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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
“processing units”in claim 20.
The “processing unit” is the placeholder, “to synthesize” is the step, and the insufficient modifier is “output speech signal”, and hence fails the three-prong test. In this instance, the bounds are not clear, since synthesizing an output speech signal requires a loudspeaker (is this the processing unit ?), and/or requires a processor accessing instructions from memory/memory cache to perform the mathematical functions that eventually produce modified coefficients, then generate a synthesized speech signal, what is the bound of the “processing unit” ?
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1,5-10,14-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by McElveen et al (20250071505).
As per claim 1, McElveen et al (20250071505) teaches a computer-implemented method comprising:
masking an input speech signal using a masking value to at least partially remove at least a portion of the input speech signal, producing a masked speech signal (as masking an audio signal from noise/silence – para 0116);
applying one or more parameters to the masked speech signal by a transformer-based neural network model to produce a vector field of coefficients in an invertible domain (as, using machine learning/NN to process the speech signal – see para 0027-0028 for the type of transform, and para 0034, teaching multiple types of neural network models – para 0034; with inverse matrices – para 0105, 01117);
and synthesizing an output speech signal corresponding to a restored version of the input speech signal by applying an inverse transform associated with the invertible domain (as, performing an inverse fourier transform after the above-mentioned – see very end of para 0117 – inverse Fourier Transform to convert the modified frequency domain back to a time domain signal).
As per claim 5, McElveen et al (20250071505) teaches the computer-implemented method of claim 1, further comprising updating the one or more parameters to reduce one or more differences between the output speech signal and the input speech signal (as, using one of the neural network based models, the CNN, to minimize the difference between the input audio signal and output – see para 0133; including different loss functions).
As per claim 6, McElveen et al (20250071505) teaches the computer-implemented method of claim 5, further comprising finetuning the one or more parameters using labeled training data for a specific task (as comparison to a ground truth label of the data – para 0133).
As per claim 7, McElveen et al (20250071505) teaches the computer-implemented method of claim 6, wherein the specific task comprises at least one of: speech denoising, codec artifact removal, or bandwidth extension (as, performing the removal of noise from speech, and improving the SNR of the system – para 0025).
As per claim 8, McElveen et al (20250071505) teaches the computer-implemented method of claim 1, further comprising applying the one or more parameters to a prompt associated with a target speaker and wherein the output speech signal comprises restored speech corresponding to the target speaker (as, modifying the input audio signals, which contain background noise and background voices, to improve the speech of the desired speaker – para 0150, reflecting back on a multi-speaker environment – para 0007).
As per claim 9, McElveen et al (20250071505) teaches the computer-implemented method of claim 8, wherein the input speech signal comprises a mixture of first speech associated with the target speaker and second speech associated with a different speaker (as, processing audio signals with multiple speakers – para 0007).
Claims 10,14-18,20 are system/processor claims that perform the steps found in method claims 1, 5-9 above and as such, claims 10,14-18,20 are similar in scope and content to claims 1,5-9; therefore, claims 10,14-18,20 are rejected under similar rationale as presented against claims 1,5-9 above. Furthermore, McElveen et al (20250071505) teaches processor/memory devices performing the disclosed steps (para 0103).
As per claim 19, McElveen et al (20250071505) teaches the system of claim 10, wherein system comprises at least one of:
a system for performing simulation operations;
a system for performing simulation operations to test or validate autonomous machine applications;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for rendering graphical output;
a system for performing deep learning operations;
a system for performing generative operations using a large language model (LLM);
a system for performing generative operations using a vision language model (VLM);
a system for performing generative operations using a multi-modal language model;
a system implemented using an edge device;
a system for generating or presenting virtual reality (VR) content;
a system for generating or presenting augmented reality (AR) content;
a system for generating or presenting mixed reality (MR) content;
a system incorporating one or more Virtual Machines (VMs);
a system implemented at least partially in a data center;
a system for performing hardware testing using simulation;
a system for synthetic data generation;
a collaborative content creation platform for 3D assets;
a system implemented at least partially using cloud computing resources;
a system using or deploying one or more inference microservices; or
a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container).
as, performing signal processing on audio, and simulating acoustic propagation (para 0039); examiner notes that the above limitations are in the ‘alternative’ – ie, “at least one of”.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim(s) 2-4, 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over McElveen et al (20250071505) in view of Sharma et al (20240185875).
As per claim 2, McElveen et al (20250071505) teaches the concept of taking a ‘glimpse’ segment of the audio signal, which comprises acoustic input data frames, and dependent upon SNR estimates – para 0111, but does not explicit mention number of frames/shifting the segment; Sharma et al (20240185875) teaches the use of neural networks in replicating background acoustic properties (see abstract), using similar convolution techniques (para 0033) attempting to improve the noise properties (i.e., improving SNR, and the like – para 0020), operating on segmented/frame basis (see para 0030, with initially operating on a window of 13 frames), however variable based on the desired target range – see para 0019. Therefore, it would have been obvious to one of ordinary skill in the art of acoustic processing to further define in the process of McElveen et al (20250071505) with varying the frame/segment lengths, as taught by Sharma et al (20240185875) because it would advantageously give the flexibility of the neural network to alter the segment length to maximize the desired acoustic properties so that a close match is performed, to the target audio signal segment (see para 0028, last 2 sentences).
As per claims 3,4, the combination of McElveen et al (20250071505) in view of Sharma et al (20240185875) teaches a random selected size (see McElveen et al (20250071505), operating on the frames/segments as noted above, based on an adapted threshold – para 0106, and operating on SNR numbers – para 0111 – e.g., 10db threshold; examiner notes, that the frame size selection is ‘random’ based on a measurement of noise (below the signal), with noise being random itself – in other words, the segment size/number of frames is based on the every changing SNR measurement – taught in para 0111 – SNR and para 0106 – adaptive parameter size).
Claims 11-13 are system/processor claims that perform the steps found in method claims 2-4 above and as such, claims 11-13 are similar in scope and content to claims 2-4; therefore, claims 11-13 are rejected under similar rationale as presented against claims 2-4 above. Furthermore, McElveen et al (20250071505) teaches processor/memory devices performing the disclosed steps (para 0103).
As per claim 2, McElveen et al (20250071505) teaches the computer-implemented method of claim 1, wherein the masking value defines a percentage of frames of the input speech signal that are at least partially removed within each span that includes a predetermined number of consecutive frames.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, the following references were found that are common to applicants specification/claim features:
Wang et al (20230306980) teaches T-F masking in convolutional processing using neural network changing frame size based on latency – see para 0007, 0005
Ranieri et al (20150030166) teaches altering frame size to match T-F characteristics between microphone features (para 0009 – 0016).
Lu et al (20150006164) teaches differing segment/frame size (para 0162) in analyzing speech features (para 0163) processing using convolution functions – para 0174.
Lidner (20140185610) teaches allowable frame erasure rates – ie, percentage of frames allowed to be replaced/erased.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 06/18/2026
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