KONINKLIJKE PHILIPS N.V.

Patent Trials and Appeals Board

Jun 23, 2020

14434286 - (D) (P.T.A.B. Jun. 23, 2020)

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.
14/434,286 04/08/2015 Bart Jacob Bakker 2012P01004WOUS 1014
24737 7590 06/23/2020
PHILIPS INTELLECTUAL PROPERTY & STANDARDS
465 Columbus Avenue
Suite 340
Valhalla, NY 10595
EXAMINER
COBANOGLU, DILEK B
ART UNIT PAPER NUMBER
3626
NOTIFICATION DATE DELIVERY MODE
06/23/2020 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):
katelyn.mulroy@philips.com
marianne.fox@philips.com
patti.demichele@Philips.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________________

Ex parte BART JACOB BAKKER,
HENDRIK JAN VAN OOIJEN,
and RENE VAN DEN HAM
__________________

Appeal 2020-000940
Application 14/434,286
Technology Center 3600
____________________

Before JAMES P. CALVE, MICHELLE R. OSINSKI, and
WILLIAM A. CAPP, Administrative Patent Judges.

CALVE, Administrative Patent Judge.

DECISION ON APPEAL

STATEMENT OF THE CASE
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the decision
of the Examiner to reject claims 1–4, 6–8, 10, 11, 13, 14, and 16–24, which
are all pending claims.2 Appeal Br. 6. We have jurisdiction under 35 U.S.C.
§ 6(b).
We AFFIRM.

1 “Appellant” refers to “applicant” as defined in 37 C.F.R. § 1.42. Appellant
identifies Koninklijke Philips Electronics N. V. as the real party in interest.
Appeal Br. 2.
2 Claims 5, 9, 12, and 15 are cancelled. Appeal Br. 2.
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CLAIMED SUBJECT MATTER
Claims 1, 16, and 18 are independent. Claim 1 is reproduced below.
1. An apparatus that determines whether an anticoagulant is
to be administered to a patient based on an estimation value of
thrombosis risk of the patient based on patient-specific input
features, the apparatus comprising:
a data interface that receives the input features;
a processor that:
calculates the estimation value by applying a
decision support algorithm as a function of numerical values
derived from the received input features;
compares the estimation value to a threshold value;
if the estimation value exceeds the threshold value,
prescribe an anticoagulant to the patient;
if the estimation value does not exceed the
threshold value, do not prescribe the anticoagulant to the patient
and
a user interface that outputs the estimation value
and, if the estimation value exceeds the threshold, outputs the
prescription of an anticoagulant treatment to be administered to
the patient;
wherein the input features include a combination
of at least one clinical risk factor and at least one protein
concentration of the patient.

REJECTIONS
Claims 10, 11, 13, 14, 16, and 17 are rejected under 35 U.S.C.
§ 112(a) for lack of written description.
Claims 1–4, 6–8, 10, 11, 13, 14, and 16–24 are rejected as directed to
a judicial exception to 35 U.S.C. § 101.
Claims 1, 2, 6, 8, 16–19, and 22–24 are rejected under 35 U.S.C.
§ 102 as anticipated by Mann (US 2009/0298103 A1, pub. Dec. 3, 2009).

