U.S. patent application number 14/796757 was filed with the patent office on 2015-11-05 for systems, methods, and media for finding and matching tremor signals.
The applicant listed for this patent is Henry Frisz, Xanadu Christina Halkias. Invention is credited to Henry Frisz, Xanadu Christina Halkias.
Application Number | 20150313508 14/796757 |
Document ID | / |
Family ID | 47519296 |
Filed Date | 2015-11-05 |
United States Patent
Application |
20150313508 |
Kind Code |
A1 |
Halkias; Xanadu Christina ;
et al. |
November 5, 2015 |
Systems, Methods, and Media for Finding and Matching Tremor
Signals
Abstract
System, methods, and media for finding and matching tremor
signals that: receive a first piece of tremor signal content;
identify a first plurality of tremor peaks that describe at least a
portion of the first piece of tremor signal content using a
Teager-Kaiser operator; form a first template of tremor peaks from
at least a portion of the first plurality of tremor peaks, the
first group of tremor peaks having a first group of parameters;
compare the first template and the first group of parameters with
at least one second template and a second group of parameters,
wherein the second group of parameters is based on a second set of
tremor peaks associated with a second piece of tremor content; and
identify a match between the first piece of tremor signal content
and the second piece of tremor signal content based on the
comparing.
Inventors: |
Halkias; Xanadu Christina;
(New York, NY) ; Frisz; Henry; (Huntington,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Halkias; Xanadu Christina
Frisz; Henry |
New York
Huntington |
NY
NY |
US
US |
|
|
Family ID: |
47519296 |
Appl. No.: |
14/796757 |
Filed: |
July 10, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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13180865 |
Jul 12, 2011 |
9107614 |
|
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14796757 |
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Current U.S.
Class: |
600/595 |
Current CPC
Class: |
A61B 5/1101 20130101;
A61B 5/0004 20130101; A61B 5/4082 20130101; A61B 5/7455 20130101;
A61B 5/7282 20130101; A61B 2560/0475 20130101; A61B 5/0022
20130101; A61B 5/7246 20130101; A61B 5/7235 20130101 |
International
Class: |
A61B 5/11 20060101
A61B005/11; A61B 5/00 20060101 A61B005/00 |
Claims
1. A system for finding and matching tremor signals, the system
comprising: a device configured to measure a first tremor signal; a
server configured to communicate with the device for exchanging
information therefrom, the server comprising: at least one hardware
processor configured to: receive the first tremor signal from the
device via a communication network; identify a first plurality of
tremor peaks that describe at least a portion of the first tremor
signal; generate a first template of tremor peaks from at least a
portion of the first plurality of tremor peaks, the first template
of tremor peaks having a first group of parameters; store the first
template of tremor peaks and the first group of parameters in an
electronic storage medium; compare the first template and the first
group of parameters with at least one second template and a second
group of parameters stored in the electronic storage medium,
wherein the second group of parameters is based on a second
plurality of tremor peaks associated with a second tremor signal;
generate a third template of tremor peaks upon matching the first
template with the at least one second template; and transmit the
third template to the device via the communication network.
2. The system of claim 1, wherein the first plurality of tremor
peaks and the second plurality of tremor peaks is identified using
a Teager-Kaiser operator.
3. The system of claim 1, wherein the third template of tremor
peaks is generated by reversing the polarity of the at least one
second template of tremor peaks.
4. The system of claim 1, wherein the at least one hardware
processor performs temporal windowing on the first tremor
signal.
5. The system of claim 1, wherein the at least one hardware
processor prunes the first plurality of tremor peaks after
identifying the first plurality of tremor peaks and before
generating the first template of tremor peaks.
6. The system of claim 5, wherein the pruning is based on a
threshold.
7. The system of claim 5, wherein pruning is repeated using a
different temporal window.
8. The system of claim 1, wherein matching comprises identifying at
least a portion of the first template of tremor peaks within the at
least one second template of tremor peaks.
9. The system of claim 1, wherein the device is further configured
to output an electromechanical output based on the third template
of tremor peaks.
10. The system of claim 9, wherein the electromechanical output
comprises a vibration.
11. The system of claim 9, wherein the electromechanical output
comprises an electrical current.
12. The system of claim 11, wherein the electromechanical output
further comprises a vibration.
13. The system of claim 1, wherein the first tremor signal and the
second tremor signal are associated with a specific action.
