U.S. patent application number 13/708525 was filed with the patent office on 2013-07-18 for systems and methods for analyzing neuro-reponse data and virtual reality environments.
The applicant listed for this patent is Ramachandran Gurumoorthy, Robert T. Knight, Anantha Pradeep. Invention is credited to Ramachandran Gurumoorthy, Robert T. Knight, Anantha Pradeep.
Application Number | 20130185144 13/708525 |
Document ID | / |
Family ID | 44719037 |
Filed Date | 2013-07-18 |
United States Patent
Application |
20130185144 |
Kind Code |
A1 |
Pradeep; Anantha ; et
al. |
July 18, 2013 |
SYSTEMS AND METHODS FOR ANALYZING NEURO-REPONSE DATA AND VIRTUAL
REALITY ENVIRONMENTS
Abstract
Example methods, systems and machine readable instructions are
disclosed for analyzing neuro-response data and virtual reality
environments. An example method includes analyzing neuro-response
data gathered from an individual exposed to a virtual reality
comprising a virtual reality setting and a marketing material. The
example method also includes determining an effectiveness of one or
more of the virtual reality setting or the marketing material based
on the neuro-response data. In addition, the example method
includes modifying one or more of the virtual reality setting or
the marketing material based on the effectiveness.
Inventors: |
Pradeep; Anantha; (Berkeley,
CA) ; Knight; Robert T.; (Berkeley, CA) ;
Gurumoorthy; Ramachandran; (Berkeley, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Pradeep; Anantha
Knight; Robert T.
Gurumoorthy; Ramachandran |
Berkeley
Berkeley
Berkeley |
CA
CA
CA |
US
US
US |
|
|
Family ID: |
44719037 |
Appl. No.: |
13/708525 |
Filed: |
December 7, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
12853197 |
Aug 9, 2010 |
8392250 |
|
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13708525 |
|
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Current U.S.
Class: |
705/14.43 ;
705/14.41 |
Current CPC
Class: |
G06Q 30/02 20130101;
A61B 5/04842 20130101; A61B 3/113 20130101; G06Q 30/0244 20130101;
A61B 5/0496 20130101; G06Q 30/0242 20130101; A61B 5/0402 20130101;
A61B 5/055 20130101; A61B 3/112 20130101; A61B 5/04009 20130101;
A61B 5/163 20170801; A61B 5/162 20130101 |
Class at
Publication: |
705/14.43 ;
705/14.41 |
International
Class: |
G06Q 30/02 20120101
G06Q030/02 |
Claims
1. A method comprising: analyzing neuro-response data gathered from
an individual exposed to a virtual reality environment comprising a
virtual reality setting and a marketing material; determining an
effectiveness of one or more of the virtual reality setting or the
marketing material based on the neuro-response data; and modifying
at least one of the virtual reality setting or the marketing
material based on the effectiveness.
2. The method of claim 1, wherein the neuro-response data comprises
electroencephalographic data.
3. The method of claim 2, wherein the effectiveness is determined
based on an interaction between two frequency bands of the
electroencephalographic data.
4. The method of claim 1, wherein the effectiveness is determined
based on user manipulation of the marketing material in the virtual
reality environment.
5. The method of claim 1, wherein modifying the at least one of the
virtual reality setting or the marketing material occurs in-real
time as the individual is exposed to the virtual reality
environment.
6. The method of claim 1, wherein the virtual reality environment
comprises a first virtual reality environment, modifying the
virtual reality setting comprises transitioning from the first
virtual reality environment to a second virtual reality
environment, and the second virtual reality environment is
different from the first virtual reality environment.
7. The method of claim 1, wherein modifying the marketing material
comprises presenting a different marketing material to the
individual.
8. The method of claim 1, further comprising determining a
resonance to the at least one of the virtual reality setting or the
marketing material based on the neuro-response data.
9. The method of claim 1, wherein the marketing material comprises
a first product, a second product and a third product, and
modifying the marketing material comprises selecting the second
product or the third product for introduction into the virtual
reality setting based on the effectiveness of the first
product.
10. A system comprising: a data analyzer to: analyze neuro-response
data gathered from an individual exposed to a virtual reality
environment comprising a virtual reality setting and marketing
material; and determine an effectiveness of at least one of the
virtual reality setting or the marketing material based on the
neuro-response data; and a modifier to modify at least one of the
virtual reality setting or the marketing material based on the
effectiveness.
11. The system of claim 10, wherein the neuro-response data
comprises electroencephalographic data and the data analyzer is to
determine the effectiveness based on an interaction between two
frequency bands of the electroencephalographic data.
12. The system of claim 10, wherein the data analyzer is to
determine the effectiveness based on user manipulation of the
marketing material in the virtual reality environment.
13. The system of claim 10, wherein the modifier is to modify the
at least one of the virtual reality setting or the marketing
material in-real time as the individual is exposed to the virtual
reality environment.
14. The system of claim 10, wherein the virtual reality environment
comprises a first virtual reality environment, the modifier is to
modify the virtual reality setting by transitioning from the first
virtual reality environment to a second virtual reality
environment, the second virtual reality environment different from
the first virtual reality environment.
15. The system of claim 10, wherein the modifier is to modify the
marketing material by presenting different marketing material to
the individual.
16. The system of claim 10, wherein the data analyzer is to
determine a resonance to one or more of the virtual reality setting
or the marketing material based on the neuro-response data.
17. The system of claim 10, wherein the marketing material
comprises a first product, a second product and a third product,
and the modifier is to modify the marketing material by selecting
the second product or the third product for introduction into the
virtual reality setting based on the effectiveness of the first
product.
18. A tangible machine readable storage medium comprising
instructions that, when executed, cause a machine to at least:
analyze neuro-response data gathered from an individual exposed to
a virtual reality environment comprising a virtual reality setting
and a marketing material; determine an effectiveness of at least
one of the virtual reality setting or the marketing material based
on the neuro-response data; and modify at least one of the virtual
reality setting or the marketing material based on the
effectiveness.
19. The tangible machine readable storage medium of claim 18,
wherein the neuro-response data comprises electroencephalographic
data and the instructions cause the machine to determine the
effectiveness based on an interaction between two frequency bands
of the electroencephalographic data.
20. The tangible machine readable storage medium of claim 18,
wherein the instructions cause the machine to determine a resonance
to one or more of the virtual reality setting or the marketing
material based on the neuro-response data.
