Ex Parte Bonas et al

Patent Trial and Appeal Board

Jan 4, 2018

13930660 (P.T.A.B. Jan. 4, 2018)

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.
13/930,660 06/28/2013 Rachel M. Bonas CHA920130006USl_8134-0069 5993
73109 7590 01/11/2018
Cuenot, Forsythe & Kim, LLC
20283 State Road 7
Ste. 300
Boca Raton, EL 33498
EXAMINER
KAZEMINEZHAD, FARZAD
ART UNIT PAPER NUMBER
2657
NOTIFICATION DATE DELIVERY MODE
01/11/2018 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):
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PTOL-90A (Rev. 04/07)
UNITED STATES PATENT AND TRADEMARK OFFICE
BEFORE THE PATENT TRIAL AND APPEAL BOARD
Ex parte RACHEL M. BONAS, EDWIN J. BRUCE,
BENJAMIN J. FLORA, and ROMELIA H. FLORES
Appeal 2017-0069041
Application 13/930,660
Technology Center 2600
Before THU A. DANG, DENISE M. POTHIER, and JOYCE CRAIG,
Administrative Patent Judges.
POTHIER, Administrative Patent Judge.
DECISION ON APPEAL
STATEMENT OF THE CASE
Appellants2 appeal under 35 U.S.C. § 134(a) from the Examiner’s
Final Rejection of claims 1—4, 6—12, and 14—20. App. Br. 34. Claims 5 and
13 have been canceled. Id. at 36, 38. We have jurisdiction under 35 U.S.C.
§ 6(b). We affirm.
1 Throughout this opinion, we refer to (1) the Final Action (Final Act.)
mailed June 7, 2016, (2) the Appeal Brief (App. Br.) filed November 7,
2016, (3) the Examiner’s Answer (Ans.) mailed January 27, 2017, and (4)
the Reply Brief (Reply Br.) filed March 27, 2017.
2 The real party in interest is listed as International Business Machines
Corporation. App. Br. 3.
Appeal 2017-006904
Application 13/930,660
Invention
Appellants’ invention relates to “computing resources for language
enhancement and, more particularly, to electronically based thesauruses.”
Spec. 11 ; see id. 11 2—3, Abstract.
Illustrative claim 1 is reproduced below:
1. A method of language enhancement, the method
comprising:
automatically gathering source text from a plurality of
text sources, wherein at least a portion of the source text is
stored as natural language documents, the plurality of text
sources including at least one social media website, and storing
the source text to a thesaurus data infrastructure;
receiving subject text being exposed to thesaurus
processing;
identifying a context of the subject text;
accessing the thesaurus data infrastructure to identify
source text having context similar to the context of the subject
text;
analyzing, using a processor, the identified source text to
identity at least one candidate word or phrase contained in the
source text to recommend as a replacement for at least one
word or phrase contained in the subject text by performing
natural language inference processing on the source text stored
as natural language documents; and
recommending the identified at least one candidate word
or phrase as the replacement for the at least one word or phrase
contained in the subject text.
The Examiner relies on the following as evidence of unpatentability:
Siegel
Zangvil
Papachristou
US 7,610,382 B1 Oct. 27, 2009
US 2010/0286979 A1 Nov. 11, 2010
US 8,392,441 B1 Mar. 5,2013
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The Rejections
Claims 1—4, 6—12, and 14—20 are rejected under 35 U.S.C. § 101 as
being directed to patent ineligible subject matter. Ans. 2-4.
Claims 1, 4, 6, 7, 9, 12, 14, 15, 17, and 20 are rejected under
35 U.S.C. § 102(a)(1) as anticipated by Siegel. Ans. 4—8.
Claims 2, 3, 10, 11, 18, and 19 are rejected under 35 U.S.C. § 103 as
unpatentable over Siegel and Papachristou. Ans. 9-11.
Claims 8 and 16 are rejected under 35 U.S.C. § 103 as unpatentable
over Siegel and Zangvil. Ans. 12—13.
THE § 101 REJECTION
Under 35 U.S.C. § 101, a patent may be obtained for “any new and
useful process, machine, manufacture, or composition of matter, or any new
and useful improvement thereof.” The Supreme Court has “long 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 Int’l, 134 S. Ct. 2347, 2354 (2014) (quoting Ass’n for Molecular
Pathology v. Myriad Genetics, Inc., 133 S. Ct. 2107, 2116 (2013)). The
Supreme Court in Alice reiterated the two-step framework previously set
forth in Mayo Collaborative Services v. Prometheus Laboratories, Inc., 566
U.S. 66, 70-72 (2012), “for distinguishing patents that claim laws of nature,
natural phenomena, and abstract ideas from those that claim patent-eligible
applications of those concepts.” Alice, 134 S. Ct. at 2355.
The first step in that analysis is to determine whether the claims are
directed to one of those patent-ineligible concepts, such as an abstract idea.
Abstract ideas may include, but are not limited to, fundamental economic
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practices, methods of organizing human activities, an idea of itself, and
mathematical formulas or relationships. Id. at 2355—57. If the claims are
not directed to a patent-ineligible concept, the inquiry ends. Otherwise, the
inquiry proceeds to the second step where the elements of the claims are
considered “individually and ‘as an ordered combination’ to determine
whether the additional elements ‘transform the nature of the claim’ into a
patent-eligible application.” Id. (quoting Mayo, 56 U.S. at 79).
Claims 1—4 and 6—8
Mayo/Alice Analysis — Step 1
Regarding representative claim 13 4and the first part of the Alice!Mayo
analysis, the Examiner concludes the claims are “directed to a series of steps
for evaluating a text and replacing certain words.” Ans. 2. The Examiner
further concludes the claims are “directed to nothing more than a method for
obtaining replacement text using a thesaurus[,] which is an abstract idea in
the form of comparing new and stored information to determine linguistic
replacement options and relies on well known, basic thesaurus usage.” Id. at
3. In the Response to Argument section, the Examiner further finds the
above abstract idea corresponds to “bullet 6” of the July 2015 Update:
Interim Eligibility Guidance Identifying Abstract Ideas A Id. at 14.
