DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In virtue of this communication, claims 6-15 are currently pending in this Office Action. See the session Response to Applicant’s Reply as set forth below with respect to claims 1-5.
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Response to Applicant’s Reply
This Office Action is in response to a communication reply filed on October 3, 2025, and the communication reply is in response to the Election/Restriction Requirement office action mailed on August 8, 2025, wherein applicant, in the reply, indicated an election of Species II, claims 6-15 for prosecution without traverse and thus, claims 1-5 are withdrawn from further consideration on the merits pursuant to 37 CFR 1.142(b), as being drawn to a non-elected invention.
However, applicant failed to update status of claim 7 in the reply filed on October 3, 2025, but elected claim 7 without traverse, and wherein claim 7 depends on independent claim 1 according to latest submitted claim list on October 24, 2024, although the feature “the third input information” recited in claim 7 was identified and not recited in claim 1, but recited in claim 6, which appears that claim 7 should be considered as further limitation to features of claim 6, and depended on independent claim 6, other than depending on independent claim 1 of the claim list of October 24, 2024, which was the ground that the Office provisionally restricted claim 7 into group II including independent claim 6 in the previous office action mailed on August 5, 2025. Therefore, also in a condition that the instant application is not in condition for allowance at this point, the Office initiated a telephone interview with applicant attorney Jae Seok Ahn (registration number 78,937) on December 1, 2025, and decided to withdraw the provisional restriction of claim 7 applied in the previous restriction/election office action mailed on August 5, 2025 and correcting election-restriction of claim 7, also all other claims, is based on the claim list submitted on October 24, 2023, and applicant maintained the election of Group II, claims 6, 8-15 without traverse, and the non-elected group I, claims 1-5, 7.
Therefore, the following office action is applied according to the corrected election of group II, claims 6, 8-15 without travers and a complete reply to a future final office action must include cancellation of non-elected claims 1-5, 7 or other appropriate action (37 CFR 1.144). See MPEP § 821.01 and applicant is also respectfully reminded that status of claims shall be updated accordingly in the future prosecution.
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
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) because the claim limitations use 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 is: a channel convert unit for function “compressing the plurality of …”, etc. as recited in claim 6.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f), it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) limitation:
“a channel convert unit” in claim 6 – an element channel conversion unit 190 by receiving microphone signals from microphone encoders and output to voice signal estimator in figure 15, and implemented by a processor, para 194, para 161 of USPGPub 20240105199 A1.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f).
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Objections
Claims 6, 8-15 are objected to because of the following informalities:
Claim 6 recited “the sum of …” which should be -- a Claims 8-12 are objected due to the dependencies to claim 1.
Claim 13 is objected for the at least similar reason as described in claim 6 above since claim 13 recited similar deficient feature as recited in claim 6. Claims 14-15 are objected due to the dependencies to claim 13.
Claim 8 further recited “The second artificial neural network” which should be -- the pre-learned second artificial neural network-- or similar if this term is referred back to “a pre-learned second artificial neural network” as recited in parent claim 6. Claims 9-11 are objected due to the dependencies to claim 8.
Claim 15 is further objected for the at least similar reason as described in claim 8 above since claim 15 recited similar deficient feature as recited in claim 8.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 6, 8-15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 6 recited “a channel convert unit for compressing the plurality of pieces of conversion information and converting them into first input information …” which is confusing of term “them” herein because it is unclear whether the term “them” herein is referred to “conversion information” to be done by “compressing” or “conversion information” that has been done for “compressing”, and thus, renders claim indefinite. Claim 6 further recited “the estimated noise echo signal” which has an insufficient antecedent basis for the limitation and causes confusing because it is unclear what this term is referred to and it is unclear what it is and thus, renders claim indefinite. Claims 8-12 are rejected due to the dependencies to claim 6.
Claim 13 is rejected for the at least similar reasons described in claim 6 above since claim 13 recited the similar deficient features as recited in claim 6 above. Claims 14-15 are rejected due to the dependencies to claim 13.