Description
RELATED APPLICATION
[0001] This patent arises from a continuation of patent application
Ser. No. 12/853,197, entitled "Neuro-Response Evaluated Stimulus in
Virtual Reality Environments," which was filed on Aug. 9, 2010, and
which is incorporated herein by reference in its entirety.
FIELD OF THE DISCLOSURE
[0002] This disclosure relates generally to advertising, and, more
particularly, to systems and methods for analyzing neuro-response
data and virtual reality environments including using
neuro-response data to evaluate marketing and entertainment in
virtual reality environments.
BACKGROUND
[0003] Conventional systems for evaluating marketing materials
typically involve monitoring and surveying individuals exposed to
materials such as products, packages, advertisements, and services.
Attempts have been made to present marketing materials in their
natural environments such as showrooms, store shelves, displays,
etc. However, mechanisms for presenting marketing materials in
natural environments are limited. In some examples, individuals are
asked to respond to surveys quickly after exposure to marketing
materials in actual environments, but information collected is
typically limited. Furthermore, conventional systems are subject to
brain pattern, semantic, syntactic, metaphorical, cultural, and
interpretive errors that prevent accurate and repeatable
analyses.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The disclosure may best be understood by reference to the
following description taken in conjunction with the accompanying
drawings, which illustrate particular examples.
[0005] FIGS. 1A-1B illustrate a particular example of a system for
evaluating stimulus material in a virtual reality environment.
[0006] FIGS. 2A-2E illustrate a particular example of a
neuro-response data collection mechanism.
[0007] FIG. 3 illustrates examples of data models that can be used
with a stimulus and response repository.
[0008] FIG. 4 illustrates one example of a query that can be used
with the neuro-response collection system.
[0009] FIG. 5 illustrates one example of a report generated using
the neuro-response collection system.
[0010] FIG. 6 illustrates one example of a technique for evaluating
stimulus material in a virtual reality environment.
[0011] FIG. 7 provides one example of a system that can be used to
implement one or more mechanisms.
DETAILED DESCRIPTION
[0012] Reference will now be made in detail to some specific
examples of the disclosure including the best modes contemplated by
the inventors for carrying out the teachings of the disclosure.
These specific examples are illustrated in the accompanying
drawings. While the disclosure is described in conjunction with
these specific examples, it will be understood that it is not
intended to limit the disclosure to the described examples. On the
contrary, it is intended to cover alternatives, modifications, and
equivalents as may be included within the spirit and scope of the
disclosure as defined by the appended claims.
[0013] For example, the techniques and mechanisms of the present
disclosure will be described in the context of particular types of
stimulus materials. However, it should be noted that the techniques
and mechanisms of the present disclosure apply to a variety of
different types of stimulus materials including marketing and
entertainment materials. It should be noted that various mechanisms
and techniques can be applied to any type of stimuli. In the
following description, numerous specific details are set forth in
order to provide a thorough understanding of the present
disclosure. Particular examples of the present disclosure may be
implemented without some or all of these specific details. In other
instances, well known process operations have not been described in
detail in order not to unnecessarily obscure the present
disclosure.
[0014] Various techniques and mechanisms of the present disclosure
will sometimes be described in singular form for clarity. However,
it should be noted that some examples include multiple iterations
of a technique or multiple instantiations of a mechanism unless
noted otherwise. For example, a system uses a processor in a
variety of contexts. However, it will be appreciated that a system
can use multiple processors while remaining within the scope of the
present disclosure unless otherwise noted. Furthermore, the
techniques and mechanisms of the present disclosure will sometimes
describe a connection between two entities. It should be noted that
a connection between two entities does not necessarily mean a
direct, unimpeded connection, as a variety of other entities may
reside between the two entities. For example, a processor may be
connected to memory, but it will be appreciated that a variety of
bridges and controllers may reside between the processor and
memory. Consequently, a connection does not necessarily mean a
direct, unimpeded connection unless otherwise noted.
Overview
[0015] The examples disclosed herein provide improved methods and
apparatus for evaluating marketing materials in natural
environments that use neuro-response data such as central nervous
system, autonomic nervous system, and effector system measurements
along with survey based data.
[0016] A system presents stimulus materials such as products,
product packages, displays, services, offerings, etc., in virtual
reality environments such as market aisles, store shelves, showroom
floors, etc. Sensory experiences output to the user via the virtual
reality environment elicit user interactivity. User activity and
responses are used to modify marketing materials and/or virtual
reality environments. Neuro-response data including
electroencephalography (EEG) data is collected from users in order
to evaluate the effectiveness of marketing materials in virtual
reality environments. In particular examples, neuro-response data
is used to modify marketing materials and virtual reality
environments presented to the user.
EXAMPLES
[0017] Marketing materials such as products, product packages,
brochures, displays, signs, offerings, and arrangements are
typically evaluated by surveying individuals exposed to the
marketing materials. Survey responses and focus groups elicit user
opinions about the marketing materials. The survey responses and
focus groups provide some limited information about the
effectiveness of the marketing materials. It is recognized that
user responses to marketing materials in a laboratory or evaluation
setting can sometimes be different than user responses to the
marketing materials in a natural environment, such as a store
shelf, a supermarket aisle, a tradeshow floor, building, or a
showroom. However, opportunities to evaluate the effectiveness of
marketing materials in natural environments are limited.
[0018] In some instances, efforts are made to elicit user responses
to marketing materials after users visit actual showrooms, stores,
or tradeshows. In other instances, model stores, displays, and mock
presentations are set up to test the effectiveness of particular
packages, displays, presentations, offerings, etc. However, using
actual displays or establishing mock presentations is cumbersome
and inflexible. It is highly inefficient to test a variety of
presentations or make changes to presentations based on user
feedback. Furthermore, even when ample user feedback is obtained,
user feedback is subject to brain pattern, semantic, syntactic,
metaphorical, cultural, and interpretive errors that prevent
accurate and repeatable analyses.
[0019] Consequently, the techniques of the present disclosure
provide mechanisms for evaluating marketing materials presented in
virtual reality environments by using neuro-response data. In some
examples, neuro-response data along with other survey and focus
group data is used to test marketing presentations and displays in
virtual reality environments. Virtual reality environments and
virtual reality environment templates can be generated to allow
reuse, customization, and integration with generated marketing
materials.