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ANALYSIS
Claims 10, 11, 13, 14, 16, and 17
for Lack of Written Description
The Examiner determines that adding “administer an anticoagulant
treatment to the patient” in claim 16 introduces new matter. Final Act. 3.
The Examiner finds the Specification recites “[s]pecifically, the proposed
solution may be used to decide, per patient, whether or not to administer
anticoagulant treatment based on estimated thrombosis risk.” Id. (quoting
Spec. 2, last two lines). The Examiner determines that this passage “teaches
suggesting whether or not to administer the treatment, it’s not describing or
reciting how to administer the treatment.” Ans. 3.
Appellant argues that this passage of the Specification supports this
limitation because the description of “deciding whether or not to administer
an anticoagulant treatment necessarily reflects to one of skill in the art that
the applicants had possession of the ability to administer the treatment at the
time the specification was filed.” Appeal Br. 6; Reply Br. 2. Appellant also
argues that administering an anticoagulant treatment is well-known in the art
and need not be described further where the original disclosure disclosed
whether or not to “administer anticoagulant treatment.” Reply Br. 2.
As an initial step, we interpret this limitation. MPEP ¶ 2163 II.A.1.
We interpret this limitation to mean to treat a patient with an anticoagulant.
We do not interpret this limitation to mean “to provide the patient an oral
medication” as the Examiner does. Final Act. 3. Claim 16 does not recite a
method of administering an anticoagulant treatment to a patient. Instead, it
recites a method of calculating an estimation value and using that value to
determine whether to administer an anticoagulant treatment to a patient.
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The Background of the Invention makes clear that it is known in the
art to administer anticoagulant treatment for thrombosis. The problem is
deciding whether to administer treatment. Spec. 1:11–24. The uncertainty
causes unnecessary thromboses in high risk patients who do not receive
treatment and bleeding in low risk patients who receive unnecessary
anticoagulant treatment. Id. at 1:25–2:2. The invention object is increasing
the accuracy of thrombosis risk estimation for patients. See id. at 2:14–15.
Claim 16 recites such a method of determining whether to administer
anticoagulant treatment to a patient, not how to do so. If an estimation value
for a patient exceeds a threshold value, claim 16 recites a step of “administer
an anticoagulant treatment to the patient; otherwise, do not administer the
anticoagulant to the patient.” Appeal Br. 17 (Claims App.).
This method calculates a thrombosis estimation value of a patient to
“help[] the physician to stratify the patients that are treated or examined for
conditions that are known to increase thrombosis risk, into high and low risk
categories.” Id. at 2:28–30. “[T]he proposed solution may be used to
decide, per patient, whether or not to administer anticoagulant treatment
based on estimated thrombosis risk.” Id. at 2:30–31 (emphasis added). The
clinical decision support system processor 20 calculates a thrombosis risk
score (R) based on input patient risk factors. If the score exceeds a threshold
value (T), anticoagulant therapy is indicated for the patient whose risk
values have been entered into the calculation. Id. at 6:24–26. “Otherwise,
preventive anti-coagulation therapy is indicated as not advisable.” Id. at
6:26. The clinical decision support system “can be accessed by a clinician
who needs to make a decision about patients’ anticoagulation treatment.”
Id. at 7:1–3 (emphasis added).
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The Patent Laws require “[t]he specification shall contain a written
description of the invention.” 35 U.S.C. § 112(a). The written description
must reasonably convey to a skilled artisan that the inventor had possession
of the claimed subject matter as of the filing date. Ariad Pharms., Inc. v. Eli
Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (citations omitted).
As our reviewing court has instructed us:
[T]he level of detail required to satisfy the written description
requirement varies depending on the nature and scope of the
claims and on the complexity and predictability of the relevant
technology. Id. For generic claims, we have set forth a number
of factors for evaluating the adequacy of the disclosure,
including “the existing knowledge in the particular field, the
extent and content of the prior art, the maturity of the science or
technology, [and] the predictability of the aspect at issue.”
Ariad, 598 F.3d at 1351 (citation omitted). The Examiner has not explained
why the written description is inadequate in terms of these factors and in fact
agrees that the Specification supports a method to determine whether or not
to administer anticoagulant treatment as recited in claim 16. See Ans. 3.
Appellant’s Figure 4 illustrates user interface 30, which displays a
patient’s calculated risk score (RS) of 0.1 compared to a threshold level (T)
of 0.5. The risk scale (RV) indicates that the patient’s score of 0.1 qualifies
as a low risk of thrombosis. Spec. 11:10–24. This visualization allows a
clinician to quickly assess a patient’s thrombosis risk and provides enhanced
support for a treatment decision. Id. at 11:25–27. We determine that the
Specification sufficiently describes the method step of “if the estimation
value exceeds the threshold value, administer an anticoagulant treatment to
the patient” as recited in claim 16 and its dependent claims 10, 11, 13, 14,
and 17. Thus, we do not sustain this rejection.
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Patent Eligibility under 35 U.S.C. § 101
Examiner’s Determination
The Examiner finds that the claims recite making a determination by
obtaining patient-specific input features, calculating an estimation value, and
comparing the estimation value to a threshold. Final Act. 4. The Examiner
determines that these steps recite a mental process that medical professionals
can perform in their heads. Id.; Ans. 3–4. The Examiner also determines
that the claims are directed to a mathematical concept of a decision support
algorithm. Ans. 3–4.
The Examiner determines that the judicial exception is not integrated
into a practical application because applying the decision support algorithm
as a function of numerical values derived from input features is done on a
generic computer and processor that do not add a meaningful limitation or
provide an inventive concept. See Final Act. 4; Ans. 4. The Examiner also
determines that the claims lack additional elements that are significantly
more than the judicial exception, and administration of an anticoagulation
treatment if an estimation value exceeds a threshold value is extra-solution
activity. Final Act. 4; Ans. 4.
Appellant’s Contentions
Appellant argues that calculating an estimation value of thrombosis
risk by applying a decision support algorithm as a function of numerical
values derived from patient-specific input features cannot be performed in a
medical professional’s head. Appeal Br. 7. Appellant argues Example 37 of
the 2019 Revised Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan.
7, 2019) recites a similar determining step that requires a processor and
cannot be applied practically in the mind. Id.
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Appellant argues that there is no known algorithm for determining a
value of thrombotic risk based on a numerical value associated with at least
one clinical risk factor and at least one protein concentration and therefore a
medical professional reviewing clinical risk factors and protein blood test
results as a mental process cannot convert clinical risk factors into numerical
values that can be combined with numerical values of protein concentrations
to calculate an estimation value of thrombotic risk as claimed. Appeal Br. 8;
Reply Br. 3. Appellant further argues that the decision support algorithm is
created using “machine learning” that uses “hidden models” that are “tuned”
to provide a reliable prediction. Reply Br. 3.
Appellant asserts that even if the claims recite a mental process, the
claims integrate the mental process into a practical application by improving
the accuracy of identifying patients with high thrombotic risk for whom
anticoagulant therapy should be administered. Appeal Br. 8–9. Appellant
contends that this improvement to technology or a technical field constitutes
a practical application of the abstract idea. Id. at 9.
Appellant asserts that the dependent claims impose further substantial
limitations to the abstract idea of calculating and comparing numbers. Id. at
9–10. For example, Appellant contends that claims 3, 4, 13, 14, 20, and 21
“limit the calculating step to specific clinical risk factors.” Id. at 10.
Appellant argues that claims 7 and 10 “add an additional optimization that
minimizes prediction error associated with the estimated thrombosis risk
value.” Id. Appellant argues that claim 11 “adds an additional training and
optimizing step to train and validate the calculation of the thrombotic risk
value.” Id.

