Ex Parte Ghosh et al

Patent Trial and Appeal Board

Sep 16, 2014

10869343 (P.T.A.B. Sep. 16, 2014)

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UNITED STATES PATENT AND TRADEMARK OFFICE
__________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
__________

Ex parte JAYATI GHOSH, CHARLES D. TROUP, and
XIANGYANG ZHOU
__________

Appeal 2012-001101
Application 10/869,343
Technology Center 1600
__________

Before DEMETRA J. MILLS, LORA M. GREEN, and
JEFFREY N. FREDMAN, Administrative Patent Judges.

MILLS, Administrative Patent Judge.

DECISION ON APPEAL

This is an appeal under 35 U.S.C. § 134. The Examiner has rejected
the claims for obviousness. We have jurisdiction under 35 U.S.C. § 6(b).
STATEMENT OF CASE
The following claims are representative.

1. A computer-implemented method of automatically separating
multiple microarray images from at least one single combined image
containing multiple microarray images thereon, said method comprising:
receiving as an input said at least one single combined image
containing multiple microarray images thereon, each microarray image
corresponding to a single microarray and comprising a plurality of features;
automatically locating said features in said microarray images using a
computer;
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automatically determining, using said computer, the boundaries of
each microarray image based on the locations of said features corresponding
to that microarray image;
automatically cropping, using said computer, said single combined
image, using said automatically determined boundaries to split said single
combined image into separate, independent images, thereby forming a group
of single independent images, each single independent image containing only
one microarray image, wherein all other image data from said single
combined image has been removed from each said single independent image;
automatically storing, using said computer, said single independent
images as separate files: and
displaying or outputting at least one of said single independent images
in a user readable format.

Cited References

Gaidoukevitch et al. US 6,498,863 B1 Dec. 24, 2002
Le Cocq US 2004/0243341 A1 Dec. 2, 2004

Jesús Angulo & Jean Serra, Automatic analysis of DNA microarray images
using mathematical morphology, 19 BIOINFORMATICS 553-562 (2003).

Grounds of Rejection
1. Claims 1–4, 7–17, 19–22, 25–33, and 36 are rejected under
35 U.S.C. § 103(a) as being unpatentable over Gaidoukevitch in
view of Angulo.
2. Claims 5, 6, 23, 24, 34, and 35 are rejected under 35 U.S.C.
§ 103(a) as being unpatentable over Gaidoukevitch in view of
Angulo and Le Cocq.

FINDINGS OF FACT
The Examiner’s findings of fact are set forth in the Answer at pages
5–12. The following facts are highlighted.
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1. The Specification, page 12, discloses that
Current methods for such pre-processing [of
microarrays] require manual cropping and naming or
cataloguing of the cropped images, as noted above.
Embodiments of the present invention eliminate the need
for such manual tasks, thereby reducing the chances for
erroneously naming or organizing the cropped images,
and simplifying pre-processing by automating it.

2. The Specification discloses that
To analyze results obtained from microarray
experiments where multiple microarrays are provided on
a single slide, the researcher or analyst first needs to have
the multiple images which are produced from the
multiple microarrays located on the single slide,
separated or cropped, so that the researcher or analyst
can work with data from a single microarray (i.e., single
image) at a time, since generally the researcher or analyst
is interested in observing the data from only one
microarray at a time. (Id.)

ISSUE
The issue is: Does the cited prior art support the Examiner’s finding
that the claimed subject matter is obvious?

