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: receiving seismic data representing a subterranean volume, the seismic data including a plurality of signals; obtaining a machine learning model trained to predict energy arrivals in the signals, the machine learning model was trained using seismic data that does not represent the subterranean volume; predicting energy arrivals in a quality control portion of the plurality of signals of the seismic data using the machine learning model; determining that the predicted energy arrivals for the quality control portion are not accurate; in response to determining that the predicted energy arrivals are not accurate, training the machine learning model to predict the energy arrivals using a training data set that represents the subterranean volume; predicting the energy arrivals in the seismic data using the machine learning model that was trained based at least in part on the training data set; and generating a velocity model based on the predicted energy arrivals.
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 “predicting energy arrivals in a quality control portion of the plurality of signals of the seismic data using the machine learning model; determining that the predicted energy arrivals for the quality control portion are not accurate; predicting the energy arrivals in the seismic data using the machine learning model that was trained based at least in part on the training data set;” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 9 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 additional element in the preamble of “receiving seismic data representing a subterranean volume, the seismic data including a plurality of signals; obtaining a machine learning model trained to predict energy arrivals in the signals, the machine learning model was trained using seismic data that does not represent the subterranean volume; and generating a velocity model based on the predicted energy arrivals” are considered by MPEP 2106.05(g) as insignificant extra solution activity, mere data gathering/outputting.
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 2-8, 10-15 and 17-20 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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 7-9,15-16 and 20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by He et al. (US11221425B1, 2022-01-11) herein referred to He.
Regarding Claim 1, He teaches a method comprising:
receiving seismic data representing a subterranean volume, the seismic data
including a plurality of signals (Col. 6, lines 40-45).
obtaining a machine learning model trained to predict energy arrivals in the
signals, the machine learning model was trained using seismic data that does not represent the subterranean volume (Col. 8, lines 58-Col. 9, lines 8);
predicting energy arrivals in a quality control portion of the plurality of signals of
the seismic data using the machine learning model (Co. 8, lines 64-67);
determining that the predicted energy arrivals for the quality control portion are
not accurate (Col. 8, lines 58-60);
in response to determining that the predicted energy arrivals are not accurate,
training the machine learning model to predict the energy arrivals using a training data set that represents the subterranean volume (Col. 9, lines 6-8);
predicting the energy arrivals in the seismic data using the machine learning
model that was trained based at least in part on the training data set (Col. 8, lines 42-44); and
generating a velocity model based on the predicted energy arrivals (Col. 8, lines 52-55).
Regarding Claim 7, He teaches the method of claim 1, wherein the energy
arrivals include first breaks representing reflected seismic signals (Col. 6, lines 43-45).
Regarding Claim 8, He teaches The method of claim 1, comprising generating a
digital display including an image representing the subterranean volume based at least in part on the velocity model (Col. 17, lines 18-21).
Regarding Claim 9, He teaches a computing system comprising one or more
processors and a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations (Col. 15, lines 22-45), the operations comprising:
receiving seismic data representing a subterranean volume, the seismic data
including a plurality of signals (Col. 6, lines 40-45).
obtaining a machine learning model trained to predict energy arrivals in the
signals, the machine learning model was trained using seismic data that does not represent the subterranean volume (Col. 8, lines 58-Col. 9, lines 8);
predicting energy arrivals in a quality control portion of the plurality of signals of
the seismic data using the machine learning model (Co. 8, lines 64-67);
determining that the predicted energy arrivals for the quality control portion are
not accurate (Col. 8, lines 58-60);
in response to determining that the predicted energy arrivals are not accurate, training the machine learning model to predict the energy arrivals using a training data set that represents the subterranean volume (Col. 9, lines 6-8);
predicting the energy arrivals in the seismic data using the machine learning
model that was trained based at least in part on the training data set (Col. 8, lines 42-44); and
generating a velocity model based on the predicted energy arrivals (Col. 8, lines 52-55).
Regarding Claim 15, He teaches the computing system of claim 9, wherein the
energy arrivals include first breaks representing reflected seismic signals (Col. 6, lines 43-45).
