Ex Parte Burgess

Patent Trial and Appeal Board

Apr 24, 2014

12041801 (P.T.A.B. Apr. 24, 2014)

UNITED STATES PATENT AND TRADEMARKOFFICE
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.
12/041,801 03/04/2008 Webb Lewis Burgess 9060-277 2154
101681 7590 04/25/2014
MYERS BIGEL SIBLEY & SAJOVEC, P. A.
P.O. BOX 37428
RALEIGH, NC 27627
EXAMINER
SCHECHTER, ANDREWM
ART UNIT PAPER NUMBER
2857
MAIL DATE DELIVERY MODE
04/25/2014 PAPER
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.
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
________________

BEFORE
THE PATENT TRIAL AND APPEAL BOARD
________________

Ex parte WEBB LEWIS BURGESS1
________________

Appeal 2012-003587
Application 12/041,801
Technology Center 2800
________________

Before CHARLES F. WARREN, MARK NAGUMO, and
KAREN M. HASTINGS, Administrative Patent Judges.

NAGUMO, Administrative Patent Judge.

DECISION ON APPEAL
Webb Lewis Burgess (“Burgess”) timely appeals
under 35 U.S.C. § 134(a) from the final rejection2 of claims 1-6, 8-19,
and 21-35, which are all of the pending claims. We have jurisdiction.
35 U.S.C. § 6. We affirm-in-part.

1 The real party in interest is listed as Eaton Corporation. (Appeal Brief,
filed 15 July 2011 (“Br.”), 1.)
2 Office action mailed 16 March 2011 (“Final Rejection,” cited as “FR”).
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Application 12/041,801

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OPINION
A. Introduction3
The claimed inventions relate to methods of estimating the service life
of a battery, especially a battery that is operated in a “float service” mode.
In float service, batteries are seldom used, and when they are not in use, they
are “float charged” at relatively low charging rates. Such batteries are
typically used for standby power applications, such as Uninterruptable
Power Supply (“UPS”) systems and emergency lighting. According to
the 801 Specification, as shown in Figure 1, below,

{Fig. 1 shows the relative capacity of a battery as a function of time. At
first, the capacity does not change significantly, but after passing a threshold
(“guarantee”) age, the battery capacity falls off rapidly.}
such a battery typically retains its initial capacity for a “threshold” or
“guarantee” time, after which its capacity falls off relatively rapidly.
Burgess seeks exclusive patent protection for computer-based
methods that in some embodiments detect the change-over from the

3 Application 12/041,801, Battery service life estimation methods,
apparatus and computer program products using state estimation techniques
initialized using a regression model, filed 4 March 2008. We refer to the
“801 Specification” and cite it as “Spec.”
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Application 12/041,801

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guarantee period to the fall-off period, and then estimate the remaining
service life of the battery after the battery has entered the fall-off period.
Figure 3, shown below, illustrates a claimed method of determining
when the battery has entered the second, declining, period of service life.

{Fig. 3 shows regression models (lines) “responsive to measurements,” and
initialization and start of Kalman filter estimates of battery service life}
Initially, successive measurements of the relative capacity will yield
regression models4 having zero or very small slopes, as shown by the first
two dots. The Specification explains that the beginning of the second period
(i.e., t0) will be marked by “a rapid acceleration in the degradation of battery
capacity.” (Id. at 13, ll. 27-28.) The Specification focuses on methods in
which a “predictive state estimator” is initialized only after predetermined
criteria (e.g., the slope is large enough, and the statistics (e.g., variance,
correlation coefficients) indicate the calculated slope and other parameters
are reliable) are met. (Spec. 2, ll. 25-31.) According to the Specification,

4 The Specification teaches that the regression model may include a
straight-line regression model. (Spec. 2, l. 31.) Thus, the term “regression”
may be thought of as the process of determining, for example, the slope,
intercept, and error estimates of a straight line (model) fit to the data.
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Application 12/041,801