PRINCIPLES OF LAW
In making our determination, we apply the preponderance of the
evidence standard. See, e.g., Ethicon, Inc. v. Quigg, 849 F.2d 1422, 1427
(Fed. Cir. 1988) (explaining the general evidentiary standard for proceedings
before the Office). The Board “determines the scope of claims in patent
applications not solely on the basis of the claim language, but upon giving
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claims their broadest reasonable construction ‘in light of the specification as
it would be interpreted by one of ordinary skill in the art.’” Phillips v. AWH
Corp., 415 F.3d 1303, 1316 (Fed. Cir. 2005) (quoting In re Am. Acad. of Sci.
Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004).
“In rejecting claims under 35 U.S.C. § 103, the examiner bears the
initial burden of presenting a prima facie case of obviousness. Only if that
burden is met, does the burden of coming forward with evidence or
argument shift to the applicant.” In re Rijckaert, 9 F.3d 1531, 1532 (Fed.
Cir. 1993) (citations omitted). In order to determine whether a prima facie
case of obviousness has been established, we consider the factors set forth in
Graham v. John Deere Co., 383 U.S. 1, 17 (1966): (1) the scope and content
of the prior art; (2) the differences between the prior art and the claims at
issue; (3) the level of ordinary skill in the relevant art; and (4) objective
evidence of nonobviousness, if present.
“The combination of familiar elements according to known methods
is likely to be obvious when it does no more than yield predictable results.”
KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007).
Our mandate is to give claims their broadest reasonable interpretation.
In re Am. Acad. of Sci. Tech Ctr, 367 F.3d at 1364. “An essential purpose of
patent examination is to fashion claims that are precise, clear, correct, and
unambiguous. Only in this way can uncertainties of claim scope be removed,
as much as possible, during the administrative process.” In re Zletz, 893
F.2d 319, 322 (Fed. Cir. 1989).
In In re Venner, the court held that broadly providing an automatic or
mechanical means to replace a manual activity which accomplished the
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same result is not sufficient to distinguish over the prior art. In re Venner,
262 F.2d 91, 95 (CCPA 1958).

ANALYSIS
Claims 1–4, 7–17, 19–22, 25–33, and 36 are rejected under 35 U.S.C.
§ 103(a) as being unpatentable over Gaidoukevitch in view of Angulo.1
We agree with the Examiner’s fact finding, statement of the rejection
and responses to Appellants’ arguments as set forth in the Answer. We find
that the Examiner has provided evidence to support a prima facie case of
obviousness. We provide the following additional comment to argument set
forth in the Answer.
Claim Interpretation
We interpret the term “cropping” in claim 1 to reasonably encompass
“separating” multiple arrays into smaller arrays. (FF2.)

Discussion Rejection 1
Claims 1 and 4
The Examiner finds that “Gaidoukevitch teach[es] how an array may
be partitioned into sub-arrays (Figures 2-3 and col. 8, lines 14-17).” (Ans.
5.) “Gaidoukevitch et al. teach storing the original image and then restoring
images after manipulation (col. 21, lines 32-46) (i.e. storing images as files),
as in claims 1 and 36.” (Ans. 6.) “Gaidoukevitch et al. teach filtering based

1 Where claims are argued as a group in the Brief, we select the first
indicated claim as representative. For example, on page 10 of the Brief,
claims 2 and 20 are argued together, and we select claim 2 as representative.
Claims separately argued by Appellants are addressed separately.
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on ‘threshold’ values (col. 7, lines 44-47) and a ‘default value’ for the
boundaries (col. 12, lines 15-19). The default values of Gaidoukevitch et al.
are applied during the setting of array boundaries (col. 12, lines 25-28), as in
‘applied during said cropping’ recited in claim 4.” (Ans. 14.)
Appellants contend that
At most, the references teach automatically finding the
boundaries of the individual micro-arrays. Neither reference
teaches cropping the images and outputting them to separate
files. Furthermore, there would be no reason to perform such an
operation within the methods of either of the references, since
each reference is dedicated to processing the entire image of the
multiple arrays to determine the properties of each of the spots.

(Reply Br. 2.) We are not persuaded. As argued by the Examiner,
Gaidoukevitch teaches how an array may be partitioned (separated) into sub-
arrays and stored. (Ans. 5.) Thus, one of ordinary skill in the art would
understand that the system of Gaidoukevitch would have been capable of
partitioning an array to multiple sub-arrays where each sub-array would be
composed of a single image. We agree with the Examiner that storing such
a single image in a single image file would have been an obvious approach
to the person of ordinary skill.
Furthermore, Appellants admit in the Specification at page 12, that
their invention is to merely automate well known current methods of
manually cropping and naming cropped images. (FF1.) It is not inventive to
automate a manual process. In In re Venner, the court held that broadly
providing an automatic or mechanical means to replace a manual activity
which accomplished the same result is not sufficient to distinguish over the
prior art. Venner, 262 F.2d at 95. Appellants have failed to rebut the
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Examiner’s prima facie case of obviousness for claims 1 and 4 by a
preponderance of the evidence and the rejections of claims 1 and 4 are
affirmed. Claims 3, 7, 9, 17, 19–22, 25–31, 33, and 36 fall with
representative claim 1. Other claims separately argued by Appellants are
addressed below.

