The MITRE Corporation

Patent Trials and Appeals Board

Oct 20, 2021

2021004846 (P.T.A.B. Oct. 20, 2021)

UNITED STATES PATENT AND TRADEMARK OFFICE
UNITED STATES DEPARTMENT OF COMMERCE
United States Patent and Trademark Office
Address: COMMISSIONER FOR PATENTS
P.O. Box 1450
Alexandria, Virginia 22313-1450
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
14/678,532 04/03/2015 Robert M. TAYLOR Jr. 699592000900 1286
25227 7590 10/20/2021
MORRISON & FOERSTER LLP
2100 L STREET, NW
SUITE 900
WASHINGTON, DC 20037
EXAMINER
LEE, TSU-CHANG
ART UNIT PAPER NUMBER
2128
NOTIFICATION DATE DELIVERY MODE
10/20/2021 ELECTRONIC
Please find below and/or attached an Office communication concerning this application or proceeding.
The time period for reply, if any, is set in the attached communication.
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PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte ROBERT M. TAYLOR and BURHAN NECIOGLU
Appeal 2021-004846
Application 14/678,532
Technology Center 2100
Before CAROLYN D. THOMAS, MICHAEL J. STRAUSS, and
MICHAEL J. ENGLE, Administrative Patent Judges.
STRAUSS, Administrative Patent Judge.
Appeal 2021-004846
Application 14/678,532
2
DECISION ON APPEAL
STATEMENT OF THE CASE1
Pursuant to 35 U.S.C. § 134(a), Appellant2 appeals from the
Examiner’s decision to reject claims 1–22. Non-Final Act. 2. We have
jurisdiction under 35 U.S.C. § 6(b).
We REVERSE.
CLAIMED SUBJECT MATTER
According to Appellant:
[T]he claims of the present application are directed to image
compression and decompression using a trained learning
machine . . . [that] enable compression ratios unachievable by
conventional methods. As explained in the specification, image
compression techniques generally utilize patterns in image data
to reduce the amount of information needed to represent an
image. For example, conventional image compression
techniques such as JPEG are generally based on the observations
that adjacent pixels in an array of pixels representing an image
are likely to be similar and that the human eye and brain have
difficulty detecting high frequency changes in pixel intensities
over a given area. The compression ratios achievable by
conventional compression techniques are limited by the patterns
that their human designers were able to determine. The systems

1 In this Decision, we refer to Appellant’s Appeal Brief filed December 14,
2020 (“Appeal Br.”); Reply Brief filed August 9, 2021 (“Reply Br.”); the
Non-Final Office Action mailed May 13, 2020 (“Non-Final Act.”); the
Examiner’s Answer mailed June 8, 2021 (“Ans.”); and the Specification
filed April 3, 2015 (“Spec.”). Rather than repeat the Examiner’s findings
and Appellant’s contentions in their entirety, we refer to these documents.

