U.S. patent application number 16/864353 was filed with the patent office on 2021-11-04 for augmented reality field of view based on sensed user data.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Divya Kannan CHAKRAVARTHI, Kriteshwar Kaur KOHLI, John A. LYONS, Vinod A. VALECHA.
Application Number | 20210343085 16/864353 |
Document ID | / |
Family ID | 1000004811686 |
Filed Date | 2021-11-04 |
United States Patent
Application |
20210343085 |
Kind Code |
A1 |
CHAKRAVARTHI; Divya Kannan ;
et al. |
November 4, 2021 |
AUGMENTED REALITY FIELD OF VIEW BASED ON SENSED USER DATA
Abstract
User-specific augmentation of a real word field of view viewable
through an augmented reality (AR) device is facilitated by a
processor(s) receiving image data representative of a real world
field of view viewable by a user through the AR device, and
receiving sensor data indicative of the user's stress level, which
is related, at least in part, to the user's real world field of
view viewable through the AR device. The processor(s) processes the
image data, based on the user's stress level, to identify one or
more stress-inducing elements to be hidden in the real world field
of view viewable through the AR device. Further, the processor(s)
provides an augmented real world field of view for display to the
user through the AR device, where the one or more stress-inducing
elements are hidden from the user in the augmented real world field
of view.
Inventors: |
CHAKRAVARTHI; Divya Kannan;
(Wappingers Falls, NY) ; KOHLI; Kriteshwar Kaur;
(White Plains, NY) ; VALECHA; Vinod A.; (Pune,
IN) ; LYONS; John A.; (Ottawa, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
1000004811686 |
Appl. No.: |
16/864353 |
Filed: |
May 1, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 20/00 20190101;
G06T 19/006 20130101; G06K 9/00671 20130101; G06F 3/011
20130101 |
International
Class: |
G06T 19/00 20060101
G06T019/00; G06N 20/00 20060101 G06N020/00; G06K 9/00 20060101
G06K009/00; G06F 3/01 20060101 G06F003/01 |
Claims
1. A computer-implemented method comprising: receiving, by one or
more processors, image data on a real world field of view viewable
by a user through an augmented reality (AR) device; receiving, by
the one or more processors, sensor data indicative of a stress
level of the user, where the user's stress level is related, at
least in part, to the real world field of view viewable by the user
through the AR device; based on the user's stress level,
processing, by the one or more processors, the image data to:
identify an element in the real world field of view inducing stress
for the user, the user and the element being in motion relative to
each other; predict, by the one or more processors, that the user
and the element may intersect; identify another element in the real
world field of view inducing stress for the user, the user and the
other element being in motion relative to each other; and predict,
by the one or more processors, that the user and the other element
will not intersect; and providing, by the one or more processors,
an augmented real world field of view for display to the user
through the AR device, where the element is not hidden from the
user in the augmented real world field of view viewable through the
AR device, and the other element is hidden from the user in the
augmented real world field of view viewable through the AR
device.
2. (canceled)
3. The computer-implemented method of claim 1, wherein the element
and the other element each comprise one or more people in the real
world field of view viewable through the AR device.
4. (canceled)
5. The computer-implemented method of claim 1, further comprising
receiving, by the one or more processors, location data for the
user to predict whether the user is approaching a crowded area, and
wherein the processing is further based on the location data
predicting that the user approaching the crowded area.
6. (canceled)
7. The computer-implemented method of claim 1, wherein the
providing comprises: based on identifying the other element for the
user to be hidden, generating, by the one or more processors, a
spatial mapping of the image data around the other element; and
using, by the one or more processors, the spatial mapping to
provide the augmented real world field of view by selectively
hiding the other element.
8. The computer-implemented method of claim 1, further comprising
using machine learning and the sensor data to classify the user's
stress level, and the processing comprises processing the image
data to identify the element and the other element based, at least
in part, on the user's classified stress level.
9. (canceled)
10. The computer-implemented method of claim 1, wherein the sensor
data comprises data indicative of the user's heart rate.
11. A system comprising: a memory; one or more processors in
communication with the memory; and program instructions executable
by the one or more processors via the memory to perform a method
comprising: receiving, by the one or more processors, image data on
a real world field of view viewable by a user through an augmented
reality (AR) device; receiving, by the one or more processors,
sensor data indicative of a stress level of the user, where the
user's stress level is related, at least in part, to the real world
field of view viewable by the user through the AR device; based on
the user's stress level, processing, by the one or more processors,
the image data to: identify an element in the real world field of
view inducing stress for the user, the user and the element being
in motion relative to each other; predict, by the one or more
processors, that the user and the element may intersect; identify
another element in the real world field of view inducing stress for
the user, the user and the other element being in motion relative
to each other; and predict, by the one or more processors, that the
user and the other element will not intersect; and providing, by
the one or more processors, an augmented real world field of view
for display to the user through the AR device, where the element is
not hidden from the user in the augmented real world field of view
viewable through the AR device, and the other element is hidden
from the user in the augmented real world field of view viewable
through the AR device.
12. The system of claim 11, wherein the element and the other
element each comprise one or more people in the real world field of
view viewable through the AR device.
13-14. (canceled)
15. The system of claim 11, wherein the providing comprises: based
on identifying the other element for the user to be hidden,
generating, by the one or more processors, a spatial mapping of the
image data around the other element; and using, by the one or more
processors, the spatial mapping to provide the augmented real world
field of view by selectively hiding the other element.
16. The system of claim 11, further comprising using machine
learning and the sensor data to classify the user's stress level,
and the processing comprises processing the image data to identify
the element and the other element based, at least in part, on the
user's classified stress level.
17. A computer program product comprising: a computer-readable
storage medium having computer-readable code embodied therein, the
computer-readable code being executable by one or more processors
to cause the one or more processors to: receive, by the one or more
processors, image data on a real world field of view viewable by a
user through an augmented reality (AR) device; receive, by the one
or more processors, sensor data indicative of a stress level of the
user, where the user's stress level is related, at least in part,
to the real world field of view viewable by the user through the AR
device; based on the user's stress level, process, by the one or
more processors, the image data to: identify an element in the real
world field of view inducing stress for the user, the user and the
element being in motion relative to each other; predict, by the one
or more processors, that the user and the element may intersect;
identify another element in the real world field of view inducing
stress for the user, the user and the other element being in motion
relative to each other; and predict, by the one or more processors,
that the user and the other element will not intersect; and
provide, by the one or more processors, an augmented real world
field of view for display to the user through the AR device, where
the element is not hidden from the user in the augmented real world
field of view viewable through the AR device, and the other element
is hidden from the user in the augmented real world field of view
viewable through the AR device.
18. The computer program product of claim 17, wherein the element
and the other element each comprise one or more people in the real
world field of view viewable through the AR device.