Regarding Alice Step 1, Appellants contend the claims are not abstract
but involve “natural language inference processing” rooted in technology.
App. Br. 16, 18—19; Reply Br. 2. Appellants argue the claim limitations “are
3 Dependent claims 2—4 and 6—8 are not separately argued. App. Br. 15—26.
We select claim 1 as representative. See 37 C.F.R. § 41.37(c)(l)(iv).
4 July 2015 Update: Interim Eligibility Guidance Quick Reference Sheet 2
(July 2015), available at
https://www.uspto.gov/sites/default/files/documents/ieg-july-2015-qrs.pdf.
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not business practices known from the pre-Computer world.” App. Br. 19.
Appellants also assert the recited arrangement “improve[s] on existing
thesaurus functionality.” App. Br. 20-21 (citing Enfish, LLC v. Microsoft
Corp., 822 F.3d 1327, 1337 (Fed. Cir. 2016)). Lastly, Appellants argue
claim 1 performs various processes to a new and useful end, which is a
tangible result and not a mere abstract idea. App. Br. 21 (citing Alice, 134
S.Ct. 2347); Reply Br. 3, 5. We are not persuaded.
At the outset, we note that the Examiner determines the claims are
directed to an idea (see Ans. 2—3) itself—not a fundamental business
practice. As such, any arguments focused on fundamental business practice
(see, e.g., App. Br. 19) are unavailing. Specifically, the Examiner states the
claims are directed to an abstract idea of “comparing new and stored
information to determine linguistic replacement options and relies on well
known, basic thesaurus usage.” Ans. 14 (quoting from Final Act. 12). We
agree with the Examiner.
In SmartGene, Inc. v. Advanced Biological Labs. SA, 555 F. App’x.
950 (Fed. Cir. 2014), the Federal Circuit analyzed whether a method,
system, and computer program product for guiding the selection of a
treatment regimen for a patient with a known disease or medical condition
was directed to an abstract idea. Id. at 951. The claimed method recited a
computer device with three knowledge bases, including a knowledge base
with rules for selecting regimens and another knowledge base with advisory
information useful for the patient treatment concerning different regimens.
Id. at 952. The process further included generating in the computer device a
ranked regimen list and advisory regimen information based on the patient
information and expert rules. Id.
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In concluding these claims involve an abstract idea, the SmartGene
court found the claims “do[] no more than call on a ‘computing device,’ with
basic functionality for comparing stored and input data and rules, to do what
doctors do routinely.” Id. at 954. The court also stated the claimed method
has a computer device with routine input, comparison, and output
capabilities (id.) and places very broad limitations on a computing device,
which like a doctor’s mind, contains a set of “expert rules for evaluating and
selecting” from different treatment regimens and “advisory information” (id.
at 955).
Similarly, the instant claims involve a computing device (e.g., a
processor) with basic functionality for guiding a selection of a candidate
word or phrase located in stored source text as a replacement for a word or
phrase in a subject text and generating options, which involve routine input,
comparison, and output. App. Br. 35 (Claims App’x). For example, claim 1
recites a “gathering source text” step, a “receiving subject text” step, an
“identifying a context of the subject text” step, an “accessing the thesaurus .
. . to identity source text having context similar to the context of the subject
text,” “analyzing ... the identified source text,” step, and a “recommending
the identified at least one candidate” step. Id.
Some, but not all of these steps, may also involve using “a processor”
(e.g., the “analyzing” step). Id. However, like SmartGene, the recited steps
in claim 1 place broad limitations on the processor. See id. Also, but for the
recitation of a generic “processor” in claim 1, we conclude the above, recited
steps or functions could be performed as mental steps, or with the aid of pen
and paper. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366,
1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable,
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even when performed by a computer, was precisely the holding of the
Supreme Court in Gottschalk v. Benson”5). That is, such steps are
equivalent to human mental work.6
Also, the Specification describes inferencing engine 130 can identify a
context for the subject text and can identify new words or phrases (e.g.,
candidates replacement words) for the subject text by leveraging data
infrastructure 120 (e.g., a database) based on the identified context. Spec.
37, 39, 41, Fig. 3; see also Ans. 18—19 (discussing some of these
paragraphs). Additionally, the disclosure indicates the inferencing engine
can include rules engine 136. Id. 140. Yet, the disclosure does not
demonstrate sufficiently that claim 1 ’s “natural language inference
processing” involves any specialized algorithm. See App. Br. 16—17; see
also Ans. 17 (discussing McRO, Inc. v. Bandai Namco Games America Inc.,
837 F.3d 1299, 1312 (Fed. Cir. 2016)).
Even so, to the extent the recited “natural language inference
processing” involves a database (e.g., thesaurus data infrastructure)
containing expert rules, such a recitation places a very broad limitation on
the processor, which like a person’s or editor’s mind, contains a set of expert
rules for evaluating and selecting from different words and phrases. See
SmartGene, 555 F. App’x at 955. Additionally, to the extent the recited
5 Gottschalk v. Benson, 409 U.S. 63 (1972).
6 See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1146-47
(Fed. Cir. 2016) (“While the Supreme Court has altered the § 101 analysis
since CyberSource in cases like Mayo and Alice, we continue to ‘treat []
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’”) (quoting Elec. Power Grp., LLC v.
Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016)).
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“natural language inference processing” in claim 1 roots the claimed subject
matter in computer technology (see App. Br. 16 (discussing artificial
intelligence)), the claims in SmartGene similarly involved a computer
system with knowledge bases’ rules but were still deemed to be directed to
an abstract idea.