2 We use the word “Appellant” to refer to “applicant” as defined in
37 C.F.R. § 1.42(a). Appellant identifies the real party in interest as The
MITRE Corporation. Appeal Br. 2
Appeal 2021-004846
Application 14/678,532
3
and methods according to the present claims do not rely on
human-determined patterns. Rather, the inventors realized that
there “are patterns in digital image data that humans have not
been able to discern or explain that may be used to push image
compression levels far beyond those achievable by conventional
methods.” The systems and methods according to the claims
utilize machine learning techniques that detect these patterns,
build a model that captures these patterns, and apply the model
parameters to compress and decompress digital images at
compression ratios unachievable by conventional methods.
Appeal Br. 8–9 (citations omitted). Claim 1, reproduced below, is
illustrative of the claimed subject matter:
1. A computer implemented method for machine learning
model parameters for image compression, comprising:
partitioning a plurality of training image files stored on a
first computer memory into a first set of regions, wherein each
region of the first set of regions is an array of pixel values;
training a probabilistic learning machine on the first set of
regions to generate a first set of machine learned model
parameters, the first set of machine learned model parameters
representing a first level of data patterns in the plurality of
training image files;
constructing a representation of each region of the first set
of regions using the first set of regions and the first set of machine
learned model parameters, wherein a representation of a
respective region of the first set of regions has an equal number
of pixel values as the respective region;
constructing representations of the plurality of training
image files by combining the representations of the regions of
the first set of regions;
partitioning the representations of the plurality of image
files into a second set of regions, wherein a region of the second
send of regions has a greater number of pixel values than at least
one region of the first set of regions;
training the probabilistic learning machine on the second
set of regions to generate a second set of machine learned model
parameters, the second set of machine learned model parameters
Appeal 2021-004846
Application 14/678,532
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representing a second level of data patterns in the plurality of
image files, wherein the number of machine learned model
parameters in the second set of machine learned model
parameters is less than the number of machine learned model
parameters in the first set of machine learned model parameters;
and
building the first set of machine learned model
parameters and the second set of machine learned model
parameters into an executable image compression application
that is configured to compress an image file that is different
from the training image files by transforming the image file into
a compressed image file based on the first set of machine
learned model parameters generated from the training image
files and the second set of machine learned model parameters
generated from the training image files.
Appeal Br. 35 (Claims App.).
REFERENCES AND REJECTIONS
The prior art relied upon by the Examiner are:
Name Reference Date
Dube et al. (“Dube.”) US 6,633,677 B1 Oct. 14, 2003
Chuang et al. (“Chuang.”) US 2012/0169842 A1 July 5, 2012
Rodriguez et al. (“Rodriguez.”) US 2015/0055855 A1 Feb. 26, 2015
Turkan et al. (“Turkan.”) US 2015/0093045 A1 Apr. 2, 2015
Kainen, P., Kůrková, V., Vogt, A. (2001). Continuity of Approximation by
Neural Networks in Lp Spaces. Annals of Operations Research, 101, pp.143–
147. (“Kainen.”)
Robinson, J, Kecman, V. (2003). Combining support vector machine learning
with the discrete cosine transform in image compression. IEEE Transactions
on Neural Networks, 14(4), pp.950–958. (“Robinson.”)
Bryt, O., Elad, M. (2008). Compression of facial images using the K-SVD
algorithm. Journal of Visual Communication and Image Representation,
19(4), pp.270–282. (“Bryt.”)
Chen, M. et al. (2010). Compressive Sensing on Manifolds Using a
Nonparametric Mixture of Factor Analyzers: Algorithm and Performance
Bounds. IEEE Transactions on Signal Processing, 58(12), pp.6140–6155.
(“Chen.”)
Appeal 2021-004846
Application 14/678,532
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The Examiner rejects:
a. claims 1–3, 7, 11, 13, 15–17, and 19–22 under 35 U.S.C. § 103 as
obvious over the combined teachings of Dube and Bryt (Non-Final
Act. 3–15);
b. claims 4–6 and 12 under 35 U.S.C. § 103 as obvious over the
combined teachings of Dube, Bryt, and Turkan (Non-Final Act.
16–18);
c. claims 8–10 and 18 under 35 U.S.C. § 103 as obvious over the
combined teachings of Dube, Bryt, and Chen (Non-Final Act. 18–
21); and
d. claim 14 under 35 U.S.C. § 103 as obvious over the combined
teachings of Dube, Bryt, Turkan, and Chen (Non-Final Act. 21–
22).
ISSUE
Has the Examiner erred in finding the combination of references
teaches or suggests the step of
training a probabilistic learning machine on [a] first set of regions
[partitioned from a plurality of training image files] to generate
a first set of machine learned model parameters, the first set of
machine learned model parameters representing a first level of
data patterns in the plurality of training image files
as recited in claim 1?
ANALYSIS
The Examiner rejects claim 1 as obvious over the combined teachings
of Dube and Bryt. Non-Final Act. 3–8. The Examiner relies upon Dube’s
disclosure of image compression and decompression processing using
hierarchical coding for teaching all but one of the limitations of claim 1. Id.
Appeal 2021-004846
Application 14/678,532
6
at 3–6 (citing Dube 4:1–3, 6:16–17, 7:62–-63, Figs. 1–3B, 5–7B, 9, 12).
However, the Examiner finds Dube does not explicitly disclose the
limitation of “building the first set of machine learned model parameters and
the second set of machine learned model parameters into an executable
image compression application that is configured to compress an image file
that is different from the training image files” are recited by claim 1. Id. at
