Ex Parte Serlie

Patent Trial and Appeal Board

Mar 22, 2017

13880858 (P.T.A.B. Mar. 22, 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.
13/880,858 09/11/2013 Iwo Willem Oscar Serlie 2010P01079WOUS 6088
24737 7590 03/24/2017
PTTTT TPS TNTFT T FfTTTAT PROPFRTY fr STANDARDS
EXAMINER
465 Columbus Avenue DELANEY, MOLLY K
Suite 340
Valhalla, NY 10595 ART UNIT PAPER NUMBER
2666
NOTIFICATION DATE DELIVERY MODE
03/24/2017 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):
marianne. fox @ philips, com
debbie.henn @philips .com
patti. demichele @ Philips, com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte IWO WILLEM OSCAR SERLIE
Appeal 2016-005705
Application 13/880,858
Technology Center 2600
Before JASON V. MORGAN, NABEEL U. KHAN, and
KAMRAN JIVANI, Administrative Patent Judges.
JIVANI, Administrative Patent Judge.
DECISION ON APPEAL
Appellant1 seeks our review, under 35 U.S.C. § 134(a), of the
Examiner’s final decision rejecting claims 1, 3—13, and 15. We have
jurisdiction under 35 U.S.C. § 6(b).
We REVERSE.
1 According to Appellant, the real party in interest is the Koninklijke
Philips, N.V. App. Br. 2.
Appeal 2016-005705
Application 13/880,858
STATEMENT OF THE CASE
The present invention is a “volume visualization . . . system
comprising a feature detector for detecting a feature like a border between
body and air (e.g. skin) in an image volume dataset.” Abst.
Claim 1 is illustrative:
1. A system for generating a two-dimensional of an image
volume dataset, comprising:
a distance computing subsystem for computing a distance from
a feature detected in the image volume data set to an image
element of the image volume dataset;
a weighting subsystem for weighting an image element value of
the image element, based on the distance, to obtain a weighted
image element value;
a view generator for generating the two-dimensional view of the
image volume dataset, based on the weighted image element
value, wherein the two-dimensional view comprises a
projection, and a pixel value of the view is based on the weighted
image element value.
REJECTIONS2
Claims 1, 3—13, and 15 stand rejected under 35 U.S.C. § 103(a) as
unpatentable over Haque (US 2007/0237379; Oct. 11, 2007) and Olstad et
al., 3D Transvaginal Ultrasound Imaging for Identification of Endometrial
Abnormality, Proc. SPIE Vol. 2432, Medical Imaging 1995: Physics of
Medical Imaging, pp. 543—53 (May 8, 1995). Ans. 2—6.
2 The Examiner has withdrawn a rejection under 35 U.S.C. § 112. Ans. 6.
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Appeal 2016-005705
Application 13/880,858
ANALYSIS
All claims stand rejected as obvious over Haque and Olstad.
Appellant argues the Examiner does not establish a teaching or suggestion of
the independent claims’ distance computing and weighting; e.g., claim 1 ’s
“computing a distance from a feature detected in the image volume data set
to an image element of the image volume dataset” and “weighting an image
element value of the image element, based on the distance, to obtain a
weighted image element value.” App. Br. 7.
The Examiner finds Haque and Olstad together meet the above recited
claim limitations. Final Act. 10—11, 14—17. Haque teaches a 2-D projection
image iMIP(y, z) (“maximum intensity projection image”) that contrastingly
displays, by variation of brightness, the overlapping tissues (e.g., blood
vessels) captured by a 3-D image I(x, y, z) (which is, itself, a stack of 2-D
images SI, S2,. . . Sm). Haque 3—7, 29-30, 43; Figs. 4A-C. The 2-D
projection image enhances/brightens targeted tissue (e.g., a targeted blood
vessel) by entirely suppressing the contribution of overlying tissue and
attenuating the contribution of underlying tissue. Id. at || 31—32, 37—38.
The intensity for each of the 2-D projection image’s pixels (for each y-z
position) is calculated as a “weighted sum” of the 3D-image’s voxels along
the respective x-direction “projection line.” Id. at 132. The voxels have:
no weight at a depth of x less than a (“xa”). Id.
Olstad teaches an algorithm for defining tissue surfaces within a 3-D
image. Olstad, abst. “As the user moves to neighboring 2D-slices [of the
3-D image], the contour from the previous edge detection result is copied
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Application 13/880,858
and readjusted to the new 2D image[;] . . . allowing] for . . . extraction of a
3D surface representation of the [tissue] boundary.” Id. at 546.
The Haque-Olstad combination detects and enhances a tissue surface
