Ex Parte Sketch

Board of Patent Appeals and Interferences

Mar 27, 2007

09681784 (B.P.A.I. Mar. 27, 2007)

The opinion in support of the decision being entered today was not written
for publication and is not binding precedent of the Board.

UNITED STATES PATENT AND TRADEMARK OFFICE
__________

BEFORE THE BOARD OF PATENT APPEALS
AND INTERFERENCES
__________

Ex parte EDWARD ALUN SKETCH
__________

Appeal 2007-0384
Application 09/681,784
Technology Center 3600
__________

ON BRIEF
__________

Before GRIMES, GREEN, and LINCK, Administrative Patent Judges.

GRIMES, Administrative Patent Judge.

DECISION ON APPEAL
This is an appeal under 35 U.S.C. § 134 involving claims to a method
or system for reducing a functional competency gap. The Examiner has
rejected the claims as obvious. We have jurisdiction under 35 U.S.C. § 6(b).
We affirm.
BACKGROUND
The specification describes “a method and online system for
interactively assessing an employee’s level of functional competency with
respect to his or her employment function” or “an employment function the
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employee aspires to” and “providing the employee with a variety of learning
solutions (e.g., classroom training, software training, online training, on-the-
job training, etc.) for eliminating gaps between the employee’s current level
of functional competency and the level of functional competency required”
or aspired to (Specification 3-4).
The specification describes
a method for reducing a functional competency gap [that]
includes defining an employment function and preferred
method of learning, assessing functional competency based on
the employment function via an online self-assessment,
identifying at least one gap between the assessed functional
competency and a predefined competency required for the
employment function, and identifying at least one learning
solution for reducing the at least one competency gap based on
the preferred method of learning.
(Id. at 4.)
The specification also describes generating “an online employee
development plan . . . based on the outcome of the competency gap analysis”
(id. at 7). The specification states that the “online employee development
plan comprises . . . , for each competency gap, at least one link to a learning
solution that has been preselected as best-in-class content for educating the
employee in a manner that tends to reduce or eliminate the corresponding
competency gap” (id.). The specification also states that, to “automatically
locate the most appropriate learning solution(s) to include within the
employee’s online development plan, a dynamic search engine queries a
database of all available learning solutions” (id.).

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In addition, the specification states that “[l]earning solutions are
acquired in a plurality of manners including . . . purchasing rights to
copyrighted online content (books, videos, recordings, etc.) . . . and
acquiring learning solutions via an online auction” (id. at 10). The
specification also states that the data collected by this system can be mined
“to identify employees best suited to perform a unique task” (id. at 13).
DISCUSSION
1. CLAIMS
Claims 1-20 are pending and on appeal. The claims subject to each
rejection have not been argued separately and therefore stand or fall
together. 37 C.F.R. § 41.37(c)(1)(vii).1 We will focus on claims 1, 10, and
12, which are representative and read as follows:
1. A method for reducing a functional competency gap, the
method comprising:
defining an employment function and preferred method of
learning;
assessing functional competency based on the employment
function via an online self-assessment;
identifying at least one gap between an assessed functional
competency and a predefined competency required for the employment
function;

