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
Claims 1-7 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A method for improving DAS signal-to-noise ratio by means of local FK transform, comprising: acquiring seismic wavefield data from a DAS acquisition instrument; segmenting the seismic wavefield data into a plurality of pieces of local data, each piece of local data having the same dimension as the seismic wavefield data; processing each piece of local data through the following steps: obtaining an intermediate signal through FK transform, removing partial FK spectral components in the intermediate signal according to scanning energy corresponding to the intermediate signal under different apparent slowness, and performing two-dimensional FFT inverse transform on the intermediate signal with the partial FK spectral components removed; and combining all processed local data to obtain new seismic wavefield data.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional element”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations or mental steps.
For example, steps of “segmenting the seismic wavefield data into a plurality of pieces of local data, each piece of local data having the same dimension as the seismic wavefield data; processing each piece of local data through the following steps: obtaining an intermediate signal through FK transform, removing partial FK spectral components in the intermediate signal according to scanning energy corresponding to the intermediate signal under different apparent slowness, and performing two-dimensional FFT inverse transform on the intermediate signal with the partial FK spectral components removed; and combining all processed local data to obtain new seismic wavefield data” are treated as belonging to mathematical process grouping or mental steps
Similar limitations comprise the abstract ideas of Claims 11 and 16.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that
integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method for improving DAS signal-to-noise ratio by means of local FK transform, comprising: acquiring seismic wavefield data from a DAS acquisition instrument.
Claim 11: A device for improving DAS signal-to-noise ratio by means of local FK transform, comprising: a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor, when executes the computer program, implements the following steps; acquiring seismic wavefield data from a DAS acquisition instrument.
Claim 16: A non-transitory computer readable storage medium, wherein the storage medium stores an instruction, and the instruction, when runs on a computer, enables the computer to execute the following steps: acquiring seismic wavefield data from a DAS acquisition instrument
The above steps of a method for improving DAS signal-to-noise ratio by means of local FK transform, comprising: is generically recited, not meaningful, does not represent a particular machine and/or eligible transformation, it does not indicate a practical application and acquiring seismic wavefield data from a DAS acquisition instrument are generically recited are generically recited and represent mere data gathering steps (insignificant extra-solution activity) necessary to execute the abstract idea. The additional element of a non-transitory computer readable storage medium, wherein the storage medium stores an instruction, and the instruction, when runs on a computer, enables the computer to execute the following steps is an example of generic computer equipment that is generically recited and therefore is not qualified as a particular machine..
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references (Almuhaidib, Willis, and Van Manen).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7,12-15, and 17-20 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mathematical/mental/organizing human activity process steps) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record.
Examiner Notes with Regards to Prior Art of Record
Regarding Claims 1,11, and 16 Almuhaidib (US20160320509) discloses a method for improving DAS signal-to-noise ratio by means of local FK transform, comprising: acquiring seismic wavefield data from a DAS acquisition instrument (Embodiments of the present invention provide a new technique to help in the processing and interpretation of seismic survey data, by correlating or otherwise comparing or associating seismic data obtained from a seismic survey with flow information obtained from a well or borehole in the surveyed area. In particular, embodiments of the present invention allow for flow data representing a flow profile along a well that is being monitored by a DAS to be determined [0006]); segmenting the seismic wavefield data into a plurality of pieces of local data, each piece of local data having the same dimension as the seismic wavefield data (At 610, seismic data of a subterranean region can be received or otherwise accessed by the data processing apparatus (for example, one or more processor(s) of the computer system 150 in FIG. 1A). The seismic data can be seismic data collected, for example, by receivers (for example, the receivers 120 in FIGS. 1A and 1B) and transmitted to the data processing apparatus [0094]); processing each piece of local data through the following steps: obtaining an intermediate signal through FK transform (Referring back to FIG. 6, at 660, the noise component can be predicted based on the dominant orientation of the section. For example, the noise component can be predicted by applying, to each of the number of sections of the seismic data in 610, a directional filter (for example, a non-linear median filter) guided by the spatially-varying dominant slope [0106]), removing partial FK spectral components in the intermediate signal according to scanning energy corresponding to the intermediate signal under different apparent slowness (The Gaussian filter in the steerable filter definition plays an important role as a regularizing (smoothing) term to avoid unwanted, oscillatory, instantaneous slowness estimates. The smoothing of this operator is determined by the variance of the Gaussian operator [0073]), and performing two-dimensional FFT inverse transform on the intermediate signal with the partial FK spectral components removed (Referring back to FIG. 6, at 670, the noise predicted based on the predicted noise image orientations can be subtracted from the received seismic data, according to the example techniques described with respect to equations (11)-(13) or another method [0109]; At 680, the filtered seismic data can be output, for example, by transmitting over a communication interface, storing in a data store, displaying on a user interface (for example, as shown in FIGS. 9B and 10A) [0110]); and combining all processed local data to obtain new seismic wavefield data (The filtered seismic data can have a greater signal to noise ratio than the received seismic data. The filtered seismic data can be used for other processing, for example, for reservoir analysis [0110]).