[0020] Neuro-response data is analyzed to determine the
effectiveness of marketing materials presented in various virtual
reality environments to particular individuals. Individuals are
provided with mechanisms to interact with the virtual reality
environment and marketing materials are manipulated in the
framework of the virtual reality environment. Sensors, cameras,
microphones, motion detectors, gyroscopes, temperature sensors,
etc., can all be used to monitor user responses to allow not only
manipulation of the virtual reality environment but modification of
the marketing materials presented. In particular examples,
neuro-response data is used to evaluate the effectiveness of
marketing materials and make real-time adjustments and
modifications to marketing materials or the virtual reality
environment presented.
[0021] Neuro-response measurements such as central nervous system,
autonomic nervous system, and effector measurements can be used to
evaluate subjects during stimulus presentation. Some examples of
central nervous system measurement mechanisms include Functional
Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG),
Magnetoencephalography (MEG), and Optical Imaging. Optical imaging
can be used to measure the absorption or scattering of light
related to concentration of chemicals in the brain or neurons
associated with neuronal firing. MEG measures magnetic fields
produced by electrical activity in the brain. fMRI measures blood
oxygenation in the brain that correlates with increased neural
activity. However, current implementations of fMRI have poor
temporal resolution of few seconds. EEG measures electrical
activity associated with post synaptic currents occurring in the
milliseconds range. Subcranial EEG can measure electrical activity
with the most accuracy, as the bone and dermal layers weaken
transmission of a wide range of frequencies. Nonetheless, surface
EEG provides a wealth of electrophysiological information if
analyzed properly. Even portable EEG with dry electrodes provides a
large amount of neuro-response information.
[0022] Autonomic nervous system measurement mechanisms include
Electrocardiograms (EKG) and pupillary dilation, etc. Effector
measurement mechanisms include Electrooculography (EOG), eye
tracking, facial emotion encoding, reaction time etc.
[0023] Multiple modes and manifestations of precognitive neural
signatures are blended with cognitive neural signatures and post
cognitive neurophysiological manifestations to more accurately
perform neuro-response analysis. In some examples, autonomic
nervous system measures are themselves used to validate central
nervous system measures. Effector and behavior responses are
blended and combined with other measures. According to various
examples, central nervous system, autonomic nervous system, and
effector system measurements are aggregated into a measurement that
allows evaluation of stimulus material effectiveness in particular
environments.
[0024] In particular examples, subjects are exposed to stimulus
material and data such as central nervous system, autonomic nervous
system, and effector data is collected during exposure. According
to various examples, data is collected in order to determine a
resonance measure that aggregates multiple component measures that
assess resonance data. In particular examples, specific event
related potential (ERP) analyses and/or event related power
spectral perturbations (ERPSPs) are evaluated for different regions
of the brain both before a subject is exposed to stimulus and each
time after the subject is exposed to stimulus.
[0025] According to various examples, pre-stimulus and
post-stimulus differential as well as target and distracter
differential measurements of ERP time domain components at multiple
regions of the brain are determined (DERP). Event related
time-frequency analysis of the differential response to assess the
attention, emotion and memory retention (DERPSPs) across multiple
frequency bands including but not limited to theta, alpha, beta,
gamma and high gamma is performed. In particular examples, single
trial and/or averaged DERP and/or DERPSPs can be used to enhance
the resonance measure and determine priming levels for various
products and services.
[0026] According to various examples, enhanced neuro-response data
is generated using a data analyzer that performs both
intra-modality measurement enhancements and cross-modality
measurement enhancements. According to various examples, brain
activity is measured not just to determine the regions of activity,
but to determine interactions and types of interactions between
various regions. The techniques and mechanisms of the present
disclosure recognize that interactions between neural regions
support orchestrated and organized behavior. Attention, emotion,
memory, and other abilities are not merely based on one part of the
brain but instead rely on network interactions between brain
regions.
[0027] The techniques and mechanisms of the present disclosure
further recognize that different frequency bands used for
multi-regional communication can be indicative of the effectiveness
of stimuli. In particular examples, evaluations are calibrated to
each subject and synchronized across subjects. In particular
examples, templates are created for subjects to create a baseline
for measuring pre and post stimulus differentials. According to
various examples, stimulus generators are intelligent and
adaptively modify specific parameters such as exposure length and
duration for each subject being analyzed.
[0028] A variety of modalities can be used including EEG, GSR, EKG,
pupillary dilation, EOG, eye tracking, facial emotion encoding,
reaction time, etc. Individual modalities such as EEG are enhanced
by intelligently recognizing neural region communication pathways.
Cross modality analysis is enhanced using a synthesis and
analytical blending of central nervous system, autonomic nervous
system, and effector signatures. Synthesis and analysis by
mechanisms such as time and phase shifting, correlating, and
validating intra-modal determinations allow generation of a
composite output characterizing the significance of various data
responses.
[0029] According to various examples, survey based and actual
expressed responses and actions for particular groups of users are
integrated with neuro-response data and stored in a stimulus
material and virtual reality environment. According to particular
examples, pre-articulation predictions of expressive response for
various stimulus material can be made by analyzing neuro-response
data.
[0030] FIG. 1A illustrates one example of a system for present
marketing materials in virtual reality environments. According to
various examples, a stimulus material generator 101 constructs
products, product packages, displays, labels, boxes, signs,
offerings, and advertising using custom or template designs. In
particular examples, companies, firms, and individuals wanting to
test marketing materials provide the designs that can be
incorporated into designs or wireframes for use in virtual reality
environments. A virtual reality environment generator 103 can
similarly construct custom or template based designs for store
aisles, shopping malls, showrooms, tradeshow floors, etc., that can
be integrated with marketing materials 101 using a stimulus
material and virtual reality integration mechanism 111.
[0031] The integrated marketing materials and virtual reality
environment are provided to a presentation device 121. The
presentation device 121 may include screens, headsets, domes,
multidimensional displays, speakers, motion simulation devices,
movable platforms, smell generators, etc., to provide the subject
123 with a simulated environment. Subject response collection
mechanism 131 may include cameras recorders, motion detectors,
etc., that capture subject activity and responses. According to
various examples, neuro-response data collection mechanisms are
also used to capture neuro-response data such as
electroencephalography (EEG) data for the subject presented with
stimulus materials. In particular examples, feedback and
modification mechanism 141 uses subject responses to modify
marketing materials and/or the virtual reality environment based on
subject actions. According to various examples, product packages
may be manipulated by a subject in the virtual reality environment.
In particular examples, store displays may be viewed from different
angles, products may be opened, etc.
