U.S. patent application number 16/548388 was filed with the patent office on 2021-02-25 for generating concept images of human poses using machine learning models.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Samarth Bharadwaj, Saneem Chemmengath, Suranjana Samanta, Karthik Sankaranarayanan.
Application Number | 20210056736 16/548388 |
Document ID | / |
Family ID | 1000005381775 |
Filed Date | 2021-02-25 |
![](/patent/app/20210056736/US20210056736A1-20210225-D00000.png)
![](/patent/app/20210056736/US20210056736A1-20210225-D00001.png)
![](/patent/app/20210056736/US20210056736A1-20210225-D00002.png)
![](/patent/app/20210056736/US20210056736A1-20210225-D00003.png)
![](/patent/app/20210056736/US20210056736A1-20210225-D00004.png)
![](/patent/app/20210056736/US20210056736A1-20210225-D00005.png)
![](/patent/app/20210056736/US20210056736A1-20210225-D00006.png)
![](/patent/app/20210056736/US20210056736A1-20210225-D00007.png)
United States Patent
Application |
20210056736 |
Kind Code |
A1 |
Bharadwaj; Samarth ; et
al. |
February 25, 2021 |
GENERATING CONCEPT IMAGES OF HUMAN POSES USING MACHINE LEARNING
MODELS
Abstract
Methods, systems, and computer program products for generating
concept images of human poses using machine learning models are
provided herein. A computer-implemented method includes identifying
one or more events from input data by applying a machine learning
recognition model to the input data, wherein the identifying
comprises (i) detecting multiple entities from the input data and
(ii) determining one or more behavioral relationships among the
multiple entities in the input data; generating, using a machine
learning interpretability model and the identified events, one or
more images illustrating one or more human poses related to the
identified events; outputting the one or more generated images to
at least one user; and updating the machine learning recognition
model based at least in part on (i) the one or more generated
images and (ii) input from the at least one user.
Inventors: |
Bharadwaj; Samarth;
(Bangalore, IN) ; Chemmengath; Saneem; (Bangalore,
IN) ; Samanta; Suranjana; (Bangalore, IN) ;
Sankaranarayanan; Karthik; (Bangalore, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
1000005381775 |
Appl. No.: |
16/548388 |
Filed: |
August 22, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 7/70 20170101; G06T
11/20 20130101; G06K 9/6263 20130101; G06T 2207/20081 20130101;
G06N 20/00 20190101; G06T 2207/30196 20130101; G06K 9/6256
20130101; G06K 9/00335 20130101 |
International
Class: |
G06T 11/20 20060101
G06T011/20; G06N 20/00 20060101 G06N020/00; G06T 7/70 20060101
G06T007/70; G06K 9/00 20060101 G06K009/00; G06K 9/62 20060101
G06K009/62 |
Claims
1. A computer-implemented method comprising: identifying one or
more events from input data by applying a machine learning
recognition model to the input data, wherein said identifying
comprises (i) detecting multiple entities from the input data, (ii)
determining one or more behavioral relationships among the multiple
entities in the input data, and (iii) generating, via the machine
learning recognition model, a vector representation of one or more
human poses related to the identified events; generating, using a
machine learning interpretability model and the identified events,
one or more images illustrating one or more human poses related to
the identified events, wherein said generating the one or more
images comprises converting the vector representation to one or
more human pose representations over one or more stick figure
images; outputting the one or more generated images to at least one
user; and updating the machine learning recognition model based at
least in part on (i) the one or more generated images and (ii)
input from the at least one user; wherein the method is carried out
by at least one computing device.
2. The computer-implemented method of claim 1, comprising: training
the machine learning recognition model using data pertaining to
human pose structures and image texture information.
3-4. (canceled)
5. The computer-implemented method of claim 1, wherein the input
data comprise image data.
6. The computer-implemented method of claim 1, wherein the input
data comprise text data.
7. The computer-implemented method of claim 1, wherein the input
data comprise multimodal data.
8. The computer-implemented method of claim 1, wherein the one or
more images comprise one or more stick figure images.
