VISIONGATE, INC.

Patent Trials and Appeals Board

Feb 8, 2021

2020002440 (P.T.A.B. Feb. 8, 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.
15/403,940 01/11/2017 Alan C. Nelson 60419USNP 3237
23430 7590 02/08/2021
GEORGE A LEONE, SR
CITADEL PATENT LAW
9125 Bridgeport Way SW
SUITE 105
Lakewood, WA 98499
EXAMINER
AEDER, SEAN E
ART UNIT PAPER NUMBER
1642
NOTIFICATION DATE DELIVERY MODE
02/08/2021 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.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
george.leone@citadelpatentlaw.com
marissa.leone@citadelpatentlaw.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte ALAN C. NELSON, MICHAEL G. MEYER, and
DANIEL J. SUSSMAN
Appeal 2020-002440
Application 15/403,940
Technology Center 1600
Before ERIC B. GRIMES, FRANCISCO C. PRATS, and
DEBRA L. DENNETT, Administrative Patent Judges.
DENNETT, Administrative Patent Judge.
DECISION ON APPEAL1
Pursuant to 35 U.S.C. § 134(a), Appellant2 appeals from the
Examiner’s decision finally rejecting claims 32–37. See Final Act. 1. We
have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM IN PART.

1 In our Decision, we refer to the Specification (“Spec.”) of Application
15/403,940 filed Jan. 11, 2017 (“the ’940 App.”); the Final Office Action
dated May 14, 2019 (“Final Act.”); the Appeal Brief filed Oct. 4, 2019
(“Appeal Br.”); the Examiner’s Answer dated Nov. 14, 2019 (“Ans.”); and
the Reply Brief filed Jan. 10, 2020 (“Reply Br.”).
2 We use the term “Appellant” to refer to “applicant” as defined in 37 C.F.R.
§ 1.42. Appellant identifies the real party in interest as VisionGate, Inc.
Appeal Br. 1.
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Application 15/403,940

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STATEMENT OF THE CASE
The subject matter of the ’940 Application is related to early lung
dysplasia and cancer detection systems and methods using patient sputum
specimens. Spec. 6, ll. 30–31. The invention uses an optical tomography
system to produce sub-micron resolution 3D cell images that are then
processed by automated feature extraction and classification algorithms to
identify with high accuracy abnormal cells in sputum. Id. 6, ll. 31–34.
Claim 32, reproduced below from the Claims Appendix of the Appeal
Brief, illustrates the claimed subject matter:
32. A method of training and using an automated dysplastic
cell algorithmic classifier directed to treating a malignancy in a
subject comprising:
a) obtaining a set of known unique dysplastic cells
indicating an abnormal lung process and a plurality of known
normal cells;
b) operating an optical tomography system to generate a
first set of 3D images of the set of known unique dysplastic
cells indicating an abnormal lung process and a second set of
3D images for the plurality of known normal cells;
c) computing a plurality of dysplastic cell feature
measurements from the first set of 3D images;
d) computing a plurality of normal cell feature
measurements from the second set of 3D images;
e) operating a training algorithm using the plurality of
dysplastic cell feature measurements and the normal cell feature
measurements to classify the set of known unique dysplastic
cells and plurality of known normal cells into classified
dysplastic cell types and classified normal cell types;
f) comparing the classified dysplastic cell types and
classified normal cell types with diagnostic truth to determine
an accuracy value;
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g) comparing the accuracy value to a predetermined
performance bound;
h) if the accuracy value does not fall within the
predetermined performance bound, then adjusting dysplastic
cell feature values for each of the plurality of dysplastic cell
feature measurements and adjusting normal cell feature values
for each of the plurality of normal cell feature measurements
according to the training algorithm;
i) repeating steps e) through h) until the accuracy value
falls within the predetermined performance bound and
providing the last adjusted dysplastic cell feature values for
each of the plurality of dysplastic cell feature measurements as
trained dysplastic cell feature measurements and providing the
last adjusted normal cell feature values for each of the plurality
of normal cell feature measurements as trained normal cell
feature measurements;
j) inputting the trained dysplastic cell feature
measurements and trained normal cell feature measurements
into a dysplastic classifier;
k) operating the optical tomography system to generate a
third set of 3D patient images of cells based on pseudo-
projections obtained from a patient specimen derived from
spontaneous cough sputum;
1) operating the dysplastic cell classifier to determine
whether cells represented by the third set of 3D patient images
comprise dysplastic cell types; and
m) when cells represented by the third set of 3D patient
images are classified as dysplastic cell types, then administering
an immunomodulating agent to the subject over a
predetermined time period.
