International Business Machines Corporation

Patent Trials and Appeals Board

Oct 1, 2021

2020005066 (P.T.A.B. Oct. 1, 2021)

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.
15/471,436 03/28/2017 Aaron K. Baughman END920170086US1 1049
108686 7590 10/01/2021
IBM IPLAW (GLF)
c/o Garg Law Firm, PLLC
800 Bonaventure Way, suite 115
Sugar Land, TX 77479
EXAMINER
SIRJANI, FARIBA
ART UNIT PAPER NUMBER
2659
NOTIFICATION DATE DELIVERY MODE
10/01/2021 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):
dpandya@garglaw.com
garglaw@gmail.com
uspto@garglaw.com
PTOL-90A (Rev. 04/07)

UNITED STATES PATENT AND TRADEMARK OFFICE
____________

BEFORE THE PATENT TRIAL AND APPEAL BOARD
____________

Ex parte AARON K. BAUGHMAN, JOHN M. GANCI JR.,
STEPHEN C. HAMMER and CRAIG M. TRIM

Appeal 2020-005066
Application 15/471,436
Technology Center 2600

Before CARL W. WHITEHEAD JR., ERIC S. FRAHM and
DAVID M. KOHUT, Administrative Patent Judges.

PER CURIAM

DECISION ON APPEAL
Pursuant to 35 U.S.C. § 134(a), Appellant1 appeals from the
Examiner’s decision to reject claims 1–25. We have jurisdiction under
35 U.S.C. § 6(b).
We AFFIRM.

1 We use “Appellant” to reference the “applicant” as defined in
37 C.F.R. § 1.42. Appellant identifies the real party in interest as
“International Business Machines Corporation.” Appeal Br. 2.
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Application 15/471,436

