Ex Parte Gross

Patent Trial and Appeal Board

Mar 16, 2015

10856579 (P.T.A.B. Mar. 16, 2015)

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.
10/856,579 05/28/2004 John N. Gross JNG.2004_09 9830
40280 7590 03/16/2015
STEVEN VOSEN
1563 SOLANO AVENUE #206
BERKELEY, CA 94707
EXAMINER
POUNCIL, DARNELL A
ART UNIT PAPER NUMBER
3621
MAIL DATE DELIVERY MODE
03/16/2015 PAPER
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.
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
___________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
___________

Ex parte JOHN N. GROSS
___________

Appeal 2012–004490
Application 10/856,579
Technology Center 3600
___________

Before ANTON W. FETTING, JOSEPH A. FISCHETTI, and
PHILIP J. HOFFMANN, Administrative Patent Judges.
FETTING, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
1

John N. Gross (Appellant) seeks review under 35 U.S.C. § 134 of a final
rejection of claims 12–19 and 21–30, the only claims pending in the
application on appeal. We have jurisdiction over the appeal pursuant to
35 U.S.C. § 6(b).

1
Our decision will make reference to the Appellant’s Appeal Brief (“App.
Br.,” filed July 1, 2011) and Reply Brief (“Reply Br.,” filed January 9,
2012), and the Examiner’s Answer (“Ans.,” mailed November 8, 2011).
Appeal 2012-004490
Application 10/856,579

2

The Appellant invented a way of correlating and coordinating operations
of electronic recommendation systems and online advertising systems
employed by online content service providers. Specification 1:11–13.
An understanding of the invention can be derived from a reading of
exemplary claim 12, which is reproduced below [some paragraphing added].
12. A method
of delivering advertising
from a third party
through presentation of preference items
embodied in recommendations
provided by a recommender computing
system
to users of an online content service
provider,
the method comprising the steps of:
(a) automatically causing
the recommender computing system to identify
a set of recommended items
to be submitted to a user
in response to a request for a recommendation on an
item;
(b) automatically determining
whether any of said set of recommended items
includes or should include one of the preference
items
associated with a preference to be given to
the preference items
based on a request provided by the third party
Appeal 2012-004490
Application 10/856,579

3

which is an entity other than said user making the
request for the recommendation;
(c) based on the results of step (b)
automatically causing the recommender computing
system
to give said preference to said third party
and
advertise said preference items by at least one of:
1) altering said set of recommended items to
be shown to said user to include one of the
preference items;
and
2) altering a presentation format of said set
of recommended items when it includes one
of the preference items;
(d) automatically computing
a compliance rate
achieved by the recommender computing system
for presenting third party preference items
in response to recommendation requests by users.
The Examiner relies upon the following prior art:
Gatto US 6,681,211 B1 Jan. 20, 2004
Smith US 6,853,982 B2 Feb. 8, 2005
Bezos US 6,963,850 B1 Nov. 8, 2005
Greenstein US 2006/0064304 A1 Mar. 23, 2006

Kazuhiro Kohara, Yoshimi Fukuhara, and Yukihiro Nakamura ,
Selectively Intensive Learning To Improve Large Change Prediction by
Neural Networks, NTT Information and Communication Systems
Laboratories, 463–466 (1996), last visited June 2010. (Hereinafter
referred to as “Kohara”).
Appeal 2012-004490
Application 10/856,579

4

Claims 12–19 and 21–30 stand rejected under 35 U.S.C. § 112, first
paragraph, as lacking a supporting written description within the original
disclosure.
Claims 12–19 and 21–30 stand rejected under 35 U.S.C. § 112, second
paragraph, as failing to particularly point out and distinctly claim the
invention.
Claims 12–16 and 21–27 stand rejected under 35 U.S.C. § 103(a) as
unpatentable over Smith, Greenstein, and Gatto.
2

Claims 17, 18, 29, and 30 stand rejected under 35 U.S.C. § 103(a) as
unpatentable over Smith, Greenstein, Gatto, and Kohara.
Claims 19 and 28 stand rejected under 35 U.S.C. § 103(a) as
unpatentable over Smith, Greenstein, Gatto, and Bezos.
ISSUES
The issues of written description turn primarily on whether there is
adequate support for each of several limitations in the originally filed
disclosure. The issues of definiteness turn primarily on whether the scope of
the claims is ascertainable. The issues of obviousness turn primarily on
whether the limitations of the independent claims are taught or otherwise
made predictable by the prior art.

