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 .
This action is in response to the amendment filed 7/08/2026.
Priority
As noted in the non-final Office Action dated 4/08/2026 (hereinafter “the previous Office Action”), the examiner acknowledges applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). As noted in the previous Office Action, the present application is a continuation application of international application no. PCT/MY2021/050101, filed on 11/16/2021, which claims foreign priority to Malaysia application no. MYPI2020006118, filed on 11/20/2020.
As also noted in the previous Office Action, a certified copy of Malaysia application No. MYPI2020006118 has been retrieved (on 05/17/2023), as required by 37 CFR 1.55.
Response to Amendment
Applicant’s amendment filed 7/08/2026 has been entered. However, as discussed below, amended claim 1 and its dependent claims 2-5 are rejected under 35 U.S.C. 112(a) because the amendments to claim 1 introduce new subject matter which is not described in the specification.
In the amendment, claims 1-3 and 8-211 were amended, and no claims were cancelled or added. Thus, claims 1-21 are pending.
Claims 1-3 are no longer being interpreted under 35 U.S.C. 112(f) in view of the 7/08/2026 amendments to these claims. However, as detailed below in the section 112(a) rejections, applicant’s original specification and claims, and the priority applications’ specifications and claims do not fully support the amendments to claim 1.
The previous objections to claims 8-21 are withdrawn in view of the 7/08/2026 amendments to these claims.
Response to Arguments
Applicant's arguments 7/08/2026 with respect to the invocation of 35 U.S.C. 112(f) in the previous office action have been fully considered and are persuasive. As indicated above, claims 1-3 are no longer being interpreted under 35 U.S.C. 112(f) in view of the amendments to the claims. However, as detailed below in the section 112(a) rejections, applicant’s original specification and claims, and the priority applications’ specifications and claims do not fully support the amendments to claim 1. In particular, as additionally detailed in the section 112(a) rejection below, the written descriptions of the current application the priority applications fail to disclose the newly presented claim limitation “the processing portion comprises one or more processors and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations> recited in amended claim 1. Applicant’s amendments have necessitated the claim rejections under 35 U.S.C. 112(a) discussed below.
Applicant's arguments with respect to the objections to claims 8-21 have been fully considered and are persuasive.
Applicant's arguments with respect to the rejections of claims 1-21 under 35 U.S.C. 103 have been fully considered, but are not persuasive.
With apparent reference to claim 1 and the primary Dusterhoft reference applied to claims 1-21, applicant alleges, which examiner does not acquiesce to, that Dusterhoft “D1 merely discloses a workflow in which geological and fracture-related parameters are modeled and estimated, incorporated into a reservoir simulator, and subsequently validated using actual well performance data (See Column 5 and 6 of D1). In other words, D1 uses well performance data (corresponding to the production data in claim 1) as feedback to validate or update generated models.” (applicant’s remarks, pages 7-8, emphasis in original).
With continued reference to Dusterhoft, applicant asserts, which examiner does not concede that “D1 does not disclose or suggest that production data is normalized based on G&G-based data, nor does D1 disclose processing a signal that is based on such normalized production data. The mere use of geological parameters for modelling and the later use of well performance data for model validation are fundamentally different from the claimed feature of using production data normalized based on G&G-based data.” (applicant’s remarks, page 8).
Examiner’s Response:
The examiner respectfully disagrees with applicant’s characterization of Dusterhoft. As a preliminary matter, the examiner notes that claim 1 does not recite “using production data normalized based on G&G-based data” as referenced in applicant’s arguments. Rather, claim 1 recites, in the relevant portion, that “the output signal is based on at least one production data normalized based on geology and geophysics (G&G)-based data”.
Further, examiner respectfully disagrees with applicant’s allegations regarding Dusterhoft vis-à-vis claim 1 and points applicant to the discussion of Dusterhoft below.
