Ex Parte Stephens et al

Patent Trial and Appeal Board

Mar 30, 2017

13370993 (P.T.A.B. Mar. 30, 2017)

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
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APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
13/370,993 02/10/2012 Alan Thomas Stephens II SP12-018 3664
22928 7590 04/03/2017
TORNTNO TNmRPORATFD
EXAMINER
SP-TI-3-1
CORNING, NY 14831
FIGUEROA, KEVIN W
ART UNIT PAPER NUMBER
2124
NOTIFICATION DATE DELIVERY MODE
04/03/2017 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):
u sdocket @ corning .com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte ALAN THOMAS STEPHENS II
and
LEON ROBERT ZOELLER III
Appeal 2015-006464
Application 13/370,9931
Technology Center 2100
Before CARLA M. KRIVAK, HUNG H. BUI, and
JEFFREY A. STEPHENS, Administrative Patent Judges.
STEPHENS, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
Appellants seek our review under 35 U.S.C. § 134(a) from the
Examiner’s Final Rejection of claims 1—15, which are all the claims pending
in the application. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm-in-part.
1 The real party in interest is identified as Coming Incorporated. App. Br. 2.
Appeal 2015-006464
Application 13/370,993
Claimed Subject Matter
The claimed invention relates to methods of using a neural network to
predict the strength of structures with cells, based on cell parameters. Spec.
11, Title, Abstract.
Independent claim 1 and dependent claim 4, reproduced below, are
exemplary of the subject matter on appeal.
1. A method of examining a cellular structure, the method
including the steps of:
(a) providing an inspecting device, a neural network and a target
cellular structure, the target cellular structure including a
plurality of target cells extending therethrough and further
including a target face exposing an arrangement of the target
cells;
(b) inspecting an arrangement of target cells on the target face
using the inspecting device;
(c) representing the arrangement of target cells with numerically
defined target cell parameters;
(d) inputting the target cell parameters into the neural network;
and
(e) generating an output from the neural network based on the
target cell parameters, the output being indicative of a
strength of the target cellular structure.
4. The method of claim 3, further including the steps of (g)
applying force on the sample cellular structure while measuring
a sample strength parameter representing a maximum force that
the sample cellular structure can endure prior to destruction.
Examiner’s Rejections & References
(1) Claims 1—3 stand rejected under 35 U.S.C. § 103(a) as obvious
over Zoeller III (US 2006/0151926 Al; July 13, 2006) (“Zoeller”), Summers
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et al. (US 2011/0240190 Al; Oct. 6, 2011) (“Summers”), and I.-C. Yeh,
Modeling of Strength of High-Performance Concrete Using Artificial Neural
Networks, 28 Cement and Concrete Research 1797—1808 (1998) (“Yeh”).
Final Act. 2—5.
(2) Claims 4 and 5 stand rejected under 35 U.S.C. § 103(a) as
obvious over Zoeller, Summers, Yeh, and Poff et al. (US 2010/0218596 Al;
Sept. 2, 2010) (“Poff’). Final Act. 5-6.
(3) Claims 6—9 and 11—15 stand rejected under 35 U.S.C. § 103(a)
as obvious over Zoeller, Summers, Yeh, Poff, and Xu et al. (US
2010/0165340 Al; July 1, 2010) (“Xu”). Final Act. 6-11.
(4) Claim 10 stands rejected under 35 U.S.C. § 103(a) as obvious
over Zoeller, Summers, Yeh, Poff, Xu, and Scarborough et al. (US
7,472,097 Bl; Dec. 30, 2008) (“Scarborough”). Final Act. 11—12.
ANALYSIS
Claim 1
The Examiner finds the combination of Zoeller, Summers, and Yeh
teaches and suggests the method recited in claim 1. Final Act. 2-4.
Appellants contend Summers is not proper for use in rejecting claim 1
because Summers is non-analogous art to the claimed invention. App. Br. 9;
Reply Br. 3^4. In particular, Appellants contend Summers is not in the same
field of endeavor as the claimed invention because claim 1 is directed to
“methods of examining a cellular structure,” while Summers “is in the field
of methods for designing a tire.” App. Br. 9; see also Reply Br. 3^4.
Appellants also assert Summers is not reasonably pertinent to the problem
with which claim 1 is involved. Appellants state the claimed invention’s
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problem to be solved is to “generat[e] output from a neural network based on
target cell parameters that are indicative of a strength of the target cellular
structure,” while Summers “addresses the problem of optimizing the shear
layer of a shear band for use in a tire.” App. Br. 9.
