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 communication is in response to Application filed on July 22, 2025. Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 19 and 20 recite:
rendering, to a display, a graphical user interface that generates a schedule for a number of drilling runs to be performed at a site;
receiving run features for the number of drilling runs to be performed at the site;
responsive to receipt of one or more interactions with the graphical user interface, executing a machine learning model that uses the run features to select a number of drill bits for the number of drilling runs to be performed at the site;
generating the schedule for the number of drilling runs to be performed at the site using the selected number of drill bits;
during performance of one of the scheduled number of drilling runs to be performed at the site, using a corresponding one of the selected number of bits, receiving sensor data from the site to determine drill bit performance;
responsive to the drill bit performance at the site being less than drill bit performance at one or more different sites, executing the machine learning model to select one or more different drill bits for one or more of the number of drilling runs that remain to be performed at the site;
regenerating the schedule for the number of drilling runs that remain using the one or more different drill bits; and
executing the schedule to perform at least a subsequent one of the number of drilling runs that remain using a corresponding one of the one or more different drill bits.
Step 1: The claims as a whole fall within one or more statutory categories.
Step 2A prong 1: At least claims 1, 19 and 20 recite limitations that are abstract ideas.
The limitations “generating a schedule for a number of drilling runs to be performed at a site”, “generating the schedule for the number of drilling runs to be performed at the site using the selected number of drill bits” and “regenerating the schedule for the number of drilling runs that remain using the one or more different drill bits” are mental steps. A user can, using pen and paper, create a schedule of drill run operations using specific criteria such as number of drill bits. Thus the claimed limitations can be performed by the human mind.
The limitations “using run features, select a number of drill bits for the number of drilling runs to be performed at the site” and “select one or more different drill bits for one or more of the number of drilling runs that remain to be performed at the site” are also mental steps. A user can mentally select drill bits for use as desired. Thus the claimed limitations can be performed by the human mind.
The limitation “determine drill bit performance” is a mental step. An administrator can mentally determine how well a drill bit performs. Thus, the claimed limitation can be performed by the human mind.
Step 2A prong 2:
Claims 1, 19 and 20 recite the limitation “rendering, to a display, a graphical user interface that generates a schedule for a number of drilling runs to be performed at a site” which is an additional element and is mere output recited at a high level of generality in response to an input file and is considered insignificant extra-solution activity as ‘selecting information for display as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claims 1, 19 and 20 recite the limitations “receiving run features for the number of drilling runs to be performed at the site”, and “during performance of one of the scheduled number of drilling runs to be performed at the site, using a corresponding one of the selected number of bits, receiving sensor data from the site to determine drill bit performance”. These limitations are additional elements and are insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Claims 1, 19 and 20 recite the limitation “executing the schedule to perform at least a subsequent one of the number of drilling runs that remain using a corresponding one of the one or more different drill bits”. This limitation is an additional element and considered to be generally linking the use of the abstract idea of executing the schedule to a field of use i.e. drill run technology. See Parker v.Flook in MPEP 2106.05(h), as providing a classic example of a field of use limitation. See, e.g., Bilski v. Kappos, 561 U.S. 593, 612, 95 USPQ2d 1001, 1010 (2010) ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable") (citing Parker v. Flook, 437 U.S. 584, 198 USPQ 193 (1978)). As such, the limitation does not provide integration into a practical application.
Furthermore, Claims 1, 19 and 20 recite the following additional elements “a system”, “a processor”, “memory”, “graphical user interface”, “machine learning model”, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "receiving” and “rendering” limitations identified as insignificant extra-solution activity above when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
With respect to the “executing the schedule to perform at least a subsequent one of the number of drilling runs that remain using a corresponding one of the one or more different drill bits” limitation as addressed in Step 2A Prong 2, merely linking the abstract idea of executing the generated schedule to a field of use of performing drill runs does not meaningfully limit the claim and does not teach significantly more than the abstract idea. See MPEP 2106.05(h), where limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
Claims 2-5 depend from claim 1 and thus include all the limitations of claim 1, therefore claims 2-5 recite the same abstract ideas of "mental processes".
Claims 2-5 furthermore recite:
(claim 2): wherein, responsive to the drill bit performance at the site being less than drill bit performance at one or more different sites, the executing occurs automatically;
(claim 3): wherein the regenerating the schedule occurs automatically; and
(claim 4): wherein the executing the schedule occurs automatically;
(claim 5): wherein the receiving the sensor data from the site occurs automatically.
