U.S. patent application number 12/159106 was filed with the patent office on 2009-06-18 for content development and distribution using cognitive sciences database.
Invention is credited to Brian E. Brooks.
Application Number | 20090158179 12/159106 |
Document ID | / |
Family ID | 38225630 |
Filed Date | 2009-06-18 |
United States Patent
Application |
20090158179 |
Kind Code |
A1 |
Brooks; Brian E. |
June 18, 2009 |
CONTENT DEVELOPMENT AND DISTRIBUTION USING COGNITIVE SCIENCES
DATABASE
Abstract
Computer implemented methods and systems facilitate development
and distribution of content for presentation on a display or a
multiplicity of networked displays, the content including content
elements. The content elements may include graphics, text, video
clips, still images, audio clips or web pages. The development of
the content is facilitated using a database comprising design rules
based on principles of cognitive and vision sciences. The database
may include design rules based on visual attention, memory, and/or
text recognition, for example.
Inventors: |
Brooks; Brian E.; (St. Paul,
MN) |
Correspondence
Address: |
3M INNOVATIVE PROPERTIES COMPANY
PO BOX 33427
ST. PAUL
MN
55133-3427
US
|
Family ID: |
38225630 |
Appl. No.: |
12/159106 |
Filed: |
December 29, 2006 |
PCT Filed: |
December 29, 2006 |
PCT NO: |
PCT/US06/49662 |
371 Date: |
October 17, 2008 |
Current U.S.
Class: |
715/762 |
Current CPC
Class: |
G06Q 10/00 20130101;
G06F 40/186 20200101; G06Q 30/0245 20130101; G06N 5/04 20130101;
G06Q 30/0246 20130101; G06F 3/04842 20130101 |
Class at
Publication: |
715/762 |
International
Class: |
G06F 3/048 20060101
G06F003/048 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 29, 2005 |
US |
11321340 |
Claims
1. A computer-assisted method, comprising: developing content for
presentation on a display, the content comprising content elements;
providing display information about the display and display
environment; facilitating, by way of computer assistance, the
development of the content using the display information and a
database comprising design rules or models based on principles of
cognitive and vision sciences.
2. The method of claim 1, wherein facilitating the development of
the content comprises generating user perceivable recommendations
for developing the content, the recommendations consistent with
design rules or models.
3. The method of claim 1, comprising alerting a user in response to
violation of one or more of the design rules or models.
4. The method of claim 1, wherein facilitating the development of
the content comprises automatically adjusting the content in
response to non-compliance with the design rules or models.
5. The method of claim 1, wherein facilitating the development of
the content comprises facilitating layout of the content elements
in compliance with the design rules or models.
6. The method of claim 1, wherein facilitating the development of
the content comprises facilitating selection of the content
elements in compliance with the design rules or models.
7. The method of claim 1, wherein facilitating the development of
the content comprises facilitating selection of one or more
attributes of the content elements in compliance with the design
rules or models.
8. The method of claim 7, wherein the one or more attributes of the
content elements comprise one or more of color, brightness, size,
orientation, font, movement, presentation duration or flash rate,
display location, and number of content elements concurrently
presented on the display.
9. The method of claim 1, wherein facilitating the development of
the content comprises facilitating selection of content element
attributes based on one or more attributes of the display or
display environment.
10. The method of claim 9, wherein the one or more attributes
comprise one or more of display type, display size, display shape,
average viewing distance from the display, average speed of viewer
movement relative to the display, viewer dwelling time, ambient
lighting at a location of the display, and time of day of content
presentation on the display.
11. The method of claim 1, comprising receiving user input data
comprising information regarding each content element, the
information comprising one or both of content goal and intended
message, wherein facilitating the development of the content
comprises facilitating development of the content using the design
rules or models and the user input data.
12. The method of claim 1, wherein the content elements comprise
graphics, text, video clips, still images, audio clips or web
pages.
13. The method of claim 1, wherein facilitating the development of
the content comprises facilitating development of the content for a
plurality of networked displays, the method further comprising
facilitating selection of content element attributes based on one
or more attributes of each of the displays.
14. The method of claim 1, wherein facilitating the development of
the content comprises facilitating development of the content for a
plurality of networked displays, the method further comprising:
receiving user input data comprising information regarding each
content element, the information comprising one or both of content
goal and intended message; facilitating user identification of
attributes of the networked displays or display environments that
have implications for content development; and facilitating the
development of the content using the design rules or models, user
input data, and display attributes.
15. The method of claim 1, further comprising facilitating, by way
of computer assistance, modification of the developed content in
compliance with the design rules or models.
16. The method of claim 15, wherein the developed content is
modified in response to a change in one or more attributes of the
displays or display environments.
17. The method of claim 16, wherein the one or more attributes
comprise one or more of display type, display size, display shape,
average viewing distance from the display, average speed of viewer
movement relative to the display, viewer dwelling time, ambient
lighting at a location of the display, and time of day of content
presentation on the display.
18. The method of claim 1, wherein facilitating the development of
the content comprises facilitating development of the content for a
plurality of networked displays, the method further comprising
modifying, by way of computer assistance, the developed content for
particular displays of the plurality of networked displays in
response to a change in an attribute of the particular displays or
environments associated with the particular displays.
19. The method of claim 1, wherein the database comprises design
rules or models based on one or more of user visual attention,
human memory, and text readability.
20. The method of claim 1, comprising performing a true experiment
that produces results useful for improving or optimizing
effectiveness of content presentation.
21. A system, comprising: a database comprising design rules or
models based on principles of cognitive and vision sciences; a user
interface comprising a display; and a processor coupled to the
database and user interface, the processor configured to facilitate
development of content for presentation on the display using the
design rules or models and information about the display or display
environment, the content comprising content elements.
22. The system of claim 21, wherein the processor is configured to
generate user perceivable recommendations for developing the
content, the recommendations consistent with design rules or
models.
23. The system of claim 21, wherein the processor is configured to
generate an alert for a user in response to violation of one or
more of the design rules or models.
24. The system of claim 21, wherein the processor is configured to
automatically adjust the content in response to non-compliance with
the design rules or models.
25. The system of claim 21, wherein the processor is configured to
facilitate layout of the content elements in compliance with the
design rules or models.
26. The system of claim 21, wherein the processor is configured to
facilitate selection of the content elements in compliance with the
design rules or models.
27. The system of claim 21, wherein the processor is configured to
facilitate selection of one or more attributes of the content
elements in compliance with the design rules or models.
28. The system of claim 27, wherein the one or more attributes of
the content elements comprise one or more of color, brightness,
size, orientation, font, movement, presentation duration or flash
rate, display location, and number of content elements concurrently
presented on the display.
29. The system of claim 21, wherein the processor is configured to
facilitate selection of content element attributes based on one or
more attributes of the display or display environment.
30. The system of claim 29, wherein the one or more attributes
comprise one or more of display type, display size, display shape,
average viewing distance from the display, average speed of viewer
movement relative to the display, viewer dwelling time, ambient
lighting at a location of the display, and time of day of content
presentation on the display.
31. The system of claim 21, wherein the processor is configured to
receive user input data comprising information regarding each
content element, the information comprising one or both of content
goal and intended message, the processor further configured to
facilitate development of the content using the design rules or
models and the user input data.
32. The system of claim 21, wherein the content elements comprise
graphics, text, video clips, still images, audio clips or web
pages.
33. The system of claim 21, wherein the processor is configured to
facilitate development of the content for a plurality of networked
displays, the processor further configured to facilitate selection
of content element attributes based on one or more attributes of
each of the displays.
34. The system of claim 21, wherein the processor is configured
facilitate development of the content for a plurality of networked
displays, the processor further configured to: receive user input
data comprising information regarding each content element, the
information comprising one or both of content goal and intended
message; facilitate user identification of attributes of the
networked displays or display environments that have implications
for content development; and facilitate the development of the
content using the design rules or models, user input data, and
display attributes.
35. The system of claim 21, wherein the processor is configured to
facilitate modification of the developed content in compliance with
the design rules or models.
36. The system of claim 35, wherein the processor is configured to
modify the developed content in response to a change in one or more
attributes of the displays or display environments.
37. The system of claim 36, wherein the one or more attributes
comprise one or more of display type, display size, display shape,
average viewing distance from the display, average speed of viewer
movement relative to the display, viewer dwelling time, ambient
lighting at a location of the display, and time of day of content
presentation on the display.
38. The system of claim 21, wherein the processor is configured to
facilitate development of the content for a plurality of networked
displays, the processor further configured to facilitate
modification of the developed content for particular displays of
the plurality of networked displays in response to a change in an
attribute of the particular displays or environments associated
with the particular displays.