We agree with the Examiner, however, that Summers is analogous art
to the claimed invention. Ans. 14. As noted by the Examiner, both
Summers and the claimed invention analyze cellular honeycomb structures
using numerical parameters to represent the structures’ cells. Ans. 14.
Therefore, we agree that Summers would have logically commended itself to
the problem of numerically examining cellular honeycomb structures
addressed in the present application. Ans. 14; see Spec. ]Hf 3, 16; Summers
112,7.
Appellants also argue none of Zoeller, Summers, and Yeh teaches or
suggests “representing the arrangement of target cells with numerically
defined target cell parameters” (step c) recited in claim 1; rather, Zoeller
uses an actual captured image, not numerically defined cell parameters, to
determine cell defects, and Summers merely “recognizes that conventional
geometric parameters of honeycombs have been used to find effective
properties of honeycomb structures.” App. Br. 7 (citing Zoeller || 41—42;
Summers 12). We agree with the Examiner, however, that Summers
represents an arrangement of honeycomb cells with numerically defined cell
parameters, such as cell height, cell length, and cell angle. Final Act. 3
(citing Summers 12).
Appellants also dispute the Examiner’s finding (Final Act. 3) that
Summers’ numerically defined cell parameters can be used to represent
Zoeller’s target honeycomb cellular structure, as required by step (c) of
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claim 1. App. Br. 7. In particular, Appellants contend Summers’ numerical
parameterization cannot be combined with Zoeller’s imaging method
because the combination would destroy Zoeller’s functionality of “us[ing]
the actual captured image to determine which cells include a defect.” App.
Br. 7 (citing Zoeller ]f]f 41 42). We do not agree. The Examiner’s rejection
does not preclude Zoeller’s imaging method from being performed, and
Appellants have not presented persuasive evidence to show how using
Summers’ numerical parameterization would destroy Zoeller’s imaging
functionality. Rather, the asserted Zoeller-Summers combination includes
an additional functionality for analyzing Zoeller’s honeycomb cellular
structure by “representing the [structure’s] arrangement of cells with
numerically defined parameters” as taught by Summers. Ans. 13.
Appellants additionally argue Yeh fails to suggest modifying Zoeller
to include “inputting the target cell parameters into the neural network” (step
d), and “generating an output from the neural network based on the target
cell parameters, the output being indicative of a strength of the target cellular
structure” (step e), as recited in claim 1. App. Br. 8. We note that
Appellants do not challenge the Examiner’s findings regarding what each of
the references teaches or suggests, but rather argue there is no reason to
combine Yeh and Zoeller because the references fail to suggest a motivation
for combining. App. Br. 8. Particularly, Appellants argue “there is no
suggestion to provide Zoeller with a system to predict the location and/or
size of the defects based on prior data . . . with [Yeh’s] neural network”
because Zoeller relies on actual observation, not neural network modeling to
identify defects. App. Br. 6 (citing Zoeller 144; Yeh 1800-1801); see also
id. at 8.
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Appellants’ arguments fail to persuasively challenge the Examiner’s
findings that one of ordinary skill would have had reason to combine the
teachings of Zoeller and Yeh. Particularly, the Examiner finds Zoeller
teaches inspecting a target cellular structure, and relies on Yeh for providing
a neural network to model the structure and predict its strength. Final Act.
3^4 (citing Zoeller 145, Fig. 4; Yeh Abstract, 1799); Ans. 12. The
Examiner reasons that it would have been obvious to one of ordinary skill in
the art to combine the teachings of Zoeller and Summers with Yeh’s neural
network because “neural networks are used in a variety of methods including
image analysis.” Final Act. 3. The Examiner finds Yeh teaches using a
neural network to predict the strength of a material from given parameters
(Ans. 12), and that “one of ordinary skill in the art would recognize that
Yeh’s teaching is equally applicable to prediction of strength of any other
material used in a cellular structure” (Ans. 13).
