and
interconnections of an exemplary delivery forecast computing
apparatus for display and streaming video advertising.
[0014] FIG. 2 is a block diagram of an exemplary digital data
processing machine.
[0015] FIG. 3 shows an exemplary storage medium.
[0016] FIG. 4 is a perspective view of exemplary logic
circuitry.
[0017] FIG. 5 is a flowchart of a computer-driven sequence for
collecting various metadata and actual delivery history for a given
host video, and transforming these into a highly accurate delivery
forecast.
[0018] FIG. 6 is a graph illustrating the adjustment of forecasted
delivery to account for actual delivery history.
DETAILED DESCRIPTION
[0019] The nature, objectives, and advantages of the invention will
become more apparent to those skilled in the art after considering
the following detailed description in connection with the
accompanying drawings.
Overview
[0020] The described system solves problems inherent in forecasting
numerous items with their own consumption patterns across numerous
distribution points. It also incorporates numerous drivers for
consumption into its forecast, including those that are not
reflective in past behavior.
[0021] The system is designed to forecast across many items that
all have their own characteristics in terms of popularity, pattern
of consumption over their life, legal display rights and on how
many and how big distribution points are they on display. In
addition, the system and method is designed to apply similar or
"template" patterns of behavior to future items that have no
history to extrapolate. Lastly, as history does accumulate and real
user consumption patterns emerge, the template patterns
automatically adjust to reflect the actual patterns of consumption
and extrapolate the future consumption off this new informed
history.
[0022] First, the system forecasts each item individually.
Forecasts at levels of many items are aggregations of forecasts at
the individual item level. By forecasting items individually, the
system is able to incorporate item level data critical to
projecting consumption in an environment where many drivers of the
items consumption are particular to that item and are not
reflective in past behavior, such as the following. [0023] LEGAL
DISPLAY RIGHTS. This includes dates when an item will start on
display and when it will end. This is important for two reasons.
First, forecasting consumption for an item before it is allowed on
display will be inaccurate and over count. However, it is critical
to be able to forecast items in advance of their display date to be
able to sell advertising into that item in advance. The television
up-fronts are an example where much advertising is sold into shows
that will not air for months. So, being aware, item by item, of
when it will start display makes this possible. Second, forecasting
consumption of an item after it is to be taken down and no longer
available for viewing would be a bad over counting of events. By
being aware of display rights end dates, the system will not
forecast consumption after the item is not available to be
consumed. [0024] CONSUMPTION PATTERN OF THAT TYPE OF ITEM.
Awareness of the type of item it is enables the system to forecast
different consumption patterns for a blockbuster movie than for a
small unknown video. [0025] AGE OF ITEM. Since consumption patterns
for content like video decay rapidly over time, if a system only
forecasts from past patterns, the system will over count and miss
the drop off that comes with aging into "old news." The present
system is aware of both the age of the item and the consumption
pattern of the item and can be aware in advance of when a drop off
in consumption is likely to occur and incorporate that into the
forecast.
[0026] Second, the system is able to apply similar type item
consumption patterns to new items that have not established their
own actual consumption pattern yet. The total library of content
items, such as videos, is in frequent and constant churn. New
videos are being added all the time. Many such new videos have not
been displayed yet or are not scheduled to be displayed for some
time. The system can apply the consumption pattern of like videos
to such new videos and forecast based on similarity to other
videos. Importantly, the system is self learning in that it will
automatically adjust this "template pattern" consumption forecast
for actual consumption as it occurs.
[0027] Third, the system can be made aware of the number and size
of distribution points on which the content items are on display.
In this way, as the size of the content companies syndication
partner set grows, the size of the possible consumption of its
content grows and the forecast reflects that.
[0028] Fourth, the system is aware of any technical or business
term constraints limiting which types of advertisements are capable
of serving into which distribution display points. In this way,
even if one hundred possible advertising inventory units were
forecast to exist, if twenty did not allow that particular
advertisement in question to display, the forecast would only show
eighty possible.
[0029] Last, the system will add up the individual forecasts of the
individual items in order to produce the forecast for any
aggregation of items. This means that as the library of individual
items ages and changes mix of types of items and age of items, the
aggregate forecast will reflect that. Simply extrapolating off past
aggregate behavior, such as existing systems do, implies the same
mix of age and type of items. In a business like video where new
episodes and types of content are constantly cycling in and out of
a company's library of items, this past assumed mix will be
inaccurate. It is necessary to add the individual forecasts of the
actual items in the current library to be accurate.