6. Addressing the noted deficiency of Dube, the Examiner finds Bryt’s
disclosure of an off-line training process producing a set of K-singular value
decomposition (K-SVD) dictionaries teaches or suggests the identified
limitation. Id. at 6–7 (citing Bryt Abstract, §§ 2.3, 3.1.1, Fig. 3). According
to the Examiner it would have been obvious “to modify the predictive
multiple level image compression/decompression system [and] method of
Dube to include a learning machine to build sets of machine learned model
parameters as taught in Bryt . . . [so as] to support a very low bit rate
compression.” Id. at 11.
Appellant contends that, inter alia, Dube fails to teach either of claim
1’s training steps, arguing as follows:
[T]he Examiner cites to Dube’s “predicted values” as specifically
teaching the claimed “machine learned model parameters” and
asserts that Dube’s prediction method teaches “implementing a
machine to learn & predict image characteristics via the
predictors.” (Id. at 4 (citing Dube, FIGs. IA, 5, 7B, and col. 7,
lines 62–63).) However, the predicted values of Dube are not
analogous to the claimed “machine learned model parameters” at
least because the predicted values of Dube are derived from only
a single image file. The predicted values of Dube are
“determined by the predictors, for the object pixel.” (Dube,
col. 7, lines 62–64.) Thus, the predicted values of Dube are a
component of Dube’s predictive multiple level image
Appeal 2021-004846
Application 14/678,532
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compression/decompression system that are derived on data
(e.g., pixels) of a single image (i.e., the image to be compressed).
By contrast, the claimed “machine learned model
parameters” are not derived from a single image file, but are
instead derived from “the first set of regions [of a plurality of
training image files]” as recited in the claim. Thus, Dube’s
predicted values are not analogous to the claimed “machine
learned model parameters” since the predicted values are only
based on data of a single image file, and not “the first set of
regions [of a plurality of training image files]” as required of the
“machine learned model parameters” recited in claim 1.
Appeal Br. 11–12.
The Examiner responds, finding that “[t]he method shown by Dube
can be used to a single or multiple images, with single image as a special
case for multiple images. There is no restriction that the method shown in
Dube can only be used in a single image file.” Ans. 17–18.
Appellant replies, arguing as follows:
Applicant does not argue here that Dube’s multi-level predictive
image compression process cannot be used on a plurality of
images, as alleged by the Examiner. . . . Applicants are simply
demonstrating that Dube’ s predicted values are not the same as
the claimed “first set of machine learned model parameters”
because they are not derived from a plurality of training image
files. Specifically, Dube’s predicted values are “determined by
the predictors, for the object pixel.” (Dube, col. 7, lines 62–64.)
Further, as explained in Dube, “predictor module 100 uses a
plurality of predictors to generate predicted values for an object
pixel, e.g., X, in an image.” (Dube, col. 6, lines 58–60.) Thus,
the predicted values of Dube are a component of Dube’s
predictive algorithm that are derived on data (e.g., a pixel) of a
single image (i.e., the image to be compressed).
As required by claim 1, the “first set of machine learned
model parameters” are generated by training a probabilistic
learning machine on a first set of regions of a plurality of training
image files. Further, the “first set of machine learned model
Appeal 2021-004846
Application 14/678,532
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parameters” represent “a first level of data patterns in the
plurality of training image files.” Thus, the claimed “first set of
machine learned model parameters” include data from a plurality
of training image files, whereas the predicted values of Dube
only include data from a single image.
Reply Br. 4–5.
We are persuaded the rejection is improper because the Examiner fails
to provide sufficient evidence or reasoning to show that Dube teaches or
suggests claim 1’s training step that generates machine learned model
parameters representing data patterns found in a plurality of training image
files. Although Dube’s image processing might be used to process multiple
images, each image is processed separately to generate corresponding
predictors. That is, Dube discloses a one-to-one relationship between each
image file and its predictor values, not using plural training images to
generate a set of predictors that are used to build an executable image
compression application to compress a different image file as required by the
disputed claim language.
Because we agree with at least one of the arguments advanced by
Appellant, we do not reach the merits of Appellant’s other arguments.
Accordingly, we do not sustain the rejection of independent claim 1,
or the rejection of independent claims 11, 16, 19, 20, and 22, which include
language similar to the argued limitation of claim 1. Nor do we sustain the
rejections of dependent claims 2–10, 12–15, 17, 18, and 21 which stand with
their respective base.
Appeal 2021-004846
Application 14/678,532
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CONCLUSION
We reverse the Examiner’s rejections of
a. claims 1–3, 7, 11, 13, 15–17, and 19–22 under 35 U.S.C. § 103 as
obvious over the combined teachings of Dube and Bryt;
b. claims 4–6 and 12 under 35 U.S.C. § 103 as obvious over the
combined teachings of Dube, Bryt, and Turkan;
c. claims 8–10 and 18 under 35 U.S.C. § 103 as obvious over the
combined teachings of Dube, Bryt, and Chen; and
d. claim 14 under 35 U.S.C. § 103 as obvious over the combined
teachings of Dube, Bryt, Turkan, and Chen.
DECISION SUMMARY
In summary:

Claims
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1–3, 7, 11,
13, 15–17,
19–22
103 Dube, Bryt 1–3, 7, 11,
13, 15–17,
19–22
4–6, 12 103 Dube, Bryt, Turkan 4–6, 12
8–10, 18 103 Dube, Bryt, Chen 8–10, 18
14 103 Dube, Bryt,
Turkan, Chen
14
Overall
Outcome
1–22

REVERSED