U.S. patent application number 15/263035 was filed with the patent office on 2018-03-15 for automatically assessing the mental state of a user via drawing pattern detection and machine learning.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Jinho Hwang, Roxana Monge Nunez, Valentina Salapura, Maja Vukovic.
Application Number | 20180075198 15/263035 |
Document ID | / |
Family ID | 61559991 |
Filed Date | 2018-03-15 |
United States Patent
Application |
20180075198 |
Kind Code |
A1 |
Hwang; Jinho ; et
al. |
March 15, 2018 |
AUTOMATICALLY ASSESSING THE MENTAL STATE OF A USER VIA DRAWING
PATTERN DETECTION AND MACHINE LEARNING
Abstract
A method and system for automatically assessing the mental state
of a user from a drawing made by the user. The mental state of the
user is automatically assessed by digitizing a drawing and
determining and analyzing drawing characteristics, including color,
objects, and location. Video analytics are used to determine and
analyze user time characteristics and mood characteristics. The
mental state of the user is automatically determined by
interpreting the color, object, location, time and mood
characteristics and to automatically select a user action. A
machine learning algorithm can learn developmental patterns of the
user from historical data about a plurality of the user's drawings
and the characteristics of the drawings to determine the mental
state of the user. The machine learning algorithm can be used in
selecting the user action.
Inventors: |
Hwang; Jinho; (Ossining,
NY) ; Monge Nunez; Roxana; (San Jose, CR) ;
Salapura; Valentina; (Chappaqua, NY) ; Vukovic;
Maja; (New York, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
61559991 |
Appl. No.: |
15/263035 |
Filed: |
September 12, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/165 20130101;
A61B 5/0077 20130101; A61B 5/7267 20130101; G16H 30/20 20180101;
G10L 25/63 20130101; G16H 70/00 20180101; G06N 20/00 20190101; G06N
3/004 20130101; G16H 50/20 20180101; G16H 20/00 20180101; G16H
30/40 20180101 |
International
Class: |
G06F 19/00 20060101
G06F019/00; G06N 5/04 20060101 G06N005/04; G06N 99/00 20060101
G06N099/00; G06K 9/00 20060101 G06K009/00; G10L 25/63 20060101
G10L025/63 |
Claims
1. A computer implemented method for automatically assessing the
mental state of a user from a drawing made by the user comprising:
digitizing a drawing and determining drawing characteristics, the
drawing characteristics including color, objects, and location;
analyzing the color characteristics; analyzing the object
characteristics; analyzing the location characteristics; performing
a video analysis of the user while making the drawing to determine
user characteristics, the user characteristics including time
characteristics and mood characteristics; requesting the user to
provide a voice description of the drawing; performing an audio
analysis of the user's voice description of the drawing; analyzing
the time characteristics; analyzing the mood characteristics;
receiving a profile of the user including personal user data and
user events data; automatically interpreting the color, object and
location characteristics of the drawing, the user's voice
description of the drawing, and the time and mood characteristics
of the user to obtain drawing analysis data; automatically
comparing the drawing analysis data with the profile and
determining developmental patterns based on the comparison;
accessing historical data about a plurality of the user's drawings,
the historical data including drawing characteristics of the
plurality of the user's drawings, video analysis of the user while
making the plurality of drawing and audio analysis of the user's
voice description of the plurality of drawings; inputting the
historical data into a machine learning algorithm computer program
module having a machine learning algorithm; inputting drawing tags
into the machine learning algorithm computer program module, the
drawing tags being at least one of care giver tags and user tags;
training the machine learning algorithm based on the historical
data and the drawing tags to learn developmental patterns of the
user; combining the developmental patterns of the user learned from
the machine learning algorithm and the developmental patterns of
the user determined from the comparison of the drawing analysis
data with the profile, to determine the mental state of the user
over time; and selecting a user action based on the mental state of
the user determined over time.
2. (canceled)
3. The computer implemented method of claim 1 further comprising
analyzing how the user interacts with others while making the
drawing.
4. The computer implemented method of claim 1 further comprising
using one or more psychology databases for inferring drawing
patterns.
5. The computer implemented method of claim 1 further comprising
providing an alert of the selection of an action, based on the
determined mental state of the user.
6. The computer implemented method of claim 1 further comprising
adapting the interpretation of color based on the profile.
7. (canceled)
8. The method of claim 1, wherein the machine learning algorithm is
used in selecting the user action based on the determined the
mental state of the user.