Hardware Components and Interconnections
Overall Structure
[0030] One aspect of this disclosure concerns a delivery forecast
computing apparatus 150. The apparatus 150 computes delivery
forecasts for display and streaming video advertising. In the
present example, the apparatus 150 is managed by an entity such as
a forecasting company (not shown).
[0031] The apparatus 150 is shown in an exemplary environment 100,
which includes the following components. A network 112 connects the
apparatus 150 to other components 122a, 124, 126. The network 112
may be implemented by the Internet, or any other network
appropriate to this disclosure regardless of protocol and
conveyance means, with some examples including LAN, WAN, HTTP,
token ring, Ethernet, wireless, fiber optics, ISDN, telephony,
satellite, and the like.
[0032] A client machine 122 includes a user interface 122a operated
by a client (not shown). In one example, the client is a seller of
online advertising opportunities that occur in conjunction with
streaming video content. The client machine 122, in this example,
is implemented by any desktop or notebook personal computer,
computer workstation, or other computing device with sufficient
power and capabilities to interface with the apparatus 150 in the
manner discussed below. In a specific example, the client machine
122 is a personal computer and the user interface 122a is a web
browser.
[0033] Among other interactions, the client machine 122 transmits
various metadata to the apparatus 150, under direction of the
client. This transmission may occur automatically ("push"), on
demand by the apparatus 150 ("pull"), or in response to direction
by a human located at the remote source or remotely. In one
example, the metadata occurs in a format that is predetermined by
the forecasting company. One example is a comma-separated-value
(CSV) format, including some prescribed identity and order of the
various constituent fields. In one example, the client machine 122
is operated by an advertising company that has hired the
forecasting company to develop forecasts as to the viewing of the
advertising opportunities provided by the advertising company.
[0034] Of course, there may be multiple clients and multiple client
machines 122, but a single one is given here for simplicity of
discussion. The apparatus 150 may additionally gather metadata
apart from the client machine 122. The sources of this metadata are
illustrated by 124. These include other computers, databases, data
entry terminals, news sources, web sites, and the like.
[0035] The display devices 126 are operated by members of the
public without any required affiliation with the advertising
company or forecasting company. The display devices 126 include
various embodiments, such as a web browser running on a computer,
television cable or other set top box, DVR, mobile telephone, PDA,
or other device capable of displaying online video content.
Operators of the display devices 126 are referred to as
consumers.
[0036] The forecasting company monitors people's viewing of online
advertisements. Namely, for certain online ads, the display devices
126 are programmed to transmit information to the apparatus 150
whenever the consumer elects to view that advertisement.
Alternatively, the devices 126 may transmit information to a third
party such as 122 or 124, which aggregates such information and
submits it to the apparatus 150. In any case, the transmitted
information includes the identity of online ad that was viewed, URL
hosting the online ad, the time when viewing began, the length of
viewing, and the like. The transmission of such information to the
apparatus 150 or third party may be conducted by a browser plug-in,
java applet, HTML code used to present the advertisement, or
another technology. The technology for achieving this is widely
known in the field of Internet advertising metrics.
[0037] As to the apparatus 150 itself, operations are managed by a
processor 106. The processor 106 may be implemented by one or more
hardware devices, software devices, a portion of one or more
hardware or software devices, or a combination of the foregoing.
The makeup of some exemplary digital data processing components is
described in greater detail below, with reference to FIGS. 2-4.
[0038] A local user interface 107 provides a means for a human to
locally communicate with the apparatus 150, and may include items
such as a video display, speakers, keyboard, mouse, touchpad,
digitizing pad, eye gaze tracking system, voice recognition module,
etc. The input/output 108 provides an interface between the
processor 106 and the network 112. Although the implementation of
the input/output 108 varies according to the type of network 112
and connection to the network 112, some examples include cable
modem, satellite modem, DSL modem, WiFi or WiMax modem, and
Ethernet card. The processor 106 uses the local database 109 to
store data accumulated from the metadata sources 124. The database
109 may be implemented by various digital data storage
technologies, as described in greater detail below.
