Jansen, Gijsbert Johan.

Patent Trials and Appeals Board

Dec 9, 2019

13122938 - (D) (P.T.A.B. Dec. 9, 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.
13/122,938 06/27/2011 Gijsbert Johan Jansen ARSI-091 3691
79782 7590 12/09/2019
Law Offices of Daniel L. Dawes
Dawes Patent Law Group
5200 Warner Blvd, Ste. 106
Huntington Beach, CA 92649
EXAMINER
LANDAU, SHARMILA GOLLAMUDI
ART UNIT PAPER NUMBER
1653
NOTIFICATION DATE DELIVERY MODE
12/09/2019 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):
ddawes@dawespatents.com
mdawes@dawespatents.com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
________________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
________________

Ex parte GIJSBERT JOHAN JANSEN1
________________

Appeal 2018-002669
Application 13/122,938
Technology Center 1600
________________

Before JEFFREY N. FREDMAN, JOHN G. NEW, and
JOHN E. SCHNEIDER, Administrative Patent Judges.

NEW, Administrative Patent Judge.

DECISION ON APPEAL

1 We use the word “Appellant” to refer to the “applicant” as defined in
37 C.F.R. § 1.142. Appellant identifies Biotrack Holding B.V. as the real
party-in-interest. App. Br. 3.
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SUMMARY
Appellant files this appeal under 35 U.S.C. § 134(a) from the
Examiner’s Final Rejection of claims 37–41, 44–46, and 51–53.
Specifically, claims 37–41 and 46 stand rejected as unpatentable under 35
U.S.C. § 103(a) as being obvious over the combination of Tibbe et al. (US
2006/0024756 A1, February 2, 2006) (“Tibbe”) and Grinon et al. (EP 1 826
548 A1, August 29, 2007) (“Grinon”).
Claims 37–41, 44–46 and 51–53 stand rejected as unpatentable under
35 U.S.C. § 103(a) as being obvious over the combination of Tibbe, Grinon,
Miller (WO 01/46382 A2, June 28, 2001) (“Miller”), and Pattyn et al. (WO
2004/004868 A1, January 15, 2004) (“Pattyn”).
We have jurisdiction under 35 U.S.C. § 6(b).
We AFFIRM.

NATURE OF THE CLAIMED INVENTION
Appellant’s claimed invention is directed to a device for automatically
analyzing micro-organisms in an aqueous sample using filter cytometry.
Abstr.

REPRESENTATIVE CLAIM
Claim 37 is representative of the claims on appeal and recites:
37. A method for automatically analyzing microorganisms in
an aqueous sample using filter cytometry, comprising the steps
of:

a) applying a predetermined volume of the sample on a filter;

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b) staining the microorganisms on the filter with one or more
labels;

c) imaging the bound labels on the filter surface to produce a
filter image, and;

d) analyzing the filter image for quantification of the
microorganisms on the filter,

wherein the step of analyzing comprises determining the optimal
grey value threshold for separating the microorganisms from the
background in the filter image, wherein determining the optimal
grey value threshold comprises:

i. thresholding the filter image using a range of grey value
thresholds by converting the filter image to an image for
each grey value threshold and calculating the number of
selected objects and unselected objects in each grey value
threshold image, wherein an object with a size larger than a
predetermined lower size threshold is selected;

ii. after step i, determining a ratio of selected and unselected
objects for each of the grey value threshold images in the
range of grey value thresholded images, and;

iii. after steps i and ii, designating a single grey value threshold
from the range of grey value thresholds resulting in the
selection of an optimal ratio from the ratios of selected and
unselected objects determined for each grey value threshold
images as the optimal grey value threshold.

App. Br. 15.

ISSUES AND ANALYSES
We agree with, and adopt, the Examiner’s findings, reasoning, and
conclusion that the claims on appeal are obvious over the combined cited
prior art. We address the arguments raised by Appellant below.
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A. Rejection of claims 37–41 and 46
Issue
Appellant argues that the Examiner erred in finding that the cited
references teach or suggest determining a ratio of selected and unselected
objects where selected objects are bigger than a biologically significant
threshold and unselected objects are smaller than the threshold. App. Br. 8.

