DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/17/2026 has been entered.
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 5-8, 11, 13, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yun in view of Ayya (WO2018/141061, hereinafter Ayyad).
Regarding claims 1 and 11, Yun discloses a method of and device for generating speech from human intent comprising:
at least one sensor for measuring signals (figure 1 and/or paragraph 23, “receiving speech command of a user” suggest a microphone or other means for receiving speech input);
a processor (figure 1);
at least one or more deep learning modules (paragraphs 37-39, neural network);
a wearable portion comprising the at least one sensor (figure 1 and/or paragraphs 22); and
a memory storing computer-executable instructions that, when executed by the processor (figure 1), cause the device to:
performing a training phase comprising training one or more deep learning modules on a first dataset collected from a first user (paragraphs 37-40, these neural-network-based models have already been trained by at least data of a first user before deployment); and
performing a deployment phase for a second user (at deployment phase, these NN-based models are put to use) comprising:
calibrating the trained one or more deep learning modules for the second user by retraining at least one, but not all, layers of the one or more deep learning modules using a second data set collected from the second user, wherein the second data set is smaller than the first dataset (paragraphs 79 and 87-88, retrain only some layers of NN, not all layers);
sensing signals (paragraphs 28-31, receiving speech input);
processing the signals using the one or more deep learning modules (paragraphs 28-31, receiving speech input); and
converting the processed signals into an output (paragraphs 28-31, receiving speech input);
wherein the signals comprise voluntary intentions (paragraphs 28-31, receiving speech input, which “voluntary intentions”).
Yun fails to explicitly disclose, however, Ayyad teaches that the sensed signals are biological signals comprising at least one of brain signals and muscle signals associated with speech production (abstract section and/or paragraph 7, “brain activity”); processing the biological signals using one or more deep learning modules directly without applying fixed signal processing algorithms (abstract section and/or paragraph 7, “providing the plurality of signals, without pre-processing, to a processing system comprising at least one deep learning module, the at least one deep learning module being configured to process the signals to generate at least one capability”); wherein the biological signals comprise voluntary intentions to speak or generate sound measured before actual generation of the intended sound (paragraph 41 and 159-160, “Brain-to-speech”).
Since Yun and Ayya are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known techniques of obtaining brain signals directly without pre-processing to generate speech. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding claims 3 and 13, Ayyad further discloses wherein processing the biological signals comprises processing raw, non-pre-processed signals directly at the one or more deep learning modules, without applying the fixed signal processing algorithms (abstract section and/or paragraph 7, “providing the plurality of signals, without pre-processing, to a processing system comprising at least one deep learning module, the at least one deep learning module being configured to process the signals to generate at least one capability”).
Since Yun and Ayyad are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known techniques of obtaining brain signals directly without pre-processing to generate speech. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding claims 5 and 15, Yun further discloses wherein the output is text or automatically generated speech (paragraphs 24-25; also see tables 1-2, output can be text or speech).
Regarding claims 6 and 16, Yun fails to explicitly disclose, however, Ayyad further teaches wherein the source is localized in auditory areas of the brain (paragraphs 169 and 172).
Since Yun and Ayyad are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that the source is localized in the auditory areas of the brain. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Regarding claims 7-8 and 17-18, Yun further discloses wherein sounds of words are provided to the deep learning modules at the training phase (paragraphs 79 and 88, training NN with speech initially and also at runtime); wherein text corresponding to words is provided to the deep learning modules at the training phase (paragraphs 37 and 77, training NN model with text).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yun in view of Ayya, and further in view of Tran.
Regarding claims 4 and 14, the combination of Yun and Ayyad further discloses wherein the brain signals are sourced, at least in part, from auditory areas of the brain of the first and/or second user (Ayyad: paragraph 43, “electrodes recorded the electrical activity in the user's brains, body parts such as arms, and hands”).
The combination of Yun and Ayyad still fails to explicitly disclose, however, Tran further teaches wherein the signals comprise the combination of brain and muscle signals (paragraph 74, “electrodes recorded the electrical activity in the user’s brains, body parts such as arms, and hands”).
Since the modified Yun and Tran are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known techniques obtain a combination of signals. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Claims 9-10 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yun in view of Ayyad, further in view of Tran, and further in view of Yang.
Regarding claims 9-10 and 19-20, the modified Tran still fails to explicitly disclose, however, Yang teaches wherein sounds of words are labelled by the deep learning modules at the training phase (paragraphs 49 and 182, “unsupervised learning” method in which a label or correct answer is not provided, and the NN determines the answer on its own); wherein sounds of words are labelled prior to being provided to the deep learning modules at the training phase (paragraphs 49 and 182, “supervised learning” method in which a label or correct answer is provided).
Since the modified Tran and Yang are analogous in the art because they are from the same field of endeavor, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use the known techniques of supervised learning and unsupervised learning to train a NN. One of ordinary skill in the art would have recognized that the results of the combination were predictable since the use of that known technique provides the rationale to arrive at a conclusion of obviousness. See KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (U.S. 2007).
Allowable Subject Matter
Claims 21-22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gallego (USPG 2019/0253812) teaches a method of using periauricular muscle signals to estimate direction of user’s auditory attention locus that is considered pertinent to the claimed invention.
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/HUYEN X VO/Primary Examiner, Art Unit 2656