Exemplary Digital Data Processing Apparatus
[0039] As mentioned above, data processing entities, such as the
processor 106, may be implemented in various forms. Some examples
include a general purpose processor, digital signal processor
(DSP), application specific integrated circuit (ASIC), field
programmable gate array (FPGA) or other programmable logic device,
discrete gate or transistor logic, discrete hardware components, or
any combination thereof designed to perform the functions described
herein. A general purpose processor may be a microprocessor, but in
the alternative, the processor may be any conventional processor,
controller, microcontroller, or state machine. A processor may also
be implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0040] As a more specific example, FIG. 2 shows a digital data
processing apparatus 200. The apparatus 200 includes a processor
202, such as a microprocessor, personal computer, workstation,
controller, microcontroller, state machine, or other processing
machine, coupled to a digital data storage 204. In the present
example, the storage 204 includes a fast-access storage 206, as
well as nonvolatile storage 208. The fast-access storage 206 may be
used, for example, to store the programming instructions executed
by the processor 202. The storage 206 and 208 may be implemented by
various devices, such as those discussed in greater detail in
conjunction with FIGS. 3 and 4. Many alternatives are possible. For
instance, one of the components 206, 208 may be eliminated;
furthermore, the storage 204, 206, and/or 208 may be provided
on-board the processor 202, or even provided externally to the
apparatus 200.
[0041] The apparatus 200 also includes an input/output 210, such as
a connector, line, bus, cable, buffer, electromagnetic link,
network, modem, transducer, IR port, antenna, or other means for
the processor 202 to exchange data with other hardware external to
the apparatus 200.
Storage Media
[0042] As mentioned above, various instances of digital data
storage may be used, for example, to provide the database 109 (FIG.
1), to embody the storage 204 and 208 (FIG. 2), to store
programming of the apparatus 150, and the like. Depending upon its
application, this digital data storage may be used for various
functions, such as storing data, or to store machine-readable
instructions. These instructions may themselves aid in carrying out
various processing functions, or they may serve to install a
software program upon a computer, where such software program is
then executable to perform other functions related to this
disclosure.
[0043] In any case, the storage media may be implemented by nearly
any mechanism to digitally store machine-readable signals. One
example is optical storage such as CD-ROM, WORM, DVD, digital
optical tape, disk storage 300 (FIG. 3), or other optical storage.
Another example is direct access storage, such as a conventional
"hard drive", redundant array of inexpensive disks ("RAID"), or
another direct access storage device ("DASD"). Another example is
serial-access storage such as magnetic or optical tape. Still other
examples of digital data storage include electronic memory such as
ROM, EPROM, flash PROM, EEPROM, memory registers, battery backed-up
RAM, etc. Storage of data or programming need not be limited to a
single one of such devices, but may be distributed across two or
more storage units.
[0044] In one example, a storage medium is coupled to a processor
so the processor can read information from, and write information
to, the storage medium. In the alternative, the storage medium may
be integral to the processor. In another example, the processor and
the storage medium may reside in an ASIC or other integrated
circuit.
Logic Circuitry
[0045] In contrast to storage media that contain machine-executable
instructions, as described above, a different embodiment uses logic
circuitry to implement processing features such as the processor
106.
[0046] Depending upon the particular requirements of the
application in the areas of speed, expense, tooling costs, and the
like, this logic may be implemented by constructing an
application-specific integrated circuit (ASIC) having thousands of
tiny integrated transistors. Such an ASIC may be implemented with
CMOS, TTL, VLSI, or another suitable construction. Other
alternatives include a digital signal processing chip (DSP),
discrete circuitry (such as resistors, capacitors, diodes,
inductors, and transistors), field programmable gate array (FPGA),
programmable logic array (PLA), programmable logic device (PLD),
and the like.
[0047] FIG. 4 shows an example of logic circuitry in the form of an
integrated circuit 400.
Operation
[0048] Having described various structural features, the next
description concerns an operational aspect. The steps of any
method, process, or algorithm described in connection with the
embodiments disclosed herein may be embodied directly in hardware,
in a software module executed by hardware, or in a combination of
these.
Overview
[0049] As mentioned above, forecasting techniques based on past
trends alone do not incorporate the key drivers of consumption and
will not be an accurate forecast. As discovered by the inventors,
this is because volume of consumption and the resultant number of
events to forecast, for aggregations of content with individual
identity, has many driving factors that are not reflected in past
behavior. Some of these factors include the following.