2
Claims 17–19 and 28depend from claims 12 and 25. Accordingly the
rejections of claims 17–19 and 28 necessarily also are based on Greenstein
and Gatto. The omission of these references from those dependent claim
rejections is treated as inadvertent harmless error.
Appeal 2012-004490
Application 10/856,579

5

FACTS PERTINENT TO THE ISSUES
The following enumerated Findings of Fact (FF) are believed to be
supported by a preponderance of the evidence.
Facts Related to Claim Construction
01. The disclosure contains no lexicographic definition of “preference
item.”
Facts Related to the Prior Art
Smith
02. Smith is directed to monitoring activities of online users, and for
recommending items to users based on such activities by
providing personalized recommendations that are relevant to a
current browsing session of a user. Smith 1:7–12.
03. A recommendation service is a computer-implemented service
that recommends items from a database of items. The
recommendations are customized to particular users based on
information known about the users. One common application for
recommendation services involves recommending products to
online customers. Smith 1:15–20.
04. One technique commonly used by recommendation services is
known as content-based filtering. Pure content-based systems
operate by attempting to identify items which, based on an
analysis of item content, are similar to items that are known to be
of interest to the user. Smith 1:28–32.
Appeal 2012-004490
Application 10/856,579

6

05. Another common recommendation technique is known as
collaborative filtering. In a pure collaborative system, items are
recommended to users based on the interests of a community of
users, without any analysis of item content. Collaborative systems
commonly operate by having the users explicitly rate individual
items from a list of popular items. Smith 4:33–37.
06. Smith describes viewing histories of users to identify, for each
product, a set of additional products that are deemed related to the
first product. Smith 1:47–53.
07. Smith describes using the current and/or recent contents of the
user’s shopping cart as inputs to the Recommendation Service.
For example, if the user currently has three items in his or her
shopping cart, these three items can be treated as the items of
known interest for purposes of generating recommendations, in
which case the recommendations may be generated and displayed
automatically when the user views the shopping cart contents.
This method of generating recommendations can also be used
within other types of recommendation systems, including content-
based systems and systems that do not use item-to-item mappings.
Smith 8:25–40.
08. Smith describes how an item that is related to two or more of the
items of known interest will generally be ranked more highly than
(and thus recommended over) an item that is related to only one of
the items of known interest. Smith 13:62–65.
Appeal 2012-004490
Application 10/856,579

7

09. Smith describes how captured search terms/phrases may be used
for a variety of purposes, such as filtering or ranking the personal
recommendations returned. Smith 27:29–31.
10. Smith describes how the related items list for a given product is
generated by retrieving the corresponding similar items list,
optionally filtering out items falling outside the product category
of the product, and then extracting the N top-rank items. Once
this related items list 64 has been generated for a particular
product, it may be re-used (e.g., cached) until the relevant similar
items table 60 is regenerated. Smith 29:63–30:5.
11. Smith describes selecting a set of additional products to present to
the user by taking into consideration a frequency with which a
candidate additional product co-occurs with a viewed product
within product viewing histories of users. Smith 3:56–60.
12. Smith describes combining sets of related items to form a ranked
set of related items, in which a ranking of a related item reflects
whether the ranked item is included within more than one of the
plurality of sets of related items, and during the browsing session,
generating and providing to the user a customized page that
includes representations of recommended items selected from the
ranked set. Smith 32:12–41.
Greenstein
13. Greenstein is directed to collecting, monitoring and directing data
from individual consumers and processing such against indicative
Appeal 2012-004490
Application 10/856,579

8

data collected with respect to a plurality of consumer purchased
and/or leased products/services. Greenstein para. 2.
14. Greenstein identifies as being needed a more accessible and easily
utilized purchasing/leasing decisions making process provides a
more customized purchasing/leasing decision solution based on
information and/or preferences of the purchaser/lessor or the
person who will utilize the products or services, which could
include providing information and preferences that are particular
to the persons who will utilize the products or services Greenstein
para. 10.
15. Greenstein describes ranking of the products and/or services by
ordering the optimal products or services, which would include
the recommendation or selection of a single product or service,
based on the unique facts and circumstances of each individual
consumer. Greenstein para. 22.
16. Greenstein describes generating a recommendation and/or a
comparison of services for a client in accordance with the
preference data for the client. Greenstein para. 37.
17. Greenstein describes providing input concerning the products and
or services that the third party expert will take into account and
even as to the formulae upon which products and/or services will
be recommended or sold. The purchaser then chooses which
products or services and selects the parameters/criteria used to
select or recommend the products and/or services. Greenstein
paras. 64–65.
Appeal 2012-004490
Application 10/856,579