9. A computer program product comprising a computer readable
storage medium having program instructions embodied therewith, the
program instructions executable by a computing device to cause the
computing device to: identify one or more events from input data by
applying a machine learning recognition model to the input data,
wherein said identifying comprises (i) detecting multiple entities
from the input data, (ii) determining one or more behavioral
relationships among the multiple entities in the input data, and
(iii) generating, via the machine learning recognition model, a
vector representation of one or more human poses related to the
identified events; generate, using a machine learning
interpretability model and the identified events, one or more
images illustrating one or more human poses related to the
identified events, wherein said generating the one or more images
comprises converting the vector representation to one or more human
pose representations over one or more stick figure images; output
the one or more generated images to at least one user; and update
the machine learning recognition model based at least in part on
(i) the one or more generated images and (ii) input from the at
least one user.
10-11. (canceled)
12. The computer program product of claim 9, wherein the input data
comprise one or more of image data, text data, and multimodal
data.
13. A system comprising: a memory; and at least one processor
operably coupled to the memory and configured for: identifying one
or more events from input data by applying a machine learning
recognition model to the input data, wherein said identifying
comprises (i) detecting multiple entities from the input data, (ii)
determining one or more behavioral relationships among the multiple
entities in the input data, and (iii) generating, via the machine
learning recognition model, a vector representation of one or more
human poses related to the identified events; generating, using a
machine learning interpretability model and the identified events,
one or more images illustrating one or more human poses related to
the identified events, wherein said generating the one or more
images comprises converting the vector representation to one or
more human pose representations over one or more stick figure
images; outputting the one or more generated images to at least one
user; and updating the machine learning recognition model based at
least in part on (i) the one or more generated images and (ii)
input from the at least one user.
14. A computer-implemented method comprising: training a machine
learning recognition model using data pertaining to human pose
structures and image texture information; identifying one or more
events from input data by applying the machine learning recognition
model to the input data, wherein said identifying comprises (i)
detecting multiple entities from the input data, (ii) determining
one or more behavioral relationships among the multiple entities in
the input data, and (iii) generating, via the machine learning
recognition model, a vector representation of one or more human
poses related to the identified events; generating, using a machine
learning interpretability model and the identified events, one or
more images illustrating one or more human poses related to the
identified events, wherein said generating the one or more images
comprises converting the vector representation to one or more human
pose representations over one or more stick figure images;
outputting, via a dialog system, the one or more generated images
to at least one human user; automatically producing at least one
machine-originated image representative of the one or more
generated images based at least in part on feedback from the at
least one human user; updating the machine learning recognition
model based at least in part on the at least one machine-originated
image; wherein the method is carried out by at least one computing
device.
15-16.
17. The computer-implemented method of claim 14, wherein the input
data comprise image data.
18. The computer-implemented method of claim 14, wherein the input
data comprise text data.
19. The computer-implemented method of claim 14, wherein the input
data comprise multimodal data.
20. The computer-implemented method of claim 14, wherein the one or
more images comprise one or more stick figure images.
Description
FIELD
[0001] The present application generally relates to information
technology and, more particularly, to image creation
technologies.
BACKGROUND
[0002] Conventional interpretability approaches are local in
nature, typically highlighting specific parts of an image which
contribute towards a classification task of the image. However,
such approaches generally fail to determine and/or learn
relationships between objects in the image, or to identify and/or
determine a global concept of the image.
SUMMARY
[0003] In one embodiment of the present invention, techniques for
generating concept images of human poses using machine learning
models are provided. An exemplary computer-implemented method can
include identifying one or more events from input data by applying
a machine learning recognition model to the input data, wherein the
identifying comprises (i) detecting multiple entities from the
input data and (ii) determining one or more behavioral
relationships among the multiple entities in the input data. Such a
method also includes generating, using a machine learning
interpretability model and the identified events, one or more
images illustrating one or more human poses related to the
identified events, and outputting the one or more generated images
to at least one user. Additionally, such a method includes updating
the machine learning recognition model based at least in part on
(i) the one or more generated images and (ii) input from the at
least one user.
