Ex Parte Wellman et al

Patent Trial and Appeal Board

Jul 21, 2017

13749618 (P.T.A.B. Jul. 21, 2017)

% United States Patent and Trademark Office
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APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO.
13/749,618 01/24/2013 Richard W. Wellman 3128.2.3 9096
36491 7590 07/25/2017
Knn7ler T aw firm in
EXAMINER
50 W. Broadway
10th Floor
GRANT, MICHAEL CHRISTOPHER
SALT LAKE CITY, UT 84101 ART UNIT PAPER NUMBER
3715
NOTIFICATION DATE DELIVERY MODE
07/25/2017 ELECTRONIC
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.
Notice of the Office communication was sent electronically on above-indicated "Notification Date" to the
following e-mail address(es):
docket @ kunzlerlaw .com
PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte RICHARD W. WELLMAN, KELLY D. PHILLIPPS, and
DAVID B. GONZALEZ
Appeal 2016-0007431
Application 13/749,618
Technology Center 3700
Before LINDA E. HORNER, ANNETTE R. REIMERS, and
NATHAN A. ENGELS, Administrative Patent Judges.
HORNER, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
Richard W. Wellman et al. (Appellants) seek our review under
35 U.S.C. § 134(a) of the Examiner’s decision rejecting claims 13-27. Non-
Final Office Action (December 22, 2014) (hereinafter “Non-Final Act.”).
Claims 1-12 are withdrawn. Non-Final Act. 1. We have jurisdiction under
35 U.S.C. § 6(b). We AFFIRM.
1 CloudVu, Inc. is an applicant as provided in 37 C.F.R. § 1.46. CloudVu,
Inc. changed its name to PurePredictive, Inc. Appellants name
PurePredictive, Inc. as the real party in interest. Appeal Brief 2 (June 18,
2015) (hereinafter “Appeal Br.”).
Appeal 2016-000743
Application 13/749,618
CLAIMED SUBJECT MATTER
Appellants’ claimed subject matter relates to “using machine learning
to identity patterns in student engagement relative to electronic learning
systems.” Spec. 11. Claims 13, 22, and 24 are the independent claims on
appeal. Claim 13 is illustrative of the claimed subject matter and is
reproduced below.
13. An apparatus for determining student engagement,
the apparatus comprising:
an activity monitor module configured to receive
monitored electronic learning interactions of one or more
students;
a machine learning module configured to compare, using
machine learning, the monitored electronic learning interactions
to a plurality of archetypal learning patterns, the machine
learning comprising a machine learning ensemble including a
plurality of learned functions from multiple machine learning
classes; and
a result module configured to send an alert for at least one
student of the one or more students based on the machine
learning comparison.
Appeal Br. 10 (Claims Appendix).
REJECTIONS
The Non-Final Office Action includes the following rejections:
1. Claims 13-27 stand rejected under 35 U.S.C. § 101 as being
directed to patent ineligible subject matter.
2. Claims 13-16 and 18-27 stand rejected under 35 U.S.C. § 102(b)
as anticipated by Sorenson (US 2013/0004930 Al, published
January 3, 2013).
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3. Claim 17 stands rejected under 35 U.S.C. § 103(a) as unpatentable
over Sorenson and Intelligent Miner.2
ANALYSIS
Rejection of claims 13-27 under 35 U.S.C. § 101
The Examiner’s rejection
The Examiner determines that “[t]he claims are directed to the
abstract idea of a method of organizing human activity” and, specifically,
“methods for determining student engagement that could be performed by
human beings alone.” Non-Final Act. 2. The Examiner further finds that
“the additional elements or combinations of elements in the claims other
than the abstract idea ... do not provide meaningful limitations to transform
the abstract idea into a patent eligible application of the abstract idea such
that the claims amount to significantly more than the abstract idea itself.”
Id. at 3 (“[T]he claims include generic computer structures and functions
that are well-understood and routine.”)
Appellants ’ arguments
Appellants contest these findings.3 Appellants argue that “the claims
are directed to machine learning, not human activity, and a human cannot
2 The Examiner identifies “Intelligent Miner” as being an application of the
Intelligent Miner computer program features as evidenced collectively by
P. Cabena et al., “Intelligent Miner for Data Applications Guide,” IBM,
pages 89-103 (March 1999) and Lo Yuk Ting et al. (US 2010/0131314 Al,
published May 27, 2010). Non-Final Act. 13.
