Ex Parte MORGANA

Patent Trials and Appeals Board

Sep 25, 2014

12142174 - (D) (P.T.A.B. Sep. 25, 2014)

UNITED STATES PATENT AND TRADEMARK OFFICE
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BEFORE THE PATENT TRIAL AND APPEAL BOARD
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Ex parte STEPHEN C. MORGANA
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Appeal 2012-008462
Application 12/142,174
Technology Center 2600
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Before: JOSEPH L. DIXON, JAMES R. HUGHES, and ERIC S. FRAHM,
Administrative Patent Judges.

DIXON, Administrative Patent Judge.

DECISION ON APPEAL

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Application 12/142,174
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STATEMENT OF CASE
Appellant appeals under 35 U.S.C. § 134 from a rejection of claims 1–
5, 8–12, and 15–19. We have jurisdiction under 35 U.S.C. § 6(b).
We reverse.

The claims are directed to reducing the number of vertices in a
clustered Delaunay mesh when generating a color gamut. Claim 1,
reproduced below, is illustrative of the claimed subject matter:
1. A method for reducing complexity when constructing a
color gamut with a Delaunay mesh, the method comprising:

receiving, into a memory, a plurality of color data points
of image pixels of a color image;

defining an n-dimensional Delaunay mesh of individual
point clusters;

performing cluster analysis on said received plurality of
color data points to determine which new point clusters need to
be added to said mesh and which existing point cluster in said
mesh is to receive a color data point;
restructuring said n-dimensional Delaunay mesh comprising:
for each point cluster in said Delaunay mesh,
determining a representative point to serve as a normal
vertex for said cluster; and

adding said normal vertex to said Delaunay mesh;

generating a color gamut from said Delaunay mesh;

storing said generated color gamut to said memory; and

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using said generated color gamut to model any of: the
gamut of said color image or the gamut of a color output device
used to render said color image.

REFERENCES
The prior art relied upon by the Examiner in rejecting the claims on
appeal is:
Cholewo
Migdal
US 6,480,301 B1
US 6,611,267 B2
Nov. 12, 2002
Aug. 26, 2003

Rizwan A. Siddiqui et al., Hierarchical Compression of Tetrahedral Meshes
Through Clustering and Vector Quantization (2007) (hereinafter “Siddiqui).

Hong Zhou et al., Energy-Based Hierarchical Edge Clustering of Graphs,
IEEE Pacific Visualisation Symposium, 55-61 (2008)(hereinafter “Zhou”).

REJECTIONS
The Examiner made the following rejections:
Claims 1, 8, and 15 stand rejected under 35 U.S.C §103(a) as being
unpatentable over Cholewo and Siddiqui.
Claims 2–4, 9–11, and 16–18 stand rejected under 35 U.S.C §103(a)
as being unpatentable over Cholewo, Siddiqui, and Migdal.
Claims 5, 12, and 19 stand rejected under 35 U.S.C §103(a) as being
unpatentable over Cholewo, Siddiqui, and Zhou.

ANALYSIS
The Examiner finds Siddiqui discloses the independent claim 1
limitations “restructuring said n-dimensional Delaunay mesh comprising:
for each point cluster in said Delaunay mesh, determining a representative
point to serve as a normal vertex for said cluster, and adding said normal
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vertex to said Delaunay mesh” (Ans. 5–6). Appellant contends Siddiqui
fails to disclose these limitations because “portions of the provided
translation of Siddiqui have, at best, unclear or ambiguous interpretations”
(Br. 9). We agree with Appellant.
Siddiqui discloses determining centroids for clusters in a Delaunay
mesh and generating error vectors from the centroids to vertex locations in
order to perform a compression algorithm on the mesh (See Siddiqui,
Abstract). The Examiner relies on the following disclosure in Siddiqui for
meeting the claimed “restructuring” limitations (Ans. 5–6, 22–23):
2.1.2 Finding the Central Node Frequency Values and Error
Vectors

K-means clustering technique after the creation of the
central nodes of the node coordinates of the centers are
determined. Central nodes in 3D space Voronoi diagram is
determined by subtracting the number of nodes in each center in
their area. The number of the node that determines the center
frequency. Classified according to the central nodes in the
nodes are sorted. Delaunay triangulation method, the wire
mesh structure is created due to the lack of importance of the
order of the node, instead of just the central nodes in the list of
key nodes in the use of frequencies will be enough.

(Siddiqui, sec. 2.1.2). We cannot ascertain from this disclosure exactly how
Siddiqui’s central node, or centroid, is used in creating a Delaunay mesh
because the language quoted above is not explicitly clear. Further, the
Examiner’s interpretation of the reference (Ans. 22–23) does not explain
why this language shows that Siddiqui’s central node is a representative
point in a cluster of points which is selected to serve as a normal vertex and
added to a Delaunay mesh, as recited in claim 1. Rather, one of ordinary
skill in the art could read the ambiguous language of the translated reference
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as disclosing that the central nodes in Siddiqui are merely abstractions
created from clusters of nodes. However, Appellant’s Specification defines
normal vertices as “data points obtained from image measurement sensors”
(Spec. ¶ 33). It is not self-evident from Siddiqui and the Examiner has not
explained why Siddiqui’s central nodes should be understood as being actual
sensed data points meeting the definition of normal vertices. Accordingly,
we find the Examiner has not shown that Siddiqui discloses “determining a
representative point to serve as a normal vertex for said cluster; and adding
said normal vertex to said Delaunay mesh,” as recited in claim 1.
We are, therefore, constrained by the record to find the Examiner
erred in rejecting independent claim 1, independent claims 8 and 15 which
recited commensurate limitations, and dependent claims 2–5, 9–12, and 16–
19 for similar reasons.

CONCLUSION
The Examiner erred in rejecting claims 1–5, 8–12, and 15–19 under
35 U.S.C. § 103(a).

DECISION
For the above reasons, the Examiner’s rejection of claims 1–5, 8–12,
and 15–19 is reversed.

REVERSED

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