U.S. patent application number 15/067663 was filed with the patent office on 2017-09-14 for image processing and text analysis to determine medical condition.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Patrick W. Fink, Kristin E. McNeil, Philip E. Parker, David B. Werts.
Application Number | 20170262583 15/067663 |
Document ID | / |
Family ID | 59786750 |
Filed Date | 2017-09-14 |
United States Patent
Application |
20170262583 |
Kind Code |
A1 |
Fink; Patrick W. ; et
al. |
September 14, 2017 |
IMAGE PROCESSING AND TEXT ANALYSIS TO DETERMINE MEDICAL
CONDITION
Abstract
A method, a processing device, and a computer program product
are provided. At least one processing device correlates textual
medical information related to the subject with characteristics of
an image of a medical condition of the subject to generate a
subject signature. The at least one processing device compares the
subject signature with multiple reference signatures to determine
at least one reference signature corresponding to the subject
signature. Each reference signature is associated with a
corresponding medical condition and is generated by correlating
textual medical information regarding the corresponding medical
condition with characteristics of an image of the corresponding
medical condition. The at least one processing device identifies
the medical condition of the subject based on the medical
conditions associated with the determined at least one reference
signature. Information is provided regarding the identified medical
condition of the subject.
Inventors: |
Fink; Patrick W.;
(Charlotte, NC) ; McNeil; Kristin E.; (Charlotte,
NC) ; Parker; Philip E.; (York, SC) ; Werts;
David B.; (Charlotte, NC) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
59786750 |
Appl. No.: |
15/067663 |
Filed: |
March 11, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 30/20 20180101;
G06F 19/321 20130101; G16H 30/40 20180101; G16H 50/20 20180101;
G06F 16/5838 20190101 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1-7. (canceled)
8. A computer program product comprising: a computer readable
storage medium having computer readable program code embodied
therewith for execution on a processing system, the computer
readable program code being configured to be executed by the
processing system to: correlate textual medical information of a
subject with characteristics of an image of a medical condition of
the subject to generate a subject signature; compare the subject
signature with a plurality of reference signatures to determine at
least one reference signature corresponding to the subject
signature, wherein each reference signature is associated with a
corresponding medical condition and is generated by correlating
textual medical information regarding the corresponding medical
condition with characteristics of an image of the corresponding
medical condition; identify the medical condition of the subject
based on the medical conditions associated with the determined at
least one reference signature; and provide information regarding
the identified medical condition of the subject.
9. The computer program product of claim 8, wherein the medical
condition includes a disease.
10. The computer program product of claim 8, wherein each of the
reference signatures is generated by: performing analytics on each
of a plurality of textual medical information corresponding to a
plurality of medical conditions to extract descriptions of each of
the plurality of medical conditions; analyzing images of each of
the plurality of medical conditions to extract image
characteristics of each of the plurality of medical conditions; and
correlating the extracted descriptions of each of the plurality of
medical conditions with corresponding image characteristics to
generate each of the reference signatures.
11. The computer program product of claim 8, wherein the
correlating textual medical information of the subject with
characteristics of an image of a medical condition of the subject
to generate a subject signature further comprises: analyzing the
textual medical information of the subject to extract a description
of the medical condition of the subject; analyzing an image of the
medical condition of the subject to extract image characteristics
of the medical condition of the subject; and correlating the
extracted description of the medical condition of the subject with
the extracted image characteristics of the medical condition of the
subject to generate the subject signature.
12. The computer program product of claim 8, wherein the comparing
the subject signature with a plurality of reference signatures
comprises; calculating a match score between the subject signature
and each of the plurality of reference signatures; and determining
the at least one reference signature corresponding to the subject
signature based on the match scores.
13. The computer program product of claim 12, wherein: the
characteristics of the image of the medical condition of the
subject and the characteristics of the image of the corresponding
medical condition include respective feature vectors; and the
calculating a match score between the subject signature and each of
the plurality of reference signatures further comprises: comparing
the feature vectors of the image of the medical condition of the
subject with a respective feature vectors of each of the images of
the corresponding medical conditions.