9. A system comprising: at least one data processor connected to at
least one memory that stores software instructions, where execution
of the software instructions by the at least one data processor
causes the system to automatically assess the mental state of a
user from a drawing made by: digitizing a drawing and determining
drawing characteristics, the drawing characteristics including
color, objects, and location; analyzing the color characteristics;
analyzing the object characteristics; analyzing the location
characteristics; performing a video analysis of the user while
making the drawing to determine user characteristics, the user
characteristics including time characteristics and mood
characteristics; requesting the user to provide a description of
the drawing; performing an audio analysis of the user's voice
description of the drawing; analyzing the time characteristics;
analyzing the mood characteristics; receiving a profile of the user
including personal user data and user events data; automatically
interpreting the color, object and location characteristics of the
drawing, the user's voice description of the drawing, and the time
and mood characteristics of the user to obtain drawing analysis
data; automatically comparing the drawing analysis data with the
profile and determining developmental patterns based on the
comparison; accessing historical data about a plurality of the
user's drawings, the historical data including drawing
characteristics of the plurality of the user's drawings, video
analysis of the user while making the plurality of drawing and
audio analysis of the user's voice description of the plurality of
drawings; inputting the historical data into a machine learning
algorithm computer program module having a machine learning
algorithm; inputting drawing tags into the machine learning
algorithm computer program module, the drawing tags being at least
one of care giver tags and user tags; training the machine learning
algorithm based on the historical data and the drawing tags to
learn developmental patterns of the user; combining the
developmental patterns of the user learned from the machine
learning algorithm and the developmental patterns of the user
determined from the comparison of the drawing analysis data with
the profile, to determine the mental state of the user over time;
and selecting a user action based on the determined the mental
state of the user determined over time.
10. (canceled)
11. The system of claim 9 further including analyzing how the user
interacts with others while making the drawing.
12. The system of claim 9 further including using one or more
psychology database for inferring drawing patterns to determine the
mental state of the user.
13. The system of claim 9 further including providing an alert of
the selection of an action based on the determined mental state of
the user.
14. The system of claim 9 further including creating a profile of
the user and determining normal or abnormal patterns based on the
profile to determine mental state of the user.
15. (canceled)
16. The system of claim 9 wherein the machine learning algorithm is
used in selecting the user action based on the determined the
mental state of the user.
17. A computer program product comprising: program instructions on
a computer-readable storage medium, where execution of the program
instructions using a computer causes the computer to automatically
assess the mental state of a user from a drawing made by the user
by: digitizing a drawing and determining drawing characteristics,
the drawing characteristics including color, objects, and location;
analyzing the color characteristics; analyzing the object
characteristics; analyzing the location characteristics; performing
a video analysis of the user while making the drawing to determine
user characteristics, the user characteristics including time
characteristics and mood characteristics; requesting the user to
provide a voice description of the drawing; performing an audio
analysis of the user's voice description of the drawing; analyzing
the time characteristics; analyzing the mood characteristics;
receiving a profile of the user including personal user data and
user events data; automatically interpreting the color, object and
location characteristics of the drawing, the user's voice
description of the drawing, and the time and mood characteristics
of the user to obtain drawing analysis data; automatically
comparing the drawing analysis data with the profile and
determining developmental patterns based on the comparison;
accessing historical data about a plurality of the user's drawings,
the historical data including drawing characteristics of the
plurality of the user's drawings, video analysis of the user while
making the plurality of drawing and audio analysis of the user's
voice description of the plurality of drawings; inputting the
historical data into a machine learning algorithm computer program
module having a machine learning algorithm; inputting drawing tags
into the machine learning algorithm computer program module, the
drawing tags being at least one of care giver tags and user tags;
training the machine learning algorithm based on the historical
data and the drawing tags to learn developmental patterns of the
user; combining the developmental patterns of the user learned from
the machine learning algorithm and the developmental patterns of
the user determined from the comparison of the drawing analysis
data with the profile, to determine the mental state of the user
over time; and selecting a user action based on the determined the
mental state of the user determined over time.
18. The computer program product of claim 17 further including
analyzing how the user interacts with others while making the
drawing.
19. The computer program product of claim 17 further including
creating a profile of the user and determining normal or abnormal
patterns based on the profile to determine mental state of the
user.
20. (canceled)
21. The computer implemented method of claim 1 wherein the machine
learning algorithm learns what user actions are correct for
different classes of users
22. The computer implemented method of claim 21 wherein the machine
learning algorithm determines that a particular user action is the
correct action for the user with a level of confidence.