As detailed in the section 103 rejection of claim 1 in the previous Office Action and below, contrary to applicant’s assertions, the above-noted limitation is disclosed by Dusterhoft. Dusterhoft explicitly discloses in Column 8 that “FIG. 3 is a diagram illustrating an example point and vector representation of a geological property data point in a volume of interest, such as a formation [i.e., based on geology and geophysics (G&G)-based data], according to aspects of the present disclosure. In the embodiment shown, the vector 300 comprises a location, a magnitude, a direction, and a length. The location corresponds to point 301, which may correspond to the physical location to which the data point represented by the vector 300 is associated”. As further detailed in the section 103 rejection of claim 1 in the previous Office Action and below, Dusterhoft also discloses in Column 6 that “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance [i.e., production data based on G&G-based data] can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis”. This teaches updating the data based on a completed result, which corresponds to normalization. Column 11, lines 22-26, 46-47 and 52-64 of Dusterhoft further disclose that “measurements within each bin can then be processed, e.g., to determine an average value for the measurements, and the processed measurements visualized or otherwise used to identify characteristics of interest in the formation.”, “using one or more clustering algorithms to group the data entries into clusters” and “locations of the points 530-538 corresponding clusters 530a-538a may be determined, for example, by selecting the average location of the points with the corresponding cluster 530a-538a or performing weighted analysis … vectors associated with each of the points 530-538 may be determined by averaging, weighted analysis, or other methods that would be appreciated by one of ordinary skill in the art. The other points shown in the model may be generated from similar clustering mechanisms, or may comprise individual data entries representing actual measurements or extrapolations from other modeling operations.”
As also detailed in the section 103 rejection of claim 1 in the previous Office Action and below, Dusterhoft additionally discloses in Column 7 that “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation”. This teaches actual data (production data) of geological property data).
With reference to the secondary Qian reference applied to claims 1-21, applicant next asserts that Qian “D2 generally discloses constructing a mathematical or machine-learning model for describing a relationship between target sweet spots and selected attributes” and “In D2, "d-dimensional" refers to the number of dimensions or features in a mathematical feature vector, not to a visually perceivable three-dimensional graphics-based volume. In other words, the "d" in Rd merely indicates that the input vector w may include d selected attributes, such as w1, w2, w3 ... It does not disclose or suggest a displayable 3D productivity volume.” (applicant’s remarks, page 8).
With continued reference to Qian, applicant then concludes “A mathematical d-dimensional feature space is fundamentally different from the claimed visually perceivable graphics-based signal that is displayable as a three-dimensional productivity volume.” (applicant’s remarks, pages 8-9).
Examiner’s Response:
The examiner respectfully disagrees with applicant’s allegations regarding Qian vis-à-vis claim 1 and points applicant to the discussion of Qian below.
Contrary to applicant’s assertions, the above-referenced “wherein the visually perceivable graphics-based signal is displayable as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is configured to be used in identifying at least one sweet spot location” is taught by the combination of Dusterhoft and Qian.
As detailed in the section 103 rejection of claim 1 in the previous Office Action and below, Section 2.2 on page 747 of Qian explicitly discloses that “The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
PNG
media_image1.png
28
322
media_image1.png
Greyscale
where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression”. This teaches a d-dimensional (3D, with three dimensions) set for the visual representation of identifying sweet spot location of structure.
Pages 745, 748 and 752 of Qian also disclose “the 3D distribution of core samples and well logs”, “interpretation results from 3D seismic data” and “We have extracted the attribute along the surface of the target reservoir and show it in Fig. 8. From Fig. 8, southwest–northeast anticlinal uplifting was predicted to result in stronger production, consistent with the analytical result of a single sweet spot attribute. By contrast, comprehensive quantitative characterization evaluation was more visual, more accessible and more credible.”
Regarding the applied combination of Dusterhoft and Qian, applicant then generally asserts “the Examiner has not articulated a sufficient rationale for why a POSITA would combine them in the specific manner claimed. D1 is directed to reservoir simulation and fracture modeling, while D2 is directed to sweet spot prediction using machine learning on geological attributes.” before concluding “even if D1 and D2 were combined, the combination would still fail to disclose or suggest all the features as recited in claim 1.” (applicant’s remarks, page 9).