14. The system of claim 1 wherein the first tremor signal and the
second tremor signal are associated with a specific time duration.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit and is a continuation of
U.S. patent application Ser. No. 13/180,865 filed on Jul. 12, 2011.
The present application is related to co-pending, commonly assigned
U.S. patent application Ser. No. ______concurrently filed with the
present application. The contents of the above-identified
applications are incorporated by reference in their entirety as if
recited in full herein.
TECHNICAL FIELD
[0002] The disclosed subject matter relates to systems, methods,
and media for finding and matching tremor signals.
BACKGROUND
[0003] Movement disorders such as Parkinson's disease have been
increasingly affecting larger and younger portions of the global
population. These disorders are usually associated with
degeneration of the central nervous system. The most common
symptoms surrounding Parkinson's disease are movement related and
include most notably shaking, rigidity, slowness of movement and
difficulty with walking and gait.
[0004] Parkinsonian tremor has long been accepted as the most
distinct symptom that indicates the presence of the disease and its
severity, and also serves as a measure of the progress and
evolution of the disease. Most Parkinson's patients find that the
most difficult part of the disease is the induced tremor attributed
mainly to the lack of dopamine in the brain. Research has
introduced two main types of tremors that affect most Parkinson's
patients: (i) resting tremor, which occurs when an individual is
resting and can inhibit activities such as sleeping; and (ii)
action tremor, which occurs when an individual is performing a
specific task such as lifting an object.
[0005] Tremor signals are a result of the lack of dopamine in a
patient's brain, leading to errors regarding the transmission of
electrical nerve impulses through the patient's central and
peripheral nervous systems. These transmission errors, appear as
involuntary, rapid movements that generally affect a person's limbs
(e.g., arms, hands, feet, etc.). Tremor signals are typically
measured using electromyograms (EMG), accelerometers, etc.
[0006] The most current advances in medicine and science regarding
alleviating Parkinson's tremors can be classified into two main
categories: firstly, pharmaceutical options that aim to balance the
amount of dopamine in the brain and that can cause known side
effects such as painful rigidity; and secondly, the use of deep
brain implants that stimulate areas of the brain responsible fir
motor movement. However, pharmaceutical options and deep brain
implants can have an inherent danger attached to them due to the
process and surgery required to place them in the brain as well as
the fact that currently, the medical community does not have an
understanding of the causes and origin of Parkinson's disease.
[0007] Accordingly, new, non-invasive, and non-pharmaceutical
driven, techniques for managing tremors are needed.
SUMMARY
[0008] In accordance with some embodiments, systems, methods, and
media for finding and matching tremor signals are provided. Such
systems, methods, and media can be used to compare tremor signals
for the purposes of categorizing pathological and physiological
tremors, removing and cancelling-out identified tremor signals,
identifying similar tremors, identifying multiple tremors in long
recordings, etc.
[0009] In some embodiments, systems for finding and matching tremor
signals are provided, the systems comprising: at least one
processor that, receives a first piece of tremor signal content,
identifies a first plurality of tremor peaks that describe at least
a portion of the first piece of tremor signal content using a
Teager-Kaiser operator; forms a first template of tremor peaks from
at least a portion of the first plurality of tremor peaks, the
first template of tremor peaks having first group parameters;
compares the at least one first template and its parameters with at
least one second template and its parameters, wherein the at least
one second group of parameters is based on a second set of tremor
peaks associated with a second piece of tremor content; and
identifies a match between the first piece of tremor signal content
and the second piece of tremor signal content based on
comparing.
[0010] In some embodiments, methods for finding and matching tremor
signals are provided, the methods comprising: receiving a first
piece of tremor signal content; identifying a first plurality of
tremor peaks that describe at least a portion of the first piece of
tremor signal content using a Teager-Kaiser operator; forming a
first template of tremor peaks from at least a portion of the first
plurality of tremor peaks, the first template of tremor peaks
having first group parameters; comparing the at least one first
template and its parameters with at least one second template and
group of parameters, wherein the at least one second group of
parameters is based on a second set of tremor peaks associated with
a second piece of tremor content; and identifying a match between
the first piece of tremor signal content and the second piece of
tremor signal content based on the comparing.