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REJECTIONS
I. Claims 32–37 under 35 U.S.C. § 103 over Nandakumar3 in view of
Asaoka,4 Gray,5 Wu,6 Lunetta,7 Cutler,8 Nelson,9 Meyer,10 Fauver,11
Neumann,12 Marsh,13 and Keith14 (“Obviousness Rejection”);

3 Nandakumar et al., Quantitative characterization of pre-neoplastic
progression using single cell computed tomography and 3D karyometry,
79(1) Cytometry 25–34 (2011) (“Nandakumar”).
4 Asaoka et al., Combining Multiple HRT Parameters Using the ‘Random
Forests’ Method Improves the Diagnostic Accuracy of Glaucoma in
Emmetropic and Highly Myoptic Eyes, 55(4) Invest. Ophthalmol. Vis. Sci.
2482–2490 (2014) (“Asaoka”).
5 Gray et al., Random forest-based similarity measures for multi-modal
classification of Alzheimer’s disease, 65 NeuroImage 167–175 (2013)
(“Gray”).
6 Wu et al., Identification of differential gene expression for microarray data
using recursive random forest, 121(24) Chin. Med. J. 2492–2496 (2008)
(“Wu”).
7 Lunetta et al., Screening large-scale association study data: exploiting
interactions using random forests, 5(32) BMC Genetics 1–13 (2004)
(“Lunetta”).
8 Cutler et al., Random Forests for Microarrays, 411 Methods Enzymol.
422–432 (2006) (“Cutler”).
9 Nelson et al., Early detection of lung cancer based on three-dimensional,
morphometric analysis of cells from sputum, ASCO Annual Meeting,
Abstract 7547 (2014) (“Nelson”).
10 Meyer et al., Automated cell analysis in 2D and 3D: A comparative study,
42 Pattern Recog. 141–146 (2009) (“Meyer”).
11 Fauver et al., Three-dimensional imaging of single isolated cell nuclei
using optical projection tomography, 13(11) Optics. Express 4210–4223
(2005) (“Fauver”).
12 Neumann et al., Premalignant and Malignant Cells in Sputum From Lung
Cancer Patients, Cancer Cytopath. 473–439 (2009) (“Neumann”).
13 Marsh et al., New Horizons in Lung Cancer Diagnosis, 37 Cancer 437–
439 (1976) (“Marsh”).
14 Keith et al., Oral Iloprost Improves Endobronchial Dysplasia in Former
Smokers, 4(6) Cancer Prev. Res. 793–802 (2011) (“Keith”).
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II. Claim 37 under 35 U.S.C. § 112(a) as failing to comply with the
written description requirement (“Written Description Rejection”);
and
III. Claim 37 under 35 U.S.C. § 101 as directed to non-statutory subject
matter and directed to a judicial exception without significantly more
(“Subject Matter Eligibility Rejection”).
Appeal Br. 3–4.
OPINION
I. Obviousness Rejection
The Examiner rejects claims 32–37 over Nandakumar in view of
Asaoka, Gray, Wu, Lunetta, Cutler, Nelson, Meyer, Fauver, Neumann,
Marsh, and Keith. Final Act. 2–12.
The Examiner relies on Nandakumar as the primary reference, finding
that it teaches a method using an optical tomography assay to differentiate
cultured dysplastic esophageal cells from normal esophageal cells. Final
Act. 3. The Examiner acknowledges that Nandakumar does not describe a
training algorithm using dysplastic and normal cell measurements. Id. The
Examiner finds that “these [and other] deficiencies are made up in the
teachings” of other cited references. Id. at 4. Then Examiner then provides
findings regarding disclosures in the eleven additional references. Id. at 4–6.
Notably, the Examiner fails to identify any disclosure in the additional
references—taken alone or together—that teaches “a training algorithm
using dysplastic and normal cell measurements.” Independent claims 32 and
36 recite methods of training an automated dysplastic cell algorithmic
classifier comprising, inter alia:
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a) obtaining a set of known unique dysplastic cells
indicating an abnormal lung process and a plurality of known
normal cells;
b) operating an optical tomography system to generate a
first set of 3D images of the set of known unique dysplastic
cells indicating an abnormal lung process and a second set of
3D images for the plurality of known normal cells;
e) operating a training algorithm using the plurality of
dysplastic cell feature measurements and the normal cell feature
measurements to classify the set of known unique dysplastic
cells and plurality of known normal cells into classified
dysplastic cell types and classified normal cell types.