Claim 7 is rejected under 35 USC 112(b) as being indefinite because applicant elected claim 7 in the reply filed on October 3, 2025, and claim 7 depends on non-elected claim 1 according to claim list submitted on October 24, 2024 and wherein the Office has withdrawn the restriction of claim 7 to group II, and corrected and restricted to group I, as discussed above. Because claim 7 depends on non-elected claim 1 at this point, it made the claim incomplete, see MPEP 608.01(n)(V) and the attached interview summary.
Claim 8 further recited “information output from the artificial neural network in the previous step” twice, and wherein it is unclear “the artificial neural network” herein is referred to “artificial neural networks” included in “the second artificial neural network” or included in “the third artificial neural network” and thus, further renders claim indefinite. Claims 9-11 are rejected due to the dependencies to claim 8.
Claim 15 is rejected for the at least similar reasons described in claim 8 above since claim 15 recited the similar deficient features as recited in claim 8 above.
Examiner Comment
Claim 6, similar to claim 13, recited “a pre-learned second artificial neural network” and “a pre-learned third artificial neural network”, but it appears that claimed “a pre-learned second artificial neural network” and “a pre-learned third artificial neural network” merely “use third input information” and/or “an estimated echo signal” and “having the third input information” and/or ”an estimated noise signal”, respectively, but contribute nothing to the following final claim step: “output an estimated voice signal” that is clear voice signal in a preferable result, a BRI is applied to the claimed “artificial neural networks”, see MPEP 2111.
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 of this title, 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 6, 8-15 are rejected under 35 U.S.C. 103 as being unpatentable over Fazeli et al. (US 20200312346 A1, hereinafter Fazeli) and in view of reference Tanaka et al. (US 20180358032 A1, hereinafter Tanaka).
Claim 6: Fazeli teaches a multi-channel based noise and echo signal integrated cancellation device (title and abstract, ln 1-18, a communication system 10 in fig. 1A, and details in fig. 10, wherein microphone and speaker signals converted to frequency domain by STFTs 212 and 222, i.e., and microphone signal comprising acoustic echo signal y(t) and additive noise n(t), para 53, i.e., multi-channel, to output near-end speech signal s(t) by an echo estimation via an element 230 and near-end estimation via an element 250 in time-frequency domain in fig. 2) using deep neural network (recurrent neural networks including deep multitask recurrent neural networks, being applied, para 197) comprising:
a microphone encoder module (a part of encoder 1110 with feature extraction module 1010 in fig. 10) that receives a microphone input signal (signal d(t) from the microphone 14 in fig. 10) including an echo signal (y(t) discussed above), and a speaker's voice signal (near-end speech signal s(t), para 53), convert the microphone input signal into conversion information (a first input information Ďk,f via microphone signal feature extraction module 1010 in fig. 10) and output a first input information (h from the encoder 1110, fig. 11A, and through a gated recurrent unit GRU layer 114 in fig. 11B);
a far-end signal encoder (including STFT 222, abs 224, etc., in fig. 2) that receives a far-end signal (x(t) as far-end signal in fig. 2), converts the far-end signal into second input information (Ẍk,f from log 226 in fig. 2), and outputs the converted second input information (through the STFT 222, abs 224, or log 226 in fig. 2, and outputted therefrom);
a pre-learned second artificial neural network (a part of AEC neural network 228 in fig. 2 and 1028 in fig. 10) that uses third input information, which is the sum of the first input information and the second input information (through concatenation layer 251 by taking Ẍk,f as the second input information, and Ďk,f as the first input information in figs. 4, or concatenation layer 1112 in fig. 11B) as input information (as input information to the deep gated recurrent unit GRU network in figs. 4, 11B), and an estimated echo signal obtained by estimating the echo signal from the second input information as output information (outputted from the echo estimator 230 in the AEC neural network in fig. 2 or part of echo canceller in fig. 10);
a pre-learned third artificial neural network (other part of AEC neural network 228 in fig. 2 and 1028 in fig. 10) having the third input information as input information (through concatenation layer 251 by taking Ẍk,f as the second input information, and Ďk,f as the first input information in figs. 4, 11A, or concatenation layer 1112 in fig. 11B) and an estimated noise signal obtained by estimating the noise signal from the second input information as output information (n(t) as noise signal different from echo signal y(t) in d(t), and nonlinearity between far-end signal and modified version of the far-end signal, etc., as noise, para 53 and e.g., white noise added, para 105 and as weights in GRU computation, para 64); and
a voice signal estimator (a part of near-end estimator 250 in fig. 2 and part of echo canceller 1028 in fig. 10 and/or including iSTFT 272/1074 and exp 272/1072 in figs. 2, 10) configured to output an estimated voice signal obtained by estimating the voice information based on the estimated echo signal, the estimated noise echo signal, and the second input information (through the contextual attention aware neural network, converters 1070, etc., to obtain estimated near-end speech signal q(t) from estimated features Qk,f in figs. 2, 10).
