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
Response to Arguments
Applicant's arguments with respect to claims 1, 11, and 21 have been considered but are moot in view of the new ground(s) of rejection.
Applicant’s arguments are directed to the amended subject matter; new prior art citations from existing art are provided to cover the concept of “unprocessed” comprising a compressed vector representation per se as it applied to the existing prior art, in light of the use of encoders and decoders for compression/decompression. Further the semantics of unprocessed vs processed and uncompressed vs compressed do not have distinguishing evidence in the specification to show a unique meaning or “unprocessed” per se. In other words, a signal which is compressed must have been processed in some capacity e.g. encoder, and will be processed again for decompression e.g. decoder, the compressed signal in which a new system or component receives, from the new systems perspective is in fact unprocessed with respect to compression versus decompression, as the system which decompresses expects a compressed signal.
Further, Applicant's arguments with respect to claim 21 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
Yes
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
No. The claims and the specification do not indicate a judicial exception as an improvement of a function or technology.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Step 2B: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT AMOUNT TO SIGNIFICANTLY MORE THAN THE JUDICIAL EXCEPTION?
Yes, the interpretation in the context of 35 USC 101 amounts to a significant use of a hearing device or hearing aid using explicitly transmitted data to a second device combined with precise encoding and two decoding operations to compress vectors while simultaneously accepting a second vector as well as noise being removed from noisy speech. A human would not seek such claim limitations as a general or routine step or mental per se or in routine humanly operations, and similarly such claim limitations are not routine per se or include extra solution activity, but rather combine components in a significant way, which would also not be construed as routine or known mathematical functions. Such claim language provides significance beyond mere data manipulation or mental decision making and is also analogous to interim amendment examples. Additionally, if one or ordinary skill in the art fails to properly consider any specified hardware or arranged components in the claims, considers such claim limitations non-generic, or simply disregards Enfish, the claims still demonstrate that there exists improvements to the functioning of a computer (or technology), e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));. Therefore, in light of the above as a whole, it is not warranted to give a rejection under 35 USC 101.
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.
Claims 1, 3-6, 11-16, and 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240355320 A1 Mohamadabadi; Behnam Babagholami et al. (hereinafter Mohamadabadi) in view of US 20240005946 A1 SHARMA; Dushyant et al. (hereinafter SHARMA).
Re claim 1, Mohamadabadi teaches
A method, comprising: (fig. 6)
receiving, at a hearable device, an auditory signal; (utilizing a hearing aid, headphones, or wearable hearing device 0062 and 0090-0091)
encoding, at the hearable device… (encoder to decoder as in fig. 6 utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
decoding, at the hearable device using a speech enhancement decoder,… (utilizing loss and noise minimization as a form of speech enhancement 0076 with 0090-0091, under the premise of a encoder to decoder as in fig. 6 utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
outputting, from the hearable device, the denoised speech; and (utilizing loss and noise minimization in final signal 0076 with 0090-0091, under the premise of a encoder to decoder as in fig. 6 utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
However, while Mohamadabadi teaches speech processing and enhancement using encoder to decoder with noise removal and multi-layered loss functions, it does not utilize vectors per se, or use ASR for a final decoder step to transcribe text such as for voice command use and fails to teach:
… the auditory signal into a compressed vector representation of the auditory signal; (SHARMA using fig. 3c, a neural network driven system 0072 capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
…the compressed vector representation of the auditory signal into denoised speech; (SHARMA using fig. 3c, a neural network driven system 0072 capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
transmitting, from the hearable device, the compressed vector representation of the auditory signal as an unprocessed vector representation (SHARMA a compressed representation embedded vector in fig. 3a is transmitted from an encoder, where the encoder itself creates the compressed vector, which is transmitted to the backend 0080 and fig. 4c with fig. 3c, in which the signal is unprocessed per se i.e. fresh from the encoder output of the compressed signal as input into fig. 3c, in which a decoder then decompresses the signal applied to an ASR 0069, NOTE: a signal which is compressed must have been processed in some capacity e.g. encoder, and will be processed again for decompression e.g. decoder, the compressed signal in which a new system or component receives, from the new systems perspective is in fact unprocessed with respect to compression versus decompression, as the system which decompresses expects a compressed signal… also isolated vectors combined for an ASR decoder as in fig. 3c, a neural network driven system 0072 capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi to incorporate the above claim limitations as taught by SHARMA to allow for combining prior art elements according to known methods to yield predictable results such as the ASR system of SHARMA to allow for voice command processing after the signal is cleaned, as well as vectorization of isolated speech vectors in SHARMA prior to the ASR, thereby leveraging both enhanced audio quality and original signal information, wherein such a fusion reduces word error rates (WER), mitigating artifacts from processing while preserving acoustic details for recognition at the final decoder (ASR), wherein using vectors that combine synthetic (processed) and real (unprocessed) data can improve accuracy when adapting models to new or complex acoustic environments for hearing aids or other devices.
