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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/23/2025 has been entered.
Priority
Current application, US Application No. 18/005,011 filed on 01/10/2023, is a National Stage entry of PCT/CN2021/103487, International Filing Date: 06/30/2021, which claims foreign priority to CN 202010931370.2 filed on 09/07/2020 and foreign priority to CN 202010928912.0 filed on 09/07/2020.
Examiner acknowledges that the certified copies of foreign priority documents have been received. However, the certified English translation copies of the original foreign documents, which are not written in English, have not been received. There is no requirement to submit certified English translation copies at this stage according to 37 CFR 1.55(g)(3). However, should the need of certified English translated copies arise according to the cases mentioned in 37 CFR 1.55(g)(3), submission may be requested in the future.
DETAILED ACTION
This office action is responsive to the amendment filed on 12/23/2025. Claims 1-7 and 9-14 are currently pending. Claim 8 is canceled and claim 14 is added new per applicant’s request.
Response to Amendment
Applicant's amendment is entered into further examination and appreciated by the examiner. However, amendment introduced new matters. The amendment lacks the description support from the specification and fails to supply applicant’s own evidentiary submission under 37 CFR 1.132 (Rule 132) as an alternative way to show description support instead of the originally disclosed specification. (See MPEP 716.01 and 1206(II)).
Response to Arguments/Remarks
Regarding remarks on the objections to the claims, the amendment is accepted and the previous objects are withdrawn.
Regarding remarks on the rejections under 35 USC 112(a), the amendment accompanied with persuasive explanation is accepted and the previous rejections are withdrawn. However, new matters are discovered in the amendment. Please see the new office action below.
Regarding remarks on the rejections under 35 USC 112(b), the amendment accompanied with persuasive explanation is accepted and the previous rejections are withdrawn.
Regarding remarks on the rejections under 35 USC 101, the amendment accompanied with persuasive remarks would have overcome the previous rejection if no new matters are introduced. Due to new matters having been introduced in the amendment, the rejections are maintained until the new matter situation is resolved.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-7 and 9-14 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention. As per claims 1 and 11,the newly added limitations “a solid seismic source device”, “a seismic motion detector”, “the model training unit includes a CPU/GPU accelerated computing host device together with a plurality of spatially distributed CPU/GPU computing nodes coupling with the CPU/GPU accelerated computing host device, wherein the computing nodes are arranged at the different logging well positions to perform deep neural network model training for each of the logging well positions” lack description support from the recent updated specification filed on 12/23/2025. For the solid seismic source device, the closest descriptions are acoustic source or artificial source (see specification - an acoustic source positioned in the borehole may be used to generate P-waves, Suitable acoustic sources may include monopole or dipole transmitters [pg. 11 line 26-30], active excitation of artificial seismic sources [pg. 12 line 1]).
No description for the solid seismic source can be found in the specification nor in the claims. For the remaining limitations, e.g. a seismic motion detector, a CPU/GPU accelerated computing host device together with a plurality of spatially distributed CPU/GPU computing nodes coupling with the CPU/GPU accelerated computing host device, and the computing nodes are arranged at the different logging well positions, their description support cannot be found in the specification.
For the sake of examination, the newly added limitations are ignored.
As per claims 2-7, 9-10 and 12-14, claims are also rejected because base claim 1 is rejected.
Claim Interpretation – 35 USC 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The current application includes limitations in claim 11 that do not use the word “means,” but are nonetheless interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because of the following reasons:
Claim 11 includes a limitation/element that use generic placeholders, various units that are coupled with functional language, “configure to acquire”, “configured to analyze”, “configured to classify”, “configured to construct”, “configured to train”, “ configured to fuse” or “configured to predict” without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
The physical structure of the units are interpreted as a general computer software or sub components of the general computer by treating units are equivalent to the modules as they are simply substituted without explanation (see specification – a program, code, executable instructions [pg. 19 line 15-29], a software functional module, the function may be stored in a computer-readable storage medium [pg. 20 line 2-3]).
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f).