39. The system of claim 21, wherein the database comprises design
rules or models based on one or more of user visual attention,
human memory, and text readability.
40. The system of claim 21, wherein the processor is configured to
perform a true experiment that produces results useful for
improving or optimizing effectiveness of content presentation.
41. The system of claim 21, comprising one or more sensors for
sensing one or more attributes of the display environment.
42. The system of claim 41, wherein the one or more sensors
comprise a video camera.
43. The system of claim 41, wherein the one or more sensors
comprise one or more proximity sensors.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to methods and systems for
developing content for presentation on a display or a multiplicity
of networked displays.
BACKGROUND
[0002] Designers of content often employ computer application
programs that are capable of importing and arranging various types
of content. Advertisements, for example, may be developed that
incorporate text, graphics, video, and audio elements, among
others. In general, the effectiveness of advertising content is a
function of a designer's experience, rather than the sophistication
of the computer application program used to generate the
advertising content.
[0003] A successful content designer generally improves his or her
skills in a trial and error fashion or by relying on tried-and-true
approaches. Imparting an accomplished designer's skills to a less
experienced designer is often difficult if not impossible, as such
skills tend to be highly stylistic and personal to the particular
designer. Because the competency of designers varies significantly,
so does the quality and effectiveness of the content that they
produce. Conventional computer application programs for generating
content generally do not provide the designer with tools that allow
the designer to exceed his or her own skills for developing
effective content.
SUMMARY OF THE INVENTION
[0004] The present invention is directed to systems and methods for
developing and distributing content through use of computer
assistance. Embodiments of the present invention are directed to a
computer-assisted method for developing content for presentation on
a display, the content comprising content elements. The content
elements may include graphics, text, video clips, still images,
audio clips or web pages. The method further involves facilitating,
by way of computer assistance, the development of the content using
a database comprising design rules or models based on principles of
cognitive and vision sciences. The database may include design
rules or models based on visual attention, memory, and/or text
readability, for example.
[0005] Facilitating the development of the content may involve
developing the content in compliance with design rules or models,
and may involve alerting a user in response to violation of one or
more of the design rules or models. Facilitating content
development may involve generating user perceivable recommendations
for developing the content, where the recommendations are
consistent with design rules or models. Facilitating content
development may involve automatically adjusting the content via
computer-assistance in response to violation of one or more of the
design rules or models.
[0006] Facilitating the development of the content may involve
facilitating selection and/or layout of the content elements or
selection of one or more attributes of the content elements in
compliance with the design rules or models. The attributes of the
content elements may include one or more of color, brightness,
size, font, orientation, movement, presentation duration or flash
rate, display location, and number of content elements concurrently
presented on the display, among others.
[0007] Facilitating the development of the content may involve
facilitating selection of content element attributes based on one
or more attributes of the display. The display attributes may
include one or more of display type, display size, display shape,
average viewing distance from the display, average speed of viewer
movement relative to the display, viewer dwelling time, ambient
lighting at a location of the display, and time of day of content
presentation on the display, among others.
[0008] According to some implementations, user input data is
received regarding each content element, the user input data
including information concerning one or both of content goal and
intended message. In such implementations, facilitating the
development of the content may involve facilitating development of
the content using the design rules or models and the user input
data.
[0009] The content may be developed for presentation on a
multiplicity of networked displays, and may involve selection of
content element attributes based on one or more attributes of each
of the displays. According to some implementations, user input data
regarding each content element is received, the information
comprising one or both of content goal and intended message.
Attributes of the networked displays are identified that have
implications for content development. Content development is
facilitated using the design rules or models, user input data, and
display attributes.
[0010] Methods of the present invention may further involve
facilitating, by way of computer assistance, modification of the
developed content in compliance with the design rules or models.
The developed content may be modified in response to a change in
one or more attributes of one or more displays of a display
network, such as display type, display size, display shape,
expected viewing distance from the display, ambient lighting at a
location of the display, and time of day of content presentation on
the display, for example.
[0011] According to other embodiments, systems of the present
invention may include a database comprising design rules or models
based on principles of cognitive and vision sciences, a user
interface comprising a display, and a processor coupled to the
database and user interface. The processor is configured to
facilitate development of content for presentation on the display
in compliance with the design rules or models. The processor may be
configured to implement one or more of the methods described
hereinabove.
[0012] Embodiments of the present invention are further directed to
systems and methods that provide for computer-assisted analysis of
content by one or more cognitive and vision sciences (CVS) models.
Content is provided or developed by a content designer. The content
is input to a computer that implements one or more CVS models, such
as a computational model of visual attention, a text readability
model or a model of human memory. The CVS model or models perform
an analysis on the content and produce an output based on the
analysis results. Information representative of environmental
conditions at the presentation locations and/or goals for the
content may be inputs to the model(s). For example, the type of
displays and average distance between displays and viewers may be
environmental condition information that is input to the
model(s).
[0013] Goal information that may be input to the model(s) may
include goals that are associated with each of the various models,
such as computational model of visual attention, a text readability
model or a model of human memory. Typical goal information may
include specific elements of the content to be perceived by viewers
and the desired order in which such specific elements are
perceived. Other goal information may include improving or
optimizing text readability based on text size and/or scrolling
text rate relative to viewer location and/or speed at which viewers
pass by a given display. Additional goal information may include
maximizing memory retention and recall of content by viewers, such
as by conforming to memory capacity and duration rules of a given
model.
[0014] In some implementations, the output represents
recommendations for changing the content in conformance with a
given model's rules or goals. The recommendations may take several
forms, such as a narrative form or images. For example, a menu of
possible attributes of the content that may be changed can be
presented to the user. The menu of attributes may include a range
of attribute values that may be changed by the user, yet still
conform with a given model's rules or goals. In other
implementations, the output represents a modified form of the
original content produced automatically by the computer implemented
CVS model or models. A number of variations of modified content may
be automatically produced, each of which satisfies the rules or
goals of the model or models. The user may then select a desired
version of the modified content for presentation. Alternatively,
the computer may select one or more of the versions for
presentation. In other implementations, the various versions of
modified content may be subject to a designed experimental process
that improves or optimizes content presentation effectiveness for a
number of networked displays, preferably on a display-by-display
basis.
[0015] According to other embodiments, content may be developed and
distributed in conformance with cognitive and vision sciences rules
or models. A true experiment may be performed to improve or
optimize presentation effectiveness of the content. A
quasi-experiment or correlational experiment may also be performed
to improve or optimize presentation effectiveness of the content.
Conducting the true experiment may include identifying dependent
variables, such as a goal of increasing sales of a particular
product. Independent variables may be identified, such as
parameters associated with one or more CVS models (e.g., text
readability, visual attention and/or memory parameters). Content
may be modified in view of the results from the true experiment or
quasi-/correlational experiment. For example, content may be
modified on a display-by-display basis, based on improved or
optimized parameters for each display. The modified content may be
presented on each of the displays. Additional true or
quasi-/correlational experimentation may be conducted to further
improve or optimize content presentation, particularly under
changing environmental conditions or a change in the goals or
intended message of the content.
[0016] The above summary of the present invention is not intended
to describe each embodiment or every implementation of the present
invention. Advantages and attainments, together with a more
complete understanding of the invention, will become apparent and
appreciated by referring to the following detailed description and
claims taken in conjunction with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIG. 1 illustrates various processes associated with the
development of content in accordance with embodiments of the
present invention;
[0018] FIG. 2 illustrates various processes associated with the
development of content in accordance with other embodiments of the
present invention;
[0019] FIG. 3 illustrates various processes associated with the
development of content in accordance with further embodiments of
the present invention;
[0020] FIG. 4A depicts an initial attempt by a designer to create a
presentation for display that includes a number of different
content elements;
[0021] FIG. 4B illustrates how the developed content shown in FIG.
4A is more appropriately arranged in a manner consistent with
design rules or models that are based on principles of cognitive
and vision sciences in accordance with embodiments of the present
invention;
[0022] FIG. 5 is a block diagram of a system for implementing
computer-assisted development of content using principles of
cognitive and vision sciences in accordance with embodiments of the
present invention;
[0023] FIG. 6 is a block diagram of a system for implementing
computer-assisted development and/or distribution of content in a
manner consistent with principles of cognitive and vision sciences
in accordance with embodiments of the present invention;
[0024] FIG. 7 is a block diagram of a digital signage system that
incorporates the capability for developing and distributing content
in accordance with embodiments of the invention;
[0025] FIG. 8 illustrates the process flow of creating and
deploying content using the components and functionality of the
digital signage system shown in FIG. 7;
[0026] FIG. 9 is a flowchart illustrating an exemplary
implementation of a digital signage system in accordance with
embodiments of the present invention;
[0027] FIG. 10 is a block diagram of a system for developing and/or
distributing content using cognitive/vision science driven software
in accordance with embodiments of the present invention;
[0028] FIG. 11 is a flowchart illustrating various processes
associated with content development and modification using one or
more cognitive/vision sciences models in accordance with the
present invention; and
[0029] FIG. 12 is a flowchart illustrating various processes
associated with content development and modification of same using
one or more cognitive/vision sciences models and results from true
experimentation preferably implemented by a digital signage system
in accordance with the present invention.