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STATEMENT OF THE CASE
Appellant’s Invention
The present invention relates to a “language model provid[ing]
probabilistic indications that a series of words are found together for a
particular subject-matter domain” (Spec. ¶ 6), e.g., “‘thoracic’ and
‘surgery’” (id. ¶ 7), and an “acoustic model determin[ing] a probability that
a given audio signal includes a series of words of human speech” (id. ¶ 10),
i.e., “the probability of phones (a phonetically distinct sound in a speech)
given a signal” (id.). The invention can “cause[] an error in the acoustic
model [to] be corrected through retraining using the language model’s
success.” Id. ¶ 17.2. The invention can “further operate[] . . . the language
model on the [of-interest] portion of the speech signal . . . [to produce a]
textual representation . . . for the training from the language model that
correctly recognized the word[(s)].” Id. ¶ 21. And, the invention can
“determine[] a severity of an error associated with the acoustic model
incorrectly recognizing the portion of the speech signal . . . [and] boost[] a
number of occurrences of the training . . . [as] a function of the severity.”
Id. ¶ 19.
Claim 1, reproduced below, is illustrative of argued subject matter.
1. A method comprising:
selecting, to recognize spoken words in a speech signal
generated from a speech, a model-pair, the model-pair
comprising an acoustic model and a language model;
computing a degree of disjointedness between the acoustic
model and the language model relative to the speech by
comparing, responsive to the model pair performing speech
recognition on the speech signal, a first recognition output
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produced from the acoustic model and a second recognition
output produced from the language model;
determining, responsive to the acoustic model incorrectly
recognizing a portion of the speech signal as a first word and
the language model correctly recognizing the portion of the
speech signal as a second word, a textual representation of the
second word;
associating with the textual representation, a set of sound
descriptors;
generating, using the textual representation and the set of sound
descriptors, a training speech pattern; and
training, using the training speech pattern to produce a retrained
acoustic model, the acoustic model to recognize the portion of
the speech signal as the second word, the training causing the
retrained acoustic model and the language model to recognize
the portion of the speech signal as the second word.
Appeal Br. 16 (Claims Appendix).
Rejections
Claims 1–3, 6, 7, 13, 15–17, and 20–25 are rejected under 35
U.S.C. § 103 as being unpatentable over Choi (US 2017/0053652 A1;
Feb. 23, 2017) and Faisman (US 2008/0319743 A1; Dec. 25, 2008).
Final Act. 14–35.
Claim 4 is rejected under 35 U.S.C. § 103 as being unpatentable over
Choi, Faisman, and Wang (US 2007/0219798 A1; Sept. 20, 2007).
Final Act. 35–37.
Claim 5 is rejected under 35 U.S.C. § 103 as being unpatentable over
Choi, Faisman, Wang, and Matsuda (US 2016/0260428 A1; Sept. 8, 2016).
Final Act. 38–39.
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Claims 8 and 10–12 are rejected under 35 U.S.C. § 103 as being
unpatentable over Choi, Faisman, and Suendermann ’536
(US 2010/0268536 A1; Oct. 21, 2010). Final Act. 39–44.
Claim 9 is rejected under 35 U.S.C. § 103 as being unpatentable over
Choi, Faisman, Suendermann ’183 (US 2012/0166183 A1; June 28, 2012),
and Rennie (US 2017/0053644 A1; Feb. 23, 2017). Final Act. 44–48.
Claim 14 is rejected under 35 U.S.C. § 103 as being unpatentable over
Choi, Faisman, and Reich (US 8,285,546 B2; Oct. 9, 2012). Final Act. 49–
50.
Claims 18 and 19 are rejected under 35 U.S.C. § 103 as being
unpatentable over Choi, Faisman, and Suzuki (US 2017/0169813 A1;
June 15, 2017). Final Act. 50–54.
OPINION
Appellant presents the same arguments for each of the independent
claims and asserts the dependent claims stand on the patentability of their
independent claims. Appeal Br. 10 et seq. We address the arguments with
reference to claim 1. For the following reasons, we are unpersuaded of error
and thus sustain the rejections of claims 1–25.
A dispositive issue before us is whether Choi teaches or suggests the
following claim limitation: “responsive to the acoustic model incorrectly
recognizing a portion of the speech signal as a first word and the language
model correctly recognizing the portion of the speech signal as a second
word.” Appeal Br. 10–11 (emphasis omitted). Appellant contends:
Choi discloses a speech recognition system using both an
acoustic model and a language model. A combiner unit
combines outputs from each model to produce a final speech
recognition result. In particular, the acoustic model provides the
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combiner unit with probabilities that the speech matches
particular letters, a syllable, or a higher-level linguistic
unit. The language model also provides the combiner unit with
probabilities that the speech matches various linguistic units,
this time considering the relationships among current and past
linguistic units. The combiner combines the highest probability
recognition results from each model to determine a final
result. . . .
Importantly, in Choi’s implementation the acoustic model
produces probabilities that the speech matches a particular
linguistic unit, not probabilities that any such match is correct
or incorrect. . . . Similarly, the language model produces
probabilities that the speech matches various linguistic units—
also not probabilities that any such match is correct or incorrect.
Without any evaluation of correctness, Choi cannot properly be
interpreted as teaching or suggesting [the disputed claim
limitation].
Appeal Br. 11–12 (emphasis and internal citations omitted).
The Examiner responds:
Applicant disputes the mapping of the concept of “correctly
recognizing a speech input” to Choi. The disputed qualifier
“correctly” is [n]ot defined in the Claim. The ordinary meaning
of “correctly recognizing a speech input,” in the context of the
art of speech recognition, is having a result with a
“confidence/likelihood/probability” value that is considered
acceptable according to some criterion. . . . Choi considers the
result with the “highest probability,” from among two results,
as the “accurate” one. The “highest probability” result of Choi
teaches “correct” result of the Claim.
Ans. 4 (formatting altered, e.g., bullet-format omitted).
Appellant replies:
Since Appellants did not define the term “correct” in the
Application, the plain meaning of the word is necessarily used.
According to the Merriam-Webster online
dictionary at https://www.merriam-webster.com/dictionary/
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correct, the term “correct” is an adjective defined as
“conforming to or agreeing with fact, logic, or known truth”.
This definition is the first definition listed for the term, and it is
this definition that is the plain meaning intended by the
Appellants[.]
Reply Br. 2 (emphasis omitted).
We are unpersuaded the Examiner erred. We rather agree with the
Examiner’s claim interpretation because the Specification explains that the
claimed language model and acoustic model each provide a “probability”
that a speech signal contains the respectively output series of words. See
supra 2 (description of Appellant’s invention). Accordingly, we find the
Specification teaches that each output provides a ‘best guess’ and
corresponding likelihood of the speaker’s intended series of words. Further,
we find (i) the claimed “language model correctly recognizing the portion of
the speech signal” to mean that a high probability for the respective guess is
found; and (ii) the claimed “acoustic model incorrectly recognizing a portion
of the speech signal” to mean that a low probability for the respective guess
is found. That is, based on the probabilities of the ‘best guesses,’ the
invention deems the language model’s guess as “correct,” the acoustic
model’s guess as “incorrect,” and then trains the acoustic model to recognize
the speech signal as the language model’s guess.
Appellant’s argued claim construction asks us to, essentially, forego
the above probabilistic determination of a “correct” output (and an
“incorrect” output) that reasonably turns on the language and acoustic
models disclosed by the Specification. See id. Appellant asks us to apply,
instead, an absolute (i.e., free from imperfection) determination of a
“correct” output that unreasonably turns upon the abstract intent of a speaker
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implied by the claim.2 See id. And in support, Appellant offers only a
single general dictionary definition of “correct,” which is “conforming to
. . . logic” (Reply Br. 2 (block-quoted above) (emphasis omitted)) and thus
comports with a probabilistic determination of “correct.” See also Merriam
Webster Dictionary, available at
https://www.merriam-webster.com/dictionary/ correct (last visited Sept. 20,
2020) (supporting a probabilistic-threshold meaning for the claimed
“correct” and “incorrect” by defining the term “correct” (adjective) as
“conforming to an approved . . . standard”). For the foregoing reasons, the
Examiner’s interpretation of “correct” is reasonable and more
comprehensive than Appellant’s interpretation. See Phillips v. AWH Corp.,
415 F.3d 1303, 1321 (Fed. Cir. 2005) (“[T]he specification is the single best
guide to the meaning of a disputed term . . . [and] acts as a dictionary when
it expressly defines terms used in the claims or when it defines terms by
implication.” (quotation marks and citations omitted)).
Appellant also contends:
Faisman also discloses using both an acoustic model and a
language model for speech recognition. A human proofreader
corrects a resulting transcript; then the corrected transcript is
used to train the language model, and used along with the
original audio to train the acoustic model.
However, in Faisman’s implementation the proofreader corrects
the final results, after both models have processed the speech.
Faisman does not disclose, and cannot be reasonably interpreted
as disclosing, “comparing, responsive to the model pair
performing speech recognition on the speech signal, a first