[0011] In some embodiments, computer readable media containing
computer executable instructions that when executed by at least one
processor cause the processor to perform a method for finding and
matching tremor signals are provided, the method comprising:
receiving a first piece of tremor signal content, identifying a
first plurality of tremor peaks that describe at least a portion of
the first piece of tremor signal content using a Teager-Kaiser
operator; forming a first template of tremor peaks from at least a
portion of the first plurality of tremor peaks, the first template
of tremor peaks having first group parameters; comparing the at
least one first template and its parameters with at least one
second template and group of parameters, wherein the at least one
second group of parameters is based on a second set of tremor peaks
associated with a second piece of tremor content; and identifying a
match between the first piece of tremor signal content and the
second piece of tremor signal content based on the comparing.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1A is a diagram of one configuration of hardware that
can be used in accordance with some embodiments.
[0013] FIG. 1B is a diagram of another configuration of hardware
that can be used in accordance with some embodiments.
[0014] FIG. 2 is a diagram of a process for identifying and
matching tremor signals that can be used in accordance with some
embodiments.
[0015] FIG. 3 is a diagram of a tremor signal and its components
that can be identified in accordance with some embodiments.
DETAILED DESCRIPTION
[0016] Movement disorders such as Parkinson's disease can exhibit
several symptoms. One of the most distinct symptoms is the
appearance of involuntary limb movements known as tremors that can
severely inhibit a patient's mobility and that are caused by tremor
signals from the brain due to the lack of dopamine in a patient's
brain.
[0017] In accordance with some embodiments, systems, methods and
media for identifying and matching tremor signals are presented
that can provide a non-invasive approach in arresting and managing
tremor signals.
[0018] In order to perform such functions, tremor signals can be
measured using several monitoring apparatus such as an
electromyogram (EMG), an accelerometer or any other suitable
device. In order to describe one or more portions of a tremor
signal, unique tremor peak sequences can then be identified and
used to form templates of tremor signal content. In some
embodiments, and depending on the tremor monitoring apparatus,
these peaks can be peaks in the rate of change of the tremor
signal, peaks in the amplitude of the tremor signal and/or peaks in
any other suitable form of tremor measurement. Unwanted peaks can
be removed from further processing through the use of pruning.
Depending on the identified peaks, sets of peaks can next be
formed. A set of unique tremor peaks at a specific instance in time
may be considered to be a descriptor of a given tremor signal and
may be used as a template. These sets of peaks can then be stored
in a database and can take the form of a table or vector space.
Upon storage in the database, these tremor peak sets can be used as
templates to identify matching tremors from the same tremor source
and/or a different tremor source that can be obtained in different
times. Once a match is identified, the template can be used to
create a new signal and/or output that can minimize and/or cancel
the tremor signal.
[0019] FIG. 3 depicts an example of a tremor signal 300 in
accordance with some embodiments. Components 310, 320, and 330
indicate tremor peaks that can be grouped together to form a
template. Each tremor peak represents an involuntary limb movement
in a specific direction. For example, tremor peak 310 indicates
that the limb movement is occurring in the opposite direction of
the movement represented by tremor peak 330. Component 340
illustrates background interference such as physiological tremor
and/or additive noise from the recording apparatus, etc., which can
be present when identifying tremor peaks. Threshold 350 can be used
for pruning the identified tremor peaks in order to minimize the
background interference.
[0020] FIG. 1A illustrates an example of hardware 100. As depicted,
hardware 100 can include an analog tremor signal input 102, an
analog-to-digital converter 104, a first processor 106, an output
interface 108, an output device 110, an input interface 112, a
second processor 114, memory 116, and a database 118. Inputs 102
can be formed by any suitable input for receiving tremor signals,
such as an accelerometer, an electromyogram (EMG), a storage medium
(such as an analog or digital storage medium), etc.