Even assuming the claimed “natural language inference processing”
involves a computer executing an algorithm, simply reciting using a
computer to execute an algorithm that can be performed in the human mind
does not turn a general purpose computer (e.g., a processor in claim 1) into a
new machine programmed to perform particular functions. See
CyberSource, 654 F.3d at 1374—75. That is, incidental use of a computer to
perform mental processes of analyzing a database and obtaining candidate
words/phrases for subject text by performing “thesaurus processing” does
not impose a sufficiently meaningful limit on the claim’s scope. See id. at
1375.
The Supreme Court also instructs: “[t]he ‘novelty’ of any element or
steps in a process, or even of the process itself, is of no relevance in
determining whether the subject matter of a claim falls within the § 101
categories of possibly patentable subject matter.” Diamond v. Diehr, 450
U.S. 175, 188—89 (1981). As such, Appellants’ allegation that the prior art
does not disclose a claim limitation is unavailing. App. Br. 17.
We further are not persuaded by Appellants’ arguments that the
claims in the instant application are similar or analogous to those in DDR
Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014) and
Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016), such that
the claims are patent eligible under § 101. See App. Br. 17—20. For
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example, we conclude independent claim 1, which recites a “processor,” is
not related to the type of patent-eligible database claim considered by the
court in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016).
Here, none of the limitations in claim 1 are directed to a “self-referential
table for a computer database.” Enfish, 822 F.3d at 1336. At best, we
conclude claim 1 merely encompass a conventional database that stores
typical types of text sources having no further specificity (e.g., “the source
text is stored as natural language documents” and “storing the source text to
a thesaurus data infrastructure”). App. Br. 35 (Claims App’x).
Moreover, Appellants have not explained adequately how the claimed
subject matter “improve[s] on existing thesaurus functionality” (App. Br. 20;
Reply Br. 4) or requires specific software that can make non-abstract
improvements on computer technology. See Enfish, 822 F.3d at 1336; see
also Ans. 16—17. Thus, we conclude Appellants’ claims are merely directed
to a generic “processor” for performing routine “thesaurus processing” (see
App. Br. 35 (Claims App’x)) and are not “directed to a specific
improvement to the way computers operate, embodied in the self-referential
table,” as was found by the court regarding the subject claims in Enfish. Id.
Likewise, we disagree that claim 1 describes a solution that is
“necessarily rooted in computer technology in order to overcome a problem
specifically arising in the realm of computer networks.” DDR, 773 F.3d at
1257. Appellants’ argument overlooks that the argued “solution” must be a
technical solution. Appellants’ argument does not explain how either the
problem or solution is technical. Unlike DDR or Enfish, Appellants merely
assert the limitations “are not business practices known from the pre-
Computer world” (App. Br. 19) but fail to indicate the solution rooted in
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computer technology that claims have overcome (Ans. 16). Additionally,
we note Appellants’ claims on appeal are silent regarding any mention of a
computer “network,” such as the claims in DDR. App. Br. 35 (Claims
App’x). Rather, Appellants quote the claim language (App. Br. 19), which
merely addresses an abstract idea through the use of generic, computer-
related recitations that do not add meaningful limitations to steps otherwise
directed to an abstract idea. Id.
Lastly, Appellants contend that the recited steps “clearly do not seek
to tie up any judicial exception of an abstract idea such that others cannot
practice it.” App. Br. 24; Reply Br. 6. Yet, we determine that Appellants’
explanation of claim 1, when viewed as a whole, does not sufficiently
address how the claims do not tie-up “the executable operation steps
generally such that others cannot practice them.” Id. at 23. Moreover, lack
of preemption does not make the claims any less abstract. See Ariosa
Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015)
(“While preemption may signal patent ineligible subject matter, the absence
of complete preemption does not demonstrate patent eligibility.”).
Mayo/Alice Analysis — Step 2
Because the claims are directed to an abstract idea, we turn to the
second part of the Alice!Mayo analysis to determine if there are additional
limitations that individually, or as an ordered combination, ensure the claims
amount to “significantly more” than the abstract idea. Alice, 134 S. Ct. at
2357.
Regarding Alice, Step 2, the Examiner finds the claim further recites
using a processor to analyze the identified source text, which requires a
computer. Ans. 3. However, the Examiner determines “the additional
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limitations simply call for the implementation of the abstract idea on a
conventional computer.” Id. The Examiner states the other elements in the
claim do not amount to significantly more than the abstract idea. Id.
Appellants disagree, contending claim 1 is “nonconventional and non­
generic arrangement of pieces” that amount to significantly more than an
abstract idea. App. Br. 22 (quoting Bascom Global Internet Services, Inc. v.
AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016)). To support this
argument, Appellants quote the claim language but provide no further
explanation. See, e.g., id. at 20—22, 25. We are not persuaded.
Returning to SmartGene, the court found the claims amount to no
more than mental steps of comparing new and stored information and using
rules to identify medical options and do not identify new computer
hardware, which would apply the idea to tangible physical objects or
advance physical implementations beyond “well understood, routine,
conventional activity” or steps which doctors routinely perform. Id. at 955
(citing Mayo, 56 U.S. at 71—72, 80—81). Specifically, the court determined
the claims “call[] on a computer to do nothing that is even arguably an
advance in physical implementations of routine mental information-
comparison and rule-application processes.” Id. Fikewise, we determine
claim 1 at issue calls on a processor to do nothing that advances physical
implementations of routine, mental information-comparison and rule-
application processes, including comparing new and stored information to
determine linguistic replacement options that rely on well known, basic
thesaurus usage.
Appellants further argue that the “analyzing” step involving “natural
language inference processing” is a specific limitation other than what is
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well understood, routine, and conventional in the field or adds
unconventional steps confining the claim to a particular useful application.
App. Br. 25. In response, the Examiner determines claim 1 and the
disclosure are silent on the specifics of the inference processing and are
described similar to well-understood, routine, and conventional processes
rather than something more. See Ans. 18—19 (citing Spec. ]Hf 39, 41).