Appeal Br. 38, 41. The disclosures in the references do not teach these
limitations, and the Examiner fails to provide any reason for a person of
ordinary skill in the art at the time of the invention to have modified the
references to arrive at the claimed invention. See KSR Int'l Co. v. Teleflex,
Inc., 550 U.S. 398, 418 (2007) (“[I]t can be important to identify a reason
that would have prompted a person of ordinary skill in the relevant field to
combine the elements in the way the claimed new invention does.”).
Nandakumar concerns using optical tomography to develop
morphological biosignatures of esophageal cells from cell cultures. See
Nandakumar Abst. Nandakumar reports on morphometric hallmarks of
cancer progression in two pre-neoplastic esophageal cell lines, and suggests
that classification models could be developed based on individual cell lines.
Id. Abst, 32. Nandakumar notes that “[t]he use of cell lines circumvents the
problems encountered in highly heterogeneous biopsies” (which would
include known unique dysplastic cells and known normal cells). Id. at 33.
The reference does not disclose obtaining dysplastic and normal lung cells
from a patient (known unique dysplastic cells and known normal cells),
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using an optical tomography system to generate two sets of 3D images—one
for the known unique dysplastic cells and another for the known normal
cells—to compute two sets of cell feature measurements, and operating a
training algorithm using the cell feature measurement to classify the cells as
either dysplastic or normal.
Asaoka reports on using a random forests classifier to statistically
evaluate Heidelberg Retina Tomograph parameters to improve the
diagnostic accuracy of glaucoma. Asaoka 2842. The reference does not
relate to dysplastic cells, either of a cell line or a patient.
Gray, Wu, Lunetta, and Cutler each concern use of random forests to
form classification models from data. Gray relates to classification of
Alzheimer’s disease (Gray 167), Wu to DNA microarray data (Wu Abst.),
Lunetta to use of random forests to evaluate single nucleotide polymorphism
data (Lunetta Abst.), and Cutler to microarrays of genes (Cutler 423). None
of these references suggests obtaining cell sets as claimed and operating a
training algorithm based upon them.
Nelson discloses analyzing biopsies of lung cancer with an optical
tomography system, but teaches nothing about using cell sets as claimed to
create a data set for use with a classifier. See Nelson Abst. 7547. Meyer
compares the results of automated cell analysis using 2D optical microscopy
and 3D tomographic reconstruction. Meyer 141. Fauver concerns
developing the optical projection tomography microscope, and employs two
lung cell lines as experimental subjects. Fauver 4212. Neumann assesses
the frequency of dysplastic and malignant cells in sputa from patients with
lung cancer, and makes a passing reference to Fauver. Neumann 474, 479.
Marsh discloses using endoscopy to localize radiologically occult lung
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tumors discovered in sputum samples. Marsh 437. Keith teaches use of oral
Iloprost as a chemopreventive agent for lung cancer. Keith Abst. None of
these references, alone or in combination with other references, teaches
limitations a), b), and e) of the claims.
The prima facie case is a procedural tool of patent examination,
allocating to the examiner the initial burden to show unpatentability. In re
Oetiker, 977 F.2d 1443, 1445 (Fed. Cir. 1992). “If examination at the initial
stage does not produce a prima facie case of unpatentability, then without
more the applicant is entitled to grant of the patent.” Id.; see also In re Fine,
837 F.2d 1071, 1074 (Fed. Cir. 1988) (When the references cited by the
examiner fail to establish a prima facie case of obviousness, the rejection is
improper and will be overturned.).
We agree with Appellant that the Examiner fails to establish a prima
facie case of obviousness of the claims over the cited references. We do not
sustain the obviousness rejection.
II. Written Description Rejection of Claim 37
Claim 37 recites:
37. An automated dysplastic cell algorithmic classifier
directed to treating a malignancy in a subject trained in
accordance with the method of claim 36.
Appeal Br. 42 (Claims App.). Claim 36 recites elements a)–j) of claim 32.
Compare id. at 41–42, with id. at 38–39.