Claim 2
With respect to Claims 2 (which we select as representative), the
Examiner finds that
Gaidoukevitch et al. teach providing images (Abstract) of
microarrays which contain more than one array, which they call
“sub-arrays” (i.e. multiple array images) (see Figures 2-3 and
col. 6, lines 44-48). Gaidoukevitch teach how an array may be
partitioned into sub-arrays (Figures 2-3 and col. 8, lines 14-17).
Gaidoukevitch teach automatically locating objects (i.e.
features) (col. 3, lines 18-21) and determining a grid overlay
(i.e. determining boundaries) (col. 3, lines 5-18) which
corresponds to the underlying sub-arrays (col. 14, lines 8-13).
Sub-grids can be identified and objects can be repositioned
within their corresponding subgrids (col. 6, lines 23-35).
Furthermore, Gaidoukevitch et al. teach that array boundaries
can be set for the image to define the area of interest so that
objects on the image outside the defined array are not included
(col. 11, line 60 to col. 12, line 4 and lines 25-29), as in claims
1 and 36.

(Ans. 5.) The Examiner further finds that

Gaidoukevitch et al. teach a grid overlay (i.e. boundaries)
(col. 9, line 44 to col. 10, line 3) and a deviation of plus or
minus two degrees from the overlaid boundaries (col. 9, lines
52-55), which reads on the limitation “offset by predetermined
boundary parameters.” Also, see Gaidoukevitch Figure 9 as
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compared to Applicant's Figure 9, both of which show an offset
in the image boundaries, as in claim 2.

(Ans. 6.)
Appellants contend that the
Examiner maintains that the passage in Gaidoukevitch at
column 9, lines 52–55 is equivalent to the teaching of an offset
used to define the cropping boundaries. The cited passage
teaches that the multiple-array image is rotated such that the
rows and columns of the spots deviate from horizontal and
vertical alignment by no more than plus or minus two degrees.
There is no teaching of an offset from the boundaries of the
edges of the individual micro-array sub-images, i.e., a linear
displacement from a boundary.

(Reply Br. 2.)

According to Appellants’ Specification, page 25 ¶ [00103], “[u]sing
the offset values, the degree of rotation [of the microarray] can be readily
calculated.” Rotation is performed if any is needed. (Page 25 ¶ [00102].)
In FIG. 11 of Gaidoukevitch
at a step 1110, the actual angle of orientation between
rows and columns of the array and boundaries of the image is
determined Finally, at a step 1112, the image is rotated to its
desired angle of orientation. This is different from prior art
approaches in which alignment of the overlay grid is either
performed manually or depends on orientation markers
included on the substrate of the array and captured in the image.
The present invention allows users to correct automatically the
orientation of the array independent of any guides that may be
included on the substrate.

(Col. 10, ll. 24–37.)

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Appellants have failed to provide evidence or scientific argument that
rebuts the Examiner’s finding that the microarray image rotation
calculations, deviations and corrections of Gaidoukevitch do not teach the
offset of claim 2. The rejection of claim 2 is affirmed. Appellants have
further failed to provide argument or evidence to rebut the Examiner’s prima
facie case of obviousness of claim 4, dependent upon claim 2 and addressed
with claim 1 above.

Claims 8, and 10–13
Appellants argue that,
With respect to Claims 8, the Examiner maintains that
Angulo teaches naming the individual cropped images, since
the images in Figure 2a refer to array 1a and array 1b. First, as
noted above, the images in Figure 2a are not images of a single
cropped micro-array image. Second, the computer did not
generate these names, the authors of the article generated those
names.

(Reply Br. 2.)

Appellants have failed to provide a preponderance of the evidence to
show that Gaidoukevitch combined with Angulo does not teach that
microarray naming and storing is known. Appellants further admit that they
are automating a well-known, manual naming process. (FF1.) The
rejections of claims 8 and 10–13 is affirmed.

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Claims 14 and 32
The Examiner finds that
Gaidoukevitch et al. teach one or more computer
processors for carrying the method (i.e. batch mode)(col. 28,
lines 13-22), as in claim 14. Since a limiting definition of batch
mode was not provided, “batch mode” as been interpreted to
mean the same as “batch processing” which is commonly
understood by those in the art to mean executing a series of jobs
on a computer. Since a plurality of processors are taught, this
reads on having a series of jobs are executed as a batch process
(i.e. in batch mode).