[0030] While the invention is amenable to various modifications and
alternative forms, specifics thereof have been shown by way of
example in the drawings and will be described in detail. It is to
be understood, however, that the intention is not to limit the
invention to the particular embodiments described. On the contrary,
the intention is to cover all modifications, equivalents, and
alternatives falling within the scope of the invention as defined
by the appended claims.
DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS
[0031] In the following description of the illustrated embodiments,
reference is made to the accompanying drawings that form a part
hereof, and in which is shown by way of illustration, various
embodiments in which the invention may be practiced. It is to be
understood that the embodiments may be utilized and structural
changes may be made without departing from the scope of the present
invention.
[0032] The present invention is directed to methods and systems for
creating content for presentation on a display or a multiplicity of
networked displays, and facilitating, by way of computer
assistance, content creation in a manner consistent with principles
based on human cognitive science and vision science. Methods and
systems of the present invention are also directed to distributing
and adjusting content for presentation on a display or a
multiplicity of networked displays in a manner consistent with
principles based on human cognitive science and vision science.
Developing and adjusting content may also involve performing true
experiments or quasi-/correlational experiments to improve or
optimize content presentation effectiveness. Creating,
distributing, and adjusting content in accordance with the present
invention advantageously enhances the effectiveness of content
presentation as perceived by a recipient, such as potential
purchaser of goods or services.
[0033] Content creation is preferably conducted in a manner
consistent with principles based on one or more of how human
perceptual systems process information, mechanisms that underlie
attention, how the human brain stores and represents information in
memory, and the cognitive basis of language and problem solving,
for example. A knowledge base that stores cognitive and vision
science information is preferably utilized during the content
design, distribution, and/or adjustment processes in order to
provide content that is easily processed by human perceptual
systems, easily comprehended, and easily stored in memory. The
knowledge base may include design rules and templates that may be
implemented by a computer to develop and modify content in
conformance with principles of cognitive and vision sciences. The
knowledge base may also include computer implementable models of
principles of cognitive and vision sciences, such as models of
visual attention, text readability, and memory principles. Computer
assisted methods and systems of the present invention allow content
designers, who typically do not have the training required to apply
principles from cognitive science and vision science, to increase
the effectiveness of content design and distribution.
[0034] In some embodiments, computer assisted methods and systems
of the present invention may be implemented to operate in a
semi-automatic mode, wherein a user is led by the computer through
one or more interactive sessions to design, develop, distribute,
and/or adjust content. In other embodiments, computer assisted
methods and systems of the present invention may be implemented in
a more fully automatic manner, with minimal or no user input or
interaction. In a fully automatic mode, for example, a
computer-based system may create a presentation based on user
selected pieces of content in a manner consistent with design rules
or models stored in a cognitive sciences database. User selected
pieces of content may be arranged, sized, and/or oriented on a
user's display based on the design rules or models, and further in
view of the goal and/or intended message of the content pieces as
indicated by the user. A fully automated implementation may involve
the computer-based system adjusting content elements of a given
presentation based on one or more of the design rules or models,
goal of the content pieces, and intended message of the contend
pieces. These are but a few illustrative examples of possible
levels of automaticity that can be achieved in accordance with the
present invention, and are not to be regarded as exhaustive or
limiting.
[0035] Aspects of the present invention will generally be discussed
herein in the context of a digital signage system (DSS) or network.
A DSS as contemplated in the particular embodiments described
herein includes a series of interconnected (e.g., networked)
display screens that are similar to traditional signs, but that can
be controlled from a remote location to deliver dynamically
changing content. Such displays or digital signs may be configured
such that people can directly interact with signage content via
touch screens or human interface devices (e.g., keyboard or mouse).
It is to be understood that principles of the present invention may
be applied in a wide variety of applications, and are not limited
to those involving a DSS. Moreover, it is to be understood that
implementations of the present invention may vary substantially in
terms of complexity, in that some implementation may utilize
relatively simple principles of cognitive science and/or vision
science (e.g., human visual perception), while others may be of
substantial complexity, drawing from multiple disciplines of the
cognitive and vision sciences (e.g., human visual attention,
memory, and text readability).
[0036] Display technology is becoming increasingly diverse such
that there are significant differences in the types of displays
that can be used to present content via a DSS. For example, the
size, shape, brightness, and viewing conditions will, in general,
vary greatly across a DSS. For example, some displays may be small,
flexible and non-rectilinear, whereas others may be standard
large-format LCD and plasma displays. This variation in display
types and viewing conditions means that any single version of a
piece of content will not be optimal for all the displays across a
DSS.
[0037] In order to overcome this problem using a conventional
approach, it would be necessary to generate unique versions of each
piece of content for each unique display type and viewing
environment, and to selectively distribute these unique versions of
content to their corresponding displays in the network. However, it
is not realistic to expect content designers to have such detailed
knowledge of the display types and viewing conditions across a
large network of displays. Furthermore, even if content designers
had such detailed knowledge, it would be prohibitively
time-consuming to manually create unique versions of content for
each display and to manually schedule the content to play on each
corresponding display at the appropriate time. Methods and systems
of the present invention advantageously allow content designers
without advanced training in the visual and cognitive sciences to
apply principles from these disciplines during the content creation
process and during content adjustment, such as during content
distribution to a network of disparate displays, in order to
improve content effectiveness.
[0038] According to embodiments of the present invention, the user
may be prompted during the content creation process to input one or
both of the goal and intended message for each piece of content to
be presented. According to various embodiments, the system may
assist the user in identifying key attributes of the DSS that have
implications for content design. The system may further guide the
user through the process of applying the cognitive and vision
sciences to design content based on the goals and key DSS
attributes. For example, the system may help users choose templates
(e.g., best layout) and elements (e.g., whether elements should be
graphical, text, involve movement, color, size, etc.) to display on
the DSS displays.
[0039] According to other embodiments, systems and methods of the
present invention may implement software that automatically
generates new templates and applies transformations to existing
content elements. New templates and content elements may be
generated for various reasons, such as to improve the content
effectiveness. Tools are preferably made available to the user that
facilitate generation of unique versions of pieces of content for
each display of the DSS. For example, software tools may be
implemented that elicit input from a user and/or other software
components regarding DSS attributes and other factors that underlie
content effectiveness, and apply information from the cognitive and
visions sciences (e.g., design rules or models accessed from a
database) to extrapolate, fill in, and otherwise explore the
information space for the particular pieces of content the system
aims to improve or optimize. Systems and methods of the present
invention provide a facility to generate completely new content
that is not simply a reconfiguration of deployed templates or
elements associated with deployed versions of content. That is, the
systems and methods of the present invention need not rely solely
on the hybridization/blending of deployed templates and elements
that data suggest are effective, although such systems and methods
are capable of hybridization/blending.
[0040] Turning now to FIG. 1, there is illustrated various
processes associated with the development of content in accordance
with embodiments of the present invention. The term content is a
broad term that refers to a wide variety of informational content,
including graphics, text, video clips, still images, audio clips,
web pages, and/or any combination of video and/or audio content,
for example. A piece of content refers to a specific set and
configuration of images, videos, text elements, etc., that is meant
to stand on its own to communicate a specific message or set of
messages (e.g., a television commercial). The term content element
refers to individual images, videos, text strings, etc., that can
be combined to make specific pieces of content.
[0041] Each piece of content can have many versions. For example,
two versions of the same piece of content could differ in that one
version uses text to represent a concept whereas another version of
that same piece of content might use an icon to represent the same
concept. There can also be many versions of each content element.
For example, one version of a text string could have 12-point font
whereas the same text string could have 24-point font.
[0042] According to the embodiment of FIG. 1, content is developed
10 for presentation on a display. The development of the content,
which includes content elements, is facilitated 12, by way of
computer assistance. Specifically, design rules or models stored in
a database are applied to 14 to facilitate computer-assisted
development of the content. The design rules or models are
preferably rules or guidelines that are based on principles of
cognitive and vision sciences. The design rules/models allow a
designer who has limited or no knowledge of principles of cognitive
and vision sciences to create effective content that is consistent
with such principles. The design rules/models stored in the
database may be used to facilitate 16 computer-assisted adjustment
of the content. The processes of generating content and revising
content in a manner consistent with principles of cognitive and
vision sciences are advantageously facilitated by computer
assistance to enhance content effectiveness.
