U.S. patent application number 17/553482 was filed with the patent office on 2022-06-16 for audio analysis of body worn camera.
This patent application is currently assigned to Truleo, Inc.. The applicant listed for this patent is Truleo, Inc. Invention is credited to Colin BROCHTRUP, Matthew GOLDEY, Tejas SHASTRY, Svyatoslav VERGUN.
Application Number | 20220189501 17/553482 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220189501 |
Kind Code |
A1 |
SHASTRY; Tejas ; et
al. |
June 16, 2022 |
AUDIO ANALYSIS OF BODY WORN CAMERA
Abstract
Machine natural language processing to analyze language in
apparatus, systems, and methods of using are provided. Audio from
camera footage can be transcribed in one exemplary method includes
extracting at least one audio segment from a body camera video
track, detecting voice activity to identify starting and ending
timestamps of voice, transcribing the at least one audio segment to
identify and separate audio of at least a first speaker, and
scoring the audio of the first speaker to identify interactions of
interest. Audio could be analyzed and scored to record verbal
performance, respectfulness, wellness, etc. and speakers from the
audio can be detected.
Inventors: |
SHASTRY; Tejas; (Chicago,
IL) ; VERGUN; Svyatoslav; (Morton Grove, IL) ;
BROCHTRUP; Colin; (Chicago, IL) ; GOLDEY;
Matthew; (Zionsville, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Truleo, Inc, |
Chicago |
IL |
US |
|
|
Assignee: |
Truleo, Inc.
Chicago
IL
|
Appl. No.: |
17/553482 |
Filed: |
December 16, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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63126368 |
Dec 16, 2020 |
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63143538 |
Jan 29, 2021 |
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63264151 |
Nov 16, 2021 |
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International
Class: |
G10L 25/57 20060101
G10L025/57; G10L 15/26 20060101 G10L015/26; G10L 25/87 20060101
G10L025/87; G10L 15/18 20060101 G10L015/18; G10L 17/00 20060101
G10L017/00 |
Claims
1. A method of using machine natural language processing to analyze
language in transcribed camera footage comprising: extracting at
least one audio segment from a body camera video track; detecting
voice activity to identify starting and ending timestamps of voice;
transcribing the at least one audio segment to identify and
separate audio of at least one speaker; scoring the audio of the at
least one speaker to identify interactions of interest.
2. The method of claim 1 wherein the at least one speaker is a
figure of authority, including one of a: police officer, emergency
technician, guard, soldier, doctor, or first responder.
3. The method of claim 2 wherein the interactions of interest
include whether the figure of authority is escalating or
de-escalating a situation.
4. The method of claim 2 wherein the interactions of interest
include whether the figure of authority is using respectful
language or negative language.
5. The method of claim 2 wherein the scoring includes analyzing for
word disfluencies or filler words to analyze speaker
confidence.
6. The method of claim 2 wherein the figure of authority is
identified based on voice quality.
7. The method of claim 6 wherein the transcribing identifies
whether audio of at least an other speaker is included on the at
least one audio segment.
8. The method of claim 1 wherein the method further includes:
identifying events that may have occurred in the body camera video
track based on language cues in the at least one audio segment.
9. The method of claim 8 wherein the method further includes:
compressing the body camera video track based on the events.
10. The method of claim 1 wherein the method is performed in
real-time.
11. A system of using machine natural language processing to
analyze language in transcribed camera footage comprising: an audio
and language analyzer, operable to: extract at least one audio
segment from a body camera video track; detect voice activity to
identify starting and ending timestamps of voice; transcribe the at
least one audio segment to identify and separate audio of at least
one speaker; score the audio of the at least one speaker to
identify interactions of interest.
12. The system of claim 11 wherein the at least one speaker is a
figure of authority, including one of a: police officer, emergency
technician, guard, soldier, doctor, or first responder.
13. The system of claim 12 wherein the interactions of interest
include whether the figure of authority is escalating or
de-escalating a situation.
14. The system of claim 12 wherein the interactions of interest
include whether the figure of authority is using respectful
language or negative language.
15. The system of claim 12 further comprising at least one other
speaker and wherein the interactions of interest include whether
the at least one other speaker is using negative language.
16. The system of claim 12 wherein the score includes an analysis
for word disfluencies or filler words to analyze speaker
confidence.
17. The system of claim 12 wherein the figure of authority is
anonymously identified based on voice quality.
