DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 2, line 16, “i.e.” renders scope indefinite.
The dependent claims 11-12 are rejected as being indefinite since they inherit the deficiencies of claim 10 which depend from.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-10 are directed to a method, claim 11 is directed to using a system comprising a processor to perform the method. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
Method for determining physical properties of rocky formations, comprising:
training a first artificial intelligence system (AI1) on a first training dataset (TR1), said first training dataset (TR1) comprising:
independent variables (V1), associated with one or more rocky formations, comprising: at least one of X-ray fluorescence measurements, XRF, X-ray diffraction measurements, XRD, and gamma-ray measurements; one or more drilling parameters;
one or more dependent variables (V2), comprising one or more physical properties of said one or more rocky formations,
wherein said first training dataset (TR1) is obtained from one or more training wells;
wherein said method further comprises:
determining operating data (OP), associated with a drilling of an operating well and comprising values of XRF and/or XRD measurements and values of said one or more drilling parameters (DP);
executing a processing operation, wherein values of one or more of said one or more physical properties (PP) of a rocky formation crossed by said operating well are computed on the basis of said operating data (OP) by means of at least said first artificial intelligence system (AI1).
Step 2A Prong 1:
“determining operating data (OP), associated with a drilling of an operating well and comprising values of XRF and/or XRD measurements and values of said one or more drilling parameters (DP)” is directed to math because this involves the collection, storage, analysis, and visualization of data generated during drilling operations, which includes drilling parameters such as weight on bit, rotary speed, and mud properties, as well as geological data such as formation type and fluid saturation. The use of mathematical formulas and calculations is essential for optimizing drilling.
“executing a processing operation, wherein values of one or more of said one or more physical properties (PP) of a rocky formation crossed by said operating well are computed on the basis of said operating data (OP) by means of at least said first artificial intelligence system (AI1)” is directed to math because determining the change between AI systems, at their core, consist of mathematical formulas, equations, and relationships. The act of "computing" values from data via an AI model is an application of mathematical calculations.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “computing” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of " determining operating data (OP), associated with a drilling of an operating well and comprising values of XRF and/or XRD measurements and values of said one or more drilling parameters (DP); executing a processing operation, wherein values of one or more of said one or more physical properties (PP) of a rocky formation crossed by said operating well are computed on the basis of said operating data (OP) by means of at least said first artificial intelligence system (AI1)” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“training a first artificial intelligence system (AI1) on a first training dataset (TR1), said first training dataset (TR1) comprising: independent variables (V1), associated with one or more rocky formations, comprising: at least one of X-ray fluorescence measurements, XRF, X-ray diffraction measurements, XRD, and gamma-ray measurements; one or more drilling parameters; one or more dependent variables (V2), comprising one or more physical properties of said one or more rocky formations” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“wherein said first training dataset (TR1) is obtained from one or more training wells” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“determining operating data (OP), associated with a drilling of an operating well and comprising values of XRF and/or XRD measurements and values of said one or more drilling parameters (DP)” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“executing a processing operation, wherein values of one or more of said one or more physical properties (PP) of a rocky formation crossed by said operating well are computed on the basis of said operating data (OP) by means of at least said first artificial intelligence system (AI1)” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying results.
This is similar to electric power: MPEP 2106.05(h) vi. 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).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 1 recites the additional element(s) of using generic AI/ML technology, i.e. “training a first artificial intelligence system (AI1) on a first training dataset (TR1)”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “training a first artificial intelligence system (AI1) on a first training dataset (TR1)” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “training a first artificial intelligence system (AI1) on a first training dataset (TR1)” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other 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.