1 Although claims 2-9, 13, 16, and 18 are discussed under a separate heading
(Br. 6), these claims have not been separately argued: pointing out that these
claims “have limitations beyond claims 1 and 15” is not a separate argument.
37 C.F.R. § 41.37(c)(1)(vii) (“A statement which merely points out what a
claim recites will not be considered an argument for separate patentability of
the claim.”). Thus, Appellant has waived the right to have these claims
separately considered.
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dynamically querying a database of available learning solutions
to locate a learning solution that (i) is currently a best-in-class learning
solution for reducing the at least one competency gap, and (ii) best matches
the employee's preferred method of learning; and
automatically defining a development plan for the employee
including [a] learning solution identified with the dynamic query.
10. The method of claim 1 additionally comprising acquiring or
selling learning solutions in an online auction format.
12. The method of claim 1 additionally comprising mining online
assessment data to select current employees for a particular employment
opportunity.
2. SABA SOFTWARE
Claims 1-9, 13, 15, 16, 18, and 20 stand rejected under 35 U.S.C.
§ 103 as obvious over Saba Software Learning Management System from
1997-2000, as evidenced by the following references:
• Saba Software web page entitled “Saba Learning Network,
Enterprise Series” (reference A);
• Burriesci, “It’s All About the Knowledge,” Intelligent Enterprise,
Vol. 2, pp. 10 and 12 (1999) (reference B);
• “Red Hat to Offer Saba Learning to Meet Global Training Needs,”
Business Wire (2000) (reference C);
• Khirallah, “Veterans Agency Turns to Online Training,”
Information Week, Vol. 779, p. 183 (2000) (reference D);
• Saba Software web page entitled “Saba Competency Content
Alliance,” web.archive.org/web/20000607004748/www.saba.com/
english/enterprises/partners/comp_content.htm (reference E);
• Saba Software web page entitled “Saba Learning Exchange,”
web.archive.org/web/20000510182442/www.saba.com/english/enterprises/
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solutions/learn_ex.htm (reference F);
• Rice, “Hyundai Revs Training with Saba,” IT Support News,
Vol. 20, pp. 18 and 20 (2000) (reference G);
• Rice, “Techies.com Signs On with Saba,” IT Support News,
Vol. 20, pp. 22-23 (2000) (reference H).
Reference A describes Saba’s “comprehensive web-based application
that enables large, globally distributed organizations . . . to measure and
close critical knowledge gaps for groups and individuals. This solution
enables [organizations] to rapidly and effectively target required knowledge,
assess knowledge gaps, [and] plan and buy learning.” Reference A states
that individual learners can “[a]ssess their knowledge against the established
profile” and “[p]lan and buy learning based on personalized
recommendations.” Reference A also states that the organizations can
“become part of the Saba Learning Exchange, a global network connecting
extended enterprises to over 20,000 learning offerings from learning
providers around the world. . . . This allows [organizations] to quickly find
best-of-breed learning offerings to close knowledge gaps.”
Reference B states that Saba’s software “uses collaborative profiling
to nonintrusively tailor lessons to individual learning styles, preferences, and
prior knowledge; to send personal email notices about upcoming classes or
the need to update a certification; to track employee skills and knowledge
inventories; and to deliver an interface in the user’s native language.”
Reference D states that Saba’s software allows companies to
“measure gaps between what an employee knows and what he or she needs
to know in a particular job.” Reference D also states that, in the E-learning
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program, “trainees will participate in a series of skills assessment tests
online. Then they’ll be able to see where there are gaps in their knowledge,
and will receive course suggestions.” References C and E-H also describe
various aspects of Saba’s software.
The Examiner argues that “Saba Software teaches a learning
management system” that meets the limitations of claim 1, except that “Saba
Software does not expressly teach that the querying is dynamic per se”
(Answer 5-7). However, the Examiner argues that it would have been
obvious “to dynamically query a database of available learning solutions”
because it is “old and well-known in the art of database management to
dynamically query databases in order to improve the likelihood of one
having access to the most up-to-date information available” (id. at 7).
The Examiner has pointed out where each limitation of claim 1 is
taught or suggested by References A-H (id. at 5-7). We conclude that the
Examiner has set forth a prima facie case that Saba’s software would have
made the method of claim 1 obvious to a person of ordinary skill in the art.
Appellant argues that “Reference A and Reference B each fail to
teach, disclose, or suggest that the Saba Software ‘automatically defin[es] a
development plan for the employee including learning solution identified
with the dynamic query’” (Br. 5). In particular, Appellant states that:
Reference B indicates that the Saba Software “uses
collaborative profiling to nonintrusively tailor lessons to
individual learning styles, preferences, and knowledge . . . ,”
(Reference B, Col. 1, line 41 - Col. 2, line 2), apparently once
the user has already identified the “lesson.” Reference B does
not indicate that the Saba Software automatically defines which
“lessons” a user should undertake. Reference A indicates that
the Saba Software “lets [a user] find offerings specific to [the
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user’s] industry, as well as offerings that are targeted for
specific roles, certifications, and competencies.” (Reference A,
lines 21-22). Reference A similarly does not indicate that the
Saba Software automatically defines which “offerings” a user
should undertake.
(Id.) Instead, Appellant argues, “in Saba, the user identifies the ‘lesson(s)’ –
a ‘development plan’ including ‘learning solutions’ is not ‘automatically
defined’ (Reply Br. 1-2).
In addition, Appellant argues, the “Examiner fails to establish a prima
facie case that ‘it would have been obvious to one of ordinary skill in the art
at the time of Applicant’s invention to modify Saba Software to dynamically
query a database of available learning solutions.’” (Br. 5.) In particular,
Appellant argues that, because neither Reference A nor Reference B teaches
or suggests that the Saba Software “‘automatically defin[es] a development
plan for the employee including learning solution identified with the
dynamic query,’ . . . there is no teaching or suggestion in Reference A or
Reference B to modify the Saba Software to dynamically, i.e., automatically,
query a database of available learning solutions” (id.).
We are not persuaded by these arguments. Reference A describes a
global network of learning offerings. In addition, Reference A states that
individual learners can “[p]lan and buy learning based on personalized
recommendations.”
Reference A does not expressly state that this network can be
automatically queried or that these “personalized recommendations” are
automatically generated based on the results of the gap analysis. However,
when the references are read in context and as a whole, we agree with the
Examiner that they teach “automatically defining a development plan for the
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employee including [a] learning solution identified with [a] dynamic query.”
Reference B, for example, states that the Saba software “uses collaborative
profiling to nonintrusively tailor lessons to individual learning styles,
preferences, and prior knowledge” (page 10, paragraph bridging the left and
center columns). Thus, the references reasonably appear to teach
“personalized recommendations” that are automatically generated based on
the gap analysis.
In addition, we agree with the Examiner that Reference B provides
further evidence that the method of claim 1 would have been obvious. In
particular, Reference B states that Saba’s Software “uses collaborative
profiling to nonintrusively tailor lessons to individual learning styles,
preferences, and prior knowledge; [and] to send personal email notices about
upcoming classes or the need to update a certification.” Id. Reference B
does not appear to expressly discuss the gap analysis features of Saba’s
software. However, we find that, when Reference B is read in context with
the other references applied by the Examiner, which clearly describe the gap
analysis (see Reference A), the references as a whole describe e-mail notices
that are automatically generated based on the results of the gap analysis.
We conclude that the Examiner has set forth a prima facie case that
claim 1 would have been obvious over Saba’s software, as evidenced by
References A-H, which Appellant has not rebutted. We therefore affirm the
rejection of claim 1 under 35 U.S.C. § 103. Claims 2-9, 13, 15, 16, 18, and
20 fall with claim 1.
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3. SABA SOFTWARE WITH COLLEGEBYTES
Claims 10, 17, and 19 stand rejected under 35 U.S.C. § 103 as obvious
over Saba’s software in view of CollegeBytes.com, as disclosed in
CommonPlaces.2 The Examiner states that “Saba Software’s learning
management system does not expressly teach . . . [a]cquiring or selling
learning solutions in an online auction format” (Answer 10). However, the
Examiner argues that “Collegebytes.com (1999) teaches a computer-based
system that sells textbooks via an online auction format” (id.). The
Examiner concludes that it would have been “obvious to one of ordinary
skill in the art at the time of invention to combine the teachings of Saba
Software and collegebytes.com to create a system and method to sell
learning solutions via an online auction format for the advantages of an
efficient marketplace” (id.). In addition, the Examiner argues that “the
ability to auction off learning solutions does not affect the competency
assessment aspects of the claims; therefore, such an auctioning service is
effectively a value added service that enhances the experience of a user by
allowing him/her to quickly and conveniently plan as much of his/her
learning experience as possibl[e] in a single interface or environment” (id. at
14).
As indicated in CommonPlaces, CollegeBytes.com is an Internet Hub
for college audiences. CollegeBytes.com includes online auction
capabilities that allow users to “buy and sell everything from dorm furniture
to textbooks to musical instruments to computers.” We conclude that one of