[0032] According to various examples, neuro-response data including
EEG data is used to make real-time modifications to marketing
materials and virtual reality environments. In particular examples,
lack of interest is detected using neuro-response data and
different marketing materials are dynamically presented to the user
as the user moves along in a grocery aisle.
[0033] FIG. 1B illustrates one example of a neuro-response data
collection mechanism that can be used with users exposed to
stimulus material in virtual reality environments. According to
various examples, the virtual reality stimulus presentation
includes a stimulus presentation in virtual reality environments
device 151. In particular examples, the virtual reality environment
presentation device 151 is merely a display, monitor, screen, etc.,
that displays stimulus material in the context of a virtual reality
environment to a user. The stimulus material may be a product,
product package, service, offering, advertisement, placard,
brochure, etc., placed in the context of a supermarket aisle,
convenience store, room, etc.
[0034] The stimuli can involve a variety of senses and occur with
or without human supervision. Continuous and discrete modes are
supported. According to various examples, the virtual reality
environment presentation device 151 also has protocol generation
capability to allow intelligent customization of stimulus and
environments provided to multiple subjects in different settings
such as laboratory, corporate, and home settings.
[0035] According to various examples, virtual reality environment
presentation device 151 could include devices such as headsets,
goggles, projection systems, display devices, speakers, tactile
surfaces, etc., for presenting the stimulus in virtual reality
environments.
[0036] According to various examples, the subjects 153 are
connected to data collection devices 155. The data collection
devices 155 may include a variety of neuro-response measurement
mechanisms including neurological and neurophysiological
measurements systems such as EEG, EOG, MEG, pupillary dilation, eye
tracking, facial emotion encoding, and reaction time devices, etc.
According to various examples, neuro-response data includes central
nervous system, autonomic nervous system, and effector data. In
particular examples, the data collection devices 155 include EEG
161, EOG 163, and fMRI 165. In some instances, only a single data
collection device is used. Data collection may proceed with or
without human supervision.
[0037] The data collection device 155 collects neuro-response data
from multiple sources. This includes a combination of devices such
as central nervous system sources (EEG), autonomic nervous system
sources (EKG, pupillary dilation), and effector sources (EOG, eye
tracking, facial emotion encoding, reaction time). In particular
examples, data collected is digitally sampled and stored for later
analysis. In particular examples, the data collected could be
analyzed in real-time. According to particular examples, the
digital sampling rates are adaptively chosen based on the
neurophysiological and neurological data being measured.
[0038] In one particular example, the system includes EEG 161
measurements made using scalp level electrodes, EOG 163
measurements made using shielded electrodes to track eye data, fMRI
165 measurements performed using a differential measurement system,
a facial muscular measurement through shielded electrodes placed at
specific locations on the face, and a facial affect graphic and
video analyzer adaptively derived for each individual.
[0039] In particular examples, the data collection devices are
clock synchronized with a virtual reality environment presentation
device 151. In particular examples, the data collection devices 155
also include a condition evaluation subsystem that provides auto
triggers, alerts and status monitoring and visualization components
that continuously monitor the status of the subject, data being
collected, and the data collection instruments. The condition
evaluation subsystem may also present visual alerts and
automatically trigger remedial actions. According to various
examples, the data collection devices include mechanisms for not
only monitoring subject neuro-response to stimulus materials, but
also include mechanisms for identifying and monitoring the stimulus
materials. For example, data collection devices 155 may be
synchronized with a set-top box to monitor channel changes. In
other examples, data collection devices 155 may be directionally
synchronized to monitor when a subject is no longer paying
attention to stimulus material. In still other examples, the data
collection devices 155 may receive and store stimulus material
generally being viewed by the subject, whether the stimulus is a
program, a commercial, printed material, or a scene outside a
window. The data collected allows analysis of neuro-response
information and correlation of the information to actual stimulus
material and not mere subject distractions.
[0040] According to various examples, the virtual reality stimulus
presentation system also includes a data cleanser device 171. In
particular examples, the data cleanser device 171 filters the
collected data to remove noise, artifacts, and other irrelevant
data using fixed and adaptive filtering, weighted averaging,
advanced component extraction (like PCA, ICA), vector and component
separation methods, etc. This device cleanses the data by removing
both exogenous noise (where the source is outside the physiology of
the subject, e.g. a phone ringing while a subject is viewing a
video) and endogenous artifacts (where the source could be
neurophysiological, e.g. muscle movements, eye blinks, etc.).
[0041] The artifact removal subsystem includes mechanisms to
selectively isolate and review the response data and identify
epochs with time domain and/or frequency domain attributes that
correspond to artifacts such as line frequency, eye blinks, and
muscle movements. The artifact removal subsystem then cleanses the
artifacts by either omitting these epochs, or by replacing these
epoch data with an estimate based on the other clean data (for
example, an EEG nearest neighbor weighted averaging approach).
[0042] According to various examples, the data cleanser device 171
is implemented using hardware, firmware, and/or software. It should
be noted that although a data cleanser device 171 is shown located
after a data collection device 155, the data cleanser device 171
like other components may have a location and functionality that
varies based on system implementation. For example, some systems
may not use any automated data cleanser device whatsoever while in
other systems, data cleanser devices may be integrated into
individual data collection devices.
[0043] In particular examples, a survey and interview system
collects and integrates user survey and interview responses to
combine with neuro-response data to more effectively perform
virtual reality stimulus presentation. According to various
examples, the survey and interview system obtains information about
user characteristics such as age, gender, income level, location,
interests, buying preferences, hobbies, etc.
[0044] According to various examples, the virtual reality stimulus
presentation system includes a data analyzer 173 associated with
the data cleanser 171. The data analyzer 173 uses a variety of
mechanisms to analyze underlying data in the system to determine
resonance. According to various examples, the data analyzer 173
customizes and extracts the independent neurological and
neuro-physiological parameters for each individual in each
modality, and blends the estimates within a modality as well as
across modalities to elicit an enhanced response to the presented
stimulus material. In particular examples, the data analyzer 173
aggregates the response measures across subjects in a dataset.
[0045] According to various examples, neurological and
neuro-physiological signatures are measured using time domain
analyses and frequency domain analyses. Such analyses use
parameters that are common across individuals as well as parameters
that are unique to each individual. The analyses could also include
statistical parameter extraction and fuzzy logic based attribute
estimation from both the time and frequency components of the
synthesized response.