Step 2B:
“training a first artificial intelligence system (AI1) on a first training dataset (TR1), said first training dataset (TR1) comprising: independent variables (V1), associated with one or more rocky formations, comprising: at least one of X-ray fluorescence measurements, XRF, X-ray diffraction measurements, XRD, and gamma-ray measurements; one or more drilling parameters; one or more dependent variables (V2), comprising one or more physical properties of said one or more rocky formations” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“wherein said first training dataset (TR1) is obtained from one or more training wells” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“determining operating data (OP), associated with a drilling of an operating well and comprising values of XRF and/or XRD measurements and values of said one or more drilling parameters (DP)” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“executing a processing operation, wherein values of one or more of said one or more physical properties (PP) of a rocky formation crossed by said operating well are computed on the basis of said operating data (OP) by means of at least said first artificial intelligence system (AI1)” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
The claim is therefore ineligible under 35 USC 101.
Claim 11 is similar to claim 1 but recites a system comprising a processor, an input interface coupled to said processor, and an output interface coupled to said processor, wherein a first artificial intelligence system is loaded in said processor, to implement a method as in claim 1. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 11 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 11.
Regarding claim 2, “wherein said one or more physical properties (PP) comprise one or more of: static Young modulus; dynamic Young modulus; static shear modulus (Shear modulus static); dynamic shear modulus (Shear modulus dynamic); static elastic modulus (Bulk modulus static); dynamic elastic modulus (Bulk modulus dynamic); uniaxial compressive strength (UCS); static Poisson's ratio; dynamic Poisson's ratio; primary wave velocity (P wave velocity); secondary wave velocity (S wave velocity); ultimate tensile strength; coefficient of friction; cohesion, i.e. that component of shear stress which is independent of friction between particles; Lamè's first parameter, λ; Lamè's second parameter, μ; porosity; density” is a mental step of identification of data.
Regarding claim 3, “wherein said one or more drilling parameters comprise one or more of: a vertical force acting upon a drill bit used for drilling said operating well (Weight On Bit, WOB); a rate of penetration (ROP) into the subsoil while drilling said operating well; a revolution speed of the drill bit (Rotation Per Minute, RPM); a torque acting upon the drill bit (Torque); a pressure in the drilling mud supply line or “flowline” (Standpipe Pressure, SPP); a vertical force (weight) acting upon the hook to which the equipment supporting the drill bit is hung (Weight On Hook, WOH); a rate of flow of drilling mud entering the hydraulic mud circuit (Flow IN); a rate of flow of mud exiting the annulus (Flow OUT); a pressure of one or more pumps for drilling mud circulation (Pump Pressure); one or more properties of the drilling mud; a parameter associated with a drilling mud flow detection device and representative of a degree of opening/inclination of a flow paddle belonging to said device and configured for intercepting said mud flow and changing its own angle as a function of the rate of said flow; bit size (BS); bit position (BP); bit type; drilling depth; data describing the gas extracted from the drilling mud returning to the surface (Mud gas data)” is a mental step of identification of data.
Regarding claim 4, “wherein the independent variables (V1) of said first training dataset (TR1) comprise gamma radiation measurements, said method comprising: training a second artificial intelligence system (AI2) on a second training dataset (TR2), said second training dataset comprising: at least one independent variable (V3), comprising values of XRF and/or XRD measurements concerning one or more rocky formations; at least one dependent variable (V4), comprising gamma radiation values for said one or more rocky formations; wherein said operating data (OP) comprise values of XRF and/or XRD measurements concerning said operating well, wherein said second training dataset (TR2) is associated with one or more test wells, wherein said method comprises: computing by means of said second artificial intelligence system (AI2), based on the XRF and/or XRD measurements concerning said operating well, gamma radiation values for said operating well; wherein, in said processing operation, the values of said one or more physical properties of said rocky formation crossed by said operating well are computed by means of said first artificial intelligence system (AI1) on the basis of the gamma radiation values computed for said operating well and the values of said one or more drilling parameters determined for said operating well” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 5, “wherein the values of said one or more physical properties (PP) are computed by said first artificial intelligence system (AI1)” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 6, “wherein said physical properties are divided into a first group and a second group; the physical properties of the first group are computed by the first artificial intelligence system (AI1); the physical properties of the second group are computed by executing a further processing step, on the basis of one or more independent variables and/or one or more physical properties of the first group” is directed to math.
Regarding claim 7, “wherein the values of physical properties computed by the first artificial intelligence system (AI1) are computed using a single artificial intelligence model” is directed to math.