Re claim 11, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
For instance, see fig. 9 which contains the necessary interfaces, processor, and memory.
Re claim 20, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see fig. 9 which contains the necessary instructions and medium.
Re claim 12, Mohamadabadi teaches
2. The method of claim 1, wherein the hearable device comprises a hearing aid. (utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
Re claims 3 and 13, Mohamadabadi teaches
3. The method of claim 1, wherein the hearable device is selected from a group consisting of: a headset, a loudspeaker, an in-wall speaker, a ceiling speaker, a soundbar, a computer speaker, or a subwoofer. (headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
Re claims 4 and 14, Mohamadabadi teaches
4. The method of claim 1, wherein at least one of the speech enhancement decoder and the speech recognition decoder is trained to perform decoding using a neural network. (in a neural network system, utilizing loss and noise minimization in final signal 0076 with 0090-0091, under the premise of a encoder to decoder as in fig. 6 utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
Re claims 5 and 15, Mohamadabadi teaches
5. The method of claim 1, further comprising:
determining a first loss function for the speech enhancement decoder and a second loss function for the speech recognition decoder; (utilizing first and second loss functions 0090-0091, under the premise of a encoder to decoder as in fig. 6 utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
computing a total loss for the speech enhancement decoder and the speech recognition decoder by performing dynamic weight balancing using the first loss function and the second loss function; and (retrained data based parameters (weights) of first and second loss function combinations 0090-0091, under the premise of a encoder to decoder as in fig. 6 utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
updating weights associated with models used to train speech enhancement decoder… (updated or retrained data based parameters (weights) of first and second loss function combinations 0090-0091 to minimize noise per se 0076, under the premise of a encoder to decoder as in fig. 6 utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
However, while Mohamadabadi teaches speech processing and enhancement using encoder to decoder with noise removal and multi-layered loss functions, it does not utilize vectors per se, or use ASR for a final decoder step to transcribe text such as for voice command use and fails to teach:
… and the speech recognition decoder to minimize the total loss. (SHARMA decoder specifically for ASR and overall optimizing a loss function 0071 in a neural network driven system 0072 capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi to incorporate the above claim limitations as taught by SHARMA to allow for combining prior art elements according to known methods to yield predictable results such as the ASR decoder with analogous loss function use e.g. WER of SHARMA to allow for voice command processing after the signal is cleaned, as well as vectorization of isolated speech vectors in SHARMA prior to the ASR, thereby leveraging both enhanced audio quality and original signal information, wherein such a fusion reduces word error rates (WER), mitigating artifacts from processing while preserving acoustic details for recognition at the final decoder (ASR), wherein using vectors that combine synthetic (processed) and real (unprocessed) data can improve accuracy when adapting models to new or complex acoustic environments for hearing aids or other devices.
Re claims 6 and 16, while Mohamadabadi teaches speech processing and enhancement using encoder to decoder with noise removal and multi-layered loss functions, it does not utilize vectors per se, or use ASR for a final decoder step to transcribe text such as for voice command use and fails to teach:
6. The method of claim 1, wherein the auditory signal comprises words spoken by a person. (capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi to incorporate the above claim limitations as taught by SHARMA to allow for combining prior art elements according to known methods to yield predictable results such as the ASR decoder with analogous loss function use e.g. WER of SHARMA to allow for voice command processing after the signal is cleaned, as well as vectorization of isolated speech vectors in SHARMA prior to the ASR, thereby leveraging both enhanced audio quality and original signal information, wherein such a fusion reduces word error rates (WER), mitigating artifacts from processing while preserving acoustic details for recognition at the final decoder (ASR), wherein using vectors that combine synthetic (processed) and real (unprocessed) data can improve accuracy when adapting models to new or complex acoustic environments for hearing aids or other devices.