[0050] (A) Rapid and spiky life cycle of consumption. This is
because initial popularity drives a lot of viewers but once it is
old news many fewer people will consume the content. This is
particularly true for video. So, past viewing patterns do now
indicate when a video is aging into old news and will start to
decline
[0051] (B) Number of distribution points or places where that
content is on display. Video, unlike traditional web pages, is
often widely syndicated and put on display on numerous locations.
The size of audience of these various locations will also directly
impact total views of the content on display. For example, if a
video is on display only on website X it will receive fewer views
than if it is also put on display on web portal A, B and C. Since
these syndication deals go on and off frequently, a forecast must
be aware of the number and size and timing of such syndication
partners to correctly forecast consumption and related events.
[0052] (C) Legal display rights. Much digital content is owned by
one company but displayed by a different company. These companies
enter into rights arrangements where the display company will
contract with the owning company for when and where they are
allowed to display particular content. These display rights windows
change, opening up and shutting down, frequently. They also change
by item of content to item of content. The present system looks
ahead, to be aware of when such rights windows open and close,
enhancing the accuracy of the forecast.
[0053] (D) Level of promotion. Much of television advertising is
really promotions running ads promoting other television shows. The
forecasted viewership for any particular item or items of content
is affected by the level to which that content is promoted. Since
such promotions may be set for the future, relying on past behavior
will not suffice.
[0054] (E) Type of content. Adding a major new release blockbuster
item or video will have a different effect on viewership than
adding in a short clip of an old show. A forecast has to be aware
of the difference between a new blockbuster item and a new clip or
small effect item.
[0055] (F) Library of content churn. The set of items available for
display is in constant churn as older items age and decay in
viewership and new items are added to the library and drive new
viewership. Looking backwards will not incorporate the new influx
of different types of content and the different ages of content in
their consumption life cycles.
Operating Sequence
[0056] FIG. 5 shows a sequence 500 to illustrate one example of the
operating sequence of this disclosure. Broadly, this sequence
serves to collect various metadata and actual delivery history for
a given item of streaming video content and its advertising
opportunities, and to computationally transform these into a highly
accurate forecast of actual delivery of online advertisement.
[0057] The sequence 500 is illustrated in the context of a
particular item of online video content, this being referred to as
the "given item" or "host video" or "content item." As described
below, the forecasting approach of the sequence 500 is based on the
core concept of the individual content item, such as an episode of
a TV show or a movie. This content item is the central
organizational theme, rather than being based on a website or
location where the content is displayed. This enables the forecast
system to intelligently incorporate the individual items
characteristic's into the forecast.
[0058] The sequence 500 is also considered in conjunction with a
proposed class of advertisements, making the ultimate forecast even
more realistic as to the availability of advertising opportunities
for the client. It is useful to consider features of the proposed
class of advertisements, since the encoding format or length or
other characteristics of the proposed ad class may limit the
available advertising opportunities.
[0059] In step 502, the apparatus 150 receives machine-readable
metadata from one or more of the remote sources 124. Some of this
metadata pertains to the host video on which the various
advertising opportunities are planned to occur via embedded
streaming advertisements or parallel display ads. This may be
referred to as "supply" metadata, and some examples include the
following. [0060] Duration of advertising opportunities. This is a
statement of the duration of advertising opportunities provided by
the host video content. For example, this may be a time in
hours-minutes-seconds for each opening, and may further include an
indication of whether the host video is one-time or episodic. In
the case of display advertising, this may additionally designate
other characteristics of advertising opportunity, such as pixel
size, screen placement, and other characteristics of display
advertising. [0061] Legal Rights. This is a statement as to legal
rights to present the host video. Digital content might be is owned
by one company but displayed by a different company. These
companies enter into rights arrangements where the display company
will contract with the owning company for when and where they are
allowed to display particular content. Such display rights windows
change, and can open and close frequently. They also change from
one host video to another. This is included in the metadata 502 so
that the present forecast can accurately consider the opening and
closing of such rights windows. In one example, the statement of
legal rights may be embodied in the form of a span of relevant
calendar dates during which the host video is permitted to run
according to contractual agreement, and may also be referred to as
"display rights." [0062] Rating. This is a rating of the host video
as to potentially offensive material. For example, this may be a G,
R, PG-13, or other rating according to the Motion Picture
Association of America or another rating body. [0063] Syndication.