Examiner’s Response:
Regarding applicant’s apparent argument that a person of skill in the art would not be motivated to combine the Dusterhoft reference with the Qian reference, or that the references are not analogous art based on the above-noted, purported deficiencies in the motivation to combine Dusterhoft and Qian in the previous office action, a review of KSR and MPEP § 2141.01(a) suggests that applicant is mistaken: “Under the correct analysis, any need or problem known in the field and addressed by the patent [or application at issue] can provide a reason for combining the elements in the manner claimed.” KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 420, 82 USPQ2d 1385, 1397 (2007). This does not require that the reference be from the same field of endeavor as the claimed invention, in light of the Supreme Court's instruction that "[w]hen a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one." Id. at 417, 82 USPQ2d 1396. Rather, a reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). See Bigio, 381 F.3d at 1325, 72 USPQ2d at 1212.”
Even assuming arguendo that Applicant’s assertion that Dusterhoft “D1 is directed to reservoir simulation and fracture modeling, while D2 [Qian] is directed to sweet spot prediction using machine learning on geological attributes” amount to an argument that a person of skill in the art would not be motivated to combine the Dusterhoft and Qian references, or that the references are not analogous art, as noted in the previous office action and below, Dusterhoft and Qian are analogous art because they are both directed to machine learning and data-driven modeling approaches for predicting and optimizing subsurface resource extraction (i.e., energy resource extraction from reservoirs) outcomes (see, e.g., Dusterhoft, Abstract and col. 3, lines 54-65, and Qian, Abstract).
Furthermore, contrary to Applicant’s assertion that “the Examiner has not articulated a sufficient rationale for why a POSITA would combine them in the specific manner claimed” the portions of Qian cited to teach the limitations of the claims in dispute, namely “process the output signal by manner of machine learning-based processing to produce at least one prediction signal corresponding to at least one visually perceivable graphics-based signal, wherein the visually perceivable graphics-based signal is displayable as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is configured to be used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure” are combinable with Dusterhoft at least the reasons noted in the previous office action and below.
As discussed in the previous office action and below, it would have been obvious to one of ordinary skill in the art (i.e., a POSITA) before the effective filing date of the claimed invention incorporate the limitations above as taught by Qian into the disclosed invention of Dusterhoft, and a POSITA would have been motivated to make this modification because “high accuracy of sweet spot prediction” can be achieved through a workflow that can “effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data”, as suggested by Qian (Qian Abstract, Page 746).
Regarding the combination of references applied to the claims, applicant is further directed to the rejections of these claims below, which are based on the detailed incremental combinations of Dusterhoft and Qian (applied to independent claims 1, 6 and 17, and dependent claims 2-5, 7-16 and 18-21).
With reference to independent claims 6 and 17, applicant then generally asserts “Claims 6 … and 17) recite features corresponding to those of independent claim 1 and are therefore patentable for the same reasons as presented above with respect to claim 1.” (applicant’s remarks, page 9).
Similarly, with regard to the dependent claims, applicant generally alleges “Each of the dependent claims is patentable at least on the basis of dependency and/or by virtue of their respective limitations.” Id.
Examiner’s Response:
Examiner respectfully disagrees with the above-noted, general assertions regarding claims 6 and 17 and the dependent claims. As discussed in detail below, the combination of Dusterhoft and Qian (i.e., Dusterhoft in view of Qian) teaches the limitations of independent claims 1, 6 and 17, and dependent claims 2-5, 8-16 and 18-21 (see the section 103 rejections below for further details).
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 1-5 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. Amended independent claim 1 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 had possession of the claimed invention.
In particular, as noted above, the claim limitation “the processing portion comprises one or more processors and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations> is recited in claim 1 as amended on 7/08/2026. However, the written description of the current application fails to disclose the above-identified limitation.
The original specifications of the instant application and the priority applications do not describe the limitation “the processing portion comprises one or more processors and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations>” recited in amended claim 1. With reference to the unlabeled elements in the high-level block diagram of FIG. 12 the instant specification states “The apparatus(es) 102 can, for example, correspond to one or more computers (e.g., laptops, desktop computers and/or electronic mobile devices having computing capabilities such as Smartphones and electronic tablets). The apparatus(es) 102 can, in one embodiment, include one or more processors (not shown) which can be configured to perform one or more processing tasks.” and “The device(s) 104 can, for example, correspond to one or more host devices (e.g., one or more computers or one or more databases). A host device can, for example, be configured to host/carry a platform (software and/or hardware platform) configured to perform one or more processing tasks.” (See, e.g., paragraphs 39-40). Thus, applicant’s specification supports “the processing portion comprises one or more processors” added to claim 1. However, this is not a disclosure of the above-noted, newly-presented limitation of claim 1 regarding “the processing portion comprises one or more processors and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations>.