3 Appellants present arguments for claims 13-27 as a group for the first
ground of rejection. Appeal Br. 3-6. We select claim 13 as representative
of the group, and claims 14-27 stand or fall with claim 13. 37 C.F.R.
§ 41.37(c)(l)(iv).
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perform the ‘machine learning’ limitations of the claims.” Appeal Br. 4.
Appellants further argue that “the claims do not preempt the entire field of
determining student engagement such that others could not practice it” and
“the claims only exclude certain applications of machine learning to
monitored electronic learning interactions.” Id. at 5. Appellants further
draw parallels to the Federal Circuit’s holding in DDR Holdings, LLC v.
Hotels.com, LP, 773 F.3d 1245 (Fed. Cir. 2014), arguing that “the problems
addressed by the claims specifically arise in the realm of computer
technology (e.g., the specifically recited electronic learning).” Appeal Br. 6.
Appellants further argue “the claims add ‘significantly more’ than an
abstract idea” because “machine learning is not a generic computer element
such as a processor or memory, but is a specialized structure that serves a
specialized technical purpose.” Id. Appellants assert:
[T]he claims, as a whole, represent a significant improvement to
the technical field of electronic learning, because comparisons
that could not previously be reliably made (except by requiring a
teacher to be present to observe and compare, thus destroying
many of the distance learning benefits of electronic learning) can
be made, via machine learning, by practicing the subject matter
of the claims.
Reply Brief 3 (October 19, 2015) (hereinafter “Reply Br.”).
Legal Principles
A patent may be obtained for “any new and useful process, machine,
manufacture, or composition of matter, or any new and useful improvement
thereof.” 35 U.S.C. § 101. The Supreme Court has held that this provision
contains an important implicit exception: Laws of nature, natural
phenomena, and abstract ideas are not patentable. Alice Corp. v. CLS Bank
Inti, 134 S. Ct. 2347, 2354 (2014); Gottschalkv. Benson, 409 U.S. 63, 67
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(1972) (“Phenomena of nature, though just discovered, mental processes,
and abstract intellectual concepts are not patentable, as they are the basic
tools of scientific and technological work.”). Notwithstanding that a law of
nature or an abstract idea, by itself, is not patentable, the application of these
concepts may be deserving of patent protection. Mayo Collaborative Servs.
v. Prometheus Labs., Inc., 566 U.S. 66, 71 (2012). In Mayo, the Court stated
that “to transform an unpatentable law of nature into a patent eligible
application of such a law, one must do more than simply state the law of
nature while adding the words ‘apply it.”’ Id. at 72 (citation omitted).
In Alice, the Court reaffirmed the framework set forth previously in
Mayo “for distinguishing patents that claim laws of nature, natural
phenomena, and abstract ideas from those that claim patent-eligible
applications of these concepts.” Alice, 134 S. Ct. at 2355. The first step in
the analysis is to “determine whether the claims at issue are directed to one
of those patent-ineligible concepts.” Id. In Enfish, LLC v. Microsoft
Corporation, 822 F.3d 1327 (Fed. Cir. 2016), the Federal Circuit explained,
“the first step in the Alice inquiry . . . asks whether the focus of the claims is
on the specific asserted improvement in computer capabilities ... or,
instead, on a process that qualifies as an ‘abstract idea’ for which computers
are invoked merely as a tool.” Id. at 1335—36.
In FairWarning IP, LLC v. Iatric Systems, Inc., 839 F.3d 1089 (Fed.
Cir. 2016), the Federal Circuit affirmed a district court determination that
claims directed to “the concept of analyzing records of human activity to
detect suspicious behavior” were not directed to patent eligible subject
matter. Id. at 1094. The claims in FairWarning recited, generally, a method
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of detecting improper access of a patient’s protected health information in a
computer environment, comprising generating a rule for monitoring
transactions/activity in an audit log, applying the rule to the audit log data,
storing hits, and providing notifications of hits. Id. The Federal Circuit
determined that the claims were directed to an abstract idea:
We have explained that the “realm of abstract ideas”
includes “collecting information, including when limited to
particular content.” Elec. Power Grp., LLC v. Alstom S.A., 830
F.3d 1350, 1353 (Fed. Cir. 2016) (collecting cases). We have
also “treated analyzing information by steps people go through
in their minds, or by mathematical algorithms, without more, as
essentially mental processes within the abstract-idea category.”