The Examiner rejects claim 37 for failing to comply with the written
description requirement of 35 U.S.C. § 112(a). Final Act. 12. More
specifically, the Examiner finds that the Specification does not adequately
describe the genus of automated dysplastic cell algorithmic classifiers
encompassed by claim 37. Id. at 12–13. The Examiner explains that the
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Specification does not describe common attributes or characteristics that
demonstrate the applicants, at the time of filing, were in possession of the
automated dysplastic cell algorithmic classifiers now claimed. Id.
Appellant argues that the claimed classifier is a part of a cell imaging
system that operates using a computer, and the training method is recited as
being that of claim 36, and is described in detail in the Specification.
Appeal Br. 32. Appellant’s argument is unpersuasive of reversible error in
the rejection.
The sufficiency of an application’s written description is a question of
fact. Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir.
2010) (en banc). For an applicant to comply with the 35 U.S.C. § 112(a)
written description requirement, the applicant’s specification must “convey
with reasonable clarity to those skilled in the art that, as of the filing date
sought, he or she was in possession of the invention.” Carnegie Mellon
Univ. v. Hoffmann-La Roche Inc., 541 F.3d 1115, 1122 (Fed. Cir. 2008)
(quoting Vas-Cath Inc. v. Mahurkar, 935 F.2d 1555, 1563‒64 (Fed. Cir.
1991)). “It is not sufficient for purposes of the written description
requirement of § 112 that the disclosure, when combined with the
knowledge in the art, would lead one to speculate as to modifications that
the inventor might have envisioned, but failed to disclose.” Lockwood v.
American Airlines, Inc., 107 F.3d 1565, 1572 (Fed. Cir. 1997). “The
purpose of the ‘written description’ requirement is broader than to merely
explain how to ‘make and use’; the applicant must also convey with
reasonable clarity to those skilled in the art that, as of the filing date sought,
he or she was in possession of the invention. The invention is, for purposes
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of the ‘written description’ inquiry, whatever is now claimed.” Vas-Cath
Inc., 935 F.2d at 1563–64.
Claim 37 claims an automated dysplastic cell algorithmic classifier
trained in accordance with the method of claim 36. See Appeal Br. 42. The
classifier may be implemented with the use of a computer, but the classifier
is not itself a computer. See id. The Specification discloses methodology
for training various classifiers, and describes what various classifiers do, but
fails to adequately describe what a classifier is. See, e.g., Spec. 5, ll. 15–16
(“A biological specimen classifier identifies cells from the sputum specimen
as normal or abnormal.”); 16, ll. 24–25 (“The classifiers used in the
cytological detection system are trained as described below”); 17, ll. 28–29
(“[T]he dysplastic classifier was trained using a set of about 150 known
dysplastic cells and about 25,000 known normal cells. The dysplastic cell
classifier 50 further identifies cells as exhibiting mild, moderate, or severe
dysplasia.”).
The Specification contains no description of the structure of an
automated dysplastic cell algorithmic classifier. It has long been held that
“apparatus claims cover what a device is, not what a device does.” Hewlett-
Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1468 (Fed. Cir. 1990);
see also In re Gardiner, 171 F.2d 313, 315-16 (CCPA 1948) (“It is trite to
state that the patentability of apparatus claims must be shown in the structure
claimed and not merely upon a use, function, or result thereof.”). However,
claim 37 and the Specification merely describe the use, function, and result
of the classifier, but not structure. “It is ‘not a question of whether one
skilled in the art might be able to construct the patentee’s device from the
teachings of the disclosure, . . . Rather, it is a question whether the
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application necessarily discloses that particular device.’” Martin v. Mayer,
823 F.2d 500, 504 (Fed. Cir. 1987) (recognized as superseded by rule on
other grounds by Kubota v. Shibuya, 999 F.2d 517, 521 (Fed. Cir. 1993)).
The ’940 Application fails to disclose adequately the specific structure
of an automated dysplastic cell algorithmic classifier; thus claim 37 fails to
comply with the written description requirement. We sustain the rejection of
claim 37 for failing to comply with 35 U.S.C. § 112(a).
III. Subject Matter Eligibility Rejection of Claim 37
The Examiner finds that claim 37 does not fall within at least one of
the four categories of patent eligible subject matter because it does not claim
a process, machine, manufacture, or composition of matter. Final Act. 14.