14. The computer program product of claim 12, wherein the match
score for a reference signature is based on a distance between the
reference signature and the subject signature.
15. A processing device comprising: at least one processor; a
memory; and a communication bus connecting the at least one
processor with the memory, wherein the memory has stored therein
instructions, which when executed by the at least one processor
cause the processing device to perform a method comprising:
correlating textual medical information related to a subject with
characteristics of an image of a medical condition of the subject
to generate a subject signature; comparing the subject signature
with a plurality of reference signatures to determine at least one
reference signature corresponding to the subject signature, wherein
each reference signature is associated with a corresponding medical
condition and is generated by correlating textual medical
information regarding the corresponding medical condition with
characteristics of an image of the corresponding medical condition;
identifying the medical condition of the subject based on the
medical conditions associated with the determined at least one
reference signature; and providing information regarding the
identified medical condition of the subject.
16. The processing device of claim 15, wherein the medical
condition includes a disease.
17. The processing device of claim 15, further comprising:
generating the plurality of reference signatures, the generating
the plurality of reference signatures further comprising:
performing analytics on a plurality of textual medical information
corresponding to a plurality of medical conditions to extract
descriptions of each of the plurality of medical conditions;
analyzing images of the plurality of medical conditions to extract
image characteristics of each of the plurality of medical
conditions; and correlating the extracted descriptions of the
plurality of medical conditions with the corresponding image
characteristics to generate the reference signatures.
18. The processing device of claim 15, wherein the correlating to
generate a subject signature comprises: analyzing the textual
medical information related to the subject to extract a description
of the medical condition of the subject; analyzing an image of the
medical condition of the subject to extract image characteristics
of the medical condition of the subject; and correlating the
extracted description of the medical condition of the subject with
the extracted image characteristics of the medical condition of the
subject to generate the subject signature.
19. The processing device of claim 15, wherein the comparing the
subject signature with a plurality of reference signatures
comprises: calculating a match score between the subject signature
and each of the plurality of reference signatures; and determining
the at least one reference signature corresponding to the subject
signature based on the match scores.
20. The processing device of claim 19, wherein: the characteristics
of the image of the medical condition of the subject and the
characteristics of the image of the corresponding medical condition
include respective feature vectors; and the calculating a match
score between the subject signature and each of the plurality of
reference signatures further comprises: comparing the feature
vectors of the image of the medical condition of the subject with a
respective feature vector of each of the images of the
corresponding medical conditions.
Description
BACKGROUND
[0001] Present invention embodiments are related to systems and
methods for image processing and textual analysis. In particular,
present invention embodiments are related to performing textual
analysis on unstructured text describing symptoms of a patient's
medical condition, correlating textual data and characteristics
generated from processing of an image of the patient's medical
condition to produce a subject signature, and finding a closest
match to a reference signature related to a textual description of
a known medical condition and an image of the known medical
condition.
[0002] Most medical conditions are best treated if identified
early. However, it is difficult for an average person to know about
various medical conditions and related symptoms. Often, one may
have a serious medical condition and not know it or one may assume
it is a different medical condition. It is inconvenient,
time-consuming and costly to go to a doctor's office for every
suspected medical condition, large or small.
SUMMARY
[0003] According to embodiments of the present invention, a
computer-implemented method, a processing device, and a computer
program product are provided. Embodiments may be implemented by at
least one processing device. Textual medical information related to
a subject may be correlated with characteristics of an image of a
medical condition of the subject to generate a subject signature.
The subject signature may be compared with multiple reference
signatures to determine at least one reference signature
corresponding to the subject signature. Each reference signature is
associated with a corresponding medical condition and is generated
by correlating textual medical information regarding the
corresponding medical condition with characteristics of an image of
the corresponding medical condition. The medical condition of the
subject may be identified based on the medical conditions
associated with the determined at least one reference signature.