[0043] FIG. 2 illustrates various processes associated with the
development of content in accordance with other embodiments of the
present invention. According to the embodiment of FIG. 2, content
is developed or adjusted 20 for presentation on a display. Design
rules or models stored in a database are accessed 22 during content
development or adjustment. The design rules are rules or guidelines
that are based on principles of cognitive and vision sciences, as
previously discussed. The models stored in the database are
typically based on a combination of rules that are associated with
a multiplicity of cognitive and vision sciences principles. A
computational model of visual attention, for example, represents
one such model that encompasses several principles of cognitive and
vision sciences. One particular computation model of visual
attention may be referred to as a saliency mapping model as is
known in the art. Useful examples of saliency mapping models are
disclosed in U.S. Patent Publication No. 2006/0215922 and in U.S.
Pat. No. 7,130,461, each of which is incorporated herein by
reference. It is understood that a wide range of cognitive and
vision science models may be used in the context of the present
invention, and are not limited to models of human visual attention
as specifically discussed above. Such other models may include
those that encompass human memory principles, for example.
[0044] A computer system, which accesses the database that stores
the design rules or models, determines 24 if development or
adjustment of the content is consistent with the design
rules/models. Various operations may be performed in response to
determining that the design rules have been violated. For example,
a user-perceivable recommendation may be generated 26 to suggest
changes the user can make during content development or adjustment
to satisfy to the design rules or models. A user-perceivable alert
may be generated 27 that indicates non-compliance with the design
rules or models. Automatic adjustment to the developed content may
be performed 28 to ensure that the content is consistent with the
design rules or models. FIG. 2 illustrates several of many other
possible events that can be triggered during development or
adjustment of content if an inconsistency with the design
rules/models has been detected. Compliance with the design
rules/models can be made mandatory or permissive depending on the
application and sophistication of the user.
[0045] FIG. 3 illustrates various processes associated with the
development of content in accordance with further embodiments of
the present invention. According to the embodiment of FIG. 3,
content is developed 30 for presentation on a multiplicity of
displays, such as a network of DSS displays. The multiplicity of
displays are preferably those associated with a DSS, but may be
displays associated with any network of displays, such as home
computer displays coupled to the internet Design rules or models
stored in a database are applied 32 during content development, the
design rules/models based on principles of cognitive and vision
sciences, as previously discussed.
[0046] Attributes of each display of the display network are
determined 34. Such attributes typically include display type,
size, shape, environment, ambient lighting, viewing distance,
viewer passing speed, among others. These attributes are preferably
determined in an automated manner, such as by reading attribute
data stored in the display (e.g., determined and stored during
display installation) or from a database that contains attribute
information for each display. These attributes may also be
determined using one or more sensors located at the viewing
locations. A video camera, for example, may be installed at viewing
locations to facilitate detection of changing environmental
conditions, such as day/night changes, density of viewers, and
distance between the viewers and the display. Proximity sensors,
such as infrared (IR) sensors, may be used at viewer locations to
determine the average number of viewers per unit time and/or
average distance between the viewers and the display.
[0047] According to one approach, the content is adjusted 36 to
accommodate the attributes of the networked displays in conformance
with the design rules/models. For example, the attributes of a 8''
display differ significantly from those of a large panel display
(e.g., 50'' LCD display). The content of a given presentation is
preferably adjusted so that the content elements are presented 38
on each of the disparate displays in conformance with the design
rules/models.
[0048] According to a further approach, as is also shown in FIG. 3,
user input data is received 35 regarding elements of the content.
The user input data preferably includes the goal and/or the
intended message of each content element. The content is adjusted
37 to accommodate the attributes of the networked displays and the
user input data in a manner consistent with the design
rules/models. The adjusted content is presented 38 in an
appropriate manner on each of the networked displays 38.
[0049] FIGS. 4A and 4B illustrate how content development for
presentation on a display 40 may be conducted in a manner
consistent with design rules developed from principles of cognitive
and vision sciences. FIG. 4A depicts an initial attempt by the
designer to create a presentation for display that includes a
number of different content elements. In this illustrative example,
the designer has selected the following content elements for
presentation on display 40: a text crawl 44, a video advertisement
42, a store logo 46, and a weather/news panel 48. Assuming that the
designer is not well acquainted with principles of cognitive and
vision sciences, the layout of these content elements 42, 44, 46,
and 48 as shown in FIG. 4A represents what the designer believes to
be an effective piece of content.
[0050] FIG. 4B illustrates how the developed content shown in FIG.
4A is more appropriately arranged in a manner consistent with
design rules or models developed from principles of cognitive and
vision sciences. The locations and size of the content elements 42,
44, 46, and 48 shown in FIG. 4B have been changed in accordance
with design rules/models developed from principles of cognitive and
vision sciences. Aspects of the content elements other than, or in
addition to, location and size relative to the display 40 may be
modified as well, such as font of text, text orientation,
foreground and background colors, color intensity, proportion of
the content elements relative to one another, relative brightness,
among others. Adjustment of the content elements may be implemented
in a semi-automatic or fully automatic manner via computer
assistance.
[0051] FIG. 5 is a block diagram of a system for implementing
computer-assisted development of content using principles of
cognitive and vision sciences in accordance with embodiments of the
present invention. The system shown in FIG. 5 includes a processor
52 coupled to a user interface 54 and a display 56. The user
interface 54 preferably includes one or more user input devices,
such as a keyboard, mouse, voice recognition facility, and the
like. A presentation 58 of content developed in accordance with the
present invention is typically presented on the display 56. Content
of the presentation 58 is preferably created and revised in
accordance with design rules or models stored in a cognitive
sciences database 50. Various templates (e.g., layouts) that are
consistent with the design rules/models may also be stored in the
cognitive sciences database 50. It is understood that the cognitive
sciences database 50 typically stores information, such as design
rules, templates, and models, that is associated with both
cognitive science and vision science, and that the use of the term
cognitive sciences database is not exclusive to cognitive science
only.
[0052] FIG. 6 is a block diagram of a system for implementing
computer-assisted development and/or distribution of content in a
manner consistent with principles of cognitive and vision sciences
in accordance with embodiments of the present invention. The system
shown in FIG. 6 includes a processor 62 coupled to a user interface
64, a display 66, a cognitive sciences database 50, and a network
interface 70. The network interface 70 facilitates communication
between the processor 62 and a multiplicity of displays 80A-80N of
a DSS. The processor 62 applies design rules accessed from the
cognitive sciences database 50 to format content in a manner
tailored for each of the displays 80A-80N, at least some of which
have differing attributes. The effectiveness of the presentations
82A-82N distributed to the various displays 80A-80N is enhanced by
adjustments made to the content by application of the design rules,
models, and templates stored in the cognitive sciences database 50,
in view of attributes of the DSS. The effectiveness of the
presentations 82A-82N distributed to the various displays 80A-80N
may be further enhanced by modification of the content elements in
view of user-indicated goals and intended message.
[0053] FIG. 7 is a block diagram of a DSS that incorporates the
capability for developing and distributing content in accordance
with embodiments of the invention. The block, diagram of FIG. 7
illustrates one configuration of a DSS divided into functional
blocks. Those skilled in the art will appreciate that the DSS may
be alternatively illustrated using different function blocks and
that various components of the DSS may be implemented as hardware,
software, firmware, or any combination of hardware, software and
firmware.
[0054] The DSS illustrated in FIG. 7 is a computerized system
configured to present informational content via audio, visual,
and/or other media formats. The DSS may include functionality to
automatically or semi-automatically generate playlists, which
provide a list of the information content to be presented, and
schedules, which define an order for the presentation of the
content. In a semi-automatic mode, a user may access a DSS control
processor 105 via an interactive user interface 110. Assisted by
the DSS control processor 105, the user may develop content by
identifying content elements to be presented, preferably in
accordance with design rules stored in a cognitive sciences
database 130. The DSS control processor 105 may then be used to
generate playlists and schedules that control the timing and order
of presentations on one or more DSS players 115. Each player 115
presents content to recipients according to a playlist and schedule
developed for the player 115. As discussed previously, the
informational content may comprise graphics, text, video clips,
still images, audio clips, web pages, and/or any combination of
video and/or audio content, for example.