18. The system of claim 16 wherein the transcription identifies
whether audio of at least an other speaker is included on the at
least one audio segment.
19. The system of claim 18 wherein the audio of the at least other
speaker is either selectively removed or analyzed by the
system.
20. The system of claim 11 wherein the system is further operable
to: identify events that may have occurred in the body camera video
track based on language cues in the at least one audio segment.
21. The system of claim 20 wherein the system is further operable
to: compress the body camera video track based on the events.
22. The system of claim 11 wherein the system operates in
real-time.
Description
[0001] In one aspect, apparatus, systems, and/or methods of
analysis of audio from body worn cameras, including through natural
language processing is detailed. The audio can be analyzed in
real-time, such as, for example, during a police encounter, or
alternatively, at least a portion of the audio can be analyzed at a
later time.
[0002] In exemplary scenarios involving police officers, while
nearly 50% of police officers wear body cameras, and while hundreds
of hours of footage is recorded each day, only a fraction of the
footage is ever analyzed and/or reviewed. As many police
departments look for better oversight and training of their police
force, few departments are able to leverage body camera data as a
source of insight into their interactions with the community.
[0003] The apparatus, systems, methods, and processes described
herein offer departments an efficient and effective way of
analyzing body camera data. The analysis can be utilized in many
aspects, including efforts to improve training tactics, provide
better oversight, etc.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The accompanying drawings, which are incorporated herein and
constitute part of this specification, illustrate the presently
preferred embodiments of the disclosure, and, together with the
general description given above and the detailed description given
below, serve to explain exemplary features of the disclosure. In
the drawings:
[0005] FIG. 1 shows an exemplary end-to-end flow of body camera or
cam audio analysis.
[0006] FIG. 2 shows Table 1 with example features extracted via
intent and sentiment analysis of a body cam transcription
segment.
[0007] FIG. 3 shows an example analysis showing intent labels and
sentiment polarity of an event extracted from body camera
audio.
[0008] FIGS. 4A and 4B show an exemplary analysis of transcribed
audio and sentiment summaries.
[0009] FIGS. 5A and 5B show an aggregate summary of metrics and
combinations with top metrics.
[0010] FIG. 6 shows exemplary summaries across various
officers.
[0011] FIG. 7 shows exemplary training of an intent and entity
model.
[0012] FIG. 8 shows exemplary weights of positive coefficients.
[0013] FIG. 9 shows exemplary weights of negative coefficients.
DETAILED DESCRIPTION
[0014] In the drawings, like numerals indicate like elements
throughout. Certain terminology is used herein for convenience only
and is not to be taken as limiting. The terminology includes the
words specifically mentioned, derivatives thereof, and words of
similar import. The embodiments illustrated below are not intended
to be exhaustive or to limit to the precise form disclosed. These
embodiments are chosen and described to best explain the
principles, application, and practical use, and to enable others
skilled in the art to best utilize the present disclosure.
[0015] The present disclosure details analysis of audio, such as
from video tracks and/or real-time interactions from audio or video
recordings. The analyses detailed herein is primarily focused on
the audio analysis of interactions. Several examples provided
herein involve body cameras, also termed body worn cameras, and
police officers. These scenarios are presented as exemplary only
and not intended to limit the disclosure in any manner. This
disclosure could be applied without limitation to audio from other
sources, with such audio able to be analyzed from any other
scenario and processed similarly. For example, such alternative
scenarios could not involve police officers, could be from cameras
that are not body worn, or could involve altercations. In other
examples, the body cam can be worn by an emergency technician, a
firefighter, a security guard, a citizen instead of a police
officer, police during interview of a suspect, interactions in a
jail or prison, such as, for example, between guards and inmates or
between inmates, or other person. Additionally, the body cam can be
worn by an animal or be positioned on or in an object, such as a
vehicle. It is understood, therefore, that this disclosure is not
limited to the particular embodiments disclosed, but it is intended
to cover modifications within the spirit and scope of the present
disclosure as defined by the appended claims. The same behavior and
emotional sentiments captured can also be applied to scenarios
including, but not limited to, conversations within sales teams,
conversations involving financial transactions, conversations
between counterparties where one party may be privy to valuable
information that they cannot share with the other, or conversations
between counterparties where one holds a degree of power (legal,
authoritative, managerial, etc.) over another.