[0046] In some examples, statistical parameters used in a blended
effectiveness estimate include evaluations of skew, peaks, first
and second moments, distribution, as well as fuzzy estimates of
attention, emotional engagement and memory retention responses.
[0047] According to various examples, the data analyzer 173 may
include an intra-modality response synthesizer and a cross-modality
response synthesizer. In particular examples, the intra-modality
response synthesizer is configured to customize and extract the
independent neurological and neurophysiological parameters for each
individual in each modality and blend the estimates within a
modality analytically to elicit an enhanced response to the
presented stimuli. In particular examples, the intra-modality
response synthesizer also aggregates data from different subjects
in a dataset.
[0048] According to various examples, the cross-modality response
synthesizer or fusion device blends different intra-modality
responses, including raw signals and signals output. The
combination of signals enhances the measures of effectiveness
within a modality. The cross-modality response fusion device can
also aggregate data from different subjects in a dataset.
[0049] According to various examples, the data analyzer 173 also
includes a composite enhanced effectiveness estimator (CEEE) that
combines the enhanced responses and estimates from each modality to
provide a blended estimate of the effectiveness. In particular
examples, blended estimates are provided for each exposure of a
subject to stimulus materials. The blended estimates are evaluated
over time to assess resonance characteristics. According to various
examples, numerical values are assigned to each blended estimate.
The numerical values may correspond to the intensity of
neuro-response measurements, the significance of peaks, the change
between peaks, etc. Higher numerical values may correspond to
higher significance in neuro-response intensity. Lower numerical
values may correspond to lower significance or even insignificant
neuro-response activity. In other examples, multiple values are
assigned to each blended estimate. In still other examples, blended
estimates of neuro-response significance are graphically
represented to show changes after repeated exposure.
[0050] According to various examples, a data analyzer 173 passes
data to a resonance estimator that assesses and extracts resonance
patterns. In particular examples, the resonance estimator
determines entity positions in various stimulus segments and
matches position information with eye tracking paths while
correlating saccades with neural assessments of attention, memory
retention, and emotional engagement. In particular examples, the
resonance estimator stores data in the priming repository system.
As with a variety of the components in the system, various
repositories can be co-located with the rest of the system and the
user, or could be implemented in remote locations.
[0051] Data from various repositories is blended and passed to a
virtual reality stimulus presentation engine to generate patterns,
responses, and predictions 175. In some examples, the virtual
reality stimulus presentation engine compares patterns and
expressions associated with prior users to predict expressions of
current users. According to various examples, patterns and
expressions are combined with orthogonal survey, demographic, and
preference data. In particular examples linguistic, perceptual,
and/or motor responses are elicited and predicted. Response
expression selection and pre-articulation prediction of expressive
responses are also evaluated.
[0052] FIGS. 2A-2E illustrate a particular example of a
neuro-response data collection mechanism. FIG. 2A shows a
perspective view of a neuro-response data collection mechanism
including multiple dry electrodes. According to various examples,
the neuro-response data collection mechanism is a headset having
point or teeth electrodes configured to contact the scalp through
hair without the use of electro-conductive gels. In particular
examples, each electrode is individually amplified and isolated to
enhance shielding and routability. In some examples, each electrode
has an associated amplifier implemented using a flexible printed
circuit. Signals may be routed to a controller/processor for
immediate transmission to a data analyzer or stored for later
analysis. A controller/processor may be used to synchronize
neuro-response data with stimulus materials. The neuro-response
data collection mechanism may also have receivers for receiving
clock signals and processing neuro-response signals. The
neuro-response data collection mechanisms may also have
transmitters for transmitting clock signals and sending data to a
remote entity such as a data analyzer.
[0053] FIGS. 2B-2E illustrate top, side, rear, and perspective
views of the neuro-response data collection mechanism. The
neuro-response data collection mechanism includes multiple
electrodes including right side electrodes 261 and 263, left side
electrodes 221 and 223, front electrodes 231 and 233, and rear
electrode 251. It should be noted that specific electrode
arrangement may vary from implementation to implementation.
However, the techniques and mechanisms of the present disclosure
avoid placing electrodes on the temporal region to prevent
collection of signals generated based on muscle contractions.
Avoiding contact with the temporal region also enhances comfort
during sustained wear.
[0054] According to various examples, forces applied by electrodes
221 and 223 counterbalance forces applied by electrodes 261 and
263. In particular examples, forces applied by electrodes 231 and
233 counterbalance forces applied by electrode 251. In particular
examples, the EEG dry electrodes operate to detect neurological
activity with minimal interference from hair and without use of any
electrically conductive gels. According to various examples,
neuro-response data collection mechanism also includes EOG sensors
such as sensors used to detect eye movements.
[0055] According to various examples, data acquisition using
electrodes 221, 223, 231, 233, 251, 261, and 263 is synchronized
with stimulus material presented to a user. Data acquisition can be
synchronized with stimulus material presented by using a shared
clock signal. The shared clock signal may originate from the
stimulus material presentation mechanism, a headset, a cell tower,
a satellite, etc. The data collection mechanism 201 also includes a
transmitter and/or receiver to send collected neuro-response data
to a data analysis system and to receive clock signals as needed.
In some examples, a transceiver transmits all collected media such
as video and/or audio, neuro-response, and sensor data to a data
analyzer. In other examples, a transceiver transmits only
interesting data provided by a filter. According to various
examples, neuro-response data is correlated with timing information
for stimulus material presented to a user.
[0056] In some examples, the transceiver can be connected to a
computer system that then transmits data over a wide area network
to a data analyzer. In other examples, the transceiver sends data
over a wide area network to a data analyzer. Other components such
as fMRI and MEG that are not yet portable but may become portable
at some point may also be integrated into a headset.
[0057] It should be noted that some components of a neuro-response
data collection mechanism have not been shown for clarity. For
example, a battery may be required to power components such as
amplifiers and transceivers. Similarly, a transceiver may include
an antenna that is similarly not shown for clarity purposes. It
should also be noted that some components are also optional. For
example, filters or storage may not be required.
[0058] FIG. 3 illustrates examples of data models that can be used
for storage of information associated with collection of
neuro-response data. According to various examples, a dataset data
model 301 includes a name 303 and/or identifier, client attributes
305, a subject pool 307, logistics information 309 such as the
location, date, and stimulus material 311 identified using user
entered information or video and audio detection.