23. The system of claim 9 wherein the machine learning algorithm
learns what user actions are correct for different classes of
users
24. The system of claim 22 wherein the machine learning algorithm
determines that a particular user action is the correct action for
the user with a level of confidence.
25. The computer program product of claim 17 wherein the machine
learning algorithm learns what user actions are correct for
different classes of users
26. The computer program product of claim 25 wherein the machine
learning algorithm determines that a particular user action is the
correct action for the user with a level of confidence.
Description
BACKGROUND OF THE INVENTION
[0001] This disclosure is directed to a method and system for
automatically assessing the mental state of a user via drawing
pattern detection and machine learning.
[0002] People, especially children, communicate and express
themselves by drawing, so this becomes an important input to
understand what the person thinks or feels at the moment. The
drawings by children change over time, for example, they become
more realistic as soon as the child becomes older. Many elderly use
painting as their hobby and express their state of mind through
drawings. Analysis of drawings for children is a developing field.
Some examples of emotions and user states can be found at
http://www.newkidscenter.com/Interpreting-Children's-Drawings.html).
[0003] There is a need to determine the mental state of a person.
The mental state is important to understand the person's
personality characteristics. The mental state, such as anger,
anxiety, depression, etc., and historical data such as abuse, loss
of loved person are important in the detection of mental problems.
Such knowledge is also important in making a determination of
whether corrective action, such as therapy, is advised, and if so,
the type of recommended therapy.
SUMMARY OF THE INVENTION
[0004] One embodiment of this invention is directed to a computer
implemented method for automatically assessing the mental state of
a user from a drawing made by the user. In one embodiment, the
mental state of the user is automatically assessed by digitizing a
drawing and determining drawing characteristics, which can include
color(s), object(s), and location. The method can further include
analyzing the characteristics. The method can also include
performing a video analysis of the user while making the drawing to
determine user characteristics, the user characteristics including
time characteristics and mood characteristics, analyzing the time
characteristics and analyzing the mood characteristics. The method
further includes automatically interpreting the color, object,
location, time and mood characteristics to determine a mental state
of the user and selecting a user action based on the determined the
mental state of the user.
[0005] In one embodiment, a computer implemented method further
includes creating a profile of the user and determining normal or
abnormal patterns based on the profile to determine mental state of
the user. In another embodiment, the computer implemented method
includes determining developmental patterns of the user from
historical data about a plurality of the user's drawings and the
characteristics of the plurality of the user's drawings using a
machine learning algorithm to facilitate a determination of the
mental state of the user. By way of further example, the machine
learning algorithm can use the determined the mental state of the
user in the selection of a user action.
[0006] Another embodiment is directed to a system having at least
one data processor connected to at least one memory that stores
computer-readable program instructions, where execution of the
computer-readable program instructions by the at least one data
processor causes the system to automatically assess the mental
state of a user from a drawing made by the user. In one embodiment,
the mental state of the user is automatically assessed by
digitizing a drawing and determining drawing characteristics, which
can include color, objects, and location. The system can further
include a drawing analyzer for analyzing the drawing
characteristics. In some embodiments, the system also performs a
video analysis of the user to determine and analyze user
characteristics, such as time and mood. In some embodiments, the
system automatically interprets drawing color(s), object(s),
location, time and mood characteristics to determine a mental state
of the user and selects a user action based on the determined the
mental state of the user.
[0007] Another embodiment is directed to a computer program product
having computer-readable program instructions on a non-transitory
computer-readable medium, where execution of the computer-readable
program instructions using a computer causes the computer to
automatically assess the mental state of a user from a drawing made
by the user by digitizing a drawing and determining drawing
characteristics, the drawing characteristics including color,
objects, and location; analyzing the color characteristics;
analyzing the object characteristics; and analyzing the location
characteristics. The computer program product can also include
instructions for performing a video analysis of the user to
determine user characteristics, the user characteristics including
time characteristics and mood characteristics; analyzing the time
characteristics; and analyzing the mood characteristics. The
computer program product may further includes instructions for
automatically interpreting the color, object, location, time and
mood characteristics to determine the mental state of the user; and
selecting a user action based on the determined mental state of the
user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a block diagram of an exemplary system of
automatically assessing the mental state of a user based on drawing
patterns and selecting an ameliorating action according to one
embodiment of the present disclosure.
[0009] FIG. 2 is flow diagram of one embodiment of the method of
automatically assessing the mental state of a user from a
drawing.