2 “CommonPlaces, in Agreement with Reuters Health Information, to Offer
Free Access to Health News Via CollegeBytes.com,” Business Wire (1999).
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ordinary skill in the art would have been motivated to incorporate an online
auction format in Saba’s software to allow users to buy learning solutions,
such as textbooks for self-study, by an online auction.
Appellant argues that the Examiner has not provided a prima facie
case that one of ordinary skill in the art would have been motivated to
combine Saba’s software with CollegeBytes.com (Br. 6). We are not
persuaded by this argument.
Reference A, among others, teaches that Saba’s software allows users
to buy learning solutions, although it does not explicitly state how these
learning solutions are bought and sold. As evidenced by CommonPlaces,
online auctions were known in the art at the time the present application was
filed. CommonPlaces also indicates that online auctions were used to buy
and sell textbooks, among other things. Based on these teachings, we agree
that one of ordinary skill in the art would have been motivated to combine
Saba’s software with the online auction capabilities of CollegeBytes.com in
order to buy and sell learning solutions (e.g., books) through an online
auction for the known advantages of online auctions.
We conclude that the Examiner has set forth a prima facie case that
claim 10 would have been obvious over Saba’s software in view of
CollegeBytes.com, as disclosed in CommonPlaces, which Appellant has not
rebutted. We therefore affirm the rejection of claim 10 under 35 U.S.C.
§ 103. Claims 17 and 19 fall with claim 10.
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4. SABA SOFTWARE WITH TUTTLE
Claims 11, 12, and 14 stand rejected under 35 U.S.C. § 103 as obvious
over Saba’s software in view of Tuttle.3 The Examiner argues that “Tuttle
teaches a computer-based system that creates a database of assessments and
analyses of skills of the workforce that can be analyzed to determine
capability gaps that can be used in recruiting and training decisions.”
(Answer 12.) In particular, the Examiner argues that Tuttle describes
“[m]ining/ searching online assessment data to select current employees for
employment opportunities” (id.). The Examiner concludes that it would
have been obvious “to combine the teachings of Saba and Tuttle to enable a
system that could assess the capabilities of . . . employees and identify
individual[s] or groups of employees to be selected for employment
opportunities and tasks for the advantage of convenience in a single system”
(id. at 13).
Tuttle describes a database containing the “technical skills of
workforce employees” (col. 1, ll. 25-29). Tuttle states that this database can
be used to generate reports that can be used to match employee skills with
workloads (col. 1, ll. 29-32). Thus, Tuttle describes a method in which a
database is mined “to select current employees for a particular employment
opportunity,” as recited in claim 12.
Through the online assessment (see Reference D), Saba’s software
provides a database of functional competency gaps, as well as functional
competencies, of employees. Thus, the database contains information
similar to the database described in Tuttle. We conclude that the Examiner

