HEALTH DISCOVERY CORPORATION

Patent Trials and Appeals Board

Oct 1, 2021

IPR2021-00555 (P.T.A.B. Oct. 1, 2021)

Trials@uspto.gov Paper No. 17
571-272-7882 Date: October 1, 2021

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

INTEL CORPORATION,
Petitioner,

v.

HEALTH DISCOVERY CORPORATION,
Patent Owner.
____________

IPR2021-00555
Patent 10,402,685 B2
____________

Before LYNNE H. BROWNE, GARTH D. BAER, and FREDERICK C.
LANEY, Administrative Patent Judges.

Opinion for the Board filed PER CURIAM.

Opinion Dissenting filed by Administrative Patent Judge BAER.

PER CURIAM.

DECISION
Denying Institution of Inter Partes Review
35 U.S.C. § 314

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I. INTRODUCTION
Intel Corp. (“Petitioner”) filed a Petition to institute an inter partes
review of claims 1–23 (the “challenged claims”) of U.S. Patent
10,402,685 B2 (Ex. 1001, the “’685 patent”) pursuant to 35 U.S.C. § 311 et
seq. Paper 2 (“Petition” or “Pet.”). Health Discovery Corp. (“Patent
Owner”) filed a Preliminary Response. Paper 9 (“Prelim. Resp.”).
Institution of an inter partes review is authorized by statute when “the
information presented in the petition . . . and any response . . . shows that
there is a reasonable likelihood that the petitioner would prevail with respect
to at least 1 of the claims challenged in the petition.” 35 U.S.C. § 314(a);
see 37 C.F.R. § 42.108. For the reasons discussed below, we deny the
Petition and do not institute inter partes review.
A. RELATED MATTERS
The parties identify the following related proceeding involving the
’685 patent: Health Discovery Corp. v. Intel Corp., Civil Action No. 6:20-
cv-666 (W.D. Texas July 23, 2020). See Pet. 8; Paper 3, 2.
B. THE ’685 PATENT
The ’685 patent addresses identifying a determinative subset of
features from within a group of features by training a support vector
machine (SVM) using training samples with class labels to determine a
value of each feature. Ex. 1001, code (57). Features with the smallest
values are removed and an updated kernel matrix is generated. Id. The
process is repeated until a predetermined number of features remain that can
accurately separate data into different classes. Id.
Figure 2 is reproduced below.
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Figure 2 is a flowchart illustrating an example method for increasing
knowledge that may be discovered using a support vector machine. Id. at
5:6–8. As shown in Figure 2, the SVM is trained using training data to
generate an optimal hyperplane. Id. at 9:61–10:18. Test data is input into
the trained SVM “to determine whether the SVM was trained in a desirable
manner.” Id. at 10:26–28. If not, the kernel selection is adjusted at step 224
and the training process is repeated from step 208. Id. at 10:61–11:4. After
the optimal kernel is selected, the SVM is further optimized through feature
selection to reduce the dimensionality of feature space. See id. at 2:37–39,
15:48–57. The ’685 patent uses recursive feature elimination (RFE), where
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one or more features are removed at each iteration and a new classifier is
trained with the remaining features. Id. at 14:61–64, 17:4–9.
C. ILLUSTRATIVE CLAIM
Petitioner challenges claims 1–23 of the ’685 patent. Pet. 1.
Challenged claims 1, 7, 12, and 18 are independent. Claim 1 is reproduced
below.
1. A method, comprising:
retrieving training data from one or more storage devices in
communication with a processor, the processor operable
for:
determining a value for each feature in a group of features
provided by the training data;
eliminating at least one feature with a minimum ranking
criterion from the group, wherein the minimum ranking
criterion is obtained based on the value for each feature in
the group;
subtracting a matrix from the kernel data to provide an updated
kernel data, each component of the matrix comprising a dot
product of two of training samples provided by at least a
part of the training data that corresponds to the eliminated
feature;
updating the value for each feature of the group based on the
updated kernel data;
repeating of eliminating the at least one feature from the group
and updating the value for each feature of the group until a
number of features in the group reaches a predetermined
value to generate a feature ranking list; and
recognizing a new data corresponding to the group of features
with the feature ranking list.
Ex. 1001, 71:56–72:12.