Analog-to-digital converter 104 can be any suitable converter for
converting an analog signal to digital form, and can include a
converter having any suitable resolution, sampling rate, input
amplitude range, etc. Input interface 112 can be any suitable input
interface for receiving tremor signal content in a digital form,
such as a network interface, a USB interface, a serial interface, a
parallel interface, a storage device interface, an optical
interface, a wireless interface, etc. First processor 106, and
second processor 114 can include any suitable processing devices
such as computers, servers, microprocessors, controllers, digital
signal processors, programmable logic devices, etc. Instructions
(e.g., software, firmware etc.) for controlling such processors can
be stored on any suitable computer readable media, such as disk
drives, compact disks, digital video disks, memory (such as random
access memory, read only memory, flash memory, etc.), and/or any
other suitable media. Memory 116 can include any suitable computer
readable media, such as disk drives, compact disks, digital video
disks, memory (such as random access memory, read only memory,
flash memory, etc.), and/or any other suitable media, and can be
used to store instructions for performing the processes described
herein such as that described in connection with FIG. 2. Database
118 can include and suitable hardware and/or software database for
storing data. Output interface 108 can include any suitable
interface for providing data to an output device, such as a video
display interface, a network interface, an amplifier, etc. The
indicators of found tremor events, or one or more of such events,
can be coupled to output devices using any suitable interface. For
example, such indicators can be displayed on display screens though
a video interface, such indicators and/or event(s) can be provided
to network devices through a network interface and/or to
electro-mechanical devices (such as piezoelectric materials)
through an amplifier, etc. Output device 110 can include any
suitable device for outputting data and can include display
screens, network devices, electro-mechanical devices, etc. In some
embodiments, output device 110 can be an external, adjustable
device that can arrest and/or cancel the tremor signal using
vibrations, pressure, electrical pulses or any other suitable
electromechanical output.
[0021] Hardware 100 can be implemented in any suitable form. For
example, hardware 100 can be implemented as a Web server, including
one or more hardware processors, that receives a potential tremor
signal from a user, analyzes the signal, and provides identifiers
for matching tremors to the user. As other examples, hardware 100
can be implemented as a combination of a user computer and a
wearable, portable signal recorder/player, a camera, a mobile
phone, a tablet computing device, an email device, etc. that
receives a potential tremor signal from a user, analyzes the
signal, provides identifiers for matching tremors to the user, and
outputs a signal that can arrest and/or cancel the tremor signal
based on the identified matching tremors. In some embodiments the
output signal can be a vibration at a specified frequency, and/or
electrical pulses, and/or pressure and/or any other suitable form
of force with the ability to effectively minimize and/or cancel a
tremor signal.
[0022] FIG. 1B shows an alternate configuration of hardware 170
that can be used to implement some embodiments of the present
invention. Components 102, 104, 108, 110, 116, and 118 in FIG. 1B
can be substantially the same as components 102, 104, 108, 110,
116, and 118 in FIG. 1A. Processor 176 in FIG. 1B can perform a
combination of the functions of processors 106, and 114 shown in
FIG. 1A. In some embodiments, hardware 170 can be implemented as a
wearable, portable device that receives a potential tremor signal
from the user, analyzes the signal, provides identifiers for
matching tremors to the user and generates a force that can arrest
and/or cancel the tremor signal.
[0023] In FIG. 2, a process 200 for identifying and matching
identical and/or similar tremor signals is illustrated. The process
starts at 202 and subsequently receives as input the tremor signal
content at 204. Step 204 can be performed by components 112 and/or
102 and 104 of hardware 100. The tremor signal content can have
matching intra-signal tremor instances (e.g., matching tremor
signals within the same signal content) or matching inter-signal
tremor instances (e.g., matching tremor signals in different signal
content).
[0024] Receipt of the tremor signal content can be achieved in
multiple ways. For example, the tremor content can be received in
digital format as a digital file (e.g., a ".txt" file, an ".xml"
file, etc.), as a digital stream, as digital content in a storage
device (e.g., memory, a database, etc.). As another example, the
tremor content can be received as an analog signal, which is then
converted to digital format using an analog to digital converter.
Such an analog signal can be received through an electromyogram, an
accelerometer, etc.
[0025] Next, at 206, process 200 can identify peaks that can
possibly describe the tremor content. A set of peaks that is
descriptive of the signal tremor content can be classified as a
template. Each template can also have identifiable parameters that
can define for example, a template's peak frequency, amplitude,
length, and/or any other suitable characteristic(s) that can be
associated to the type of tremor (e.g., action tremor, resting
tremor, etc.)