2 That is, Appellant asks us to interpret “correct” as meaning the claimed
language model’s output is the exact series of word intended by the speaker
and “incorrect” as meaning the claimed acoustic model’s output is not the
exact series of words intended by the speaker.
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recognition output produced from the acoustic model and a
second recognition output produced from the language model.”
Appeal Br. 12 (emphasis and internal citations omitted).
The Examiner responds: “[T]he Claim is entirely mapped to Choi but
for the last limitation of ‘training’ for which Faisman was added. . . . [T]he
[Choi-Faisman combination is a] tandem of the recognition output of the
process of Choi providing input to the training process of Faisman.” Ans. 7.
Appellant does not reply to the Examiner’s above clarification of how
Faisman is applied, nor address Faisman whatsoever. Reply Br. 2 et seq.
As Appellant does not address the Examiner’s clarification of the
Choi-Faisman combination, the arguments misapprehend and thereby fail to
address the Examiner’s actual reliance on Faisman.
OVERALL CONCLUSION
For the foregoing reasons, we affirm the Examiner’s decision to reject
claims 1–25.
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DECISION SUMMARY
Claim(s)
Rejected 35 U.S.C. § Reference(s)/Basis Affirmed Reversed
1–3, 6, 7,
13, 15–17,
20–25
103 Choi, Faisman
1–3, 6, 7,
13, 15–17,
20–25

4 103 Choi, Faisman, Wang 4
5 103 Choi, Faisman, Wang, Matsuda 5
8, 10–12 103 Choi, Faisman, Suendermann ’536 8, 10–12
9 103
Choi, Faisman,
Suendermann ’183,
Rennie
9
14 103 Choi, Faisman, Reich 14
18, 19 103 Choi, Faisman, Suzuki 18, 19
Overall
Outcome 1–25

TIME PERIOD FOR RESPONSE
No time period for taking any subsequent action in connection with
this Appeal may be extended under 37 C.F.R. § 1.136(a).
See 37 C.F.R. § 1.136(a)(1)(iv).

AFFIRMED