19-20. (canceled)
Description
BACKGROUND
[0001] Agoraphobia is a type of anxiety disorder in which an
individual is anxious in situations or places where the individual
perceives that their environment is unsafe, with no easy way to
escape. For instance, the individual can fear situations such as
using public transportation, being in open or enclosed spaces,
standing in line, being in a crowd, or simply being outside their
home. An individual with agoraphobia often has a hard time feeling
safe in any public place, especially where crowds gather. The
individual's anxiety can be so overwhelming that the individual may
feel unable to leave their home. Agoraphobia treatment can be
challenging, because it often involves confronting the patient's
fears. Without treatment, it is uncommon for agoraphobia to
resolve. Treatment is typically with a type of counseling referred
to as cognitive behavioral therapy (CBT), which is helpful in
resolving the disorder for only about half of the individuals
counseled.
SUMMARY
[0002] Certain shortcomings of the prior art are overcome and
additional advantages are provided through the provision, in one or
more aspects, of a computer-implemented method, which includes
receiving, by one or more processors, image data representative of
a real world field of view viewable by a user through an augmented
reality (AR) device, and receiving, by the one or more processors,
sensor data indicative of a stress level of the user, where the
user's stress level is related, at least in part, to the real world
field of view viewable by the user through the AR device. Based on
the user's stress level, the one or more processors process the
image data to identify one or more stress-inducing elements for the
user to be hidden in the real world field of view viewable through
the AR device. The one or more processors further provide an
augmented real world field of view for display to the user through
the AR device, where the one or more stress-inducing elements are
hidden from the user in the augmented real world field of view
viewable by the user through the AR device.
[0003] Systems and computer program products relating to one or
more aspects are also described and claimed herein. Further,
services relating to one or more aspects are also described and can
be claimed herein.
[0004] Additional features and advantages are realized through the
techniques described herein. Other embodiments and aspects of the
invention are described in detail herein and are considered a part
of the claimed aspects.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] One or more aspects of the present invention are
particularly pointed out and distinctly claimed as examples in the
claims at the conclusion of the specification. The foregoing and
other objects, features, and advantages of the invention are
apparent from the following detailed description taken in
conjunction with the accompanying drawings in which:
[0006] FIG. 1 is a workflow that illustrates certain aspects of
some embodiments of the present invention;
[0007] FIG. 2 depicts one embodiment of a system, illustrating
certain aspects of an embodiment of the present invention, in
accordance with one or more aspects of the present invention;
[0008] FIG. 3 depicts a block diagram of a computing system which,
in one embodiment, can implement one or more aspects of an
embodiment of the present invention;
[0009] FIG. 4 illustrates various aspects of some embodiments of
the present invention;
[0010] FIG. 5 is a further workflow that illustrates certain
aspects of some embodiments of the present invention;
[0011] FIGS. 6A & 6B depict one embodiment of a real world
field of view viewable through an AR device, and an augmented real
world field of view viewable through the AR device, respectively,
in accordance with one or more aspects of the present
invention;
[0012] FIGS. 7A-7B depict a further workflow illustrating certain
aspects of one or more embodiments of the present invention;
[0013] FIG. 8 depicts one embodiment of another computing system to
implement, or facilitate implementing, one or more aspects of a
field of view processing and augmentation facility, in accordance
with one or more aspects of the present invention;
[0014] FIG. 9 depicts an embodiment of a cloud computing
environment which can facilitate implementing, or be used in
association with, certain aspects of an embodiment of the present
invention; and
[0015] FIG. 10 depicts abstraction model layers according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0016] The accompanying figures, in which like reference numerals
refer to identical or functionally similar elements throughout the
separate views, and which are incorporated in and form a part of
the specification, further illustrate the present invention and,
together with the detailed description, serve to explain aspects of
the present invention. Note in this regard that descriptions of
well-known systems, devices, processing techniques, etc., are
omitted so as not to obscure the invention in detail. It should be
understood, however, that the detailed description and this
specific example(s), while indicating aspects of the invention, are
given by way of illustration only, and not limitation. Various
substitutions, modifications, additions, and/or other arrangements,
within the spirit or scope of the underlying inventive concepts
will be apparent to those skilled in the art from this disclosure.
Note further that numerous inventive aspects and features are
disclosed herein, and unless inconsistent, each disclosed aspect or
feature is combinable with any other disclosed aspect or feature as
desired for a particular application of one or more of the concepts
disclosed herein.
[0017] Note also that illustrative embodiments are described below
using specific code, designs, architectures, protocols, layouts,
schematics, or tools only as examples, and not by way of
limitation. Furthermore, the illustrative embodiments are described
in certain instances using particular software, tools, or data
processing environments only as example for clarity of description.
The illustrative embodiments can be used in conjunction with other
comparable or similarly purposed structures, systems, applications,
or architectures. One or more aspects of an illustrative embodiment
can be implemented in hardware, software, or a combination
thereof.
[0018] As understood by one skilled in the art, program code, as
referred to in this application, can include both software and
hardware. For example, program code in certain embodiments of the
present invention can include fixed function hardware, but other
embodiments can utilize a software-based implementation of the
functionality described. Certain embodiments combine both types of
program code.
[0019] One example of program code, also referred to as one or more
programs or program instructions, is depicted in FIG. 8 as
program/utility 840, having a set (at least one) of program modules
842, which can be stored in memory 823. As a further example, FIG.
8 depicts additional, or alternative, program code implemented as a
field of view processing and augmentation facility or module 801.
As a further example, in FIG. 3, program code implementing one or
more aspects described herein could be stored or resident in main
memory 308, read-only memory 324, disc storage 326, CD-ROM 330,
and/or other peripheral devices of computing environment 300.
[0020] As noted initially, agoraphobia is a type of disorder in
which an individual can become stressed in one or more situations
that might cause the individual to feel anxious and trapped.
Depending on the individual disorder, stressful situations can
include open spaces, public transport, shopping centers, standing
in line, or being in a crowd. People with agoraphobia often have a
hard time feeling safe in any public space, especially where crowds
gather.
[0021] To assist in addressing this disorder, disclosed herein, in
one or more aspects, is the use of augmented reality (AR) to
dynamically modify a real world situational experience of the user
by overlapping or hiding one or more stress-inducing elements to
the user in the real world field of view viewed by the user through
the AR device. For instance, where the user is an agoraphobic
patient, or a patient with social anxiety disorder, large crowds
are overlaid with other objects, or individuals within the crowd
can be removed or hidden entirely from view using spatial mapping
techniques, providing the user with a perception of a smaller
gathering, personalizing the AR viewable image to reduce the user's
anxiety, and making the space feel more open to the user, thereby
reducing the user's stress or anxiety. Over time, as the user's
condition improves, the field of view processing and augmentation
facility can dynamically expose more the of real world field of
view to the user based on the user's currently sensed health data
to assist in the user's treatment plan.