Regarding Claim 16, He teaches a computer-readable medium storing
instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations (Col. 19, lines 1-9), the operations comprising:
receiving seismic data representing a subterranean volume, the seismic data
including a plurality of signals (Col. 6, lines 40-45);
obtaining a machine learning model trained to predict energy arrivals in the
signals, the machine learning model was trained using seismic data that does not represent the subterranean volume (Col. 8, lines 58-Col. 9, lines 8);
predicting energy arrivals in a quality control portion of the plurality of signals of
the seismic data using the machine learning model (Co. 8, lines 64-67);
determining that the predicted energy arrivals for the quality control portion are
not accurate (Col. 8, lines 58-60);
in response to determining that the predicted energy arrivals are not accurate,
training the machine learning model to predict the energy arrivals using a training data set that represents the subterranean volume (Col. 9, lines 6-8);
predicting the energy arrivals in the seismic data using the machine learning
model that was trained based at least in part on the training data set (Col. 8, lines 42-44); and
generating a velocity model based on the predicted energy arrivals (Col. 8, lines 52-55).
Regarding Claim 20, He teaches the medium of claim 16, wherein the energy
arrivals include first breaks representing reflected seismic signals (Col. 6, lines 43-45).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 5, 10, 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over He, in view of Yuan et al. (Yuan et al., CN110308484A, 2019-10-08 ), herein referred to as Yuan.
Regarding Claim 2, He teaches the method of claim 1, wherein determining that the predicted energy arrivals for the quality control portion are not accurate. He fails to
teach receiving human-generated labels of predicted energy arrivals for the quality control portion; and comparing the human-generated labels with the predicted energy arrivals for the quality control portion. However, in a related field, Yuan teaches receiving human-generated labels of predicted energy arrivals for the quality control portion; and comparing the human-generated labels with the predicted energy arrivals for the quality control portion (Step S21-Step S22 (pg. 4)).Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order to continuously improve interpretation and selection of seismic data.
Regarding Claim 5, He teaches the method of claim 1. He fails to teach wherein the training data set includes ground-truths that are human-applied. However, in a related field, Yuan teaches wherein the training data set includes ground-truths that are human-applied (Step S21-Step S22 (pg. 4)). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order to continuously improve interpretation and selection seismic data.
Regarding Claim 10, He teaches the method of claim 9, wherein determining that the predicted energy arrivals for the quality control portion are not accurate. He fails to teach receiving human-generated labels of predicted energy arrivals for the quality control portion; and comparing the human-generated labels with the predicted energy arrivals for the quality control portion. However, in a related field, Yuan teaches receiving human-generated labels of predicted energy arrivals for the quality control portion; and comparing the human-generated labels with the predicted energy arrivals for the quality control portion (Step S21-Step S22 (pg. 4)).Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order to continuously improve interpretation and selection of seismic data.
Regarding Claim 13, He teaches the method of claim 9. He fails to teach wherein the training data set includes human-applied ground-truths labels. However, in a related field, Yuan teaches wherein the training data set includes human-applied ground-truths labels (Step S21-Step S22 (pg. 4)). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order to continuously improve interpretation and selection seismic data.
Regarding Claim 17, He teaches the medium of claim 16, wherein determining that the predicted energy arrivals for the quality control portion are not accurate. He fails to teach receiving human-generated labels of predicted energy arrivals for the quality control portion; and comparing the human-generated labels with the predicted energy arrivals for the quality control portion. However, in a related field, Yuan teaches receiving human-generated labels of predicted energy arrivals for the quality control portion; and comparing the human-generated labels with the predicted energy arrivals for the quality control portion (Step S21-Step S22 (pg. 4)).Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order to continuously improve interpretation and selection of seismic data.
Claims 4, 6, 12, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over He, in view of Yuan et al. (Yuan et al., CN110308484A, 2019-10-08 ), herein referred to as Yuan.
Regarding Claim 4, He teaches the method of Claim 1. He fails to teach wherein the training data set includes a portion of the seismic data that was received. However, in a related field, Yuan teaches wherein the training data set includes a portion of the seismic data that was received (Fig. 2). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order for the model to adapt to the new dataset with few training samples and few updates.