Id. And we have found that “merely presenting the results of
abstract processes of collecting and analyzing information,
without more (such as identifying a particular tool for
presentation), is abstract as an ancillary part of such collection
and analysis.” Id. Here, the claims are directed to a combination
of these abstract-idea categories. Specifically, the claims here
are directed to collecting and analyzing information to detect
misuse and notifying a user when misuse is detected. See id.
Id. at 1094-95; see also Intellectual Ventures I LLC v. Capital One
Financial Corp., 850 F.3d 1332, 1340 (Fed. Cir. 2017) (noting that the
Federal Circuit has held on numerous occasions that an invention directed to
collection, manipulation, and display of data was an abstract process).
The court in FairWarning distinguished the claims before it from the
claims in McRO, Inc. v. Bandai Nanco Games America Inc., 837 F.3d 1299
(Fed. Cir. 2016), which were directed to “a specific asserted improvement in
computer animation, i.e., the automatic use of rules of a particular type.”
839 F.3d at 1094 (citing McRO, 837 F.3d at 1314). The court in McRO
explained that “it [was] the incorporation of the claimed rules, not the use of
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the computer that ‘improved [the] existing technological process’ by
allowing the automation of further tasks.” McRO, 837 F.3d at 1313
(alteration in original) (quoting Alice, 134 S. Ct. at 2358). By contrast,
“FairWaming’s claims merely implement an old practice in a new
environment” using “the same questions . . . that humans in analogous
situations detecting fraud have asked for decades, if not centuries.”
FairWarning, 839 F.3d 1094-95. “Although FairWaming’s claims require
the use of a computer, it is this incorporation of a computer, not the claimed
mle, that purportedly ‘improve[s] [the] existing technological process’ by
allowing the automation of further tasks.” Id. at 1095 (quoting Alice, 134
S.Ct.at2358).
If the claims are directed to a patent-ineligible concept, then the
second step in the analysis is to consider the elements of the claims
“individually and ‘as an ordered combination’” to determine whether there
are additional elements that ‘“transform the nature of the claim’ into a
patent-eligible application.” Alice, 134 S. Ct. at 2355 (quoting Mayo, 566
U.S. at 79). In other words, the second step is to “search for an ‘inventive
concept’— i.e., an element or combination of elements that is ‘sufficient to
ensure that the patent in practice amounts to significantly more than a patent
upon the [ineligible concept] itself.’” Id. (brackets in original) (quoting
Mayo, 566 U.S. at 72-73). The prohibition against patenting an abstract
idea “cannot be circumvented by attempting to limit the use of the formula
to a particular technological environment or adding insignificant post­
solution activity.” Bilski v. Kappos, 561 U.S. 593, 610-11 (2010) (citation
and internal quotation marks omitted). The Court in Alice noted that
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‘“[s]imply appending conventional steps, specified at a high level of
generality,’ was not ‘enough’ [in Mayo] to supply an ‘inventive concept.’”
Alice, 134 S. Ct. at 2357 (quoting Mayo, 566 U.S. at 82, 77, 72).
In DDR Holdings, the Federal Circuit held that claims “directed to
systems and methods of generating a composite web page that combines
certain visual elements of a ‘host’ website with content of a third-party
merchant” contained the requisite inventive concept. 773 F.3d at 1248. The
court explained that the claims at issue involved a technological solution that
overcame a specific challenge unique to the Internet. Id. at 1259. In
BASCOM Global Internet Servs., Inc. v. AT&T Mobility LLC, 827 F.3d
1341, 1348 (Fed. Cir. 2016), the court similarly held that claims “directed to
filtering content on the Internet” contained an inventive concept. The court
found “an inventive arrangement” of “known, conventional pieces” through
“the installation of a filtering tool at a specific location, remote from the
end-users, with customizable filtering features specific to each end user.”
Id. at 1350. The claimed custom filter could be located remotely from the
user because the invention exploited the ability of Internet service providers
to associate a search request with a particular individual account. Id. This
technical solution overcame defects in prior art embodiments and elevated
an otherwise abstract idea to a patentable invention. Id.