[0004] In another embodiment of the invention, an exemplary
computer-implemented method includes training a machine learning
recognition model using data pertaining to human pose structures
and image texture information, and identifying one or more events
from input data by applying the machine learning recognition model
to the input data, wherein the identifying comprises (i) detecting
multiple entities from the input data and (ii) determining one or
more behavioral relationships among the multiple entities in the
input data. Such a method also includes generating, using a machine
learning interpretability model and the identified events, one or
more images illustrating one or more human poses related to the
identified events, and outputting, via a dialog system, the one or
more generated images to at least one human user. Further, such a
method includes automatically producing at least one
machine-originated image representative of the one or more
generated images based at least in part on feedback from the at
least one human user, and updating the machine learning recognition
model based at least in part on the at least one machine-originated
image.
[0005] Another embodiment of the invention or elements thereof can
be implemented in the form of a computer program product tangibly
embodying computer readable instructions which, when implemented,
cause a computer to carry out a plurality of method steps, as
described herein. Furthermore, another embodiment of the invention
or elements thereof can be implemented in the form of a system
including a memory and at least one processor that is coupled to
the memory and configured to perform noted method steps. Yet
further, another embodiment of the invention or elements thereof
can be implemented in the form of means for carrying out the method
steps described herein, or elements thereof; the means can include
hardware module(s) or a combination of hardware and software
modules, wherein the software modules are stored in a tangible
computer-readable storage medium (or multiple such media).
[0006] These and other objects, features and advantages of the
present invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a diagram illustrating system architecture,
according to an exemplary embodiment of the invention;
[0008] FIG. 2 is a diagram illustrating system architecture,
according to an exemplary embodiment of the invention;
[0009] FIG. 3 is a diagram illustrating active learning and machine
teaching, according to an exemplary embodiment of the
invention;
[0010] FIG. 4 is a flow diagram illustrating techniques according
to an embodiment of the invention;
[0011] FIG. 5 is a system diagram of an exemplary computer system
on which at least one embodiment of the invention can be
implemented;
[0012] FIG. 6 depicts a cloud computing environment according to an
embodiment of the present invention; and
[0013] FIG. 7 depicts abstraction model layers according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0014] As described herein, an embodiment of the present invention
includes generating concept images of human poses using machine
learning models. At least one embodiment includes generating visual
concept images of human poses for higher-order concepts of human
behavior. In contrast to conventional approaches for model
interpretability, one or more embodiments include using multimodal
descriptions as input to generate the concept images. Such an
embodiment includes using a multimodal machine learning algorithm,
which learns a generalized concept of one or more higher-order
human behaviors and produces one or more visual concept images.
Such higher-order and/or generalized concepts are defined as
concepts which identify multiple entities and relationships between
them.
[0015] At least one embodiment includes implementing a generation
model which is pre-trained to create stick figures which represent
human poses, wherein the model is image-based, text-based, or
multimodal. Such an embodiment includes providing an input to an
interpretation pipeline with a pre-trained stick figure generator,
wherein an interpretability model generates various human poses.
Additionally, in such an embodiment, a multimodal machine learning
algorithm learns a generalized concept of a higher-order concept to
generate a set of training images. As noted above and herein, an
interpretation pipeline can include a model or a series of modules
which can be artificial intelligence- (AI-) based, non-AI-based, or
mixed. In at least one embodiment, an interpretation pipeline
includes a diagnostic or evaluation module separate from the
actual/primary model (which may be image-based, text-based, or
multimodal). In such an embodiment, a purpose of the interpretation
model is to confirm, by generating visually consistent scenes via
stick figures, that the actual/primary model has understood the
concept correctly.
[0016] One or more embodiments include using a vector
representation of a human pose (learnt by a machine learning model)
and converting the vector to one or more human pose representations
over one or more stick figures. In such an embodiment, a human user
can subsequently interpret the one or more stick figures and
confirm the accuracy of the machine learning model output.
Accordingly, at least one embodiment includes generating images of
human poses by enabling the model to manipulate the pose of the
stick figure(s) and create an image that is indicative (to a human)
of a given concept.