Analysis
The Examiner finds that Tibbe teaches determining the optimal gray
value threshold for separating the microorganism from the background in the
filter image. Final Act. 3 (citing Tibbe ¶ 95). The Examiner finds that
Tibbe teaches that the determination of the optimal gray value threshold
comprises thresholding the image using a gray value threshold by converting
the filter image to an image for a range of gray value thresholds and
calculating the number of selected objects in each gray value threshold
image, in which an object with a size larger than a predetermined lower size
threshold is selected. Id. at 3–4 (citing Tibbe ¶¶ 92, 96, Fig. 5). The
Examiner finds that Tibbe teaches next determining a ratio of selected and
unselected objects, and then designating a single gray value threshold from
the range of gray value thresholds, which results in the selection of an
optimal ratio from the ratios of selected and unselected objects determined
for each of the gray value threshold images as the optimal gray value
threshold. Id. at 4 (citing Tibbe ¶¶ 102–103, 116, Fig. 12).
The Examiner acknowledges that Tibbe does not expressly teach
calculating the number of unselected objects and the ratio of selected to
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unselected objects for each of the gray value threshold images in the range
of gray value threshold images. Final Act. 4. However, the Examiner finds
that the reference teaches that it is desirable to calculate the signal-to-noise
ratio, which is the ratio of selected to unselected objects. Id. (citing Tibbe
¶ 102).
The Examiner therefore concludes that a person of ordinary skill in
the art would have been motivated to calculate this ratio for a range of gray
value threshold images, because Tibbe teaches that the accurate detection of
cells requires maximizing the signal-to-noise ratio (i.e., the ratio of selected
to unselected objects, Final Act. 4 (citing Tibbe ¶ 102). The Examiner’s
conclusion further relies on Tibbe’s teaching that selection of an optimal
gray value threshold from a range of gray value threshold images by
choosing a point at which the noise (unselected objects) is below the
threshold and the all of the cells (the selected objects) are above the
threshold. Id. (citing Tibbe ¶ 96). The Examiner further concludes that a
skilled artisan would therefore have been motivated to modify the teachings
of Tibbe to arrive at a method wherein the number of unselected objects and
ratio of selected to unselected objects is calculated for each of the plurality
of gray value threshold images, and could have done so with a reasonable
expectation of success. Id. at 4–5.
Appellant argues that Tibbe teaches selecting a fixed threshold by
creating a binary image using a threshold to separate cells from background
and adapting the images to a fixed threshold. App. Br. 8. Appellant argues
that Tibbe does not teach or suggest determining a ratio of selected and
unselected objects, where selected objects are bigger than a biologically
significant threshold and unselected objects are smaller than the threshold.
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Id. Rather, Appellant asserts, Tibbe characterizes objects as “counted,” and
not on the basis of any type of size criterion. Id. Appellant reasons that,
without selecting objects in the image based on size, Tibbe cannot teach or
suggest determining a ratio of “selected” and “unselected” objects in the
manner required by the claims. Id.
Appellant disputes the Examiner’s findings, arguing that Tibbe does
not teach determining an optimal grey value threshold for each image. App.
Br. 9. Rather, Appellant contends, Tibbe teaches using a constant preset
threshold for all of the images, because the signal-to-noise ratio is
“surprisingly high and nearly constant,” and because the “easiest way to
count the cells is by using a preset threshold level.” Id. Appellant asserts
that the claimed step of determining a threshold optimal grey value for each
image is not the same as using a constant threshold for every image. Id.
Appellant next argues that Tibbe also does not teach determining an
optimal grey value threshold with the recited steps of thresholding the image
using a grey value threshold, by converting the image to an image for a
range of grey value thresholds and calculating the number of selected objects
in each grey value threshold image, such that an object is selected when it is
bigger than a predetermined lower size. App. Br. 9. Appellant asserts that,
to the contrary, Tibbe teaches that fluorescently-labeled cells are located at
random positions in an object plane, which can be modeled in an image
according to a two-dimensional Gaussian distribution. Id. Appellant
contends that, when the threshold level is substantially equal to the signal-to-
noise ratio, the number of counted objects is high. Id. However, argues
Appellant, there is no teaching or suggestion in Tibbe, including paragraphs