This refers to a representation as to use of syndication partners
if any. This indicates where the host video is planned to run, and
may include web addresses and/or identification of a syndication
partner such as CBS.TM., YAHOO.TM., AOL.TM., YOUTUBE.TM., and the
like. [0064] Type of content. Adding a major new release
blockbuster item or video will have a different effect on your
viewership than adding in a short clip of an old show. This is
included in the metadata 502 so that the present forecast can
consider the difference between a new blockbuster item and a new
clip or small effect item. In one example, the host video is
classified according to the most appropriate one of the following
content types: (1) a "clip," which lasts for five minutes or ten
minutes, (2) an "episode," which lasts for twenty-two minutes or
forty-four minutes, or (3) a "movie" which lasts for one hour or
more greater. Host videos are classified according to the closest
one of these types. [0065] Constraints. This is a consideration of
business terms, such as whether the producer of the host video
prohibits advertising of certain subject matter or content rating.
The constraints may also consider whether the displayer of the
video allows pre-rolls, which are typically brief streaming
advertisements that appear prior to the host video. Another example
of a constraints is "blacklisting." For example, the client may
input information pertaining to a known contract between the
producer of the host video and a particular advertising buyer.
Thus, if the host video producer has a contract to advertise
COKE.TM. products, then during later computing steps below, this
constraint would be helpful to exclude excluding PEPSI.TM. as a
buyer of advertising during later computational steps discussed
below. [0066] Encoding. This refers to machine-readable encoding
format of the host video. The given item may take many forms, such
as H.264, Quicktime.TM., MPEG-2, MP4, WMV, AVI, and MOV, to name a
few. In the case of display advertising, some additional formats
may include JPG, GIF, PNG, and the like, as well as display size in
pixels. Also, this may consider the features of the particular
display environment, such as hardware, operating system, and the
like.
[0067] The previously described metadata items pertain to the host
video and refers to "supply" metadata. Step 502 additionally
receives various items "demand" metadata, these pertaining to the
characteristics of a proposed class of advertisements for which the
present delivery forecast will be applicable. This may be an actual
advertising item that the client has in mind, or alternatively a
class of items characterized by the restrictions of this metadata.
Some examples of demand metadata of step include the following.
[0068] Time window. This is a span of relevant calendar dates for
which placement of the proposed class of advertisements is being
forecast. [0069] Permissible Content. This is the client's
specification as to a required content rating of the advertising to
be placed into the host video. For instance, regardless of the
content rating of the host video, the client may impose a
requirement that all advertising to be placed into the host video
must adhere to a PG-13 rating. The content ratings of advertising,
in this example, may use the same or different rating system as the
host videos. Nevertheless, the client's specification of content
rating as to permissible advertising is independent of the rating
applied to the host video. [0070] Encoding. This refers to the
machine-readable encoding format of the proposed class of
advertising items. The format of advertisements of the proposed
class is independent of the format of the host video item, and must
be separately considered. [0071] Duration. The duration of
advertisements of the proposed class is important to consider,
because proposed ads that are too long might not fit inside the
advertising opportunities provided in the host video.
[0072] Although receipt of metadata is limited to step 502 as
illustrated, this is merely for ease of explanation, and such
metadata may be may received over time, in batch, randomly, on a
schedule, or other basis. Moreover, step 502 may be performed upon
machine or user-initiated demand by any of the apparatus 150,
client machine, or other source. In one embodiment, the processor
106 stores metadata from step 502 in the local database 109.
[0073] In step 506, the apparatus 150 monitors actual delivery of
advertising related to the given host video item. This occurs by
receiving transmissions from consumers' display devices 126 via the
input/output 108. As mentioned above, the display devices 126 are
operated by members of the public without any required affiliation
with the advertising companies or forecasting company. Yet, in step
506, the apparatus 150 is able to monitor peoples' viewing of
display and streaming video advertisements. Namely, the display
devices 126 are programmed to transmit information to the apparatus
150 whenever the consumer elects to view that advertisement. For
example, this may be triggered by a consumer mouse-clicking on the
advertisement. The transmitted information includes the identity of
ad that was viewed, URL that hosted the online ad, time when
viewing began, length of viewing, duration of the host video viewed
before exiting, and any other relevant information. The
transmission of such information to the apparatus 150 may be
conducted according to a browser plug-in, Java applet, HTML code
used to present the advertisement, or another technology. The
technology for achieving this is widely known in the field of
Internet advertising metrics.