The Examiner also finds that claim 37 is directed to a judicial
exception without significantly more. Id. More specifically, the Examiner
finds that “an automated dysplastic cell algorithmic classifier directed to
treating a malignancy in a subject trained in accordance with the method of
instant claim 36” is a mathematical concept, which is an abstract idea, and
thus qualifies as a judicial exception to patentability. Id.
Appellant argues that the claimed classifier is a component of a larger
lung cancer test system, and “includes at least a computer running an
algorithm . . . that has been further trained with the method of claim 36. . . .
It is a machine.” Appeal Br. 33–34; see also Reply Br. 11–12.
A. Principles of Law
An invention is patent-eligible if it claims a “new and useful process,
machine, manufacture, or composition of matter.” 35 U.S.C. § 101. The
Supreme Court, however, has long interpreted 35 U.S.C. § 101 to include
implicit exceptions: “[l]aws of nature, natural phenomena, and abstract
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ideas” are not patentable. Alice Corp. v. CLS Bank Int’l, 573 U.S. 208, 216
(2014). In determining whether a claim falls within an excepted category,
we are guided by the Supreme Court’s two-step framework, described in
Alice (see id. at 217–18), and Mayo Collaborative Services v. Prometheus
Laboratories, Inc., 566 U.S. 66, 75–77 (2012). In accordance with that
framework, we first determine what concept the claim is “directed to.” See
Alice, 573 U.S. at 219.
Concepts determined to be abstract ideas, and thus patent ineligible,
include certain methods of organizing human activity, such as fundamental
economic practices (id. at 219–20; Bilski v. Kappos, 561 U.S. 593, 611
(2010)); mathematical formulas (Parker v. Flook, 437 U.S. 584, 594–95
(1978)); and mental processes (Gottschalk v. Benson, 409 U.S. 63, 69
(1972)). Concepts determined to be patent eligible include physical and
chemical processes, such as “molding rubber products” (Diamond v. Diehr,
450 U.S. 175, 191 (1981)); “tanning, dyeing, making water-proof cloth,
vulcanizing India rubber, smelting ores” (id. at 182 n.7 (quoting Corning v.
Burden, 56 U.S. 252, 267–68 (1854))); and manufacturing flour (Benson,
409 U.S. at 69 (citing Cochrane v. Deener, 94 U.S. 780, 785 (1876))).
If a claim is “directed to” an abstract idea, we turn to the second step
of the Alice and Mayo framework, where “we must examine the elements of
the claim to determine whether it contains an ‘inventive concept’ sufficient
to ‘transform’ the claimed abstract idea into a patent-eligible application.”
Alice, 573 U.S. at 221. “A claim that recites an abstract idea must include
‘additional features’ to ensure ‘that the [claim] is more than a drafting effort
designed to monopolize the [abstract idea].’” Id. (quoting Mayo, 566 U.S. at
77).
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In January 2019, the PTO published revised guidance on the
application of Section 101. 2019 Revised Patent Subject Matter Eligibility
Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) (“Guidance”) (updated in October
2019 (“Guidance Update”)). Under the Guidance, we first look to whether a
claim recites (1) any judicial exceptions, including certain groupings of
abstract ideas (i.e., mathematical concepts, certain methods of organizing
human activity such as a fundamental economic practice, or mental
processes) (“Guidance Step 2A, Prong One”), and (2) additional elements
that integrate the judicial exception into a practical application (see MPEP §
2106.05(a)–(c), (e)–(h)) (“Guidance Step 2A, Prong Two”). Only if a claim
(1) recites a judicial exception and (2) does not integrate that exception into
a practical application, do we then look to whether the claim (3) adds a
specific limitation beyond the judicial exception that is not “well-
understood, routine, conventional” in the field (see MPEP § 2106.05(d)), or
(4) simply appends well-understood, routine, conventional activities
previously known to the industry, specified at a high level of generality, to
the judicial exception (“Guidance Step 2B”).
B. Guidance Step 1 – Is the claim to patentable subject
matter?
The subject matter of claim 37 is an algorithmic classifier. See
Appeal Br. 42. Appellant contends that the classifier is part of a larger lung
cancer test system, and includes at least a computer running an algorithm,
thus is a machine. Appeal Br. 33–34.