(hereinafter “targeted surface”) within Haque’s 3-D image. Final Act. 10-
11; Ans. 7. More particularly, the targeted surface is detected in Haque’s
3-D image via Olstad’s teachings and then enhanced in Haque’s 2-D
projection image via Haque’s teachings. Id. The Examiner reads claim 1 ’s
detected feature on the targeted surface. Id.
The Examiner reads the image element of claim 1 on Haque’s “image
surface.” Id. In the Haque-Olstad combination, Haque’s image surface is
located at x=0; such that, for each pixel of the 2-D projection image, there is
a projection line distance a between the image surface and targeted surface
(located at departure point x=a). See Ans. 7—8. The Examiner finds
distance a meets the claimed computed distance between the image element
and detected feature because distance a is computed as a function of the
given y-z position, namely computed via a function expressed by Haque’s
paragraph 32 as “a(y, z).” Id.
Appellant argues in part:
[Haque’s weighted sums] depend on relative positioning, not
distance, of x with respect to a(y, z), where a(y, z) is a set
distance from a departure point of a projection line in the x
direction to an image surface (not to a feature detected in the
image volume data set), and varies depending on (y, z) of a
three-dimensional image data I(x, y, z). See paras. [0031]—
[0032], . . . Indeed, the actual distance between x and a(y, z)
appears to be irrelevant, since x being greater than, less than or
equal to a(y, z) dictates the weighting function
Wm(x). . . . Then, a series of images Si (where i = 1, 2, . . . , m)
may be captured by performing maximum intensity projection
(MIP) in the x direction on a value obtained by multiplying three
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Application 13/880,858
dimensional image data I(x, y, z) with the appropriate weighting
function Wm(x). There is no teaching of computing the distance
from a detected feature to an image element (both in the image
volume data set).
App. Br. 8 (emph. omitted).
We agree that the Examiner has not shown a teaching or suggestion of
the claimed computed distance between the detected feature (read on
Haque’s targeted surface of depth x=a, i.e., “departure point”) and image
element (read on Haque’s image surface of depth x=0). As argued by
Appellant (above block quote), though Haque expresses a projection line
(x-direction) distance of “a(y, z)” between the image surface and targeted
surface, this disclosure merely reflects that the distance a varies throughout
the 3-D image. Stated differently, the “a(y, z)” terminology reflects that a
projection line distance a between the image surface and targeted surface
will vary as the projection line is translated in the y and z directions. Even
assuming the varying distances a(y, z) are known—i.e., the image surface
and departure point depths are known throughout Haque’s 3D-image (as
respectively x=0 and x=a)—that is not to say the distances a(y, z) are
computed from the respective y-z positions of the projection lines. Even
further assuming a disclosed need and/or step for computing the distances
a(y, z), no such disclosure is established by the Examiner’s finding that: “In
Haque [0033], the distance is calculated between the projection point
[(i.e., at x=a)] and the image surface, as it is stated that this variable is
dependent on the positioning of (y, z) this distance is calculated.” Ans. 7—8.
As also argued by Appellant (above block quote), there is no apparent
need to compute the projection line distances a(y, z) between the image
surface and targeted surface. Rather, there is only an apparent need to know
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the targeted surface’s varying depth such that: the pixels thereover can be
given no weight (entirely suppressed); all pixels thereon can be given an
initial weight (greatest brightness); and all pixels thereunder can be given
exponentially less weight as their depth increases (dimmed with depth). See
supra at 3 (explaining Haque). Thus, there is only an apparent need to
compute respective distances between the targeted surface and pixels
thereunder, so as to thereby determine each such pixel’s exponential
decrease in weight (see id.).
For the foregoing reasons, we reverse the rejection of independent
claims 1,13, and 15. Because the same error persists for the dependent
claims, we also reverse the rejection of claims 3—12.
DECISION
We reverse the Examiner’s rejection of claims 1, 3—13, and 15.
REVERSED
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