We agree with the Examiner and further note that the document
discussed in the Appeal Brief7 underscores that “natural language inference
processing” technology, which learns rules using document sets, was known
and conventional. We further find Appellants’ claimed data gathering,
receiving, identifying, accessing, analyzing, and recommending are
consistent with “well-understood, routine, [and] conventional activities]
previously engaged in by [people] in the field.” Mayo, 566 U.S. at 73. That
is, the practice of identifying a context of subject text (e.g., a context for the
word to expose to a thesaurus), analyzing the source text to identity
candidate words and phrases, and recommending identified candidates as a
replacement for the subject text has been widespread for writers and editors
of all types.
Further, “simply appending conventional steps, specified at a high
level of generality, to . . . abstract ideas cannot make those . . . ideas
patentable.” Id. at 82. For example, as noted above, we conclude
7 Miles Osborne et al., Processing Natural Language Software Requirement
Specifications, IEEE Proc. of 2d Int’l Conf. Requirements Eng’g
299-36 (1996), cited in App. Br. 17 n.l and quoted in id. at 27—28. This
publication was not provided in the record. Cf. 37 C.F.R. § 41.33(d).
Additionally, the discussion indicates NLP (natural language processing)
algorithms are often but not always grounded in statistical inference.
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Appellants’ claims merely encompass the customary use of general-use
processor and conventional techniques of storing data in a database and
natural language inference processing (e.g., “storing the source text to a
thesaurus data infrastructure” and “analyzing ... the identified source text”
using a processor “to identify at least one candidate word or phrase ... by
performing natural language inference processing.”) App. Br. 35 (Claims
App’x). Thus, we conclude Appellants’ claims are merely directed to a
generic “processor” and conventional processing techniques that are not
“directed to a specific improvement to the way computers operate, embodied
in the self-referential table,” as was found by the court regarding the subject
claims in Enfish. Enfish, 822 F.3d at 1336.
We further determine claim 1 is limited to a thesaurus processing
without significantly more to transform the abstract idea and is similarly
directed to “merely selecting information, by content or source, for
collection [and] analysis[, which] does nothing significant to differentiate a
process from ordinary mental processes.” Electric Power Group, LLC v.
Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016). Further regarding the
use of a generic processor, see Alice, 134 S. Ct. at 2358 (holding that “the
mere recitation of a generic computer cannot transform a patent-ineligible
abstract idea into a patent-eligible invention”); Bascom, 827 F.3d at 1348
(Fed. Cir. 2016) (“An abstract idea on ‘an Internet computer network’ or on
a generic computer is still an abstract idea.”); Intellectual Ventures I LLC v.
Capital One Fin. Corp., 850 F.3d 1332, 1341 (Fed. Cir. 2017) (“Rather, the
claims recite both a generic computer element—a processor—and a series of
generic computer ‘components’ that merely restate their individual functions
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.... That is to say, they merely describe the functions of the abstract idea
itself, without particularity. This is simply not enough under step two.”).
We thus conclude Appellants’ claim 1 is not directed to an
improvement in computer or database functionality, such that none of the
claim limitations, viewed “both individually and ‘as an ordered
combination,’” amount to significantly more than the judicial exception in
order to transform the nature of the claims sufficiently into patent-eligible
subject matter. See Alice, 134 S. Ct. at 2355 (internal quotations omitted)
(quoting Mayo, 56 U.S. at 79).
For at least the above reasons, and on this record, Appellants have not
persuaded us the Examiner erred in rejecting claims 1—4 and 6—8 under
§101. Accordingly, we sustain the Examiner’s rejection.
Claims 9—12 and 14—20
Independent 9 claims recites “[a] system” with similar limitations to
those in claim 1. App. Br. 37 (Claims App’x). Independent claim 17 recites
“[a] computer program product” with similar limitations to those in claim 1.
Id. at 39-40 (Claims App’x). For these claims, Appellants do not present
separate arguments, relying on the arguments presented for claim 1. Id. at
26. We are not persuaded for the above-discussed reasons. Accordingly, we
sustain the Examiner’s rejection of claims 9 and 17, as well as dependent
claims 10-12, 14—16, and 18—20, not argued separately. Id.
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THE ANTICIPATION REJECTION OVER SIEGEL
Claims 1, 6, 7, 9, 14, 15, and 17
Regarding representative claim l,8 the Examiner finds that Siegel
discloses all its limitations, including the step of “analyzing ... the
identified source text to identify at least one candidate word or phrase ... to
recommend as a replacement for at least one word or phrase contained in the
subject text by performing natural language inference processing on the
source text.” Ans. 6 (citing Siegel 13:60-63, 65—66). Appellants argue
Siegel’s “synonym substitution” does not perform natural language
inference processing as recited, which includes machine learning grounded
in statistical inference. App. Br. 27—28.
ISSUE
Under § 102, has the Examiner erred in rejecting claim 1 by finding
that Siegel discloses
analyzing, using a processor, the identified source text to
identify at least one candidate word or phrase contained in the
source text to recommend as a replacement for at least one
word or phrase contained in the subject text by performing
natural language inference processing on the source text stored
as natural language documents
(hereafter “the analyzing step”)?
8 Appellants argue claims 1, 9, and 17 as a group. See App. Br. 27—29. We
select claim 1 as representative. See 37 C.F.R. § 41.37(c)(l)(iv).
Additionally, dependent claims 6, 7, 14, and 15 are not argued. See App. Br.
27—33 (addressing only claims 1, 4, 9, 12, 17, and 20 with regards to the
§ 102 rejection). The appeal is taken from the rejection of all claims. See 37
C.F.R. 41.31(c).