[0059] In particular examples, a subject attribute data model 315
includes a subject name 317 and/or identifier, contact information
321, and demographic attributes 319 that may be useful for review
of neurological and neuro-physiological data. Some examples of
pertinent demographic attributes include marriage status,
employment status, occupation, household income, household size and
composition, ethnicity, geographic location, sex, race. Other
fields that may be included in data model 315 include shopping
preferences, entertainment preferences, and financial preferences.
Shopping preferences include favorite stores, shopping frequency,
categories shopped, favorite brands. Entertainment preferences
include network/cable/satellite access capabilities, favorite
shows, favorite genres, and favorite actors. Financial preferences
include favorite insurance companies, preferred investment
practices, banking preferences, and favorite online financial
instruments. A variety of subject attributes may be included in a
subject attributes data model 315 and data models may be preset or
custom generated to suit particular purposes.
[0060] Other data models may include a data collection data model
337. According to various examples, the data collection data model
337 includes recording attributes 339, equipment identifiers 341,
modalities recorded 343, and data storage attributes 345. In
particular examples, equipment attributes 341 include an amplifier
identifier and a sensor identifier.
[0061] Modalities recorded 343 may include modality specific
attributes like EEG cap layout, active channels, sampling
frequency, and filters used. EOG specific attributes include the
number and type of sensors used, location of sensors applied, etc.
Eye tracking specific attributes include the type of tracker used,
data recording frequency, data being recorded, recording format,
etc. According to various examples, data storage attributes 345
include file storage conventions (format, naming convention, dating
convention), storage location, archival attributes, expiry
attributes, etc.
[0062] A preset query data model 349 includes a query name 351
and/or identifier, an accessed data collection 353 such as data
segments involved (models, databases/cubes, tables, etc.), access
security attributes 355 included who has what type of access, and
refresh attributes 357 such as the expiry of the query, refresh
frequency, etc. Other fields such as push-pull preferences can also
be included to identify an auto push reporting driver or a user
driven report retrieval system.
[0063] FIG. 4 illustrates examples of queries that can be performed
to obtain data associated with neuro-response data collection.
According to various examples, queries are defined from general or
customized scripting languages and constructs, visual mechanisms, a
library of preset queries, diagnostic querying including drill-down
diagnostics, and eliciting what if scenarios. According to various
examples, subject attributes queries 415 may be configured to
obtain data from a neuro-informatics repository using a location
417 or geographic information, session information 421 such as
timing information for the data collected. Location information 423
may also be collected. In some examples, a neuro-response data
collection mechanism includes GPS or other location detection
mechanisms. Demographics attributes 419 include household income,
household size and status, education level, age of kids, etc.
[0064] Other queries may retrieve stimulus material recorded based
on shopping preferences of subject participants, countenance,
physiological assessment, completion status. For example, a user
may query for data associated with product categories, products
shopped, shops frequented, subject eye correction status, color
blindness, subject state, signal strength of measured responses,
alpha frequency band ringers, muscle movement assessments, segments
completed, etc.
[0065] Response assessment based queries 437 may include attention
scores 439, emotion scores, 441, retention scores 443, and
effectiveness scores 445. Such queries may obtain materials that
elicited particular scores. Response measure profile based queries
may use mean measure thresholds, variance measures, number of peaks
detected, etc. Group response queries may include group statistics
like mean, variance, kurtosis, p-value, etc., group size, and
outlier assessment measures. Still other queries may involve
testing attributes like test location, time period, test repetition
count, test station, and test operator fields. A variety of types
and combinations of types of queries can be used to efficiently
extract data.
[0066] FIG. 5 illustrates examples of reports that can be
generated. According to various examples, client assessment summary
reports 501 include effectiveness measures 503, component
assessment measures 505, and neuro-response data collection
measures 507. Effectiveness assessment measures include composite
assessment measure(s), industry/category/client specific placement
(percentile, ranking, etc.), actionable grouping assessment such as
removing material, modifying segments, or fine tuning specific
elements, etc, and the evolution of the effectiveness profile over
time. In particular examples, component assessment reports include
component assessment measures like attention, emotional engagement
scores, percentile placement, ranking, etc. Component profile
measures include time based evolution of the component measures and
profile statistical assessments. According to various examples,
reports include the number of times material is assessed,
attributes of the multiple presentations used, evolution of the
response assessment measures over the multiple presentations, and
usage recommendations.
[0067] According to various examples, client cumulative reports 511
include media grouped reporting 513 of all stimulus assessed,
campaign grouped reporting 515 of stimulus assessed, and
time/location grouped reporting 517 of stimulus assessed. According
to various examples, industry cumulative and syndicated reports 521
include aggregate assessment responses measures 523, top performer
lists 525, bottom performer lists 527, outliers 529, and trend
reporting 531. In particular examples, tracking and reporting
includes specific products, categories, companies, brands.
[0068] FIG. 6 illustrates one example of evaluation of stimulus
presentation in a virtual reality environment. At 601, user
information is received from a subject provided with a
neuro-response data collection mechanism. According to various
examples, the subject provides data including age, gender, income,
location, interest, ethnicity, etc. At 603, stimulus material is
received. In particular examples, stimulus material is received
from companies, firms, individuals, etc., seeking to evaluate their
products, product labels, displays, brochures, services, offerings,
etc., in a virtual reality environment. In particular examples,
stimulus material is dynamically generated using information
provided by advertisers. According to various examples, a virtual
reality environment appropriate for the stimulus material is
selected and customized at 605. Virtual reality environments may
include super market aisles, shopping centers, store shelves,
showrooms, trade show floors, offices, etc.
[0069] At 607, stimulus material is integrated into a virtual
reality environment and presented to a user. At 609, interaction
data is received from users exposed to stimulus material.
Interaction data may be received from haptic gloves, platforms,
sensors, cameras, microphones, platforms, magnetic fields,
controllers, etc.
[0070] At 611, neuro-response data is received from the subject
neuro-response data collection mechanism. In some particular
examples, EEG, EOG, pupillary dilation, facial emotion encoding
data, video, images, audio, GPS data, etc., can all be transmitted
from the subject to a neuro-response data analyzer. In particular
examples, only EEG data is transmitted. At 613, stimulus material
and the virtual reality environment is modified based on user
interaction. In particular examples, products may be manipulated by
the user in the virtual reality environment. According to various
examples, stimulus material and/or the virtual reality environment
can also be modified based on neuro-response data at 615. In
particular examples, if a user is determined to be losing interest
in a product, a different product may be presented. Alternatively,
a different environment displaying the product may be presented
after a transition from one store to another. According to various
examples, neuro-response and associated data is transmitted
directly from an EEG cap wide area network interface to a data
analyzer. In particular examples, neuro-response and associated
data is transmitted to a computer system that then performs
compression and filtering of the data before transmitting the data
to a data analyzer over a network.