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the predictive state estimator may include a deterministic model, a
probabilistic model, or an adaptive model, and may include a Kalman filter.
(Id. at 2, l. 32 to 3, l. 1.)
The 801 Specification defines the term “Kalman filter” with the
following words: “[a] Kalman filter is an algorithm for obtaining a minimum
mean-square error point estimate of a random process. It is a method of
least squares filtering that is obtained from a state space formulation.”
(Spec. 8, ll. 32-35.) Somewhat more concretely, a Kalman filter provides a
way of predicting, at successive time steps, the changes in a system. Certain
parameters of the system are measured from time to time and are used to
refine the prediction of the changes as well as of the errors associated with
each parameter.
Claim 8 refers to a “state estimator of future battery capacity,” which
must be initialized in response to some condition being met. Claim 8 reads:
A method of estimating service life of a battery, the method
comprising the following steps implemented in a computer:
generating, using the computer, a set of measures of
battery capacity responsive to a series of discharges of the
battery;
generating, using the computer, respective regression
models that relate battery capacity to time based on the set
of measures of battery capacity for respective ones of a
series of times;
initializing, using the computer, a state estimator of future
battery capacity responsive to one of the regression
models meeting a predetermined criterion; and
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Application 12/041,801

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generating, using the computer, a prediction of service life
using the state estimator.
(Claims App., Br. 17; indentation and emphasis added.)
Independent claim 23 covers apparatuses corresponding to claim 8,
while independent claim 33 covers non-transitory computer-readable media
containing computer programs coding processes corresponding to claim 8.
Claim 1, representative of claims limited to the use of Kalman filters,
reads:
A method of estimating service life of a battery, the method
comprising the following steps implemented in a computer:
generating, using the computer, a measure of capacity of
the battery responsive to a discharge of the battery;
detecting, using the computer, an acceleration of a
decrease in battery capacity; and
generating, using the computer, a prediction of service
life from the measure of capacity using a Kalman filter
responsive to the detected acceleration of a decrease in
battery capacity.
(Claims App., Br. 16; indentation and emphasis added.)
Independent claim 13 covers apparatuses corresponding to claim 1,
while independent claim 30 covers non-transitory computer-readable media
containing computer programs coding processes corresponding to claim 1.
Claims 2-5 (dependent from claim 1), 14-17 (dependent from
claim 13), and 31, and 32 (dependent from claim 30) are similar to claim 8
in that they require, in the words of claim 2, that “generating a prediction of
service life from the measure of capacity using a Kalman filter comprises
initializing the Kalman filter responsive to the regression model meeting a
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Application 12/041,801

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predetermined criterion.” (Claims App., Br. 16; emphasis added. Cf.
claims 14 and 31, id. at 18 and 21, respectively.)
We address the claims in two groups. The first group consists of the
“initializing” claims, namely, independent claims 8, 23, and 33, and their
associated dependent claims, as well as dependent claims 2-5, 14-17, 31,
and 32. The second group consists of the “Kalman filter claims,” namely,
independent claims 1, 13, and 30, and associated dependent claims 6, 18, 19,
21, and 22, which do not expressly require initialization of the Kalman filter
responsive to some predetermined criterion.
The Examiner maintains the following grounds of rejection:5
A. Claims 1-6, 13-19, and 30-32 stand rejected under
35 U.S.C. § 103(a) in view of the combined teachings of
Blessing6 and Laig-Hoerstebrock.7
A1. Claims 21 and 23 stand rejected under 35 U.S.C. § 103(a) in
view of Blessing, Laig-Hoerstebrock, and Eisenberger.8

5 Examiner’s Answer mailed 7 October 2011 (“Ans.”).
6 Alf Blessing and Hans-Peter Schoner, Process for monitoring the residual
charge and capacity of a battery, U.S. Patent Application Publication
2001/0020849 A1 (2001).
7 Helmut Laig-Hoerstebrock and Eberhard Meissner, Method for
determining the amount of charge which can be drawn from a storage
battery and a monitoring device for a storage battery, U.S.
Patent 6,949,911 B2 (2005).
8 Dean Eisenberger and Jeff Legg, Uninterruptible DC power supply for
equipment associated with a mobile site, U.S. Patent Application Publication
2004/0124709 A1 (2004).
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Application 12/041,801

7
B. Claims 8-12, 23-27, and 33-35 stand rejected under
35 U.S.C. § 103(a) in view of the combined teachings of
Blessing and Greitzer.9
B1. Claims 28 and 29 stand rejected under 35 U.S.C. § 103(a) in
view of Blessing, Greitzer, and Eisenberger.
B. Discussion
Findings of fact throughout this Opinion are supported by a
preponderance of the evidence of record.
Initially, we find Burgess addresses only the process claims in detail.
Accordingly, claims 8, 23, and 33, and the corresponding dependent claims
stand or fall with claim 8. Similarly, independent claims 1, 13, and 30 stand
or fall together.
Initializing claims: claim 8
As illustrated in modified Figure 6, reproduced on the following page,
processes covered by claim 8 require that a set of regression models of
battery capacity, e.g., fits to a straight line, be generated [step 615], and that
a state estimator of future battery capacity be initialized [step 626] in
response to the meeting of some predetermined criterion by one of the
regression models.