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ANALYSIS
Based on the record before us, we find no error in the Examiner’s
rejection of claim 1. As the Examiner notes, neither the claims nor the
Specification defines the recited “natural language inference processing” in
terms of machine learning. See Ans. 20 (citing Spec. 39, 41); see also
Reply Br. 7—8 (reproducing Spec. 29, 36). Moreover, the cited
references9 used to support Appellants’ purported “machine learning”
understanding of the recitation “natural language inference processing” have
not been provided in the record for review. In any event, a quote from one
of these references reproduced in the Appeal Brief indicates that natural
language processing (NLP) algorithms “often, although not always, [are]
grounded in statistical inference.” App. Br. 28 (emphasis added). As such,
consistent with the disclosure and the related citations, the recited “natural
language inference processing” is not limited to machine learning processing
or grounded in statistical inferences. On the other hand, at least one
customary meaning describes a “natural language processing” as a technique
that recognizes human language.10
Turning to Siegel, the reference discloses a mechanism (e.g., synonym
substitution mechanism 208) used to select a synonym for a word among
candidate synonyms (e.g., permutations) by consulting synonym database
256. Siegel 13:58—68, Figs. 5A-B. Additionally, the candidate words in
9 App. Br. 17 n.l—2 (citing Miles Osborne et al., Processing Natural
Language Software Requirement Specifications, IEEE Proc. of 2d Int’l
Conf. Requirements Eng’g 299-36 (1996) (“Osborne”) and
http://www.dictionary.com/browse/natural-language-processing); Reply
Br. 7.
10 Natural-language processing (def.), Microsoft Computer Dictionary, 5th
ed. 358 (2002).
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database 256 are in a natural language format (e.g., English). See id. Thus,
Siegel’s process involves analyzing a database (e.g., analyzing the identified
source text to identify a candidate word or phrase contained in the source
text stored to a thesaurus data infrastructure) to determine candidate words
for synonym substitution (e.g., recommend a replacement for a word or
phase contained in the subject text, such as the word “movie” for “film” in
Figure 5B), which involves a technique that recognizes natural language
(e.g., English). As such, based on the arguments presented (App. Br. 27—
28), we are not persuaded of error in the Examiner’s rejection.
Although not presented or argued by Appellants (see id.), Siegel
discloses the recited “natural language inference processing” even if limited
by at least one common meaning of an “inference engine.”11 That is, Siegel
teaches applying various algorithms (e.g., rules) to select a synonym (e.g., a
conclusion) among candidate synonyms from a database, including hashing
excerpts (e.g., inputs) and using the hash to decide which words are eligible
for synonym substitution (e.g., matching inputs with facts/rules). Siegel
7:57—61, 13:1—5. Even further, Siegel teaches different sets of words may
be substituted for particular words for different types of textual data, such as
different words/synonyms for movie reviews and fictional works, including
using indications (e.g., context) as to which words/synonyms are appropriate
for particular types of textual works (e.g., matching inputs with facts and
rules to derive a conclusion). Id. at 13:40—50.
11 Inference engine (def.), Microsoft Computer Dictionary, 5th ed. 270
(2002).
17
Appeal 2017-006904
Application 13/930,660
For the foregoing reasons, Appellants have not persuaded us of error
in the anticipation rejection of independent claim 1 and claims 6, 7, 9, 14,
15, and 17, which are not separately argued.
Claims 4, 12, and 20
We reach the opposite conclusion for claim 4. Claim 4 recites
“processing the source text using dynamically created rules to identify the
source text having context similar to the context of the subject text, the
dynamically created rules generated by performing initial processing on the
source text when the source text is gathered.” App. Br. 36 (Claims App’x).
The Examiner finds Siegel discloses this limitation. Ans. 7 (citing Siegel
13:16—18, 65—67). Among other arguments, Appellants argue that the
citations to Siegel disclose different permutations of the excerpt to perform
synonym substitution but does not disclose the recited “the dynamically
created rules generated by performing initial processing on the source text
when the source text is gathered.” App. Br. 31; Reply Br. 10.
Turning to the cited passages in Siegel, Siegel discloses synonym
database 256 that may be adapted from various sources, such as a thesaurus.
Siegel 13:16—18. Siegel also discloses synonym substitution mechanism
208 uses synonym database 256 to generate a permutation (e.g., a candidate)
of the excerpt. Id. at 13:65—67. These passages do not disclose dynamically
created rules, let alone generating these rules during “initial processing on
the source text when the source text is gathered” as recited in claim 4.
Granted, other passages, not cited by the Examiner, discuss editing
and modifying synonyms in database 256 when certain words create
ambiguities or potentially inappropriate interpretations such that Siegel
teaches “dynamically created rules.” Siegel 13:11—16,29—39. Nevertheless,
18
Appeal 2017-006904
Application 13/930,660
the Examiner has not sufficiently explained how these rules or how the cited
passages disclose generating rules during “initial processing on the source
text when the source text is gathered,’ '' as recited. See Ans. 7, 22—23. At
best, the Examiner discusses adapting synonym database 256 “requires
‘rules associated with initial processing’” and the database being altered
using permutations. See Ans. 22—23. This explanation, however, does not
address dynamically creating rules—as opposed to synonyms— and further
does not address the rules are generated necessarily when the source text is
gathered in order to anticipate claim 1. Whether such a teaching to create
rules dynamically by initially processing the source text when the text is
gathered is obvious to one skilled in the art is not before us. Nor will we
speculate in the first instance on appeal.
For the foregoing reasons, Appellants have persuaded us of error in
the anticipation rejection of (1) dependent claim 4 and (2) dependent claims
12 and 20, for similar reasons.
THE OBVIOUSNESS REJECTIONS
Appellants do not argue the obviousness rejections of dependent
claims 2, 3, 8, 10, 11, 16, 18, and 19. See generally App. Br. These claims
depend directly or indirectly from independent claims 1, 9, and 17 discussed
previously. We sustain these rejections for the same reasons as their
independent claims. Accordingly, Appellants have not persuaded us of error
in the rejections of claims 2, 3, 8, 10, 11, 16, 18, and 19.