[0017] As used herein, an interpretability model provides some
provision to indicate the reason for a decision, such as the
presence of correlated attributes and/or a sequence of steps. By
way merely of example, one instance of an interpretability model
can include one or more decision trees. As also used herein, an
explainability model justifies a decision by providing
human-interpretable evidence in a feature space. By way of example,
with image/vision data or text data, such evidence can include
feature weight maps and/or heat maps (generated, for example, via
guided backpropagation techniques, gradient-weighted class
activation mapping techniques, etc.). Additionally, as used herein,
concepts in the visual domain generally refer to colors, shapes,
actions, etc., and higher-order concepts are defined via learning
and/or understanding across data points.
[0018] FIG. 1 is a diagram illustrating system architecture,
according to an embodiment of the invention. By way of
illustration, FIG. 1 depicts a new knowledge (training) component
102, which applies model 104 (e.g., a machine learning model) to a
new concept (i.e., input data pertaining to one or more human poses
and/or behaviors). The output of model 104 is at least one image,
which is provided to a visual confirmation system 106. Upon a
positive determination and/or approval by the visual confirmation
system 106, the model output is deemed a concept image 108 and is
output by the visual confirmation system 106 to a human approver
110 for analysis. Upon approval by the human approver 110, the
model 104 is updated based on the concept image 108. As noted
above, the visual confirmation system 106 is another term for an
interpretation pipeline (detailed herein). Accordingly, the visual
confirmation pipeline (which can be one or more modules with AI
components) is a diagnostic or evaluation module separate from the
actual/primary model 104 (which may be image-based, text-based, or
multimodal). As noted herein, a purpose of the visual confirmation
system 106 is to confirm, by generating visually consistent scenes
via stick figures, that the model 104 has understood a concept
correctly.
[0019] In an example embodiment such as depicted in FIG. 1, the
model 104 performs concept detection using pre-trained entity
generation tools. The concept detected by the model 104 can be
expressed in one or more suitable forms, and output as an image or
series of images (e.g., concept image 108) that are subsequently
presented to an end-user and/or human annotator (e.g., human
approver 110) for confirmation and/or validation.
[0020] FIG. 2 is a diagram illustrating system architecture,
according to an exemplary embodiment of the invention. By way of
illustration, FIG. 2 depicts a model 203 (e.g., a machine learning
model), which is applied to input data and outputs an identified
and/or determined primary task. Additionally, FIG. 2 depicts a new
knowledge (training) component 202, which updates model 203,
thereby creating a new model 204, using a new concept (i.e., input
data pertaining to one or more human poses and/or behaviors). The
output of model 204 is at least one image, which is provided to
visual confirmation system 206. Upon a positive determination
and/or approval by the visual confirmation system 206, the model
output is deemed a concept image 208 and is output by the visual
confirmation system 206 to a human approver 210 for analysis. Upon
approval by the human approver 210, the model 204 can be further
updated based on the concept image 208.
[0021] Accordingly, at least one embodiment includes providing
visual interpretability of higher-order (human pose and/or
behavior) concepts. Such an embodiment includes utilizing a
paradigm of image retrieval and/or generation for visual imaging of
the concepts. In one or more embodiments, pre-trained models with
pose structure and texture of images (such as, for example, stick
figures) are used for image generation. Higher-order concepts often
require many words to describe spatial aspects via text; however,
one or more embodiments include encompassing spatial configurations
in one or more images.
[0022] As detailed herein, at least one embodiment includes
implementing one or more machine learning models to learn
higher-order concepts from input data. As also noted herein,
higher-order concepts include those that require identifying
multiple entities and relationships between them (e.g., sports
including multiple players, a playing surface, multiple items of
playing equipment, etc.).
[0023] In one or more embodiments, a (machine learning) generation
model is pre-trained to create stick figures representative of
human poses. In such an embodiment, the human pose-to-stick figure
model can include a fully generative model and/or image retrieval
from a corpus. As an example, in such an embodiment, an event
recognition model can be trained to learn and/or understand various
events pertaining to human poses and/or human behaviors. Such a
model can be image-based, text based, or multimodal. Also, in such
an embodiment, the model is passed, as input, to a proposed
interpretation pipeline with a pre-trained stick figure generator.