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Principles of Law
Section 101 of the Patent Act states:
Whoever invents or discovers any new and useful process,
machine, manufacture, or composition of matter, or any new
and useful improvement thereof, may obtain a patent therefor,
subject to the conditions and requirements of this title.
35 U.S.C. § 101. This provision contains an implicit exception: “Laws of
nature, natural phenomena, and abstract ideas are not patentable.” Alice
Corp. Pty. v. CLS Bank Int’l, 573 U.S. 208, 216 (2014).
To distinguish patents that claim laws of nature, natural phenomena,
and abstract ideas from those that claim patent-eligible applications, we first
determine whether the claims are directed to a patent-ineligible concept. Id.
at 217. If they are, we consider the elements of each claim, individually and
“as an ordered combination,” to determine if additional elements “‘transform
the nature of the claim’ into a patent-eligible application” as an “inventive
concept” sufficient to ensure the claims in practice amount to significantly
more than a patent on the ineligible concept itself. See id. at 217–18.
The USPTO has issued guidance about this framework. 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019)
(“Revised Guidance”). Under the Revised Guidance, to determine whether a
claim is “directed to” an abstract idea, we evaluate whether the claim recites
(1) any judicial exceptions, including certain groupings of abstract ideas
listed in the Revised Guidance (i.e., mathematical concepts, certain methods
of organizing human activities such as a fundamental economic practice, or
mental processes); and (2) additional elements that integrate the judicial
exception into a practical application (see MPEP §§ 2106.05(a)–(c), (e)–(h)
(9th ed. rev. 08.2017 Jan. 2018) (“MPEP”)). Id. at 52–55.
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Only if a claim (1) recites a judicial exception and also (2) does not
integrate that exception into a practical application, do we then consider
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. Id. at 56.
Grouping of Claims
Because Appellant argues independent claims 1, 16, and 18 as a group
(Appeal Br. 7–9), we select claim 1 as the representative claim of the group.
See 37 C.F.R. § 41.37(c)(1)(iv).
Appellant’s arguments summarizing dependent claim limitations and
asserting, without any analysis, that these dependent claims impose further
meaningful limits on the abstract idea (see Appeal Br. 9–10) do not present
cognizable argument for the separate patentability of those claims. See 37
C.F.R. § 41.37(c)(1)(iv) (“A statement which merely points out what a claim
recites will not be considered an argument for separate patentability of the
claim.”); see also In re Lovin, 652 F.3d 1349, 1357 (Fed. Cir. 2011) (holding
that the Board reasonably interpreted 37 C.F.R. § 41.37(c)(1)(vii), which is
the predecessor to § 41.37(c)(1)(iv), “to require more substantive arguments
in an appeal brief than a mere recitation of the claim elements and a naked
assertion that the corresponding elements were not found in the prior art”).
Therefore, we address Appellant’s patent-eligibility arguments in
regards to independent claim 1. Claims 2–4, 6–8, 10, 11, 13, 14, and 16–24
stand or fall with claim 1.
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Step 1: Is Claim 1 Within a Statutory Category?
Claim 1 recites an “apparatus” which is within a statutory category of
35 U.S.C. § 101, namely, a machine. Therefore, we next consider whether
claim 1 considered as a whole recites a judicial exception.
Step 2A, Prong 1: Does Claim 1 Recite a Judicial Exception?
We determine claim 1 recites an abstract idea. The Revised Guidance
enumerates this abstract idea as (1) mathematical concepts––mathematical
calculations and (2) mental processes––concepts that can be performed in
the human mind. See Revised Guidance, 84 Fed. Reg. at 52.
The invention calculates an estimation value of thrombosis risk based
on patient-specific input features. Spec. 1:1–3. The calculation provides a
“patient-specific measure to estimate the personal thrombosis risk and
facilitate an informed choice on whether or not to treat.” See id. at 1:15–17.
The focus is to reduce uncertainty about patient specific risk of thrombosis
that causes unnecessary thromboses in high risk patients who do not receive
anticoagulant treatment and bleeding in relatively low risk patients who
receive unnecessary anticoagulant treatment. Id. at 1:25–2:2.
The Specification describes this innovation as implemented on a
computer-based “clinical decision support system (CDSS),” which is
any software designed to directly aid in clinical decision
making in which characteristics of individual patients are
matched to a computerized knowledge base for the purpose of
generating patient-specific assessments or recommendations
that are then presented to clinicians for consideration and
decision making.
Id. at 1:4–8. CDSSs are used to improve the quality of health care by
supporting clinical decision making. Id. at 1:8–10.
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The preamble recites this concept as embodied in an “apparatus that
determines whether an anticoagulant is to be administered to a patient based
on an estimation value of thrombosis risk of the patient based on patient-
specific input features.” Appeal Br. 15 (Claims App.).
The Specification describes this “apparatus” as a CDSS that involves
a clinical decision support algorithm and/or software. Spec. 5:19–21, Fig. 1.
Appellant’s Figure 1, which we reproduce below, illustrates an exemplary
CDSS configuration as a schematic block diagram.