Regarding Claims 1,11, and 16, Willis (US20200102821) discloses a method for improving DAS signal-to-noise ratio by means of local FK transform, comprising: acquiring seismic wavefield data from a DAS acquisition instrument (A distributed acoustic sensing (DAS) system is one type of seismic sensor system utilized for VSP. The DAS system utilizes a downhole distributed acoustic sensor such as optical fibers as sensing elements to detect seismic waves incident on the distributed acoustic sensor resulting from an acoustic source outputting acoustic energy at or near the surface of the wellbore [0003]); segmenting the seismic wavefield data into a plurality of pieces of local data, each piece of local data having the same dimension as the seismic wavefield data (In some examples, the seismic data is filtered. For example, the seismic data in the raw seismic profile is filtered. The filtered seismic profile enhances any resonant noise. The filtering may take the form of a median filter or some other type of filter that enhances the resonant noise [0047]); processing each piece of local data through the following steps: obtaining an intermediate signal through FK transform, removing partial FK spectral components in the intermediate signal according to scanning energy corresponding to the intermediate signal under different apparent slowness (At block 908, a Fast Fourier Transform (FFT) is performed on the filtered seismic data (or unfiltered if block 906 is not performed) to convert the seismic data from a time domain representation of the seismic data for each channel into a frequency domain representation or frequency spectrum of the seismic data for each channel [0060]), and performing two-dimensional FFT inverse transform on the intermediate signal with the partial FK spectral components removed (Instead of adjusting the resonant noise in the seismic data associated with the raw seismic profile, the resonant noise may be adjusted in the frequency domain representation of the seismic data. Then, the adjusted frequency domain representation of the seismic data may be transformed into the time domain by an inverse FFT and used to produce a resonant noise reduced raw seismic profile [0069]); and combining all processed local data to obtain new seismic wavefield data (At block 814, a determination is made whether seismic data in each region of the raw seismic profile has been compared to the predetermined pattern. If the seismic data in each of the regions have been compared to the predetermined pattern, then processing continues to block 816. If the seismic data in each region has not been compared to the predetermined pattern, at block 820 another region is selected then processing returns to block 806 to process another region [0052]).
Regarding Claims 1,11, and 16, Van Manen (US20190072686) discloses a method for improving DAS signal-to-noise ratio by means of local FK transform, comprising: acquiring seismic wavefield data from a DAS acquisition instrument (It is well known, for example, that due to the “uncertainty principle”, a function and its Fourier transform cannot both have bounded support. As (seismic) data are necessarily acquired over a finite spatial (and temporal) extent, the terms “bounded support” and “limited support” herein are used not in the strict mathematical sense, but rather to describe an “effective numerical support”, that can be characterized, e.g., by the (amplitude) spectrum being larger than a certain value [0039]); segmenting the seismic wavefield data into a plurality of pieces of local data, each piece of local data having the same dimension as the seismic wavefield data (The present disclosure relates to methods for separating contributions from two or more different sources in a common set of aliased measured signals representing a wave field, particularly of seismic sources and of sets of aliased recorded and/or aliased processed seismic signals [0002]); processing each piece of local data through the following steps: obtaining an intermediate signal through FK transform, removing partial FK spectral components in the intermediate signal according to scanning energy corresponding to the intermediate signal under different apparent slowness (While various embodiments of the present disclosure have been described above, it should be understood that they have been presented by way of example only, and not of limitation. For example it should be noted that where filtering steps are carried out in the frequency-wavenumber space, filters with the equivalent effect can also be implemented in many other domains such as tau-p or space-time [0152]), and performing two-dimensional FFT inverse transform on the intermediate signal with the partial FK spectral components removed (This equation can be solved using an iterative solver for linear equations, for instance the conjugate gradient method. The operators in A.sub.F are realized using standard FFT, and the operators in A.sub.D are computed using a combinations of Fourier transforms and differential schemes, that also can be implemented by using FFT [0107]).
In regards to Claim 1, the claims are allowed because the closest prior art Almuhaidib, Willis, and Van Manen either singularly or in combination, fail to anticipate or render obvious, selecting said integer charge value ([Qi]) and therewith calculating a plurality of different candidate image-charge/current signal frequency values (ficand) according to said selected measured signal frequency (fo) and according to a corresponding one of one or more different candidate charge states of the ion and/or of ion isotope or isotopologue in combination with all other limitations in the claim as claimed and defined by applicant.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mike Warner (US20160238729) discloses the method includes providing observed seismic dataset having three distinct nonzero data values derived from three distinct nonzero seismic measured values of said portional volume of the Earth, generating a predicted seismic dataset having three distinct nonzero data values, generating a nontrivial convolutional filter including three nonzero filter coefficients, generating a convolved observed dataset by convolving the convolutional filter with said observed seismic dataset.
William Curry (US20150117151) discloses the method comprises decomposing the seismic data (102) by a transformation to the local slowness domain, preferably using Gaussian slowness period packets as the local slowness or slope decomposition technique, thereby avoiding problems with the data stationary assumption.
Roald van Borselen (US20130329520) discloses Techniques are described for predicting surface-related multiples from measurements performed at varying depths. One or more operations, such as wavefield decompositions and/or extrapolations, may be performed on scattered wavefield data obtained by underwater sensors at different underwater depths to determine one or more surface-related multiple wavefield contributions at a selected depth or at the different underwater depths where the scattered wavefield data is collected from measurements.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAH ZAAB whose telephone number is (571)272-4973. The examiner can normally be reached Monday - Friday 7:00 am - 4:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on 571-272-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARAH ZAAB/Examiner, Art Unit 2857
/Catherine T. Rastovski/ Supervisory Primary Examiner, Art Unit 2857
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