In the amendment filed 7/08/2026, Applicant generally refers to “Amended claims: 1-3” and states that amended “Claim 1 recites, "an input portion comprising communication circuitry," "the processing portion comprises one or more processors and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to .... "” without pointing to any support in applicant’s specification or original claims for the amendments. (see, remarks, page 6).
As such, applicant does not point to any portion of applicant’s original specification as disclosing or describing the limitation “the processing portion comprises one or more processors and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations>” recited in amended claim 1. As indicated above, the specification is silent regarding analyzing any “computer-readable memory”, RAM, ROM or storage device for “storing instructions” let alone any “non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations>”. That is, the specification does not mention, let alone describe any computer-readable storage or memory that stores executable instructions, program code or software, much less any “non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations>” as recited in amended claim 1. As such, the specification does not describe “a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to:” <perform operations>”as recited in amended claim 1. Accordingly, claim 1 is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Claims 2-5, which each depend directly or indirectly from claim 1, are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement under the same rationale as claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Dusterhoft et al. (U.S. Patent No. 10,621,500 B2, hereinafter “Dusterhoft”) in view of non-patent literature Qian et al. (“Intelligent prediction and integral analysis of shale oil and gas sweet spots”, Petroleum Science, 15(4), 744-755, 2018, hereinafter “Qian”).
Claim 1.
With respect to claim 1, Dusterhoft discloses the invention as claimed including an input portion comprising communication circuitry (Column 3, lines 15-23, “system may include … network ports for communication with external devices as well as various input and output (I/O) devices … system may also include one or more buses operable to transmit communications between the various hardware components.”) configured to receive at least one output signal communicated from at least one apparatus coupled to the device, wherein the output signal is based on at least one production data normalized based on geology and geophysics (G&G)-based data (Column 8 “FIG. 3 is a diagram illustrating an example point and vector representation of a geological property data point in a volume of interest, such as a formation [i.e., based on geology and geophysics (G&G)-based data], according to aspects of the present disclosure. In the embodiment shown, the vector 300 comprises a location, a magnitude, a direction, and a length. The location corresponds to point 301, which may correspond to the physical location to which the data point represented by the vector 300 is associated” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance [i.e., production data based on G&G-based data] can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches updating the data based on a completed result, which corresponds to normalization, Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches actual data (production data) of geological property data); and
a processing portion coupled to the input portion (Column 6 “The design tool may receive the input 202 and output suggested design and formation/reservoir parameters 204” discloses that a design tool receives the input) wherein, the processing portion comprises one or more processors and a non-transitory computer-readable memory storing instructions that, when executed by the one or more processors, cause the processing portion to: <perform operations> (Column 3, lines 11-15 and 24-26 “system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) … ROM, and/or other types of nonvolatile memory.”, “computer-readable media may include any instrumentality or aggregation of instrumentalities that may retain data and/or instructions” and col. 10, lines 44-45, “data blocks 450-455 may be stored sequentially within a storage medium” and claim 8, “A non-transitory computer readable medium comprising a set of instructions that, when executed by a processor, cause the processor to perform the steps”).
Although Dusterhoft substantially discloses the claimed invention, Dusterhoft is not relied on to explicitly disclose a device suitable for sweet spot-based machine learning (SSML), the device comprising… wherein: the processing portion is configured to process the output signal by manner of machine learning-based processing to produce at least one prediction signal corresponding to at least one visually perceivable graphics-based signal, further wherein the visually perceivable graphics-based signal is displayable as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is configured to be used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure.
