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Patent Trials and Appeals Board

Nov 22, 2019

15284575 - (D) (P.T.A.B. Nov. 22, 2019)

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.
15/284,575 10/04/2016 Illapha Gustav Lars CUBA GYLLENSTEN 2015P01142US01 4418
24737 7590 11/22/2019
PHILIPS INTELLECTUAL PROPERTY & STANDARDS
465 Columbus Avenue
Suite 340
Valhalla, NY 10595
EXAMINER
MALAMUD, DEBORAH LESLIE
ART UNIT PAPER NUMBER
3792
NOTIFICATION DATE DELIVERY MODE
11/22/2019 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 ILLAPHA GUSTAV LARS CUBA GYLLENSTEN,
ROHAN JOSHI, and HERMAN JAN TER HORST

Appeal 2019-003879
Application 15/284,575
Technology Center 3700
Before JILL D. HILL, LEE L. STEPINA, and ARTHUR M. PESLAK,
Administrative Patent Judges.
HILL, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
Appellant1 appeals under 35 U.S.C. § 134(a) from the Examiner’s
decision rejecting claims 1–18. We have jurisdiction under
35 U.S.C. § 6(b).
We REVERSE.

1 We use the word Appellant to refer to “applicant” as defined in 37 C.F.R.
§ 1.42. Appellant identifies the real party in interest as Koninklijke Philips
Electronics N.V. Appeal Br. 2.
Appeal 2019-003879
Application 15/284,575

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BACKGROUND
Appellant’s invention relates to a system and method for predicting
heart failure decompensation. Claim 1, reproduced below, illustrates the
claimed invention with certain limitations italicized:
1. A system configured to predict decompensation in a
subject with heart failure, the system comprising:
one or more hardware processors configured by machine-
readable instructions to:
determine baseline information for the subject based on
physiological data of other patients with heart failure, the
physiological data of the other patients corresponding to a
period of time when the other patients have stable physiological
parameters that precedes heart failure;
receive weight information that includes a weight of the
subject generated by one or more weight sensors;
receive blood pressure information that includes a blood
pressure of the subject generated by one or more blood pressure
sensors;
receive heart rate information that includes a heart rate of
the subject generated by one or more heart rate sensors;
determine one or more weight parameters associated with
a change in the weight of the subject based on the weight
information with respect to the baseline information;
determine one or more blood pressure parameters
associated with a change in the blood pressure of the subject
based on the blood pressure information with respect to the
baseline information;
determine one or more heart rate parameters associated
with a change in the heart rate of the subject based on the heart
rate information with respect to the baseline information;
determine individual weight, blood pressure, and heart
rate parameters with higher predictive strength for predicting
decompensation relative to other weight, blood pressure, and
heart rate parameters with lower predictive strength, the
individual weight, blood pressure, and heart rate parameters
with higher predictive strength comprising at least one
predictive weight parameter, at least one predictive blood
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Application 15/284,575

