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 1-20 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, comprising: obtaining, from a seismic acquisition system, a seismic dataset pertaining to a subterranean region of interest, wherein the seismic dataset comprises a plurality of samples in a plurality of dimensions; using a seismic processing system: forming a plurality of nodes, wherein each node of the plurality specifies a location in the plurality of dimensions and wherein each node has a plurality of neighboring nodes adjacent to it in the plurality of dimensions, for each node: determining, a traveltime operator, based on a portion of the seismic dataset within an aperture surrounding the node; and assigning a canonical operator based on the traveltime operator, forming a plurality of windows, wherein each window of the plurality of windows comprises a neighboring node; for each window of the plurality of window: determining a modal canonical operator, based on the canonical operator for each neighboring node within the window; determining modal-nodes within the window, wherein the canonical operator of each modal-node is equal to modal canonical operator; determining deviating-nodes withing the window, wherein the canonical operator of each deviating-node deviates from the modal canonical operator; and determining a replacement traveltime operator for each deviating-node based on the traveltime operator of modal-nodes within the window, and forming a seismic image of the subterranean region of interest based on the seismic dataset and the traveltime operator of the modal-nodes within the plurality of nodes and the replacement traveltime operator of the deviating-nodes within the plurality of nodes.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
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 grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “determining, a traveltime operator, based on a portion of the seismic dataset within an aperture surrounding the node; and assigning a canonical operator based on the traveltime operator, forming a plurality of windows, wherein each window of the plurality of windows comprises a neighboring node; determining a modal canonical operator, based on the canonical operator for each neighboring node within the window; determining modal-nodes within the window, wherein the canonical operator of each modal-node is equal to modal canonical operator; determining deviating-nodes withing the window, wherein the canonical operator of each deviating-node deviates from the modal canonical operator; and determining a replacement traveltime operator for each deviating-node based on the traveltime operator of modal-nodes within the window, and forming a seismic image of the subterranean region of interest based on the seismic dataset and the traveltime operator of the modal-nodes within the plurality of nodes and the replacement traveltime operator of the deviating-nodes within the plurality of nodes” are treated as belonging to mental process grouping and/or mathematical concept.
Similar limitations comprise the abstract ideas of Claims 11.
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:
In Claim 1: a seismic acquisition system
In Claim 11: a seismic acquisition system, seismic processing system
The additional element of: a seismic acquisition system, seismic processing system are generally recited and are not qualified as particular machines.
Further the limitations of: “obtaining, from a seismic acquisition system, a seismic dataset pertaining to a subterranean region of interest, wherein the seismic dataset comprises a plurality of samples in a plurality of dimensions; using a seismic processing system: forming a plurality of nodes, wherein each node of the plurality specifies a location in the plurality of dimensions and wherein each node has a plurality of neighboring nodes adjacent to it in the plurality of dimensions, for each node” are considered by MPEP 2106.05(g) and MPEP 2106.05(h) as insignificant extra-solution activity, mere data gathering and generally linking the use of a judicial exception to a particular technological environment or field of use.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. 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).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-10 and 12-20 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Allowable Subject Matter
Claims 1-20 would be allowable if written overcome the 101 rejection set forth in this office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 1, Colombo, Kim and others teach a method, comprising: obtaining, from a seismic acquisition system, a seismic dataset pertaining to a subterranean region of interest, wherein the seismic dataset comprises a plurality of samples in a plurality of dimensions; using a seismic processing system: forming a plurality of nodes, wherein each node of the plurality specifies a location in the plurality of dimensions and wherein each node has a plurality of neighboring nodes adjacent to it in the plurality of dimensions. No prior art, individually or in combination, teach nor suggest A method, comprising: obtaining, from a seismic acquisition system, a seismic dataset pertaining to a subterranean region of interest, wherein the seismic dataset comprises a plurality of samples in a plurality of dimensions; using a seismic processing system: forming a plurality of nodes, wherein each node of the plurality specifies a location in the plurality of dimensions and wherein each node has a plurality of neighboring nodes adjacent to it in the plurality of dimensions, for each node: determining, a traveltime operator, based on a portion of the seismic dataset within an aperture surrounding the node; and assigning a canonical operator based on the traveltime operator, forming a plurality of windows, wherein each window of the plurality of windows comprises a neighboring node; for each window of the plurality of window: determining a modal canonical operator, based on the canonical operator for each neighboring node within the window; determining modal-nodes within the window, wherein the canonical operator of each modal-node is equal to modal canonical operator; determining deviating-nodes withing the window, wherein the canonical operator of each deviating-node deviates from the modal canonical operator; and determining a replacement traveltime operator for each deviating-node based on the traveltime operator of modal-nodes within the window, and forming a seismic image of the subterranean region of interest based on the seismic dataset and the traveltime operator of the modal-nodes within the plurality of nodes and the replacement traveltime operator of the deviating-nodes within the plurality of nodes.