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D. ASSERTED GROUND OF UNPATENTABILITY
Petitioner asserts the following ground of unpatentability. Pet. 4.
Petitioner submits the Declaration of Dr. Theodoros Evgeniou, (Ex. 1003) in
support of its arguments.
Claims Challenged 35 U.S.C. §1 References/Basis
1–23 103 Mukherjee2, Bradley3, Cortes4
II. DISCUSSION
A. LEVEL OF SKILL IN THE ART
Petitioner contends a person of ordinary skill in the art at the time of
the alleged invention of the ’685 patent (a “POSITA”) would have had “at
least a Master’s degree in electrical engineering, computer science, or the
equivalent with three years of experience in machine learning and data-
analysis techniques.” Pet. 20 (citing Ex. 1003 ¶ 15). Further, “[a]dditional
education could substitute for professional experience, and vice versa.” Id.
Patent Owner does not contest that a skilled artisan would have this kind of
background knowledge and experience; nor does Patent Owner suggest that
Petitioner’s proposed level of skill leads to an improper understanding of the

1 The Leahy-Smith America Invents Act (“AIA”) amended 35 U.S.C. § 103.
See Pub. L. No. 112–29, 125 Stat. 284, 285–88 (2011). As the application
that issued as the ’685 patent was filed before the effective date of the
relevant amendments, the pre-AIA version of § 103 applies.
2 Mukherjee et al., “Support Vector Machine Classification of
Microarray Data,” Technical Report C.B.C.L. Paper No. 182, A.I.
Memo No. 1677, M.I.T. (1998) (Ex. 1005, “Mukherjee”).
3 Bradley, Paul, “Mathematical Programming Approaches to Machine
Learning and Data Mining,” University of Wisconsin-Madison (Aug. 27,
1998) (Ex. 1015, “Bradley”).
4 Cortes, C., et al, “Support-Vector Networks,” Machine Learning, 20, 273–
297 (1995) (Ex. 1011, “Cortes”).
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prior art or an improper patentability analysis. See generally Prelim. Resp.
At this stage in the proceeding, we adopt Petitioner’s articulation of the level
of ordinary skill in the art, which is supported by Dr. Evgeniou’s testimony
and appears commensurate with the level of ordinary skill as reflected in the
asserted prior art and the ’685 patent.
B. CLAIM CONSTRUCTION
We find no claim terms require express construction for us to
determine whether or not to institute inter partes review. See Nidec Motor
Corp. v. Zhongshan Broad Ocean Motor Co., 868 F.3d 1013, 1017 (Fed.
Cir. 2017) (“[W]e need only construe terms ‘that are in controversy, and
only to the extent necessary to resolve the controversy.’”) (quoting Vivid
Techs., Inc. v. Am. Sci. & Eng’g, Inc., 200 F.3d 795, 803 (Fed. Cir. 1999)).
C. UNPATENTABILITY ANALYSIS
Petitioner asserts that claims 1–23 would have been obvious over
Mukherjee, Bradley, and Cortes. Pet. 20–78. As explained below, we find
Petitioner has not shown a reasonable likelihood that it would prevail in its
obviousness challenge based on Mukherjee, Bradley, and Cortes.
1. Summary of Mukherjee (Ex. 1005)
Mukherjee, titled “Support Vector Machine Classification of
Microarray Data” describes applying Support Vector Machines (SVMs) to a
classification of DNA microarray data samples. Ex. 1005, Abstract. In
addition to its SVM disclosure, Mukherjee describes performing feature
selection using an iterative algorithm that is based in part on optimized
pattern weight values. Ex. 1005, 5; see Ex. 1003 ¶ 91 n.12.
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2. Summary of Bradley (Ex. 1015)
Bradley is titled “Mathematical Programming Approaches to Machine
Learning and Data Mining,” and describes various “mathematical
programming approaches to supervised and unsupervised machine learning
problems.” Ex. 1015, 1.
3. Summary of Cortes (Ex. 1011)
Cortes is titled “Support-Vector Networks.” Ex. 1011, 1. Cortes
describes a “new learning machine for two-group classification problems.”
classifying objects using support vector machines. Id.
4. Feature Values
Independent claims 1, 7, 12, and 18 recite the iterative determination
and use of feature values. Claim 1, for example, recites “determining a
value for each feature in a group of features provided by the training data;
eliminating at least one feature with a minimum ranking criterion . . .
updating the value for each feature of the group . . . [and] repeating of
eliminating the at least one feature from the group and updating the value for
each feature of the group until a number of features in the group reaches a
predetermined value . . . .” Ex. 1001, 71:60–72:9. Claims 7, 12, and 18, the
other challenged independent claims, have similar language requiring the
iterative determination and use of feature values. Ex. 1001, 72:41–59,
73:17–33, 74:18–32. According to Petitioner, Mukherjee teaches feature
value determination because “Mukherjee’s decision function includes
variables 𝑥𝑥 (training patterns) [and] α (weight value for each pattern).”
Pet. 35 (quoting Ex. 1003 ¶ 130). Further, “[i]n comparison to the ‘primal’
space decision function D(𝑥𝑥) = 𝒘𝒘 ∙ 𝒙𝒙 + 𝑏𝑏, Mukherjee’s decision function is
presented in ‘dual’ space, where the ‘primal’ weight vector 𝒘𝒘 is replaced by
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the summation of patterns (observations/samples) 𝑥𝑥k with corresponding
pattern weight values 𝛼𝛼k.” Id. Thus, according to Petitioner, “Mukherjee’s
linear SVM therefore either (a) discloses ‘determining a value [(w)] for each
feature [(x)] in a group of features provided by the training data’ or (b)
renders the limitation obvious to try as there are only two known alternative
representations (‘primal’ and ‘dual’) for the decision function.” Id. at 36.
Patent Owner contends that Mukherjee does not teach the claimed
feature values because Mukherjee instead “only computes pattern weights,
expressed as 𝛼𝛼s.” Prelim Resp. 27. We agree with Patent Owner that
Petitioner has not adequately bridged the gap between the claimed feature
values and Mukherjee’s pattern weights5. Although Petitioner’s expert
contends that feature weight and pattern weight “are equivalent,” he
implicitly concedes they are not in his additional assertion that they are
“related and can be derived from one another.” See Ex. 1003, 91 n.12. We
are also not persuaded by Petitioner’s argument that “a relationship exists
between feature weight vectors, 𝒘𝒘, and pattern weight vectors, 𝜶𝜶, such that
one can be mathematically derived and calculated from the other.” Pet. 35.
Even if correct, Petitioner’s argument falls short because Petitioner offers no
explanation as to why a skilled artisan would have done so. As Patent
Owner notes, “a conclusion of obviousness is not dependent upon what a
person of ordinary skill in the art could do, but rather what the hypothetical
individual would do, and there was no motivation for Mukherjee or any
person of ordinary skill in the art to [determine feature weight values].”