[0022] An augmented reality (AR) device is, for instance, a
wearable glass device, or headset-mounted device, with an
incorporated, or associated, augmented reality system that provides
an interactive experience of a real world environment to a user
where objects or elements that reside in the real world can be
enhanced or modified by computer-generated perceptual information,
including across multiple sensory modalities, if desired. An
augogram is a computer-generated image, in whole or in part, used
to create an augmented reality field of view. An AR device or
system can combine real and virtual worlds, is real-time
interactive, and provides accurate 3-D registration of virtual and
real objects. The overlaid sensory information can be constructive,
that is, additive to the natural environment, or destructive, that
is, masking of the natural environment. The experience can be
seamlessly interwoven with the physical world such that it is
perceived as an immersive aspect of the real environment. In this
way, augmented reality can alter the user's ongoing perception of a
real world environment.
[0023] Advantageously, through a combination of real-time stress
level sensing or measurement, and diminished reality techniques
using an augmented reality (AR) device/system, a
computer-implemented method, system and computer program product
are provided herein which allow an individual or user with, for
instance, social anxiety or agoraphobia, to function in the real
world without fully having addressed their disorder. Over time, the
computer-implemented method, system and computer program product
disclosed herein allow the individual to resolve their condition by
gradually confronting their fears, dependent on the real-time
stress level data obtained for the user as the user functions in
the real world.
[0024] More particularly, embodiments of the present invention
include a computer-implemented method, system, and computer program
product, where program code executing on one or more processors
receives image data representative of a real world field of view
viewable by a user through an augmented reality (AR) device, and
receives sensor data indicative of a current stress level of the
user, where the user's stress level is related, at least in part,
to the real world field of view viewable by the user through the AR
device. Embodiments of the present invention also include program
code executing on one or more processors which processes, based on
the user's stress level, the image data to identify one or more
stress-inducing elements for the user to be hidden in the real
world field of view viewable by the user through the AR device.
Further, embodiments of the present invention include program code
executing on one or more processors that provides an augmented real
world field of view for display to the user through the AR device,
where the one or more stress-inducing elements are hidden from the
user in the augmented real world field of view viewable through the
AR device.
[0025] In certain embodiments of the present invention, providing
the augmented real world field of view for display to the user
through the AR device includes selectively hiding, by the one or
more processors, only the identified one or more stress-inducing
elements in the augmented real world field of view for display to
the user through the AR device.
[0026] In one or more embodiments of the present invention, the one
or more stress-inducing elements include one or more people in the
real world field of view viewable through the AR device. Further,
in one embodiment, program code executing on the one or more
processors determines that the user and the one or more people are
in motion relative to each other, and the identifying includes
predicting by the program code that the user and the one or more
people will not intersect. In one or more embodiments of the
present invention, the program code executing on the one or more
processors receives location data for the user to predict whether
the user is approaching or in a crowded area, and the processing of
the image data is further based on the location data predicting
that the user is approaching or in the crowded area.
[0027] In certain embodiments of the present invention, the real
world field of view viewable by the user through the AR device
includes multiple stress-inducing elements for the user, and the
one or more stress-inducing elements identified to be hidden are
only a portion of the multiple stress-inducing elements viewable by
the user through the AR device, with the portion being less than
all of the multiple stress-inducing elements.
[0028] In one or more embodiments of the present invention,
providing the augmented real world field of view for display to the
user includes, based on identifying the one or more stress-inducing
elements for the user to be hidden, generating by the program code
executing on the one or more processors, a spatial mapping of the
image data around the one or more stress-inducing elements, and
using the spatial mapping to provide the augmented real world field
of view by selectively hiding the one or more stress-inducing
elements.
[0029] In certain embodiments of the present invention, program
code executing on one or more processors uses machine learning and
the sensor data to classify the user's stress level, and the
processing includes processing the image data to identify the one
or more stress-inducing elements for the user based, at least in
part, on the user's classified stress level.
[0030] In one embodiment, the sensor data includes data indicative
of the user's heart rate.
[0031] Embodiments of the present invention are inextricably tied
to computing and provide significantly more than existing
approaches to addressing an individual's anxiety disorder. For
instance, embodiments of the present invention provide program code
executing on one or more processors to exploit the
interconnectivity of various systems, as well as to utilize various
computing-centric data analysis and handling techniques, in order
to receive image data representative of a real world field of view
viewable by a user through an augmented reality (AR) device, and
receive sensor data indicative of a stress level of the user, where
the user's stress level is related, at least in part, the real
world field of view viewable by the user through the AR device, and
based on the user's stress level (e.g., based on the user's stress
level exceeding a threshold), process the image data to identify
one or more stress-inducing elements to be hidden and provide an
augmented real world field of view for display to the user through
the AR device, where the one or more stress-inducing elements are
hidden from the user in the augmented real world field of view.
Both the interconnectivity of the devices and/or computing systems
utilized, and the computer-exclusive data processing techniques
utilized by the program code, enable various aspects of the present
invention. Further, embodiments of the present invention provide
significantly more functionality than existing approaches to
treating an individual with an anxiety disorder, by advantageously
allowing the individual to continue to function in the real world,
while simultaneously addressing the individual's anxiety disorder
through conditioning.
[0032] In embodiments of the present invention, the program code
provides significantly more functionality, including but not
limited to: 1) program code that receives image data representative
of a real world field of view viewable by a user through an
augmented reality (AR) device; 2) program code that receives sensor
data indicative of a stress level of the user, where the user's
stress level is related, and least in part, to the real world field
of view viewable by the user through the AR device; 3) program code
that processes, based on the user's stress level, the image data to
identify one or more stress-inducing elements for the user to be
hidden in the real world field of view viewable through the AR
device; and 4) program code that provides an augmented real world
field of view for display to the user through the AR device, where
the one or more stress-inducing elements are hidden from the user
in the augmented real world field of view viewable through the AR
device.
[0033] By way of example, FIG. 1 depicts one embodiment of a
workflow or process illustrating one or more aspects of some
embodiments of the present invention. In one or more embodiments of
the present invention, program code executing on one or more
processors receives sensor data indicative of a user's stress
level, where the user's stress level is related, at least in part,
the user's real world field of view 100 as viewed through an
augmented reality (AR) device, such as AR glasses or an AR headset.
For example, the sensor data can be from one or more health
measurement or sensor devices worn by or associated with the user,
such as a heart rate monitor or smart watch capable of measuring
the user's heart rate. Program code executing on the one or more
processors processes received image data representative of the
user's real world field of view to identify one or more
stress-inducing elements for the user to be hidden in the real
world field of view viewable by the user through an augmented
reality (AR) device 102. In one or more implementations, based on
the user's stress level, program code executing on one or more
processors selectively hides or removes the one or more
stress-inducing elements from the real world field of view viewable
by the user through the AR device 104. For instance, in one or more
embodiments, only the identified one or more stress-inducing
elements are removed from the modified or augmented real world
field of view displayed to the user through the AR device.