[0016] In at least one example detailed herein involving police
officers, research shows that language used in police interactions
as measured by humans reviewing body worn camera (BWC) video shows
disparities in officer behavior based on the use of respectful or
disrespectful language (see, e.g.,
https://www.pnas.org/content/114/25/6521). Simply put: using more
respectful language leads to fewer escalated scenarios. However,
the vast amount of footage to be reviewed to make determinations of
the use of respectful language across a department is nearly
impossible to process with solely human review.
[0017] In at least one aspect, the present disclosure details
transcription of BWC audio and separation of the audio into
individualized, anonymous speakers. In at least one example, the
speaker wearing the camera is tagged anonymously as the officer.
The systems and methods described involve natural language
processing (NLP) models operable to run on the speaker-separated
transcript, identifying key phrases associated with risky or
respectful interactions. Features are weighted based on a
department's preference for detection (e.g., directed profanity is
worse than informality). In addition, the present systems and
methods tag events, like arrests and use of force, as a further
dimension to analyze risk. In at least one embodiment, the officer
identification allows selectively transcribing and/or analyzing
only either the officer or only transcribing and/or analyzing the
civilian (or other non-officer) audio. While there may be several
reasons for allowing selective transcription or analyzing, in at
least one instance, this option could be important for legal
mandates, including, to not analyze, or specifically redact,
civilian or officer audio in relevant cases. In other aspects and
exemplary scenarios, redaction of officer, civilian, or other audio
may apply to sections or entire segments of transcripts or
selections.
[0018] In at least one aspect, the detailed systems and methods
utilize NLP models that use a modern architecture termed a
"transformer". These models learn based on context, not keywords.
Thus, seeing a word used in context, the models can automatically
extrapolate synonyms or other potential variations of the word. In
this way, the models of the present detailed systems and methods
are able to capture key phrases associated with risk and respect
with only a handful of examples.
Officer Detection
[0019] Given a set of anonymous speakers, it is nearly impossible
to figure out who the officer is on conventional methods like voice
fingerprinting. Instead, in at least one aspect, the present
detailed systems and methods use an assumption common to body-worn
camera usage: that the person wearing the camera is the
officer.
[0020] The present detailed systems and methods measure the voice
quality of each speaker using a set of metrics that include: [0021]
Short Time Intelligibility Measure (stoi) [0022] time domain
segmental signal to noise ratio (SNRseg) [0023] frequency weighted
segmental signal to noise ratio (fwSNRseg) [0024] Normalized
Covariance Metric (ncm)
[0025] The highest signal quality is labeled as a potential
officer. In some cases, multiple speakers may still have high
quality signal, if for example the officer is facing away from the
microphone and a civilian is talking directly. In these cases, the
present detailed systems and methods use an additional text-based
classifier that is trained on officer-specific language
patterns.
Scalable, Compliant Ingestion of Body Camera Audio
[0026] FIG. 1 shows an exemplary method for reducing the footprint
of data for efficient analysis. As many police departments produce
hundreds to thousands of hours per day of body camera recordings
across their police force, it is challenging, if not prohibitive,
to process such a large amount of data in a cost effective
form.
[0027] FIG. 1 shows an exemplary analysis flowchart 100 of body
camera video footage 110. The footage is first processed such that
the audio track is isolated from the video at 120. Discarding video
information initially by retaining only the audio can greatly
reduce the cost of and increasing the speed of transferring data
and analyzing data with machine learned models. For example, audio
may only be a fraction (e.g., for example, 5%) of the information
in a selection of footage, which could, in some instances, markedly
increase the speed of transfer and analysis while markedly reducing
the cost. Next, the audio 130 is streamed through a voice activity
detection model at 140, which identifies the starting and ending
timestamps within the audio track where voice is detected at 150.
These sections of audio 150 are streamed into an automatic speech
recognition model that outputs the text transcription of the
selection of audio at 160. At 170, speaker diarization is performed
to create speaker segmented segments at 180. At 190, intent and
sentiment classification is performed and a body cam audio analysis
report is issued at 200.
[0028] In at least one embodiment, during the entire pipeline
process, audio is only retained in temporary memory and not written
to disk, enabling a privacy-compliant method for transcribing
sensitive audio. The separated audio data can be streamed in
real-time for analysis or from an already stored file. Subsequent
analysis of the file, including based on the features of interest
documented below, can be used as a determination of whether a
recording should be maintained long-term, including if it contains
data of interest. In at least one embodiment, the original audio
from the video file is added in "long term storage", and can be
analyzed at a subsequent time. In one example, the analysis
documented could be used as a way to determine videos of interest.