The interpretability model then generates various human poses
(illustrated via stick figures), and the visualized stick figures
of concepts are provided to one or more end-users for confirmation
and/or validation that the model is accurate and/or not biased.
[0024] FIG. 3 is a diagram illustrating active learning and machine
teaching, according to an exemplary embodiment of the invention. By
way of illustration, FIG. 3 depicts an embodiment that includes
active learning and machine teaching. In such an embodiment, a
visual confirmation model 324 generates a stick figure collage to
confirm the understanding of specific human behavior(s). As also
depicted in FIG. 3, such an embodiment includes utilizing a
pre-trained visual recognition system 322 that is built on various
modules of predicted sub-tasks. Such an embodiment also includes
utilizing a machine teaching scenario 326, which includes using a
dialog system 320 to interact with a human teacher and/or approver
to understand and/or confirm higher-order visual concepts. In such
an embodiment, the dialog system 320 is used to understand
higher-order-encompassing concepts from known lower-order concepts
related to human behavior(s). Additionally, in at least one
embodiment, the visual confirmation model 324 is constrained to
generate stick figures of humans (and human poses and/or
behaviors). Based on the dialog with the human teacher (e.g., the
machine teaching scenario 326), the visual confirmation model 324
learns at least one concept and confirms this knowledge with at
least one machine-generated image.
[0025] Also, one or more embodiments include image generation using
white box multimodal settings. A white box model, as used herein,
refers to an AI model which allows access to its internal workings
and/or components. For example, one such white box model can
include a neural network that provides access to intermediate
layers and/or representations. A goal of such an embodiment
includes creating images that confirm a higher-order understanding
of visual concepts related to human poses and/or behavior(s). In at
least one such embodiment, images of specific (new) pose concepts
are input to a multimodal learning model, which outputs a primary
task and one or more generalized images that confirm the
concept.
[0026] Further, in such an embodiment, a pre-trained human pose
estimation component, in connection with the multimodal learning
model, computes join positions and gaze determine poses. The
multimodal learning model then generalizes over specific instances
of pose estimation that are labeled as a single higher-order
concept (such as, for example, "group exercise classes"). The
multimodal machine learning algorithm (used as part of the model)
attempts to learn a generalized concept of this higher-order
concept. Additionally, a visual confirmation model uses the
intermediary representation of the base model and generates concept
images to generalize the concept images (such as, for example,
depicted in FIG. 2). Further, in one or more embodiments, the
visual confirmation is separate from the primary task.
[0027] Also, one or more embodiments include incorporating
situational understanding in generating concept images of human
poses using machine learning models. Such an embodiment includes
extending the visual confirmation system in the machine teaching
situation to propagate the understanding in one situation into
another situation. In such an embodiment, the visual confirmation
system not only confirms the pose or higher-order concept, but also
generalizes the pose or higher-order concept to one or more other
situations. In an example embodiment, the visual confirmation
system, upon receiving human approval to update the model (such as
depicted in FIG. 3, for instance) updates a graphical model and/or
a knowledge graph with this new confirmed understanding of the
scene. The graphical model and/or knowledge graph can then be used
to infer anew relationship between other objects in the scene. For
example, the machine teaching component can learn that cold and
windy weather causes humans to shiver, and the update to the
graphical model can reduce the chance of determining an action of
flying a kite accordingly.
[0028] FIG. 4 is a flow diagram illustrating techniques according
to an embodiment of the present invention. Step 402 includes
identifying one or more events from input data by applying a
machine learning recognition model to the input data, wherein
identifying comprises (i) detecting multiple entities from the
input data and (ii) determining one or more behavioral
relationships among the multiple entities in the input data. The
input data can include image data, text data, and/or multimodal
data. At least one embodiment additionally includes training the
machine learning recognition model using data pertaining to human
pose structures and image texture information.
