Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
The office action sent in response to Applicant’s communication received on 1/15/2025 for the application number 19021279. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
Status of the claims
Claims 1-5 are presented for examination.
Information Disclosure Statement
The information disclosure was submitted on 5/2/2025 before the mailing data of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner’s Note:
Rejection under 101 was not given since the claims describes a non-conventional method/device for an automatic speech recognition.
Claim Rejections - 35 USC § 112
Claim limitation “encoder, bias encoder and bias decoder” in claim 5 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. These components are merely software and nowhere in the specification describes them as a structure. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
And
KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination.
Claim(s) 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Prabhavalkar ( US 20200357387) and further in view of Wang ( Towards Contextual Spelling Correction for Customization of End-to-End Speech Recognition Systems)
Regarding claim 1, Prabhavalkar teaches a speech recognition method that generates text from speech data, the speech recognition ( speech to text, Fig 1, Para 0034) method comprising: transforming, by an audio encoder, an audio feature sequence of the speech data to hidden state vectors ( hidden state 212, Fig 1, Para 0050) ; transforming, by a bias encoder, registered bias phrases to a phrase-level feature sequence ( bias encoder, the bias encoder 220 receives each bias phrase 114 in the set as a corresponding sequence of graphemes 116 (e.g., grapheme data) and generates a corresponding fixed-dimensional representation, referred to as a bias vector 222 or bias embedding, for each bias phrase 114, Para 0039) ; recursively estimating, by a bias decoder, from a previous token estimated as the text, a next token based on the hidden state vectors and the phrase-level feature sequence( the first encoder 210, the first attention module 218, the bias encoder 220, the bias attention module 228, and the decoder 240 are trained jointly to predict a sequence of graphemes from a sequence of acoustic feature frames., Para 0081) and estimating bias phrase index probabilities for the next token ( as the decoder context state 238 changes, the bias attention module 228 calculates a different summary or aggregation of the bias vectors 222, thus changing the probabilities that the decoder will indicate for elements of the different biasing phrases 114., Para 0043, 0066, 0069) ; and performing a beam search for the estimated next token ( beam search, This scalability issue can be addressed with bias conditioning by providing lists of bias prefixes 114F such that the CLAS model 200 only “enables” bias phrases 114 when the model 200 detects a corresponding prefix 114F in the partially decoded hypothesis (e.g., partial transcript 151) on the beam y<t., Para 0075)
Prabhavalkar does not explicitly teach and estimating a bias phrase index for the next token based on the bias phrase index probabilities, increasing a token probability for the bias phrase corresponding to the bias phrase index,
However, Wang teach estimating a bias phrase index for the next token based on the bias phrase index probabilities ( Context index is the output of similarity layer, which is the expected index of the ground-truth context phrase in the bias list for this position, See Section 2) , increasing a token probability for the bias phrase corresponding to the bias phrase index ( the probability of Joe Biden in second place is increased ( it will be selected) since it’s a bias term, See 2) Contextual Biasing Mechanism)
It would have been obvious having the teachings of Prabhavalkar to further incorporate the concept of Wang for POSITA before the effective filing date to improve the word error rate in the ASR system (See Introduction, Wang)
Regarding claim 2, Prabhavalkar modified by Wang as above in claim 1, teach, wherein the bias phrase index probabilities for the next token are estimated for each bias phrases by the bias decoder, and the bias phrase index for the next token is estimated based on a maximum value of the bias phrase index probabilities ( ( the probability of Joe Biden in second place is increased since it’s a bias term using argmax (eq 2) See 2) Contextual Biasing Mechanism)
Regarding claim 3, Prabhavalkar modified by Wang as above in claim 1, teach, wherein increased by weighting the token probability ( the context phrase “Joe Biden” is recognized as “John Bide” by the ASR system, and the index of target context phrase “Joe Biden” is 2 in the bias list. By design, the wordpieces of “John Bide” should be labeled as [B,I,L] for CLS tag, and the corresponding context index output should be the same to where “Joe Biden” is in context list. It should be noted that the final output sequence length can be different from the input due to the replace operation., here based on the position the token probability is high for “Joe Bide” See 2) Contextual Biasing Mechanism) and D. D. Inference - 1) Context Pre-Selection Mechanism)
Regarding claim 4, Prabhavalkar as above in claim 1, teach wherein the bias encoder comprises a bias attention layer that estimates the bias phrase index probabilities (attention layer, Fig 1)
Regarding claim 5, arguments analogous to claim 1, are applicable.
Conclusion
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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