Information regarding the identified medical condition of the
subject may be provided.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Generally, like reference numerals in the various figures
are utilized to designate like components.
[0005] FIG. 1 illustrates an example environment in which
embodiments may be implemented.
[0006] FIG. 2 illustrates an example of a processing device capable
of performing functions of various embodiments.
[0007] FIG. 3 is a flowchart illustrating example processing
regarding generation of reference signatures.
[0008] FIG. 4 is a flowchart illustrating example processing
regarding generating a subject signature and finding a closest
match to one or more reference signatures.
DETAILED DESCRIPTION
[0009] With reference now to FIG. 1, an example environment 100 for
implementation of embodiments is shown. Example environment 100 may
include one or more servers 102, a network 104, and one or more
databases, which may include unstructured textual input 112, an
image of a medical condition of a subject 110, natural language
processing (NLP) rules and dictionaries 108 and medical documents
including images of known medical conditions 106. Although FIG. 1
shows four databases, each of which may include unstructured
textual input 112, an image of a medical condition of a subject
110, natural language processing (NLP) rules and dictionaries 108
and medical documents including images of known medical conditions
106, other embodiments may include this data in a single database
or this data may be included in a different number of
databases.
[0010] Network 104 may be implemented by any number of any suitable
communications media (e.g., wide area network (WAN), local area
network (LAN), Internet, Intranet, etc.). In some embodiments,
server(s) 102 and NLP rules and dictionaries 108 may be local to
each other and may communicate with each other via any appropriate
local communication medium (e.g., local area network (LAN),
hardwire, wireless link, Intranet, etc.). In other embodiments,
server(s) 102 and NPL rules and dictionaries 108 may be remotely
located from each other and may communicate with each other via a
WAN, Internet, etc.
[0011] In a training phase, one or more server(s) 102 may receive
copies of medical documents 106, which may include images of known
medical conditions as well as doctors' notes, medical journal
entries, and academic medical articles related to the known medical
conditions. Server(s) 102 may use NLP rules and dictionaries 108 to
extract known medical condition descriptions from unstructured text
included in medical documents 106. Images of the corresponding
known medical conditions may be analyzed to extract image
characteristics. For each of the known medical conditions, a
corresponding extracted known medical condition description may be
correlated with respective extracted image characteristics to
produce a reference signature for each of the known medical
conditions.
[0012] In a runtime phase, one or more processing devices 102 may
receive unstructured textual input 112 including a description of a
subject's medical condition and may use NLP rules and dictionaries
108 to extract a subject's medical condition description from
unstructured textual input 112. A subject may be a person, animal,
or other entity having a medical or other condition. An image of
the subject's medical condition may be analyzed to extract image
characteristics. The subject's extracted medical condition
description and the extracted image characteristics of the
subject's medical condition may be correlated to produce a
subject's medical condition signature. One or more reference
signatures that are closest to the subject's medical condition
signature may be selected and results displayed to a user. The
embodiments may alternatively be utilized for any type of entity
(persons, animals, objects, etc.) having any desired visual
characteristics to determine a condition of the entity.
[0013] Referring now to FIG. 2, a schematic of an example
processing device 210 is shown, which may implement a server of
server(s) 102. Processing device 210 is only one example of a
suitable processing device for the environment of FIG. 1 and is not
intended to suggest any limitation as to the scope of use or
functionality of embodiments of the invention described herein.
Regardless, processing device 210 is capable of being implemented
and/or performing any of the functionality set forth herein.
[0014] In processing device 210, there is a computer system 212
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer system
212 include, but are not limited to, personal computer systems,
server computer systems, thin clients, thick clients, hand-held or
laptop devices, multiprocessor systems, microprocessor-based
systems, set top boxes, programmable consumer electronics, network
PCs, minicomputer systems, mainframe computer systems, and
distributed cloud computing environments that include any of the
above systems or devices, and the like.
