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 .
This Office Action is in response to the reply filed July 31, 2026. Claims 13-20 are pending. Claim 20 is withdrawn.
Election/Restrictions
Applicant’s election without traverse of Invention I (claims 13-19) in the reply filed on July 31, 2026 is acknowledged.
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.
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.
Claim(s) 13-15, 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chiang et al (US Patent Application Publication NO. 20230067657), hereinafter Chiang, in view of Busch et al (US Patent Application Publication No. 20190251426), hereinafter Busch.
Regarding claim 13, Chiang teaches an all-analog natural language processing method, comprising: receiving an analog audio signal from an analog microphone at an analog filter bank; filtering the analog audio signal, (as processing speech/audio signal – para 0019 – see analog signal, performing VAD on said signal; and operating on the front-end—para 0002) by a plurality of analog bandpass filters configured for detecting voice activity, to extract features; classifying extracted features, by a voice activity analog neural network to generate an output of voice features (as, using an acoustic feature extraction circuit to extract voice features, and utilizing band-pass filters – para 0006; examiner notes that this processing is operating on analog signals; wherein the classifier is an analog neural network circuit – para 0022) and classifying the extracted features, by a word detection analog neural network to generate an output of a keyword or a wake word. (see mapping above, for the analog neural network, generating outputs). Chiang analog neural network operates on VAD/voice activity, as mapped above, but does not expand upon word/wakeword detection; Busch teaches neuromorphic integrated circuits performing keyword detection/spoken word identification on audio signals (see para 0049), wherein the neural network operates in the analog multiplier array (para 0051). Therefore, it would have been obvious to one of ordinary skill in the art of analog neural networks to modify the analog neural network architecture of Chiang to include analog neural networks that perform keyword identification/word spotting, as taught by Busch because it would advantageously perform artificial intelligence algorithms at much less power (see Busch, para 0005).
Regarding claim 14, the combination of Chiang in view of Busch teaches the all-analog natural language processing method of claim 13, further comprising: filtering the analog audio signal, by a plurality of analog bandpass filters configured for noise cancellation and audio band thresholding (see Chiang, para 0024 – bandpass filtering, for the purpose of determining noise – para 0022, and amplifying while removing noise – para 0020 – focused on maintaining a certain Signal-to-Noise Ratio).
Regarding claim 15, Chiang in view of Busch teaches the all-analog natural language processing method of claim 13, further comprising: accumulating and outputting energy within a frequency band of the bandpass filter over a period of time (see Chiang as, using a stabilizing capacitor in the bandpass filter – para 0030; examiner notes, that “stabilizing” is equivalent to a ‘gathering of charge/voltage over time’; ie, wherein the average voltage/energy is an accumulation of energy of the signal).
Regarding claim 17, the combination of Chiang in view of Busch teaches the all-analog natural language processing method of claim 13, wherein classifying extracted features by the voice activity analog neural network, and classifying extracted features by the word detection analog neural network is performed in parallel (see Busch , figure 4a/4b, parallel processing).
Regarding claim 18, the combination of Chiang in view of Busch teaches the all-analog natural language processing method of claim 17, further comprising: gating the output of the word detection analog neural network by the output of the voice activity analog neural network (as, in Chiang , performing VAD as mapped in claim 13 above; in Busch, the gating of voltages on the transistors, determine the weight values – para 0052, which are used for the word detection aspect – para 0012-0014) .
Regarding claim 19, the combination of Chiang in view of Busch teaches the all-analog natural language processing method of claim 13, further comprising: training the word detection analog neural network for keyword and wake word classification using at least one of a keyword classification dataset and a wake word classification dataset (see Busch , para 0012-0014, using weights to train the analog neural network model).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Chiang in view of Busch in view of Lesso (US Patent Application Publication No. 20200401373).
Regarding claim 16, the combination of Chiang in view of Busch teaches the limitations of claim 13 (from which claim 16 depends, and mapped above); furthermore, Chiang et al (2023006657) teaches that as part of the BPF, using switched-capacitor circuitry to modify the bandpass filtering (see Chiang et al (2023006657), para 0028-0030). However, neither Chiang et al (2023006657) nor Busch teach the analog transistors to be of “charge trap” architecture. Lesso (20200401373) teaches the use of charge trap based analog circuitry in generating analog neural networks for voice activity detection (para 0090 showing charge-trap; and VAD in para 0028, and analog neural networks – para 0111). Therefore, it would have been obvious to one of ordinary skill in the art of analog neural networks to use charge-trap transistors in the analog network, as taught by Lesso (20200401373), because it would advantageously allow for an alternative technology, to perform the equivalent of resistant memory selective control of the two operating states (see Lesso (20200401373), para 0090, reflecting back on para 0089).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659