However, in the same field, analogous art Qian teaches a device suitable for sweet spot-based machine learning (SSML), the device comprising (2.2 Model training & Page 747 “The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes” teaches sweet spot detection for a machine learning model):
wherein: the processing portion is configured to process the output signal by manner of machine learning-based processing to produce at least one prediction signal corresponding to at least one visually perceivable graphics-based signal (2.2 Model training & Page 747 “selected from the attributes fed into the fuzzy mathematics workflow can be used as inputs for the training of an intelligent prediction model that uses machine learning methods…The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
PNG
media_image1.png
28
322
media_image1.png
Greyscale
where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches machine learning based on processing prediction, prediction produce in a multi-dimensional set),
further wherein the visually perceivable graphics-based signal is displayable as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is configured to be used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure (2.2 Model training & Page 747 “The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
PNG
media_image1.png
28
322
media_image1.png
Greyscale
where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches d-dimensional (3D dimension) set for the visual representation of identifying sweet spot location of structure).
Dusterhoft and Qian are analogous art because they are both directed to machine learning and data-driven modeling approaches for predicting and optimizing subsurface resource extraction (i.e., energy resource extraction from reservoirs) outcomes (see, e.g., Dusterhoft, Abstract and col. 3, lines 54-65, and Qian, Abstract).
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitations above as taught by Qian into the disclosed invention of Dusterhoft.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “high accuracy of sweet spot prediction” can be achieved through a workflow that can “effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data”, as suggested by Qian (Qian Abstract, Page 746).
Claim 2.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 1.
Dusterhoft further discloses the device further comprising an output portion coupled to the processing portion, wherein the output portion is configured to transmit the prediction signal to the apparatus, and the apparatus is capable of being configured to display the visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches transmit the predictions and the display the visual representation).
Claim 3.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 1.
Dusterhoft further discloses wherein the output portion is configured to display the visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 9 “the single CRS may comprise a geographic coordinate system that identifies a location based on its latitude and longitude on the surface of the Earth, as well as its depth under the surface at that latitude and longitude. The coordinates of the point 301 within the CRS may be measured directly, or calculated and extrapolated from indirect sources. For example, the latitude and longitude of the drilling rig may be known based on a global positioning system at the rig site, and the orientation of the point with respect to the drilling rig may be known from downhole sensors (e.g., accelerometers, magnetometers, etc.)” teaches display the geographic data that is graphic based signal).
Claim 4.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 1.
Dusterhoft further discloses wherein the structure corresponds with a completed structure (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to a completion operation).
Claim 5.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 4.
Dusterhoft further discloses wherein the structure corresponds with a completed oil well (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to a completion operation for an oil well and actual performance of a completed oil well).
Independent claim 6.
Dusterhoft discloses the invention as claimed including a processing method in association with completion-based machine learning (COMML), the processing method comprising (Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches a machine learning with completion results and completion-based learning by the machine learning algorithm):
receiving data associated with a completed structure, the data associated with the completed structure corresponding with completion (COM) data (Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches receiving completion results (completion data));
collecting production data (Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches actual data (production data));
normalizing production data based on completion (COM) data to produce at least one output signal (Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance [i.e., production data] can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches updating the data based on completed result, which corresponds to normalization and comparing the result with actual data (production data)).
Although Dusterhoft substantially discloses the claimed invention, Dusterhoft is not relied on to explicitly disclose processing the output signal by manner of machine learning-based processing to generate at least one prediction signal, wherein at least one predictive machine learning model is derivable based on the prediction signal.
In the same field, analogous art Qian teaches and processing the output signal by manner of machine learning-based processing to generate at least one prediction signal, wherein at least one predictive machine learning model is derivable based on the prediction signal (2.2 Model training & Page 747 “selected from the attributes fed into the fuzzy mathematics workflow can be used as inputs for the training of an intelligent prediction model that uses machine learning methods…The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes” teaches machine-learning comprising prediction).
Dusterhoft and Qian are analogous art because they are both directed to machine learning and data-driven modeling approaches for predicting and optimizing subsurface resource extraction (i.e., energy resource extraction from reservoirs) outcomes (see, e.g., Dusterhoft, Abstract and col. 3, lines 54-65, and Qian, Abstract).
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitations above as taught by Qian into the disclosed invention of Dusterhoft.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “high accuracy of sweet spot prediction” can be achieved through a workflow that can “effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data”, as suggested by Qian (Qian Abstract, Page 746).