(Ans. 15.)
Appellants argue that,
[A] system with more than one processor could as easily
employ all of the processors to carry out one single job or even
the same thread within a single job as employ them to carry out
a series of different jobs. The additional processors merely
allow the computer to execute more instructions at the same
time, but such execution is not inherently equivalent to batch
processing

and that “[t]he Examiner has not pointed to any teaching in Gaidoukevitch
of performing multiple image array analysis on the multiple processors, such
that each processor works on a separate image.” (Br. 14–15.)
We are not persuaded. Appellants have failed to provide a
preponderance of the evidence to show that the multiple processors of
Gaidoukevitch are not capable of performing batch processing on a separate
image. Further, the claims do not require each processor to operate on a
separate image, but simply require a “batch mode.” The Examiner’s
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interpretation is reasonable in the absence of any evidence or substantive
argument to the contrary. The rejection of claims 14 and 32 are affirmed.

Claims 15 and 16
Claims 15 and 16 are affirmed for the reasons of record set forth on
pages 7 and 16 of the Examiner’s Answer. Claim 17 falls with claim 16.

Rejection 2
Claims 5, 6, 23, 24, 34, and 35 are rejected under 35 U.S.C. § 103(a)
as being unpatentable over Gaidoukevitch in view of Angulo and Le Cocq.

The Examiner admits that
Gaidoukevitch et al. in view of Angulo et al. do not teach the
limitations of claims 5, 6, 24, 34 and 35 which recite locating features
using a projection based algorithm, projecting the two dimensional
array to form one dimension, peak picking the one dimensional data
set, estimating spacing between features, projecting the two
dimensional array in the second of two dimensions, peak picking the
one dimensional data set representative of the values of the second
dimension, and generating coordinates.

(Ans. 10.)

However, the Examiner relies on Le Cocq to make up for this
deficiency in Gaidoukevitch and Angulo.

Le Coqc [sic] teaches processing of microarray data by
automatically locating features on the microarray using a
projection based algorithm (¶[16). LeCoqc [sic] teach reducing
(i.e. projecting) the two dimensional array in a first of the two
dimensions to form a one dimensional dataset representative of
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the values in the first dimension (¶ [016); peak picking the one
dimensional dataset and determining which picked peaks to
retain for further processing, based on predetermined peak
height and peak width thresholds (¶ [0020 and 0058);
estimating spacing between the features based on a statistical
determination of a most frequent distance between centers of
retained peaks which are adjacent one another; projecting the
two dimensional array in the second of the two dimensions to
form a one dimensional dataset representative of the values in
the second dimension ¶ [0011, ¶ [0016, and ¶ [0058); peak
picking the one dimensional dataset representative of the values
in the second dimension, and determining which picked peaks
to retain for further processing, based on predetermined peak
height and peak width thresholds (¶ [0126); estimating spacing
between the features based on a statistical determination of a
most frequent distance between centers of retained peaks which
are adjacent one another (¶ [0016); and generating coordinates
for the features on the array, relative to X and Y axes referring
to the first and second dimensions, based on the picked peaks
and peak spacing (¶ [0016 and ¶ [0080).

(Ans. 10–11.)
Appellants contend that
As noted above with respect to independent claims 1, 19,
and 33 from which claims 5-6, 23-24 and claim 34 respectively
depend, the combination of Gaidoukevitch and Angulo does not
teach the limitations of the base claims regarding the cropping
of one image into sub-images and storing those sub-images as
separate files. Independent claim 35 also includes the
limitations in question. LeCocq does not provide the missing
teachings. Second, the algorithm used in LeCocq is different
from that used in Gaidoukevitch or Angulo. The Examiner has
not pointed to any teaching that the method of LeCocq would
shorten the processing time of the method taught in
Gaidoukevitch. Accordingly, Applicant submits that the
Examiner has not made a prima facie case for obviousness with
respect to claims 5, 6, 23, 24, 34, and 35.
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(Br. 16.)
We have concluded above that Gaidoukevitch and Angulo teach the
limitations of the base claims regarding the cropping of one image into sub-
images and storing those sub-images as separate files. In addition, LeCocq
is in the same field of endeavor as the claimed invention and it is analogous
prior art as it is “reasonably pertinent to the particular problem with which
the inventor is involved.” In re Bigio, 381 F.3d 1320, 1325 (Fed. Cir. 2004).
Appellants have not shown otherwise. Appellants have failed to rebut the
Examiner’s prima facie case of obviousness by a preponderance of the
evidence and the rejection of representative claim 5 is affirmed. Claims
6, 23, 24, 34, and 35 fall with claim 5.

CONCLUSION OF LAW
The cited references support the Examiner’s obviousness rejections
which are affirmed for the reasons of record.
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

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