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 1-7 and 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. 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 reservoir parameter prediction method based on geological characteristic constraint, comprising: (1.A)
Acquiring, by a data acquisition unit, seismic attributes of different types from a target stratum, (1.A.1)
Selecting, by a data acquisition unit, dominant seismic attributes from seismic attributes of different types according to relevance between the seismic attributes of different types of the target stratum and reservoir parameters; (1.B)
classifying, by a waveform classification unit, seismic waveforms of the target stratum by a preset waveform classification network model, according to waveform features and on the basis of the dominant seismic attributes, so as to obtain a waveform classification result, waveforms of different types correspondingly representing different geological characteristics; (1.C)
constructing, by a model construction unit, different deep neural network models corresponding to the different geological characteristics with the seismic attributes as an input, the reservoir parameters as an output, and the waveform classification result as constraint; (1.D)
training, by a model training unit, the different deep neural network models by seismic data and well logging data of the target stratum, so as to optimize model parameters of each of the deep neural network models; (1.E)
fusing a plurality of trained deep neural network models into a set of spatial variation neural network prediction models; (1.F)
predicting the reservoir parameters of the target stratum by the set of spatial variation neural network prediction models; (1.G) and
applying, by a computing system, the predicted reservoir parameters to well placement planning (1.H)”.
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 or Method).
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 exception. 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, highlighted limitations/steps (1.A), (1.B) – (1.D) and (1.E) - (1.H) are treated by the Examiner as belonging to Mathematical Concept grouping or Mental Process grouping or a combination of Mathematical Concept and Mental Process groupings as the limitations include Mathematical Calculations or show Mathematical Relationship, or involves mental evaluation/judgement, or the optional combination of the two recited groups.
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: (Side Note: duplicated elements are not repeated)
In Claim 1: “A reservoir parameter prediction method based on geological characteristic constraint”, “Acquiring seismic attributes of different types from a target stratum”, “training the different deep neural network models by seismic data and well logging data of the target stratum” and a plurality of “units”;
In Claim 11: “A reservoir parameter prediction apparatus based on geological characteristic constraint” and various units;
In Claim 12: “A computer storage medium storing thereon a computer program executable by a processor”;
In Claim 13: “A computer device, comprising a memory and a processor”;
As per claim 1, the additional element in the preamble “A reservoir parameter prediction method based on geological characteristic constraint” is not a meaningful limitation because the preamble generally links the method to an abstract idea, i.e. predicting a reservoir parameter based on geological characteristic constraint, and fails to link the method with a particular operation or field of use.
The limitation/step “acquiring seismic attributes of different types from a target stratum” represents a standard data collection step in the art and only adds insignificant extra solution to the judicial exception because the elements, e.g. different types of seismic attributes and a target stratum, are generically recited in a high level generality without specific details.
The limitation/step “training the different deep neural network models by seismic data and well logging data of the target stratum” can be interpreted as a mathematical calculation of the model parameters using mathematical relationship between the model and data. Even if the limitation/step is treated as extra solution activity, the limitation/step represents a standard model training step in the art and only adds insignificant extra solution activity to the judicial exception because the limitation is recited in a high level of generality without specific details.
The limitations/elements, the plurality of “unit”, represents sub components of the general computer (see claim interpretation at 35 USC112(f) above) and they are not particular.
As per claim 11, the additional element in the preamble “A reservoir parameter prediction apparatus based on geological characteristic constraint” is not a meaningful limitation because the preamble generally links the apparatus to an abstract idea, i.e. predicting a reservoir parameter based on geological characteristic constraint, and fails to link the apparatus with a particular operation or field of use. The limitations/elements, e.g. various units, represent sub components of a general computer and they are not particular in the art.
As per claim 12, the additional element in the preamble “A computer storage medium storing thereon a computer program executable by a processor” is not a meaningful limitation because the preamble even fails to link the medium to a particular operation or field of use. The elements “a computer storage medium”, “computer program” and “a processor” represents sub components of a general computer and they are not particular in the art.
As per claim 13, the additional element in the preamble “A computer device, comprising a memory and a processor” is not a meaningful limitation because the preamble even fails to link the medium to a particular operation or field of use. The recited elements represents sub components of a general computer and they are not particular in the art.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements as a whole do not reflect an improvement to the computer technology or other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. No particular machine or real-world transformation are claimed. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Under Step 2B analysis, the above claims fail to include additional elements that are sufficient to amount to significantly more than the judicial exception as shown in the prior art of record.
The limitations/elements listed as additional elements above are well understood, routine and conventional steps/elements in the art according to the prior art of record. (See Jiang, Pandey, Chopra, Suwa, Wei, all and others in the list of prior art cited below)
Claims 1-7 and 9-14, therefore, are not patent eligible.
Claim 12 is also rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because “A computer storage medium” does not belong to a statutory category as a machine readable media can be interpreted as the transitory signals under BRI (see MPEP 2106 II Nuijten. 851 F.3d at 1294, 112 USPQ2d at 1133).
Allowable Subject Matter
Claims 1 and 11 recite subject matter which is allowable over the prior art, and would be allowable if rewritten or amended to overcome current objections and rejections.