Step 1: Claims 2-4 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 2-4 recite limitations that are abstract ideas because they depend from claim 1 that recites mental steps.
The limitation “wherein the regenerating the schedule occurs automatically” in claim 3 further refines the ‘regenerating’ step in claim 1 which is considered a mental step. As such, this limitation is also a mental step.
Step 2A prong 2: Claims 2 and 4 recite the limitations “wherein, responsive to the drill bit performance at the site being less than drill bit performance at one or more different sites, the executing occurs automatically” and “wherein the executing the schedule occurs automatically”. These limitations are additional elements and further define the ‘executing the schedule’ step in claim 1, which was considered to be generally linking the use of the abstract idea of executing the schedule to a field of use i.e. drill run technology. See Parker v.Flook in MPEP 2106.05(h). As such, this limitation also does not provide integration into a practical application.
Claim 5 recites the limitation “wherein the receiving the sensor data from the site occurs automatically” which is an additional element and further defines the ‘receiving ’ step in claim 1, which was considered to be insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. As such, this limitation also does not provide integration into a practical application.
Step 2B:
With respect to the “executing the schedule” limitations as addressed in Step 2A Prong 2, merely linking the abstract idea of executing the generated schedule to a field of use of performing drill runs does not meaningfully limit the claim and does not teach significantly more than the abstract idea. See MPEP 2106.05(h), where limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
With respect to the "receiving” limitation identified as insignificant extra-solution activity above when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
Claims 6-13 depend from claim 1 and thus include all the limitations of claim 1, therefore claims 2-5 recite the same abstract ideas of "mental processes".
Claims 6-13 furthermore recite:
(claim 6): wherein the machine learning model comprises a feature set generated through feature engineering;
(claim 7): wherein the machine learning model comprises a feature set that comprises features extractable from images of drill bits;
(claim 8): wherein the features comprise number of blades;
(claim 9): wherein the features comprise cutter diameter;
(claim 10): wherein the machine learning model classifies drill bit images based on features extractable from the drill bit images;
(claim 11): wherein the machine learning model outputs predicted drill bit features based at least in part on the run features;
(claim 12): wherein the machine learning model outputs predicted drill bit features as a coded image decodable to provide a set of a combination of desirable drill bit features for each of the number of drilling runs;
(claim 13): rendering the coded image to the display.
Step 1: Claims 6-13 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 6-13 recite limitations that are abstract ideas because they depend from claim 1 that recites mental steps.
The limitation “classifying drill bit images based on features extractable from the drill bit images” in claim 10 is a mental step. A user can mentally determine how to group images based on certain criteria. As such, this limitation is a mental step.
The limitations “outputting predicted drill bit features based at least in part on the run features” and “outputting predicted drill bit features as a coded image decodable to provide a set of a combination of desirable drill bit features for each of the number of drilling runs” are mental steps. A user can mentally select which drill bit feature would be most desirable to consider based on given run features. As such, these limitations are mental steps.
Step 2A prong 2:
Claim 13 recites “rendering the coded image to the display”. This is an additional element and is mere output recited at a high level of generality to display an image and is considered insignificant extra-solution activity as ‘selecting information for display as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, claims 6-12 recite the following additional elements “a machine learning model”, note that these recited additional elements are a high-level recitation of generic computer software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "rendering” limitation identified as insignificant extra-solution activity above when re-evaluated these elements are well-understood, routine, and conventional as evidenced by the court cases in 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
Claims 14-16 depend from claim 1 and thus include all the limitations of claim 1, therefore claims 14-16 recite the same abstract ideas of "mental processes".
Claims 14-16 furthermore recite:
(claim 14): wherein the schedule specifies drilling fluid for each of the number of drilling runs;
(claim 15): executing drilling fluid machine learning model that uses the run features and the selected number of drill bits for the number of drilling runs to be performed at the site to select a drilling fluid for each of the number of drilling runs;
(claim 16): wherein the drilling fluid machine learning model utilizes microscopic imagery data of drilling fluids.
Step 1: Claims 14-16 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 14-16 recite limitations that are abstract ideas because they depend from claim 1 that recites mental steps.
Claim 14 recites the limitation “wherein the schedule specifies drilling fluid for each of the number of drilling runs” which further defines the schedule in the “generating” step in claim 1 which was considered a mental step. As such, this limitation is also a mental step.