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pressure parameter, and at least one predictive heart rate
parameter; and
predict decompensation in the subject based on the at
least one weight parameter, the at least one blood pressure
parameter, and the at least one heart rate parameter with
higher predictive strength for predicting decompensation
relative to the other weight, blood pressure, and heart rate
parameters with lower predictive strength.
REJECTIONS
I. Claims 1–7 and 10–18 stand rejected under 35 U.S.C.
§ 102(a)(1) as being anticipated by Sarkar (US 2012/0253207 A1, pub. Oct.
4, 2012). Final Act. 7.
II. Claims 8 and 9 stand rejected under 35 U.S.C. § 103 as
unpatentable over Sarkar. Final Act. 10.
OPINION
Rejection I – Anticipation by Sarkar
Appellant argues that Sarkar’s determination of “the heart failure risk
level based on all patient metrics … is not the same as predicting
decompensation based on parameters with higher predictive strength.”
Appeal Br. 14. According to Appellant, Sarkar “does not describe which
parameters might be more predictive and which parameters might be less
predictive.” Id. at 15. Appellant thus contends that Sarkar does not predict
decompensation based on “the at least one weight parameter, [the] at least
one blood pressure parameter, and the at least one heart rate parameter with
higher predictive strength for predicting decompensation relative to the other
weight, blood pressure, and heart rate parameters with lower predictive
strength,” as required by the independent claims. Id.
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The Examiner responds that Sarkar relates the risk of worsening heart
failure to decompensation and “discloses weighting different patient metrics
to produce a risk level for the patient.” Ans. 7 (citing Sarkar ¶¶ 46, 50–51).
The Examiner notes that, although intrathoracic impedance is used as an
example of a parameter having higher predictive strength, Sarkar also
discloses weight, blood pressure, and heart rate as parameters. Ans. 8.
Appellant replies that Sarkar’s disclosure that “some factors may be
weighted differently than others … is not the same as describing the
presently claimed algorithm for predicting heart failure decompensation”
based on “determining individual weight, blood pressure, and heart rate
parameters with higher predictive strength.” Reply Br. 2–3. According to
Appellant, Sarkar “does not describe predicting decompensation in the
subject based on at least one weight parameter, at least one blood pressure
parameter, and at least one heart rate parameter with higher predictive
strength for predicting decompensation relative to other weight, blood
pressure, and heart rate parameters with lower predictive strength.” Id. at 3.
For the following reasons, Appellant’s arguments are persuasive.
Appellant defines “decompensation” as “a period of worsening heart
failure symptoms until [the patients] eventually require hospitalization.”
Spec. ¶ 20. Sarkar discloses that “a relatively rapid worsening of the
patient’s condition, e.g., a decompensation, precipitates hospitalization and,
in some cases, death.” Sarkar ¶ 27; see also Ans. 7. As such, we agree with
the Examiner that Sarkar discloses decompensation as claimed. Sarkar also
discloses that “lower resolution diagnostic information based on the patient
metrics may be transmitted to the user, which may be used to determine a
risk level of the patient being re-admitted to the hospital due to heart
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failure.” Id. ¶ 6. Sarkar discloses that “patient metrics may include, as
examples, therapy use statistics (e.g., pacing or shocks), thoracic impedance,
heart rate, heart rate variability, patient activity, and a percentage of time
receiving cardiac resynchronization therapy. Other example patient metrics
include weight, blood pressure, [and] respiration rate.” Id. ¶ 30; see also
Ans. 7. Sakar discloses that “external electrodes or other sensors may be
used by IMD 16 to … sense and detect patient metrics used to generate the
high and lower resolution diagnostic information, e.g., a heart failure risk
level.” Id. ¶ 76. Thus, Sarkar discloses determining weight, blood pressure,
and heart rate using sensors.
Appellant uses sensed information to determine weight, blood
pressure, and heart rate parameters based on a change with respect to
baseline information. Specifically, the Specification discloses determining a
change in weight, blood pressure, and heart rate over time, or “window
size.” Spec. ¶ 31. Sarkar discloses that “metric-specific thresholds may be
rate of change thresholds or relative change thresholds.” Sarkar ¶ 61; see
also Final Act. 8. Thus, we agree with the Examiner that Sarkar determines
weight, blood pressure and heart rate parameters, as recited.
However, although Sarkar discloses that “some states or metrics may
be more indicative of the risk of re-hospitalization” and “may provide a
greater contribution to the determined risk level” (Sarkar ¶ 51), Sarkar is
comparing one patient metric to a different patient metric to determine the
risk level. Sarkar discloses, for example, that “intrathoracic impedance may
be a greater risk factor to the patient than patient inactivity.” Sarkar ¶ 51. In
Sarkar, “the risk level may be determined by the sum, average, or other
combination of weighted scores for each of the patient metrics.” Id. Claim
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1, by contrast, requires a comparison of parameters with higher predictive
strength for predicting decompensation relative to the same parameters with
lower predictive strength. See Spec. ¶ 34 (“prediction component 24 is
configured to identify and/or predict decompensation for subject 12 based on
individual parameters with higher predictive strength relative to individual
parameters with lower predictive strength.”).
Figure 5 assists us in ascertaining the meaning of higher and lower
“predictive strength,” by showing various parameters having different
predictive strengths (“predictive strength of the classifier was evaluated by
calculating the area under the curve (AUC)). Spec. ¶ 63. For example, a
change in blood pressure over a two day window has a higher predictive
strength, i.e., a larger area under the curve than a change in blood pressure
over an eight day window. As claimed, each of weight, blood pressure and
heart rate parameters are compared to other weight, blood pressure, and
heart rate parameters to predict decompensation. In Sarkar, “the risk level
may be determined based on a predetermined number of patient metrics
exceeding their representative thresholds,” not based on relative predictive
strengths of parameters. Because Sarkar does not predict decompensation
based on higher predictive strength parameters relative to the same
parameters with lower predictive strength, the Examiner erred in finding that
Sarkar is able to predict decompensation in the subject based on the at least
one weight parameter, the at least one blood pressure parameter, and the at
least one heart rate parameter with higher predictive strength for predicting
decompensation relative to the other weight, blood pressure, and heart rate
parameters with lower predictive strength. We, therefore, do not sustain the
rejection of independent claims 1, 6, and 11 based on Sarkar. Claims 2–5, 7,
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10, and 12–18 depend directly or indirectly from claim 1, claim 6, or claim
11, and include the limitations thereof. Independent claims 6 and 11 contain
substantially the same limitations as claim 1. We, therefore, do not sustain
Rejection I.

Rejection II – Obviousness of Claims 8 and 9 Based on Sarkar
Claims 8 and 9 depend from claim 6. The Examiner does not
conclude that it would have been obvious to one skilled in the art to predict
decompensation in the subject based on the at least one weight parameter,
the at least one blood pressure parameter, and the at least one heart rate
parameter with higher predictive strength for predicting decompensation
relative to the other weight, blood pressure, and heart rate parameters with
lower predictive strength. We therefore do not sustain the rejection of
claims 8 and 9 as unpatentable over Sarkar.
CONCLUSION
In summary:
Claims
Rejected
35 U.S.C. § Basis Affirmed Reversed
1–7, 10–18 102(a)(1) Sarkar 1–7, 10–18
8, 9 103 Sarkar 8, 9
Overall
Outcome
1–18
REVERSED