Here, for example, a video termed "of interest" could be retained
for long term storage, while a video not termed "of interest" could
be deleted, classified, or recommended for deletion, including for
further analysis before deletion. Additionally, in at least one
embodiment, metadata relating to the officer wearing the camera,
the date and time, and location data can be maintained along with
the corresponding audio during processing.
Speaker Diarization of Audio
[0029] In at least one exemplary embodiment, as the audio stream is
transcribed into text, each word is assigned a start and stop time.
Segments of the audio transcript are generated by the speech
recognition model based on natural pauses in conversation.
[0030] In at least one embodiment, the audio and text is then
further streamed to a speaker diarization model that analyzes the
audio stream for speaker changes, as shown at 170 in FIG. 1. If a
speaker change occurs and is measured by the model, the text is
periodically re-segmented such that segments contain only a single
speaker, e.g. at 180 in FIG. 1. In at least one embodiment, this
process is performed after transcription, rather than during or
before, such that noises and other disruptions that are common in
body cam audio do not adversely affect a continuous single speaker
transcription. If diarization is performed before transcription,
these breaks can break the continuity of the transcription in a way
that can lower transcription accuracy.
Intent and Entity Classification and Sentiment Analysis of
Transcribed Audio
[0031] In at least one embodiment, after transcription and
diarization, the speaker-separated text transcription is analyzed
through an intent classification model. The intent classification
model utilizes a deep-learned transformer architecture and, in at
least one example, is trained from tagged examples of intent types
specific for police interactions. Specifically, in at least one
exemplary embodiment, intent labels classify words or phrases as:
`aggression`, `anxiety`, `apology`, `arrest`, `bias`, `bragging`,
`collusion`, `de-escalation`, `fear`, `general`, `gratitude`,
`manipulation`, `mistrust`, `reassurance`, `secrecy`, etc. The
classifier can also tag "events" by words and phrases, in at least
one example, effectively tagging as the consequence of a speaker's
intent. In at least one exemplary scenario, such a classifier can
identify "get your hands off of me" as a "use of force" event, or
"you have the right to remain silent" as an "arrest" event.
[0032] In one aspect, the intent classification leverages types of
features to determine the correct intent with one or more models or
model layers. First, the entire text of the segment is chunked into
words up to a maximum defined sequence length. Second, each segment
of text is run through one or more transformer-based models. Each
transformer model either outputs a single intent label (as
mentioned above) or a set of entity labels (such as person,
address, etc.). For models where a single intent label is captured,
that single intent label is used as is. For models where entity
labels are captured, those captured labels are subject to further
analysis by a layer of the model that determines the final intent
label. Many transformer architectures lend themselves to stacking
similar model layers. Thus, the intent and entity models can be
combined for some or all of the labels listed above, such that a
single model performs both tasks and outputs a single intent
label.
[0033] In at least one embodiment, alongside the intent classifier,
a sentiment analysis model tags each segment in three ways:
[0034] First, in at least one exemplary embodiment, the labels of
`very positive`, `positive`, `neutral`, `negative`, and `very
negative` are output by the sentiment classifier trained in a
similar way to the intent classifier, each with a probability. The
aggregate probability of "positive" labels is subtracted from the
aggregate probability of "negative" labels to produce a sentiment
polarity. The probability of the top label subtracted from 1 is
used as a "subjectivity" score. The subjectivity score gives an
estimate of how likely it is that two human observers would differ
in opinion on the interpretation of the polarity. Thus, sentiment
labels can be filtered for ones with "low subjectivity", which may
provide more "objective" negative or "objective" positive
sentiments and be used to objectively quantify the sentiment of an
event. Where highly objective negative statements can identify
interactions of interest where either an officer or a person of
interest is escalating a situation, likewise, highly objective
positive statements can identify successful de-escalation of a
situation (see, for example, the conversation in FIGS. 4A and
4B).
[0035] Second, in at least one exemplary embodiment, the
transcribed text output is analyzed for word disfluencies.
Disfluencies are instances of filler words (uh, uhms, so) and
stutters. These disfluencies can be an indicator of speaker
confidence, and the log-normalized ratio of disfluencies in each
segment compared to the number of words is output as a second
sentiment metric.
[0036] Third, entities detected by the intent classifier previously
mentioned can be given manual weights that correlate with positive
or negative sentiment, such as an entity capturing "profanity"
weighted as "very negative" with a score of -1.0.