3 Tuttle, U.S. Patent No. 6,591,246 B1, issued July 8, 2003.
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has set forth a prima facie case that it would have been obvious to mine this
data “to select current employees for a particular employment opportunity.”
Appellant argues that the Examiner has not provided a prima facie
case that one of ordinary skill in the art would have been motivated to
combine Saba’s software with Tuttle (Br. 6). We are not persuaded by this
argument.
As discussed above, Saba’s software provides a database containing
information that is similar to the database described in Tuttle. In addition,
Reference A states that Saba’s software “empowers managers and team
leaders to
• Target required knowledge for roles within their group
• Assess knowledge gaps for their group
• Plan and buy learning, and approve learning requests
• Learn and track results for the group
• Improve performance by modifying knowledge targets and repeating
the learning process.”
Thus, Reference A describes using the data collected by Saba’s
software in making management decisions. We agree with the Examiner
that one of ordinary skill in the art would have been motivated to mine (i.e.,
analyze) the data generated by Saba’s software “to select current employees
for a particular employment opportunity,” in order to efficiently match
employees with appropriate jobs.

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We conclude that the Examiner has set forth a prima facie case that
claim 12 would have been obvious over Saba’s software in view of Tuttle,
which Appellant has not rebutted. We therefore affirm the rejection of
claim 12 under 35 U.S.C. § 103. Claims 11 and 14 fall with claim 12.
SUMMARY
The Examiner’s position is supported by the preponderance of the
evidence of record. We therefore affirm the rejection of claims 1-20 under
35 U.S.C. § 103.
No time period for taking any subsequent action in connection with
this appeal may be extended under 37 C.F.R. § 1.136(a).

AFFIRMED

ERIC GRIMES )
Administrative Patent Judge )
)
)
) BOARD OF PATENT
LORA M. GREEN )
Administrative Patent Judge ) APPEALS AND
)
) INTERFERENCES
)
NANCY J. LINCK )
Administrative Patent Judge )

EB/MLM
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Brooks Kushman P.C./FGTL
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22nd Floor
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