19
Appeal 2017-006904
Application 13/930,660
DECISION
We affirm the Examiner’s rejection of claims 1—4, 6—12, and 14—20
under § 101.
We affirm the Examiner’s rejection of claims 1, 6, 7, 9, 14, 15, and 17
under § 102.
We reverse the Examiner’s rejection of claims 4, 12, and 20 under
§102.
We affirm the Examiner’s rejections of claims 2, 3, 8, 10, 11, 16, 18,
and 19 under § 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)(l)(iv).
AFFIRMED
20
Application/Control No. Applicant(s)/Patent Under Patent
Notice of References Cited 13/930,660
Appeal No.
2017-006904
Examiner Art Unit
2657 Page 1 of 1
U.S. PATENT DOCUMENTS
* Document Number
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Date
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A us-
B us-
C US-
D US-
E US-
F US-
G US-
H US-
1 US-
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K US-
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M US-
FOREIGN PATENT DOCUMENTS
* Document Number
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Date
MM-YYYY Country Name Classification
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NON-PATENT DOCUMENTS
* Include as applicable: Author, Title Date, Publisher, Edition or Volume, Pertinent Pages)
U Inference&Natural Language - Dictionary - Microsoft Computer Dictionary, 5th Edition (2002)
V
w
X
*A copy of this reference is not being furnished with this Office action. (See MPEP § 707.05(a).)
Dates in MM-YYYY format are publication dates. Classifications may be US or foreign.
U.S. Patent and Trademark Office
PTO-892 (Rev. 01-2001) Notice of References Cited Part of Paper No.

PUBLISHED BY
Microsoft Press
A. Division of Microsoft Corporation
One Microsoft Way
F.edmond, Washington 98052-6399
Copyright © 2002 by Microsoft Corporation
All rights reserved. No part of the contents of tins book may be reproduced or transmitted in any form
or by any means without the written permission of the publisher.
Library of Congress Cataloging-in-Publication Data
Microsoft Computer Dictionary.—5th ed.
p. cm.
ISBN 0-7356-1495-4
1. Computers—Dictionaries. 2. Microcomputers—Dictionaries.
AQ76.5. M52267 2002
004'.03—dc2i 200219714
Printed and bound in the United States of America.
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Send comments to mspmptti@micmsoft.com.
Active Desktop, Active Directory, Ac tiveMovie, AcfiveStore, ActiveSync, ActiveX, Authenticode,
BackOffice, BizTalk, CiearType. DirectsD, DirectAniniation, DirectDraw, Directlnput, DirectMusic,
DirectPlay, DtrectShow, DirectSomid, DirectX, Entourage, FoxPro, FrontPage, Hotmail, hitelliEye,
InteiliMouse, IntelfiSense, JSeript, MapPoint, Microsoft, Microsoft Press, Mobile Explorer, MS-DOS,
MSN, Music Central, NetMeeting, Outlook, PhotoDraw, PowerPoint, SharePoint, UltimateTV, Visio,
Visual Basic, Visual C++, Visual FoxPro, Visual InterDev, Visual J++, Visual SourceSafe, Visual Studio,
Win32, Win32s, Windows, Windows Media, Windows NT, Xbox are either registered trademarks or
trademarks of Microsoft Corporation in the United States and/or other countries. Other product and
company names mentioned herein may be the trademarks of their respective owners.
The example companies, organi zations, products, domain names, e-mail addresses, logos, people, places,
and events depicted herein are fictitious. No association with any real company, organization, product,
domain name, e-mail address, logo, person, place, or event is intended or should be inferred.
Acquisitions Editor: Alex Blanton
Project Editor: Sandra Haynes
Body Part No. X08-41929
card
either on the system board or. on cards that are plugged
into expansion connectors. Common expansion buses
included on the system board are USB. PC Card, and PCI .
■See also AT bus.
expansion card n. See card (definition 1), expansion
board.
expansion slot n, A socket in a computer, designed to
hold expansion boards and connect them to tire system bus
(data pathway). Expansion slots are a means of adding or
enhancing tire computer's features and capabilities. In lap­
top and other portable computers, expansion slots come in
the form of PCMCIA slots designed to accept PC Cards
See also expansion board, PC Card PCMCIA slot.
experience points n. Often used in role-playing games
(RPGs), experience points are a way of measuring how
much -a player'has experienced or learned. As a player
moves through a game, additional benefits, often in the
form of increased statistics or skills, are earned. These
points are frequently spent or used by tire player to
increase his or her score. See also computer game,
role-playing game.
expert system n. An application program that makes
decisions or solves problems in a particular'field, such as
finance or medicine, by using knowledge and analytical
rules defined by experts in the field. It uses two compo­
nents, a knowledge base and an inference engine, to form
conclusions. Additional tools include user interfaces and
explanation facilities, which enable the system to justify
or explain its conclusions as well as allowing developers
to run checks on the operating system. See also artificial
intelligence, inference engine, intelligent database, knowl­
edge base.
expiration date n. The date on which a shareware, beta,
or trial version of a program stops functioning, pending
purchase of the full version or the entry of an access code.
expire vl\ To stop functioning in wdiole or in part. Beta
versions of software are often programmed to expire when
a new version .is released. See also beta2.
Explicitly Parallel Instruction Computing n. See EPIC
exploded view n. A form of display that show's a struc­
ture with its parts separated but depicted in relation to
each other. See the illustration.
Top sheil.
Pliiil
,v xx, V
« / . '■N. XvX,' } '
Hub
Woven liner
/ Write-protect tabXT
Bolton: shell
Lifter presses,
liners against
disk to trap dust
Shutter spring
'V
XX
■Shutter
Exploded view.
Explorer n. See Internet Explorer, Window's Explorer.