[0071] According to various examples, data is also passed through a
data cleanser to remove noise and artifacts that may make data more
difficult to interpret. According to various examples, the data
cleanser removes EEG electrical activity associated with blinking
and other endogenous/exogenous artifacts. Data cleansing may be
performed before or after data transmission to a data analyzer.
[0072] At 617, neuro-response data is synchronized with timing,
environment, and other stimulus material data. In particular
examples, neuro-response data is synchronized with a shared clock
source. According to various examples, neuro-response data such as
EEG and EOG data is tagged to indicate what the subject is viewing
or listening to at a particular time.
[0073] At 619, data analysis is performed. Data analysis may
include intra-modality response synthesis and cross-modality
response synthesis to enhance effectiveness measures. It should be
noted that in some particular instances, one type of synthesis may
be performed without performing other types of synthesis. For
example, cross-modality response synthesis may be performed with or
without intra-modality synthesis.
[0074] A variety of mechanisms can be used to perform data analysis
609. In particular examples, a stimulus attributes repository is
accessed to obtain attributes and characteristics of the stimulus
materials, along with purposes, intents, objectives, etc. In
particular examples, EEG response data is synthesized to provide an
enhanced assessment of effectiveness. According to various
examples, EEG measures electrical activity resulting from thousands
of simultaneous neural processes associated with different portions
of the brain. EEG data can be classified in various bands.
According to various examples, brainwave frequencies include delta,
theta, alpha, beta, and gamma frequency ranges. Delta waves are
classified as those less than 4 Hz and are prominent during deep
sleep. Theta waves have frequencies between 3.5 to 7.5 Hz and are
associated with memories, attention, emotions, and sensations.
Theta waves are typically prominent during states of internal
focus.
[0075] Alpha frequencies reside between 7.5 and 13 Hz and typically
peak around 10 Hz. Alpha waves are prominent during states of
relaxation. Beta waves have a frequency range between 14 and 30 Hz.
Beta waves are prominent during states of motor control, long range
synchronization between brain areas, analytical problem solving,
judgment, and decision making Gamma waves occur between 30 and 60
Hz and are involved in binding of different populations of neurons
together into a network for the purpose of carrying out a certain
cognitive or motor function, as well as in attention and memory.
Because the skull and dermal layers attenuate waves in this
frequency range, brain waves above 75-80 Hz are difficult to detect
and are often not used for stimuli response assessment.
[0076] However, the techniques and mechanisms of the present
disclosure recognize that analyzing high gamma band (kappa-band:
Above 60 Hz) measurements, in addition to theta, alpha, beta, and
low gamma band measurements, enhances neurological attention,
emotional engagement and retention component estimates. In
particular examples, EEG measurements including difficult to detect
high gamma or kappa band measurements are obtained, enhanced, and
evaluated. Subject and task specific signature sub-bands in the
theta, alpha, beta, gamma and kappa bands are identified to provide
enhanced response estimates. According to various examples, high
gamma waves (kappa-band) above 80 Hz (typically detectable with
sub-cranial EEG and/or magnetoencephalography) can be used in
inverse model-based enhancement of the frequency responses to the
stimuli.
[0077] Various examples of the present disclosure recognize that
particular sub-bands within each frequency range have particular
prominence during certain activities. A subset of the frequencies
in a particular band is referred to herein as a sub-band. For
example, a sub-band may include the 40-45 Hz range within the gamma
band. In particular examples, multiple sub-bands within the
different bands are selected while remaining frequencies are band
pass filtered. In particular examples, multiple sub-band responses
may be enhanced, while the remaining frequency responses may be
attenuated.
[0078] An information theory based band-weighting model is used for
adaptive extraction of selective dataset specific, subject
specific, task specific bands to enhance the effectiveness measure.
Adaptive extraction may be performed using fuzzy scaling. Stimuli
can be presented and enhanced measurements determined multiple
times to determine the variation profiles across multiple
presentations. Determining various profiles provides an enhanced
assessment of the primary responses as well as the longevity
(wear-out) of the marketing and entertainment stimuli. The
synchronous response of multiple individuals to stimuli presented
in concert is measured to determine an enhanced across subject
synchrony measure of effectiveness. According to various examples,
the synchronous response may be determined for multiple subjects
residing in separate locations or for multiple subjects residing in
the same location.
[0079] Although a variety of synthesis mechanisms are described, it
should be recognized that any number of mechanisms can be
applied--in sequence or in parallel with or without interaction
between the mechanisms.
[0080] Although intra-modality synthesis mechanisms provide
enhanced significance data, additional cross-modality synthesis
mechanisms can also be applied. A variety of mechanisms such as
EEG, Eye Tracking, GSR, EOG, and facial emotion encoding are
connected to a cross-modality synthesis mechanism. Other mechanisms
as well as variations and enhancements on existing mechanisms may
also be included. According to various examples, data from a
specific modality can be enhanced using data from one or more other
modalities. In particular examples, EEG typically makes frequency
measurements in different bands like alpha, beta and gamma to
provide estimates of significance. However, the techniques of the
present disclosure recognize that significance measures can be
enhanced further using information from other modalities.
[0081] For example, facial emotion encoding measures can be used to
enhance the valence of the EEG emotional engagement measure. EOG
and eye tracking saccadic measures of object entities can be used
to enhance the EEG estimates of significance including but not
limited to attention, emotional engagement, and memory retention.
According to various examples, a cross-modality synthesis mechanism
performs time and phase shifting of data to allow data from
different modalities to align. In some examples, it is recognized
that an EEG response will often occur hundreds of milliseconds
before a facial emotion measurement changes. Correlations can be
drawn and time and phase shifts made on an individual as well as a
group basis. In other examples, saccadic eye movements may be
determined as occurring before and after particular EEG responses.
According to various examples, time corrected GSR measures are used
to scale and enhance the EEG estimates of significance including
attention, emotional engagement and memory retention measures.
[0082] Evidence of the occurrence or non-occurrence of specific
time domain difference event-related potential components (like the
DERP) in specific regions correlates with subject responsiveness to
specific stimulus. According to various examples, ERP measures are
enhanced using EEG time-frequency measures (ERPSP) in response to
the presentation of the marketing and entertainment stimuli.