[0029] Step 404 includes generating, using a machine learning
interpretability model and the identified events, one or more
images illustrating one or more human poses related to the
identified events. In at least one embodiment, the one or more
images include one or more stick figure images.
[0030] Also, in one or more embodiments, identifying one or more
events can include generating, via the machine learning recognition
model, a vector representation of one or more human poses related
to the identified events. In such an embodiment, generating the one
or more images includes converting the vector representation to one
or more human pose representations over one or more stick figure
images.
[0031] Step 406 includes outputting the one or more generated
images to at least one user. Step 408 includes updating the machine
learning recognition model based at least in part on (i) the one or
more generated images and (ii) input from the at least one
user.
[0032] Also, an additional embodiment of the invention includes
training a machine learning recognition model using data pertaining
to human pose structures and image texture information, and
identifying one or more events from input data by applying the
machine learning recognition model to the input data, wherein the
identifying comprises (i) detecting multiple entities from the
input data and (ii) determining one or more behavioral
relationships among the multiple entities in the input data. Such
an embodiment also includes generating, using a machine learning
interpretability model and the identified events, one or more
images illustrating one or more human poses related to the
identified events, and outputting, via a dialog system, the one or
more generated images to at least one human user. Further, such an
embodiment includes automatically producing at least one
machine-originated image representative of the one or more
generated images based at least in part on feedback from the at
least one human user, and updating the machine learning recognition
model based at least in part on the at least one machine-originated
image.
[0033] The techniques depicted in FIG. 4 can also, as described
herein, include providing a system, wherein the system includes
distinct software modules, each of the distinct software modules
being embodied on a tangible computer-readable recordable storage
medium. All of the modules (or any subset thereof) can be on the
same medium, or each can be on a different medium, for example. The
modules can include any or all of the components shown in the
figures and/or described herein. In an embodiment of the invention,
the modules can run, for example, on a hardware processor. The
method steps can then be carried out using the distinct software
modules of the system, as described above, executing on a hardware
processor. Further, a computer program product can include a
tangible computer-readable recordable storage medium with code
adapted to be executed to carry out at least one method step
described herein, including the provision of the system with the
distinct software modules.
[0034] Additionally, the techniques depicted in FIG. 4 can be
implemented via a computer program product that can include
computer useable program code that is stored in a computer readable
storage medium in a data processing system, and wherein the
computer useable program code was downloaded over a network from a
remote data processing system. Also, in an embodiment of the
invention, the computer program product can include computer
useable program code that is stored in a computer readable storage
medium in a server data processing system, and wherein the computer
useable program code is downloaded over a network to a remote data
processing system for use in a computer readable storage medium
with the remote system.
[0035] An embodiment of the invention or elements thereof can be
implemented in the form of an apparatus including a memory and at
least one processor that is coupled to the memory and configured to
perform exemplary method steps.
[0036] Additionally, an embodiment of the present invention can
make use of software running on a computer or workstation. With
reference to FIG. 5, such an implementation might employ, for
example, a processor 502, a memory 504, and an input/output
interface formed, for example, by a display 506 and a keyboard 508.
The term "processor" as used herein is intended to include any
processing device, such as, for example, one that includes a CPU
(central processing unit) and/or other forms of processing
circuitry. Further, the term "processor" may refer to more than one
individual processor. The term "memory" is intended to include
memory associated with a processor or CPU, such as, for example,
RAM (random access memory), ROM (read only memory), a fixed memory
device (for example, hard drive), a removable memory device (for
example, diskette), a flash memory and the like. In addition, the
phrase "input/output interface" as used herein, is intended to
include, for example, a mechanism for inputting data to the
processing unit (for example, mouse), and a mechanism for providing
results associated with the processing unit (for example, printer).
The processor 502, memory 504, and input/output interface such as
display 506 and keyboard 508 can be interconnected, for example,
via bus 510 as part of a data processing unit 512. Suitable
interconnections, for example via bus 510, can also be provided to
a network interface 514, such as a network card, which can be
provided to interface with a computer network, and to a media
interface 516, such as a diskette or CD-ROM drive, which can be
provided to interface with media 518.
