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 .
DETAILED ACTION
1. Claims 1-20 are presented for examination.
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.
2. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) The claim 1-8 recite steps or acts including predicting hydrocarbon show indicators; thus, the claims are to a process, which is one of the statutory categories of invention. The claim 9- 16 recite a non-transitory, computer-readable medium while the claim 17-20 recite a system comprising processors and non-transitory computer-readable storage medium and therefore is a machine, which is a statutory category of invention.
(Step 2A – Prong One) For the sake of identifying the abstract ideas, a copy of the claim is provided below. Abstract ideas are bolded.
The claims 1, 9 and 17 recite:
receiving input data identifying, for different depths of a well that is being drilled, a drill bit location, a depth, a weight on bit, rotations per minute, a rate of penetration, lagged lithology percentages, and real-time mud gas logs (insignificant extra-solution activity – data gathering);
performing data cleaning on the input data (under its broadest reasonable interpretation, “mathematical concepts” group of abstract ideas) using an isolation forest algorithm to remove outliers (insignificant extra-solution activity for the act of outputting itself and “apply it”);
identifying, from the input data, a sequence of attributes for the well being drilled (under its broadest reasonable interpretation, “a mental process” that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion), wherein the sequence of attributes includes the input data measured at a sequence of depths in the well (insignificant extra-solution activity – data gathering); and
predicting, in real time using machine learning on the sequence of attributes received while drilling the well (insignificant extra-solution activity for the act of outputting itself and “apply it”), hydrocarbon show indicators classifying a presence of hydrocarbons at a pre-determined distance ahead of a drilling bit (under its broadest reasonable interpretation, a mental process that convers performance in the human mind or with the aid of pencil and paper including an observation, evaluation, judgment or opinion).
Therefore, the limitations, under the broadest reasonable interpretation, have been identified to recite judicial exceptions, an abstract idea.
(Step 2A – Prong Two: integration into practical application) This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements of “computer-implemented” (Claim 1-8), “non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations” (Claim 9), “non-transitory, computer-readable medium” (Claim 10-16), “computer-implemented system, comprising: one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations” (Claim 17), “computer-implemented system” (Claim 18-20) which is recited at high level generality and recited so generally that they represent more than mere instruction to apply the judicial exception on a computer (see MPEP 2106.05(f)). The limitation can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(d)). Further, the additional elements of “computer” does not (1) improve the functioning of a computer or other technology, (2) is not applied with any particular machine (except for generic computer components), (3) does not effect a transformation of a particular article to a different state, and (4) is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Further the additional elements of “machine learning” is an insignificant extra-solution activity which is generally linking the use of a judicial exception to a particular technological environment or field of use.
Claims 1, 9, and 17 recite the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering (see MPEP 2106.05(g)): “receiving input data identifying, for different depths of a well that is being drilled, a drill bit location, a depth, a weight on bit, rotations per minute, a rate of penetration, lagged lithology percentages, and real-time mud gas logs (insignificant extra-solution activity – data gathering);
wherein the sequence of attributes includes the input data measured at a sequence of depths in the well (insignificant extra-solution activity – data gathering);”.
The claims 1, 9, and 17 recite the limitation which insignificant extra-solution activity for the act of outputting itself , is equivalent to “apply it”, and/or generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)): “using an isolation forest algorithm to remove outliers (insignificant extra-solution activity for the act of outputting itself and “apply it”),
in real time using machine learning on the sequence of attributes received while drilling the well (insignificant extra-solution activity for the act of outputting itself and “apply it”)”.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
(Step 2B - inventive concept) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer-implemented” (Claim 1-8), “non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations” (Claim 9), “non-transitory, computer-readable medium” (Claim 10-16), “computer-implemented system, comprising: one or more processors; and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations” (Claim 17), “computer-implemented system” (Claim 18-20) which is recited at high level generality and recited so generally that they represent more than mere instruction to apply the judicial exception on a computer (see MPEP 2106.05(f)). The limitation can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(d)). Further the additional elements of “machine learning” is an insignificant extra-solution activity which is generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)).