[0037] An example output of these metrics for a particular phrase
is shown at 300 in Table 1 in FIG. 2. The phrase "calm down sir uh
calm down" is transcribed and labeled for intent (de-escalation),
sentiment label (positive), sentiment polarity (0.25), sentiment
subjectivity (0.11), and word disfluency (7.0).
Identification of De-Escalation Events and Analysis of Bias
[0038] In at least one exemplary embodiment, the combination of
sentiment and intent labels across speaker-separated segments of
the body cam audio transcript enables the identification of
de-escalation events and their efficacy. FIG. 3 shows an example of
an event analyzed by one exemplary method described in at least one
aspect herein. FIG. 3 shows an exemplary method that involves a
communication between police and community participants, where both
instances of positive and negative sentiment can be seen (note
extensions from centralized vertical line). Following negative
sentiment, in this exemplary embodiment shown in FIG. 3,
de-escalation phrases are used to attempt to resolve sentiment to a
neutral or positive position. In the exemplary event shown in FIG.
3, several de-escalation events are necessary before sentiment
stabilizes, but the event eventually escalates to an arrest (note
third from bottom entry).
[0039] In FIG. 3, the time from the initial negative sentiment
event to the arrest can be determined as the "de-escalation time",
and, for example, the transcript of the segments of de-escalation
can be further analyzed and compared to other events to determine
which phrases lead to the fastest and most successful
de-escalations.
[0040] For events such as the one shown as represented in FIG. 3, a
police report is typically generated documenting features such as
the gender and race of the suspects or participants involved. The
report and analysis can provide joint value in two ways. Features
within the transcript that are identified, such as persons,
addresses, weapons, etc., for example, can be used to populate the
report automatically. Second, the report data can be compared
against event analyses such as the one shown in FIG. 3, to identify
whether sentiment polarity or word disfluency differs between
interactions of participants of different races, which, among other
things, can be indications of racial bias.
[0041] Further, since the features extracted effectively classify
body cam videos as ones with "content of interest" (including,
e.g., strongly negative or positive sentiment, a large number of
sentences with strong emotions, such as aggression, misconduct,
etc.), the analysis performed by the engine can be used as a method
to identify videos that should be retained long term and/or enable
departments to delete videos that are not of interest, e.g., due to
lack of interesting content. This deletion could save storage costs
for police departments.
[0042] Exemplary usage of the analysis is shown in FIGS. 4A-6 in
the form of summarized reports generated from the analysis on audio
from FIG. 3. FIG. 4a shows an analysis of the transcribed audio
where particular phrases and entities are tagged as positive
sentiment, negative sentiment, or various behaviors and emotions.
The average sentiment polarity as described above can be
interpreted as a "respect score", and a summary of the number of
times respectful vs disrespectful interactions can be generated as
shown in FIG. 4b. These aggregate metrics enable tracking, for
example: (1) the overall respectfulness of officers over time by
comparing the number of respectful vs disrespectful interactions,
e.g., month over month, and (2) the ways in which officers are
being respectful or disrespectful in an effort to expose areas of
improvement for police training, etc.
[0043] FIGS. 5A and 5B show interpretations of the FIG. 3 analysis
from FIGS. 4A and 4B. As previously mentioned, points of negative
sentiment can be identified as beginning of event escalations, and
contrastly, points of positive sentiment can be identified as end
of escalations. The classified behaviors and emotions between those
points can be identified as de-escalation tactics, and the efficacy
of these tactics can be measured by comparing the time required for
sentiment to resolve from negative to positive. FIG. 5A shows an
aggregate summary of these metrics and, combined with a list of top
metrics in FIG. 5B, a department can utilize these metrics to
identify police tactics, behaviors, and phrases that are most
successful at de-escalation of events.
[0044] The timeline of events in FIG. 3 can also be summarized as
shown in FIG. 6 across various officers. By summarizing the number
of negative sentiments, undesirable behaviors and emotions (such as
aggression), and other features, the analysis conducted by the
methods described herein can act as an early warning system for
officers that may either be (1) conveying negative sentiment often
and may be unnecessarily escalating situations, or (2) receiving
negative sentiment from their interactions and may be at risk for
burn-out.
Analyzing Risky/Respectful Language with Intent and Entity
Detection
[0045] In at least one exemplary embodiment, an intent classifier
identifies the event occurring (accident, arrest, etc.) and a
sentiment model simply labels the language as positive or negative.
As shown in FIG. 7, the system and methods detailed herewithin
train an intent and entity model that identifies many linguistic
features.