It is for this reason, Claim 1 and all of its dependencies would be allowed.
Claim 11 include analogous, though not necessarily coextensive, features in conjunction with Claim 1, and therefore, would be, along with its dependencies, for similar rationale as disclosed above, allowed.
Conclusion
The prior art made record and not relied upon is considered pertinent to applicant’s disclosure.
Colombo et al. ( MACHINE LEARNING INVERSION USING BAYESIAN INFERENCE AND SAMPLING, 2023-09-14) teaches a system and methods for determining an updated geophysical model of a subterranean region of interest are disclosed. The method includes obtaining a preprocessed observed geophysical dataset based, at least in part, on an observed geophysical dataset of the subterranean region of interest, and forming a training dataset composed of a plurality of geophysical training models and corresponding simulated geophysical training datasets. The method further includes iteratively determining a simulated geophysical dataset from a current geophysical model, determining a data loss function between the preprocessed observed geophysical dataset and the simulated geophysical dataset, training a machine learning (ML) network, using the training dataset, to predict a predicted geophysical model and determining a model loss function between the current and predicted geophysical models. The method still further includes updating the current geophysical model based on an inversion using the data loss and model loss functions;
Zhang et al. (SYSTEM AND METHOD OF HYDROCARBON DETECTION USING NONLINEAR MODEL FREQUENCY SLOPE, 2022-09-22) teaches a method is disclosed that includes: obtaining a seismic data volume for a subterranean region of interest; transforming, by a computer processor using a non-stationary series analysis, the seismic data volume into a seismic spectral volume where the seismic spectral volume includes a seismic spectrum for each of a plurality of voxels; and determining a seismic attribute volume composed of a seismic attribute for each of the plurality of voxels. The seismic attribute for a voxel of the plurality of voxels is based, at least in part, on an integral of the seismic spectrum for the voxel over a range bounded by a first frequency and a second frequency. The method further includes determining a presence of hydrocarbon in the subterranean region of interest based on the seismic attribute volume. A system for performing the method is also disclosed and described;
Bakulin et al. (PROVIDING SEISMIC IMAGES OF THE SUBSURFACE USING ENHANCEMENT OF PRE-STACK SEISMIC DATA, 2022-06-23) teaches a system provides seismic images of the subsurface by enhancing pre-stack seismic data. The system obtains seismic data comprising a plurality of seismic traces that are generated by measuring reflections of seismic waves emitted into a geological formation. The system sorts seismic data into at least one multidimensional gather comprising a data domain. The system determines local kinematical attributes of a seismic trace. The system forms an ensemble of seismic traces, each representing a reference point. The system applies local moveout corrections to each seismic trace of the ensemble. The system applies residual statics and phase corrections for each seismic trace that is corrected by the local moveout corrections. The system sums the seismic traces of the ensemble to obtain an output seismic trace having an increased signal-to-noise ratio (SNR) relative to the seismic trace that represents the reference point for the ensemble of seismic traces;
Kim et al. (FULL WAVEFORM INVERSION VELOCITY GUIDED FIRST ARRIVAL PICKING, 2022-06-16) teaches a method of determining an arrival-time of a first seismic event in a seismic data set including, obtaining the seismic data set and an initial seismic velocity model, and determining an updated seismic velocity model based on the seismic data set. Furthermore, the method includes determining a simulated arrival-time of the first seismic event based on the updated seismic velocity model and defining a predicted time-window based on the simulated arrival-time of the first seismic event, and picking the arrival-time of the first seismic event in the seismic data set based on the predicted time-window;
Fu et al. (Seismic Velocity Derived Hydrocarbon Indication, 2021-09-07) teaches A velocity model is generated based upon seismic waveforms via any seismic model building method, such as full waveform inversion or tomography. Data representative of a measurement of a physical attribute of an area surrounding a well is received and an attribute model is generated based upon the velocity model and the data. An image is rendered based upon the attribute model for use with seismic exploration above a region of a subsurface comprising a hydrocarbon reservoir and containing structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.
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/MICHAEL J SINGLETARY/Examiner, Art Unit 2863