[0026] In some embodiments, these sets of peaks can be obtained
using the discrete Teager-Kaiser (TK) operator. As is known in the
art, the Teager-Kaiser operator is a non-linear, quadratic operator
introduced to measure the real physical energy of a system. This
operator differs from the traditional way of measuring the energy
of a signal by suppressing unwanted background noise and
highlighting candidate tremor peaks. The discrete time TK operator
for real valued tremor signals can be given by:
E.sub.D[x(n)]=E(n)=x.sup.2(n)-x(n-1)*x(n+1)
[0027] As another example, in some embodiments, the discrete TK
operator can be used in windowed portions of the tremor signal
content. Temporal windowing of the tremor signal can provide a
faster, more-efficient and more-sensitive method for identifying
the tremor peaks. Examples of temporal windows can vary. In some
embodiments, initial windows can range anywhere from half a second
to two seconds, although any suitable temporal windows can be
used.
[0028] Tremor peaks cam be pruned using a temporal window. For
example, in some embodiments, temporal windowing can be combined
with half-wave rectification of the discrete Teager-Kaiser operator
which takes advantage of the symmetric nature of the tremor signal
and also minimizes the need for using multiple thresholds.
Denoising filters such as moving average filters or any other
suitable filters may be used in combination with the TK operator to
increase the sensitivity of identifying candidate tremor peaks.
[0029] In some embodiments, temporal windowing can be used and
repeated with different sized windows in order to achieve different
resolutions of analysis. For example, after the tremor signal
content is analyzed in a given time window the same analysis can be
repeated in a smaller or larger time window to increase or
decrease, respectively, the sensitivity and accuracy in identifying
the tremor peaks.
[0030] After identifying tremor peaks in 206, process 200 can then
prune tremor peaks at 208 in some embodiments.
[0031] Tremor peaks may need to be pruned, for example, because
when peaks are identified using the discrete TK operator, there is
a possibility of also identifying non-tremor peaks (e.g.,
physiological tremor peaks). On average, a person suffering from
Parkinson's disease will exhibit an action tremor (e.g.,
involuntary movement when performing an intentional action) or a
resting tremor (e.g., involuntary movement when resting), or a
combination of both. These tremors can appear as peaks in the
tremor content signal. The set of these tremor peaks can be unique
for each individual. The frequency of the tremor peaks can occur
with a range of 4 Hertz-6 Hertz, or any other suitable frequency
range.
[0032] In some embodiments, one can initially use a temporal window
of one second length for analyzing and identifying tremor signal
content to be pruned. Using the rectified discrete TK operator, one
can then calculate the probability density function of the tremor
peaks by forming a histogram of the resulting signal. Once the
histogram has been obtained, one can choose to use those bins of
the histogram that contain less than six peaks but at least one
peak or any other suitable characteristic as a way to identify a
threshold for pruning. Once the minimum value of a potential
candidate tremor peak is selected, it can be used as a threshold in
the original tremor signal content to obtain a first initial set of
pruned tremor peak candidates. In some embodiments multiple
thresholds can be used for different portions of the tremor signal
content.
[0033] In some embodiments, one can provide an additional layer of
pruning of already-identified candidate tremor peaks by using a
multi-resolution approach. For example in doing so, the
already-identified candidate tremor peaks can be further pruned by
using a larger temporal window for analysis. More particularly, for
example, one can use a two-second temporal window and then only
keep the twelve highest peaks as the final tremor peaks or any
other suitable number of peaks.
[0034] In some embodiments, one can chose different temporal
windows for the analysis and identification of the tremor peaks for
pruning. Multiple instances of temporal windows at different sizes
can also be added, which can increase the sensitivity and
specificity of the pruning process.
[0035] As a result of pruning the candidate tremor peaks at 208, a
set of unique sequenced tremor peaks can be obtained. At 210, this
set of unique sequenced tremor peaks can be grouped to any suitable
temporal arrangement to form a template. In some embodiments, such
temporal arrangements can represent, for example, different time
frames (e.g., a resting tremor template can represent a patient's
set of tremor peaks during sleep). In some embodiments these
temporal arrangements can represent, for example, different actions
(e.g., a writing tremor template can represent a patient's set of
tremor peaks during an attempt to write).
[0036] Each set of tremor peaks that forms a template can have
descriptive parameters. Such set parameters can include, for
example, the frequency of occurrence, the first order or second
order inter-peak distance, the amplitude of the peaks, etc. These
parameters can be extracted at 212. In some embodiments, energy
levels can be obtained and/or any other suitable characteristic can
also be included as part of the extracted set of parameters.
[0037] Theses parameters along with the templates of tremor peaks
can then be stored in a database (or any other suitable storage
mechanism) at 214. This database can also contain parameters and
other templates previously stored during other iterations of
process 200 for other tremor signal content.