However, Fazeli does not explicitly teach a plurality of microphone encoders that receive a plurality of microphone input signals including an echo signal and he plurality of microphone input signals into a plurality of conversion information; a channel convert unit for compressing the plurality of pieces of conversion information and converting them into first input information having a size of a single channel and outputting the converted first input information;
Tanaka teaches an analogous field of endeavor by disclosing a multi-channel based noise and echo signal integrated cancellation device (title and abstract, ln 1-10 and a sound emitting and collecting device 10 in figs. 1-2 and having functions in fig. 3A-3B and multiple microphone signals as the multi-channel based in fig. 3A) and wherein a plurality of microphone encoders is disclosed (elements 31, 32, 33) to receive a plurality of microphone input signals (applied to the microphone signals from the microphones 11, 12, 13, respectively in fig. 3A) including an echo signal (echo in the microphone signals above and cancelled by the elements 31, 32, 33 in fig. 3A, para 18) and the plurality of microphone input signals into a plurality of conversion information (through FIR filters and subtraction process within the element 31, 32, 33, para 18), and output the converted information (signal E and provided to a beam forming unit 20 in fig. 3A, para 20-21); a channel convert unit (element 20 in fig. 3A) for compressing the plurality of pieces of conversion information and converting them into first input information having a size of a single channel (outputting a beamed audio signal as input to a second echo canceller 40, i.e., receiving the microphone signals from microphone encoders outputted from the elements 31, 32, 33, and output to a AEC2 as the voice signal estimator in fig. 3A and details in fig. 7) and outputting the converted first input information (outputting from FIR filters 21, 22, 23 to an adder in fig. 7) and a far-end signal encoder that receives a far-end signal (part of AEC2 by receiving the speaker signals 70 L/R in fig. 3A), converts the far-end signal into second input information (spectrum of the echo component estimated in the subtraction process in the first echo cancellers, para 23), and outputs the converted second input information (outputted from ERLE CALC. in fig. 3B) for
Improving cleanup of noise more completely (avoiding the leak of the noise from one of the multiple microphone signals in the estimated speech signal, and increasing a voice quality, para 2-3, e.g., one of multiple microphone signals used for voice detection while rest of microphone signals for estimating useful DOA with the result of the voice detection in fig. 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the plurality of microphone encoders that receive the plurality of microphone input signals including the echo signal and the plurality of microphone input signals into the plurality of conversion information; the channel convert unit for compressing the plurality of pieces of conversion information and converting them into the first input information having the size of the single channel and outputting the converted first input information, as taught by Tanaka, to the microphone encoder module in the multi-channel based noise and echo signal integrated cancellation device, as taught by Fazeli, for the benefits discussed above.
Claim 13 is a method claim and essentially associated with the features of multi-channel based noise and echo signal integrated cancellation device of claim 1 and thus, rejected according to claim 1 above.