[0092], [0096], and Figure 5, of converting the filter image to a range of
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grey value thresholds and calculating the number of selected object in each
grey value threshold image. Id. Instead, Appellant asserts Tibbe teaches
only that the plateau is the desired value that is chosen so as to arrive at the
desired result of counting the actual number of cells. Id.
Next, Appellant argues that Tibbe neither teaches nor suggests
determining the ratio of selected and unselected objects. App. Br. 10.
Appellant contends, rather, that Tibbe characterizes objects as “counted,”
and that this designation is not made on the basis of any metric of size. Id.
Appellant points to paragraphs [0102] and [0103] of Tibbe, which,
Appellant argues, are directed to maximizing signal-to-noise ratio (“SNR”),
and how the SNR is determined in the absence of background noise. Id.
Appellant contends that the rejection is in error at least because the
Examiner has not demonstrated that Tibbe teaches calculating the number of
unselected objects in the image, much less the ratio of selected to unselected
objects. Id. Rather, argues Appellant, the Examiner has demonstrated only
that Tibbe teaches maximizing the SNR, which, Appellant asserts, is not the
same as determining the ratio of selected and unselected objects in the
image. Id. According to Appellant, an optimal ratio from a plurality of
ratios of selected and unselected objects determined for each grey value
threshold image cannot be determined by simply maximizing the SNR. Id.
Appellant argues that the Examiner has not demonstrated how calculating
and maximizing the SNR is equivalent to determining the optimal ratio from
the selected and unselected objects determined for each grey value threshold.
Id.
Furthermore, Appellant argues, and in contrast to the claims, Tibbe
teaches that determination of the SNR is executed prior to filtering and
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thresholding of the image. App. Br. 10. Appellant points to paragraph
[0115] of Tibbe, which states that after the SNR determination and
application, “[t]he filtered image is now ready for thresholding.” Id.
According to Appellant, Tibbe thus teaches determining the SNR before
thresholding an image, not after thresholding the image, and therefore
cannot teach determining the optimal ratio from the selected and unselected
objects determined for each grey value threshold in the manner claimed,
because the claims require “determining a ratio of selected and unselected
objects for each of the grey value threshold images” after thresholding the
filtered image.” Id.
Finally, Appellant notes that Tibbe neither teaches nor suggests
designating a single grey value threshold from a range of grey value
thresholds giving an optimal ratio of selected and unselected objects, as
claimed. App. Br. 11. Appellant contends that, in contrast to the claims,
paragraph [0116] and Figure 12 of Tibbe disclose the counted number of
objects in the image plotted against the threshold level with and without the
use of a template-matching algorithm (i.e., a filter). Id. According to
Appellant, Tibbe teaches that the application of the template facilitates
establishing a predetermined threshold level that is applicable to all images.
Id. (citing Tibbe ¶ 116). However, argues Appellant, the teachings of Tibbe
neither teach nor suggest a single grey value threshold from a range of grey
value threshold that results in the selection of an optimal ratio for each of the
grey value threshold images, by selecting an optimal ratio from the ratios of
selected and unselected objects. Id.
We are not persuaded by Appellant’s arguments, and we address each
in turn. Appellant first argues that Tibbe teaches selecting a fixed threshold
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by creating a binary image using a threshold to separate cells from
background and adapting the images to a fixed threshold. See App. Br. 8.
Appellant asserts that “Tibbe teaches using a constant preset threshold for all
of the images, because the signal-to-noise ratio is ‘surprisingly high and
nearly constant,’ and because the ‘easiest way to count the cells is by using a
preset threshold level.’” Id. at 9.
We disagree. Tibbe does indeed state, in paragraph [0095] the
phrases quoted by Appellant. However, in the same and subsequent
paragraphs, Tibbe teaches the unsuitability of the method proposed by
Appellant:
Obviously, the easiest way to count the cells is by using a preset
threshold level, which is constant for images. In practice,
however, this method was found to be very dependent on the
chosen threshold level. This is visualized in FIG. 5, which
contains curves that are defined as threshold level curves. These
curves show the number of counted objects in a cell image,
plotted against the applied threshold level. Three threshold level
curves of typical cell images are presented.
….