[0034] Note that the particular stress-inducing element to be
hidden is dependent on the individual user, and the user's
condition being addressed. Agoraphobia, or a social anxiety
disorder, is discussed herein in connection with one or more
embodiments of the invention, by way of example only. For instance,
in one or more implementations, the one or more stress-inducing
elements could be one or more animals, such as one or more dogs,
cats, etc., or any other stress-inducing element or object for the
particular user. Advantageously, the computer-implemented method,
system, and program product disclosed herein allow a user to
continue to function in the real world by selectively hiding or
blocking one or more stress-inducing elements from the augmented
real world field of view display to the user through the AR device
based on the received sensor data indicative of the user's current
stress level. Note also, although described with reference to heart
rate, the sensor data could measure other biological
characteristics indicative of stress or anxiety, such as blood
pressure, perspiration, or breathing.
[0035] FIG. 2 depicts one embodiment of a system 200, illustrating
certain aspects of an embodiment of the present invention. System
200 includes various computing devices, including one or more
mobile devices and one or more sensor(s) 201, such as an augmented
reality (AR) device 210, one or more sensors 220, such as sensors
worn by or associated with the user of the system, and one or more
mobile computing resources 230, such as a smartphone or other
mobile computing resource associated with the user. In the
embodiment depicted, system 200 also includes one or more remote
computing resources 240 in communication with AR device 210,
sensors 220 and/or mobile computing resource(s) 230 across one or
more networks 205. By way of example, in one or more embodiments,
AR device 210, sensor(s) 220, mobile computing resource(s) 230, and
remote computing resource(s) 240, can each have a wireless
communication capability for communicating data to facilitate
processing, as described herein. By way of example, network(s) 205
can be, for instance, a telecommunications network, a local-area
network (LAN), a wide-area network (WAN), such as the Internet, or
a combination thereof, and can include wired, wireless, fiber-optic
connections, etc. The network(s) can include one or more wired
and/or wireless networks that are capable of receiving and
transmitting data, including image data, sensor data, and location
data, such as discussed herein.
[0036] By way of example, AR device 210 can include or have
associated therewith digital image capture components 211, such as
conventional image or video camera components and related sensors.
Further, computing resource(s) 210 can include an image processing
module 212. Note in this regard that, in the embodiment of FIG. 2,
system 200 includes, by way of example, image processing module 212
associated with AR device 210, as well as, or alternatively, image
processing module 231 associated with mobile computing resource(s)
230, and image processing module 244 associated with remote
computing resource(s) 240. This is one implementation only. In one
or more other implementations, the image processing module (or
program code) could be associated with only one of the computing
resources or AR device, or otherwise located. In one embodiment,
image processing module 212 can include image-video-based
processing for, for instance, object detection or element detection
using conventional detection algorithms. For instance, where people
are the element to be detected in the image data, facial
recognition code can be used to detect people in the user's field
of view. Additionally, AR device 210 includes transmitter and/or
receiver logic or circuitry 213, and a display 214 for displaying,
for instance, the real world field of view of the user of the
system, or an augmented version of the real world field of view,
such as disclosed herein. In one or more embodiments, display 214
of AR device 210 can include augmented reality glasses or an
augmented reality headset worn by the user.
[0037] In the embodiment illustrated, sensors 220 include, by way
of example, one or more stress-related sensors 221, one or more
vision sensors 222, and one or more geolocation sensors 223. Note
that sensors 220 can be associated with or worn by the user, and
can be separate from AR device 210 and mobile computing resource(s)
230, or integrated within one or more both of AR device 210 and
mobile computing resource(s) 230. In one or more embodiments,
stress-related sensor(s) 221 can be, or can include, for instance,
a heart rate sensor, blood pressure sensor, perspiration sensor,
etc., worn by the user, and which produces sensor data related to
or indicative of the user's current level of stress or anxiety.
Vision sensor(s) 222 can include, for instance, image capture
components and/or object or element recognition software to, for
instance, facilitate identifying one or more stress-inducing
elements (e.g., people) within image data representative of a real
world field of view viewable by the user through AR device 210.
Geolocation sensor(s) 223 can be, for instance, a global
positioning sensor, to identify a geographic location of a user,
and to facilitate correlating that geographic location to an area
of historically high-traffic, such as an area that is typically
crowded, such as an airport, train station, arena, etc. Further,
geolocation sensor(s) 223 and related program code could assist in
identifying a currently congested area, such as by identifying the
presence of a large number of mobile devices in close proximity,
where the devices are associated with different people.
[0038] Mobile computing resource(s) 230 can be, for instance,
associated with AR device 210, or separate from AR device 210, in
which case mobile computing resource(s) 230 can be in wireless
communication with AR device 210. By way of example, mobile
computing resource(s) 230 can be a smartphone, wireless computer,
tablet, personal digital assistant (PDA), a laptop computer, etc.,
owned by or associated with the user of system 200. In the
embodiment illustrated, mobile computing resource(s) 230 can
further include an image processing module 231 with program code
configured to perform one or more aspects of the image processing
and augmentation facility disclosed herein. Mobile computing
resource(s) 230 further includes transmitter and/or receiver logic
or circuitry 232 for facilitating data transfer from or to AR
device 210 and sensor(s) 220, as well as remote computing
resource(s) 240.
[0039] Note that AR device 210, sensor(s) 220, and mobile computing
resource(s) 230 can include additional and/or different components,
modules, sensors, sub-systems, etc., without departing from the
spirit of the present invention.
[0040] Remote computing resource(s) 240 can be, in one or more
embodiments, a cloud-based computing resource which includes
program code 241 executing on one or more processors to implement
one or more aspects of the image processing and augmentation
facility disclosed herein. In the embodiment illustrated, program
code 241 includes, or has associated therewith, a learning agent
242, such as a neural network, which uses one or more models to
provide one or more functional aspects disclosed herein, and an
image processing module 244, again, to facilitate implementing one
or more aspects of image processing and augmentation as disclosed
herein.
[0041] Note again that although image processing module 212 is
shown associated with AR device 210, image processing module 231 is
associated with mobile computing resource(s) 230, and image
processing module(s) 244 is associated with remote computing
resource(s) 240, this represents one distributed embodiment only of
the concepts disclosed. For instance, in one or more other
embodiments, AR device 210 may be in communication with mobile
computing resource(s) which processes the image and provides the
augmented real world field of view for display to the user, and/or
can be in communication with remote computing resource(s) 240 for
image processing module 244 to process the image data and provide
the augmented real world field of view for display to the user
through the AR device. Note that one or more of the image
processing modules of AR device 210, mobile computing resource(s)
230, and/or remote computing resource(s) 240 can include program
code to execute on one or more processors to implement processing
as described herein to, for instance, allow an individual with an
anxiety disorder to continue to function in the real world, while
simultaneously helping the individual in addressing the disorder
through conditioning tailored specifically to the user's current
level of stress. This is accomplished by selectively overlaying or
hiding one or more identified stress-inducing elements for the user
within the user's augmented field of view as seen through the AR
device.
[0042] By way of example, FIG. 3 is one example of a processing or
computing environment in which illustrative embodiments can be
implemented. FIG. 3 is only an example, and not intended to imply
limitation with regard to the environment in which different
embodiments can be implemented. A particular implementation can
have any number of modifications to the depicted environment.