Appellant’s Figure 1 above illustrates a CDSS, which comprises data
interface (DI) 10, processor (P) 20, user interface (UI) 30, optimization unit
(O) 40, and database (DB) 50. Id. at 5:19–30.
The first limitation of claim 1 recites “a data interface that receives the
input features.” Appeal Br. 15 (Claims App.). The final limitation recites
“wherein the input features include a combination of at least one clinical risk
factor and at least one protein concentration of the patient.” Id.
The Specification describes the clinical risk factors as immobilization
within the last three months, surgery within the last month, family history of
venous thrombosis, pregnancy, or puerperium within the last three months,
current use of estrogens, and obesity. Spec. 6:1–13.
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The Specification describes protein concentrations as concentration of
the coagulation protein FVIII, FXI, and TFPI. Id. at 6:14–16.
Receiving or collecting data pertaining to the “input features” recites
an abstract mental process or steps people can and have performed in their
minds. See Revised Guidance, 84 Fed. Reg. at 52 n.14; Content Extraction
and Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1347
(Fed. Cir. 2014) (holding steps of collecting data, recognizing certain data in
the collected data set, and storing the recognized data in a memory recited
functions that humans always have performed); Classen Immunotherapies,
Inc. v. Biogen IDEC, 659 F.3d 1057, 1067 (Fed. Cir. 2011) (holding the idea
of collecting and comparing known information recited a mental step).
The next limitations recite functions performed by “a processor” as
calculates the estimation value by applying a decision support
algorithm as a function of numerical values derived from the
received input features;
compares the estimation value to a threshold value;
if the estimation value exceeds the threshold value, prescribe an
anticoagulant to the patient;
if the estimation value does not exceed the threshold value, do
not prescribe the anticoagulant to the patient
Appeal Br. 15 (Claims App.).
We determine that calculating an estimation value (described as a risk
score in the Specification) using a generic “decision support algorithm” as a
function of numerical values derived from the input features recites an
abstract mathematical calculation that can be performed as a mental process
in the human mind or by using pen and paper to make the calculations.
Similarly, comparing the estimation and threshold values also recites a
mental process that can be performed in the human mind.
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The Specification describes these functions as processor 20 calculates
a numerical risk score based on numerical values derived from input features
using a “clinical decision support algorithm.” Spec. 6:19–22; see 5:19–27
(processor 20 applies an interpretative algorithm). If the risk score exceeds a
threshold, anti-coagulation therapy is indicated for the patient whose values
were input. Id. at 6:19–26. Otherwise, therapy is not indicated. Id. at 6:26.
The Specification provides no description of the algorithm or process
by which a risk score (“estimation value”) is calculated. Appeal Br. 4 (citing
Spec. 6:19–25 as support for this claimed subject matter). The Specification
describes the “input features” and their “numerical values” as follows:
immobilization (plaster cast, extended bed rest at
home for at least 4 days, hospitalization) within the last
three months (e.g. “1” for true, “0” for false);
surgery within the last month ( e.g. “1” for true,
“0” for false);
family history of venous thrombosis ( considered
positive if at least one parent, brother, or sister
experienced venous thrombosis (e.g. “l” for true, “0” for
false));
pregnancy or puerperium within the last three
months (e.g. “1” for true, “0” for false);
current use of estrogens ( oral contraceptives or
hormone replacement therapy (e.g. “1” for true, “0” for
false));
obesity (body mass index over 30 (e.g. “1” for
true, “0” for false));
concentration (U/mL) of the coagulation protein
FVIII in blood;
concentration (U/mL) of the coagulation protein
FXI in blood; and
concentration (ng/ml) of the coagulation protein
TFPI in blood.
Spec. 6:4–16.
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The risk score is a number between one and zero. One is the highest
thrombosis risk. Zero is the lowest risk. Id. at 6:19–22. The risk score is
compared to a threshold value between zero and one. The threshold may be
fixed or adjustable to bias results. Low thresholds indicate higher risk and
potential overtreatment. High thresholds do the opposite. Id. at 6:27–33.
These limitations recite mental processes that can be performed in the
human mind by simply comparing a risk score to a threshold to determine if
the risk score exceeds the threshold. See Elec. Power Grp., LLC v. Alstom
S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (“[W]e have treated analyzing
information by steps people go through in their minds, or by mathematical
algorithms, without more, as essentially mental processes within the
abstract-idea category.”). The claim in Electric Power received input data
measurements and analyzed the data based on “limits, sensitivities and rates
of change for one or more measurements.” Id. at 1351–52.
So too, in Intellectual Ventures I LLC v. Capital One Bank (USA), 792
F.3d 1363, 1367 (Fed. Cir. 2015), a method of storing a user identity profile
in a database and then “tracking financial transactions to determine whether
they exceed a pre-set spending limit” was determined to involve an abstract
idea. Here, comparing an estimation value to a threshold value to determine
if the estimation value exceeds the threshold value recites a similar abstract
idea as identified above.
Essentially, the processor replicates mental processes of doctors and
clinicians. The Specification describes the computer-based clinical decision
support system aiding clinical decisions by matching traits of patients to a
computerized knowledge base to generate patient-specific assessments for
clinicians to consider in decision-making. Spec. 1:4–10.
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When recited at such an abstract, high level of generality as claim 1
does here, the claim recites an abstract idea as identified above. Similar
claims to such computerization of human mental processes and functions
have been held to be abstract ideas by our reviewing court.
In University of Florida Research Foundation, Inc. v. General Elec.
Co., 916 F.3d 1363 (Fed. Cir. 2019), the court held a method of receiving
patient physiologic treatment data, converting the data to a specific format,
performing at least one programmatic action on the data, and presenting the
results in a graphical user interface recited an abstract idea because it merely
replaced “pen and paper methodologies” with “data synthesis technology.”
Id. at 1367. The court held that:
This is a quintessential “do it on a computer” patent: it
acknowledges that data from bedside machines was previously
collected, analyzed, manipulated, and displayed manually, and
it simply proposes doing so with a computer. We have held
such claims are directed to abstract ideas.
Id. Here, claim 1 similarly collects data about a patient as “input features”
through a generic data interface, calculates an “estimation value” using a
generic decision support algorithm and “numerical values derived from the
received input features,” and “compares the estimation value to a threshold
value” to determine if the estimation value exceeds the threshold or not.
The Specification admits that clinical risk factors such as immobility
and contraceptive use are known in the art and used to estimate and diagnose
thrombosis risk. Spec. 2:2–6. Guidelines based on clinical risk factors have
been used to estimate thrombosis risk. Id. at 2:6–8. Protein measurements
such as a thrombin generation assay are used to estimate thrombotic risk. Id.
at 2:8–10. Thus, the claimed “input features” are known data types.
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The court in In re Grams, 888 F.2d 835 (Fed. Cir. 1989) held similar
claims where the sole physical process step was performing clinical tests on
individuals to obtain input data and the remaining steps essentially recited a
mathematical algorithm for solving a given type of mathematical problem
recited an abstract idea of a mathematical algorithm. Id. at 837, 840. Like
claim 1 here, the claim in Grams recited a method of diagnosing abnormal
conditions of an individual by performing clinical laboratory tests on the
individual to obtain a set of parameter values and comparing the values to
predetermined values to determine if the individual’s condition is abnormal.
Id. at 836. Here, claim 1 similarly receives data as “input features” through
a generic data interface, calculates an “estimation value,” and compares that
value to a “threshold value” to decide whether to prescribe an anticoagulant.
In In re Meyer, 688 F.2d 789 (CCPA 1982), the court held that claims
reciting a mathematical algorithm that represented the mental processes that
neurologists follow in examining patients and comparing responses to what
should be received recited an abstract idea. Id. at 793, 795–96. Replacing
thinking processes of a neurologist with a computer was abstract. Id. at 795.
Similarly, in SmartGene, Inc. v. Advanced Biological Labs., SA, 555
F. App’x 950 (Fed. Cir. 2014), the court held a claim to using a computing
device with basic functionality for comparing stored and input data and rules
replicated the mental processes that doctors routinely perform. Id. at 954–
55. Here, claim 1 compares an “estimation value” calculated by a generic
decision support algorithm (a rule) from numerical values of “input features”
to a threshold value. Doctors routinely compare patient data from tests and
examinations to thresholds to diagnose medical conditions as Grams and
Meyer indicate. See Grams, 888 F.2d at 836; Meyer, 688 F.2d at 793, 795.
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As also indicated by these cases, mere recitation of an algorithm as
part of the claimed process is not sufficient to render the claim non-abstract.
Here, the “decision support algorithm” is applied so the processor
“calculates the estimation value” “as a function of numerical values derived
from the received input features.” Appeal Br. 15 (Claims App.).
The Specification describes this process generically as well. Indeed, it
indicates that the clinical decision support system uses “a clinical decision
support algorithm and/or software.” Spec. 5:19–21. “[T]he processor 20
calculates a numerical function of the . . . numerical inputs by applying the
clinical decision support algorithm.” Id. at 6:19–21.
The Specification also describes an “interpretative clinical decision
support algorithm,” which may be a complex mathematical function that
takes numerical (or Boolean) values for the nine input features (described
above in this decision) as input and uses them in a series of non-linear
calculations to return a numerical value between zero and one. Id. at 7:6–9.
In addition, a “numerical function” can use a combination of “classifier
functions that are common in the field of machine learning, such as neural
network functions or support vector machines or Bayesian network.” Id. at
7:9–12. Classifiers are optimized by optimization unit 40 and database 540
of subjects (thrombosis patients and health controls) whose numerical values
for the nine input features are available. Id. at 7:12–14.
None of these features of the decision support algorithm are claimed.
Claim 1 requires “at least one clinical risk factor” and “at least one protein
concentration” without reciting any of the nine input features described in
the Specification (id. at 6:4–16). Argument regarding an algorithm created
by machine learning is not commensurate with claim 1. See Reply Br. 3.
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“Absent claim language carrying a narrow meaning, the PTO should
only limit the claim based on the specification or prosecution history when
those sources expressly disclaim the broader definition.” In re Bigio, 381
F.3d 1320, 1325 (Fed. Cir. 2004)); see In re Hiniker, 150 F.3d 1362, 1368
(Fed. Cir. 1998) (“Although operational characteristics of an apparatus may
be apparent from the specification, we will not read such characteristics into
the claims when they cannot be fairly connected to the structure recited in
the claims.”); In re Paulsen, 30 F.3d 1475, 1480 (Fed. Cir. 1994) (“Although
an inventor is indeed free to define the specific terms used to describe his or
her invention, this must be done with reasonable clarity, deliberateness, and
precision.”). Appellant asserts no definition or disclaimer for claim 1.
These claim construction tenets govern our patent-eligibility analysis.
See Ericsson Inc. v. TCL Commc’n Tech. Holdings Ltd., 955 F.3d 1317,
1325 (Fed. Cir. 2020) (holding that the specification must always yield to
the claim language when identifying the true focus of a claim); see also
Two-Way Media Ltd. v. Comcast Cable Commc’ns, LLC, 874 F.3d 1329,
1340 (Fed. Cir. 2017) (“As with claim 1 of the ’187 patent, the problem is
that no inventive concept resides in the claims.”).
The claimed “user interface” “outputs the estimation value” resulting
from these mental processes and mathematical calculation as extra-solution
activity. See Elec. Power, 830 F.3d at 1354 (“And we have recognized that
merely presenting the results of abstract processes of collecting and
analyzing information, without more (such as identifying a particular tool for
presentation), is abstract as an ancillary part of such collection and
analysis.”). The “user interface” is generic rather than one that organizes
icons as in claim 2 of Example 37 of the 2019 Patent Eligibility Guidelines.
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Application 14/434,286