FIG. 5 shows that only a narrow range is available where a preset
level results in an accurate cell count. Furthermore, variations in
background intensity would shift the curves horizontally, thus
making the cell count very dependent on the chosen threshold
level. Hence, a method is desired to make the counting more
robust and less dependent on the chosen threshold level.
Therefore, it was necessary to develop methods to elongate the
plateau corresponding to the actual number of cells in FIG. 5 and
the selected approach uses a matched filter algorithm to enhance
the image prior to thresholding.

Tibbe ¶¶ 95, 99. Consequently, we disagree that Tibbe teaches simply
teaches “using a constant preset threshold for all of the images,” as asserted
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by Appellant, because Figure 5, as well as Figure 12 of Tibbe, and the
related texts, teach applying a series of thresholds.
Appellant next argues that Tibbe does not teach determining an
optimal grey value threshold via thresholding the image using a grey value
threshold, by converting the image to an image for a range of grey value
thresholds and calculating the number of selected objects in each grey value
threshold image, such that an object is selected when it is bigger than a
predetermined lower size. See App. Br. 9.
We again disagree. Tibbe expressly selects for cell diameter (i.e., cell
size) in filtering the image prior to thresholding:
Although the cells in the sample may be shaped and sized
differently, all the cells in the cell image are of similar shape and
are approximately of equal size. This is due to the magnification
factor of the optical system, and the resultant influence of the
point spread function. Since the approximate width δP of the cells
in the image is known, it can be directly used in the matched filter
algorithm. It has been shown in the previous section that the filter
performs best if it exactly matches the cell sizes. By visual
inspection of several cells in the cell images, an average value of
δP=2 has been determined.

Tibbe ¶ 113. Tibbe thus teaches filtering the image by the size of the
detected cell by excluding images with diameters less than a determined cell
size δP.
Appellant argues further that that Tibbe neither teaches nor suggests
determining the ratio of selected and unselected objects, but, rather, that
Tibbe characterizes objects as “counted,” and that this designation is not
made on the basis of any metric of size. See App. Br. 10.
We are not persuaded. We have explained supra why we find that
Tibbe incorporates a metric of size, δP, into its filtered images. Furthermore,
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Tibbe teaches that: “The optimized method consists of the application of a
threshold to create a binary image in which cells get the value 1 (white),
background and noise gets the value 0 (black) and the “white” spots in the
image are counted.” Tibbe ¶ 95. More explicitly, Tibbe teaches:
The whole image f(x,y) with randomly distributed cells,
including background and noise signals, is described by the
following model:

where Ci are the peak intensities of the cells. C0 represents a
slowly varying background level, which adds to the cells. This
background signal is caused by free, unbound dye in the sample
and can slowly fluctuate as a result of inhomogeneous
illumination. A stochastic white noise component is modeled by
the component n. Sources of noise include thermal and readout
noise from the CCD camera. Based on this model we can define
the signal-to-noise ratio (SNR) of cell i in the image:

where δn is the standard deviation of the noise component n.