5 Neither party distinguishes weights from values. Rather, the parties’
dispute centers on whether Mukherjee’s pattern weight/value teaches the
claimed feature weight/value. See Prelim Resp. 25–33; Paper 15, 4–7.
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Prelim. Resp. 27–28. In short, we find that Petitioner has not produced the
required “articulated reasoning with some rational underpinning to support
the legal conclusion of obviousness.” See KSR Int’l Co. v. Teleflex Inc., 550
U.S. 398, 418 (2007). Therefore, Petitioner has not shown a reasonable
likelihood that it would prevail in establishing independent claims 1, 7, 12,
or 18 (or claims 2–6, 8–11, 13–17, and 19–23, which depend from those
claims) would have been obvious over the asserted combination of
Mukherjee, Bradley, and Cortes.
III. CONCLUSION
For the foregoing reasons, we determine that the information
presented in the Petition does not establish a reasonable likelihood that
Petitioner would prevail in showing claims 1–23 are unpatentable. We
therefore do not institute an inter partes review of claims 1–23.
IV. ORDER
Accordingly, it is:
ORDERED that the Petition is denied as to the challenged claims of
the ʼ685 patent; and
FURTHER ORDERED that no inter partes review is instituted.

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UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

INTEL CORPORATION,
Petitioner,

v.

HEALTH DISCOVERY CORPORATION,
Patent Owner.
____________

IPR2021-00555
Patent 10,402,685 B2
____________

Before LYNNE H. BROWNE, GARTH D. BAER, and FREDERICK C.
LANEY, Administrative Patent Judges.

BAER, Administrative Patent Judge, dissenting.

I respectfully dissent from the majority’s decision denying institution
of inter partes review. Although Mukherjee uses pattern weights instead of
the claimed feature values, Petitioner’s expert, Dr. Evgeniou, explains that
pattern weight and feature weight are related—feature weight (𝒘𝒘), used in
primal space, is replaced with pattern weight (𝛼𝛼) in dual space
representations. Ex. 1003 ¶¶ 48, 130. Dr. Evgeniou further explains that “a
relationship exists between feature weight vectors, 𝒘𝒘, and pattern weight
vectors, 𝜶𝜶 . . . such that one can be mathematically derived and calculated
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from the other.” Id. ¶ 131. Last, according to Dr. Evgeniou, “there are only
two known alternative representations (‘primal’ and ‘dual’) for the decision
function.” Id. ¶ 132. In light of that uncontested testimony, see Prelim.
Resp. 25–33, I agree with Petitioner that “Mukherjee’s linear SVM . . .
renders the [feature-value] limitation obvious to try.” Pet. 34 (citing
Ex. 1003 ¶ 132); see KSR, 550 U.S. at 402 (explaining that “[w]hen there is
a design need or market pressure to solve a problem and there are a finite
number of identified, predictable solutions, a person of ordinary skill in the
art has good reason to pursue the known options within his or her technical
grasp”).
For the foregoing reasons, in my opinion, Petitioner has shown a
reasonable likelihood of prevailing in showing the unpatentability of at least
one of the challenged claims of the ’685 patent.

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PETITIONER:
Lori Gordon
Lauren May Eaton
PERKINS COIE LLP
Gordon-ptab@perkinscoie.com
Eaton-ptab@perkinscoie.com

PATENT OWNER:
Tarek Fahmi
Jonathan Tsao
ASCENDA LAW GROUP, PC
tarek.fahmi@ascendalaw.com
jonathan.tsao@ascendalaw.com

Eleanor Musick
MUSICK DAVISON LLP
eleanor@mdiplaw.net