We agree with the Examiner that one of ordinary skill in the art would
have recognized that the ability of Yeh’s neural network to analyze complex
structures and determine impact of various parameters on a structure’s
strength would be useful not only in the specific environment described in
Yeh, but also in evaluating the parameters identified by Summers from
Zoeller’s inspection images. See Ans. 12—13; Final Act. 3^4. “When a
work is available in one field of endeavor, design incentives and other
market forces can prompt variations of it, either in the same field or a
different one. If a person of ordinary skill can implement a predictable
variation, § 103 likely bars its patentability.” KSRInt’l Co. v. Teleflex Inc.,
550 U.S. 398, 417 (2007). In making this determination, we “must ask
whether the improvement is more than the predictable use of prior art
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elements according to their established functions.” Id. As correctly
recognized by the Examiner, Yeh’s neural network analysis of strength
based on a structure’s parameters is a known technique that can be applied to
Zoeller’s target cellular structure to yield predictable results, and the steps of
inputting data and generating an output are part of that analysis. Ans. 12—
13.
Appellants also argue combining Yeh’s neural network technique with
Zoeller would destroy Zoeller’s functionality and intended purpose, and
change Zoeller’s principle of operation from “identifying the location and
size of a defective cell in a honeycomb structure” to Yeh’s “predicting the
compressive strength of concrete with a neural network.” App. Br. 6—8; see
also Reply Br. 2—3. We disagree. The Examiner’s rejection does not
require Zoeller to analyze concrete; rather, Zoeller would employ Yeh’s
“concept of using a neural network to predict the strength of a material in a
‘cellular’ structure,” as Yeh’s teaching is “equally applicable to prediction of
strength of any other material” in addition to concrete. Ans. 12—13. Thus,
we are not persuaded by Appellants’ argument that using Yeh’s neural
network with Zoeller’s honeycomb structures’ inspection images would
change Zoeller’s fundamental principle of operation. App. Br. 8; Reply Br.
2-3.
In view of the above, we are not persuaded the Examiner erred in
rejecting claim 1 under 35 U.S.C. § 103(a) over Zoeller, Summers, and Yeh.
Thus, we sustain the rejection of claim 1.
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Claims 2 and 3
Claim 2 depends from claim 1 and recites that “target cell parameters
include one or more of outer skin shape, outer skin thickness, cell location,
cell comer angle, horizontal cell pitch, vertical cell pitch, horizontal wall
thickness, vertical wall thickness, horizontal wall bow and vertical wall
bow.” Claim 3 depends from claim 1 and recites, inter alia, “representing
the arrangement of sample cells with numerically-defined sample cell
parameters.”
Appellants contend the cited references fail to teach or suggest
numerically defined cell parameters as recited in claim 2, and representing a
cell arrangement with numerically defined cell parameters as recited in
claim 3. App. Br. 10-11. We do not agree. Appellants’ arguments do not
address the Examiner’s specific findings that Summers represents a cell
arrangement with numerically defined cell parameters including a cell angle
parameter. Final Act. 4—5 (citing Summers 12); Ans. 14—15.
Appellants’ additional argument that Summers teaches away from
conventional cell angle parameters (Reply Br. 4) is not persuasive because
Summers merely teaches other cell parameters (effective height and
horizontal separation) may be better than conventional parameters. See
Summers 2, 62. “[M]ere disclosure of alternative designs does not teach
away.” In re Fulton, 391 F.3d 1195, 1201 (Fed. Cir. 2004).
Appellants additionally argue representing Zoeller’s target cells’
arrangement with numerically defined target cell parameters would destroy
Zoeller’s functionality. App. Br. 10. As discussed supra with respect to
claim 1, we are not persuaded that using numerically defined cell parameters
would impact Zoeller’s functionality.
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Accordingly, we sustain the Examiner’s rejection of claims 2 and 3
under 35 U.S.C. § 103(a) as obvious over Zoeller, Summers, and Yeh.
Claims 10 and 11
Claim 10 depends from claim 1 and recites “the output is pass or fail.”
Appellants contend the cited references fail to teach or suggest a pass or fail
output. App. Br. 29. Appellants further contend modifying Zoeller to
output pass or fail would destroy Zoeller’s functionality and change
Zoeller’s principle of operation. App. Br. 29.
We do not agree. Appellants’ arguments do not address the
Examiner’s specific findings that Scarborough teaches a pass or fail output.
Final Act. 11—12 (citing Scarborough Fig. 8); Ans. 25. Appellants’
arguments also do not explain how Zoeller’s principle of operation would be
changed by the combination with Scarborough, and do not address the
Examiner’s reasoning for using Scarborough’s decision system to deduce
whether a sample tested in Zoeller passes or fails a quality test. Final Act.
12. We find the Examiner has sufficiently articulated a rational basis to
support a finding of obviousness based on the Zoeller-Scarborough
combination that does not change the basic principle of operation of Zoeller,
contrary to Appellants’ assertions. Accordingly, we sustain the Examiner’s
rejection of claim 10 under 35 U.S.C. § 103(a) as obvious over Zoeller,
Summers, Yeh, Poff, Xu, and Scarborough.