19
The Specification indicates that the calculated risk score (estimation
value) is shown as a number or other graphical representation on a computer
screen or user interface 30 in comparison to the threshold. Spec. 8:6–15.
Appellant’s Figure 4 is reproduced below to illustrate this user interface.

Appellant’s Figure 4 above illustrates user interface 30 of Figure 1
with nine “input features” including binary values (“0” or “1”) for recent
surgery (RS), obesity (O), family history (FH), immobility (I), contraceptive
use (CU), and pregnancy (P), and protein concentrations. Id. at 11:10–18.
The patient’s calculated risk score (RS) is 0.1, which is graphed with
the Threshold (T) value of 0.5 on graphical visualization (RV) to indicate
that the calculated risk score qualifies as a low risk (LR). Id. at 11:18–25.
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20
We find no description of the user interface outputting a prescription.
See id. at 6:19–33, 8:6–9:14. The Specification indicates the visualization
illustrated in Figure 4 allows quick assessment by a clinician and provides
enhanced support for a treatment decision. Id. at 11:25–27.
Figure 4 illustrates why the other limitations recite an abstract idea. A
person can collect input features as binary values or protein concentrations
as a mental process. Use of the decision support algorithm to calculate an
estimation value recites a mathematical calculation and comparison of that
value to a threshold value is a mental process that doctors may perform in
their minds to diagnose medical conditions and thrombosis risk.
Such steps also are similar to claims that recited a mental process in
CyberSource Corporation v. Retail Decisions, Inc., 654 F.3d 1366 (Fed. Cir.
2011). There, the claims recited a method of detecting credit card fraud in
online transactions. The first step obtained information about transactions
that used an Internet address identified with a credit card transaction and
“can be performed by a human who simply reads records of Internet credit
card transactions from a preexisting database.” CyberSource, 654 F.3d at
1372. The second step constructed a map of credit card numbers and can be
performed by a person writing down a list of credit card transactions made
from an IP address. Id. (“There is no language in claim 3 or in the ’154
patent’s specification that requires the constructed ‘map’ to consist of
anything more than a list of a few credit card transactions.”). The third step
used the map of credit card numbers to determine if a credit card transaction
was valid. It can be performed in the human mind by a person observing
that numerous transactions used different credit cards with different user
names and billing addresses originating at the same IP address. Id. at 1373.
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21
Comparing the estimation and threshold values recites an abstract
mental step. See In re BRCA1- and BRCA2-Based Hereditary Cancer Test
Patent Litig., 774 F.3d 755, 763 (Fed. Cir. 2014) (holding that claims to
comparing two different nucleotide sequences recited an abstract mental
step). The Revised Guidance provides the following guidance:
If a claim, under its broadest reasonable interpretation,
covers performance in the mind but for the recitation of generic
computer components, then it is still in the mental processes
category unless the claim cannot practically be performed in the
mind.
Revised Guidance, 84 Fed. Reg. at 52 n.14; see also Intellectual Ventures I
LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016) (‘‘[W]ith the
exception of generic computer implemented steps, there is nothing in the
claims themselves that foreclose them from being performed by a human,
mentally or with pen and paper.’’).
Appellant’s argument that claim 1 cannot recite a mental process
because there is no known arithmetic algorithm for determining a value of
thrombotic risk based on a numerical value associated with at least one
clinical risk factor and at least one protein concentration is not persuasive.
See Appeal Br. 8. First, the claimed “decision support algorithm” is recited
at a high level of generality as being applied to calculate an estimation value
from numerical values derived from received input features. As claimed,
such a broad mathematical calculation is amenable to mental processing.
Even if we could read limitations from the Specification into claim 1, there
are no such details to be found regarding this algorithm and its operation.
Second, the calculation of the estimation value recites the abstract
idea of mathematical calculations. Revised Guidance, 84 Fed. Reg. at 52.
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22
As our reviewing court held in similar circumstances involving a
claim to performing mathematical calculations and techniques on input data:
The claims in this case are directed to abstract ideas. The focus
of the claims, as reflected in what is quoted above, is on
selecting certain information, analyzing it using mathematical
techniques, and reporting or displaying the results of the a
analysis. That is all abstract.
SAP Am., Inc. v. Investpic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) (also
holding that “analyzing information . . . by mathematical algorithms, without
more” is an abstract idea as is merely presenting results of abstract processes
as an ancillary part of the collection and analysis of information).
Even if claim 1 recited a new algorithm, the claim is still abstract.
“The ‘novelty’ of any element or steps in a process, or even of the process
itself, is of no relevance in determining whether the subject matter of a claim
falls within the § 101 categories of possibly patentable subject matter.”
Diamond v. Diehr, 450 U.S. 175, 188–89 (1981); see Mayo Collaborative
Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 88–90 (2012) (the patent
eligibility of an abstract idea does not depend on its alleged novelty or non-
obviousness); SAP America, 898 F.3d at 1163 (“No matter how much of an
advance in the finance field the claims recite, the advance lies entirely in the
realm of abstract ideas, with no plausibly alleged innovation in the non-
abstract application realm. An advance of that nature is ineligible for
patenting.”); Two-Way Media, 874 F.3d at 1340 (Fed. Cir. 2017) (holding
that “[e]ligibility and novelty are separate inquiries.”).
Accordingly, we determine that claim 1 recites the abstract idea of
mathematical concepts––mathematical calculations and mental processes––
concepts performed in the human mind as identified above.
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23
Step 2A, Prong Two: Integration into a Practical Application
We next consider whether claim 1 recites additional elements that
integrate the abstract idea into a practical application. Revised Guidance, 84
Fed. Reg. at 54 (Revised Step 2A, Prong Two). We determine that claim 1
lacks any such additional elements because any additional elements recited
in claim 1 do not improve a computer or other technology. They do not
implement the abstract idea in conjunction with a particular machine or
manufacture that is integral to the claim. They do not transform or reduce a
particular article to a different state or thing. They do not apply the abstract
idea in a meaningful way beyond linking it to a particular environment. See
Revised Guidance, 84 Fed. Reg. at 55 and MPEP sections cited therein).
Appellant argues that the system improves the accuracy of identifying
patients with high thrombotic risk to whom coagulation therapy should be
administered and therefore is a practical application of the calculating and
comparing of numbers. Appellant also argues that using numerical values of
clinical risk factors in combination with numerical values of protein
concentrations to calculate a thrombosis risk value imposes substantial limits
on the calculating and comparing process. Appeal Br. 9.
This argument is not persuasive because “[i]nformation as such is an
intangible” and collecting, analyzing, and displaying that information,
without more, is an abstract idea. See Interval Licensing LLC v. AOL, Inc.,
896 F.3d 1335, 1344–45 (Fed. Cir. 2018) (quoting Elec. Power Grp., 830
F.3d at 1353–54 and citing similar decisions holding that displaying
different types or sets of information from various sources on a generic
display is abstract absent a specific improvement to the way computers or
other technologies operate).
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24
In addition, “[i]t has been clear since Alice that a claimed invention’s
use of the ineligible concept to which it is directed cannot supply the
inventive concept that renders the invention ‘significantly more’ than that
ineligible concept.” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281,
1290 (Fed. Cir. 2018); see id. at 1291 (“As a matter of law, narrowing or
reformulating an abstract idea does not add ‘significantly more’ to it.”); see
also RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327 (Fed. Cir.
2017) (“Adding one abstract idea (math) to another abstract idea (encoding
and decoding) does not render the claim non-abstract.”); Synopsys, Inc. v.
Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“But, a
claim for a new abstract idea is still an abstract idea.”); Versata Dev. Grp.,
Inc. v. SAP Am., Inc., 793 F.3d 1306, 1335 (Fed. Cir. 2015) (holding claims
that improved an abstract idea but did not recite the supposed computer
improvements were not patent eligible).
Finally, there is no indication that the apparatus, as claimed, improves
the accuracy of identifying thrombotic risk in patients. The Specification
indicates the clinical decision support system provides increased accuracy
because “[t]he smallest set of risk factors and protein concentrations that
together have an optimal predictive value for thrombosis risk are selected
and a numerical algorithm is created that translates the numerical values of
the chosen factors and concentrations to a single numerical value specifying