[0043] Referring to FIG. 3, a block diagram of a data processing
system in which illustrative embodiments can be implemented is
shown by way of further example. Data processing system 300 is an
example of a computing system, such as AR device 210, mobile
computing resource(s) 230, and/or remote computing resource(s) 240
in FIG. 2, in which computer-usable program code or instructions
implementing processes such as disclosed herein can be located, in
one or more embodiments.
[0044] In the depicted example, data processing system 300 includes
a hub architecture including a north bridge and memory controller
hub (NB/MCH) 302 and a south bridge and input/output (I/O)
controller hub (SB/ICH) 304. Processing unit 306, main memory 308,
and graphics processor 310 are coupled to north bridge and memory
controller hub 302. Processing unit 306 can contain one or more
processors and can even be implemented using one or more
heterogeneous processor systems. Graphics processor 310 can be
coupled to the NB/MCH through an accelerated graphics port (AGP),
for example.
[0045] In the depicted example, a local area network (LAN) adapter
312 is coupled to south bridge and I/O controller hub 304 and audio
adapter 316, keyboard and mouse adapter 320, modem 322, read only
memory (ROM) 324, universal serial bus (USB) and other ports 332,
and PCI/PCIe devices 334 are coupled to south bridge and I/O
controller hub 304 through bus 338, and hard disk drive (HDD) 326
and CD-ROM 330 are coupled to south bridge and I/O controller hub
304 through bus 340. PCI/PCIe devices can include, for example,
Ethernet adapters, add-in cards, and PC cards for notebook
computers. PCI uses a card bus controller, while PCIe does not. ROM
324 can be, for example, a flash binary input/output system (BIOS).
Hard disk drive 326 and CD-ROM 330 can use, for example, an
integrated drive electronics (IDE) or serial advanced technology
attachment (SATA) interface. A super I/O (SIO) device 336 can be
coupled to south bridge and I/O controller hub 304.
[0046] An operating system runs on processing unit 306 and
coordinates and provides control of various components within data
processing system 300 in FIG. 3. The operating system can be a
commercially available operating system. An object oriented
programming system can run in conjunction with the operating system
and provide calls to the operating system from programs or
applications executing on data processing system 300.
[0047] Instructions for the operating system, the object-oriented
programming system, and applications or programs can be located on
storage devices, such as hard disk drive 326, and can be loaded
into main memory 308 for execution by processing unit 306. The
processes of the illustrative aspects discussed herein can be
performed by processing unit 306 using computer implemented
instructions, which can be located in a memory such as, for
example, main memory 308, read only memory 324, or in one or more
peripheral devices.
[0048] Note that the hardware embodiment depicted in FIG. 3 can
vary depending on the desired implementation. Other internal
hardware or peripheral devices, such as flash memory, equivalent
non-volatile memory, or optical disk drives and the like, can be
used in addition to or in place of certain hardware depicted. Also,
the processes of the illustrative aspects described herein can be
applied to other hardware environments, such as to a multiprocessor
data processing system.
[0049] In one or more implementations, data processing system 300
can be a mobile electronic device or a server computer resource,
and can be generally configured with flash memory to provide
non-volatile memory for storing operating system files and/or
user-generated data. A bus system can include one or more buses,
such as a system bus, an I/O bus and a PCI bus. Of course the bus
system can be implemented using any type of communications fabric
or architecture that provides for a transfer of data between
different components or devices attached to the fabric or
architecture. A communications unit can include one or more devices
used to transmit and receive data, such as a modem or a network
adapter. A memory can be, for example, main memory 308 or a cache
such as found in north bridge and memory controller hub 302. A
processing unit can include one or more processors or CPUs. Those
skilled in the art should note that the depicted system example of
FIG. 3, as well as other examples referenced herein, are not meant
to imply architectural limitations. As noted, data processing
system 300 can be implemented as part of AR device 210, mobile
computer resource(s) 230 and/or remote computer resource(s) 240 in
FIG. 2, and is presented by way of example only.
[0050] The illustrated systems of FIGS. 2-3 can vary depending on
the implementation. Other components, hardware or peripheral
devices, such as flash memory, equivalent non-volatile memory, or
optical disk drives and the like, can be used in addition to or in
place of certain components or hardware depicted in FIGS. 2-3. In
addition, the processes of the illustrative embodiments can be
applied to a multiprocessor data processing system. Examples of
additional computing resource(s) or computer system(s) which can
implement one or more aspects disclosed herein are also described
below with references to FIGS. 8-10. Note also that, depending on
the implementation, one or more aspects of the AR device and/or the
computing resources can be associated with, licensed by, subscribed
to by, etc., a company or organization operating, owning, etc., the
AR device/system.
[0051] As illustrated in FIG. 2, and as noted above, program code
241 executing on computing resource(s) 240 can include a learning
agent which continually learns (in one embodiment) and updates the
patterns that form one or more models 243 used, for instance, by
the image processing module 244 to, for instance, process sensor
data indicative of a stress level of the user, identify one or more
stress-inducing elements for a particular user to be hidden in the
real world field of view viewable by the user through the AR
device, as well as to provide an augmented real world field of view
for display to the user through the AR device, where the one or
more stress-inducing elements are hidden from the user in the
augmented real world field of view viewable through the AR device.
In particular, the number of stress-inducing elements to be hidden,
location of the stress-inducing elements to be hidden, type of
stress-inducing elements to be hidden, etc., can all be customized
to the particular user based on the user's disorder and health
condition, including the user's current stress level dynamically
monitored via the sensor data. Note that these aspects can change
over time, for instance, as the user makes improvements to
overcoming the disorder. Examples of how the process can be used in
one or more applications are described further below, by way of
example.
[0052] In one or more embodiments, program code 241 executing on
remote computing resource(s) 240 applies machine learning
algorithms of machine learning agent 242 to generate and train the
one or more models 243, which the program code then utilizes to
process the sensor data and the image data, and to provide the
augmented real world field of view for display to the user through
the AR device, as described herein. In an initialization or
learning stage, program code 241 can train the algorithm(s) based
on patterns for the given user of the AR device/system. Note again
that this is one embodiment only. In one or more other embodiments,
the machine learning agent and models could run on or be associated
with mobile computing resource(s) 230 and/or AR device 210.