Explore Zip n. A destructive vims that attacks computers
running Windows, where it first appears as an e-mail attach­
ment namedzipped_files.exe. ExploreZip affects local
drives, mapped drives, and accessible network machines
and destroys both document and source-code files by open­
ing and immediately closing them, leaving a zero-byte file.
Described as both a Trojan horse (because it requires the
victim to open the attachment) and a worm (because it can
propagate itself in certain instances), ExploreZip spreads by
mailing itself to the return address of every unread e-mail in
the inbox of the computer's e-mail program, as well as by
Searching for—and copying itself to—the Windows direc­
tory on mapped drives and networked machines. See also
Trojan horse, virus, worm.
exponent n. In mathematics, a number that shows how
many times a number is used as a factor in a calculation;
in other words, an exponent show's that number’s power.
Positive exponents, as in 23, indicate multiplication (2
times 2 times 2). Negative exponents, as in 2 s, indicate
division (1 divided by 23). Fractional exponents, as m 81/3,
indicate the root of a number (the cube root of 8).
201
it ■■-i=-xe-'J .'id'll-s* .nfe*e f! r- :■£! cur mure
pointing to stored data. 2. In a database, to find data by
using keys such as words or field names to locate records.
3. In indexed file storage, to find files stored on disk by
using an index of file locations ( addresses). 4. In program­
ming and information processing, to locate-information"
stored in a table by adding an offset amount, called the
index, to.the base address of the fable.
Indexed address n. The location in memory of a. particu­
lar item of data within a collection of Items, such as an
entry' in a table. An indexed address is calculated by start­
ing with a base address and adding to it a value stored in a
register called an index register.
Indexed search n. A search for an item of data that uses
an index to reduce the amount of time required
indexed sequential access method n. A scheme for
decreasing tire time necessary to locate a data record
within a large database, given a key value that identifies
the record. A smaller index file is used to store the keys
along with pointers that locate tire corresponding records
in the large main database file. Given a key, first the index
file is searched for the key and then the associated pointer
is used to access the remaining data of the record in the
main file. Acronym.: ISAM.
Index hole n. The small,.round hole near the large, rou ad
spindle opening at the center of a 5.25-inch floppy disk.
The index hole marks the location of the first data sector,
enabling a computer to synchronize its read/wrife operations
with the disk’s rotation.
Indexing Service Query Language n. A query language
available in addition to SQL for the Indexing Service-in
Windows 2000. Formerly known as Index Server, its orig­
inal function was to index the content of Internet Informa­
tion Services (IIS) Web servers. Indexing Service now
creates indexed catalogs for the contents and properties of
both file systems and virtual Webs.
Index mark n. 1. A.magnetic indicator signal placed on.a
soft-sectored disk during formatting to mark the logical
start of each track. 2. A visual information locator, such, as!
a line, on a microfiche,
indicator n. A dial or light that displays information
about tire status of a device, such as a liehr connected to a
disk drive that glows when the disk is being accessed.
Indirect address n. See relative address,
inductance n. The ability to store energy in the form of a
magne tic field. Any length of wire has some inductance,
and coiling the wire, especially around a ferromagnetic
core, increases the inductance. The' unit of inductance is
the hetoy. Compare capacitance, induction.
Induction n. The creation of a voltage or current in a
material by means of electric or magnetic fields, as in the
secondary winding of a transformer when exposed to the
changing magnetic field caused by an alternating current
in the primary winding. See also, impedance; • Compare
inductance.
Inductor n. A component designed to have a specific
amount of inductance. An inductor passes direct current
but impedes alternating current to a degree dependent on
its frequency. An inductor usually consists of a length of
Wire coiled in a cylindrical or toroidal (doughnut-shaped)
form, sometimes with a ferromagnetic core Sec the illus­
tration. Also called' choke.
Inducton-One oifseveral kinds of inductors.
Industry Standard Architecture s. See ISA.
I NET n. I. Shat fm Internet, 2. An annual conference
held by the Internet Society.
•Inf A The file extension for device informatim files,
those files con laming script* used to control hardware
operations.
Infection n. The presence of a virus or Trojan horse in a
computer system See -also Trojan hofse,. virus, worm,
Infer vb. To formulate a conclusion based on specific
information, either by applying the rules of formal logic or
by generalizing from a set of observations. For example,
from the facts that canaries are birds and birds have feath­
ers, one can infer (draw the inference) that canaries have
feathers.
Inference engine n. The processing portion of an expert
system. It matches-input propositions with facts and rules
contained in a knowledge base and then derives a conclu­
sion, on which tire 'expert, system then acts.
Inference programming n. A method of'programming
(asin Prolog) in which programs yield results based on
270
iff nv- ioot: nfoimat' -'f: e\;«at on
logical inference from a set of facts and rales. See also
Prolog.
infinite loop n. 1. A loop that, because of semantic or
logic errors, can never terminate through normal means,
2. A loop that is intentionally written with no explicit ter­
mination condition but will terminate as a result of side
effects or direct intervention. See also loop1 (definition 1),
side effect.
Infix notation n. A notation, used for writing expres­
sions, in which binary operators appear between their
arguments, as in 2 + 4. Unary7 operators usually appear
before their arguments, as in -1. See also operator prece­
dence, postfix notation, prefix notation, unary operator,
Jnfo n. One of seven new top-level domain names
approved in 2001 by the Internet. Corporation for Assigned
Names and Numbers (ICANN). Unlike the other new
domain names, which focus on specific types of Web sites,
info is meant for unrestricted use,
Infobahn n. The Internet. Infobahn is a mixture of the
terms information and Autobahn, a German highway
known for the high speeds at which drivers can legally
travel. Also called: Information Highway, Information
Superhighway, the Net.
Infomediary n. A term created from the phrase informa­
tion intermediary. A service provider that positions itself
between buyers and sellers, collecting, organizing, and
distributing focused information that improves the interac­
tion of consumer and online business.