The limitation “uses the run features and the selected number of drill bits for the number of drilling runs to be performed at the site to select a drilling fluid for each of the number of drilling runs” in claim 15 is a mental step. One can mentally make a selection of which drilling fluid to use for each of the drilling runs based on certain criteria. Thus, the claimed limitation can be performed by the human mind.
Step 2A prong 2:
Furthermore, claims 15-16 recite the following additional elements “drilling fluid machine learning model”, note that these recited additional elements are a high-level recitation of generic computer software components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
Therefore, claims 14-16 as a whole do not change this conclusion and the claims are ineligible.
Claims 17-18 depend from claim 1 and thus include all the limitations of claim 1, therefore claims 17-18 recite the same abstract ideas of "mental processes".
Claims 17-18 furthermore recite:
(claim 17): training the machine learning model;
(claim 18) wherein training comprises use of training data that comprise drill bit images.
Step 1: Claims 17-18 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 17-18 recite limitations that are abstract ideas because they depend from claim 1 that recites mental steps.
Step 2A prong 2:
Claims 17 and 18 recite the limitations “training the machine learning model”, “wherein training comprises use of training data that comprise drill bit images”. These limitations are additional elements are training the machine learning model by inputting drill bit images as training data. These limitations recite enabling the input of drill bit images into a machine learning model. Furthermore, Claims 17 and 18 recite “a machine learning model”, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
Therefore, claims 17-18 as a whole are ineligible.
Claim 20 is 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 the broadest reasonable interpretation of the “computer readable storage media” encompasses signals per se. The specification discloses that “The computer readable storage media may be a non-transitory medium (as an example) that is not a carrier wave [see para 311], however the specification does not explicitly preclude the computer readable storage media from comprising transitory signals. A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. See MPEP 2106.03(II). It is suggested that the claim be amended to recite a “non-transitory” computer readable storage media to overcome this rejection.
Accordingly, claim 20 fails to recite statutory subject matter under 35 U.S.C. 101.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory} above are further rejected as set forth below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
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 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 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 1-5, 11-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2018/0094517 by Foubert et al (hereafter Foubert), in view of US 2005/0015229 by Huang, and further in view of US 2015/0300151 Mohaghegh et al (hereafter Mohaghegh).
Referring to claim 1, Foubert discloses a method [Abstract] comprising:
rendering, to a display, a graphical user interface that generates a schedule for a number of drilling runs to be performed at a site [GUI 1300 includes various scheduled subsystem tasks that are a part of a well plan, e.g. rig up, casing, cement, drilling and rig down subsystems, para 188, Fig 13; well plan pertains to a wellsite, para 28-30; detailed well plan, para 153];
receiving run features for the number of drilling runs to be performed at the site [determination of well plan implementation options, para 172-174, Fig 10, element 1020; receiving and analyzing drilling options, para 204, Fig 18, element 1810];
responsive to receipt of one or more interactions with the graphical user interface, using the run features to select a number of drilling options for the number of drilling runs to be performed at the site [selection of one of the implementation options to implement portion of well plan- see selection block, para 172, Fig 10, element 1040, para 172; GUI user-selection of options, para 188-192, Fig 13-14; drilling selection options, Fig 13; Fig 18, element 1810];
generating the schedule for the number of drilling runs to be performed at the site using the selected number of drill options [one of the drilling options is implemented, Fig 10, element 1050, para 172; para 204, Fig 18, element 1830];
during performance of one of the scheduled number of drilling runs to be performed at the site, using a corresponding one of the selected number of options, receiving sensor data from the site to determine drill bit performance [information received via wellsite system with sensors 266 during implementation of the one of the options, Fig ; para 65, 141; LWD/AWD sensor, para 146; LWD and MWD modules employed include measuring devices that log and measure data while drilling, para 54-56; receive information indicative of washout, para 204, Fig 18, element 1850];
responsive to the drill bit performance at the site being less than drill bit performance at one or more different sites, selecting one or more different drill options for one or more of the number of drilling runs that remain to be performed at the site [adjusted option, para 198; sensor data used to update drilling plan, para 149];
regenerating the schedule for the number of drilling runs that remain using the one or more different drill options [adjust the well engineering plan according to the real-life drilling parameters (e.g., based on acquired data in the field, para 154; change/adjustment to selected implementation option can trigger a change to a schedule of tasks, para 177; adjusting the drilling option, para 204, 205, 209, Fig 18, element 1850]; and
executing the schedule to perform at least a subsequent one of the number of drilling runs that remain using a corresponding one of the one or more different drill options [implementing the selected adjusted option, para 172, Fig 10, element 1050; selected drilling option implemented, para 204, Fig 18, element 1860].