Claim 8: the combination of Fazeli and Tanaka further teaches, according to claim 6 above,
wherein The second artificial neural network includes a plurality of artificial neural networks connected in series (Fazeli, fully connected from the concatenated layer, fig. 11B and other layers including contextual attention module 1130 and networks, including encoder and decoder fully connected in series in figs. 11A/11B and detailed contextual attention module in connected networks in series in fig. 11C), and the third artificial neural network includes a plurality of artificial neural networks connected in series on a par with the second artificial neural network (Fazeli, from Ck-T+1, Ck-T+2, …, Ck with associated and connected to several GRU layers 1114, 1192, 1194 in the contextual attention neural network 1100 in fig. 11B, and cross linked from one volume to another volume each of the volumes constructed a neural network for both echo and noise estimation to be removed, e.g., noise is added to padded signal for estimation, para 73), wherein the plurality of artificial neural networks of the second artificial neural network re-estimates the echo signal based on information output from the artificial neural network in the previous step (Fazeli, e.g., layer or network K-T+2 applied result of layer or network K-T+1 or at training period, the estimated near-end features Q is re-evaluated according to loss function based on the echo and noise estimations of current frame and previous frame by a formula in para 84-85); wherein the plurality of artificial neural networks of the third artificial neural network re-estimates the noise signal based on the information output from the artificial neural network in the previous step (Fazeli, discussed above, wherein the echo and the noise estimations are performed and are to be removed, e.g., noise is added to padded signal for estimation, para 73).
Claim 9: the combination of Fazeli and Tanaka further teaches, according to claim 8 above, wherein the second artificial neural network re-estimates the echo signal using second input information, the estimated echo signal, and the noise signal as input information (Fazeli, by using far-end features representing the second input information, microphone signal features representing the noise signal, and difference or error signal outputted from the contextual attention network as the estimated echo signal or residual echo signal in fig. 14B, para 167-171); wherein the third artificial neural network re-estimates the noise signal by using second input information, the estimated echo signal, and the noise signal as input information (Fazeli, from Ck-T+1, Ck-T+2, …, Ck with associated and connected to several GRU layers 1414, 1492, 1494 in the contextual attention neural network 1400 in fig. 14B, and cross linked from one volume to another volume each of the volumes constructed a neural network for both echo and noise estimation to be removed, e.g., noise is added to padded signal for estimation, para 73, 167-171).
Claim 10: the combination of Fazeli and Tanaka further teaches, according to claim 8 above, wherein the second artificial neural network includes a 2-A artificial neural network (Fazeli, two stacked GRU networks included in the contextual attention neural networks in figs. 11B, 14B and an example in fig. 11C, e.g., through multiple layer Norm 1133 and multi-head attention 1134 by taking input from h and processed input h to as inputs to layer Norm and similar to multi-head attention 1134 by taking the input h and processed input h for multi-head attention 1134 in fig. 11C, e.g., used in echo estimation with in the echo canceller carrying the attention recurrent deep neural network in figs. 2, 10, and 13) and a 2-B artificial neural network (Fazeli, through summing operations in figure 11C combined with subtraction operations in fig. 13, wherein the subtraction applied to the modified far-end speech signal and the microphone signal level |Dk,f|so that an error signal level obtained as input to the echo canceller in fig. 13 and figs. 14A/14B), and the third artificial neural network includes a 3-A artificial neural network and a 3-B artificial neural network (Fazeli, similar to 2A and 2B, and applied to noise estimation which is included in the microphone signals, e.g., used in noise estimation with in the echo canceller carrying the attention recurrent deep neural network in figs. 2, 10, and 13),
wherein the 2-A artificial neural network includes a pre-learned artificial neural network which takes third input information as input information and second output information including information obtained by estimating the echo signal based on the third input information as output information (Fazeli, through multiple layer Norm 1133 and multi-head attention 1134 by taking input from h and processed input h to as inputs to layer Norm and similar to multi-head attention 1134 by taking the input h and processed input h for multi-head attention 1134 in fig. 11C, e.g., used in echo estimation with in the echo canceller carrying the attention recurrent deep neural network in figs. 2, 10, and 13 and the echo signal x(t) is included in the microphone signal, para 53) wherein the 3-A artificial neural network includes a pre-learned artificial neural network which takes third input information as input information and third output information including information (Fazeli, through multiple layer Norm 1133 and multi-head attention 1134 by taking input from h and processed input h to as inputs to layer Norm and similar to multi-head attention 1134 by taking the input h and processed input h for multi-head attention 1134 in fig. 11C, e.g., used in noise estimation with in the echo canceller carrying the attention recurrent deep neural network in figs. 2, 10, and 13 and the noise n(t) is included in the microphone signal, para 53, 73) obtained by estimating the noise signal based on the third input information as output information (Fazeli, through multiple layer Norm 1133 and multi-head attention 1134 by taking input from h and processed input h to as inputs to layer Norm and similar to multi-head attention 1134 by taking the input h and processed input h for multi-head attention 1134 in fig. 11C, e.g., used in noise estimation with in the echo canceller carrying the attention recurrent deep neural network in figs. 2, 10, and 13).
