Ex Parte Kindo et al

Board of Patent Appeals and Interferences

May 13, 2008

09989151 (B.P.A.I. May. 13, 2008)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________________

BEFORE THE BOARD OF PATENT APPEALS
AND INTERFERENCES
____________________

Ex parte TOSHIKI KINDO, HIDEYUKI YOSHIDA,
and TAKEHIKO SHIDA
____________________

Appeal 2007-3780
Application 09/989,1511
Technology Center 2100
____________________

Decided: May 13, 2008
____________________

Before ALLEN R. MACDONALD, JAY P. LUCAS, and
ST. JOHN COURTENAY III, Administrative Patent Judges.

LUCAS, Administrative Patent Judge.

1 Application filed November 21, 2001. Appellants claim the benefit under
35 U.S.C. § 119 of Japan patent application JP2000-359,044, filed
11/27/2000. The real party in interest is Matusshita Electric Industrial Co.
Ltd.

Appeal 2007-3780
Application 09/989,151

2
DECISION ON APPEAL
STATEMENT OF CASE
Appellants appeal from a final rejection of claims 20 and 23 to 28
under authority of 35 U.S.C. § 134. The Board of Patent Appeals and
Interferences (BPAI) has jurisdiction under 35 U.S.C. § 6(b).
Appellants’ invention relates to an information distribution system
and method in which two content providers are managed so as to present
user information to a client filtered for his needs. The information from one
of the providers (e.g. news articles) is carefully scored and weighed against a
user profile of the user’s preferences, and undergoes a learning process; the
information from the other source (e.g., ads, perhaps related to the news
subjects) does not undergo a learning process of its own. In the words of the
Appellants:
In an information distribution system that rates distribution
information pieces from a distribution information provider based on
a personal profile to distribute to a client where the personal profile
has registered therewith various keywords contained in the
distribution information pieces provided from the distribution
information provider and evaluation values corresponding to the
keywords and the evaluation values are learned in advance based on
preferences of the client, distribution information pieces from another
distribution information provider different from the distribution
information provider are rated based on the personal profile to
distribute to the client.

(Spec., 49.)

Appeal 2007-3780
Application 09/989,151

3
Claim 20 is exemplary:
20. An information distribution system, comprising:

a profile storer that stores a personal profile that includes at least one
evaluation value of a keyword contained in distribution information
provided from a first information distribution provider, wherein the at least
one evaluation value is calculated based upon a user's past selection of
distribution information containing the keyword; and

an information distributor that rates the distribution information
provide from the first information distribution provider with the keyword
based on the personal profile and sends the distribution information to a
client, wherein said information distributor rates distribution information
provided from a second information distribution provider with a keyword
based on the personal profile and sends the distribution information to the
client, said information distributor comprising:

a first information filter that rates the distribution information from
the first information distribution provider based on the at least one
evaluation value included in the personal profile in correspondence to the
keyword contained in the distribution information, sends the distribution
information to the client, and performs a learning process that changes the at
least one evaluation value of the keyword contained in the distribution
information from said first information distribution provider in the personal
profile, based on the distribution information and preference information of
the client about the distribution information; and

a second information filter that rates the distribution information from
the second information distribution provider based on the at least one
evaluation value included in the personal profile in correspondence to the
keyword contained in the distribution information, and sends the distribution
information to the client, wherein said second information filter does not
perform the learning process based on the distribution information from the
second information distribution provider.
Appeal 2007-3780
Application 09/989,151

4
The prior art relied upon by the Examiner in rejecting the claims on
appeal is:
Driscoll US 5,717,913 Feb. 10, 1998
Klein US 5,872,850 Feb. 16, 1999
Ariyoshi US 6,408,288 B1 Jun. 18, 2002
(filed Feb. 24, 1998)

Rejections:
R1: Claims 20, 23, and 24 stand rejected under 35 U.S.C. § 103(a) for
being obvious over Klein in view of Ariyoshi and further in view of
Driscoll.
R2: Claims 25 to 28 stand rejected under 35 U.S.C. § 103(a) for being
obvious over Klein in view of Ariyoshi.
Appellants contend that the claimed subject matter is not rendered
obvious by Klein in combination with Ariyoshi or Ariyoshi and Driscoll, for
failure of the references to teach important claimed limitations. The
Examiner contends that each of the claims is properly rejected.
Rather than repeat the arguments of Appellants or the Examiner, we
make reference to the Brief and the Answer for their respective details.
Only those arguments actually made by Appellants have been considered in
this opinion. Arguments which Appellants could have made but chose not to
Appeal 2007-3780
Application 09/989,151

5

make in the Brief has not been considered and are deemed to be waived. See
37 C.F.R. § 41.37(c)(1)(vii) (2004).2
We affirm-in-part.
ISSUE
The issue is whether Appellants have shown that the Examiner erred
in rejecting the claims under 35 U.S.C. § 103(a). The issue turns on whether
the references teach the claimed limitations, specifically the limitation
concerning whether a second information filter is performing a learning
process.

FINDINGS OF FACT
The record supports the following findings of fact (FF) by a
preponderance of the evidence.
1. Appellants have invented an information distribution system that ranks
information and presents it to a client user. The information is from two
(or more) content providers. (Figs. 2, 10; pages 6, top and 35, top). A
typical use of this system and method is to score and rank newspaper

2 Appellants have not presented any substantive arguments directed
separately to the patentability of the dependent claims or related claims in
each group, except as will be noted in this opinion. In the absence of a
separate argument with respect to those claims, they stand or fall with the
representative independent claim. See In re Young, 927 F.2d 588, 590 (Fed.
Cir. 1991).

Appeal 2007-3780
Application 09/989,151

6
information pieces from a content provider (page 14, middle)
customizing the content for a user with a user profile stored in the
system. The information is updated by a learning process which
customizes the information to the needs of the client user. (Page 16,
bottom, and continuing). Along with the first information is provided to
the user a second type of information, typically advertising. (Page 8, l.
11+). The advertising is selected based on the same keywords as learned
for the client user. (Page 23, l. 16+). However, the advertising keyword
ranking is not separately learned, but rather relies on the processing that
is done for the related news information. (Page 27, l. 7+).
2. Klein teaches a system for efficiently filtering content for a user viewing
information, goods or services over the Internet. (Col. 1, l. 27). The
system stores one or more user profiles for each of a plurality of users,
with ratings updated either by explicit or inferred actions of the user.
(Col. 5, ll. 1-25). By reviewing the ratings of items by a plurality of
users, a generalized rating value for the item can be calculated. (Col. 5, l.
50). The validity of various ratings can be cross checked among users,
resulting in confidence factors for the ratings. (Col. 6, l. 7).
PRINCIPLES OF LAW
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
Appeal 2007-3780
Application 09/989,151

7
rejection [under § 103] by showing insufficient evidence of prima facie
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)).
References within the statutory terms of 35 U.S.C. § 103 qualify as
prior art for an obviousness determination only when analogous to the
claimed invention. In re Clay, 966 F.2d 656, 658 (Fed. Cir. 1992). Two
separate tests define the scope of analogous prior art: (1) whether the art is
from the same field of endeavor, regardless of the problem addressed and,
(2) if the reference is not within the field of the inventor's endeavor, whether
the reference still is reasonably pertinent to the particular problem with
which the inventor is involved. In re Deminski, 796 F.2d 436, 442 (Fed. Cir.
1986); see also In re Wood, 599 F.2d 1032, 1036 (CCPA 1979) and In re
Bigio, 381 F.3d 1320, 1325 (Fed. Cir. 2004). Furthermore, “‘there must be
some articulated reasoning with some rational underpinning to support the
legal conclusion of obviousness’ . . . [H]owever, the analysis need not seek
out precise teachings directed to the specific subject matter of the challenged
claim, for a court can take account of the inferences and creative steps that a
person of ordinary skill in the art would employ.” KSR Int’l v. Teleflex Inc.,
127 S. Ct. 1727, 1741 (2007) (quoting In re Kahn, 441 F.3d 977, 988 (Fed.
Cir. 2006)).
Appeal 2007-3780
Application 09/989,151

8
ANALYSIS
From our review of the administrative record, we find that the
Examiner has presented a prima facie case for the R1 and R2 rejections of
Appellants’ claims under 35 U.S.C. § 103. The prima facie case is presented
on pages 3 to 8 of the Examiner’s Answer.
In opposition, Appellants present a number of arguments. The first
argument, relevant to claims 20, 23, and 24, contends that
“Klein fails to disclose or suggest a filter which rates distribution
information based on evaluation values included in a stored profile,
but does not perform a learning process that changes the evaluation
values based on the distribution information and preference
information of a client about the distribution information, as recited in
Appellants’ claim 20” [emphasis added] (App. Br., 10, bottom).

The Examiner first asserts that “Klein and Ariyoshi combination does
not disclose the second filter that does not perform the learning process.
Driscoll discloses an information filtering system for retrieving relevant text
data from a database ([cites]). Driscoll clearly discloses the filtering of text
data in the database not based on the learning process”. (Answer 5, top).
Thus, in restating the rejection the Examiner is relying on Driscoll to teach a
first information filter that “performs a learning process that changes the at
least one evaluation value of the keyword …” and a second information
filter that “does not perform the learning process…” as required by Claim
20. However, as shown in Figure 1, the information filter in Driscoll does
contain a modification loop #26 that performs the learning process. The
Appeal 2007-3780
Application 09/989,151

9
Examiner has not presented evidence in Driscoll for the differentiated
learning process.
Later in the Examiner’s Answer, the Examiner points to Klein as
being the reference which teaches the two filters, one of which learns and
one of which does not learn. (Answer 9, top). While we agree that Klein
teaches a system with a multiple number of users, and multiple filters for the
various data stores for each of the users (see FF #2 above), we fail to find in
any of the Klein citations proffered by the Examiner the two filters, one of
which performs a learning process and one of which does not perform the
learning process.
We thus agree with the Appellants that the Examiner has erred in
rejecting claims 20, 23, and 24 over the cited art. As claim 28 contains a
limitation similar in scope to that of the three other claims just listed, we find
error in the rejection of that claim as well.
Appellants argue against the rejection R2 of claims 25, 26, and 27
“that Kein’s system merely rates items (such as sound recordings, movies,
restaurants, vacation destinations, novels, or World Wide Web pages), but
does not send these items to a client, as recited in Appellants’ claims 25 and
26”. (App. Br. 17). Appellants’ claims clearly recite that the distribution
information is sent to the client, not necessarily the items themselves. In
Klein, column 1, lines 20 to 46, it is clearly stated that the prior art teaches
sending rated information to the users. Various filtering methods are then
described.
Appeal 2007-3780
Application 09/989,151

10
Appellants also argue that the Klein and Ariyoshi references cannot be
properly combined, as there is no motivation for one to use the two
teachings. As both Klein and Ariyoshi are addressed to the same field of
endeavor (filtering information over the Internet) we find that the
combination is proper. (See In re Clay cited above.)
We do not find error with the Examiner’s rejection of claims 25, 26,
and 27.

CONCLUSION OF LAW
Based on the findings of facts and analysis above, we conclude that
the Examiner erred in rejecting claims 20, 23, 24, and 28. We do not find
error in the rejection of claims 25 to 27.

DECISION
The Examiner's rejection of claims 20, 23, 24, and 28 is reversed. The
rejection of claims 25 to 27 is affirmed.

Appeal 2007-3780
Application 09/989,151

11
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-IN-PART

rwk

GREENBLUM & BERNSTEIN, P.L.C.
1950 ROLAND CLARKE PLACE
RESTON VA 20191