[0015] Computer system 212 may be described in the general context
of computer system executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system 212 may
be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0016] As shown in FIG. 2, computer system 212 is shown in the form
of a general-purpose computing device. Components of computer
system 212 may include, but are not limited to, one or more
processors or processing units 216, a system memory 228, and a bus
218 that couples various system components including system memory
228 to one or more processors 216.
[0017] Bus 218 represents one or more of any of several types of
bus structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0018] Computer system 212 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system 212, and includes both volatile
and non-volatile media, and removable and non-removable media.
[0019] System memory 228 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
230 and/or cache memory 232. Computer system 212 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 234 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 218 by one or more data
media interfaces. As will be further depicted and described below,
memory 228 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
[0020] Program/utility 240, having a set (at least one) of program
modules 242, may be stored in memory 228 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, the one or more application programs, the other
program modules, and program data or some combination thereof, may
include an implementation of a networking environment. Program
modules 242 generally carry out the functions and/or methodologies
of embodiments of the invention as described herein.
[0021] Computer system 212 may also communicate with one or more
external devices 214 such as a keyboard, a pointing device, a
display 224, etc.; one or more devices that enable a user to
interact with computer system 212; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system 212 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 222.
Still yet, computer system 212 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 220. As depicted, network adapter 220 communicates
with the other components of computer system 212 via bus 218. It
should be understood that, although not shown, other hardware
and/or software components could be used in conjunction with
computer system 212. Examples, include, but are not limited to:
microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
[0022] FIG. 3 is a flowchart that illustrates example processing in
a training phase of an embodiment. The process may begin with one
or more processing devices 102 inputting medical documents 106,
which may include unstructured textual input as well as images of
known medical conditions (act 302). The unstructured textual input
may include, but not be limited to, doctors' notes, medical journal
articles and academic medical articles related to known medical
conditions or other unstructured text having a description of the
subject's medical condition. One or more processing devices 102 may
use NLP rules and dictionaries 108 to extract known medical
condition descriptions from the unstructured textual input included
in medical documents 106 (act 304).
[0023] One industry standard for context analytics is Unstructured
Information Management Architecture (UIMA). UIMA is an architecture
that includes software systems for analyzing large volumes of
unstructured information in order to discover knowledge that is
relevant to an end user. For example, a UIMA application may
process text and identify entities, such as persons, places,
organizations, or relations such as works-for or located-at. A UIMA
pipeline is a list of individual stages, or Annotators, which are
run serially. When a document is processed by the UIMA pipeline, a
first annotator stage may create annotations covering sections of
text. When the first stage is completed, the second annotator stage
may then process the text. Each subsequent stage may read
annotations created by earlier stages and may add or modify the
annotations, thus building up a more complex analysis of contents
of the document. The annotations could be for an entire document, a
paragraph or sentence, a token or an annotation that one can define
by creating a custom dictionary or a parsing rule including, but
not limited to, a city, a disease, or a date of birth.
[0024] Another product for context analysis is IBM Advanced Care
Insights from International Business Machines of Armonk, N.Y. IBM
Advanced Care Insights has dictionaries for identifying various
medical conditions and symptoms. Further, one may define custom
dictionaries and rules for use with various embodiments. Some
examples of custom dictionaries may include a date dictionary
having words including, but not limited to, today, yesterday,
January, February, March, etc. An example symptom dictionary may
have words including, but not limited to, headache, pain, anxiety,
bleeding, and swollen. An example rule may be as follows, where a
token is a span of text:
<Date><tokens><Symptom>
<Symptom><tokens><Date>
[0025] Returning to the flowchart of FIG. 3, server(s) 102 may
input images of known medical conditions from medical documents 106
(act 306). Conventional image analysis techniques may be used to
extract image characteristics of the input images (act 308). Some
conventional image analysis techniques, which may be used in
various embodiments, include, but are not limited to, detecting
boundaries within an image and facial recognition.