We disagree. “Classifier” is not defined in the Specification. See
generally, Spec. The Specification states that “cell imaging system 20 [Cell-
CT 3D Cell Imaging system] includes a process implemented through
computer software executed, for example, by a personal computer
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interfacing with opto-mechanical devices to correct for motion arising
during image capture.” Spec. 10, ll. 29–31. We find no disclosure in the
Specification—and Appellant points us to none—that indicates a computer
is part of the claimed classifier, rather than part of the cell imaging system.
The claimed classifier appears to assign data regarding cell features
to preset categories based on analyzing sets of training data. See Spec. 22, l.
12–26, l. 16. The claimed classifier may be implemented with use of a
computer, but that does not make the computer part of the classifier, or make
the classifier “include” a computer (and therefore be a machine).
Although we agree with the Examiner’s determination that claim 37 is
not drawn to patentable subject matter, for the sake of completeness, we
continue our analysis of claim 37 as if it were.
C. Guidance Step 2A, Prong One – Judicial Exception
As indicated above, under Guidance Step 2A, Prong One, we consider
whether claim 37 recites a judicial exception to the statutory categories of
patent-eligible subject matter, including one of the following groupings of
abstract ideas: (1) mathematical concepts, e.g., mathematical relationships,
mathematical formulas or equations, and mathematical calculations; (2)
mental processes, e.g., concepts performed in the human mind, including
observations, evaluations, judgments, and opinions; and (3) certain methods
of organizing human activity. See Guidance, 84 Fed. Reg. at 52.
Claim 37 recites an “algorithmic classifier.” Appeal Br. 42 (Claims
App.). Thus, claim 37 describes mathematical relationships, which fall
within the “mathematical concepts” grouping identified by the courts as an
abstract idea. See 84 Fed. Reg. at 52.
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The Supreme Court has established that a mathematical concept
without more does not constitute patent-eligible subject matter. See Parker
v. Flook, 437 U.S. 584–85, 587–96 (1978) (“Here it is absolutely clear that
respondent’s application contains no claim of patentable invention. . . .
Respondent’s application simply provides a new and presumably better
method for calculating alarm limit values.”); Mackay Radio & Tel. Co. v.
Radio Corp. of America, 306 U.S. 86, 94 (1939) (“[A] scientific truth, or the
mathematical expression of it, is not patentable invention . . . .”).
Having determined that claim 37 recites the abstract idea of
mathematical concepts, we next look to determine whether the claim recites
additional elements that integrate the judicial exception into a practical
application. Guidance, 84 Fed. Reg. at 53–54.
D. Guidance Step 2A, Prong Two – Integration into a
Practical Application
According to the Guidance, even if a claim recites any one of three
groupings of abstract ideas, the claim is still not “directed to” a judicial
exception (abstract idea), and thus is patent eligible, if “the claim as a whole
integrates the recited judicial exception into a practical application of that
exception.” Id. at 53. Limitations that are indicative of “integration into a
practical application” include: (1) improvements to the functioning of a
computer, or to any other technology or technical field (see MPEP
§ 2106.05(a)); (2) applying the judicial exception with, or by use of, a
particular machine (see id. § 2106.05(b)); (3) effecting a transformation or
reduction of a particular article to a different state or thing (see id.
§ 2106.05(c)); and (4) applying or using the judicial exception in some other
meaningful way beyond generally linking the use of the judicial exception to
a particular technological environment, such that the claim as a whole is
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more than a drafting effort designed to monopolize the exception (see id.
§ 2106.05(e)). See Guidance, 84 Fed. Reg. at 54–55 (“Prong Two”). In
contrast, limitations that are not indicative of “integration into a practical
application” include: (1) adding the words “apply it” (or an equivalent) with
the judicial exception, merely including instructions to implement an
abstract idea on a computer, or merely using a computer as a tool to perform
an abstract idea (see MPEP § 2106.05(f)); (2) adding insignificant extra-
solution activity to the judicial exception (see id. § 2106.05(g)); and (3)
generally linking the use of the judicial exception to a particular
technological environment or field of use (see id. § 2106.05(h)). See
Guidance, 84 Fed. Reg. at 54–55 (“Prong Two”). The Step 2A Prong Two
analysis excludes consideration of whether a limitation is well-understood,
routine, conventional activity. Guidance, 84 Fed. Reg. at 55.