Further as discussed above Claims 1, 9, and 17 recite the limitation which is an insignificant extra-solution activity because it is a mere nominal or tangential addition to the claim, amounts to mere data gathering/outputting (see MPEP 2106.05(g)) which is the element that the courts have recognized as well-understood, routine, conventional activity (see MPEP 2106.05(d) II. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93): “receiving input data identifying, for different depths of a well that is being drilled, a drill bit location, a depth, a weight on bit, rotations per minute, a rate of penetration, lagged lithology percentages, and real-time mud gas logs (insignificant extra-solution activity – data gathering);
wherein the sequence of attributes includes the input data measured at a sequence of depths in the well (insignificant extra-solution activity – data gathering);”.
Also the claims recite the limitation which insignificant extra-solution activity for the act of outputting itself , is equivalent to “apply it”, and/or generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h)): “using an isolation forest algorithm to remove outliers (insignificant extra-solution activity for the act of outputting itself and “apply it”),
in real time using machine learning on the sequence of attributes received while drilling the well (insignificant extra-solution activity for the act of outputting itself and “apply it”)”.
Further dependent claims 2-7, 10-16 and 18-20 recite:
(Claim 2, 10 and 18) further comprising scaling and normalizing the input data (a mental process and a mathematical concept).
(Claim 3, 11, and 19) wherein the hydrocarbon show indicators include hydrocarbon wetness (insignificant extra-solution activity – data gathering).
(Claim 4, 12, and 20) wherein predicting the hydrocarbon show indicators includes using a Haworth Wetness formula to determine, using mud gases logs, if oil is productive ahead of the drilling bit (a mental process and a mathematical concept).
(Claim 5 and 13) wherein predicting the hydrocarbon show indicators includes determining that Haworth Wetness formula yields a value within a specified range of 0.5 to 40%, indicating productive hydrocarbons (a mental process and a mathematical concept).
(Claim 6 and 14) wherein the sequence of attributes includes attributes for 100 feet of drilling (insignificant extra-solution activity – data gathering).
(Claim 7 and 15) wherein the pre-determined distance is 1000 feet ahead, in a downhole direction of the drilling bit (insignificant extra-solution activity – data gathering).
(Claim 8 and 16) wherein predicting hydrocarbon show indicators classifying the presence of hydrocarbons includes predicting a presence or absence of productive amounts of one or more of oil and natural gas (a mental process).
Considering the claim both individually and in combination, there is no element or combination of elements recited contains any “inventive concept” or adds “significantly more” to transform the abstract concept into a patent-eligible application.
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.
3. Claims 1-4, 6-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Molla et al. (US 20210062650 A1), in view of Ritzmann et al. (US 20160312609 A1), and further in view of Jefferson (“MACHINE LEARNING FOR SUBSURFACE DATA ANALYSIS: APPLICATIONS IN OUTLIER DETECTION, SIGNAL SYNTHESIS AND CORE & COMPLETION”).
As per Claim 1, 9 and 17, Molla et al. teaches a computer-implemented method (Fig. 3 and 5-7)/ system comprising: one or more processors; and a non-transitory computer-readable storage medium non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations (Fig. 13), comprising:
receiving input data identifying, for different depths of a well that is being drilled, …, a depth, …, lagged lithology percentages, and real-time mud gas logs ([0004] “obtaining input data regarding at least one measured property. The at least one measured property includes an amount of each of predetermined hydrocarbons in a gas sample extracted from drilling fluid after the drilling fluid exits a wellbore.”; [0023], [0036]-[0040], [0050], [0059], “The molar gas composition is matched with the depth from which the hydrocarbon originated during drilling.”, “The preexisting database 205 is accessed to collect 210 gas composition for C1-C5.”, “the existing fluid database 102 is utilized to build and train the machine learning, fluid type classification model 104, which is then utilized with real-time FLAIR measurements 106 to predict the reservoir fluid type 108.”, “the relevant measurements (e.g., lithology, gamma ray, resistivity, density, nuclear magnetic resonance, etc.) obtained during drilling may also be incorporated into the workflow”);