[0046] The system and methods detailed herewithin can utilize the
features from FIG. 7 and assign them department-tunable weights of
importance. Transcripts can be scored based on each of these
weights, and the highest risk videos can then be surfaced by
ranking based off of a single risk score. In at least one instance,
the risk score can be used to rank videos, officers, precincts, and
departments, which, for example, can surface outliers and trends.
Additionally, in at least one instance, the features of the risk
score not only score the interaction, but side effects on
participants. An analogous officer wellness model can include the
same features to score which officers may be at most risk of
wellness issues based on the same risk score. All analysis detailed
herein, including analysis of risk score, can be done in real-time
or even on the body camera device itself. An example set of weights
of positive coefficients (more risk) are shown in FIG. 8.
Additionally, an example set of weights of negative coefficients
(more respect) are shown in FIG. 9.
[0047] In at least one aspect, the present disclosure includes an
audio analysis method to identify behavior, emotion, and sentiment
within a body worn camera video. The audio detailed herein can be
analyzed in real-time or in historical fashion. The methods
detailed herewithin can perform voice activity detection on an
audio stream to reduce the amount of audio that needs to be
analyzed. Methods shown and/or described herein can identify
emotion, behavior, and sentiment using machine-learned classifiers
within the transcribed audio. Further, methods shown and/or
described herein can measure disfluencies and other voice patterns
that are used to further the analysis. Methods shown and/or
described herein can include determining which videos should be
retained long-term based on an abundance of features of interest.
Further still, systems and methods detailed herein can use natural
language processing, including via a machine learned model, to
analyze body cam audio for behavior and/or emotional sentiment.
Even further, linguistic features can be identified in the present
systems and methods. In other aspects, systems and methods detailed
herein can weight positive and negative coefficients.
[0048] In examples involving police officers, natural language
processing can be used to score officer performance,
respectfulness, wellness, etc. Further, officers can be anonymously
detected and identified. Additionally, methods and systems detailed
herein can selectively process officer or civilian audio.
[0049] The present disclosure can be understood more readily by
reference to the instant detailed description, examples, and
claims. It is to be understood that this disclosure is not limited
to the specific systems, devices, and/or methods disclosed unless
otherwise specified, as such can, of course, vary. It is also to be
understood that the terminology used herein is for the purpose of
describing particular aspects only and is not intended to be
limiting.
[0050] The instant description is provided as an enabling teaching
of the disclosure in its best, currently known aspect. Those
skilled in the relevant art will recognize that many changes can be
made to the aspects described, while still obtaining the beneficial
results of the present disclosure. It will also be apparent that
some of the desired benefits of the present disclosure can be
obtained by selecting some of the features of the present
disclosure without utilizing other features. Accordingly, those who
work in the art will recognize that many modifications and
adaptations to the present disclosure are possible and can even be
desirable in certain circumstances and are a part of the present
disclosure. Thus, the instant description is provided as
illustrative of the principles of the present disclosure and not in
limitation thereof.
[0051] As used herein, the singular forms "a," "an" and "the"
include plural referents unless the context clearly dictates
otherwise. Thus, for example, reference to a "body" includes
aspects having two or more bodies unless the context clearly
indicates otherwise.
[0052] Ranges can be expressed herein as from "about" one
particular value, and/or to "about" another particular value. When
such a range is expressed, another aspect includes from the one
particular value and/or to the other particular value. Similarly,
when values are expressed as approximations, by use of the
antecedent "about," it will be understood that the particular value
forms another aspect. It will be further understood that the
endpoints of each of the ranges are significant both in relation to
the other endpoint, and independently of the other endpoint.
[0053] As used herein, the terms "optional" or "optionally" mean
that the subsequently described event or circumstance may or may
not occur, and that the description includes instances where said
event or circumstance occurs and instances where it does not.
[0054] Although several aspects of the disclosure have been
disclosed in the foregoing specification, it is understood by those
skilled in the art that many modifications and other aspects of the
disclosure will come to mind to which the disclosure pertains,
having the benefit of the teaching presented in the foregoing
description and associated drawings. It is thus understood that the
disclosure is not limited to the specific aspects disclosed
hereinabove, and that many modifications and other aspects are
intended to be included within the scope of the appended claims.
Moreover, although specific terms are employed herein, as well as
in the claims that follow, they are used only in a generic and
descriptive sense, and not for the purposes of limiting the
described disclosure.
* * * * *
References