[0055] In some implementations, after a playlist and schedule are
developed, the DSS control processor 105 determines the content
required for the playlist, downloads the content from a content
server, and transfers the content along with the playlist and
schedule to a player controller 120 that distributes content to the
players 115. Although FIG. 7 shows only one player controller 120,
multiple player controllers may be coupled to a single DSS control
processor 105. Each player controller 120 may control a single
player 115 or multiple players 115. The content and/or the
playlists and schedules may be transferred from the DSS control
processor 105 to the one or more player controllers 120 in a
compressed format with appropriate addressing providing information
identifying the player 115 for which the content/playlist/schedule
is intended. In some applications, the players 115 may be
distributed in stores and the content presented on the players 115
may be advertisements.
[0056] In other implementations, the DSS control processor 105 may
transfer only the playlists and schedules to the player controller
120. If the content is not resident on the player controller 120,
the player controller 120 may access content storage 125 to acquire
the content to be presented. In some scenarios, one or more of the
various components of the DSS system, including the content storage
125, may be accessible via a network connection, such as an
intranet or Internet connection. The player controller 120 may
assemble the desired content, or otherwise facilitate display of
the desired content on the players according to the playlist and
schedule. The playlists, schedules, and/or content presented on the
players 115 can be modified periodically or as desired by the user
through the player controller 120, or through the DSS control
processor 105, for example. Such modifications can be made in
accordance with design rules, models or templates stored in the
cognitive sciences database 130.
[0057] In some implementations, the DSS control processor 105
facilitates the development and/or formatting of a program of
content to be played on a player. For example, the DSS control
processor 105 may facilitate formatting of an audiovisual program
through the use of a template. The template includes formatting
constraints and/or rules that are applied in the development of an
audiovisual program to be presented. For example, the template may
include rules associated with the portions of the screen used for
certain types of content, what type of content can be played in
each segment, and in what sequence, font size, orientation, and/or
other constraints or rules applicable to the display of the
program. A separate set of rules and/or constraints may be
desirable for each display configuration. These rules, templates,
and constraints (e.g., design rules/models/templates) are
preferably stored and accessed from the cognitive sciences database
130. In some embodiments, formatting a program for different
displays may be performed automatically by the DSS control
processor 105 in accordance with the design rules. models, and
templates.
[0058] The information stored in the cognitive sciences database
130 may be used automatically or semi-automatically to control,
adjust, and/or monitor one or more processes of the DSS including
creation of templates, content design, selection of content,
distribution of content, assembly of programs, and/or formatting of
programs for display. The cognitive sciences database 130 used in
conjunction with the programming of the DSS yields advertisements
or other digital signage programs that are enhanced by the
teachings of cognitive science, while relieving the system user
from needing specific training in the field.
[0059] In development of a digital signage program, e.g., ad
campaign or the like, the DSS control processor 105 may guide a
user through various processes that are enhanced using knowledge
acquired through the cognitive sciences. For example, information
stored in the cognitive sciences database 130 may be applied to the
choice of templates to produce an optimal program layout and/or to
the selection of content, such as whether content elements should
be graphical, text, involve movement, color, size, and/or to the
implementation of other aspects of program development. The DSS
preferably includes the capability for designing alternative
versions of a digital signage program to accommodate diverse
display types and viewing conditions in a manner consistent with
the information stored in the cognitive sciences database 130.
[0060] FIG. 8 illustrates the process flow of creating and
deploying content using the components and functionality of the DSS
described above. The process guides the user through a series of
tools and scripts, and creates 210 a number of alternative
templates that specify how categories of content elements might
appear on the screen (e.g., the location, size, and orientation of
elements such as text, graphics and videos). The tools and scripts
suggest recommended templates by drawing on three sets of
information: a) principles from the cognitive and vision sciences
regarding effective display of information, b) the goals for the
content (e.g., way-finding, advertising), and c) the known
attributes of the digital signage network (e.g., size and shape of
the different displays, different viewing distances, and viewer
demographics across the network).
[0061] For example, the tools and scripts might help a user
determine whether an element should be represented graphically or
via text. The tools and scripts might also help a user determine
which of a large number of pre-defined templates are appropriate
given the viewing conditions across the network, goals for the
content, and if available, metrics regarding the types of templates
that have been effective from previous campaigns. The tools and
scripts might further help a user determine whether target and
distractor elements of the content are properly positioned,
dimensioned or otherwise presented (e.g., proper color, intensity,
etc.), and whether the desired order of target
attention/recognition by the viewer is achievable given the state
of the content.
[0062] The process walks the user through a series of tools and
scripts to generate 220 the particular content elements that will
later be placed within the templates created at block 210. The
individual content elements can include specific text messages,
static images, animations, movie clips, sound bites, etc. Each
element could have many variants, and software helps the user
determine which elements of content can be combined within a
template, the rules for how those elements can be combined, and the
parameters on which the content elements can be manipulated during
the content creation process. For example, it may be legal to
change the color or color intensity of a font during deployment,
but not the color of the face of a famous person used in the
template.
[0063] The software tools and scripts may facilitate content
generation by drawing on multiple sets of information, including:
a) data regarding the types of content elements that were effective
in previous campaigns, b) principles from the cognitive and vision
sciences, and c) the known attributes of the digital signage
network. After the content is created, in this example, user
interaction is no longer necessary.
[0064] Content creation is enhanced at block 230. The process may
involve various constraints to combine elements and templates to
create a number of versions of content. The first time through this
process, the constraints may be based on: a) the factors previously
used in creation of templates and content elements above, b)
pre-programmed guidelines for how to combine elements and
templates, and c) goals for the piece of content being deployed. On
subsequent passes through this block, the process may also use
effectiveness data (e.g., sales or inventory data, data resulting
from performing true or quasi-/correlational experimentation) to
alter existing content/templates or create novel templates (through
interpolation) and elements before creating new versions of
content. Because each display in a network may have different
attributes (e.g., different lighting levels, noise levels, shape,
size, and mean viewing distances), a unique version of content may
be created for each display in the network. The content is
distributed 240 across the digital signage network, with
adjustments made thereto in view of the DSS/display attributes.
[0065] FIG. 9 is a flowchart illustrating an exemplary
implementation of the DSS system in accordance with an embodiment
of the invention. The implementation involves a sporting goods
retailer with 200 stores. The retailer desires to advertise four
overstocked products and four products that are not overstocked but
that have higher profit margins than the overstocked products. The
goal of the campaign is to maximize gross profit while eliminating
excessive inventory of the overstocked items. That is, once the
excessive inventory is eliminated, the goal will simply reduce to
maintaining a balanced inventory at each store location.
[0066] Using cognitive/vision science driven software, the signage
manager of the retailer creates 310 a number of different templates
that will be used to develop content for each of the eight product
lines. These templates include layout of messages, color schemes,
and/or other variables that make up the program. These templates
can be used for each of the eight product lines, and are not
specific to a single product. Additionally, pre-existing or stock
templates are available for use during this phase.
[0067] After creating the base templates for this campaign, the
signage manager creates 320 individual content elements that are
needed to populate the templates. The individual elements are
specific to the product lines being promoted, and include product
branding and messages for given products. As in the template
creation process, creation of individual elements is guided by
software wizards using cognitive/vision science driven
software.
[0068] The templates are automatically populated 330 with the
individual content elements to generate a number of different
content packages for each of the eight products that the signage
network is promoting. Potentially hundreds of differing versions of
each content piece are created for each product line by merging
elements with templates to accommodate varying signage attributes
such as screen size or viewing distance.
[0069] Using pre-existing or learned knowledge about the signage
network, content is distributed 340, such as by using algorithms
that enable collection of success metrics for individual pieces of
content. According to some implementations, the content is
distributed across the network in a way that ensures proper
counterbalancing, blocking, and confound-free measurement can be
made (e.g., in conformance with performing a true experiment).
Additionally, the deployment algorithm ensures that relevant
content is sent to the appropriate signs in the network,
considering network attributes, viewer demographics, and viewing
conditions among others.
[0070] In some implementations, point of sale and sensor data is
used which allows the impact of the various content packages to be
monitored and analyzed to determine what templates and content
elements, and their combinations, are most effective for each
screen on the network. From this information, cause and effect, as
well as return on investment can be analyzed, enabling value-based
billing. This example may determine whether across all 200 stores,
the signage system itself was responsible for X % increase in
profits and Y % decrease in excessive inventory. Exploratory data
analysis generates new possible network attributes. For example,
there is a spike in sales when customers pick up product X and when
content Y is concurrently shown. On the next iteration, this new
network attribute will be tested experimentally, not just measured
from a correlation study. For example, the system may determine
whether content pieces presented on X type screens is most
effective using Y-type templates, and that the most effective
content elements have XYZ properties.
[0071] Based on effectiveness data that may be acquired
automatically (e.g., via true experiments implemented by the
signage network) or manually (e.g., sales information, inventory
levels) 350, the system may automatically generate 360 new
templates, new content elements, and new combinations thereof.