9

18. Greenstein describes how a recommendation or selection can be
precisely calculated to the need of and therefore unique to the
individual purchaser that can also reflect the preferences of the
participant and/or vendors. Greenstein para. 80.
Gatto
19. Gatto is directed to managing and viewing historical data
including security analysts’ predictions and actual reported data;
measuring, analyzing, and tracking the historical performance of
security analysts’ predictions; and creating, managing,
backtesting, and using models that use such historical and
performance data, attributes and other information to
automatically produce better predictors of future events. Gatto
1:16–25.
20. Gatto describes how a user may create a model by identifying
various factors to be taken into account in the model. For each
factor, a user specifies rules by which each non-excluded analyst
is assigned an N-score (normalized) according to the rules. Such
factors may include, for example, accuracy, rating, broker list,
experience, estimate age, and other factors. Each factor is
assigned a weight to enable a user to place greater emphasis on
one or more factors for a given model. For each model, the
analyst's N-score for each factor is multiplied by the factor weight
and those weighted N-scores are summed for each analyst. The
actual emphasis placed on an analyst's current estimate is
determined by taking the sum of the analyst's weighted factor
Appeal 2012-004490
Application 10/856,579

10

scores divided by the sum of the weighted factor scores for all
analysts. Gatto 4:50–65.
Kohara
21. Kohara is directed to selective intensive learning techniques for
improving the ability of back-propagation heuristic networks to
learn and predict large changes. Gatto, Abstract.
22. Kohara describes reducing the learning rate for small change data
as compared to large change data to increase the effectiveness in
training a neural network.
ANALYSIS
Claims 12–19 and 21–30 rejected under 35 U.S.C. § 112, first paragraph, as
lacking a supporting written description within the original disclosure
We are persuaded by the Appellant’s argument that the Specification
provides support for automating the steps in the claims.
We are not persuaded by the Appellant’s argument that the
Specification’s use of the word “preference” shows possession of
“possession items” as used in the claims. The phrase “preference item” is
not lexicographically defined and does not occur in the originally filed
disclosure. The word “preference” in the originally filed disclosure refers to
policy rather than items of goods or services. Claims 12 and 25 recite
determining whether the recommended items includes preference items.
This domain equivalence of preference and recommended items differs from
the use in the originally field disclosure of preferences as policies.
Appeal 2012-004490
Application 10/856,579

11

We are persuaded by the Appellant’s argument that the Specification
supports the “compliance rate” limitation. App. Br. 16.
We are not persuaded by the Appellant’s argument that the preference
items of claim 13 are supported for the same reasons as claim 12, supra.
We are persuaded by the Appellant’s argument that the bias
determination recited in claim 19 is supported by the Specification. App. Br.
18.
We are not persuaded by the Appellant’s argument that the originally
filed disclosure supports the “learning rate” of claims 29 and 30.
Appellant’s argument shows only that it might have been obvious, not that it
was disclosed.
One shows that one is “in possession” of the invention by
describing the invention, with all its claimed limitations, not
that which makes it obvious. Id. (“[T]he applicant must also
convey to those skilled in the art that, as of the filing date
sought, he or she was in possession of the invention. The
invention is, for purposes of the ‘written description’ inquiry,
whatever is now claimed.”) (emphasis in original). One does
that by such descriptive means as words, structures, figures,
diagrams, formulas, etc., that fully set forth the claimed
invention. Although the exact terms need not be used in haec
verba, see Eiselstein v. Frank, 52 F.3d 1035, 1038 . . .
(Fed.Cir.1995) (“[T]he prior application need not describe the
claimed subject matter in exactly the same terms as used in the
claims . . . .”), the specification must contain an equivalent
description of the claimed subject matter.