thrombotic risk.” Spec. 2:14–23. The Specification identifies this set of risk
factors as six (6) clinical factors and three (3) protein concentrations. Id. at
6:1–16. However, claim 1 calculates an estimation value based only on at
least one clinical risk factor and at least one protein concentration. Thus,
any improved accuracy from using particular risk factors is not claimed.
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25
The Specification indicates an estimation value can be based on some
of the input features but does not indicate which ones to use. Id. at 6:19–21.
Figure 4 uses all nine. Even if claim 1 recited a particular input feature, they
are data values that are measured in different ways. See id. at 13–16.
Furthermore, the accuracy of any thrombotic risk prediction depends
not only on a calculation performed by a generically-claimed algorithm on a
generically-claimed risk factor and protein concentration, but it also depends
on the threshold value that is established. Claim 1 recites no parameters for
that value. The Specification indicates that the threshold value can be set as
a fixed value or tuned by the user at user interface 30 to balance sensitivity
and specificity of the system. Id. at 6:27–29. Low threshold values set a
bias toward indications of high risk (i.e., they are more likely to be exceeded
by an estimated value), while high threshold values have the opposite effect
and tend to under treatment. Id. at 6:29–32. “The specific choice of T is the
responsibility of the user, e.g. clinician, and may be subject of a clinical
study, but is not further discussed here.” Id. at 6:32–33.
Therefore, we are not persuaded that merely inputting a clinical risk
factor and a blood concentration factor and calculating an estimation value
based on their “numerical values” necessarily improves the accuracy of the
thrombotic risk estimation or any other technology, as recited in claim 1.
The Specification indicates machine learning may be used to exploit
patterns hidden in numerical data values to predict an output. See id. at 8–9.
Claim 1 does not recite machine learning, and generic machine learning used
to optimize parameters (see id. at 7–10), by itself, does not integrate claim 1.
See id. at 7–10; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1362–
63 (Fed. Cir. 2015) (holding generic price optimization is an abstract idea).
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26
The “apparatus,” “data interface,” “processor,” and “user interface”
are recited as generic elements that perform generic functions. The data
interface receives data “input features.” The processor performs calculations
and analyzes/compares numerical values. The user interface outputs results
of the abstract calculations and comparisons performed by the processor.
Claim 1 recites the use of these components as tools that implement
the abstract idea. This implementation is not sufficient to integrate the
abstract idea into a practical application. See Alice, 573 U.S. at 223 (“[T]he
mere recitation of a generic computer cannot transform a patent-ineligible
abstract idea into a patent-eligible invention.”); Elec. Power, 830 F.3d at
1354 (holding that the focus of the claims is on certain abstract ideas that use
computers as tools); Bancorp Svcs., L.L.C. v. Sun Life Assur. Co. of Canada
(U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he use of a computer in an
otherwise patent-ineligible process for no more than its most basic function–
–making calculations or computations––fails to circumvent the prohibition
against patenting abstract ideas and mental processes.”); Accenture Glob.
Servs., GmbH v. Guidewire Software, Inc., 728 F.3d 1336, 1345 (Fed. Cir.
2013) (“Accenture attempts to limit the abstract idea of claim 1 by applying
it in a computer environment and within the insurance industry. However,
those types of limitations do not ‘narrow, confine, or otherwise tie down the
claim.’ As we . . . recently held, simply implementing an abstract concept
on a computer, without meaningful limitations to that concept, does not
transform a patent-ineligible claim into a patent-eligible one.”). “[T]he
complexity of the implementing software or the level of detail in the
specification does not transform a claim reciting only an abstract concept
into a patent-eligible [one].” Accenture, 728 F.3d at 1345.
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The Specification describes the apparatus as follows:
[T]he apparatus may be implemented as a discrete hardware
circuitry with discrete hardware components, as an integrated
chip, as an arrangement of chip modules, or as a signal
processing device or chip controlled by a software routine or
program stored in a memory, written on a computer readable
medium, or downloaded from a network, such as the Internet.
Spec. 4:17–21. The processor, data interface, and user interface also are
described generically in the Specification in addition to being claimed as
generic components. Id. at 5:19–6:33. Therefore, claim 1 does not link the
abstract idea to a particular machine that is integral to the claim.
Nor does claim 1 effect a treatment of thrombosis as Appellant argues.
Reply Br. 4–5. Claim 1 does not recite a method of treating thrombosis with
anticoagulants. Outputting a generic prescription does not effect a treatment
either. See Classen, 659 F.3d at 1068; see also Vanda Pharms. Inc. v. West-
Ward Pharms. Int’l Ltd., 887 F.3d 1117, 1135 (Fed. Cir. 2018).
The mere data gathering and extra-solution activity recited in claim 1
does not integrate the abstract idea into a practical application. See Revised
Guidance, 84 Fed. Reg. at 55 n.33; MPEP § 2106.05(g); Elec. Power, 830
F.3d at 1355 (“But merely selecting information, by content or source, for
collection, analysis, and display does nothing significant to differentiate a
process from ordinary mental processes.”).
The decision in Classen Immunotherapies illustrates the distinction.
Claim 1 of the ’283 patent recited steps of collecting and comparing data for
immunization results without applying the data for immunization purposes
and thus recited an abstract mental step. Classen, 659 F.3d at 1067. Other
patents recited a specific application of immunization steps in accordance
with a lower-risk schedule that were held to be patent-eligible. Id. at 1068.
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The claims of the ’139 and ’739 patent recited methods of lowering
the risk of chronic immune-mediated disorder by including the physical step
of immunization on a determined schedule. Id. at 1066. Therefore, these
claims recited a physical implementation of the mental step recited in the
’283 patent. Id. at 1067. Requiring the act of immunization with a lower-
risk schedule moved the abstract idea to specific patent-eligible application.
Id. at 1068. The exemplary claim of the ’739 patent recited a method of
immunizing a mammalian subject by immunizing the subject according to a
subject immunization schedule that administered at least one infectious
disease-causing organism associated immunogen of a lower risk schedule in
accordance with the lower risk screened immunization schedule. See id. at
1060–61.
In contrast to these claims in Classen, claim 1 recites an apparatus that
performs data collection and analysis using mathematical calculations and
mental processes with an extra-solution step of outputting a prescription that
is not described in the Specification. There is no method or step of treating
thrombosis in claim 1 or administering an anticoagulant treatment according
to a schedule. Claim 1 does not effect a particular treatment as Appellant
contends. See Reply Br. 4. The Specification provides no description of any
such anticoagulant treatment to be administered to a patient. Instead, the
system determines whether to administer an anticoagulant treatment to a
patient (id.) as an extra-solution activity of the mathematical calculations
and mental processes recited in the claim without imposing a meaningful
limitation on the claim.
Accordingly, we determine that claim 1 does not include additional
elements that integrate the abstract idea into a practical application.
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Step 2B: Does Claim 1 Include an Inventive Concept?
We next consider whether claim 1 recites elements, individually, or as
an ordered combination, that provide an inventive concept. Alice, 573 U.S.
at 217–18. The second step of the Alice test is satisfied when the claim
limitations involve more than performance of well-understood, routine, and
conventional activities previously known to the industry. Berkheimer v. HP
Inc., 881 F.3d 1360, 1367 (Fed. Cir. 2018); see Revised Guidance, 84 Fed.
Reg. at 56 (explaining that the second step of the Alice analysis considers
whether a claim adds a specific limitation beyond a judicial exception that is
not “well-understood, routine, conventional” activity in the field).
Individually, the limitations of claim 1 recite aspects of the abstract
idea or insignificant extra-solution activity identified above. As an ordered
combination, the limitations add nothing that is not the sum of the individual
parts. Even if the steps are groundbreaking, innovative, or brilliant, that is
not enough for eligibility. See Ass’n for Molecular Pathology v. Myriad
Genetics, Inc., 569 U.S. 576, 591 (2013); accord SAP Am., Inc. v. InvestPic,
LLC, 898 F.3d 1161, 1163 (“No matter how much of an advance in the
finance field the claims recite, the advance lies entirely in the realm of
abstract ideas, with no plausibly alleged innovation in the non-abstract
application realm. An advance of that nature is ineligible for patenting.”).
Accordingly, we determine that claim 1 does not recite any elements,
individually or as an ordered combination, that provide an inventive concept
sufficient to transform the abstract idea into patent eligible subject matter.
Thus, we sustain the rejection of claim 1 and claims 2–20, which fall with
claim 1 as directed to a judicial exception under 35 U.S.C. § 101.