Information n. The meaning of data as it is intended to be
interpreted by people. Data consists of facts, which
become information when they are seen in context and
convey meaning to people. Computers process data with­
out any understanding of what the data represents.
Information Analysis Center n. See IAC.
Information and Content Exchange n. See ICE
(definition 1).
Information appliance n. A specialized computer
designed to perform a limited number of functions and,
especially, to provide access to'the Internet Although
devices such as electronic address books or appointment
calendars might be considered information appliances, the
tennis more typically used for devices Ural are less expensive
and less capable than a fully functional personal computer.
Set-top boxes are a current example; other devices,
envisioned for the future, would include network-aware
microwaves, refrigerators, watches, and the like. Also
called: appliance.
Information center n. 1. A large computer center and its
associated offices; the hub of an information management
and dispersal facility in an organization. 2. A specialized
type of computer system dedicated toinfotmation retrieval
and decision-support functions, Tire information in such a.
system is usually read-only and consists of data extracted
or downloaded from oilier production systems.
Information engineering n. See IE (definition I).
Information explosion 1. The current period in human
history, in which the possession and dissemination of
information has supplanted mechanization or industrial­
ization as a driving force in society. 2. The rapid growth
in the amount of information available today. Also
called: information revolution.
Information hiding n. A design practice in which imple­
mentation details for both data structures and algorithms
within a module or subroutine are hidden from routines using
that module or subroutine, so as to ensure that those routines
do not depend onsome particular detail of the implementa­
tion. In theory', information hiding allows the module or sub­
routine to be changed without breaking the routines that use
it. See also break, module, routine, subroutine.
Information Highway or Information highway n. See
Information Superhighway.
Information Industry Association n. See SIIA.
Information kiosk n. See kiosk.
Information management n. The process of defining,
evaluating, safeguarding, and distributing data within an
organization or a system.
Information packet n, .Stic packet (definition 1),
Information processing n. The acquisition, storage,
manipulation, and presentation of data, particularly by
electronic means.
Information resource management n. The process of
managing the resources for the collection, storage, and
manipulation of data, within an organization, or system.
Information retrieval n. The process of finding,
organizing, arid displaying information, particularly
by electronic means.
Information revolution n. See information explosion.
271
'A?t|.;n3'. |i~fcfi"3t on :n^- ap.’-L,i‘;rur^ n3'. iesMor - “\S
National information infrastructure n. AU.S, govern­
ment program to extend and oversee the development of
the Information Superhighway. The National. Information
Infrastructure is made up of a high-bandwidth, wide area
network that can carry data, fax, video, and voice trans­
missions to users throughout the United States. The net­
work is being developed mostly by private carriers. Many
of the services, which-are aimed at enabling the efficient
creation and dissemination of information, are alreadv
available on the Internet itself, including increased acces­
sibility' to quality education through distance learning and
increased access to government services. Acronym: NIL
See also Information Superhighway, Internet?., Next Gen­
eration Internet. Compare Internet.
National Institute of Standards and Technology n, A
branch of the U.S. Commerce Department that works to
develop -and encourage standards for measurement, science,
and technology' in order to promote commerce and improve
productivity in the marketplace. Prior to 1988, .the National
Institute of Standards and Technology was known as the
National Bureau of Standards. Acronym: NIST.
national language support n. l.The practice of creat­
ing programs that can display text in any language neces­
sary-', 2, A function in Windows that enables you to specify
system and user locale information. Acronym: NLS.
National Science Foundation ;? AXIS, government
agency intended to promote scientific research by funding
both research projects and projects that facilitate scientific
communication, such as NSFnet, the former backbone of
tire Internet. Acronym: NSF, See also backbone (defini­
tion 1), NSFnet.
National Television System Committee n. See NTSC.
native adj. Of, pertaining to, or characteristic of some­
thing that is in its original form. For example, many appli­
cations are able to work with files in a number of formats;
the format the application uses internally is its native file
format. Files in other formats must be converted to the
application’s native format before they can be processed
by the application.
native application >i. A program that is designed specifi­
cally for a particular type of microprocessor, that is, a pro-
gram that is binary' compatible with a processor. A native
application generally will run much faster than a normative
application, which mast be run with'the help of an emulator
program. See also binary compatibility, emulator.
native code a. Code that has been c-ompiled to processor-
specific machine code.
native compiler n. A compiler that produces machine
code for the computer on which it is running, as opposed
to a Cross-compiler, which produces code for another type
of computer. Most compilers are native compilers. See
also compiler (definition 2), cross-compiler.
native file format m The format, an application uses
internally to process data. The application must convert
files in other formats to the native format before it can
work with them. For example, a word processor might rec­
ognize text files in ASCII text format, but it will convert
them to its own native format.before it displays them.
native language n. See host language.
natural language n. A .language spoken or written by
humans, as opposed to a programming language or a
machine language. Understanding natural language and
approximating it in a computer environment is one goal of
research in artificial intelligence.
natural-language processing n, A field of computer
science and linguistics that studies computer systems that
can recognize and react to human language, either spoken
or written. See also artificiaiintelligence. Compare, speech
recognition.
natural language query n, A query to a.database, system
that is composed in a subset of a natural language, such as
English or Japanese. The query' must conform to some
restrictive syntax rales so that the system can parse it. See
also parse, syntax,
natural-language recognition n. See speech recognition.
natural language support n, A voice recognition sys­
tem that allows the user to use verbal commands in his
or her own language to direct- a computer’s actions.
Acronym: NLS.
natural number n. An integer, or whole number, that is
equal to or greater than zero. See also integer.
navigation bar n. On a Web page, a grouping of hyper­
links for getting around in that particular Web site. See
also hyperlink.
navigation keys n. The keys on a keyboard controlling
cursor movement, including the four arrow keys and the
Backspace, End, Home, Page Down, and Page Up keys.
See also arrow key. Backspace key, End key. Home key.
Page Down key, Page Up key.