Referring to claim 1, while Foubert discloses all of the above claimed subject matter and also discloses selection of drilling options [Foubert, drilling selection options, Fig 13; Fig 18, element 1810] and determining that a drill bit that has dulled or ceased to drill efficiently is to be replaced [para 39], it remains silent as to executing a machine learning model to select drill bits.
Huang teaches selection of drill bit design parameters for a fixed cutter drill bit [para 14, 39].
Foubert and Huang are analogous art because they are directed to the same field of endeavor- management of drilling operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the selection of drilling options in Foubert with the selection of fixed cutter drill bit parameters in Huang because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the fixed cutter drill bit parameters further refines the drilling options selection in Foubert.
Still referring to claim 1, while Foubert/Huang discloses all of the above claimed subject matter, it remains silent as to the execution a machine learning model. Mohaghegh teaches the use of a machine learning model as a real-time drilling management tool that makes predictions with respect to drilling operations [MIDAS 30, para 59-60].
Foubert, Huang and Mohaghegh are analogous art because they are directed to the same field of endeavor- management of drilling operations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the well plan generation in Foubert to include the functions of the machine learning model in Mohaghegh because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the machine learning model in Mohaghegh allows for predictions pertaining to drilling options to be made within the well plan GUI of Foubert.
Referring to claim 19, the limitations of the claim are similar to those of claim 1 in the form of a system [Foubert, system 500, Fig 5; system 770, Fig 7] comprising a processor [processor(s) 502, Fig 5; one or more processors 772, Fig 7]; memory [Foubert, memory 503, Fig 5; memory 774, Fig 7]; and processor executable instructions [Foubert, para 101; 116]. As such, claim 19 is rejected for the same reasons as claim 1.
Referring to claim 20, the limitations of the claim are similar to those of claim 1 in the form of a computer readable storage medium storing executable instructions [Foubert, para 101; 116]. As such, claim 20 is rejected for the same reasons as claim 1.
Referring to claim 2, Foubert/Huang/Mohaghegh discloses that responsive to the drill bit performance at the site being less than drill bit performance at one or more different sites, the executing occurs automatically [Foubert, where measured value exceeds a limit set as to a radial dial of the GUI 1500, that condition may trigger an adjustment decision by the decision block 1250, para 197; automation mechanisms of the well drilling system to automate one or more operations at a wellsite, para 30; model simulation layer provides domain objects for display in graphical user interface, wherein the domain objects represent wells, surfaces, reservoirs, para 80-82; Huang, selection of fixed cutter drill bit, para 14, 39].
Referring to claim 3, Foubert/Huang/Mohaghegh discloses that the regenerating the schedule occurs automatically [Foubert, automation mechanisms of the well drilling system to automate one or more operations at a wellsite, para 30].
Referring to claim 4, Foubert/Huang/Mohaghegh discloses that the executing the schedule occurs automatically [Foubert, automation mechanisms of the well drilling system to automate one or more operations at a wellsite, para 30; dynamic plan management, para 154; implementation of selected implementation option for portion of well plan is automatic, para 169].
Referring to claim 5, Foubert/Huang/Mohaghegh discloses that the receiving the sensor data from the site occurs automatically [Foubert, automation mechanisms of the well drilling system to automate one or more operations at a wellsite, para 30].
Referring to claim 11, Foubert/Huang/Mohaghegh discloses that the machine learning model outputs predicted drill bit features based at least in part on the run features [Foubert, performance metrics, para 164-166; Huang, drilling performance parameter optimization for fixed cutter drill bit design, para 14; prediction, para 38, 41].
Referring to claim 12, Foubert/Huang/Mohaghegh discloses that the machine learning model outputs predicted drill bit features as a coded image decodable to provide a set of a combination of desirable drill bit features for each of the number of drilling runs [Foubert, encoder-decoder used for sensor data, para 67; Mohaghegh, MIDAS 30, predictions, para 59-60].