Again, using signage network attributes (both old and new), the
software deploys these new pieces of content across the network.
During the remainder of the campaign, the processes described in
blocks 330 through 360 may be repeated, for example, without user
interaction. The signage network manager is able to monitor the
impact that the content has on sales at any given point during the
campaign while the system automatically attempts to achieve the
campaign goals.
[0072] Upon completion of this campaign, templates and elements
that were manually or automatically generated during the campaign
are available for future campaigns as well. Furthermore, the
knowledge that was gained regarding the types of templates and
elements that are effective for particular displays, demographics,
or other factors, is used to create and distribute content more
effectively across the network during future campaigns.
[0073] Determination of whether an experiment is a true experiment
can be performed proactively or retroactively with respect to
running the experiment. According to some embodiments, a computer
may be used to determine if an experiment that is yet to be
performed is a true experiment. According to other embodiments, a
computer may be used to determine if an experiment that was
previously performed is a true experiment. According to one
approach, the computer determines, based on information provided by
the user, whether an experimental design eliminates or controls
confounds. In this example, the user enters information about the
experiment, including the independent and dependent variables of
the experiment.
[0074] The computer identifies situations that may produce
confounds in the experiment. The user selects the
confound-producing situations identified by the computer that are
present in the context of the experiment. The computer prompts the
user to identify steps taken to eliminate or control the identified
confounds. The computer determines if the combination of steps is
sufficient to eliminate confounds in the experiment. Details of
performing a true experiment in the context of the present
invention are further disclosed hereinbelow and in commonly owned
U.S. patent application Ser. No. 11/321/340, filed Dec. 29, 2005
under Attorney Docket No. 61290US002, which is hereby incorporated
herein by reference.
[0075] FIG. 10 is a block diagram of a system for developing and/or
distributing content using cognitive/vision science driven software
in accordance with embodiments of the present invention. The system
shown in FIG. 10 includes a computer 402 coupled to a display 404
and a network interface 406. The network interface 406 is coupled
to a network of displays 410, such as those of a DSS. The computer
system 402 is also coupled to a cognitive sciences database
450.
[0076] The cognitive sciences database 450 includes several sets of
rules or models each developed from principles of human cognitive
and vision sciences. In this illustrative example, the rules and
models, also referred to herein as design rules or design models,
include visual attention and perception rules 420, text readability
rules 430, and memory rules 440.
[0077] The visual attention and perception rules 420 may include
rules or models that are based on how human perceptual systems
process visual information. An illustrative example of a visual
attention and perception model 420 is referred to as a saliency
mapping model. In general terms, those portions of a given image
which elicit a strong, rapid and automatic response from viewers,
independent of the task they are trying to solve, may be referred
to as being visually salient. A red object among green objects or
horizontal lines among vertical lines represent two examples of
such salient locations of an image.
[0078] The computer system 402 may be configured to provide for
automatic detection of salient parts of image information based on
a saliency mapping model. Saliency may be computed in a number of
ways as is known in the art. Examples of such approaches which may
be implemented in the context of the present invention are
disclosed in U.S. Patent Publication No. 2006/0215922 and in U.S.
Pat. No. 7,130,461, which are incorporated herein by reference
hereinabove. Further details of saliency mapping models are
described in Koch, C. and Ullman, S. "Shifts in Selective Visual
Attention: Towards the Underlying Neural Circuitry," Human
Neurobiology, 4:219-227, 1985; and two detailed computer
implementations: Itti, L., Koch, C. and Niebur, E., "A Model of
Saliency-Based Visual Attention for Rapid Scene Analysis," IEEE
Trans. Pattern Analysis & Machine Intell. (PAMI) 20:1254-1259,
1998 and Itti, L. and Koch, C. "A Saliency-Based Search Mechanism
for Overt and Covert Shifts of Visual Attention," Vision Research
40:1489-1506, 2000, each of which is hereby incorporated herein by
reference.
[0079] According to one approach, the system shown in FIG. 10 may
be configured for determining a saliency map, which may be a
two-dimensional map that encodes salient objects in a visual
environment. The saliency map of a given scene, for example,
expresses the saliency of all locations in this image. The saliency
map is the result of competitive interactions among feature maps
for image features including color, orientation, texture, motion,
and depth, among others, that interact within and across each map.
At any time, the currently strongest location in the saliency map
corresponds to the most salient object. The value in the map
represents the local saliency of any one location with respect to
its neighborhood. By default, the system directs attention towards
the most salient location. A second most salient location may be
found by inhibiting the most salient location, causing the system
to automatically shift to the next most salient location.
[0080] By way of example, original content may be input to a
saliency mapping model, such as in the form of a scanned or
digitized image of the original content. The computer system 402
may produce a saliency map of the content image, indicating the
most salient locations of the image preferably in order. The output
of the saliency mapping model may indicate these salient locations
using a box or other shape in combination with a number or letter,
thus indicating the locations and order of saliency of the image.
These locations/order indicators can be used to provide a
comparison between the content designer's intended saliency
locations/ordering and the actual saliency locations/ordering as
determined by the computer system 402.
[0081] The computer system 402 may generate recommendations to the
designer via narrative or imagery output that can improve saliency
and/or achieve the desired saliency/ordering of salient locations.
The computer system 402 may alternatively produce altered forms of
the original content automatically in a manner that achieves the
designers desired saliency mapping/ordering requirements. In this
manner, the computer system 402 may, without user intervention,
analyze original content, develop a saliency map therefrom,
determine if saliency requirements of the user or rule/model have
been met, and, if not, generate one or more versions of adjusted
content that meets the saliency requirements of the user or
rule/model.
[0082] Other visual attention/perception rules 420 may be defined
for visual attention guiding attributes that can enhance the visual
attention of viewers to displayed content, effectively "guiding"
the viewers to allocate attention to the display or portions of the
display. Guiding attributes define aspects of individual content
elements or relationships between multiple content elements.
Guiding attributes can be used in a first mode, to attract the
visual attention of viewers to a display, and be used in a second
mode, during presentation of content once the viewer is present
within the display space. For example, a rule may be defined that
regulates the number and spatial combination of specific strong
guiding attributes that are present in the displayed content at any
moment in time in order to maximize the attractiveness of the
displayed content to the viewer, given the specific combination of
strong attributes that exist in the visual environment in which the
display is located. Once the visual attention of the viewer has
been attracted and is within the display space, as indicated by a
camera or proximity sensor, for example, the rule may allow for the
combination of both strong and weak guiding attributes, or allow
use of combinations of strong and weak attributes for guiding the
viewer's visual attention within the display content.
[0083] It is understood that there are two categories of guiding
attributes, strong and weak guiding attributes. Strong guiding
attributes include: size, color, orientation, motion, curved vs.
straight, stereoscopic depth, aspect ratio, monocular depth, and
line termination. Weak guiding attributes include: novelty,
intersection, color changes, semantic category, and faces.
[0084] A rule 420 may be defined that limits the number of strong
guiding attributes present in the display of content at any given
time. It is understood in the art that the presence of greater than
a small number of instances (e.g., four instances) of any one
strong guiding attribute in a content presentation at any given
time weakens the "strength" of this strong guiding attribute with
respect to guiding visual attention. The computer system 402 may be
configured to track strong and, optionally, weak guiding attributes
in a visual array of content presented on a display at any given
time. If greater than 4 instances of any one of the strong guiding
attributes are detected at any given time, the computer system 402
may alert the designer or take automatic corrective action by
modifying the content to eliminate the duplicative strong guiding
attribute(s) in excess of 4 or other numeric threshold.
[0085] In another illustrative example, it is assumed that the
content designer wishes to increase the likelihood that newly added
content be seen by the viewer. The computer system 402 may scan the
content to determine the identity and number of strong guiding
attributes already used in the content, and recommend use of an
unused (or least used) strong guiding attribute to draw attention
to the newly added content element. In another illustrative
example, the environment may be evaluated, such as by use of a
camera or other sensors, to determine the type and number of strong
guiding attributes present in the display environment. Based on
this environmental knowledge, the computer system 402 may recommend
alteration (or automatically alter) of the content so that the
combined number of strong guiding attributes present in the content
at any one time and in the display environment at the same time
does not exceed the "maximum number of strong guiding attributes"
threshold discussed above. This content may be adjusted dynamically
by the computer system 402 in view of both content and display
environmental visual attributes to increase the effectiveness of
content display.