[0074] As an alternative to receiving data directly from viewers'
browsers, the browsers may transmit such information to a given
different server, such as the company on whose behalf the
advertisement is being placed. In this scenario, such a server
collects viewing information from the browsers, and forwards this
to the apparatus 150. At any rate, step 506 is performed on an
ongoing basis, as shown by 506a.
[0075] In step 508, the apparatus 150 determines whether the host
video is permitted to be shown during the requested time window for
placement. This determination is made by comparing the requested
legal display rights from step 502 with the proposed time window,
also from step 502. Forecasting consumption for advertising
opportunities of a content item before it is allowed to display
will be inaccurate and over-count. However, it is important to be
able to forecast items in advance of their display date to be able
to sell advertising into that item in advance. Television up-fronts
are an example where much advertising is sold into shows that will
not air for months. So, being aware, item by item, of when it will
start display makes this possible. Forecasting consumption of an ad
after the host video is to be taken down and no longer available
for viewing would over-count. By being aware of display rights end
dates, the system will avoid forecasting consumption after
advertising items are no longer available for consumption.
[0076] If display is not permitted according to step 508, the
apparatus 150 issues an error message in step 509. This message may
be presented locally at the interface 107, displayed on a web page
that the apparatus 150 presents to the client, or transmitted to a
remote site.
[0077] If step 508 answers "YES," then the apparatus 150 develops
certain forecasting inputs in step 510. In one example, these
forecasting inputs are referred to by the names feed-1, feed-2,
feed-3, and described as follows. Also as discussed below, the
forecasting inputs in step 510 are developed predictively based on
similar host videos, and apart from any actual observations, which
are discussed separately below.
[0078] Feed-1 is a forecast of how many times viewing of the host
video will be commenced. In one example, the database 109 contains
a variety of pre-prepared profiles for feed-1, each linked to a
different "type" of host video. As mentioned above, the host video
types included clips, episodes, and movies. In the present example,
step 508 recalls the pre-prepared feed-1 profile appropriate to the
host video content type received in the metadata of step 502. Each
feed-1 profile, according to one example, includes a curve
representing a number of views by consumers over time, such as the
curve 602 in FIG. 6. These have been developed previously by
collecting and then sorting history data by content type, and
statistically developing common patterns of magnitude of maximum
views, growth, and decay curve of views from day one to a time when
host video views decay to a negligible amount.
[0079] Feed-2 is distinct from feed-1 and comprises a forecast, as
to the given host video, of how many advertising opportunities will
be requested by consumers' display devices. Namely, each time a
consumer begins viewing a host video, the web browser or other
display device submits a request indicating a certain number of
advertising opportunities appropriate to the host video. In step
510, feed-2 may be predicted by multiplying feed-1 by the number of
advertising opportunities within the currently considered host
video. The number of advertising opportunities within the host
video may be input as part of the metadata 502, or this may be a
standard number, such as X advertising opportunities of thirty
seconds during a clip, Y advertising opportunities during an
episode, and Z advertising opportunities during a movie.
[0080] Feed-3 is a forecast of how many advertising opportunities
will be actually viewed or "consumed." Consumers may not view the
entire host video, in which case, the later advertising
opportunities of the host video do not come to fruition. In the
present operation, feed-3 is developed by recalling a pre-prepared
profile appropriate to the content type of the host video. Such
profiles are generated based on historical data for host videos of
the same content type, such as clip, episode, and movie in the
present example.
[0081] Thus, step 510 develops forecasting inputs comprising
feed-1, feed-2, and feed-3 based on historical data for host videos
of similar content type. Next, in step 512, the apparatus 150 asks
if any actual consumption data is available, arising from
consumers' views of the given host video in step 506. This would
provide actual data for feed-1, feed-2, and feed-3.
[0082] If actual data is available, the apparatus 150 adjusts the
forecasting inputs of step 510 according to the actual consumption
history, in step 514. In one example, the apparatus 150 conditions
the purely predictive forecasting inputs of step 510 by the actual
data to arrive at some forecasting inputs of improved accuracy. In
a different example, step 514 substitutes the actual data for the
predictive inputs of step 510.