[0010] FIG. 3 is a flow diagram of further embodiments of the
method of automatically assessing the mental state of a user from a
drawing
[0011] FIG. 4 is a block diagram of an exemplary computing system
suitable for implementation of the embodiments of the invention
disclosed in this specification.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0012] This disclosure is directed to a system and a method for
automatically assessing the mental state of a user based on the
drawing patterns and selecting an ameliorating action such as
therapeutic treatment. The treatment for example may help with the
user's recovery process from trauma, personal troubles at home and
other circumstances that may affect the user's mental state.
[0013] FIG. 1 is a block diagram of a system of automatically
assessing the mental state of a user based on drawing patterns and
selecting an ameliorating action according to one embodiment of the
present disclosure. FIG. 1 is a more detailed example of the
program module 102 of FIG. 4 to be described later. The components
of the system 10 include a drawing analyzer 12. The analyzer 12
includes one or more modules for performing various functions.
These modules include a drawing digitizer 14, time analyzer 16,
color analyzer 18, size analyzer 20, location analyzer 22, voice
description analyzer 24, mood analyzer 26 and interaction analyzer
28. A drawing 30 is scanned and input into the drawing analyzer 12
for analysis.
[0014] The drawing analyzer 12 infers drawing patterns by analyzing
drawing characteristics determined using drawing digitizer 14, time
analyzer 16, color analyzer 18, size analyzer 20 and location
analyzer 22. The drawing digitizer 14 performs analytics to
determine visual characteristics of the drawing. The
characteristics include data relating to color, objects, location,
placement and composition, lines, marks, weight, style, people,
faces, flowers, florals, geometric shapes, random abstract shapes,
houses, windows, 3D boxes, ladders, arrows, walls, stars and
hearts. The time analyzer 16 considers how much time the user
needed to complete the drawing and other time related
characteristics input to the drawing analyzer 12. An added benefit
of the method and system of this disclosure is in the ability
detect potential issues with the user over an extended period, by
observing drawing styles over time, such as months and years. The
color analyzer 18 considers number of colors used, the most
dominant colors and other color characteristics. The size analyzer
20 considers the objects presented, the position on the paper, the
relative size of the objects to each other and other size related
characteristics. The location analyzer 22 considers the relative
grouping of objects to each other, the location being depicted by
the drawing and other location related characteristics.
[0015] The system of FIG. 1 performs a computer implemented method
for automatically assessing the mental state of a user from a
drawing made by the user. The method includes step S100 digitizing
a drawing and determining drawing characteristics, the drawing
characteristics including color, objects, and location; step S101
analyzing the color characteristics; step 102 analyzing the object
characteristics; and step S103 analyzing the location
characteristics. The method also includes step S104 performing a
video analysis of the user while making the drawing to determine
user characteristics, the user characteristics including time
characteristics and mood characteristics; step S105 analyzing the
time characteristics; and step S106 analyzing the mood
characteristics. The method further includes step S107
automatically interpreting the color, object, location, time and
mood characteristics to determine the mental state of the user; and
step S108 selecting a user action based on the determined mental
state of the user.
[0016] In one embodiment, the system and method may also interpret
the voice description by the user of the drawing. The meaning of a
drawing becomes more accurate when the user is asked to explain
what the drawing means. The system has a speaker or other voice
recorder 32 that requests the user to explain what is being drawn
or what was drawn. The drawing characteristics and the user's
clarifications are analyzed by the voice description analyzer 24 to
provide the assessment and actions accordingly. As shown in FIG. 3,
the method includes step S110 interpreting a user voice description
of the drawing and step S111 analyzing how the user interacts with
others while making the drawing. The analysis of the user
interaction with others is combined in step S115 with the step S107
in determining the mental state of the user.
[0017] In one embodiment, system has a video camera 34 that
provides inputs regarding user's mood. The mood analyzer 26
analyzes those inputs for determining such as aspects as low
self-esteem, fears, anxiety, low imagination, creativity, passion,
love and other emotional states of the user.
[0018] In cases where more than one person is drawing, the video
camera 34 provides input to interaction analyzer 14 for analyzing
how each person reacts and draws by interacting with others. The
parents or caregivers may optionally set up an identification
device 36 such as little chip in their clothing or other device to
identify the user and the user's drawings.
[0019] In one embodiment, a database module 29 contains various
commercially available psychology databases for inferring drawing
patterns. The module 29 may also include a categorical database to
diagnose the mental state of drawers.