Re claim 19, Mohamadabadi teaches
9. The method of claim 8, wherein the user device is a wearable device. (utilizing a hearing aid, headphones, or wearable hearing device 0061-0062 and 0090-0091 with 0113)
Re claim 21, while Mohamadabadi teaches audio processing, the reference fails to teach:
21. (New) The method of claim 1, wherein the encoding comprises applying an encoder and a vector quantizer associated with a codebook to generate the compressed vector representation as a sequence of quantized embeddings. (SHARMA using a codebook 0059 and quantizer 0071-0073 with fig. 3a as output embeddings, a compressed representation embedded vector in fig. 3a is transmitted from an encoder, where the encoder itself creates the compressed vector, which is transmitted to the backend 0080 and fig. 4c with fig. 3c, in which the signal is unprocessed per se i.e. fresh from the encoder output of the compressed signal as input into fig. 3c, in which a decoder then decompresses the signal applied to an ASR 0069, NOTE: a signal which is compressed must have been processed in some capacity e.g. encoder, and will be processed again for decompression e.g. decoder, the compressed signal in which a new system or component receives, from the new systems perspective is in fact unprocessed with respect to compression versus decompression, as the system which decompresses expects a compressed signal… also isolated vectors combined for an ASR decoder as in fig. 3c, a neural network driven system 0072 capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi to incorporate the above claim limitations as taught by SHARMA to allow for combining prior art elements according to known methods to yield predictable results such as the ASR system of SHARMA to allow for quantization using codebooks which enables massive compression ratios without catastrophic losses in representation accuracy, and for voice command processing after the signal is cleaned, as well as vectorization of isolated speech vectors in SHARMA prior to the ASR, thereby leveraging both enhanced audio quality and original signal information, wherein such a fusion reduces word error rates (WER), mitigating artifacts from processing while preserving acoustic details for recognition at the final decoder (ASR), wherein using vectors that combine synthetic (processed) and real (unprocessed) data can improve accuracy when adapting models to new or complex acoustic environments for hearing aids or other devices.
Claims 7-9, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240355320 A1 Mohamadabadi; Behnam Babagholami et al. (hereinafter Mohamadabadi) in view of US 20240005946 A1 SHARMA; Dushyant et al. (hereinafter SHARMA) and further in view of WO 2022066604 A1 SEED DALE et al. (hereinafter SEED).
Re claims 7 and 17, while Mohamadabadi teaches speech processing and enhancement using encoder to decoder with noise removal and multi-layered loss functions, it does not utilize vectors per se, or use ASR for a final decoder step to transcribe text such as for voice command use and fails to teach:
…text corresponding to the denoised speech. (SHARMA optimizing a loss function 0071 in a neural network driven system 0072 capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi to incorporate the above claim limitations as taught by SHARMA to allow for combining prior art elements according to known methods to yield predictable results such as the ASR decoder with analogous loss function use e.g. WER of SHARMA to allow for voice command processing after the signal is cleaned, as well as vectorization of isolated speech vectors in SHARMA prior to the ASR, thereby leveraging both enhanced audio quality and original signal information, wherein such a fusion reduces word error rates (WER), mitigating artifacts from processing while preserving acoustic details for recognition at the final decoder (ASR), wherein using vectors that combine synthetic (processed) and real (unprocessed) data can improve accuracy when adapting models to new or complex acoustic environments for hearing aids or other devices.
However, while Mohamadabadi in view of SHARMA while teaching speech enhancement and ASR for a word error rate reduction, such that ASR inherently outputs text per se, with wearable device, it fails to teach:
7. The method of claim 1, wherein the auditory signal is received via a near field communication protocol. (SEED display of text per using voice commands under near field protocols on an advanced device such as smart device e.g. watch/glasses 0006 0032 0060 0084 with fig. 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi in view of SHARMA to incorporate the above claim limitations as taught by SEED to allow for combining prior art elements according to known methods to yield predictable results such as adding near-field to the existing far-field approaches of SHARMA to provide both improved close and far sound pickup.