Id. at ¶ 93. More simply put, the determination of the signal-to-noise ratio,
as depicted in Figure 5 of Tibbe, is necessarily the ratio of selected and
unselected objects, indeed, that is the definition of a signal-to-noise ratio.
Appellant has advanced no argument that persuades us otherwise.
Appellant also argues that Tibbe teaches that determination of the
SNR is executed prior to filtering and thresholding of the image. App. Br.
10 (citing Tibbe ¶ 115).
Claim 37 recites, in relevant part:
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wherein the step of analyzing comprises determining the optimal
grey value threshold for separating the microorganisms from the
background in the filter image, wherein determining the optimal
grey value threshold comprises:

i. thresholding the filter image using a range of grey value
thresholds by converting the filter image to an image for
each grey value threshold and calculating the number of
selected objects and unselected objects in each grey value
threshold image, wherein an object with a size larger than a
predetermined lower size threshold is selected;

ii. after step i, determining a ratio of selected and unselected
objects for each of the grey value threshold images in the
range of grey value thresholded images, and;

iii. after steps i and ii, designating a single grey value threshold
from the range of grey value thresholds resulting in the
selection of an optimal ratio from the ratios of selected and
unselected objects determined for each grey value threshold
images as the optimal grey value threshold.

Claim 37 thus requires, essentially: (1) thresholding the filter image using a
range of grey value thresholds and calculating the number of selected and
unselected objects; (2) determining a ratio of selected and unselected objects
for each; and (3) designating a single threshold resulting in an optimal ratio
from the ratios of selected and unselected objects at each threshold.
Tibbe teaches:
The effect of the template-matching algorithm is presented in
FIG. 12. Again the counted number of objects in the image is
plotted against the threshold level. In this case, a 9x9 pixel
template was used with δp=2. The filter was applied on three
different cell images. The figures show that the threshold range,
for which there are a constant number of cells, is longer than in
the unfiltered case. Also, the curves have shifted to the left as a
result of the DC removal. This is an unexpected advantage since
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the plateau always has the same starting point regardless of the
background level in the original image. This discovery makes it
much easier to establish a predetermined threshold level that is
applicable to all images.

Tibbe ¶ 116. Figure 12 of Tibbe is reproduced below:

Figure 12 of Tibbe depicts threshold level curves of three
typical cell images. (a). Before filtering. The number of counted
cells is strongly dependent on the chosen threshold level. (b).
After filtering. The filter elongates the plateau of the threshold
curves, making it easier to establish a predetermined threshold
level for all images.

In other words, Tibbe teaches: (1) varying the threshold level; (2)
binarily plotting the counted number of objects versus uncounted objects in
the image at each threshold level (which determines the ratio of counted to
uncounted objects); and (3) determining the optimal threshold range for
which there are a constant number of cells. See also Tibbe Fig. 5 and
associated text. We agree with the Examiner that Tibbe thus teaches steps
(1)–(3) of claim 37, as described supra. Although Tibbe teaches
determining a range of optimal threshold values (corresponding to the
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flattened portion of the curve in Figure 12) rather than a single optimal
value, it also teaches that a value can be selected from this range. See id. at
¶ 116.
We acknowledge that Tibbe teaches additional steps pre-thresholding,
i.e., the application of the matched filter algorithm to the images. See, e.g.,
Tibbe ¶¶ 100–116. However, the language of the claim reciting “wherein
determining the optimal grey value threshold comprises…” does not
preclude the inclusion of such additional steps. See Crystal Semiconductor
Corp. v. TriTech Microelectronics Int’l, Inc., 246 F.3d 1336, 1348 (Fed. Cir.
2001) (holding that “[u]se of the transition ‘comprising’ in the language of a
claim creates a presumption ... that the claim does not exclude additional,
unrecited elements”).
We consequently find that Tibbe teaches the limitations of the claims
disputed by Appellant.

Issue 2
Appellant contends that that, in considering the cited references, a
person of ordinary skill in the art, would have had no objective reason to
modify the Tibbe with the teachings of Grinon. App. Br. 11.