9 Frank L. Greitzer et al., Method and apparatus to predict the remaining
service life of an operating system, U.S. Patent 7,457,785 B1
(25 November 2008), accorded a § 371(c)(1), (2), (4) date
of 18 August 2003.
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Appeal 2012-003587
Application 12/041,801

9
Hoerstebrock for not teaching such an initialization; id. at 12, ll. 17-20, and
para. bridging 13-13, similarly criticizing Greitzer.)
The Examiner finds that “Blessing does initialize the Kalman filter
responsive to a decrease in capacity (i.e., slope),” but that Blessing does not
expressly teach initialization in response to an acceleration in the decrease in
capacity. (FR 2, last full para.) The Examiner finds that Laig-Hoerstebrock
provides such teachings for a Kalman filter, (id. at 2-3), and that Greitzer
teaches “providing, and selecting from, a plurality of regression models for
respective ones of a series of times . . . for application to a state estimator”
(id. at 11, 2d full para.)
Blessing describes embodiments in which the limiting current reserve
is estimated by “state observers” such as Kalman filters.
(Blessing 3 [0020].) We find, however, no credible evidence in the passages
cited by the Examiner that Blessing describes initializing a Kalman filter in
response to a detected acceleration of a decrease in battery capacity, or of a
change in any other battery parameter. Although the suggestion by Blessing
in paragraph [0020] that a Kalman filter may be used as a state observer
implies a necessary initialization, the Examiner has not directed our attention
to any disclosure in Blessing suggesting when that initialization should
occur, let alone that initialization should occur after the detection of the
change-over to the second phase of battery service life.
Laig-Hoerstebrock describes methods in which “[i]t is particularly
advantageous . . . to use the relative change in the degrees of change to
determine the relative change in the amount of charge which can be drawn.”
(Laig-Hoerstebrock col. 4, ll. 46-51.) The relative change of a degree of
Appeal 2012-003587
Application 12/041,801

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change corresponds to a second derivative with respect to time, i.e., to an
acceleration. We find, however, no credible support for a teaching that a
Kalman filter or other state observer be initialized upon detection of an
acceleration of a decrease in battery capacity, or of a change in any other
battery parameter. Rather, it appears that Laig-Hoerstebrock teaches that a
Kalman filter or other state estimator could be used to monitor and use a
detected acceleration is battery capacity to predict the remaining service life.
Similarly, the disclosures in Greitzer, at column 8, cited by the
Examiner, would have suggested using shorter windows to monitor
parameters for analyzing regression lines when operating system conditions
are changing to obtain more accurate estimates. However, the Examiner has
not explained why such general suggestions (including the use of Kalman
filtering for estimating future conditions of the operating system) would
have suggested the initialization in response to one of the regression models
meeting a predetermined criterion.10

10 To the extent that Burgess may have been trying to persuade us that it
would not have been obvious in view of Blessing and Laig-Hoerstebrock to
use a Kalman filter to follow the life-history of a rechargeable storage
battery, and that such a process would have been purely “deterministic
function,” using only “statistical (i.e., a backward looking) process” (Br. 7,
last para.; emphasis omitted), rather than a “probabilistic (i.e., predictive)
state estimator” (id. at 8, l. 1; emphasis omitted), Burgess has failed.
A Kalman filter, as described in the 801 Specification at pages 8-16, is
inherently “probabilistic (i.e., predictive),” and we do not understand
Burgess to be claiming a new Kalman filter, but rather, a way of using a
well-known Kalman filter. Thus, the Kalman filter suggested by Blessing
would have had these properties. Moreover, we are not persuaded that the
routineer would not have adapted Laig-Hoerstebrock’s teachings that
acceleration of capacity changes—relative change in the degree of change—
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Application 12/041,801