Claim 7.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses pre-processing the completion (COM) data to generate pre-processed data, wherein the production data is normalized based on the pre-processed data to produce at least one output signal (Column 6-7 “the central data repository can be further leveraged to at least partially automate the design process. Specifically, one or more machine learning algorithms may use the design and simulation data as well as the actual measurements and model predictions within the repository to provide a starting point for design operations…Analytic analysis 212 may be run to improve the suggested parameter values and reduce the uncertainty in the models, with the process being iteratively repeated until an optimized design solution 214 is output, similar to the process described above with respect to FIG. 1… the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches processing completion data and actual data, actual data updated based on the completion data).
Claim 8.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the completed structure corresponds with a completed oil-well (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to completion operation and oil well).
Claim 9
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the prediction signal is further processable for the purpose of one or both of: model validation, and model evaluation and optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model validation, evaluation, and optimization).
Claim 10.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the prediction signal is further processable for the purpose of model validation (Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is provide model validation).
Claim 11.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the prediction signal is further processable for the purpose of model evaluation and optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Columns 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is provide model evaluation and optimization).
Claim 12.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the prediction signal is further processable for the purpose of model validation, model evaluation and model optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Columns 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model validation, evaluation, and optimization).
Claim 13.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the prediction signal is further processed for the purpose of model validation (Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model validation).
Claim 14.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the prediction signal is further processed for the purpose of model evaluation and optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Columns 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model evaluation, and optimization).
Claim 15.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein the prediction signal is further processed for the purpose of model validation, model evaluation and model optimization (Column 6 “In order to improve the resulting completion operation, sensitivity analyses 114 may be run to evaluate uncertainties in the estimated variables, such as formation permeability and drainage area, formation stress, fracture propagation, etc” and Column 3-4 “actual production data resulting from the completed well design and build can be used to self-validate the simulation models generated and stored at the central repository. With the implementation, an engineer could identify rapidly which simulation design would give an optimized production. The well-design can be further optimized based on predictive tools and production data” teaches the prediction is for the purpose of model evaluation, and optimization).
Claim 16.
As discussed above, Dusterhoft in view of Qian teaches the processing method according to claim 6.
Dusterhoft further discloses wherein at least one predictive machine learning model is derived based on the prediction signal (Column 5 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation” teaches the processing method is based on prediction).
Independent claim 17.
Dusterhoft discloses a device comprising: an input portion receiving at least one output signal communicated from at least one apparatus coupled to the device, wherein: the at least one output signal is based on at least one production data, and the at least one production data is normalized based on geology and geophysics (G&G)-based data (Column 8 “FIG. 3 is a diagram illustrating an example point and vector representation of a geological property data point in a volume of interest, such as a formation [i.e., based on geology and geophysics (G&G)-based data], according to aspects of the present disclosure. In the embodiment shown, the vector 300 comprises a location, a magnitude, a direction, and a length. The location corresponds to point 301, which may correspond to the physical location to which the data point represented by the vector 300 is associated” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance [i.e., production data based on G&G-based data] can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches updating the data based on a completed result, which corresponds to normalization, Column 7 “the machine learning algorithm may compare actual measurements of the simulation and completion results within the repository 210 to improve the reservoir simulation and reduce the uncertainty of the variables used within the reservoir simulation” teaches actual data (production data) of geological property data));
and a processing portion coupled to the input portion (Column 6 “The design tool may receive the input 202 and output suggested design and formation/reservoir parameters 204” teaches design tool receive the input),
Although Dusterhoft substantially discloses the claimed invention, Dusterhoft is not relied on to explicitly disclose wherein: the processing portion processes the at least one output signal by manner of machine learning-based processing to produce at least one prediction signal, the at least one prediction signal corresponds with at least one visually perceivable graphics-based signal, the at least one visually perceivable graphics-based signal is displayed as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure.