[0083] The following is an example, as to the adjustment of feed-1
per actual consumption history. In FIG. 6, curve 608 represents the
feed-1 for the given host video, according to actual consumption
history. In step 512, the apparatus 150 conditions the curve 602
according to the curve 608, taking into account the curve 608's
shape, slope, magnitude, and other characteristics. The result is
the curve 610. In one example, the curves 602, 608 are nonlinear
curves that increase exponentially to a certain maximum, and then
decay for some length. As to feed-2 and feed-3, these are numbers
or multiplicative coefficients rather than curves over time, and
the actual data from step 506 may be used to scale these numbers
higher or lower as appropriate.
[0084] If actual data is not available in step 512, then the
sequence skips step 514, and proceeds to step 516. In step 516, the
apparatus 150 predicatively adjusts the forecasting inputs feed-1,
feed-2, and feed-3 based on the current or planned distribution
points. Distribution points include, for example, syndication
partners, web addresses, web sites, and the like. For example, the
database 109 may contain a listing of known syndication partners
and empirically developed coefficients for each syndication partner
indicating how a given syndication partner has historically
affected forecasts. Thus, in a simple case, step 516 is carried out
by multiplying the forecasting inputs from step 514, or 510, by the
coefficients for the applicable syndication partners.
[0085] In step 518, the apparatus 150 computes a supply forecast.
In the present example, this is feed-1 multiplied by feed-2, and
reduced by feed-3. This represents a prediction of how many
theoretical opportunities are available to place an ad in
conjunction with the host video, and which will actually be viewed
by a human being. This quantity may be referred to as
"unconstrained available supply."
[0086] In step 519, the apparatus 150 reduces the supply forecast
according to the duration of the proposed class of advertising
items. Namely, the predicted advertising opportunities of step 518
are reduced by the number of opportunities that are not long enough
to accommodate the duration, from step 502, of the proposed class
of advertisements.
[0087] In step 520, the apparatus 150 further reduces the supply
forecast, according to the constraints received in metadata 502
that would prevent use of the propose class of advertisements with
the host video. For instance, step 519 may reduce the current
forecast by the number of advertising opportunities for which the
host video producer does not allow advertisements of the content
rating of the proposed class of advertisements. Other constraints
are applied in step 520, such as whether pre-rolls are allowed,
client-specified blacklisting, machine-readable encoding format,
and the like.
[0088] In step 522, the apparatus 150 computes a forecast of net
available advertising opportunities. This is the supply forecast,
from step 520, reduced by the existing commitments, namely, the
advertising opportunities that are known to be sold already. In
other words, step 522 considers advertising opportunities in the
relevant time window that are already booked and sold for other
ads. This information may arise from the client, via previous
input, or by other ongoing operations of the apparatus 150
pertaining to management of the client's advertising resources.
[0089] Also in step 522, the apparatus 150 provides a
human-readable output of the forecast of net available advertising
opportunities. For example, the apparatus 150 may provide a visual
display at the interface 107, or transmit machine-readable signals
over the network 112 for viewing the forecast on a remote computer.
In a more specific example, the output occurs via the user
interface 122a in the form of an interactive web page.
[0090] In step 524, the apparatus 150 considers whether it has
received client-submitted changes to any of the underlying metadata
502, 504. If changes have occurred, the apparatus 150 in step 526
re-computes the forecast of step 522. The re-computation may occur
automatically, according to metadata changes, or manually in
response to client request or other input. For instance, the
apparatus 150 may receive these via the user interface 122a in the
form an interactive web page. The re-computation of step 526 may be
carried out by repeating all of the steps from 508-520, or by
repeating those steps appropriate to the metadata that was changed.
Thus, the apparatus 150 permits the client to change the metadata
on the fly to study various "what-if" scenarios. Steps 524 and 526
are performed on an ongoing basis, as shown by 526a.
Other Embodiments
[0091] While the foregoing disclosure shows a number of
illustrative embodiments, it will be apparent to those skilled in
the art that various changes and modifications can be made herein
without departing from the scope of the invention as defined by the
appended claims. Accordingly, the disclosed embodiment are
representative of the subject matter which is broadly contemplated
by the invention, and the scope of the present invention fully
encompasses other embodiments which may become obvious to those
skilled in the art, and that the scope of the present invention is
accordingly to be limited by nothing other than the appended
claims.
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