Regarding claim 8, “wherein said first artificial intelligence system (AI1) comprises one or more artificial intelligence subsystems (S1-S5), each one dedicated to a subset of the physical properties computed by said first artificial intelligence system (AI1)” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 9, “wherein: the independent variables (V1) of the first training dataset (TR1) comprise a lithological indication of said one or more rocky formations of one or more training wells; said operating data (OP) comprise a lithological indication (IND) of one or more rocky formations of the operating well” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 10, “wherein one or more of said one or more dependent variables (V2) included in said first training dataset (TR1) are computed on the basis of sonic logs and/or density logs concerning said training wells” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Hence the claims 1-11 are treated as ineligible subject matter under 35 U.S.C. § 101.
Other Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Elkatatny ("Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models") disclose Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective is to evaluate the ability of the optimized
adaptive neuro-fuzzy inference system (ANFIS), functional neural networks (FN), random forests (RF), and support vector machine (SVM) models to predict the ROP in real time from the drilling parameters in the S-shape well profile, for the first time, based on the drilling parameters of weight on bit (WOB), drillstring rotation (DSR), torque (T), pumping rate (GPM), and standpipe pressure (SPP). Data from two wells were used for training and testing (Well A and Well B with 4012 and 1717 data points, respectively), and one well for validation (Well C) with 2500 data points. Well A and Well B data were combined in the training-testing phase and were randomly divided into a 70:30 ratio for training/testing. The results showed that the ANFIS, FN, and RF models could effectively predict
the ROP from the drilling parameters in the S-shape well profile, while the accuracy of the SVM model was very low. The ANFIS, FN, and RF models predicted the ROP for the training data with average absolute percentage errors (AAPEs) of 9.50%, 13.44%, and 3.25%, respectively. For the testing data, the ANFIS, FN, and RF models predicted the ROP with AAPEs of 9.57%, 11.20%, and 8.37%, respectively. The ANFIS, FN, and RF models overperformed the available empirical correlations for ROP prediction. The ANFIS model estimated the ROP for the validation data with an AAPE of 9.06%, whereas the FN model predicted the ROP with an AAPE of 10.48%, and the RF model predicted the ROP with an AAPE of 10.43%. The SVM model predicted the ROP for the validation data with a very
high AAPE of 30.05% and all empirical correlations predicted the ROP with AAPEs greater than 25%.
Zang et al. (US 2021/0089897 A1) disclose training a model based on a set of training data comprising at least a subset of: well logs, seismic volumes (both pre and post-stack), geologic maps, initial information from wells, core data, horizons, seismic images, synthetic log data, and the like. In certain embodiments, training data for training the model includes attributes at each of a plurality of depth points along with adjacent waveforms in a plurality of directions at each depth point. Some embodiments involve using waveforms in a forward, backward, left, right, upward, and/or downward direction in three-dimensional space along with attributes at a given depth point for training data. The model may, for example, be a machine learning model such as an artificial neural network, deep neural network, deep belief network, recurrent neural network, convolutional neural network, or the like that is trained using machine learning methods. Once trained, the model is used to determine various output parameters such as reservoir properties including lithology, porosity, permeability, water saturation, impedance (p or s), density, and the like.
Dursun et al. (US 10,657,441) disclose a method, comprising: receiving raw data sets comprising dynamic data and static data, wherein the dynamic data comprises drilling parameter and operating condition values generated during subterranean drilling operations, wherein the static data is indicative of one or more types of the subterranean drilling operations that generated the dynamic data; separating the raw data sets into training data sets based, at least in part, on the one or more types of the subterranean drilling operations identified in the static data of the raw data sets; generating at least one predictive model based, at least in part, on at least one training data set of the training data sets, wherein the at least one predictive model determines a rate of penetration (ROP) for the one or more types to which the at least one training data set corresponds, wherein generating the at least one predictive model comprises for each training data set of the training data sets generating a different context-specific predictive model associated with the static data used to generate the at least one predictive model.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857