Lockwood v. Am. Airlines, Inc., 107 F.3d 1565, 1572 (Fed. Cir. 1997)

Appeal 2012-004490
Application 10/856,579

12

Claims 12–19 and 21–30 rejected under 35 U.S.C. § 112, second
paragraph, as failing to particularly point out and distinctly claim the
invention
We are persuaded by the Appellant’s arguments that the claims are
definite in scope. App. Br. 20.
Claims 12–16 and 21–27 rejected under 35 U.S.C. § 103(a) as unpatentable
over Smith, Greenstein, and Gatto
Claim 12 has four steps. The first step simply provides product or
service recommendations. Appellant admits that Smith, as a generic
reference describing such recommender systems, describes this step. The
second and third steps determine whether the recommendations contain a
preference item and (1) if not, show a preference item, or (2) if so, somehow
alter the display of recommended items. The Examiner applies Greenstein
to Smith primarily for this. Appellant contends these limitations are not
described by either of the references. The last step is essentially an audit
step reporting compliance. The Examiner applies Gatto to Smith for this,
and again Appellant contends this is not described.
As Smith describes both providing recommendations and altering the
presentation based on some preference items, the issues extant are whether
the types of preferences in the art differ from the claim, if so, then whether
weight should be afforded to such difference, and whether it was predictable
to report some compliance rate.
Appeal 2012-004490
Application 10/856,579

13

The phrase “preference item” is not lexicographically defined and indeed
does not even occur in the Specification. The only limitation in claim 12
concerning a preference item is that it be associated in some manner with
some preference to be given to the preference items based on a request
provided by the third party. There is no step or operation that forms such an
association or bases the giving on a third party request. Thus, although
Appellant contends that preference items are not merely labels, in the claims
as drafted, the designation of data as preference items is no more than a
label, as there are no operations that directly use or produce such preference
items.
In any event, the Examiner found that Greenstein describes using vendor
preferences in making recommendations that are tailored based on those
preferences. See FF 18. These recommendations and displays also rely on
third party experts. See FF 17. Thus Greenstein shows it was predictable to
use preferences from third party vendors or experts in altering the
presentation from recommender systems in showing those preference items.
Gatto describes managing and viewing historical data and tracking the
historical performance. FF 19. Thus, Gatto shows it was at least predictable
to track actual transaction history and rates providing statistical measures of
that history. Gatto also shows it was known to normalize results and track
accuracy and other factors for model evaluation. FF 20. As compliance is
just a measure of accuracy in meeting policy, Gatto shows it was at least
predictable to track a compliance rate.
As to claim 17, reciting “identifying a learning rate for new items by the
recommender system,” the Examiner found that Kohara discloses the
Appeal 2012-004490
Application 10/856,579

14

computation of a learning rate. Ans. 13. Kohara does describe explicitly
reducing the learning rate in some circumstances to train an expert system.
As a recommender system is a form of expert system and therefore is
predictably implemented with a neural network, Kohara’s description of
tweaking learning rates to more effectively train the net is predictable in
implementing Smith.
As to claim 22, reciting “adjusting advertising for said preference
items,” the Examiner found that Smith discloses selecting additional
products to present. Ans. 12.
CONCLUSIONS OF LAW
The rejection of claims 12–19 and 21–30 under 35 U.S.C. § 112, first
paragraph, as lacking a supporting written description within the original
disclosure for automation and compliance rate limitations is improper.
The rejection of claims 12–19 and 21–30 under 35 U.S.C. § 112, first
paragraph, as lacking a supporting written description within the original
disclosure for the preference items and learning rate limitations is proper.
The rejection of claims 12–19 and 21–30 under 35 U.S.C. § 112, second
paragraph, as failing to particularly point out and distinctly claim the
invention is improper.
The rejection of claims 12–16 and 21–27 under 35 U.S.C. § 103(a) as
unpatentable over Smith, Greenstein, and Gatto is proper.
The rejection of claims 17, 18, 29, and 30 under 35 U.S.C. § 103(a) as
unpatentable over Smith, Greenstein, Gatto, and Kohara is proper.
Appeal 2012-004490
Application 10/856,579

15

The rejection of claims 19 and 28 under 35 U.S.C. § 103(a) as
unpatentable over Smith, Greenstein, Gatto, and Bezos is proper.
DECISION
The rejection of claims 12–19 and 21–30 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). See 37 C.F.R.
§ 1.136(a)(1)(iv) (2011).

AFFIRMED
rvb