[0038] At 216, matches between tremor signals can be obtained by
searching through the database and identifying similar and/or
identical matches between different templates.
[0039] Step 218 of process 200 can identify matches between tremor
signals. Matching tremor signals can be identified by either
comparing directly the different templates or by identifying those
templates that share common parameters. These parameters can either
be the same or their values can fall within a predetermined range.
This range can be either manually or automatically set based on the
number of tremor peaks and their frequency of occurrence. In some
embodiments, identical or similar content can also be identified
using the stored set of tremor peaks. Identical or similar sets of
tremor peaks and/or corresponding parameters can be referred to as
matching tremor signals. In some embodiments, real time or near
real time identification of tremor peaks can be performed
[0040] In some embodiments, the comparison can be achieved by the
use of a cross-correlation coefficient calculated between the
stored set of tremor peaks and the new tremor signal content. Such
a cross-correlation coefficient represents the similarity between
the template and the new tremor signal content.
[0041] In some embodiments, the comparison at step 218 can be
performed using matched filtering. A matched filter can be used to
correlate a known signal/template with an unknown signal to detect
the presence of the template in the signal. The matched filter can
be a linear filter that maximizes the signal to noise ratio in the
presence of additive noise in some embodiments.
[0042] In some embodiments, tremor signal content recordings can be
obtained and analyzed off-line to create and learn individual
tremor models. These tremor models can be the result of generative
approaches such as Hidden Markov Models or Gaussian Mixture Models.
Once these models are created, the extracted parameters from the
tremor signal can be used to identify the likelihood of an unknown
tremor signal matching existing tremor signals stored in the
database as shown in step 218.
[0043] In some embodiments, discriminative learning can be used to
identify tremor peaks in an unknown tremor signal content as
depicted in 218. For example, Support Vector Machines, Neural
Networks and other suitable techniques can be trained using the
parameters and sets of tremor peaks stored in the database. An
unknown signal can then be evaluated by the models and its peaks
can be determined to be tremor peaks or not.
[0044] Steps 206-218 of process 200 can occur in either or both of
the two processors depicted in hardware 100 (106, 114).
[0045] Finally, at 220, the device can generate an anti-tremor
force. This force can be a vibration at a specified frequency
obtained from the matching template, and/or electrical pulses,
and/or pressure and/or any other suitable form of force with the
ability to effectively minimize a patient's tremor signal. In some
embodiments, an adjustable, inflatable band may be used to apply
pressure based on parameters obtained from the matching template.
In some embodiments, a band can have an electromechanical circuit
including a piezoelectric transducer and an amplifier, and/or any
other suitable combination of electromechanical circuit, that
results in the generation of anti-tremor force.
[0046] In some embodiments, the techniques used to identify
matching tremor signals in recordings can be used for any suitable
application. For example, in some embodiments, these techniques can
be used to identify a repeating tremor in a single recording. As
another example, in some embodiments these techniques can be used
to identify a similar or identical tremor peak or set of tremor
peaks in two or more signals. As yet another example, in some
embodiments, these techniques can be used to identify two or more
sets of tremor peaks as being recorded during a single temporal
window based on matching tremor content in the sets.
[0047] In some embodiments, any suitable computer-readable media
can be used for storing instructions for performing the functions
described herein. For example, in some embodiments, computer
readable media can be transitory or non-transitory. For example,
non-transitory computer readable media can include media such as
magnetic media (such as hard disks, floppy disks, etc.), optical
media (such as compact discs, digital video discs, Blu-ray discs,
etc.), semiconductor media (such as flash memory, electrically
programmable read only memory (EPROM), electrically erasable
programmable read only memory (EEPROM), etc.), any suitable media
that is not fleeting or devoid of any semblance of permanence
during transmission, and/or any suitable tangible media. As another
example, transitory computer readable media can include signals on
networks, in wires, conductors, optical fibers, circuits, any
suitable media that is fleeting and devoid of any semblance of
permanence during transmission, and/or any suitable intangible
media.
[0048] Although the invention has been described and illustrated in
the foregoing illustrative embodiments, it is understood that the
present disclosure has been made only by way of example, and that
numerous changes in the details of implementation of the invention
can be made without departing from the spirit and scope of the
invention, which is limited only by the claims which follow.
Features of the disclosed embodiments can be combined and
rearranged in various ways.
* * * * *