Besides the abstract ideas, claim 37 recites that the classifier is
“directed to treating a malignancy in a subject trained in accordance with the
method of claim 36.” Appeal Br. 42 (Claims App.). If the additional
language reflects an improvement to a technology or technical field, the
claim integrates the judicial exception (abstract idea) into a practical
application and thus imposes a meaningful limit on the judicial exception.
Guidance Update 11.
Appellant contends:
[T]he underlying training method is based on the use of known
normal and known unique dysplastic cells together with cell
feature measurements in a repeated way that adjusts the cell
feature measurements until an accuracy value falls within a
performance bound.

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Even if that training method were an abstract idea, which
it is not, [the Guidance] is clear that to be directed to an abstract
idea, the claim must not integrate the exception into a practical
application. The Examiner never considered the practical
application part of [the Guidance]. All the Examiner said was
that “generic computer implementation of an abstract process
adds nothing of substance to an abstract idea and fails to
transform an abstract idea into a patent-eligible invention.” . . .
That observation . . . ignores the requirement under [the
Guidance] to consider whether there is a practical application at
step 2A.
The Examiner’s rejection fails here because the Examiner
did not analyze whether a trained cell classifier is a practical
application of the training method, which it is.
Appeal Br. 34–35.
Appellant’s argument is not persuasive. Claim 37 is directed to an
algorithmic classifier, not a method (which is claimed in claim 36).15 The
classifier of claim 37 manipulates data; it does not require treating a
malignancy in a subject. See id. at 41–42. “Treating a malignancy in a
subject” is intended use, which does not confer patentability on the
classifier. Moreover, “[i]nclusion of the material or article worked upon by
a structure being claimed does not impart patentability to the claims.” In re
Otto, 312 F.2d 937, 940 (CCPA 1963). To the extent that claim 37 claims a
device, nothing is accomplished with the device. The classifier does nothing
with the training method, which accumulates data. We discern no practical
application here.
Appellant argues that our informative decision in Ex parte Fautz
supports its position. Appeal Br. 35–36 (citing Ex parte Fautz, Appeal No.

15 In the event of further prosecution, we encourage the Examiner to
consider whether claim 36 is directed to a judicial exception.
Appeal 2020-002440
Application 15/403,940

18
2019-000106, 2019 WL 2244873 (PTAB May 15, 2019) (informative)).
Fautz is readily distinguished as reciting a magnetic resonance (MR)
tomography apparatus comprising, inter alia, an MR data acquisition unit
and a processor—both of which are pieces of machinery that exist in the
physical realm, unlike a classifier. See id. At best, claim 37 merely
generally links the use of the judicial exception to a particular technological
environment or field of use. See MPEP § 2106.05(h); see also Guidance, 84
Fed. Reg. at 54–55 (“Prong Two”).
E. Guidance Step 2B – Additional Elements
In Step 2B, we evaluate whether claim 37 recites additional elements
that amount to significantly more than the judicial exception, viewing any
additional elements both individually and in combination with other claim
elements. Guidance, 84 Fed. Reg. at 51; see also Alice, 573 U.S. at 225.
In addition to the automated dysplastic cell algorithmic classifier,
claim 37 recites “directed to treating a malignancy in a subject in accordance
with the method of claim 36.” Appeal Br. 42 (Claims App.). This language
merely links the abstract idea to a particular technological environment, and
thus the claim as a whole is no more than a drafting effort designed to
monopolize the exception and does not recite any elements or combinations
thereof that amount to significantly more than the judicial exception. See
MPEP § 2106.05(e).
We sustain the rejection of claim 37 over 35 U.S.C. § 101 as directed
to non-statutory subject matter.
Appeal 2020-002440
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19
IV. CONCLUSION
We do not sustain the rejection of claims 32–37 as obvious under 35
U.S.C. § 103. We sustain the rejection of claim 37 under 35 U.S.C. §§ 101
and 112(a). Thus, we affirm in part the Examiner’s decision.
More specifically,
DECISION SUMMARY
Claim(s)
Rejected
35 U.S.C. § Reference(s)/Basis Affirmed Reversed
32–37 103 Nandakumar,
Asaoka, Gray, Wu,
Lunetta, Cutler,
Nelson, Meyer,
Fauver, Neumann,
Marsh, Keith
32–37
37 112(a) Written
Description
37
37 101 Eligibility 37
Overall
Outcome
37 32–36
TIME PERIOD FOR RESPONSE
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R.
§ 1.136(a)(1)(iv).
AFFIRMED IN PART