By contrast, if the claim language “provides only a result-oriented
solution, with insufficient detail for how a computer accomplishes it,” then
the claims do not contain an “inventive concept” under Alice step 2.
Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1342
(Fed. Cir. 2017); see also Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d
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1350, 1354 (Fed. Cir. 2016) (explaining that claims are directed to an
abstract idea where they do not recite “any particular assertedly inventive
technology for performing [conventional] functions”).
Step 1 of Alice
We agree with the Examiner that the subject matter of claim 13 is
directed generally to the abstract idea of organizing human activity. In
particular, claim 13, when viewed as a whole, is directed to determining
student engagement through a combination of the abstract-idea categories of
collection, manipulation, and display4 of student interaction data.
We disagree with Appellants’ characterization of the claims as being
directed to machine learning. Appeal Br. 4. Appellants’ argument considers
only individual elements of the claims and does not consider the claimed
subject matter as a whole. The recitation of “machine learning” at most
limits the claimed invention to a particular field of use. Limitations of the
invention to a particular technological environment in which to apply the
underlying abstract concept, “do not make an abstract concept any less
abstract under step one.” Intellectual Ventures /, 850 F.3d at 1340.
Although the use of machine learning adds a degree of particularity to the
claims, the underlying concept embodied by the limitations merely
encompasses the abstract idea itself of collection, manipulation, and display
of student interaction data. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d
709, 715 (Fed. Cir. 2014) (“[A]ny novelty in implementation of the idea is a
factor to be considered only in the second step of the Alice analysis.”).
4 We use “display” to refer to the claimed “result module.”
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Appellants’ attempt to analogize the claimed subject matter with the
claims at issue in DDR Holdings, LLC v. Hotels.com, LP, 773 F.3d 1245
(Fed. Cir. 2014) is misplaced. Appeal Br. 5-6 (Appellants arguing that “the
problems addressed by the claims specifically arise in the realm of computer
technology”). Unlike the situation in DDR Holdings, in which the problem
of retaining website visitors was a challenge particular to the Internet {id. at
1247), the problem addressed by the claims of evaluating a student’s
interaction with course material is not a challenge particular to a computer
environment. The claims here recite the performance of a business practice
(determining student engagement) known from the pre-electronic learning
world along with the requirement to perform it on a computer. See Appeal
Br. 4—5 (citing Spec. Tflf 2—3); Spec. 12 (“Th[e] lack of personal contact
[between teachers and students] can make it difficult for academic
institutions to determine whether students using electronic learning materials
are engaged by the material, whether students are likely to succeed
academically, or the like.”). The fact that the claimed solution involves
computer technology is not surprising considering that the challenge was to
implement the pre-electronic learning business practice on a computer.
In DDR Holdings the court focused on the problem solved by the
invention and not on the solution. The court characterized the problem in
DDR as “a problem specifically arising in the realm of computer networks”
because in the pre-Internet world, there were no websites. Id. at 1257. In
this case, the problem addressed by the claims is one of determining student
engagement. Even in an electronic learning environment, the problem does
not specifically arise in the realm of computer networks. Indeed, Appellants
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acknowledge that humans routinely assess student engagement, although
Appellants contend increasing use of electronic learning materials computer-
based learning and a decrease in student-teacher interactions making it more
difficult for humans to assess student engagement. See Spec. 12; Appeal
Br. 2—A. The fact that the claim is directed to a mechanism for using a
computer to monitor these interactions and process the data to form
observations does not remove the claim from the realm of an abstract idea.
Appellants further argue that “machine learning, by its very nature
and the plain definition of ‘machine,’ must be performed by a statutory
machine.” Appeal Br. 5. “The bare fact that a computer exists in the
physical rather than purely conceptual realm ‘is beside the point.’” DDR,
773 F.3d at 1256 (quoting Alice, 132 S. Ct. at 2358). For these reasons, we
determine that claim 13 is directed to an abstract idea of determining student
engagement for which a computer is invoked merely as a tool.
Step 2 of Alice
Claim 13 does not add significantly more to the abstract idea.
Appellants do not contest the fact that “machine learning” was a known
technique at the time of the invention. In fact, “machine learning” is a term
of art as evidenced by the Examiner’s definition of the term as found in a
dictionary. Ans. 4. Claim 13 amounts to a recitation of using conventional
and generic machine learning to automate the processing of monitored
interactions between a user and a computer.