Claim 11: the combination of Fazeli and Tanaka further teaches, according to claim 10 above, wherein the 2-B artificial neural network includes a pre-learned artificial neural network which mixes the second output information from the second input information and uses fourth input information obtained by subtracting the third output information as input information, and based on the fourth input information, uses fourth output information including information obtained by estimating an echo signal as output information (Fazeli, through summing operations in figure 11C combined with subtraction operations in fig. 13, wherein the subtraction applied to the modified far-end speech signal and the microphone signal level |Dk,f|so that an error signal level obtained as input to the echo canceller in fig. 13 and figs. 14A/14B, and applied in the echo estimation with in the echo canceller carrying the attention recurrent deep neural network in figs. 2, 10, and 13 and the echo signal x(t) is included in the microphone signal d(t), para 53); wherein the 3-B artificial neural network mixes the third output information from the third input information and uses fifth input information obtained by subtracting the second output information as input information, and based on the fifth input information, uses the fifth output information including information estimating the noise signal as output information (Fazeli, through summing operations in figure 11C combined with subtraction operations in fig. 13, wherein the subtraction applied to the modified far-end speech signal and the microphone signal level |Dk,f|so that an error signal level obtained as input to the echo canceller in fig. 13 and figs. 14A/14B, and applied in the noise estimation with in the echo canceller carrying the attention recurrent deep neural network in figs. 2, 10, and 13 and the noise signal n(t) including room impulse response, etc. is included in the microphone signal d(t), para 53).
Claim 12: the combination of Fazeli and Tanaka further teaches, according to claim 6 above, wherein the microphone encoder converts the microphone input signal in the time-domain into a signal in the latent-domain (Fazeli, converted from time domain to a log of spectrum magnitude, i.e., a latent domain, and Tanaka, by AEC1, AEC2, AEC3 by employing a frequency spectrum amplitude multiplication process, para 22, or spectrum amplitude domain or latent domain), and further comprising a decoder for converting the estimated voice signal in the latent domain into an estimated voice signal in the time domain (Fazeli, through iSTFT 1074 to estimated near-end signal q(t) in time-domain in fig. 10, and Tanaka, residual echo spectrum calculated based on the output from AEC1, AEC2, AEC3 through the beamformer 20 in fig. 7 and to far-end in time domain in fig. 3B and through I/F 80 in fig. 3A).
Claim 14 has been analyzed and rejected according to claims 13, 7 above.
Claim 15 has been analyzed and rejected according to claims 13, 8 above.
The prior art (US 20160127527 A1 by Mani et al.) made of record and not relied upon is considered pertinent to applicant's disclosure because Mani disclosed noise estimation and echo estimation based on near-end microphone signal, far-end speech signal (204), and error signal from the echo-alone estimator (214 in fig. 2), which is part of the disclosures disclosed by the application specification.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LESHUI ZHANG whose telephone number is (571)270-5589. The examiner can normally be reached Monday-Friday 6:30amp-4:00pm EST.
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/LESHUI ZHANG/
Primary Examiner,
Art Unit 2695