Regarding Claim 6, He teaches the method of Claim 1, wherein training the machine learning model in response to determining that the predicted energy arrivals are not accurate (Col. 8, lines 58-60). He fails to teach includes:
again predicting the energy arrivals in the quality control portion of the plurality of signals using the machine learning model after training the machine learning model using the training data
set;
determining that the again predicted energy arrivals are not accurate;
in response to determining that the again predicted energy arrivals are not accurate:
receiving human-applied labels for the quality control portion of the seismic data; and training the machine learning model based at least in part on the quality control portion and the human-applied labels for the quality control portion.
However, in a related field, Yuan teaches again predicting the energy arrivals in the quality control portion of the plurality of signals using the machine learning model after training the machine learning model using the training data set (Fig. 2);
determining that the again predicted energy arrivals are not accurate (Fig. 2);
in response to determining that the again predicted energy arrivals are not accurate:
receiving human-applied labels for the quality control portion of the seismic data (Fig. 2); and
training the machine learning model based at least in part on the quality control portion and the human-applied labels for the quality control portion (Fig. 2).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order for the model to adapt to the new dataset with few training samples and few updates.
Regarding Claim 12, He teaches the computing system of Claim 9. He fails to teach wherein the training data set includes a portion of the seismic data that was received. However, in a related field, Yuan teaches wherein the training data set includes a portion of the seismic data that was received (Fig. 2). Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order for the model to adapt to the new dataset with few training samples and few updates.
Regarding Claim 14, He teaches the computing system of Claim 9, wherein training the machine learning model in response to determining that the predicted energy arrivals are not accurate (Col. 8, lines 58-60). He fails to teach includes:
again predicting the energy arrivals in the quality control portion of the plurality of signals using the machine learning model after training the machine learning model using the training data set;
determining that the again predicted energy arrivals are not accurate;
in response to determining that the again predicted energy arrivals are not accurate:
receiving human-applied labels for the quality control portion of the seismic data; and
training the machine learning model based at least in part on the quality control portion and the human-applied labels for the quality control portion.
However, in a related field, Yuan teaches again predicting the energy arrivals in the quality control portion of the plurality of signals using the machine learning model after training the machine learning model using the training data set (Fig. 2);
determining that the again predicted energy arrivals are not accurate (Fig. 2);
in response to determining that the again predicted energy arrivals are not accurate:
receiving human-applied labels for the quality control portion of the seismic data (Fig. 2); and
training the machine learning model based at least in part on the quality control portion and the human-applied labels for the quality control portion (Fig. 2).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order for the model to adapt to the new dataset with few training samples and few updates.
Regarding Claim 19, He teaches the computing system of Claim 16, wherein training the machine learning model in response to determining that the predicted energy arrivals are not accurate (Col. 8, lines 58-60). He fails to teach includes:
again predicting the energy arrivals in the quality control portion of the plurality of signals using the machine learning model after training the machine learning model using the training data
set;
determining that the again predicted energy arrivals are not accurate;
in response to determining that the again predicted energy arrivals are not accurate:
receiving human-applied labels for the quality control portion of the seismic data; and
training the machine learning model based at least in part on the quality control portion and the human-applied labels for the quality control portion.
However, in a related field, Yuan teaches again predicting the energy arrivals in the quality control portion of the plurality of signals using the machine learning model after training the machine learning model using the training data set (Fig. 2);
determining that the again predicted energy arrivals are not accurate (Fig. 2);
in response to determining that the again predicted energy arrivals are not accurate:
receiving human-applied labels for the quality control portion of the seismic data (Fig. 2); and
training the machine learning model based at least in part on the quality control portion and the human-applied labels for the quality control portion (Fig. 2).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified He to incorporate the teachings of Yuan by including: the limitations above in order for the model to adapt to the new dataset with few training samples and few updates.
Allowable Subject Matter
Claims 3, 11 and 18 would be allowable if written to overcome the 101 rejection set forth in this office action and rewritten in independent form to incorporate all the limitations of their base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 3, He teaches the method of claim 1, wherein the machine learning model includes a plurality of machine learning models, predicting the energy arrivals in the quality control portion includes predicting the energy arrivals in the quality control portion (Co. 8, lines 64-67).