Appellants argue that “machine learning is not a generic computer
element such as a processor or memory, but is a specialized structure that
serves a specialized technical purpose.” Appeal Br. 6 (comparing machine
learning to the thermocouple in Diamond v. Diehr, 450 U.S. 175, 178 (1981)
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and arguing that “machine learning is not unknown, but neither is it
generic.”). Although machine learning is not simply a generic computer, it
is a known technique in computing, and Appellants fail to claim a specific
type of machine learning. Rather, Appellants’ claim 13 recites using
machine learning generally on raw data to identify patterns in the data,
which is a fundamental concept of machine learning. Thus, claim 13 does
not present a new technical solution, and we perceive no “inventive concept”
that transforms the abstract idea of collecting, manipulating, and displaying
student interaction data into a patent-eligible application of that abstract
idea. Such a general and generic recitation of machine learning, without
particularity, is simply not enough under step two of the Alice test. See
Ultramercial, 772 F.3d at 715—16 (holding the claims insufficient to supply
an inventive concept because they did not “do significantly more than
simply describe [the] abstract method,” but rather are simply “conventional
steps, specified at a high level of generality”) (quoting Alice, 134 S. Ct. at
2357).
Appellants contend that the claims recite “significantly more” than
just apply an abstract idea because:
using machine learning to compare monitored electronic learning
interactions to archetypal learning patterns, and to send an alert
for a student or an evaluation for electronic learning material,
based on the machine learning comparison, especially using “a
machine learning ensemble including a plurality of learned
functions from multiple machine learning classes,” is a
significant improvement to the technical field of electronic
learning.
Reply Br. 5. We conclude, however, that the machine learning ensemble of
claim 13 does not sufficiently transform the abstract concept into a
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patentable invention under step two. In particular, we fail to see, and the
Specification fails to clearly evidence, how the use of a machine learning
ensemble is a technological improvement over, or differs from, the general
concept of machine learning. In particular, “a machine learning ensemble
including a plurality of learned functions from multiple machine learning
classes” —although technical sounding—simply describes the general
concept of machine learning itself. The mere fact that Appellants applied
coined labels to conventional processes does not make the underlying
concept inventive. See, e.g., Alice, 134 S. Ct. at 2352 n.2, 2360 (finding the
claims abstract despite the recitation of technical-sounding names such as
“shadow credit record[s]” and “shadow debit record[s]”). The recited
“machine learning” and “machine learning ensemble” provide little more
than an unspecified set of rules for recognizing patterns based on raw data
and comparing monitored student interaction data to these patterns, akin to
the general known process of machine learning. As such, the claim
language amounts to the application of known “machine learning” to an
abstract idea and does not impart an inventive concept to the abstract
apparatus.
For these reasons, we sustain the rejection of claim 13, and claims 14-
27 which fall with claim 13, under 35 U.S.C. § 101.
Rejection of claims 13-16 and 18-27 under 35 U.S.C. § 102(b) as
anticipated by Sorenson
The Examiner’s Rejection
The Examiner finds that “‘[mjachine learning’ is a term of art and one
definition of it is ‘a branch of artificial intelligence in which a computer
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generates rules underlying or based on raw data that has been fed into it.”
Ans. 4 (citing http://dictionary.reference.com/browse/machine+leaming).
The Examiner further states “[i]n other words, the broadest reasonable
interpretation of the term ‘machine learning’ would describe computer
software whereby the algorithm that the computer employs may change over
time based on data that is fed into the algorithm.” Ans. 4.
Appellants ’ Position
Appellants do not disagree with the dictionary definition, but
Appellants contend that the Examiner’s restatement of the definition is
unreasonably broad because “significant elements of the dictionary
definition are omitted” and that “a computer that ‘generates’ rules
underlying (or based on) raw data is creating new rules based on raw data,
not merely changing an algorithm (in a possibly predetermined way) based
on data.” Reply Br. 3. Appellants contend that Sorenson teaches merely
adjusting baselines and behavioral observations as data is acquired and fails
to teach generating a rule that underlies raw data. Reply Br. 4. Thus,
Appellants argue that Sorenson does not use machine learning. Id. at 6 (“the
measures of ‘Text Reading Approach,’ and the like, identified by the
Answer as Teamed functions’ are in no way Teamed’ within any reasonable
interpretation consistent with the [Specification, but are programmatically
defined instead”).