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In a very generalized sense, it appears that a Kalman filter can be said
to be re-initialized at every step, in that the parameters and the associated
estimates of errors are updated based on the most recent measurements. As
illustrated in Figure 6, supra, however, such a broad reading is inconsistent
with the disclosed processes covered by claim 8, as well as those covered by
claim 2. As simple and “obvious” as it might seem to choose to initiate a
Kalman filter only after some triggering event, the legal conclusion of
obviousness requires a demonstration based on evidence in the prior art of
record that every limitation was either known or would have been suggested
by the prior art. The Examiner has not come forward with such evidence
based on Blessing, Greitzer, or Laig-Hoerstebrock.
The Examiner’s analyses of the remaining limitations and of the
additional reference Eisenberger do not cure the deficiencies of the
rejections of independent claim 8.
We therefore reverse the rejections of independent claims 8, 23, 33,
and the corresponding dependent claims, as well as similarly limited
claims 2-5, 14-17, 31, and 32.
Kalman filter claims: claim 1
Independent claims 13, and 30, and dependent claims 6, 18, 19, 21,
and 22, stand or fall with claim 1, because there is no substantial argument
for the separate patentability of these claims.

provide improved measures of the wear (and hence the projected lifetime) of
the battery into a Kalman filter process suggested by Blessing.
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Application 12/041,801

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Several lines of inquiry lead to the conclusion that these claims do not
require initialization of the Kalman filter only after some triggering change
of parameter has occurred.
First, these claims require, as does claim 1, the step (or structures
providing for) “generating, using the computer, a prediction of service life
from the measure of capacity using a Kalman filter responsive to the
detected acceleration of a decrease in battery capacity.” (Claims App.,
Br. 16; emphasis added.) Adjectival phrases typically modify the nearest
noun. In this case, the Kalman filter is the nearest noun, and it is the Kalman
filter that is required to be responsive to the detected acceleration of a
decrease in battery capacity. This reading is strengthened by claim 2, which
depends from claim 1, and which requires that “generating a prediction of
service life from the measure of capacity using a Kalman filter comprises
initializing the Kalman filter responsive to the regression model meeting a
predetermined criterion.” (Id.; emphasis added.) Here, the generation of the
prediction of service life comprises initializing the Kalman filter responsive
to some criterion being met. If claim 1 required initializing the Kalman
filter in response to the detected acceleration of a decrease in battery
capacity, the further requirements of claim 2 would be nugatory because
they are much broader than detecting an acceleration of a decrease in battery
capacity. Thus, our reading of the claims preserves the meaning of all the
limitations recited in the claims, and is therefore to be preferred over a
narrower reading that would render claim 2 superfluous in this regard.
A Kalman filter loop, as illustrated by the 801 Specification, uses
additional discharges of the battery (block 630 in Fig. 6, supra) to generate a
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new measure of battery capacity (block 625) that serves as input to the
Kalman filter, which then generates a new prediction of battery service life
(block 640). (Spec. 17, l. 33 to 8, l. 4.) Thus, the Kalman filter will be
responsive to changes in the discharge of the battery and parameters derived
from the discharge, including any detected acceleration of decrease in
battery capacity.
Blessing itself suggests the use of a Kalman filter to predict the
limiting current reserve. (Blessing 2 [0020].) Burgess’s arguments, which
are based on the failure of Blessing or Lars-Hoerstebrok to suggest
initialization of the Kalman filter in response to the detected acceleration of
battery capacity are misplaced, as this limitation is not present in claim 1.
As our reviewing court has observed in similar circumstances,
[w]e decline to attempt to harmonize the applicants’
interpretation with the application and prior art. Such an
approach puts the burden in the wrong place. It is the
applicants’ burden to precisely define the invention, not the
PTO’s. See 35 U.S.C. § 112 ¶ 2 (“The specification shall
conclude with one or more claims particularly pointing out
and distinctly claiming the subject matter which the applicant
regards as his invention.”).
In re Morris, 127 F.3d 1048, 1056 (Fed. Cir. 1997).
We conclude that Burgess has failed to show harmful error in the
rejection of claims 1, 6, 13, 18, 19, 21, 22, and 30.
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C. Order
We affirm the rejection of claims 1, 6, 13, 18, 19, 21, 22, and 30.
We reverse the rejection of claims 2-5, 8-12, 14-17, 23-29, and 31-35.
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a).
AFFIRMED-IN-PART
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