[0020] The state of user assessment module 38 takes in all the
inputs from the drawing analyzer 12 and in some cases caregiver
input 39, and automatically determines the mental state of the
user. The assessment module 38 may illustrate the different mental
states with different parameter values.
[0021] The module 38 will also automatically select a corrective,
therapeutic and/or enhancing action and output the selected action
at 40. The mental state may indicate a need for a corrective
action, such as, therapy and the type of the recommended therapy.
The actions may include soothing the user by using ambient music,
recommending relocating the user due to, for example, undesired
people around or an unsuitable room or area. The assessment module
38 may also provide an alert or warning 42 to a caregiver or parent
that system is recommending an action based on the mental state of
the user.
[0022] The mental state or states detected may, for example,
include anger, anxiety, abuse, loss of loved person, depression and
others states. The mental state detected may be the personality
characteristics of the user.
[0023] In one embodiment, the method and system interprets the
following personality characteristics from the associated drawing
characteristics:
[0024] Impulsive child: Big figures, no necks, and asymmetry of
limbs.
[0025] Anxious child: Clouds, rain, flying birds, no eyes on the
figures
[0026] Shy child: Short figures, no nose or mouth, tiny figures and
arms close to the body
[0027] Angry child: Big hands and teeth, long arms, crossed
eyes
[0028] Insecure child: Monstrous figures, tiny heads, no hands, and
slanted figures
[0029] In one embodiment a profile and events data module 44 is
used to create a profile for the user which may include data such
as age, gender, and other related personal data. In addition, the
profile may include data on events and any other relevant
situations that are happening currently that are affecting the
person, such as divorce, new school or job and other events. The
data input from module 44 will allow the state of user assessment
module 38 to compare the drawing analyzer data with the user
information in determining normal or abnormal patterns and other
affects on the user mental state. As shown in FIG. 3, the method
includes step S112 creating a profile of the user and and step S113
determining normal or abnormal patterns based on the profile. The
normal or abnormal patterns are combined in step S115 with the step
S107 in determining the mental state of the user.
[0030] In one embodiment, the color analyzer 18 relies on visual
associations of the colors in the drawing in the mental state
assessment, such as, sky blue, green plants, red blood, brown
earth, yellow sun. Colors also have a `temperature`. Red, orange
and yellow are warm colors, while green, blue, blue-grey and violet
are cool. Examples of such associations and related meanings are
found at:
(http://drawsketch.about.comics/tipsandideas/p/doodle_color.htm).
[0031] In one embodiment, the meaning of certain colors is
adaptable depending on the circumstances of the user and the user's
profile input from module 44. For example, red often represents
power. However, red can indicate anger (`seeing red`) or a need to
impose authority, but red is also a seasonal, festive color
associated with holly berries and Santa Claus. Looking at red
increases the heart rate. In China, red is the color of prosperity
and joy.
[0032] Blue, black and gray are conventional colors, indicating
stability and strength. Dark blue is often chosen as a corporate
color in the financial sector to indicate security. Sea-green and
blue can also have connotations of water and the ocean. Gray is
sometimes called `the color of sorrow`. In some cultures black is
the color of the life-giving earth, while in western culture it is
usually associated with darkness.
[0033] Purple may represent sexual frustration or a need to appear
unorthodox, and is a favorite color of artists. Historically,
purple has represented royalty, and in darker hues suggests magic
and mystery. Brown can suggest the earth and autumn. A preference
for brown can indicate a conservative personality, even repression.
Of course, in clothing and furnishings that depends on fashion,
though trends can indicate the mood of the times. Green usually
regarded as a restful color suggesting well-being, and is favored
by well-balanced individuals. This is at odds with some
associations, `green with envy`, `going green` with nausea, and
money (due to the `greenback`).
[0034] In one embodiment, the color analyzer 18 looks for the right
emotion from the drawing through the emotional color wheel, one
example of which is found at:
(http://www.do2learn.com/organizationtools/EmotionsColorWheel/).
[0035] In one embodiment historical data 50 about the drawings and
the context of the drawings is input to a machine learning module
46 to enable learning about the user's development patterns. Care
giver or user tags 48 may be input for the drawing. The state of
assessment module 38 extracts patterns of behavior from the machine
learning data and determines the mental state of the user based on
the drawing analysis trained through historical data with correct
tags. As shown in FIG. 3, the method includes step S114 using a
machine learning algorithm to learn developmental patterns. The
developmental patterns are combined in step S115 with the step S107
in determining the mental state of the user.