[0086] Text readability may be defined in terms of one or more
design rules or a model. For example, text readability may be
defined in terms of several parameters, including text size,
reading speed (based on moving text and/or speed of moving viewer,
viewer dwelling time), font style, luminance, contrast, color, and
viewing distance, among others. According to one approach, a
minimum font or text size as a function of text contrast may be
defined as:
font size=7.434*exp(-contrast/0.6297)+5.028,
where font size is given in angular size (arc min.), and contrast
represents text contrast defined as (L.sub.t-L.sub.b)/L.sub.b,
where L.sub.t is the text luminance and L.sub.b is the background
luminance. Additional details of this model are described in Krebs,
W. and Ahumada, Jr., A, "A Simple Tool for Predicting the
Readability of a Monitor," Proceedings of the Human Factors and
Ergonomics Society 46.sup.th Annual Meeting--2002, pp. 1659-1663,
which is hereby incorporated herein by reference. The computer
system 402 may be configured to measure font size of content text
and determine if the minimum font size of such text as defined
above is met. If not, the computer system 402 may indicate
violation of this rule and/or alter the text in a manner that
satisfies the font size rule. Other text readability parameters may
similarly be determined and adjusted by the computer system
402.
[0087] For example, as sensor or data from other sources regarding
the distances of viewers relative to a display is acquired, the
system may automatically adjust the text size to improve
readability according to the distance information. Font size, which
is measured in retinal arc minutes, may be adjusted systematically
in relation to changes in viewer distances from the display to
maintain readability according to the equation above.
[0088] Memory rules or models 440 may also be implemented by the
computer system 402 to enhance viewer coding (e.g., visual,
phonological, and/or semantic coding), retention, and recall. Rules
regarding working and long-term memory may be defined and
implemented by the computer system 402. Memory rules 440 may be
developed for meeting particular goals, such as the goal of viewers
comprehending a comparison of information and remembering desired
information resulting from the comparison.
[0089] It is well understood that the duration of human working
memory without rehearsal is about 2 seconds. In other words, absent
rehearsal or repetition, information in working memory can be lost
in about 2 seconds. It is assumed, in this illustrative example,
that a content designer wishes to design content such that a viewer
encodes a first piece of information in working memory and also
wishes that the viewer retain this first piece of information in
working memory when a second piece of information is presented. In
order to ensure that the first piece of information is not lost
prior to presentation of the second piece of information, a memory
rule 440 may ensure or recommend that the second piece of
information be presented within 2 seconds of presentation of the
first piece of information.
[0090] For example, the content designer may have the goal of
presenting a comparison of a client bank's interest rate and that
of a competitor bank. In order to ensure that the two interest
rates are retained in working memory for the comparison, the second
of the two interest rates is to be presented within 2 seconds of
presentation of the first interest rate, per the working memory
duration rule 440.
[0091] Principles of primacy and recency may also be defined in
terms of memory rules 440. For example, the computer system 402 may
be configured to order or re-order presentation of a sequence of
information in a manner that increases the likelihood that the more
important information in this sequence is transferred to long-term
memory. For example, a sequence, series or pattern of information
may be presented in an advertisement for display. The information
may be text or graphic objects, such as numbers, letters, icons,
pictures (e.g., of product on sale) or other information. Primacy
and recency memory rules 440 may be applied that order or re-order
the informational objects so that the more important objects are
preferentially positioned at the beginning and end of the sequence,
with the less important (e.g., less profitable) informational
objects being positioned in the middle portion of the sequence,
series or pattern.
[0092] The principle of rehearsal may also be defined by one or
more memory rules 440. For example, a more important product of
several products may be shown more frequently than other less
important products. In this way, rehearsal or repetition of
presentation of the more important products in an advertisement
increases the likelihood that the more frequently presented
products will be remembered by the viewer.
[0093] The principle of memory capacity may be defined in terms of
one or more memory rules 440. It is understood in the art that the
capacity of working memory is about four "chunks" of information. A
"chunk" of information represents anything that has a unitary
representation in long-term memory. Four chunks may be represented
by four letters or numbers that have little association. However, a
multiplicity of letters, numbers, objects, and the like that have a
strong association may define a chunk. For example, the acronym
NATO is formed from multiple letters, but is defined as a chunk, as
NATO has a unitary representation in long-term memory to most
adults, for example.
[0094] A memory rule 440 may be defined that limits the number of
chunks that are presented at any given time in order to maximize
the likelihood that the presented chunks are processed by the
viewer and transferred to long-term memory. For example, the
computer system 402 may scan for chunks and notify the content
designer if greater than four chunks have been presented at any
given time.
[0095] These and other principles of cognitive and vision sciences
may be defined in terms of rules or models, including those
described in Goldstein, E. Bruce, "Cognitive Psychology, Connecting
Mind, Research, and Everyday Experience," Thompson/Wadsworth 2005,
which is hereby incorporated herein by reference.
[0096] As was discussed previously, the complexity of the cognitive
sciences database may vary from relatively simple to very complex.
It is understood that the rules and models shown in FIG. 10 are for
illustrative purposes only, and that a cognitive sciences database
of the present invention may incorporate one or more aspects of one
or more of these rules and models. These and other rules and models
may be developed that associate a particular cognitive/vision
science principle or set of principles to a content development
rule or model that can be implemented by a computer to detect or
ensure adherence to such rule/model.
[0097] Those skilled in the art will appreciate that
cognitive/vision science principles other than, or in addition to,
those described herein may be incorporated into a cognitive
sciences database for use in content development and distribution
in accordance with the present invention.
[0098] FIG. 11 is a flowchart illustrating various processes
associated with content development and modification using one or
more cognitive/vision sciences models in accordance with the
present invention. FIG. 11 is directed to methods that provide for
computer-assisted analysis of content by one or more cognitive and
vision sciences (CVS) models. Content is provided or developed 502
by a content designer. The content is input 504 to a computer
system that implements one or more CVS models, such as a
computational model of visual attention, a text readability model
or a model of human memory. The CVS model or models perform an
analysis 506 on the content and produce 512 an output based on the
analysis results. Information representative of environmental
conditions at the presentation locations and/or goals for the
content may be inputs 508, 510 to the model(s). For example, the
type and configuration of displays, average distances between
displays and viewers, average speeds or dwelling times as between
viewers and displays may be environmental condition information 508
that is input to the model(s).
[0099] Goal information 510 that may be input to the model(s) may
include goals that are associated with each of the various models,
such as a computational model of visual attention, a text
readability model or a model of human memory. Typical goal
information may include saliency mapping goals, such as specific
elements of the content to be perceived by viewers and the desired
order in which such specific elements are to be perceived. Other
goal information 510 may include improving or optimizing text
readability based on text size and/or scrolling text rate relative
to viewer location and/or speed at which viewers pass by a given
display. Additional goal information 510 may include maximizing
memory coding, retention, and/or recall of content by viewers, such
as by conforming to memory capacity and duration rules of a given
model.
[0100] In some implementations, the output represents
recommendations for changing 516 the content in conformance with a
given model's rules or goals. The recommendations may take several
forms, such as a narrative form or images. For example, a menu of
possible attributes of the content that may be changed 514 can be
presented to the user. The menu of attributes may include a range
of attribute values that may be changed by the user, yet still
conform with a given model's rules or goals.
[0101] In other implementations, the output represents a modified
form of the original content produced automatically 518 by the
computer implemented CVS model or models. A number of variations of
modified content may be automatically produced, each of which
satisfies the rules or goals of the model or models. The user may
then select a desired version of the modified content 514 for
presentation 520. Alternatively, the computer may select one or
more of the versions for presentation. In other implementations,
the various versions of modified content may be subject to a
designed experimental process that improves or optimizes content
presentation effectiveness for a number of networked displays,
preferably on a display-by-display basis, as is discussed in
greater detail with reference to FIG. 12 below.
[0102] FIG. 12 is a flowchart illustrating various processes
associated with content development and modification of same using
one or more cognitive/vision sciences models and results from true
experimentation preferably implemented by a digital signage system
in accordance with the present invention. According to the
embodiment shown in FIG. 12, content may be developed and
distributed 602 in conformance with cognitive and vision sciences
rules or models, such as in manners discussed hereinabove. A true
experiment may be performed 604 to improve or optimize presentation
effectiveness of the content. Conducting the true experiment may
include identifying 606 dependent variables, such as a goal of
increasing sales of a particular product. Independent variables may
be identified 608, such as parameters associated with one or more
CVS models (e.g., text readability, visual attention and/or memory
parameters). Content may be modified 610 in view of the results
from the true experiment. For example, content may be modified 612
on a display-by-display basis, based on improved or optimized
parameters for each display. The modified content may be presented
614 on each of the displays in a manner optimized for each
display.