Re claims 8 and 18, Mohamadabadi in view of SHARMA while teaching speech enhancement and ASR for a word error rate reduction, such that ASR inherently outputs text per se, with wearable device, it fails to teach:
8. The method of claim 1, further comprising: displaying, on a screen of a user device,… (SEED display of text per using voice commands under near field protocols on an advanced device such as smart device e.g. watch/glasses 0006 0032 0060 0084 with fig. 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi in view of SHARMA to incorporate the above claim limitations as taught by SEED to allow for combining prior art elements according to known methods to yield predictable results such as displaying otherwise inherent ASR results (text) on an existing display such as a smart phone, improving the ability of the combination to now display the ASR results which otherwise is an obvious and common next step in the use of ASR.
Re claim 9, Mohamadabadi in view of SHARMA while teaching speech enhancement and ASR for a word error rate reduction, such that ASR inherently outputs text per se, with wearable device, it fails to teach:
9. (Currently Amended) The method of claim 8, wherein the user device is a wearable device selected from a group consisting of: a smart watch, an augmented reality device, and a smart glasses device. (SEED display of text per using voice commands under near field protocols on an advanced device such as smart device e.g. watch/glasses 0006 0032 0060 0084 with fig. 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi in view of SHARMA to incorporate the above claim limitations as taught by SEED to allow for combining prior art elements according to known methods to yield predictable results such as adding near-field to the existing far-field approaches of SHARMA to provide both improved close and far sound pickup.
Claim 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20240355320 A1 Mohamadabadi; Behnam Babagholami et al. (hereinafter Mohamadabadi) in view of US 20240005946 A1 SHARMA; Dushyant et al. (hereinafter SHARMA) and further in view of US 20230421211 A1 SONG; Hyoung Kyu et al. (hereinafter SONG).
Re claim 22, while Mohamadabadi teaches audio processing, it fails to teach:
22. (New) The method of claim 21, wherein transmitting the compressed vector representation comprises transmitting indices of codebook vectors corresponding to the sequence of quantized embeddings and wherein the processing device… (SHARMA using a codebook 0059 and quantizer 0071-0073 with fig. 3a as output embeddings, a compressed representation embedded vector in fig. 3a is transmitted from an encoder, where the encoder itself creates the compressed vector, which is transmitted to the backend 0080 and fig. 4c with fig. 3c, in which the signal is unprocessed per se i.e. fresh from the encoder output of the compressed signal as input into fig. 3c, in which a decoder then decompresses the signal applied to an ASR 0069, NOTE: a signal which is compressed must have been processed in some capacity e.g. encoder, and will be processed again for decompression e.g. decoder, the compressed signal in which a new system or component receives, from the new systems perspective is in fact unprocessed with respect to compression versus decompression, as the system which decompresses expects a compressed signal… also isolated vectors combined for an ASR decoder as in fig. 3c, a neural network driven system 0072 capable of extracting words inherent in ASR 0049 as well as denoising 0081 using a reference or embedding vector claim 26 along side a compressed vector claim 12 for the purposes of ASR using a decoder thereof claim 25)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi to incorporate the above claim limitations as taught by SHARMA to allow for combining prior art elements according to known methods to yield predictable results such as the ASR system of SHARMA to allow for quantization using codebooks which enables massive compression ratios without catastrophic losses in representation accuracy, and for voice command processing after the signal is cleaned, as well as vectorization of isolated speech vectors in SHARMA prior to the ASR, thereby leveraging both enhanced audio quality and original signal information, wherein such a fusion reduces word error rates (WER), mitigating artifacts from processing while preserving acoustic details for recognition at the final decoder (ASR), wherein using vectors that combine synthetic (processed) and real (unprocessed) data can improve accuracy when adapting models to new or complex acoustic environments for hearing aids or other devices.
However, while the combination teaches codebooks and ASR for decoding, it fails to teach:
…upon receiving the indices of the codebook vectors, converts the indices into a sequence of codewords for decoding by the speech recognition decoder. (SONG codewords extracted i.e. converted per se under BRI, from codebook indices 0008-0009)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Mohamadabadi in view of SHARMA to incorporate the above claim limitations as taught by SONG to allow for combining prior art elements according to known methods to yield predictable results such as the ASR and codebook quantization processes of the combined prior art which demonstrates quantization using codebooks which enables massive compression ratios without catastrophic losses in representation accuracy, to now allow for highly efficient, noise-robust speech synthesis and compression using ASR decoding to map discrete, abstract semantic tokens back into high-fidelity acoustic representations without the need for large redundant raw audio files.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220405745 A1 PRATZ; Sterling et al.
Near field communication and smart technology
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov
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