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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more. This judicial exception is not integrated into a practical application because the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because: Claims 1 and 9:
The claims recite obtaining a digital acoustic signal, trimming a window of the signal, locating zero-crossings (positive-bound and negative-bound), locating local maxima and minima between zero-crossings, identifying an overall maximum and minimum, determining a valid sample range and amplitude threshold, screening local maxima/minima based on the range and threshold, selecting a first number of top local maxima and a second number of bottom local minima, expanding the selected extrema into sets of lags, calculating normalized autocorrelations for the lags, and determining an approximated maximum normalized autocorrelation. This amounts to collecting data, performing mathematical calculations on that data, analyzing it through thresholding and selection operations, and determining results based on the analysis, which are related to mathematical concepts and mental processes/data analysis.
The claims do not recite a specific improvement in the functioning of the computer, processor, ADC, or microphone (e.g., no new signal acquisition mechanism, codec structure, processor architecture, or concrete autocorrelation calculation hardware beyond generic “processor configured to” perform mathematical operations).
The claims use generic computer components (microphone, analog-to-digital converter, processor) performing their conventional functions. The mathematical operations (zero-crossing detection, extrema identification, thresholding, autocorrelation calculations) are result-oriented computational logic implemented on generic processors.
Taken individually and as an ordered combination, the claim elements do not add significantly more than the abstract idea. The use of generic “microphone,” “analog-to-digital converter,” “one or more processors,” and the result-oriented “configured to” perform mathematical operations is conventional in signal processing systems. The thresholding operations (valid sample range, amplitude threshold), selection heuristics (selecting top N maxima and bottom M minima), and mathematical computations (normalized autocorrelation) are routine control and calculation techniques in digital signal processing. The claims lack additional elements amounting to an inventive concept.
Claim 2: “the valid sample range corresponds to a typical human pitch range.” This merely specifies a field-of-use limitation or data constraint applied to the mathematical process. It defines the numerical bounds of an input parameter but does not recite a specific technological implementation or improvement to hardware. No integration into a practical application beyond use of conventional data filtering.
Claim 3: “the valid sample range and the amplitude threshold define at least one target region.” This further defines the abstract mathematical filtering/thresholding operation by naming the result (“target region”). It is a data classification step without concrete technical implementation details beyond the abstract mathematical concept.
Claim 4: “screening the one or more local maxima and the one or more local minima comprises determining whether each of the one or more local maxima and each of the one or more local minima are within the at least one target region.” This elaborates on the screening step as a conditional evaluation (determining whether extrema fall within the target region). This is result-oriented and lacks specific technical means beyond routine comparison operations performed by a generic processor.
Claim 5: “the first set of lags further comprises one or more previous lags to each of the first number of top local maxima.” This adds previous lag values to the data set, which is a mathematical data-manipulation step. It does not recite unconventional hardware, an improved data structure, or a specific technology that improves processor or memory operation. Generic control heuristic applied to the abstract algorithm.
Claim 6: “the second number is between four and five.” This recites a numerical range for a selection parameter in the abstract algorithm. Narrowing a numeric variable does not provide a technical improvement or transform the abstract idea into significantly more.
Claim 7: “the third number is between two and four.” Same analysis as claim 6.
Claim 8: “the one or more processors are further configured to analyze one or more pitch characteristics of the acoustic signal based on the approximated maximum normalized autocorrelation.” This recites a further abstract analysis step (analyzing pitch characteristics) based on the mathematical result. “Analyze” is result-oriented functional language; no particular technical means of how pitch analysis is implemented or how it improves the functioning of the system.
Claim 10: “obtaining the digital acoustic signal in the digital form comprises: receiving, by a microphone, the acoustic signal; and converting, by an analog-to-digital converter, the acoustic signal into the digital form.” This explicitly recites conventional signal acquisition steps performed by generic, well-understood hardware components (microphone and ADC). These are routine functions; the claim does not recite an improvement to microphone or ADC technology.
Claim 11: “the valid sample range corresponds to a typical human pitch range.” Same limitation as claim 2.
Claim 12:
“the valid sample range and the amplitude threshold define at least one target region.” Same limitation as claim 3.
Claim 13:
“screening the one or more local maxima and the one or more local minima comprises determining whether each of the one or more local maxima and each of the one or more local minima are within the at least one target region.” Same limitation as claim 4.
Claim 14: “the first set of lags further comprises one or more previous lags to each of the first number of top local maxima.” Same limitation as claim 5.
Claim 15: “the second number is between four and five.” Same limitation as claim 6.
Claim 16: “the third number is between two and four.” Same limitation as claim 7.
Claim 17: “analyzing one or more pitch characteristics of the acoustic signal based on the approximated maximum normalized autocorrelation.” Same limitation as claim 8.
As such, the claims do not recite a specific improvement to the functioning of the computer, processor, ADC, microphone, or signal-processing architecture. The additional elements (generic microphone, ADC, processors) amount to no more than well-understood, routine, conventional activity. Accordingly, the claims are rejected because they are directed to an abstract idea.
Allowable Subject Matter
Claims 1-17 would be in condition for allowance once the above rejection is resolved.
It should be noted that applicants have requested rejoinder of withdrawn claims 18-20 upon a notice of allowance of a generic claim. However, those claims were properly restricted. For the rejoinder to be granted substantial amendment would need to be made in claim 18, together with ensure said claim is not directed to an abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (Also reason for indicating allowability).
US 10460749, Ru et al., “Voice Activity Detection Using Vocal Tract Area Information.” The reference uses a completely different approach to analyze acoustic signals. The reference teaches using vocal tract area functions (Log-Area-Ratios) and machine learning to detect voice activity, while the claimed invention uses autocorrelation-based pitch detection. In fact, the reference explicitly criticizes autocorrelation methods as having “high computation complexity” that makes them unsuitable for low-power applications. While both systems use zero-crossings, they use them differently: the reference simply counts zero-crossings to make a binary decision, whereas the claims use zero-crossings to locate local maxima and minima, screen them based on amplitude and sample range, select the top extrema to define lags, and then calculate normalized autocorrelations to determine pitch. The reference is missing this entire workflow and the autocorrelation calculations that are central to the claimed invention.
US 20020010575, Haase et al., “Method And System For The Automatic Segmentation Of An Audio Stream Into Semantic Or Syntactic Units.” eference automatically finds sentence boundaries in continuous speech by computing the fundamental frequency (F0) across the entire signal, detecting voiced/unvoiced transitions, and extracting prosodic features like pauses and pitch changes at those transitions. While it does use autocorrelation to compute F0, it applies a windowed autocorrelation across the entire signal and finds maxima within the autocorrelation function itself. It doesn’t start by finding zero-crossings in the time-domain signal, doesn’t identify amplitude extrema between crossings, doesn’t screen those extrema, and doesn’t define specific lag values from selected peaks. It’s performing global pitch analysis for speech segmentation, not the targeted autocorrelation approach claimed.
US 20210304730, Ru Powen, “Beamforming System based on Delay Distribution Model Using High Frequency Phase Difference.” The reference aligns audio signals from multiple microphones to enhance sound from a specific direction. It converts signals to the frequency domain using Fourier transforms, calculates phase differences between microphones, and resolves ambiguities when microphones are spaced far apart by considering multiple possible delays. The entire approach of the reference operates in the frequency domain, not the time domain. It doesn’t detect zero-crossings, doesn’t identify amplitude peaks in the signal, and doesn’t calculate autocorrelations. It’s solving a spatial audio problem (which microphone heard the sound first) rather than analyzing the pitch characteristics of a single signal.
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/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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