[0053] FIG. 4 is an example machine-learning training system 400
that can be utilized to perform machine-learning, such as described
herein. Training data 410 used to train the model in embodiments of
the present invention can include a variety of types of data, such
as data generated by the AR device and/or sensors. Program code, in
embodiments of the present invention, can perform machine-learning
analysis to generate data structures, including algorithms utilized
by the program code to perform the image processing and
augmentation facility, as disclosed herein. Machine-learning (ML)
solves problems that cannot be solved by numerical means alone. In
this ML-based example, program code extracts various
features/attributes from training data 410, which can be stored in
memory or one or more databases 420. The extracted features 415 are
utilized to develop a predictor function, h(x), also referred to as
a hypothesis, which the program code utilizes as a machine-learning
model 430. In identifying machine-learning model 430, various
techniques can be used to select features (elements, patterns,
attributes, etc.), including but not limited to, diffusion mapping,
principle component analysis, recursive feature elimination (a
brute force approach to selecting features), and/or a random
forest, to select the attributes related to the user's condition,
and/or to the image processing and augmentation. Program code can
utilize a machine-learning algorithm 440 to train machine-learning
model 430 (e.g., the algorithms utilized by the program code),
including providing weights for conclusions, so that the program
code can train any predictor or performance functions included in
the machine-learning model 440, such as whether the user is likely
to intersect with one or more stress-inducing element(s) based on
determined trajectories. The conclusions can be evaluated by a
quality metric 450. By selecting a diverse set of training data
410, the program code trains the machine-learning model(s) 440 to
identify and weight various attributes (e.g., features, patterns)
that correlate to enhance performance of the machine-learning
implemented by the computing resource(s) and/or the AR device.
[0054] The model(s) used by each respective AR device and/or
computing resource(s) can be self-learning, as program code updates
the model(s) based on feedback received during performance of the
stress level evaluation, image processing, and/or image
augmentation, as described herein. For instance, as the user's
condition improves, and the sensor data indicates that the user's
stress level is lower, a fewer number of the stress-inducing
elements, such as a fewer number of people, can be hidden from the
user in the augmented real world field of view presented to the
user through the AR device.
[0055] In some embodiments of the present invention, the program
code executing on the respective computing resource(s) of system
200 (FIG. 2) utilizes existing machine-learning analysis tools or
agents to create, and tune, each respective model, based, for
instance, on data obtained, for instance, from the AR device, or
the sensors.
[0056] Some embodiments of the present invention can utilize IBM
Watson.RTM. as learning agent. IBM Watson.RTM. is a registered
trademark of International Business Machines Corporation, Armonk,
N.Y., USA. In embodiments of the present invention, the respective
program code can interface with IBM Watson application programming
interfaces (APIs) to perform machine-learning analysis of obtained
data. In some embodiments of the present invention, the respective
program code can interface with the application programming
interfaces (APIs) that are part of a known machine-learning agent,
such as the IBM Watson.RTM. application programming interface
(API), a product of International Business Machines Corporation, to
determine impacts of data on an operational model, and to update
the respective model, accordingly.
[0057] In some embodiments of the present invention, certain of the
APIs of the IBM Watson API include a machine-learning agent (e.g.,
learning agent) that includes one or more programs, including, but
not limited to, natural language classifiers, Retrieve-and-Rank
(i.e., a service available through the IBM Watson.RTM. developer
cloud that can surface most-relevant information from a collection
of documents), concepts/visualization insights, tradeoff analytics,
document conversion, natural language processing, and/or
relationship extraction. In an embodiment of the present invention,
one or more programs can be provided to analyze data obtained by
the program code across various sources utilizing one or more of,
for instance, a natural language classifier, Retrieve-and-Rank
APIs, and tradeoff analytics APIs. In operation, the program code
can collect and save machine-learned data used by the
machine-learning agent.
[0058] In some embodiments of the present invention, the program
code utilizes a neural network to analyze collected data relative
to a user to generate the operational model(s). Neural networks are
a programming paradigm which enable a computer to learn from
observational data. This learning is referred to as deep learning,
which is a set of techniques for learning in neural networks.
Neural networks, including modular neural networks, are capable of
pattern (e.g., state) recognition with speed, accuracy, and
efficiency, in situations where data sets are multiple and
expansive, including across a distributed network, including but
not limited to, cloud computing systems. Modern neural networks are
non-linear statistical data modeling tools. They are usually used
to model complex relationships between inputs and outputs, or to
identify patterns (e.g., states) in data (i.e., neural networks are
non-linear statistical data modeling or decision making tools). In
general, program code utilizing neural networks can model complex
relationships between inputs and outputs and identify patterns in
data. Because of the speed and efficiency of neural networks,
especially when parsing multiple complex data sets, neural networks
and deep learning provide solutions to many problems in
multi-source processing, which the program code, in embodiments of
the present invention, can accomplish when managing machine-learned
data sets between devices.
[0059] In general, the image processing and augmentation facilities
disclosed herein use augmented reality to simplify, via an
augmented reality device, the real world field of view viewable by
a user so that, for instance, a user with social anxiety disorder,
sees fewer people within the user's viewable environment than are
actually there. In one or more embodiments, any person within the
field of view of the user whose path is unlikely to cross the
user's path can be digitally edited out in the augmented real world
field of view displayed to the user through the AR device, or can
be overlaid with another object, creating a more calming
environment for the user to see. This process is implemented
dynamically, using the systems disclosed herein, in response to
sensor data from sensors worn by the user. In this manner, the
amount of alternation to the user's viewable environment is
increased or decreased according to the user's current level of
stress or anxiety. Advantageously, real-time anxiety-reducing
content is delivered to the user through the AR device using the
image processing and augmentation facility disclosed herein. For
instance, a real-time reduction in the number of people in the
user's field of view can be achieved where the individual user has
a fear of large crowds. Further, displayed content can be modified
dynamically whenever sensor data indicates a change in stress or
anxiety in the user, all while allowing the user to operate in the
real world. In this manner, the user is able to gradually be
reintroduced to the real environment as their stress levels drop.
In one or more embodiments, sensor data and machine learning are
used to detect an elevated stress level, for instance, above one or
more predetermined thresholds, and to take action to alter or
augment the user's real world field of view viewable through the AR
device. Through the combination of real-time stress level
measurement and diminished reality AR techniques, the system allows
the user with social anxiety or agoraphobia to function in public,
while also conditioning the user to overcome the disorder.
[0060] As a specific example, an individual user may suffer from a
social anxiety disorder, but need to go out in public to run
errands. Public shopping centers present a challenge for the
individual due to the large number of people present, and the
individual's fear of having to interact with strangers or casual
acquaintances. The individual makes use of the system disclosed
herein, which combines one or more sensors to dynamically measure
the user's level of stress, and an augmented reality headset to
provide assistance to the user. As the user enters the shopping
center, sensors in the system track people within the user's field
of view through the AR device, and calculate their trajectory,
along with the user's trajectory, to determine probability that
trajectories might intersect. If the system detects that the user
is experiencing an elevated level of stress, and the probability
that one or more people within the shopping center have a
trajectory unlikely to intersect with the user's, for instance,
below a configurable threshold, then the system automatically
removes those people from the user's augmented field of view
viewable through the AR device. People whose trajectory might
intersect with the user's trajectory would remain in view to avoid
potential collision. The system can further make use of the user's
measured stress levels to determine how many people to remove.