Analysis
Appellant argues that Tibbe is directed to image acquisition used to
count the number of captured light-emitting cells. App. Br. 11. Appellant
asserts that the paragraphs in Tibbe cited by the Examiner to support the
assertion that Tibbe teaches methods for reducing the selection of an optimal
grey value threshold of an image for quantifying immobilized cells on a
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surface in fact, disclose image acquisition used to count the number of
captured light-emitting cells. Id. (citing Tibbe Abstr., ¶ 12).
Appellant asserts that Grinon teaches that filtering the background
noise eliminates various effects, that filtered light is integrated over a period
of time, and that integration results are compared to a threshold that depends
on the detection sensitivity required by the user. App. Br. 12. Appellant
asserts that Grinon does not teach that an optimal image threshold should be
found, much less how such threshold could be acquired. Id.
We are not persuaded by Appellant’s arguments. Grinon is directed,
in relevant part, to:
A method of fast microbiological analysis of a support liable to
contain microorganisms, characterized in that it includes:

- the step of procuring a device of the above kind;

- the step of placing said support at said predetermined location;

- the step of spraying said reagent onto said support;

- simultaneously with said spraying step, the step of measuring the
quantity of light emitted in response to said reagent.

Grinon ¶ 15. Furthermore, Grinon teaches, with respect to the last step, that:
Before effecting the lysis of the microorganisms, with the
carriage 5 in the first processing position and at the same time as
spraying the reagent, the closure member 52 is opened in order
for the photomultiplier 50 to measure continuously the quantity
of light emitted by the membrane and its immediate surroundings
during the spraying step. The microcomputer compares this
measurement to a predetermined threshold value and spraying of
the reagent continues for as long as the quantity of light measured
by the photomultiplier is above that threshold.

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Grinon ¶ 111.
The Examiner relies upon Grinon as teaching a method for the
automatic analysis of microorganisms by applying a predetermined volume
of a sample to a filter, staining the cells with a label, imaging the bound
labels on the filter, and analyzing the filter image for quantification of the
cells on the filter. Final Act. 5 (citing Grinon Abstr., ¶¶ 23, 42–43, 79). The
Examiner reasons that a skilled artisan would have been motivated to
combine the teachings of Tibbe and Grinon because Tibbe teaches methods
for reducing the selection of an optimal gray value threshold of an image for
quantifying immobilized cells on a surface, and Grinon teaches methods for
imaging immobilized cells on a surface in order to quantify the cells. Id.
(citing Tibbe Abstr., ¶ 21; Grinon Abstr.). Furthermore, the Examiner also
finds that Grinon teaches the importance of reducing background noise and
for finding an optimal image threshold, and Tibbe is directed to methods for
the reduction of background noise and for finding an optimal image
threshold. Id. (citing Grinon ¶¶ 95–96).
We agree with the Examiner’s findings and conclusions and adopt
them. In particular, Grinon teaches that: “The quantity of light filtered in
this way is then integrated from time t1 to time t2 (operation 93) and a test
(94) compares the results of this integration to a threshold corresponding to
the microorganism detection sensitivity required by the user.” Grinon ¶ 96.
Tibbe, as we have explained, teaches determining an optimal threshold value
for ascertaining the optimal threshold value ratio of an image of immobilized
microorganisms. We therefore conclude that the Examiner has articulated a
rational reason to combine the references, and we affirm the rejection of the
claims on this ground.
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B. Rejection of claims 37–41, 44–46 and 51–53
Appellant argues that Miller and Pattyn fail to cure the alleged
deficiencies of Tibbe and Grinon that Appellant argue supra. App. Br. 12.
We have explained why we do not find Appellant’s arguments with
respect to Tibbe and Grinon persuasive, and we incorporate the same
reasoning with respect to these claims. We consequently affirm the
Examiner’s rejection of claims 37–41, 44–46 and 51–53.

CONCLUSION
The Examiner’s rejection of claims 37–41, 44–46, and 51–53 under
35 U.S.C. § 103(a) is 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)(1)(iv).

AFFIRMED
Claims
Rejected
35 U.S.C. § Basis Affirmed Reversed
37–41, 46 103(a) Tibbe, Grinon 37–41, 46
37–41, 44–46,
51–53
103(a) Tibbe, Grinon,
Miller, Pattyn
37–41, 44–46,
51–53

Overall
Outcome
37–41, 44–46,
51–53