Referring to claim 13, Foubert/Huang/Mohaghegh discloses rendering the coded image to the display [Huang, modeling fixed cutter drill bit including displaying a meshed model image of a cutter, para 103, 110, 119].
Referring to claim 14, Foubert/Huang/Mohaghegh discloses that the schedule specifies drilling fluid for each of the number of drilling runs [Foubert, drilling liquid, para 40; 67; Huang, dynamic modeling takes into account hydraulic pressure from mud column, para 124].
Referring to claim 15, Foubert/Huang/Mohaghegh discloses executing drilling fluid machine learning model that uses the run features and the selected number of drill bits for the number of drilling runs to be performed at the site to select a drilling fluid for each of the number of drilling runs [Foubert, well plan, para 28-30, 153; drilling liquid, para 40; 67; Huang, dynamic modeling takes into account hydraulic pressure from mud column, para 124; Mohaghegh, MIDAS 30, para 59-60].
Referring to claim 17, Foubert/Huang/Mohaghegh discloses training the machine learning model [Mohaghegh, training neural network in real time with portion of cleansed raw data from drilling operation(s), para 86-90, Fig 5].
Referring to claim 18, Foubert/Huang/Mohaghegh discloses that training comprises use of training data that comprise drill bit images [Mohaghegh, training neural network in real time with portion of cleansed raw data from drilling operation(s), para 86-90, Fig 5; Huang, drilling performance parameter optimization for fixed cutter drill bit design, para 14; prediction, para 38, 41].
Claims 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Foubert, in view of Huang, in view of Mohaghegh, and further in view of US 2019/0138946 by Asher et al (hereafter Asher).
Referring to claims 6, 7 and 10, Foubert/Huang/Mohaghegh discloses all of the above claimed subject matter and also discloses executing a machine learning model [Mohaghegh, para 59-60], modeling fixed cutter drill bit including displaying a meshed model image of a cutter [Huang, para 103, 110, 119] and that machine learning model classifies drill bit images [Mohaghegh, intelligent classification of MIDAS 30, para 65-66; meshed cutter image, Huang, para 103, 110, 119]. However remains silent as to the machine learning model comprising a feature set generated through feature engineering, wherein the feature set comprises extracted features.
Asher discloses a process of generating a predictive machine learning model generating a plurality of features 415 formed based on a data set (i.e. feature engineering) [para 48], e.g. features associated with email addresses [para 48].
Foubert, Huang, Mohaghegh and Asher are analogous art because they are directed to the same field of endeavor- analysis of data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning model of Mohaghegh to include the feature engineering/feature extraction step in Asher because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the feature generation employed by the machine learning model in Asher further refines the predictive capabilities of the machine learning model in Mohaghegh.
Referring to claim 8, Foubert/Huang/Mohaghegh/Asher discloses that the features comprise number of blades [Huang, bit design parameters include number of blades, para 65; Asher, feature set, para 48].
Referring to claim 9, Foubert/Huang/Mohaghegh/Asher discloses that the features comprise cutter diameter [Huang, bit design parameters include cutter sizes diameters, para 65; Asher, feature set, para 48].
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Foubert, in view of Huang, in view of Mohaghegh, as applied to claim 15, and further in view of US 2017/0032285 by Sharma et al (hereafter Sharma).
Referring to claim 16, while Foubert/Huang/Mohaghegh discloses all of the above claimed subject matter and also discloses executing a machine learning model [Mohaghegh, para 59-60], and the drilling fluid machine learning model utilizing drilling fluid data [Foubert, drilling liquid, para 40; 67; Huang, dynamic modeling takes into account hydraulic pressure from mud column, para 124]. It remains silent as to the drilling fluid data being microscopic imagery data.
Sharma discloses training a machine learning model on microscopic images [para 6, 15].
Foubert, Huang, Mohaghegh and Sharma are analogous art because they are directed to the same field of endeavor- analysis of data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the drilling fluid data of Foubert and Huang with the microscopic imagery data of Sharma because it would achieve predicable results.
The ordinary skilled artisan would have been motivated to make this modification because the microscopic imagery data of Sharma further refines the type of drilling fluid data in Foubert and Huang.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
King et al (US 20010042642) directed to: an iterative drilling simulation, see proposed bits section [para 67; 2, 3, 6-8 and related portions of specification].
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S/
/AMY NG/ Supervisory Patent Examiner, Art Unit 2164
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