Ex Parte Lei et al

Board of Patent Appeals and Interferences

Aug 25, 2008

10201420 (B.P.A.I. Aug. 25, 2008)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE BOARD OF PATENT APPEALS
AND INTERFERENCES
____________

Ex parte HUI LEI, YIMING YE, and PHILLIP S. YU

Appeal 2008-0720
Application 10/201,4201
Technology Center 2100
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Decided: August 25, 2008
____________

Before HOWARD B. BLANKENSHIP, JEAN R. HOMERE, and
JAY P. LUCAS, Administrative Patent Judges.

HOMERE, Administrative Patent Judge.

DECISION ON APPEAL
I. STATEMENT OF CASE
Appellants appeal under 35 U.S.C. § 134 from the Examiner’s final
rejection of claims 1 through 12 and 14 through 26. Claim 13 has been
canceled. We have jurisdiction under 35 U.S.C. § 6(b). We affirm.

1 Filed on July 22, 2002. The real party in interest is IBM Corp.
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Appellants invented a method and system for sorting inline web
objects (e.g., images, sound, and video clips) embedded in a hyper-text
markup language (HTML) document being deployed to a client device.
(Spec. 4.) As depicted in Figure 2, the system first constructs a content
feature vector and an attribute feature vector for the HTML document. The
content feature vector characterizes the content of the webpage by extracting
from the HTML text certain words based on their frequency of occurrence in
the text. The attribute feature vector characterizes attributes of the webpage
document such as the location, type, and size of the webpage. (Spec. 6.)
The system then constructs a content feature vector and an attribute feature
vector for each inline web object included in the HTML document. (Id. 6-
7.) Further, the system compares the computed feature vectors for the
HTML document with those feature vectors for each inline object
encountered in the HTML document. (Id. 7.) The system subsequently sorts
the inline objects based on their degree of similarity with the HTML
document as indicated by the computed distance between the compared
feature vectors. (Id.)
Independent claim 1 further illustrates the invention. It reads as
follows:
1. A computer-implemented method for prioritizing information
items embedded in a document for deployment on a client device,
comprising the steps of:

a) constructing one or more feature vectors for said embedding
document, said feature vectors including: a content feature vector and an
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attribute feature vector, or both, said content feature vector characterizing
content of said document, said attribute feature vector characterizing
attributes of the document;

b) constructing one or more feature vectors for an embedded item in
said document, said feature vectors including: a content feature vector and
an attribute feature vector, or both;

c) computing a similarity measure between said item embedded in
said document and said embedding document, said similarity measure based
on a comparison of a respective content feature vector, an attribute feature
vector, or both, constructed for each embedded item and a respective content
feature vector, an attribute feature vector, or both, constructed for said
embedding document; and,

d) assigning a priority to said embedded item for deployment on the
client device based on said computed similarity measures.

The Examiner relies on the following prior art:

Li US 6,345,279 B1 Feb. 05, 2002

Cullen US 6,397,213 B1 May 28, 2002

Panda US 6,904,560 B1 Jun. 07, 2005
(filed Mar. 23, 2000)

The Examiner rejects the claims on appeal as follows:
A. Claims 1 through 3, 5 through 12, 14 through 18, and 20
through 26 stand rejected under 35 U.S.C. § 103 as being unpatentable over
the combination of Panda and Li.
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B. Claims 4 and 19 stand rejected under 35 U.S.C. § 103 as being
unpatentable over the combination of Panda, Li, and Cullen.

FINDINGS OF FACT
The following findings of fact (FF) are supported by a preponderance
of the evidence.

Panda
1. Panda discloses a publishing software for identifying key images in
a document to organize them for different types of presentations. (Col. 1,
ll. 19-25.)
2. As depicted in Figure 2, Panda discloses an image selection
process (114) that extracts keywords that occur at high frequency, exceeding
a set threshold, within a document. (Col. 2, ll. 51-58.)
3. The image selection process (114) then sorts the extracted
keywords according to a predetermined criterion thereby ranking the
importance of the keywords within the document. (Col. 3, ll. 1-5.)
4. The image selection process (114) then identifies each image
within the document, and subsequently determines the location of each
identified image along with its directly associated descriptive text (image
text) within the document. (Col. 3, ll. 23-26.)
5. The image selection process (114) then identifies key images
within the document by comparing the image text with the extracted
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keywords to generate a proximity factor reflecting the correlation between
each collected image and an extracted keyword. (Col. 3, ll. 37-49.)
6. The image selection process (114) selects the key images in the
document according to an image metric, which combines the proximity
factor with the corresponding rank of the extracted keyword. (Col. 3, ll. 52-
56.)

Li
7. Li discloses a method and system of adapting multimedia content
of a document to a client device. Particularly, the system converts the
multimedia content into a plurality of transcoded content versions having
different modalities and resolutions associated therewith. The system then
filters out versions that are incompatible with the client device, and allocates
the remaining versions thereto. (Abstract, Col. 2, ll. 22-26.)
8. Li discloses that the author of a Web document may assign a
priority to the multimedia content thereof. (Col. 7, ll. 3-7.)

PRINCIPLES OF LAW
OBVIOUSNESS
Appellants have the burden on appeal to the Board to demonstrate
error in the Examiner’s position. See In re Kahn, 441 F.3d 977, 985-86
(Fed. Cir. 2006) (“On appeal to the Board, an applicant can overcome a
rejection [under § 103] by showing insufficient evidence of prima facie
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obviousness or by rebutting the prima facie case with evidence of secondary
indicia of nonobviousness.”) (quoting In re Rouffet, 149 F.3d 1350, 1355
(Fed. Cir. 1998)).
Section 103 forbids issuance of a patent when “the differences
between the subject matter sought to be patented and the prior art are
such that the subject matter as a whole would have been obvious at
the time the invention was made to a person having ordinary skill in
the art to which said subject matter pertains.”

KSR Int'l Co. v. Teleflex Inc., 127 S. Ct. 1727, 1734 (2007).

The question of obviousness is resolved on the basis of underlying factual
determinations including (1) the scope and content of the prior art, (2) any
differences between the claimed subject matter and the prior art, (3) the level
of skill in the art, and (4) where in evidence, so-called secondary
considerations. Graham v. John Deere Co., 383 U.S. 1, 17-18 (1966). See
also KSR, 127 S. Ct. at 1734 (“While the sequence of these questions might
be reordered in any particular case, the [Graham] factors continue to define
the inquiry that controls.”)
“The combination of familiar elements according to known methods
is likely to be obvious when it does no more than yield predictable results.”
Leapfrog Enter., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161 (Fed. Cir.
2007) (quoting KSR Int’l v. Teleflex, Inc., 127 S. Ct. 1727, 1739 (2007)).
“One of the ways in which a patent's subject matter can be proved obvious is
by noting that there existed at the time of invention a known problem for
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which there was an obvious solution encompassed by the patent's claims.”
KSR, 127 S. Ct. at 1742.
The reasoning given as support for the conclusion of obviousness can
be based on interrelated teachings of multiple patents, the effects of demands
known to the design community or present in the marketplace, and the
background knowledge possessed by a person having ordinary skill in the
art. KSR, 127 S. Ct. at 1740-41. See also Dystar Textilfarben GmbH & Co.
Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1368 (Fed. Cir. 2006).

ANALYSIS
Independent claim 1 recites in relevant part (1) constructing one or
more feature vectors for an embedding document, (2) constructing one or
more feature vectors for each embedded item, (3) and comparing the feature
vector of the embedding document with a respective feature vector of the
embedded item, the feature vectors including a content feature vector and an
attribute feature vector, or both. (Claims Appendix.) Appellants argue that
the combination of Panda and Li does not teach these limitations. (App. Br.
15-16.) Particularly, Appellants argue that the combination of Panda and Li
teaches a proximity factor for displaying images in different formats,
whereas the claimed invention requires prioritizing multimedia content by
computing similarity measures between feature vectors of an embedding
document and the multimedia content thereof. (Id.)
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In response, the Examiner avers that Panda’s disclosure of comparing
an image text with document keywords to generate corresponding proximity
factors, teaches the cited limitations. (Ans. 19-24.)
Therefore, the pivotal issue before us is whether one of ordinary skill
in the art would have found that Panda’s comparison of an image text with
keywords within a document to generate corresponding proximity factors
teaches the limitations in question, as recited in independent claim 1. We
answer this inquiry in the affirmative.
We begin by considering the scope and meaning of the limitation
“constructing one or more feature vectors for said embedding document,
said feature vectors including: a content feature vector and an attribute
feature vector, or both...” which must be given its broadest reasonable
interpretation consistent with Appellant’s disclosure, as explained in In re
Morris, 127 F.3d 1048, 1054 (Fed. Cir. 1997):
[T]he PTO applies to the verbiage of the proposed claims the
broadest reasonable meaning of the words in their ordinary
usage as they would be understood by one of ordinary skill in
the art, taking into account whatever enlightenment by way of
definitions or otherwise that may be afforded by the written
description contained in the applicant's specification.
Id. at 1054. See also In re Zletz, 893 F.2d 319, 321 (Fed. Cir. 1989) (stating
that “claims must be interpreted as broadly as their terms reasonably allow.”
Appellants’ Specification states the following:
FIG. 2 is a flow chart depicting the prioritization process 1050
of the present invention according to a preferred embodiment. In step
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2010, there involves the step of constructing a content feature vector
and an attribute feature vector for the embedding web (e.g., HTML)
page. The content feature vector characterizes the content of the page.
It is generally known to those of ordinary skill in the art how such a
content feature vector may be constructed. The content feature vector,
for example, may be composed of words extracted from the HTML text
where each word is given a weight equal to the frequency of the
word's appearances in the page. The attribute feature vector
characterizes the attributes of the embedding page. The attributes
refer to the location, and the type and size, etc. of the page.
[Emphasis added.]
(Spec. 6, ll. 10-19.)

In step 2040, a content feature vector and an attribute feature
vector are constructed for the inline object. The content feature
vector characterizes the content of the inline object. According to a
preferred embodiment of the present invention, the content feature
vector for an inline object is built from text that appears in a window
surrounding the immediately enclosing HTML element (URL
reference). For example, in one embodiment, this window may
comprise the enclosed URL reference plus a predetermined number of
words, e.g., 50 words surrounding the enclosed inline object (i.e.,
before and after the enclosed URL reference). [Emphasis added.]
(Spec. 7, ll. 3-11.)

Our reviewing court further states, “the ‘ordinary meaning’ of a claim
term is its meaning to the ordinary artisan after reading the entire patent.”
Phillips v. AWH Corp., 415 F.3d 1303, 1321 (Fed. Cir. 2005.)
Upon reviewing Appellants’ Specification, we find that the
construction of the feature vectors includes constructing both the content
feature vector and the attribute feature vector for the embedding document
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and the embedded items. We also find that the construction of the content
feature vector of the embedding document is exemplified as extracting from
an HTML document words based on their frequency of occurrence therein.
Similarly, we find that the content feature vector of the embedded item is
characterized as the text that appears in a window surrounding the embedded
item. Further, we note that the claim does not recite multimedia items.
Rather, the claim recites embedded items. We find, however, no definition
in Appellants’ Specification for an embedded item. We therefore construe it
consistently with its ordinary meaning. We also note that the claim recites
constructing one or more feature vectors including a content feature vector
and an attribute feature vector or both. Given, this alternative language, we
broadly but reasonably construe the claimed limitation in question to require
(1) constructing a content feature vector for an embedding document, (2)
constructing a content feature vector for each embedded item in the
document, (3) and subsequently comparing the constructed content feature
vector of the embedding document with the content feature vector or each
embedded item to thereby prioritize the embedded items.
As set forth in the Findings of Fact section, Panda discloses extracting
frequently occurring keywords from a document, and ranking the extracted
keywords based on a predetermined criterion. (FF 2-3.) Further, Panda
discloses identifying image text within the document, and comparing each
identified image text with the extracted keywords to generate a proximity
factor for each image, thereby correlating said each image with an extracted
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keyword. (FF. 4-5.) Additionally, Panda discloses ranking the proximity
factors based on the rank of the extracted keywords. (FF. 6.) One of
ordinary skill in the art would readily recognize that, as exemplified in
Appellants’ Specification, Panda’s extraction of frequently occurring
keywords from the document teaches constructing a content feature vector
for the embedding document. Similarly, the ordinarily skilled artisan would
recognize that, as characterized in Appellants’ Specification, Panda’s
identification of image text for each image within the document teaches
constructing a content feature vector for each embedded item within the
document. Consequently, the ordinarily skilled artisan would readily
appreciate that Panda’s comparison of the content feature vectors to generate
proximity factors thereby prioritizing the images within the document
teaches the claimed limitation in question.
Appellants further argue that Li does not remedy Panda’s deficiencies,
and there is insufficient rationale for properly combining the cited
references. (App. Br. 15-17.) These arguments are unavailing. As noted
above, we find no such deficiencies in Panda for Li to cure. Further, as set
forth in the Findings of Facts, Li discloses a method for adapting multimedia
contents of an HTML document to a client device. (FF. 7-8.) One of
ordinary skill in the art would have readily appreciated that Panda with Li
disclose known elements that are performing their routine functions to
predictably result in a system that prioritize embedded items in an HTML
document. It follows that Appellants have not shown that the Examiner
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erred in concluding that the combination of Panda and Li renders
independent claim 1 unpatentable.
Appellants do not provide separate arguments with respect to the
rejection of claims 2, 3, 5 through 12, 14 through 18, and 20 through 26.
Consequently, these claims fall together with independent claim 1. 37
C.F.R. § 41.37(c)(1)(vii).
Regarding dependent claims 4 and 19, Appellants argue that Cullen
does not cure the deficiencies of Panda and Li, as discussed in their
discussion of independent claim 1. (App. Br. 18.) We find no such
deficiencies in the Panda and Li combination for Cullen to cure. It follows
that Appellants have not shown that the Examiner erred in concluding that
the combination of Panda, Li, and Cullen renders claims 4 and 19
unpatentable.
CONCLUSION OF LAW
Appellants have not shown that the Examiner erred in concluding that
claims 1 through 12 and 14 through 26 are unpatentable under 35 U.S.C.
§ 103.
DECISION
We affirm the Examiner’s decision rejecting claims 1 through 12 and
14 through 26.
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).

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AFFIRMED

ce

Steven Fischman
Scully, Scott, Murphy & Presser
400 Garden City Plaza
Garden City NY 11530