performing data cleaning on the input data using an… algorithm to remove outliers ([0040], [0050], [0058] “The collected 210 data is processed and ingested 215, such as to remove outliers,”, “To ensure data consistency, statistical tools (e.g., Mahalanobis distance) may be used to identify and remove outliers from the database.”);
identifying, from the input data, a sequence of attributes for the well being drilled, wherein the sequence of attributes includes the input data measured at a sequence of depths in the well ([0037], [0040], [0059], “As schematically depicted in FIG. 7, the existing fluid database 102 is utilized to build and train the machine learning model 304, which is then utilized with real-time FLAIR measurements 106 to output continuous variables (response variables) corresponding to the input gas composition 106, including C6+ fraction 306 and/or another answer product 308 (e.g., GOR and/or STO density) that may also be based on the predicted C6+ fraction 306. Other relevant measurements (e.g., lithology, gamma ray, resistivity, density, nuclear magnetic resonance, etc.) obtained during drilling may also be incorporated into the workflow”); and
predicting, in real time using machine learning on the sequence of attributes received while drilling the well, hydrocarbon show indicators ([0040] “ in FIG. 3, in which the existing fluid database 102 is utilized to build and train the machine learning, fluid type classification model 104, which is then utilized with real-time FLAIR measurements 106 to predict the reservoir fluid type 108.”; Fig. 7, [0059] “train the machine learning model 304, which is then utilized with real-time FLAIR measurements 106 to output continuous variables (response variables) corresponding to the input gas composition 106, including C6+ fraction 306 and/or another answer product 308 (e.g., GOR and/or STO density) that may also be based on the predicted C6+ fraction 306.”)....
Molla et al. fails to teach explicitly data identifying a drill bit location, a weight on bit, rotations per minute, and a rate of penetration, isolation forest, and classifying a presence of hydrocarbons at a pre-determined distance ahead of a drilling bit.
Ritzmann et al. teaches data identifying a drill bit location, a weight on bit, rotations per minute, and a rate of penetration ([0020]-[0024]);
classifying a presence of hydrocarbons at a pre-determined distance ahead of a drilling bit ([0044]-[0050] “indicators that may be used include an oil indicator and an inverse oil indicator, which are calculated as stated below. These indicators yield information about the fluid type and indicate whether an interval might be productive or not. The data may be displayed on a continuous log as demonstrated by an example shown in FIG. 7.”, “Haworth ratios, may be plotted in a depth by depth basis on a continuous log”).
Molla et al. and Ritzmann et al. are analogous art because they are both related to a method for hydrocarbon resource analysis during drilling.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Ritzmann et al. into Molla et al.’s invention for purpose of determining hydrocarbon resource characteristics via mud logging to provide an improved techniques which automatically generate a fluid log that displays an indication of the type at each of the plurality of sample times; thus the fluid type and/or ratio information is used to monitor the operation in real time and adjust operational parameters and/or control a production operation (Ritzmann et al.: Abstract, [0034], [0051]).
Further Jefferson teaches isolation forest (Abstract, Pg 37).
Molla et al., Ritzmann et al., Jefferson are analogous art because they are all related to data driven methods in the oil and gas industry.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Jefferson as Jefferson teaches data pre-processing including data cleaning (Figure I-1 on Pg 20) to be properly fit by an algorithm to provide an accurate model results using Isolation forest (IF) in log data (Conclusion) for machine learning modeling (Pg 37) and to provide a model which is efficient in detecting a wide range of outlier types with a balanced accuracy (Abstract).
As per Claim 2, 10 and 18, Molla et al. fails to teach explicitly further comprising scaling and normalizing the input data.
Jefferson teaches further comprising scaling and normalizing the input data (Pg 44 “scaling the features, normalizing samples,”).
As per Claim 3, 11 and 19, Molla et al. fails to teach explicitly wherein the hydrocarbon show indicators include hydrocarbon wetness.
Ritzmann et al. teaches wherein the hydrocarbon show indicators include hydrocarbon wetness ([0038]-[0050] “first Pixler ratio (C1C2) indicates the fluid type present in the selected interval, where low values are an indication for heavier hydrocarbons and high values an indication for lighter hydrocarbons.”, “(e.g., Oil Indicator (OI), Haworth Ratios (HW), Pixler Ratios)… C1C2 ratio (indicating gas, light-, medium- and low gravity oil)”).
