Ex Parte He et al

Patent Trial and Appeal Board

Mar 29, 2017

13597890 (P.T.A.B. Mar. 29, 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
www.uspto.gov
APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
13/597,890 08/29/2012 Yuan He 10829-9066.US00 9641
46844 7590 03/31/2017
PFRKTNS TOTF TIP- Mirrnn
EXAMINER
PATENT-SEA POTTS, RYAN P
PO BOX 1247
SEATTLE, WA 98111 -1247 ART UNIT PAPER NUMBER
2666
NOTIFICATION DATE DELIVERY MODE
03/31/2017 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):
patentprocurement @perkinscoie. com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte YUAN HE and HONG CHEN
Appeal 2017-000587
Application 13/597,890
Technology Center 2600
Before JOSEPH L. DIXON, JAMES R. HUGHES, and ERIC S. FRAHM,
Administrative Patent Judges.
DIXON, Administrative Patent Judge.
DECISION ON APPEAL
Appeal 2017-000587
Application 13/597,890
STATEMENT OF THE CASE
Appellants appeal under 35 U.S.C. § 134 from a rejection of claims 1—
30. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.
The invention relates to detecting defects in a microelectronic device,
such as incompletely filled via holes (called “keyholes”), by analyzing an
image of the microelectronic device using a fast marching method that takes
image intensity as a speed function and converts it into arrival time
information for identifying targeted features, such as a keyholes, where there
is significant acceleration, i.e., a change in image intensity (see Spec, 24—
32). Claim 1, reproduced below, is illustrative of the claimed subject matter:
1. A method for analyzing an image of a microelectronic
device, comprising:
processing an image of a microelectronic device using a
fast marching method to obtain arrival time information
representative of features in the image of the microelectronic
device; and
analyzing the arrival time information using a targeted
feature descriptor to identify targeted features, if any, in the
image of the microelectronic device.
REFERENCES
The prior art relied upon by the Examiner in rejecting the claims on
appeal is:
Gleason US 6,456,899 B1 Sept. 24,2002
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Roshan Dharshana Yapa & Koichi Harada, Breast Skin-Line Estimation and
Breast Segmentation in Mammograms Using Fast-Marching Method, 1:5
Int’l Journal of Medical, Health, Pharmaceutical and Biomedical
Engineering 217-25 (2007) (“Yapa”).
Jiayong Yan, et al., Lymph Node Segmentation from CT Images Using Fast
Marching Method, Computerized Medical Imaging and Graphics 28,
33-38 (2004) (“Yan”).
REJECTIONS
The Examiner made the following rejections:
Claims 1—6 and 8—30 stand rejected under 35 U.S.C. § 103(a) as being
unpatentable over Gleason and Yapa.
Claim 7 stands rejected under 35 U.S.C. § 103(a) as being
unpatentable over Gleason, Yapa, and Yan.
ANALYSIS
Claims 1—9 and 12—15
Regarding claim 1, Appellants contend one of ordinary skill in the art
would not have been motivated to combine Yapa’s fast marching method
with Gleason (see App. Br. 15—17). Appellants also contend combining
Yapa’s fast marching method with Gleason would have changed the basic
principle of operation of Gleason (see id. at 17—18). We disagree with
Appellants.
Yapa describes using a fast marching method to detect the breast skin­
line in a digital mammogram image when performing computer-aided
diagnosis of breast cancer (see Yapa 217 (“I. Introduction”)). Yapa notes
that “[f]ailure to detect breast skin-line accurately, would lead to overlook a
lesion location near the skin line” (id.). Accordingly, Yapa describes
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implementing the “fast marching method for boundary detection” in order to
“extract the skin-line boundary of a given mammogram image” (Yapa 219
(“III. Fast Marching Method”)). Yapa’s fast marching method considers
“the special case of a surface moving with speed F(x,y)>(F and lets “T(x,yj
be the time at which the surface crosses a given point (x,y). The function
T(x,y) then satisfies the equation: |V7]/*-l.” (Id.)
For a given image with intensity values I(x,y), often this speed
function F(x,y) is defined as a decreasing function based on the
image local gradient \VI(x,y)\. . . . Thus the fast marching
algorithm can be briefed as follows. A small front (typically a
single seed point) is initialized inside the desired region, grows
outwards and stops at the sharp boundary as the speed function
A reduces to near zero.
(Id.)
Based on the above disclosure, the Examiner finds Yapa teaches
“using a fast marching method to obtain arrival time information
representative of features” and “analyzing the arrival time information using
a targeted feature descriptor to identify targeted features,” as recited in claim
1 (Final Act. 8). Accordingly, the Examiner only needs to rely on Gleason
for teaching an image of a microelectronic device as the subject of an image
analysis.