[0037] Accordingly, computer software including instructions or
code for performing the methodologies of the invention, as
described herein, may be stored in associated memory devices (for
example, ROM, fixed or removable memory) and, when ready to be
utilized, loaded in part or in whole (for example, into RAM) and
implemented by a CPU. Such software could include, but is not
limited to, firmware, resident software, microcode, and the
like.
[0038] A data processing system suitable for storing and/or
executing program code will include at least one processor 502
coupled directly or indirectly to memory elements 504 through a
system bus 510. The memory elements can include local memory
employed during actual implementation of the program code, bulk
storage, and cache memories 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 implementation.
[0039] Input/output or I/O devices (including, but not limited to,
keyboards 508, displays 506, pointing devices, and the like) can be
coupled to the system either directly (such as via bus 510) or
through intervening I/O controllers (omitted for clarity).
[0040] Network adapters such as network interface 514 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
currently available types of network adapters.
[0041] As used herein, including the claims, a "server" includes a
physical data processing system (for example, system 512 as shown
in FIG. 5) running a server program. It will be understood that
such a physical server may or may not include a display and
keyboard.
[0042] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0043] 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.
[0044] 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.
[0045] 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.
[0046] 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.
[0047] These computer readable program instructions may be provided
to a processor of a 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.
[0048] 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.
[0049] 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 accomplished as one step, executed concurrently,
substantially concurrently, in a partially or wholly temporally
overlapping manner, 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.
[0050] It should be noted that any of the methods described herein
can include an additional step of providing a system comprising
distinct software modules embodied on a computer readable storage
medium; the modules can include, for example, any or all of the
components detailed herein. The method steps can then be carried
out using the distinct software modules and/or sub-modules of the
system, as described above, executing on a hardware processor 502.
Further, a computer program product can include a computer-readable
storage medium with code adapted to be implemented to carry out at
least one method step described herein, including the provision of
the system with the distinct software modules.
[0051] In any case, it should be understood that the components
illustrated herein may be implemented in various forms of hardware,
software, or combinations thereof, for example, application
specific integrated circuit(s) (ASICS), functional circuitry, an
appropriately programmed digital computer with associated memory,
and the like. Given the teachings of the invention provided herein,
one of ordinary skill in the related art will be able to
contemplate other implementations of the components of the
invention.
[0052] Additionally, it is understood in advance that
implementation of the teachings recited herein are not limited to a
particular computing environment. Rather, embodiments of the
present invention are capable of being implemented in conjunction
with any type of computing environment now known or later
developed.
[0053] For example, cloud computing is a model of service delivery
for enabling convenient, on-demand network access to a shared pool
of configurable computing resources (for example, 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.
[0054] Characteristics are as follows:
[0055] 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.
[0056] 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).
[0057] 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 (for
example, country, state, or datacenter).
[0058] 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.
[0059] 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 (for
example, 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.
[0060] Service Models are as follows:
[0061] 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 (for
example, web-based e-mail). 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.
[0062] 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.
[0063] 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 (for example, host
firewalls).
[0064] Deployment Models are as follows:
[0065] 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.
[0066] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (for example, 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.
[0067] 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.
[0068] 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 (for example, cloud bursting for load-balancing between
clouds).
[0069] 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.
[0070] Referring now to FIG. 6, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes 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. 6 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).
[0071] Referring now to FIG. 7, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 6) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 7 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:
[0072] 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.
[0073] 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. 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.
[0074] In one example, these resources may include 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.
[0075] 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
concept image generation 96, in accordance with the one or more
embodiments of the present invention.
[0076] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a," "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, steps,
operations, elements, and/or components, but do not preclude the
presence or addition of another feature, step, operation, element,
component, and/or group thereof.
[0077] At least one embodiment of the present invention may provide
a beneficial effect such as, for example, using a multimodal
machine learning algorithm to learn a generalized concept of
higher-order human behavior, and based at least thereon, generating
one or more visual concept images as output.
[0078] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *