Ex Parte Kurapati

Board of Patent Appeals and Interferences

Feb 12, 2010

09736908 (B.P.A.I. Feb. 12, 2010)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE BOARD OF PATENT APPEALS
AND INTERFERENCES
____________

Ex parte KAUSHAL KURAPATI
____________

Appeal 2009-006709
Application 09/736,908
Technology Center 2400
____________

Decided: February 12, 2010
____________

Before MAHSHID D. SAADAT, THOMAS S. HAHN, and ELENI
MANTIS MERCADER, Administrative Patent Judges.

MANTIS MERCADER, Administrative Patent Judge.

DECISION ON APPEAL
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STATEMENT OF THE CASE
Appellant appeals under 35 U.S.C. § 134(a) from the Final Rejection
of claims 1-22. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.
Rather than reiterate the arguments of Appellant and the Examiner,
reference is made to the Brief (filed May 6, 2008) and the Answer (dated
July 21, 2008) for the respective details. Only those arguments actually
made by Appellant have been considered in this decision. Arguments which
Appellant could have made but chose not to make in the Brief have not been
considered and are deemed to be waived. See 37 C.F.R. § 41.37(c)(1)(vii).

Appellant’s Invention
Appellant’s invention is directed to a television programming
recommender for generating recommendations for one or more items based
on the consistency with which an item was selected relative to the number of
times the item was offered. The present invention adjusts a conventional
program recommender score based on a consistency metric. See Spec. 3 and
Fig. 4.
Claim 1 is illustrative of the invention and reads as follows:
1. A method for recommending items using a recommending
device, comprising the steps of:
obtaining a list of one or more available items;
obtaining a recommendation score, R, for said one or more available
items;
calculating, using a processor of the recommending device, an
adjustment, A, to said recommendation score, R, based on a consistency
which is a ratio of an item being selected by a user relative to the number of
times the item was offered, wherein the number of times the item was
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offered is greater than one, and wherein the number of times the item was
offered and the number of times the item was selected by the user are stored
in a memory;
generating, using said processor, a combined recommendation score,
C, based on said recommendation score, R, and said adjustment, A; and
displaying said list on a display unit, wherein said items are displayed
in order based on a value of said combined recommendation score, C.

The Examiner’s Rejections
The Examiner’s Answer cites the following prior art references:
Herz US 5,758,257 May 26, 1998

Ukai US 7,096,486 B1 Aug. 22, 2006

1. The Examiner rejected claims 1-2, 4, 7-8, 10-12, 14, 17-18, and
20-22 under 35 U.S.C. § 102(e) as being anticipated by Ukai.
2. The Examiner rejected claims 3, 5-6, 9, 13, 15-16, and 19 under
35 U.S.C. § 103(a) as being unpatentable over Ukai in view of Herz.
We note that Appellant argues rejected claims 1-2, 4, 7-8, 10-12, 14,
17-18, and 20-22 together as a group, making particular reference solely to
independent claim 1. See Br. 6-10. Accordingly, we select claim 1 as
representative. See 37 C.F.R. § 41.37(c)(1)(vii).
We further note that Appellant argues rejected claims 3, 5-6, 9, 13,
15-16, and 19 as a group, making particular reference solely to claim 3. See
Br. 10-11. Accordingly, we select claim 3 as representative. See 37 C.F.R.
§ 41.37(c)(1)(vii).

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ISSUES
With respect to claim 1, Appellant (Br. 7) argues that Ukai’s view
scores 502 and 503 are not used to recommend programs for recording and
future viewing. Appellant (Br. 7) further argues that while Ukai’s Table 700
is used to calculate a viewer’s preference measure and to schedule programs,
Table 700 is created based on Table 600 and the database 300, and neither
include the view scores 502 and 503.
Appellant (Br. 7-8) asserts that the view scores 502 and 503 are
simply historical viewing records for the program and not recommendation
scores. Appellant (Br. 8) also argues that the view measure 504 does not
constitute an adjustment to the view scores 502 and 503. Furthermore,
Appellant (Br. 9) argues that the view measure 504 for a program is the
average viewing length based on the total length of the program, and not a
ratio of an item being selected by a user relative to the number of times the
item was offered and thus, does not constitute “a consistency” as recited in
claim 1.
With respect to claim 3, Appellant (Br. 10-11) argues that Herz does
not teach that the recommendation score is provided by an explicit program
recommender, but only that a customer profile is created based on a
customer questionnaire.
Thus, the pivotal issues before us are:
(1). with respect to claim 1, whether Appellant has demonstrated that
the Examiner erred in determining that Ukai teaches the limitations of:
“calculating, using a processor of the recommending device, an adjustment,
A, to said recommendation score, R, based on a consistency which is a ratio
of an item being selected by a user relative to the number of times the item
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was offered, wherein the number of times the item was offered is greater
than one” and “generating, using said processor, a combined
recommendation score, C, based on said recommendation score, R, and said
adjustment, A;” and
(2). with respect to claim 3, whether Appellant has demonstrated that
the Examiner erred in determining that Herz teaches that the
“recommendation score, R, is provided by an implicit program
recommender.”
FINDINGS OF FACT
The record supports the following relevant findings of fact (FF) by a
preponderance of the evidence:
1. It remains uncontested by Appellant that Ukai’s Table 700 is used to
calculate a viewer’s preference based on the Table 600 and the
database 300 (Br. 7).
2. Ukai teaches that the viewer’s view history is reviewed to determine
the viewer’s “favorite program” and the viewer is informed of the
result of presumption so that the viewer’s selecting work can be
lightened (col. 2, ll. 11-34).
3. Ukai further teaches that the view history table 500 includes the
program name 501, the view scores 502 and 503, and the program
view measure 504 obtained by dividing the sum of the view scores by
the number of serials of the series program, i.e., mean view score (col.
5, ll. 40-48, Fig. 5).
4. Ukai teaches that the Table 600, which analyzes the viewer’s
preferences, includes adding “a view score shown in the view history
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table 500” to the value of the corresponding item of the total view
score column 602 (col. 5, l. 56-col. 6, l. 7, Fig. 6).
5. Ukai in Table 700 indicates the viewer’s preferences for programs
including a weighted program view measure in the weighted program
view measure section 703 obtained by multiplying a program view
measure column 504 of the corresponding program shown in the view
history table 500 by a value not smaller than 1 (col. 6, ll. 21-38).
6. Ukai further teaches that the program view measure 504 is updated
every time the view score 502 or 503 is entered (col. 5, ll. 51-53).
7. Ukai teaches that the view score (i.e., view score 502) is obtained by
dividing a view time period by a program time period and thus, a view
score of 1 indicates that the program was viewed in its entirety and a
view score greater than 1.0 indicates that a recorded program was
viewed repeatedly (col. 5, ll. 49-51, col. 5, ll. 53-55, Fig. 5).
8. Appellant’s specification includes no teaching that the actual value of
the recommendation score is adjusted or changed based on the
calculated adjustment value (Fig. 3, Spec. 7:29-8:7).
9. Appellant’s Figure 3 only shows two separate fields for the
recommender score R and adjusted recommender score A—not any
adjustment values to the recommendation score R.
10. Herz teaches that a customer profile is created based on a
questionnaire or ballot (col. 12, ll. 11-20), an agreement matrix is
calculated taking into account the customer profile to determine the
customer’s preferences for different programs (col. 19, l. 6-col. 21, l.
67), and the customer profiles/agreement matrix are used in
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scheduling television recordings and monitoring is performed to
update/adjust those profiles (col. 24, l. 56-col. 27, l. 6).
11. Herz teaches an example wherein the agreement matrix indicates
based on scoring that “John prefers ‘Star Trek’, ‘Terminator IT’, and
‘Aliens’” (col. 21, ll. 63-67).
12. In Herz’s example the program Star Trek had a score of 0.418, the
program Terminator IT had a score of 0.429, and the program Aliens
had a score of 0.365 (see the first table in col. 21 and the final
agreement matrix in col. 21, ll. 55-61).

PRINCIPLES OF LAW
Anticipation
“A claim is anticipated only if each and every element as set forth in
the claim is found, either expressly or inherently described, in a single prior
art reference.” Verdegaal Bros., Inc. v. Union Oil Co. of Cal., 814 F.2d 628,
631 (Fed. Cir. 1987).
Obviousness
Appellant has to demonstrate error in the Examiner’s position. See In
re Kahn, 441 F.3d 977, 985-86 (Fed. Cir. 2006).
The claim terms should be given their broadest reasonable meaning in
their ordinary usage as such claim terms would be understood by one skilled
in the art by way of definitions and the written description. In re Morris,
127 F.3d 1048, 1054 (Fed. Cir. 1997).
The claims, of course, do not stand alone. Rather, they are part
of “a fully integrated written instrument,” . . . consisting
principally of a specification that concludes with the claims.
For that reason, claims “must be read in view of the
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specification, of which they are a part.” . . . . [T]he
specification “is always highly relevant to the claim
construction analysis. Usually, it is dispositive; it is the single
best guide to the meaning of a disputed term.”
Phillips v. AWH Corp., 415 F.3d 1303, 1315 (Fed. Cir. 2005).

ANALYSIS
I. The 35 U.S.C. § 102(e) rejection of claims 1-2, 4, 7-8, 10-12,
14, 17-18, and 20-22 as being anticipated by Ukai

It is uncontested that Ukai’s Table 700 is used to calculate a viewer’s
preference based on the Table 600 and the database 300 (FF 1). However,
we do not agree with Appellant’s assertion (Br. 7) that Tables 600 and 700
have nothing to do with the view scores 502 and 503.
Ukai teaches that the viewer’s view history is reviewed to determine
the viewer’s “favorite program” and the viewer is informed of the result of
presumption so that the viewer’s selecting work can be lightened (FF 2).
Ukai further teaches that the view history table 500 includes the program
name 501, the view scores 502 and 503, and the program view measure 504
obtained by dividing the sum of the view scores by the number of serials of
the series program, i.e., mean view score (FF 3).
Ukai teaches that the Table 600, which analyzes the viewer’s
preference, includes adding “a view score shown in the view history table
500” to the value of the corresponding item of the total view score column
602 (FF 4). Furthermore, Ukai teaches that Table 700 indicates the viewer’s
preference for programs including a weighted program view measure in the
weighted program view measure section 703 obtained by multiplying a
program view measure column 504 of the corresponding program shown in
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the view history table 500 by a value not smaller than 1 (FF 5). Ukai further
teaches that the program view measure 504 is updated every time a view
score 502 or 503 is entered (FF 6).
Accordingly, we are not persuaded by Appellant’s argument (Br. 7)
that Ukai’s Table 700 is used to calculate a viewer’s preference measure and
to schedule programs based on Table 600 and the database 300, and not
based on the view scores 502 and 503. As stated supra, Ukai teaches that
Table 600 includes view score column 602 which is updated based on the
view scores 502 and 503 of the Table 500 and that the Table 700 provides a
recommendation based on the program view measure column 504, which is
in turn updated every time a view score 502 or 503 is entered (FF 3-6).
Accordingly, we also do not agree with Appellant (Br. 7) that Ukai’s view
scores 502 and 503 are not used to recommend programs for recording and
future viewing because the view scores 502 and 503 are used to calculate a
viewer’s preference measure (Table 700) and to make a recommendation to
the viewer as to a “favorite program” (FF 2-6).
Accordingly, Ukai teaches generating, a combined recommendation
score, C, (i.e., Table 700 recommendation) based on said recommendation
score, R, (i.e., view scores 502 and/or 503) and an adjustment, A (i.e., view
measure column 504).
We also do not agree with Appellant’s argument (Br. 7-8) that the
view scores 502 and 503 are simply a historical viewing record for the
program and not a recommendation score.
As stated supra, Ukai teaches that the viewer’s view history is
reviewed to determine the viewer’s “favorite program” and the viewer is
informed of the result of presumption so that the viewer’s selecting work can
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be lightened (FF 2). The view score (i.e., view score 502) is obtained by
dividing a view time period by a program time period and thus, a view score
of 1 indicates that the program was viewed in its entirety and a view score
greater than 1.0 indicates that a recorded program was viewed repeatedly
(FF 7). Thus, we agree with the Examiner’s reasoning that view scores (i.e.,
502 or 503) constitute preference/recommendation scores of how much the
user enjoys viewing the program. The preference/recommendation is
indicated as a factor of 1 wherein a score of 1 would indicate that the viewer
saw the program in its entirety, and whereas a view score of less than 1 (e.g.,
0.8) would indicate that the viewer did not see the program in its entirety. A
view score greater than 1 would indicate that the viewer saw the recorded
program repeatedly (FF 7).
We also remain unpersuaded by Appellant’s argument (Br. 8) that the
view measure 504 does not constitute an adjustment to the view scores 502
and 503, and by Appellant’s argument (Br. 9) that the view measure 504 for
a program is the average viewing length based on the total length of the
program, and not a ratio of an item being selected by a user relative to the
number of times the item was offered (i.e., “a consistency”).
At the outset we agree with the Examiner (Ans. 10-11), that the claim
1 limitation only recites: “calculating, using a processor of the
recommending device, an adjustment, A, to said recommendation score, R”
and does not positively recite that the value of R is adjusted or changed but
rather that an adjustment is calculated to the recommendation score. Claim
1 defines an “adjustment, A” as being “based on a consistency which is a
ratio of an item being selected by a user relative to the number of times the
item was offered”.
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As stated above, Ukai teaches that the view history table 500 includes
the program name 501, the view scores 502 and 503, and the program view
measure 504 obtained by dividing the sum of the view scores by the number
of serials of the series program, i.e., mean view score (FF 3). Thus, clearly
view measure 504 is the ratio of a program being selected by a user (i.e., the
sum of the view scores 502, 503, etc.) relative to the number of times the
item was offered (i.e., the total number of view scores for only view scores
502 and 503 would be 2). Accordingly, the view measure 504 constitutes an
adjustment based on “a consistency.” Furthermore, Ukai teaches that the
program view measure 504 is updated every time the view score 502 or 503
is entered (FF 6). Thus, the view measure 504 is determined relative to the
number of times the item was offered and updated accordingly.
We also agree with the Examiner (Ans. 11) that by using a first view
score 502 and a second view score 503, the calculation of a program view
measure 504 is representative of an adjustment to the first and the second
video scores 502/503 because the program view measure 504 is
representative of an average of two view scores. Thus, if a first view score
is 0.6 and a second view score is 0.8, a program view measure for the two
view scores would be 0.7, therefore the first view score is being adjusted by
an adjustment of +0.1 and the second view score is being adjusted by an
adjustment of -0.1. We also agree with the Examiner (Ans. 11) that this
interpretation is consistent with Appellant’s specification which fails to
teach that the actual value of the recommendation score is adjusted or
changed based on the calculated adjustment value (FF 8). Appellant’s
Figure 3 only shows two separate fields for the recommender score R and
adjusted recommender score A—not any adjustment values to the
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recommendation score R (FF 9). As stated supra, Appellant’s specification
is the single best guide to the meaning of a disputed term. See Phillips, 415
F.3d 1315.
For the foregoing reasons, since Appellant has not shown error in the
Examiner’s position, the 35 U.S.C. § 102(e) rejection of independent claim 1
is sustained. We will also sustain the rejection of claims 2, 4, 7-8, 10-12, 14,
17-18, and 20-22, which fall with claim 1.
II. The 35 U.S.C. § 103(a) rejection of claims 3, 5-6, 9, 13, 15-16,
and 19.

Herz teaches that a customer profile is created based on a
questionnaire or ballot, an agreement matrix is calculated taking into account
the customer profile to determine the customer’s preference of different
programs, and the customer profiles/agreement matrix are used in
scheduling television recordings and monitoring is performed to
update/adjust those profiles (FF 10). Herz provides an example wherein the
agreement matrix indicates based on scoring that “John prefers ‘Star Trek’,
‘Terminator IT’, and ‘Aliens’” (FF 11). In Herz’s example the program Star
Trek had a score of 0.418, the program Terminator IT had a score of 0.429,
and the program Aliens had a score of 0.365 (FF 12). Thus, contrary to
Appellant’s argument (Br. 10-11), Herz provides the missing teaching from
Ukai of the recommendation score, R, being provided by an explicit program
recommender, i.e., input provided by customer in a customer profile’s which
is being used to determine the recommendation scores for different
programs.
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Accordingly, we will also sustain the Examiner’s 35 U.S.C. § 103(a)
rejection of claim 3 and claims 5-6, 9, 13, 15-16, and 19, which fall with
claim 3.

CONCLUSIONS
Appellant has not demonstrated that the Examiner erred in
determining that Ukai teaches the limitations of: “calculating, using a
processor of the recommending device, an adjustment, A, to said
recommendation score, R, based on a consistency which is a ratio of an item
being selected by a user relative to the number of times the item was offered,
wherein the number of times the item was offered is greater than one” and
“generating, using said processor, a combined recommendation score, C,
based on said recommendation score, R, and said adjustment, A.”
Appellant has not demonstrated that the Examiner erred in
determining that Herz teaches that the “recommendation score, R, is
provided by an implicit program recommender.”

DECISION
The Examiner’s decision rejecting claims 1-22, all of the appealed
claims, 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

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