However, in the same field, analogous art Qian teaches wherein: the processing portion processes the at least one output signal by manner of machine learning-based processing to produce at least one prediction signal, the at least one prediction signal corresponds with at least one visually perceivable graphics-based signal (2.2 Model training & Page 747 “selected from the attributes fed into the fuzzy mathematics workflow can be used as inputs for the training of an intelligent prediction model that uses machine learning methods…The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
PNG
media_image1.png
28
322
media_image1.png
Greyscale
where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches machine learning based to produce prediction, prediction produce in multi-dimensional set),
the at least one visually perceivable graphics-based signal is displayed as a three-dimensional (3D) productivity volume, the three dimensional (3D) productivity volume is used in identifying at least one sweet spot location, and the at least one sweet spot location corresponds with a placement of a structure (2.2 Model training & Page 747 “The purpose of machine learning is to establish a complex mathematical model expressing the relationship between y and X, namely the construction of a model that can describe the relationship between target sweet spots and selected attributes. The equation is listed below:
PNG
media_image1.png
28
322
media_image1.png
Greyscale
where w is weight vector, b is the constant deviation, and <w; X> indicates a generated function which shows the linear or nonlinear relationship between them. R means real number set, and Rd indicates a d-dimensional set. Machine learning is a process of multivariate nonlinear regression” teaches d-dimensional set for the visual representation, identifying sweet spot location of structure).
Dusterhoft and Qian are analogous art because they are both directed to a data-driven modeling approach for predicting and optimizing subsurface resource extraction outcomes.
Dusterhoft and Qian are analogous art because they are both directed to machine learning and data-driven modeling approaches for predicting and optimizing subsurface resource extraction (i.e., energy resource extraction from reservoirs) outcomes (see, e.g., Dusterhoft, Abstract and col. 3, lines 54-65, and Qian, Abstract).
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Qian into the disclosed invention of Dusterhoft.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “high accuracy of sweet spot prediction” achieve through a workflow that can “effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data”, as suggested by Qian (Qian Abstract, Page 746).
Claim 18.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 17
Dusterhoft further discloses the device further comprising an output portion coupled to the processing portion, wherein the output portion transmits the at least one prediction signal to the apparatus, and the apparatus displays the at least one visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 6 “Once well performance can be captured based on the implementation of the completion solution 124, the data resulting from the well performance can be used to validate and enable the modelled results to be compared to the actual output. This will enable self-validation of the generated models to verify the quality of the predictions, which can then be updated in the central repository for further use and analysis” teaches transmit the predictions and the display the visual representation).
Claim 19.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 17.
Dusterhoft further discloses wherein the output portion displays the at least one visually perceivable graphics-based signal (Column 3 “Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display” and Column 9 “the single CRS may comprise a geographic coordinate system that identifies a location based on its latitude and longitude on the surface of the Earth, as well as its depth under the surface at that latitude and longitude. The coordinates of the point 301 within the CRS may be measured directly, or calculated and extrapolated from indirect sources. For example, the latitude and longitude of the drilling rig may be known based on a global positioning system at the rig site, and the orientation of the point with respect to the drilling rig may be known from downhole sensors (e.g., accelerometers, magnetometers, etc.)” teaches display the geographic data that is graphic based signal).
Claim 20.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 17.
Dusterhoft further discloses wherein the structure corresponds with a completed structure (Column 12, lines 6-12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to a completion operation).
Claim 21.
As discussed above, Dusterhoft in view of Qian teaches the device according to claim 20.
Dusterhoft further discloses wherein the structure corresponds with a completed oil well (Column 12 “including advanced earth models predicting the performance of a particular oil well or the results of a completion operation, as well as the actual performance of the well or completion operation and the design information used to plan the oilwell and completion operation” teaches the structure corresponds to a completion operation for an oil well and actual performance of a completed oil well).
Conclusion
Applicant's amendment and new claim necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant's disclosure.
For instance, Anderson et al. (U.S. Patent Application Pub. No. 20170364795, hereinafter “Anderson”) discloses that “the System Integration Database (SID) receives data from field devices, the data including geology, geophysics and completion data” where “data including production data is retrieved, compared and combined into a uniform data repository using normalization based on unique identifiers
which include geology, geophysics and completion data” [i.e., geology and geophysics (G&G)-based data] (see, e.g., paragraphs [0004]-[0005] and [0011]).
The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RANDY K BALDWIN whose telephone number is (571)270-5222. The examiner can normally be reached on Mon - Fri 9:00-6:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached on 571-272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RANDALL K. BALDWIN/Primary Examiner, Art Unit 2125
1 Former claims 7-20 were amended to correct their claim numbering, and former claims 7-20 are now correctly numbered as claims 8-21.
2 FIG. 1 includes reference numerals mentioned in the specification, but no text labels, descriptive legends, or any other details.