He along with all other references fail to teach receiving predictions from the plurality of machine learning models; determining that a first subset of the predictions are low-confidence predictions based on inconsistency in the predictions by the different machine learning models; and determining that a second subset of the predictions are high-confidence predictions based on consistency between the predictions received from the plurality of machine learning models, the predicted energy arrivals include the high-confidence predictions and not the low-confidence predictions. It is for this reason, Claim 3 would be allowable.
Regarding Claim 11, He teaches the computing system of claim 9, wherein the machine learning model includes a plurality of machine learning models, predicting the energy arrivals in the quality control portion includes predicting the energy arrivals in the quality control portion (Co. 8, lines 64-67).
He along with all other references fail to teach receiving predictions from the plurality of machine learning models; determining that a first subset of the predictions are low-confidence predictions based on inconsistency in the predictions by the different machine learning models; and determining that a second subset of the predictions are high-confidence predictions based on consistency between the predictions received from the plurality of machine learning models, the predicted energy arrivals include the high-confidence predictions and not the low-confidence predictions. It is for this reason, Claim 3 would be allowable.
Regarding Claim 18, He teaches the medium of claim 16, wherein the machine learning model includes a plurality of machine learning models, predicting the energy arrivals in the quality control portion includes predicting the energy arrivals in the quality control portion (Co. 8, lines 64-67).
He along with all other references fail to teach receiving predictions from the plurality of machine learning models; determining that a first subset of the predictions are low-confidence predictions based on inconsistency in the predictions by the different machine learning models; and determining that a second subset of the predictions are high-confidence predictions based on consistency between the predictions received from the plurality of machine learning models, the predicted energy arrivals include the high-confidence predictions and not the low-confidence predictions. It is for this reason, Claim 3 would be allowable.
Conclusion
The prior art made record and not relied upon is considered pertinent to applicant’s disclosure.
Goncharuk et al. (GENERATING REALISTIC SYNTHETIC SEISMIC DATA ITEMS, 2024-02-08) teaches methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic synthetic seismic data items. One of the methods includes obtaining a plurality of synthetic seismic data items; obtaining a plurality of real seismic data items; processing each of the plurality of synthetic seismic data items using a machine learning model; processing each of the plurality of real seismic data items using the same machine learning model; determining a range for values for one or more parameters of a synthetic seismic data generator by comparing the synthetic seismic data items and the real seismic data items in an embedding space of the machine learning model; and selecting, as realistic synthetic seismic data items, a plurality of synthetic seismic data items that have been generated with a respective combination of values for the one or more parameters that is within the determined range;
Schumaker et al. (TRANSFER LEARNING FOR ML-ASSISTED SEISMIC INTERPRETATION, 2023-12-28) teaches a method includes receiving field seismic data that represents a subsurface, identifying features in the field seismic data using a machine learning model that was trained using at least one first synthetic seismic data set that includes one or more features and one or more labels of the features, and at least one second synthetic seismic data set, the first and second synthetic seismic data sets both generated based on a geological model. Noise is injected into the second synthetic seismic data based on the geological model. The method also includes generating a model of the subsurface based at least in part on the features that were identified in the field seismic data using the machine learning model;
Zhang (FIRST-BREAK PICKING OF SEISMIC DATA AND GENERATING A VELOCITY MODEL, 2019-10-24) teaches a new method for iteratively picking the seismic first breaks and conducting imaging of the near-surface velocity structures in an iterative fashion is provided that the first-break picks of the input seismic data are applied to image the near-surface velocity structures and the calculated travel times associated with the updated velocity structures are applied to help refine the first-break picks in the first break picking process until first-break picks satisfy a number of quality control criteria, statics solutions are optimized, and the near surface imaging reaches an acceptable data misfit. This invention produces a velocity model that can be used for near surface statics corrections or for the prestack depth migration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SINGLETARY whose telephone number is (571)272-4593. The examiner can normally be reached Monday-Friday 8:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at 571-270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL J SINGLETARY/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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