As per Claim 4, 12 and 19, Molla et al. fails to teach explicitly wherein predicting the hydrocarbon show indicators includes using a Haworth Wetness formula to determine, using mud gases logs, if oil is productive ahead of the drilling bit.
Ritzmann et al. teaches wherein predicting the hydrocarbon show indicators includes using a Haworth Wetness formula to determine, using mud gases logs, if oil is productive ahead of the drilling bit ([0044]-[0050] “Haworth ratios, may be plotted in a depth by depth basis on a continuous log ”).
As per Claim 6 and 14, Molla et al. fails to teach explicitly wherein the sequence of attributes includes attributes for 100 feet of drilling.
Ritzmann et al. teaches wherein the sequence of attributes includes attributes for 100 feet of drilling (Fig. 5A, [0044]-[0050] “Haworth ratios, may be plotted in a depth by depth basis on a continuous log as shown in FIG. 8.”).
As per Claim 7 and 15, Molla et al. fails to teach explicitly wherein the pre-determined distance is 1000 feet ahead, in a downhole direction of the drilling bit.
Ritzmann et al. teaches wherein the pre-determined distance is 1000 feet ahead, in a downhole direction of the drilling bit (Fig. 5A, [0044]-[0050] “Haworth ratios, may be plotted in a depth by depth basis on a continuous log as shown in FIG. 8.”).
As per Claim 8 and 16, Molla et al. fails to teach explicitly wherein predicting hydrocarbon show indicators classifying the presence of hydrocarbons includes predicting a presence or absence of productive amounts of one or more of oil and natural gas.
Ritzmann et al. teaches wherein predicting hydrocarbon show indicators classifying the presence of hydrocarbons includes predicting a presence or absence of productive amounts of one or more of oil and natural gas (Fig. 4A-4B, [0037]-[0040] “. 4a represents a productive oil zone and FIG. 4b represents a productive gas zone.” “This intersection point gives an indication whether the selected interval is potentially productive (e.g., it is productive if within the ellipse 405).”).
4. Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Molla et al. (US 20210062650 A1), in view of Ritzmann et al. (US 20160312609 A1) and further Jefferson (“MACHINE LEARNING FOR SUBSURFACE DATA ANALYSIS: APPLICATIONS IN OUTLIER DETECTION, SIGNAL SYNTHESIS AND CORE & COMPLETION”), and further in view of Melo (“Formation fluid prediction through gas while drilling analysis”).
Molla et al. as modified by Ritzmann et al. and Jefferson teaches most all the instant invention as applied to claims 1-4, 6-12, and 14-20 above.
As per Claim 5 and 13, Molla et al. as modified by Ritzmann et al. and Jefferson fails to teach explicitly wherein predicting the hydrocarbon show indicators includes determining that Haworth Wetness formula yields a value within a specified range of 0.5 to 40%, indicating productive hydrocarbons.
Melo wherein predicting the hydrocarbon show indicators includes determining that Haworth Wetness formula yields a value within a specified range of 0.5 to 40%, indicating productive hydrocarbons (Pg 13-14).
Molla et al., Ritzmann et al. Jefferson, and Melo are analogous art because they are all related to data driven methods in the oil and gas industry.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Melo into Molla et al. as modified by Ritzmann et al. and Jefferson’s invention for purpose of determining hydrocarbon resource characteristics via mud logging to provide an improved techniques which automatically generate a fluid log that displays an indication of the type at each of the plurality of sample times; thus the fluid type and/or ratio information is used to monitor the operation in real time and adjust operational parameters and/or control a production operation (Ritzmann et al.: Abstract, [0034], [0051]) and to provide an accurate model results (Jefferson: Abstract). Further Haworth is commonly used an indicator that helps to identify formation fluid changes (Melo: Pg 13) for early availability of analysis results with producing reliable quality gas data (Melo: Pg 12).
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sahu, et al. (“Application of Integrated Compositional Gas Ratio Analysis to Understand Reservoir Hydrocarbon Potential and Enhance Confidence of Testing and
Reservoir Lateral Drilling: With Examples from Krishna Godavari Basin, India”).
Tittlemier et al. (“Integrated Reservoir Characterization aids target selection, production fluid prediction and completions optimization in the Southern Delaware Basin Resource Plays”)
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571)272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/Primary Examiner, Art Unit 2188