Legal Principles
“Both anticipation under § 102 and obviousness under § 103 are two-
step inquiries. The first step in both analyses is a proper constmction of the
claims .... The second step in the analyses requires a comparison of the
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properly construed claim to the prior art.” Medichem, S.A. v. Rolabo, S.L.,
353 F.3d 928, 933 (Fed. Cir. 2003) (internal citations omitted).
We determine the scope of the claims in patent applications not solely
on the basis of the claim language, but giving claims “their broadest
reasonable interpretation consistent with the specification” and “in light of
the specification as it would be interpreted by one of ordinary skill in the
art.” In re Am. Acad. ofSci. Tech Ctr., 367 F.3d 1359, 1364 (Fed. Cir.
2004).
Claim Construction of “Machine Learning ”
We agree with and adopt the dictionary definition of “machine
learning” that was proffered by the Examiner and not contested by
Appellants. This definition defines “machine learning” as “a branch of
artificial intelligence in which a computer generates rules underlying or
based on raw data that has been fed into it.” See Ans. 4 (citing
http://dictionary.reference.com/browse/machine+leaming). We decline to
adopt the Examiner’s broadened restatement of this definition to encompass
“computer software whereby the algorithm that the computer employs may
change over time based on data that is fed into the algorithm.” Ans. 4. This
restatement omits a key feature of machine learning, which, as we
understand the term as used in the art, requires the computer to generate new
rules based on raw data and not simply to change existing rules based on
data. Reply Br. 3.
Anticipation by Sorenson
Based on the interpretation of “machine learning” set forth above, for
the reasons that follow, the Examiner’s finding that Sorenson discloses
“machine learning” as recited in claims 13-16 and 18-27 is not supported by
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a preponderance of the evidence. Specifically, Sorenson discloses an
analyzing function 150 that uses information gathered by monitoring a
user’s interaction with course material to generate behavioral observations
about the interaction. Sorenson, para. 97. For example, Sorenson describes
that the analyzing function 150 can compare the time it took a user to read a
passage with a baseline reading rate assigned to the passage and generate a
behavioral observation (e.g., the reader is not devoting sufficient time to the
task of reading the passage). Id. Sorenson discloses further updating this
behavioral observation based on additional input (e.g., if the user’s scores
for quizzes about the passage show that the user had a thorough
understanding of the material). Id. at para. 98. The table provided in
paragraph 108 of Sorenson details further examples of rules employed by the
computer to derive various learning behaviors based on data gathered from
monitoring the user’s interactions with course materials. For example, if the
collected data show that the user engaged in intra-directional navigation
through the material, left the browser environment to engage other
applications, and/or made multiple exits and entrances into a course, the data
would indicate that the user is distracted.
Sorenson does not, however, clearly disclose that these rules, such as
set forth in paragraph 108, are generated by the computer based on raw data
received from monitoring the user’s interactions. As pointed out by
Appellants, it is possible that the rules set forth in Sorenson are predefined
as part of the algorithm employed by the computer and the computer does
not generate new rules based on raw data. Instead, as discussed in Sorenson,
the computer may update the behavioral observations based on further data,
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but these updates to the observations could also be based on predefined rules
as part of the computer algorithm.
For these reasons, we do not sustain the Examiner’s rejection of
claims 13-16 and 18-27 under 35 U.S.C. § 102(b) as anticipated by
Sorenson.
Rejection of claim 17 under 35 U.S.C. § 103(a) as unpatentable over
Sorenson and Intelligent Miner
In the rejection of claim 17, the Examiner relies on the deficient
finding that Sorenson discloses machine learning as the basis for further
modifying Sorenson with the disclosure of Intelligent Miner. Non-Final
Act. 13-14. For the same reasons set forth above in the analysis of the
anticipation rejection, we likewise do not sustain the rejection of claim 17
under 35 U.S.C. § 103(a) as unpatentable over Sorenson and Intelligent
Miner.
DECISION
The decision to reject claims 13-27 under 35 U.S.C. § 101 is
affirmed.
The decision to reject claims 13-16 and 18-27 under 35 U.S.C.
§ 102(b) and claim 17 under 35 U.S.C. § 103(a) is reversed.
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)(l)(iv).
AFFIRMED
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