Claim 11 depends from claim 1 and recites “the output is a numerical
prediction of the strength of the target cellular structure.” Appellants
contend Zoeller cannot be modified to include the claimed strength
prediction because Zoeller is not concerned with predicting cellular structure
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strength and Zoeller’s modification would change Zoeller’s principle of
operation. App. Br. 17; Reply Br. 6. We do not agree.
Appellants’ arguments do not address the Examiner’s specific
findings that Yeh is concerned with neural network-based strength
prediction and outputs a numerical prediction of a structure’s strength, as
claimed. Ans. 18; Final Act. 8 (citing Yeh 1803, Fig. 1). Appellants also do
not explain why or how Zoeller’s principle of operation would be changed
by Zoeller’s combination with Yeh. Further, the Examiner provides
reasonable findings supported by evidence regarding the combination of
Zoeller and Yeh that does not change the basic principle of operation of the
primary Zoeller reference, contrary to Appellants’ assertions. Ans. 11—13,
18.
Accordingly, we sustain the Examiner’s rejection of claim 11 under
35 U.S.C. § 103(a) as obvious over Zoeller, Summers, Yeh, Poff, and Xu.
Claims 4—9 and 12—15
Claim 4 depends (indirectly) from claim 1 and recites the additional
step of “applying force on the sample cellular structure while measuring a
sample strength parameter representing a maximum force that the sample
cellular structure can endure prior to destruction.”
The Examiner finds Poff measures a sample strength parameter
representing a maximum force as claimed because Poff measures a
minimum adhesion threshold via a maximum sustained force at a
circumferential skin portion bonded to honeycomb cell walls. Ans. 15
(citing Poff 125); Final Act. 5 (citing Poff 127).
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Appellants argue Poff does not teach or suggest measuring a strength
parameter representing a maximum force, as claimed. App. Br. 11—12.
Appellants contend Poff at most determines “if the adherence [of the
circumferential portion to the honeycomb walls] is at least strong enough to
withstand the imposed force,” but Poff “fails to determine the actual strength
of adherence, i.e., the maximum force the circumferential skin can
experience prior to destruction.” App. Br. 11—12 (citing Poff 127).
We agree with Appellants the Examiner has not shown that Poff
teaches the claimed strength parameter measurement. The cited portions of
Poff disclose a minimum adhesion force is applied to a honeycomb’s
circumferential skin portions for identifying non-marketable, deficient cells
having weakly bonded skin portions. See Poff H 25 (“where the bond
strength is below a minimum threshold, it is generally more likely that the
circumferential skin portion will separate at least partially from the core
portion”), 27 (“the minimum threshold can be determined as a minimum
adherence or adhesion force that maintains one example skin portion bonded
to one example core portion”). Thus, Poff determines if sample honeycomb
cells withstand a preset minimum adhesion force, but Poff does not measure
a sample’s strength parameter using that force, and does not determine “a
maximum force that the sample cellular structure can endure prior to
destruction.” App. Br. 12; see Poff 127. The Examiner also has not shown
that the additional teachings of Zoeller, Summers, Yeh, Xu, and
Scarborough cure the above-noted deficiencies of Poff.
As the Examiner has not identified sufficient evidence supporting the
Examiner’s finding that the references teach or suggest the claimed
“applying force on the sample cellular structure while measuring a sample
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strength parameter representing a maximum force that the sample cellular
structure can endure prior to destruction,” we do not sustain the Examiner’s
§ 103(a) rejection of claim 4 and claims 5—9 dependent therefrom.
For the same reasons, we do not sustain the Examiner’s § 103(a)
rejection of independent claim 12, which recites the limitation of “applying
force on each the sample cellular structures while measuring a sample
strength parameter representing a maximum force the sample cellular
structure can endure prior to destruction.” We similarly do not sustain the
rejection of claims 13—15, which depend from claim 12. Because the above-
discussed issue is dispositive as to the obviousness rejections of claims 4—9
and 12—15, we do not reach additional issues raised by Appellants’
arguments as to the § 103(a) rejections of these claims.
DECISION
The Examiner’s decision rejecting claims 1—3, 10, and 11 is affirmed.
The Examiner’s decision rejecting claims 4—9 and 12—15 is reversed.
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)(l)(iv).
AFFIRMED-IN-PART
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