[0103] Additional true experimentation may be conducted to further
improve or optimize content presentation, particularly under
changing environmental conditions or a change in the goals or
intended message of the content. It is understood that
quasi-experiments and correlational experiments may be performed in
addition to, or to the exclusion of, a true experiment. Details of
suitable quasi-/correlational experimental methods that may be
adapted in accordance with the present invention are disclosed in
U.S. Patent Publication No. 2005/039206, which is hereby
incorporated herein by reference.
[0104] According to various embodiments, an expert system may be
configured to implement a true experiment in the context of the
present invention. The expert system may include a design processor
having various hardware components including a central processing
unit (CPU) and memory, among other components. The memory stores
computer instructions that control the processes for designing the
experiment and stores information acquired from the user that are
needed for the experimental design. Under control of the software,
the CPU algorithmically selects or generates questions to elicit
information from a user. The questions are presented to the user
via an output device of a user interface that is coupled to the
design processor. For example, the user interface typically
includes a display device, such as a liquid crystal display (LCD)
or other type of display device for presenting the questions to the
user. The user interface also includes one or more input devices,
such as a touch screen responsive to a finger or stylus touch, a
mouse, keyboard, voice recognition, or other type of input device.
The user enters responses to the questions via one or more input
devices(s) of the user interface. The design processor can
determine the appropriate descriptive and inferential statistics
for the experiment based on the experimental design and the
characteristics of the independent and dependent variables.
[0105] The design processor may be configured to identify the
information required to design a true experiment and selects or
generates a series of questions that elicit responses from the user
providing the required information. The questions are presented to
the user via a user interface. User responses to the questions are
received via the user interface and are transferred to the design
processor. The design processor extracts information from the user
responses and designs a true experiment based on the information.
The expert system has the capability to collect information at
specific steps that is relevant to other steps. For example,
knowledge that the dependent variable is continuous in step X means
a particular type of statistical analysis should be used in step Y.
The system uses data from previous steps to complete later steps.
For example, if the data has already been acquired, the system
would not ask the user for the same information again. The user
would not need to know that the information was relevant to both
steps. If the data were not available from previous steps, the
system would ask the user for the needed data.
[0106] A true experiment includes development of a hypothesis or
objective. Dependent and independent variables are identified, and
at least two levels of one or more independent variable are used. A
control group and treatment groups are formed and samples are
randomly assigned to levels of the independent variable. There is
some kind of method for controlling for or eliminating confounding
variables. For example, in a digital signage experiment, the system
would guide the user through the process of controlling for carry
over effects by 1) balancing and counterbalancing the order with
which pieces of content are shown at locations across the network;
and or 2) ensuring that two pieces of experimental content are not
shown within a block of time in which viewers could see both pieces
of content while in the store; and or 3) ensuring that sufficient
time has elapsed before data are collected between when the content
switches from one version of experimental content and another
version of experimental content such that at least 95% of possible
viewers who were in the store at the time of the content change
would have left the store. If all of these elements are
appropriately applied, the experiment produces results that can be
used to make statistical inferences about the relationship between
the dependent and independent variables. The expert system
described herein allows a user who is unsophisticated in the
complexities of true experimental design to design an experiment
that produces substantially confound-free results and can be used
to determine and quantify any causal relationship between
independent and dependent variables.
[0107] Embodiments of the invention are directed to an expert
system that has the capability of designing a true experiment based
on user input. As previously mentioned, the use of the expert
system relieves the user of having any foundation in the theory or
practice of experimental design. A true experiment has at least two
levels of an independent variable. The expert system elicits
information from a user required to choose independent and
dependent variables for the experiment. For example, in a digital
signage experiment, the expert system might ask the user questions
such as: "If content X (where X is any piece of content in which
the user wants to experimentally evaluate) is effective, what are
the changes in the world that you would expect to happen as a
result of showing content X? The system would provide a number of
possible changes such as: sales of a particular product will
increase; foot traffic in a particular location in the store will
increase; consumers will inquire with staff regarding the features
of a particular product; consumers will pick a particular product
off the shelf; and other, where other is any other change that is
not included in the system's stored set of possible changes.
[0108] Those skilled in the art will appreciate that each of these
possible "changes in the world" correspond to a possible dependent
variable that could be measured in an experiment designed to test
the effectiveness of content X. Likewise, the expert system could
guide the user through the process of picking control content
analogues to a placebo in a drug study. For example, the expert
system would ask the user to identify content that would not be
related in any way to the goal of content X. With respect to
threats to internal validity, the expert system, via the sequence
of questions and user responses, identifies threats to internal
validity, and may initiate processes for controlling these threats,
such as through balancing, counterbalancing and/or blocking, and/or
randomization.
[0109] The expert system, based on user input, is capable of
implementing processes for assigning samples randomly to groups so
that each sample in an experiment is equally likely to be assigned
to levels of the independent variable. The expert system is also
capable of designing an experiment that includes randomization,
counterbalancing and/or blocking. The system may assist the user in
selecting independent variables or levels of independent variables,
and assists the user in selecting dependent variables based on
factors associated with internal and/or external validity of the
experiment. For example, the system could obtain the necessary
information to conduct power analyses on various combinations of
independent and dependent variables, provide the user with the
results of the various power analyses, the domain specific terms,
and values that the user understands ("Using sales data to measure
the effectiveness of this piece of content would take 8 weeks and
cost $1400 whereas using sensor data would take 2 weeks and cost
$800").
[0110] In some configurations, in addition to designing the true
experiment, the expert system may aid the user in performing one or
more of conducting true experiments, collecting data, statistically
analyzing the data, and interpreting the results of the
experiments. In addition to the experiment design processor and
user interface previously described, the expert system may also
include an experiment control processor configured to automatically
or semi-automatically control the execution of the experiment. An
experiment analysis processor may also be included that is
configured to analyze the experimental data and/or interpret the
results of the experiment. The functions of the control processor
and the analysis processor are enhanced through knowledge of how
the experiment was designed by the design processor.
[0111] For example, because the analysis processor will have
received information regarding the independent and independent
variables (e.g., whether the independent variables (IVs) and
dependent variables (DVs) are continuous or discrete), the analysis
processor would have much of the necessary information to choose
the appropriate statistical test to apply to the data from the
experiment. For example, if there is one IV with two discrete
levels and one continuous DV, then a T-Test may be selected by the
analysis processor for the inferential statistical test whereas if
there is one IV with two discrete levels and one DV with two
discrete levels, then a Chi-Squared test may be used for the
inferential statistical test. Likewise, because the analysis
processor will have access to information from the design processor
regarding which experimental conditions are diagnostic of
particular hypotheses, the analysis processor would have most or
all of the information needed to determine which experimental and
control conditions should be statistically compared and reported to
the user. Additional details regarding methods and systems for
designing and implementing true experiments in the context of the
present invention are disclosed in commonly owned U.S. patent
application Ser. No. 11/321/340, filed Dec. 29, 2005 under Attorney
Docket No. 61290US002, which is incorporated by reference
hereinabove.
[0112] Application of cognitive and vision sciences, alone or in
combination with designing and implementing true experiments in
accordance with the present invention, allows users with little or
no background in the cognitive and vision sciences (or designing
true experiments) to apply these disciplines in order to create
more effective content. This functionality can be used in either a
single or multi-screen environment. On a system-wide level,
application of cognitive and vision sciences provides input and
constraints for the automated content design system in order to
tailor content on a screen-by screen basis. For example, if the
average viewing distance is known for each network sign, then the
component for applying the cognitive and vision sciences will
determine the ideal font size for each display, and this
information will be used by the automated content design component
to generate text with those font-size parameters.
[0113] Automated content design and development according to the
present invention may also provide for the automatic generation of
new templates and application of transformations to existing
elements. New templates and elements may be generated to improve
the content effectiveness. Content development tools of the present
invention may also be used to generate unique versions of pieces of
content for each player in the system.
[0114] In some implementations, users may be prompted to provide
input or may use information supplied from other components
regarding the network attributes and factors that underlie content
effectiveness. Knowledge from the cognitive and visions sciences
may be used to extrapolate, fill in, and otherwise explore the
information space for the particular pieces of content the system
aims to enhance. The functionality of the content development tools
provides the ability to generate completely new content that is not
simply a reconfiguration of deployed templates or elements
associated with deployed versions of content.
[0115] The foregoing description of the various embodiments of the
invention has been presented for the purposes of illustration and
description. It is not intended to be exhaustive or to limit the
invention to the precise form disclosed. Many modifications and
variations are possible in light of the above teaching. For
example, embodiments of the present invention may be implemented in
a wide variety of applications. It is intended that the scope of
the invention be limited not by this detailed description, but
rather by the claims appended hereto.
* * * * *