Thus, as the user becomes more used to crowds, and the user's
stress level lowers in the presence of crowds, the system will make
fewer adjustments in the augmented real world field of view that
the user sees, for instance, removing fewer people as the user's
anxiety level lowers, or more people as the user's anxiety level
increases.
[0061] FIG. 5 depicts one detailed implementation of image
processing and augmentation, in accordance with one or more aspects
of the present invention. In FIG. 5, a user goes out in public
wearing the AR device and associated sensors 500, with the AR
device being switched on 502, and the sensors collecting data. In
the embodiment of FIG. 5, the sensors include sensor data to
measure the user's stress level 504, as well as vision sensors to
identify stress-inducing elements in the user's path 506, and a
geolocation sensor to provide data to assist in identifying crowded
areas 508. The sensor data can be provided as streaming sensor data
510 to one or more computing resources 522. In one or more
embodiments, the image processing and augmentation facility 520
includes one or more machine learning algorithms to, for instance,
classify a stress-inducing event of the user, determine and
identify one or more stress-inducing elements in the user's field
of view, and determine trajectories of the one or more
stress-inducing elements 524. For instance, streaming sensor data
is passed to one or more machine learning algorithms to, for
instance, predict potentially anxiety-causing situations for the
user. Heart rate monitoring, geolocation data, traffic data, and
vision sensor data, can all be used as time-series features in an
LSTM or RNN, which continuously updates predictions of a user's
propensity to have a high-stress or anxiety event. Continuous
determination of stress or anxiety levels can be used, and the
number of stress-inducing elements can be reduced, as the user's
stress level reduces. For instance, LSTM predictions can be
gathered throughout the use of the augmented reality device/system,
and as the user's level of anxiety is reduced, the amount of
diminished reality displayed to the user by the AR device can also
be reduced.
[0062] As illustrated in FIG. 5, processing determines whether
there are one or more candidate stress-inducing elements for
removal 526. If "no", then there is no modification, or no further
modification, to the real world field of view viewable by the user
through the augmented reality device 529. Assuming that there are
one or more stress-inducing elements in the user's field of view to
be removed, then spatial mapping of the environment surrounding the
one or more stress-inducing elements to be removed can be employed
530. This can be done through a mesh-mapping system that is
in-built in standard AR devices. Region tracking with 3-D positions
can then be performed through simultaneous localization and mapping
(SLAM) techniques 532. Existing APIs for augmented reality systems
help manage spatial mapping of an environment, as well as
performing post-processing operations on the spatial mapping. In
one or more implementations, stress-inducing element tracking can
be automatic due to the SLAM being performed during the spatial
mapping stage. SLAM continually determines, for instance, the
camera's position relative to the origin of the environment, and
all 3-D locations are mapped relative to the origin as well. Once
an object or element has been selected, its 3-D location within the
spatial mapping is all that is needed to locate the object per
frame. Processing can then perform element removal or diminishing
534. For instance, in-painting can be used, which relies on the
idea that patterns are common in nature and often repeated. By
repeating nearby patterns in front of the selected region, the
element will appear to vanish. A neural network can also be used to
learn and repeat patterns from similar images to provide a
realistic diminished result. Post-processing 536 can then be
performed to provide the augmented real world field of view image
to the user's AR display 536. The augmented real world field of
view is then displayed to the user via the AR device 538.
[0063] FIGS. 6A & 6B depict one embodiment of a real world
field of view 600 viewable through an AR device 602 which includes
one or more stress-inducing elements 601, that is, one or more
people in the case of a user with a social anxiety disorder. In
these figures, FIG. 6A represents the actual real world field of
view without any augmentation, while FIG. 6B depicts an augmented
real world field of view seen by the user through the augmented
reality device, where multiple stress-inducing elements (multiple
people in this example) have been removed or hidden from the user's
view, while others remain. The ones remaining may be selected or
identified to remain, since machine learning predicts there is a
possibility or probability that the user's path may intersect with,
or come close to the paths of, those remaining individuals.
[0064] FIGS. 7A-7B depict a further embodiment of program code
processing, in accordance with one or aspects of the present
invention.
[0065] Referring collectively to FIGS. 7A & 7B, program code
executing on one or more processors implements a process 700 which
includes receiving, by one or more processors, image data
representative of a real world field of view viewable by a user
through an augmented reality (AR) device 702, and receiving, by the
one or more processors, sensor data indicative of a stress level of
a user, where the user's stress level is related, at least in part,
to the real world field of view viewable by the user through the AR
device 704. Based on the user's stress level, the one or more
processors process the image data to identify one or more
stress-inducing elements for the user to be hidden in the real
world field of view viewable through the AR device 706. The one or
more processors provide an augmented real world field of view for
display to the user through the AR device, where the one or more
stress-inducing elements are hidden from the user in the augmented
real world field of view viewable through the AR device 708.
[0066] In one or more embodiments, providing the augmented real
world field of view for display includes selectively hiding, by the
one or more processors, only the identified one or more
stress-inducing elements in the augmented real world field of view
for display to the user through the AR device 710.
[0067] In certain embodiments, the one or more stress-inducing
elements include one or more people in the real world field of view
viewable through the AR device 712. In one embodiment, where the
user and one or more people are in motion relative to each other,
the identifying includes predicting, by the one or more processors,
that the user and the one or more people will not intersect 714. In
one or more embodiments, the process also includes receiving, by
the one or more processors, location data for the user to predict
whether the user is approaching a crowded area, and the processing
is further based on the location data resulting in a prediction
that the user is approaching a crowded area 716.
[0068] In one or more implementations, the real world field of view
viewable by the user through the AR device includes multiple
stress-inducing elements for the user, with the one or more
stress-inducing elements being only a portion of the multiple
stress-inducing elements, the portion being less than all of the
multiple stress-inducing elements 718.
[0069] In one or more embodiments, providing the augmented real
world field of view for display further includes a process 720,
which includes generating, based on identifying the one or more
stress-inducing elements to be hidden, a spatial mapping of the
image data around the one or more stress-inducing elements 722, and
using the spatial mapping to provide the augmented real world field
of view by selectively hiding the one or more stress-inducing
elements 724.
[0070] In one or more embodiments, the process further includes
using machine learning and the sensor data to classify the user's
stress level, and the processing includes processing the image data
to identify the one or more stress-inducing elements for the user
based, at least in part, on the user's classified stress level
726.
[0071] In one or more embodiments, the user and the one or more
stress-inducing elements are in motion relative to each other, and
the identifying includes predicting, by the one or more processors,
that the user and the one or more stress-inducing elements will not
intersect 728.
[0072] In one embodiment, the sensor data includes data indicative
of the user's heart rate 730.
[0073] Further exemplary embodiments of a computing environment to
implement one or more aspects of the present invention are
described below with reference to FIGS. 8-10.
[0074] By way of further example, FIG. 8 depicts one embodiment of
a computing environment 800, which includes a computing system 812.
Examples of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer system
812 include, but are not limited to, a server, a desktop computer,
a work station, a wireless computer, a handheld or laptop computer
or device, a mobile phone, a programmable consumer electronic
device, a tablet, a personal digital assistant (PDA), and the
like.