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Claims Anticipated by Mann
The Examiner relies on Mann to disclose an apparatus, method, and
non-transitory computer readable medium in independent claims 1, 16, and
18 to include obtaining input features of at least one clinical risk factor and
at least one protein concentration of the patient and calculating an estimation
value by applying a decision support algorithm as a function of numerical
values derived from the input features. Final Act. 5–7. The Examiner cites
paragraph 32, 34, 37, and 112 as disclosing the claimed “input features” of a
protein concentration and at least one clinical risk factor. Id.
Appellant argues that Mann discloses that hemostatic risk is related to
the level of thrombin in a patient’s blood and thus determines the amount of
thrombin based on concentrations of three blood factors in samples taken
from a patient without considering clinical risk factors. Appeal Br. 10–13;
Reply Br. 5–7. Appellant also argues that Mann does not consider clinical
risk factors “because Mann believes that the levels of protein in a patient
sufficiently captures the patient’s clinical history.” Appeal Br. 11 (citing
Mann ¶ 30). Appellant contends that paragraph 34 of Mann uses the
concentrations of three blood factors in a biologic sample taken from the
subject to determine hemostatic risk in the patient. Id.
In response, the Examiner points out that Mann also describes risk
factors such as acute coronary syndrome event and in vitro fertilization for
thromboembolic complications. Ans. 5 (citing Mann ¶¶ 32, 37, 270–72).
Appellant responds that Mann does not estimate thrombosis risk based on a
combination of such clinical risk factors and protein concentrations. Reply
Br. 5–7. Appellant argues that Mann determines thrombosis risk based only
on blood protein factors for coagulation measured in blood samples. Id.
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The calculation of an estimation value of thrombosis risk is based on
numerical values derived from input features that “include a combination of
at least one clinical risk factor and at least one protein concentration.”
Appeal Br. 15–18 (Claims App.) (emphasis added).
Mann’s description of acute coronary syndrome event and in vitro
fertilization risk factors corresponds to the claimed “clinical risk factors”
that are used as input features to calculate an estimation value of thrombosis
risk. The Specification describes the clinical risk factors as including patient
immobilization in the last three months, surgery in the last month, family
history of venous thrombosis, pregnancy, use of estrogens, and obesity. See
Spec. 6:1–13. However, Mann’s teachings cited by the Examiner do not use
these or any other clinical risk factors to estimate a patient’s thrombosis risk.
Paragraphs 32, 34, 37, and 112, which are cited to disclose the input
features (see Final Act. 5–6), do not use clinical risk factors to calculate a
thrombosis risk as required by the claims. Paragraph 32 describes a method
of assessing hemostatic risk by simulating, in silico, thrombin concentration
based on biological input of a sample taken from a subject. Paragraph 34
describes determining concentrations of three blood factors in a biological
sample taken from the subject. Paragraph 37 describes generating as output
a thrombin concentration that is taken to be indicative of blood coagulation.
Paragraph 112 describes inputting patient-specific concentrations of pro- and
anti-coagulants. We find no disclosure of the use of clinical risk factors
such as acute coronary syndrome event or in vitro fertilization to estimate a
thrombosis risk in combination with the blood concentration factors. Mann
estimates thrombosis risk based on blood protein concentrations without
considering clinical risk factors. Thus, we do not sustain this rejection.
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CONCLUSION
In summary:
Claims
Rejected
35 U.S.C. § Reference(s)/
Basis
Affirmed Reversed
10, 11, 13,
14, 16, 17
112(a) Written
Description
10, 11, 13,
14, 16, 17
1–4, 6–8, 10,
11, 13, 14,
16–24
101 Eligibility 1–4, 6–8,
10, 11, 13,
14, 16–24

1, 2, 6, 8, 16–
19, 22–24
102 Mann 1, 2, 6, 8, 16–
19, 22–24
Overall
Outcome
1–4, 6–8,
10, 11, 13,
14, 16–24

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