[0036] In one embodiment, a machine learning module 52 uses a
machine learning algorithm to learn what actions are best for
different classes of users so that other users can benefit as the
system learns. Tags 54 may be added for a recommended action when
the user likes it. The machine learning module 52 enables the state
of user assessment module to determine with a certain level of
confidence that a particular action is the correct action for the
user.
[0037] In one embodiment, categories of the assessment analysis
include various states of mind and drawing types. In addition, the
output can be multiple with different probabilities. The analysis
categories may include:
[0038] Emotions: Deal with emotions like anger and sadness through
these helpful exercises.
[0039] Relaxation: Level of stress or relaxation.
[0040] Happiness: how much drawers appreciate and focus on the
good. This is all about reflecting on personal happiness.
[0041] Portraits: a great way to get to know self and relationships
with others is through portraits.
[0042] Trauma and Loss: This will ask one to face some unpleasant
aspects of life, but with the goal of overcoming them.
[0043] Collaging: a different way of drawing based on
preferences.
[0044] Self: Examine aspects if who one is and how one sees the
world through drawings.
[0045] Gratitude: that will help one be happy about what one has
and expresses his/her gratitude for something.
[0046] Inside the Mind: Take a look inside mind to see what's going
on.
[0047] The method and system of analyzing drawing patterns as
disclosed herein serves several the purposes, including but not
limited to:
[0048] Identification/Referral: To identify children who need
special education and related services;
[0049] Eligibility: To determine whether a child is a "child with a
disability" under the Individuals with Disabilities Education Act
(IDEA) definition;
[0050] Planning an Individualized Education Program (IEP) for
children ages 3-22: To develop goals and plan service delivery;
[0051] Planning an Individualized Family Service Plan (IFSP) for
children ages birth-2: To identify the major results or outcomes
expected to be achieved for the child and family;
[0052] Instructional strategies: To determine ways to help a child
learn; and
[0053] Measuring progress: To provide a present level of
performance for measuring a child's progress.
[0054] FIG. 4 illustrates a schematic of an example computer or
processing system that may implement the method and system for
assessing the mental state of a user via drawing pattern detection
and machine learning in one embodiment of the present disclosure.
The computer system is only one example of a suitable processing
system and is not intended to suggest any limitation as to the
scope of use or functionality of embodiments of the methodology
described herein. The processing system shown may be 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 the processing system shown in FIG. 4 may
include, but are not limited to, personal computer systems, server
computer systems, thin clients, thick clients, handheld 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.
[0055] The computer system 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. The computer system 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.
[0056] The components of computer system may include, but are not
limited to, one or more processors or processing units 100, a
system memory 106, and a bus 104 that couples various system
components including system memory 106 to processor 100. The
processor 100 may include a program module 102 that performs the
methods described herein. The module 102 may be programmed into the
integrated circuits of the processor 100, or loaded from memory
106, storage device 108, or network 114 or combinations
thereof.
[0057] Bus 104 may represent 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.
[0058] Computer system may include a variety of computer system
readable media. Such media may be any available media that is
accessible by computer system, and it may include both volatile and
non-volatile media, removable and non-removable media.
[0059] System memory 106 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
and/or cache memory or others. Computer system may further include
other removable/non-removable, volatile/non-volatile computer
system storage media. By way of example only, storage system 108
can be provided for reading from and writing to a non-removable,
non-volatile magnetic media (e.g., 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 104 by one or more data media interfaces.
[0060] Computer system may also communicate with one or more
external devices 116 such as a keyboard, a pointing device, a
display 118, etc.; one or more devices that enable a user to
interact with computer system; and/or any devices (e.g., network
card, modem, etc.) that enable computer system to communicate with
one or more other computing devices. Such communication can occur
via Input/Output (I/O) interfaces 110.
[0061] Still yet, computer system can communicate with one or more
networks 114 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 112. As depicted, network adapter 112
communicates with the other components of computer system via bus
104. It should be understood that although not shown, other
hardware and/or software components could be used in conjunction
with computer system. 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.
[0062] 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
non-transitory computer readable storage medium (or media) having
computer readable program instructions thereon for causing a
processor to carry out aspects of the present invention.
[0063] 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.
[0064] 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.
[0065] 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, 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 conventional 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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 block 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.
[0070] 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" and/or "comprising," 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.
[0071] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements, if any, 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
embodiment was 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.
[0072] In addition, while preferred embodiments of the present
invention have been described using specific terms, such
description is for illustrative purposes only, and it is to be
understood that changes and variations may be made without
departing from the spirit or scope of the following claims.
* * * * *
References