[0075] Computing system 812 can be described in the general context
of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types.
[0076] As depicted in FIG. 8, computing system 812, is shown in the
form of a general-purpose computing device. The components of
computing system 812 can include, but are not limited to, one or
more processors or processing units 816, a system memory 823, and a
bus 818 that couples various system components including system
memory 823 to processor 816.
[0077] In one embodiment, processor 816 may be based on the
z/Architecture offered by International Business Machines
Corporation, or other architectures offered by International
Business Machines Corporation or other companies.
[0078] Bus 818 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 Interconnect
(PCI) bus.
[0079] Computing system 812 can include a variety of computer
system readable media. Such media may be any available media that
is accessible by computing system 812, and it includes both
volatile and non-volatile media, removable and non-removable
media.
[0080] System memory 823 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
830 and/or cache memory 832. Computing system 812 can further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 834 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 could be provided. In such
instances, each can be connected to bus 818 by one or more data
media interfaces. As described below, memory 823 can include at
least one program product having a set (e.g., at least one) of
program modules or code that are configured to carry out the
functions of embodiments of the invention.
[0081] Program/utility 840, having a set (at least one) of program
modules 842, can be stored in memory 832 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, one or more application programs, other program
modules, and program data or some combination thereof, can include
an implementation of a networking environment. Program modules 842
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein. Alternatively, a
field of view processing and augmentation facility, module, logic,
etc., 801 can be provided within computing environment 812, as
disclosed herein.
[0082] Computing system 812 can also communicate with one or more
external devices 814 such as a keyboard, a pointing device, a
display 824, etc.; one or more devices that enable a user to
interact with computing system 812; and/or any devices (e.g.,
network card, modem, etc.) that enable computing system 812 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 822.
Still yet, computing system 812 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 820. As depicted, network adapter 820 communicates
with the other components of computing system, 812, via bus 818. It
should be understood that although not shown, other hardware and/or
software components could be used in conjunction with computing
system 812. 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.
[0083] One or more aspects may relate to or use cloud
computing.
[0084] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of certain teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0085] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0086] Characteristics are as follows:
[0087] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0088] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0089] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0090] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0091] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0092] Service Models are as follows:
[0093] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based email). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0094] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0095] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0096] Deployment Models are as follows:
[0097] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0098] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0099] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0100] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0101] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0102] A cloud computing node can include a computer system/server,
such as the one depicted in FIG. 8. Computer system/server 812 of
FIG. 8 can 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. Computer system/server 812 is capable of being implemented
and/or performing any of the functionality set forth
hereinabove.
[0103] Referring now to FIG. 9, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 can comprise one or more cloud computing nodes 10 with which
local computing devices used by cloud consumers, such as, for
example, personal digital assistant (PDA) or cellular telephone
54A, desktop computer 54B, laptop computer 54C, and/or automobile
computer system 54N may communicate. Nodes 10 may communicate with
one another. They may be grouped (not shown) physically or
virtually, in one or more networks, such as Private, Community,
Public, or Hybrid clouds as described hereinabove, or a combination
thereof. This allows cloud computing environment 50 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 54A-N shown in FIG. 9 are intended to be illustrative only
and that computing nodes 10 and cloud computing environment 50 can
communicate with any type of computerized device over any type of
network and/or network addressable connection (e.g., using a web
browser).
[0104] Referring to FIG. 10, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 9) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 10 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
[0105] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0106] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0107] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
[0108] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and field
of view and augmentation processing 96.
[0109] 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 skills 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 skills in the art to understand the embodiments disclosed
herein.
[0110] 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 can 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.
[0111] 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.
[0112] 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.
[0113] 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.
[0114] 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.
[0115] 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.
[0116] 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.
[0117] 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.
[0118] In addition to the above, one or more aspects may be
provided, offered, deployed, managed, serviced, etc. by a service
provider who offers management of customer environments. For
instance, the service provider can create, maintain, support, etc.
computer code and/or a computer infrastructure that performs one or
more aspects for one or more customers. In return, the service
provider may receive payment from the customer under a subscription
and/or fee agreement, as examples. Additionally or alternatively,
the service provider may receive payment from the sale of
advertising content to one or more third parties.
[0119] In one aspect, an application may be deployed for performing
one or more embodiments. As one example, the deploying of an
application comprises providing computer infrastructure operable to
perform one or more embodiments.
[0120] As a further aspect, a computing infrastructure may be
deployed comprising integrating computer readable code into a
computing system, in which the code in combination with the
computing system is capable of performing one or more
embodiments.
[0121] As yet a further aspect, a process for integrating computing
infrastructure comprising integrating computer readable code into a
computer system may be provided. The computer system comprises a
computer readable medium, in which the computer medium comprises
one or more embodiments. The code in combination with the computer
system is capable of performing one or more embodiments.
[0122] Although various embodiments are described above, these are
only examples. For example, computing environments of other
architectures can be used to incorporate and use one or more
embodiments. Further, different instructions, instruction formats,
instruction fields and/or instruction values may be used. Many
variations are possible.
[0123] Further, other types of computing environments can benefit
and be used. As an example, a data processing system suitable for
storing and/or executing program code is usable that includes at
least two processors coupled directly or indirectly to memory
elements through a system bus. The memory elements include, for
instance, local memory employed during actual execution of the
program code, bulk storage, and cache memory which provide
temporary storage of at least some program code in order to reduce
the number of times code must be retrieved from bulk storage during
execution.
[0124] Input/Output or I/O devices (including, but not limited to,
keyboards, displays, pointing devices, DASD, tape, CDs, DVDs, thumb
drives and other memory media, etc.) can be coupled to the system
either directly or through intervening I/O controllers. Network
adapters may also be coupled to the system to enable the data
processing system to become coupled to other data processing
systems or remote printers or storage devices through intervening
private or public networks. Modems, cable modems, and Ethernet
cards are just a few of the available types of network
adapters.
[0125] 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 "comprise" (and any form of comprise, such as
"comprises" and "comprising"), "have" (and any form of have, such
as "has" and "having"), "include" (and any form of include, such as
"includes" and "including"), and "contain" (and any form contain,
such as "contains" and "containing") are open-ended linking verbs.
As a result, a method or device that "comprises", "has", "includes"
or "contains" one or more steps or elements possesses those one or
more steps or elements, but is not limited to possessing only those
one or more steps or elements. Likewise, a step of a method or an
element of a device that "comprises", "has", "includes" or
"contains" one or more features possesses those one or more
features, but is not limited to possessing only those one or more
features. Furthermore, a device or structure that is configured in
a certain way is configured in at least that way, but may also be
configured in ways that are not listed.
[0126] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below, if any, 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 one or more aspects of the invention and
the practical application, and to enable others of ordinary skill
in the art to understand one or more aspects of the invention for
various embodiments with various modifications as are suited to the
particular use contemplated.
* * * * *