Gleason describes a method for detecting and classifying defects in an
image of a semiconductor chip. Specifically, Gleason’s method “involves
the mathematical manipulation of pixel data in the acquired reference and
defect images 21, 22, and utilizes stored information in the knowledge
database 23 to derive a useful classification of the type of defect contained in
the defect image” (Gleason, col. 5,11. 36-40), where “the reference image 21
and defect image 22 can be generated, for example, by any of several in-line
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defect inspection systems such as optical microscopes, scanning electron
microscopes . . (Gleason, col. 2,11. 58—61).
We agree with the following obviousness conclusion made by the
Examiner:
The mere fact that the specific content of the images used by
Gleason and Yapa is different would not deter one of ordinary
skill in the art from combining Yapa with Gleason. . . . Both
Gleason and Yapa are concerned with image analysis
algorithms that determine the boundaries of regions within
images. One of ordinary skill in the art would look to
inventions such as that disclosed by Yapa in order to improve
another invention in the same field of image analysis, such as
that disclosed by Gleason.
(Ans. 8—9). In other words, one of ordinary skill in the art faced with the
problem of detecting features in an image of a semiconductor device would
have been motivated to look for an alternative image analysis technique such
as Yapa’s fast marching method. Although Gleason and Yapa describe
disparate image content, both references relate to the broad field of image
analysis, and Yapa’s technique would have been applicable to Gleason’s
image of a semiconductor device. Such “substitution of one element for
another known in the field . . . must do more than yield a predictable result”
to avoid being found obvious. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398,
416 (2007). Here, the result of the Examiner’s combination is predictable:
it would allow one to use a fast marching method to detect boundaries, i.e.,
areas with a certain image intensity gradient, in an image of a semiconductor
chip. Thus, we are not persuaded by Appellants’ argument that one would
not have been motivated to combine Yapa with Gleason.
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We are also not persuaded by Appellants’ argument that the
Examiner’s combination would change the basic principle of operation of
Gleason. The thrust of the Examiner’s obviousness conclusion is that one of
ordinary skill in the art would have substituted one method of image analysis
for another (see Ans. 8—9). Thus, Gleason’s method would indeed be altered
by Yapa’s fast marching method, although it would be altered in a manner
obvious to one of ordinary skill in the art that yielded only predictable
results, as noted above.
Further, we are not persuaded by Appellants’ Reply Brief arguments
that the “Examiner’s proposed rationale relies on impermissible hindsight
and is not a convincing line of reason for combining the applied references
to arrive at the claimed invention,” “the Examiner improperly proposes
modifying the Gleason technology to arrive at the claimed invention while
ignoring the fact that Gleason’s sub-pixel registration and alignment is
performed without determining defect boundaries,” and “[b]y segmenting
the defects in the defect image, one of ordinary skill in the art would
understand the Examiner’s modification could obviate Gleason’s
segmentation process” (Reply Br. 2—3). Claim 1 does not include any
limitations that specifically define how to implement a fast marching method
with respect to a microelectronic device image in a way that is different than
how to implement a fast marching method with respect to any other type of
image. That is, aside from limiting the method to analysis of “an image of a
microelectronic device,” there are no steps in claim 1 specifically tailored to
the distinct features of a microelectronic device image.
Accordingly, Appellants’ discussion of the details of integrating a fast
marching method into Gleason’s particular defect detection and
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classification method for an image of a semiconductor chip (see Reply Br.
2—3) is not commensurate with the broad scope of claim 1. Here, it suffices
to show obviousness by concluding, as we do above, that one of ordinary
skill in the art would have applied Yapa’s fast marching method to identify
features of Gleason’s semiconductor chip image because this merely
amounts to substituting a known method in the field of image analysis.
We are, therefore, not persuaded the Examiner erred in rejecting claim
1, and claims 2—9 and 12—15 not specifically argued separately.
Claims 10 and 11
Regarding claims 10 and 11, Appellants rely on the same arguments
discussed above with respect to claim 1 (App. Br. 19), which are not
persuasive. Additionally, Appellants contend “Gleason’s system is not
designed to identify features in an arrival time image of a semiconductor
device, much less identify targeted features for visual inspection” (id.). We
are not persuaded by Appellants’ additional arguments.
First, we note the Examiner relies on Yapa, not Gleason, for
disclosing the use of an arrival time image to identify features (see Final
Act. 8; Ans. 11). Second, the claim 10 limitation “for visual inspection” is
merely an intended use limitation, and is thus, not given patentable weight.
That is, claim 10 does not positively recite actually performing a visual
inspection.
We are, therefore, not persuaded the Examiner erred in rejecting claim
10, and claim 11 not specifically argued separately.
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Claims 16—18
Regarding claims 16—18, Appellants rely on the same arguments
discussed above with respect to claim 1 (App. Br. 20-21), which are not
persuasive. Additionally, Appellants contend “Gleason fails to disclose fast
marching methods and arrival time images. Ya[p]a also fails to disclose
using arrival time images” {id. at 21). Further, Appellants contend Gleason
“fails to disclose evaluating arrival time images using a targeted feature
descriptor” and “Ya[p]a discloses using breast-line segmentation techniques
to identify breast skin lines, not targeted features, in an input mammogram
image” {id.). We are not persuaded by Appellants’ additional arguments.