[0026] Conventional machine learning techniques may be employed in
a correlation module engine in order to correlate each extracted
known medical condition description with image characteristics of a
corresponding image of the known medical condition (act 310) to
produce reference signatures (act 312).
[0027] An example signature could be a description of a cancerous
mole on the skin collocated with an image of the ailment.
"Irregular shaped", "darkened skin", "surrounded by slightly
reddish irritation area" could all appear in the text surrounding
the image, along with text unrelated to the image. Key components
could be represented as a vector such as [0 0 1 0 0 1 1 0 0], where
the 1s may be positional markers for image features that can be
detected and are described in a given sentence. The 0s may be
features that can be looked for in all images, but are not present
in a textual description. An example of things in the image that do
not appear in the textual description may include, but not be
limited to, many small discolorations, raised skin, linear marks
(scars), linear tears (cuts), etc.
[0028] Using conventional techniques, image analytics can identify
shapes and colors, and can identify patterns such as, for example,
facial recognition when seeing 2 circles for eyes, a line for a
nose, and so on. A deviation from a pure circle/oval can be
captured if it exceeds a threshold as an "irregular shaped"
feature. Darkened skin can be captured via color filters as another
feature. A combination of a color filter and a shape detector could
capture a reddish ring as another feature. Again, the image
analytics would produce a feature vector representing the things
found in the image.
[0029] The feature vector produced by the image analytics may be
correlated with the feature vector produced by NLP to arrive at a
disease causing the anomalies to appear. For example, if the
sentence was "Skin cancer symptoms can include irregular shaped
darkened skin surrounded by slightly reddish irritation area", the
signature could be associated with "skin cancer". Additional
symptoms described in the text could be stored along with symptoms
of the main disease for later display to a user regarding things
not apparent in the image.
[0030] FIG. 4 is a flowchart that illustrates example processing in
a runtime phase of an embodiment. The process may begin with
server(s) 102 inputting textual input 112 (act 402). Textual input
112 may include unstructured textual input including, but not
limited to, doctors' notes, subject's notes, social media messages,
email and text messages. Server(s) 102 may use NLP rules and
dictionaries 108 to extract medical condition descriptions from
textual input 112 (act 404). Conventional analytics including, but
not limited to, UIMA and IBM Advanced Care Insights may be used to
annotate textual input 112 and extract medical condition
descriptions in substantially the same manner as described
above.
[0031] An image of a subject's medical condition may be received
(act 406) and may be analyzed, using conventional image analysis
techniques, to extract image characteristics (act 408). Server(s)
102 may correlate the extracted medical condition descriptions with
the image characteristics (act 410) to produce a patient or subject
signature (act 412). The subject signature may be compared with
each of a number of reference signatures to determine one or more
closest matching reference signatures (act 414). A match score may
be computed. The one or more closest matching reference signatures
may be determined by the match score, which may be based on
computing a distance of a feature vector of the subject signature
from a corresponding feature vector of each of the reference
signatures. The one or more closest matching reference signatures
have a minimum distance with respect to the subject signature.
[0032] Distance computation is typically defined as a Euclidean
distance between the vectors. In Euclidean space R.sup.n, this is
defined as the square root of the sums of the squared differences
of each position, i.e. {square root over
(.SIGMA..sub.i=1.sup.n|v.sub.1(x.sub.i)-v.sub.2(x.sub.i)|.sup.2)}.
Other possibilities may include magnitude difference or Hamming
distance (simple count of how many bits are different in
total).
[0033] Results, which may include information regarding the one or
more closest matching reference signatures, may then be displayed
to a user (act 416).
[0034] In one embodiment, a user, or doctor to prevent undue
hypochondria, may use a user interface to take an image of
something he or she is curious about, and may be presented with a
descending list of matches about what the image shows. In another
embodiment, a system may scan social media, and if a certain
threshold of confidence is achieved, a message (social media
message, email, text if known, etc.) may be sent to the person in
the image to inform them of a likely medical condition.