Specific portions are extracted and isolated to identify ERP, DERP
and ERPSP analyses to perform. In particular examples, an EEG
frequency estimation of attention, emotion and memory retention
(ERPSP) is used as a co-factor in enhancing the ERP, DERP and
time-domain response analysis.
[0083] EOG measures saccades to determine the presence of attention
to specific objects of stimulus. Eye tracking measures the
subject's gaze path, location and dwell on specific objects of
stimulus. According to various examples, EOG and eye tracking is
enhanced by measuring the presence of lambda waves (a
neurophysiological index of saccade effectiveness) in the ongoing
EEG in the occipital and extra striate regions, triggered by the
slope of saccade-onset to estimate the significance of the EOG and
eye tracking measures. In particular examples, specific EEG
signatures of activity such as slow potential shifts and measures
of coherence in time-frequency responses at the Frontal Eye Field
(FEF) regions that preceded saccade-onset are measured to enhance
the effectiveness of the saccadic activity data.
[0084] According to various examples, facial emotion encoding uses
templates generated by measuring facial muscle positions and
movements of individuals expressing various emotions prior to the
testing session. These individual specific facial emotion encoding
templates are matched with the individual responses to identify
subject emotional response. In particular examples, these facial
emotion encoding measurements are enhanced by evaluating
inter-hemispherical asymmetries in EEG responses in specific
frequency bands and measuring frequency band interactions. The
techniques of the present disclosure recognize that not only are
particular frequency bands significant in EEG responses, but
particular frequency bands used for communication between
particular areas of the brain are significant. Consequently, these
EEG responses enhance the EMG, graphic and video based facial
emotion identification.
[0085] Integrated responses are generated at 621. According to
various examples, the data communication device transmits data to
the response integration using protocols such as the File Transfer
Protocol (FTP), Hypertext Transfer Protocol (HTTP) along with a
variety of conventional, bus, wired network, wireless network,
satellite, and proprietary communication protocols. The data
transmitted can include the data in its entirety, excerpts of data,
converted data, and/or elicited response measures. According to
various examples, data is sent using telecommunications, wireless,
Internet, satellite, or any other communication mechanisms that is
capable of conveying information from multiple subject locations
for data integration and analysis. The mechanism may be integrated
in a set top box, computer system, receiver, mobile device,
etc.
[0086] In particular examples, the data communication device sends
data to the response integration system. According to various
examples, the response integration system combines analyzed and
enhanced responses to the stimulus material while using information
about stimulus material attributes. In particular examples, the
response integration system also collects and integrates user
behavioral and survey responses with the analyzed and enhanced
response data to more effectively measure and track neuro-responses
to stimulus materials. According to various examples, the response
integration system obtains attributes such as requirements and
purposes of the stimulus material presented.
[0087] Some of these requirements and purposes may be obtained from
a variety of databases. According to various examples, the response
integration system also includes mechanisms for the collection and
storage of demographic, statistical and/or survey based responses
to different entertainment, marketing, advertising and other
audio/visual/tactile/olfactory material. If this information is
stored externally, the response integration system can include a
mechanism for the push and/or pull integration of the data, such as
querying, extraction, recording, modification, and/or updating.
[0088] The response integration system can further include an
adaptive learning component that refines user or group profiles and
tracks variations in the neuro-response data collection system to
particular stimuli or series of stimuli over time. This information
can be made available for other purposes, such as use of the
information for presentation attribute decision making. According
to various examples, the response integration system builds and
uses responses of users having similar profiles and demographics to
provide integrated responses at 621. In particular examples,
stimulus and response data is stored in a repository at 623 for
later retrieval and analysis.
[0089] According to various examples, various mechanisms such as
the data collection mechanisms, the intra-modality synthesis
mechanisms, cross-modality synthesis mechanisms, etc. are
implemented on multiple devices. However, it is also possible that
the various mechanisms be implemented in hardware, firmware, and/or
software in a single system. FIG. 7 provides one example of a
system that can be used to implement one or more mechanisms. For
example, the system shown in FIG. 7 may be used to implement a data
analyzer.
[0090] According to particular examples, a system 700 suitable for
implementing particular examples of the present disclosure includes
a processor 701, a memory 703, an interface 711, and a bus 715
(e.g., a PCI bus). When acting under the control of appropriate
software or firmware, the processor 701 is responsible for such
tasks such as pattern generation. Various specially configured
devices can also be used in place of a processor 701 or in addition
to processor 701. The complete implementation can also be done in
custom hardware. The interface 711 is typically configured to send
and receive data packets or data segments over a network.
Particular examples of interfaces the device supports include host
bus adapter (HBA) interfaces, Ethernet interfaces, frame relay
interfaces, cable interfaces, DSL interfaces, token ring
interfaces, and the like.
[0091] In addition, various very high-speed interfaces may be
provided such as fast Ethernet interfaces, Gigabit Ethernet
interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI
interfaces and the like. Generally, these interfaces may include
ports appropriate for communication with the appropriate media. In
some cases, they may also include an independent processor and, in
some instances, volatile RAM. The independent processors may
control such communications intensive tasks as data synthesis.
[0092] According to particular examples, the system 700 uses memory
703 to store data, algorithms and program instructions. The program
instructions may control the operation of an operating system
and/or one or more applications, for example. The memory or
memories may also be configured to store received data and process
received data.
[0093] Because such information and program instructions may be
employed to implement the systems/methods described herein, the
present disclosure relates to tangible, machine readable media that
include program instructions, state information, etc. for
performing various operations described herein. Examples of
machine-readable media include, but are not limited to, magnetic
media such as hard disks, floppy disks, and magnetic tape; optical
media such as CD-ROM disks and DVDs; magneto-optical media such as
optical disks; and hardware devices that are specially configured
to store and perform program instructions, such as read-only memory
devices (ROM) and random access memory (RAM). Examples of program
instructions include both machine code, such as produced by a
compiler, and files containing higher level code that may be
executed by the computer using an interpreter.
[0094] Although the foregoing disclosure has been described in some
detail for purposes of clarity of understanding, it will be
apparent that certain changes and modifications may be practiced
within the scope of the appended claims. Therefore, the present
examples are to be considered as illustrative and not restrictive
and the disclosure is not to be limited to the details given
herein, but may be modified within the scope and equivalents of the
appended claims.
* * * * *