We note the Examiner relies on Yapa for disclosing the claim 16
limitation of “an arrival time image” {see Final Act. 8; Ans. 11), and
Appellants have not specifically explained why the relied upon description
in Yapa fails to meet this limitation. Further, we disagree with Appellants
that detecting the breast skin-line in a digital mammogram image, as
described in Yapa, fails to disclose identifying a targeted feature. Claim 16
does not recite a specific targeted feature that is identified, and thus, does not
preclude Yapa’s detection of a boundary that corresponds to the breast skin­
line in a digital mammogram image {see Yapa 219 (“III. Fast Marching
Method”)), from teaching the identification of a targeted feature. In other
words, detecting boundaries where there is a certain image intensity gradient
{see id.) meets the claim 16 limitation of “identifying targeted features.”
Further, as discussed above regarding claim 1, it would have been obvious to
use Yapa’s fast marching method to identify features in Gleason’s
semiconductor chip image.
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We are, therefore, not persuaded the Examiner erred in rejecting claim
16, and claims 17 and 18 not specifically argued separately.
Claims 19—24
Regarding claims 19-24, Appellants rely on the same arguments
discussed above with respect to claim 1 (App. Br. 21—22), which are not
persuasive. Additionally, Appellants contend both Gleason and Yapa fail to
disclose using an arrival time image and using a defect descriptor {id. at 22).
We are not persuaded by Appellants’ additional arguments.
We note the Examiner relies on Yapa for disclosing the claim 19
limitation of “an arrival time image” {see Final Act. 8; Ans. 11), and
Appellants have not specifically explained why the relied upon description
in Yapa fails to meet this limitation. Further, the Examiner relies on
Gleason for disclosing the use of a “defect descriptor,” as recited in claim 19
(Final Act. 16; Ans. 12). We agree with the Examiner because Gleason
discloses “utilizing] stored information in the knowledge database 23 to
derive a useful classification of the type of defect contained in the defect
image” {see Gleason, col. 5,11. 37-40). That is, in order for Gleason’s
method to classify a defect, there must be some description of the defect, for
example, in Gleason’s knowledge database.
We are, therefore, not persuaded the Examiner erred in rejecting claim
19, and claims 20-24 not specifically argued separately.
Claims 25—3 0
Regarding claims 25—30, Appellants rely on the same arguments
discussed above with respect to claim 1 (App. Br. 22), which are not
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persuasive. Additionally, Appellants contend Gleason and Yapa fail to
disclose “the programmable processing device is configured to execute
instructions from the memory to analyze the arrival time information using a
targeted feature descriptor to identify targeted features, if any, in the image
of the microelectronic device” (App. Br. 22—23). We disagree with
Appellants’ additional argument.
We agree with the Examiner’s interpretation of the claimed
“programmable processing device” as a means under 35 U.S.C. § 112, sixth
paragraph) (Final Act. 4—6). That is, “programmable processing device”
does not denote “sufficiently definite structure” for performing the functions
of claim 25. Williamson v. Citrix Online, LLC, 792 F.3d 1339, 1349 (Fed.
Cir. 2015). As noted by the Examiner (Final Act. 5), we look to the
Specification to determine what structures perform the recited functions in
claim 25. See Personalized Media Comm ’ns, LLC v. Int 7 Trade Comm ’n,
161 F.3d 696, 703 (Fed. Cir. 1998) (Ҥ 112[(f)] operates to restrict claim
limitations drafted in such functional language to those structures, materials,
or acts disclosed in the specification (and their equivalents) that perform the
claimed function.”). Here, the Specification discloses a processing unit for
performing the inventive method that can be “one or more programmable
processors, computers, central processing units, processing devices,
microprocessors, digital signal processors (DSPs), and/or application-
specific integrated circuits (ASICs)” (Spec. 1 61). Accordingly, Appellants’
“programmable processing device” can simply be a general purpose
computer programmed to perform the claimed functions.
We find Yapa discloses performing a computer-aided image analysis
that involves using a fast marching method (see Yapa 217 (“I.
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Introduction”)), and thus, meets the claim 25 limitation of a “programmable
processing device.” Further, as discussed above with regard to claim 1, the
combination of Gleason and Yapa discloses using arrival time information to
identify targeted features in an image of a microelectronic device.
We are, therefore, not persuaded the Examiner erred in rejecting claim
25, and claims 26—30 not specifically argued separately.
CONCLUSION
The Examiner did not err in rejecting claims 1—30 under 35 U.S.C.
§ 103(a).
DECISION
For the above reasons, the Examiner’s rejections of claims 1—30 are
affirmed.
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a)(l)(iv).
AFFIRMED
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