[0035] The environment of the present invention embodiments may
include any number of computer or other processing systems (e.g.,
client or end-user systems, server systems, etc.) and databases or
other repositories arranged in any desired fashion, where the
present invention embodiments may be applied to any desired type of
computing environment (e.g., cloud computing, client-server,
network computing, mainframe, stand-alone systems, etc.). The
computer or other processing systems employed by the present
invention embodiments may be implemented by any number of any
personal or other type of computer or processing system (e.g.,
desktop, laptop, PDA, mobile devices, etc.), and may include any
commercially available operating system and any combination of
commercially available and custom software (e.g., browser software,
communications software, server software, etc.). These systems may
include any types of monitors and input devices (e.g., keyboard,
mouse, voice recognition, etc.) to enter and/or view
information.
[0036] It is to be understood that the software of the present
invention embodiments may be implemented in any desired computer
language and could be developed by one of ordinary skill in the
computer arts based on the functional descriptions contained in the
specification and flowcharts illustrated in the drawings. Further,
any references herein of software performing various functions
generally refer to computer systems or processors performing those
functions under software control. The computer systems of the
present invention embodiments may alternatively be implemented by
any type of hardware and/or other processing circuitry.
[0037] The various functions of the computer or other processing
systems may be distributed in any manner among any number of
software and/or hardware modules or units, processing or computer
systems and/or circuitry, where the computer or processing systems
may be disposed locally or remotely of each other and may
communicate via any suitable communications medium (e.g., LAN, WAN,
Intranet, Internet, hardwired, modem connection, wireless, etc.).
For example, the functions of the present invention embodiments may
be distributed in any manner among the various systems, and/or any
other intermediary processing devices. The software and/or
algorithms described above and illustrated in the flowcharts may be
modified in any manner that accomplishes the functions described
herein. In addition, the functions in the flowcharts or description
may be performed in any order that accomplishes a desired
operation.
[0038] The software of the present invention embodiments may be
available on a non-transitory computer useable medium (e.g.,
magnetic or optical mediums, magneto-optic mediums, floppy
diskettes, CD-ROM, DVD, memory devices, etc.) of a stationary or
portable program product apparatus or device for use with
stand-alone systems or systems connected by a network or other
communications medium.
[0039] The communication network may be implemented by any number
of any type of communications network (e.g., LAN, WAN, Internet,
Intranet, VPN, etc.). The computer or other processing systems of
the present invention embodiments may include any conventional or
other communications devices to communicate over the network via
any conventional or other protocols. The computer or other
processing systems may utilize any type of connection (e.g., wired,
wireless, etc.) for access to the network. Local communication
media may be implemented by any suitable communication media (e.g.,
local area network (LAN), hardwire, wireless link, Intranet,
etc.).
[0040] The system may employ any number of any conventional or
other databases, data stores or storage structures (e.g., files,
databases, data structures, data or other repositories, etc.) to
store information. The database system may be implemented by any
number of any conventional or other databases, data stores or
storage structures (e.g., files, databases, data structures, data
or other repositories, etc.) to store information. The database
system may be included within or coupled to a server and/or client
systems. The database systems and/or storage structures may be
remote from or local to the computer or other processing systems,
and may store any desired data.
[0041] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises", "comprising", "includes", "including",
"has", "have", "having", "with" and the like, when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0042] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
act for performing the function in combination with other claimed
elements as specifically claimed. The description of the present
invention has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limited to the
invention in the form disclosed. Many modifications and variations
will be apparent to those of ordinary skill in the art without
departing from the scope and spirit of the invention. The
embodiments were chosen and described in order to best explain the
principles of the invention and the practical application, and to
enable others of ordinary skill in the art to understand the
invention for various embodiments with various modifications as are
suited to the particular use contemplated.
[0043] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
[0044] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0045] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0046] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0047] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0048] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0049] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0050] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0051] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
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