The following is a statement of reasons for the indication of allowable subject matter: As per claims 1 and 11, the closest prior art of record, Jiang (US 20210181362 A1), hereinafter ‘Jiang’, Pandey (US 20200160173 A1), hereinafter ‘Pandey’, Chopra (Chopra, Satinder, and Kurt Marfurt. "Seismic Attributes–a promising aid for geologic prediction." CSEG recorder 31, no. 5 (2006): 110-120), hereinafter ‘Chopra’, Suwatjanapornpong (Suwatjanapornpong, S., and et al. "Multi-Attributes Analysis and Neural Network: A New Approach of Reservoir Characterisation in Thap Raet and Greater Sirikit East, Phitsanulok Basin." In International Petroleum Technology Conference, p. D031S049R001. IPTC, 2016), hereinafter ‘Suwa’ and Wei (CN 111025384 A), hereinafter ‘Wei’ either singularly or in combination, fail to anticipate or render obvious limitations
“classifying, by a waveform classification unit, seismic waveforms of the target stratum by a preset waveform classification network model, (or classify, on a basis of the dominant seismic attributes, seismic waveforms of the target stratum by a preset waveform classification network model and) according to waveform features and on the basis of the dominant seismic attributes, so as to obtain a waveform classification result, waveforms of different types correspondingly representing different geological characteristics“,
“constructing, by a model construction unit, (or construct) different deep neural network models corresponding to the different geological characteristics with the seismic attributes as an input, the reservoir parameters as an output, and the waveform classification result as constraint”, and
“fusing (or fuse) a plurality of (or different) trained deep neural network models into a set of spatial variation neural network prediction models”, emphasis added by highlighting the limitations with bold face fonts, in combination with other limitations..
Jiang discloses receiving seismic data correlating to a subterranean formation, deriving a set of seismic attributes from the seismic data using classifier, determining parameterized results using a deep learning neural network and training the NN model in order to accurately predict the subterranean formation faults (predict subterranean formation faults, by (1) receiving seismic data correlating to a subterranean formation, (2) deriving a set of seismic attributes from the seismic data, (3) determining parameterized results by analyzing the seismic data and the set of seismic attributes using a deep learning neural network (DNN) [0004], and by selecting seismic attributes using classifier [0037-0039, 0061, Fig. 3B, claims 10], more accurate fault models [0049, Fig. 5], train the model [0060, Fig. 7-8]), but is silent regarding classifying waveforms using dominant attributes and setting the classification result as constraint for constructing different DNN models and fusing different DNN models into a set of spatial variation neural network.
Pandey discloses (deep learning based reservoir modelling by receiving input data comprising information associated with one or more well logs in a region of interest, determines, based at least in part on the input data, an input feature associated with a first deep neural network ‘DNN’ for predicting a value of a property at a location within the region of interest, trains, using the input data and based at least in part on the input feature, the first DNN, predicts, using the first DNN, the value of the property at the location in the region of interest, and utilizes a second DNN that classifies facies based on the predicted property in the region of interest [abs]), but is silent regarding the above allowable limitations.
Chopra discloses (neural network application for multi-attribute analysis [pg. 7 par. 2], multi-attribute classification approach, incorporating neural network training techniques, and generate sand probability volumes derived from P-wave and S-wave impedances estimated using AVO inversion [pg. 18 par. 1]), but is silent regarding the above allowable limitations.
Suwa discloses (predict reservoir properties by utilizing a combination of well and seismic data, select the best group of attributes, the neural network algorithm is used to improve the correlation between well log and seismic attribute, the correlation result is validated by correlation error and then applied to a seismic cube in order to predict reservoir properties from multi-attributes, and not only are the multi-attributes used to predict the reservoir characteristics, but also a neural network is able to improve the correlation value [abs]), but is silent regarding the above allowable limitations.
Wei also discloses (acquiring, according to seismic data and logging data, seismic data within a time window range corresponding to different types of reservoirs; using a pre-trained neural network model to perform waveform classification on the seismic data within the time window range corresponding to different types of reservoirs; and fusing waveform classification results corresponding to the different types of reservoirs, to realize reservoir prediction. [0070 - 0077]), but is silent regarding the above allowable limitations.
As per claims 2-7, 9-10 and 12-14, claims would be allowable because base claim 1 would be allowable.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS KAY whose telephone number is (408) 918-7569. The examiner can normally be reached on M, Th & F 8-5, T 2-7, and W 8-1.
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/DOUGLAS KAY/Primary Examiner, Art Unit 2857