DETAILED ACTION
Notice of 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 § 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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the USPTO’s eligibility analysis entails considering whether the claimed subject
matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-5 and 9-17 are directed to a method (process) and claim 18 is directed to a system (machine). As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis
in Step 2A. Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance is a two prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claims 1 and 18 recite abstract limitations including (or substantially similar to): “transforming the data series into a frequency domain representation using Fourier transforms; processing the frequency domain representations for by aggregating them into discrete frequency bins; applying a detection method to the processed frequency domain representations, wherein the detection method comprises a discriminative system based on processed spectral features; and identifying a rig state selected from a group consisting of rig heave and downlinking from analysis of the frequency domain data representations.”
These limitations, as drafted, are a process that, under its broadest reasonable
interpretation, cover performance of the limitations in the mind, or by a human using pen and
paper, and therefore recite mental processes. The mere recitation of generic computing elements does not take the claim out of the mental process grouping. Mental processes cover concepts performed in the human mind (including an observation, evaluation, judgment, opinion) as well as decision-making steps which encompasses the limitations listed above. The claims do not require any action as currently worded. Thus, the claims recite abstract ideas.
If the claim recites a judicial exception (i.e., an abstract idea enumerated in Section I of
the 2019 Revised Patent Subject Matter Eligibility Guidance, a law of nature, or a natural
phenomenon), the claim requires further analysis in Prong Two. In Prong Two, examiners
evaluate whether the claim recites additional elements that integrate the exception into a
practical application of that exception.
Claim 1 recites the additional element of “receiving time series of individual sensor data from real time data system located on a drilling rig”. Claim 18 recites the additional elements of “a processor”, “a memory coupled to the processor”, “receive time series of individual sensor data from a real time data system located on a drilling rig” and “a display device that presents output of the identified rig state to a user”.
The recitation of “receiving time series of individual sensor data from real time data system located on a drilling rig”, “a processor”, “a memory coupled to the processor” and “a display device that presents output of the identified rig state to a user” amount to insignificant extra-solution activity.
Accordingly, in combination, these additional elements do not integrate the abstract ideas into practical applications because they do not impose any meaningful limits on practicing the abstract ideas.
If the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
The recitation of (or substantially similar to) “receiving time series of individual sensor data from real time data system located on a drilling rig” amounts to mere data gathering because this step uses sensors to measure data to perform the abstract idea. As such, this additional element does not amount to significantly more than the abstract idea. CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011).
The recitation of “a processor” and “a memory coupled to the processor” merely amounts to “apply it” because these elements contain mere instructions to implement the abstract ideas on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer. Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984.
The recitation of “a display device that presents output of the identified rig state to a user” is post-solution activity and the courts have held that merely displaying data does not add significantly more to the judicial exception. Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Thus, even when viewed as an ordered combination, nothing in the claims add
significantly more (i.e. an inventive concept) to the abstract idea.
Claim 2 recites “wherein the rig state is the downlinking rig state, and wherein the downlinking rig state comprises a state where communication occurs between sensors in downhole tools and a surface computing system.” which is well-understood, routine and conventional because this step merely requires transmission of data and does not integrate the abstract idea into a practical application. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. Therefore, similar to claim 1, this claim does not provide a practical application of the abstract idea, and is not significantly more.
Claims 3-5 and 9-17 further add to the abstract ideas because nothing in the claims preclude the recited steps in the claims from practically being performed in the human mind, or by a human using pen and paper. Therefore, similar to claim 1, these claims do not provide a practical application of the abstract idea, and are not significantly more.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 5, 9-12 and 14-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhan et al. (U.S. Publication No. 20230175394).
Regarding claim 1, Zhan teaches a method for recognizing specific rig states in oilfield drilling and completion operations ( monitoring the real-time condition of the PDC cutters in the drill bit during drilling; pp[0019], [0023]) comprising:
receiving time series of individual sensor data (The one or more sensors may be embedded sensors or systems in the PDC drill bit cutter substrate with different types of sensors that are used to measure real-time wear and dimension changes of PDC cutters and related drill bits; pp[0032])) from an external real time data system located on a drilling rig (a telemetry transceiver (130B) may be installed in the BHA (123) of a drilling system (100) to transmit data and signals through a telemetry channel (132) from the BHA (123) to a telemetry transceiver (130A) located on the drilling rig (102). The telemetry channel (132) may use acoustic signals transmitted through the drilling fluid; pp[0024]);
transforming the data series into a frequency domain representation using Fourier transforms (Processing the data may involve aggregating the data from different on-cutter PDC sensors, synchronizing the data, and transforming the data (e.g., using Fourier transforms) to remove noise; pp[0053]);
processing the frequency domain representations for by aggregating them into discrete frequency bins ( Processing the data may involve aggregating the data from different on-cutter PDC sensors; pp[0053]);
applying a detection method to the processed frequency domain representations, wherein the detection method comprises a discriminative system based on processed spectral features (the wear of the cutting surface (634) of the instrumented PDC cutter (630) may be determined from the spectrum of the reflected ultrasonic wave (653); pp[0042]); and
identifying a rig state selected from a group consisting of rig heave and downlinking from analysis of the frequency domain data representations (The data and signals transmitted through the telemetry channel (132) may be processed and analyzed to determine by a computer system (134). The computer system (134) may be located on the drilling rig (102) or at a remote location; pp[0024]. The data collected by the on-cutter sensors (802) includes measurement of drilling conditions such as real-time wear and dimension changes of PDC cutters and related drill bits; pp[0058]).
Regarding claim 2, Zhan teaches the method of claim 1, wherein the rig state is the downlinking rig state, and wherein the downlinking rig state comprises a state where communication occurs between sensors in downhole tools and a surface computing system (The data and signals transmitted through the telemetry channel (132) may be processed and analyzed to determine by a computer system (134). The computer system (134) may be located on the drilling rig (102) or at a remote location; pp[0024]. The data collected by the on-cutter sensors (802) includes measurement of drilling conditions such as real-time wear and dimension changes of PDC cutters and related drill bits; pp[0058]).
Regarding claim 5, Zhan teaches the method of claim 1, further comprising interpolating time series data points to ensure maintain a uniform frequency sampling (the sensing data collected from the on-cutter sensors for similar intervals of offset wells (i.e., existing wellbores close to a proposed well that provides information for planning the proposed well) is integrated as a training set for deriving artificial intelligence (AI) models, including machine-learning (ML) and deep-learning (DL) models, to predict drill bit performance, bit dull grad; pp[0020]).
Regarding claim 9, Zhan teaches the method of claim 1, wherein the discriminative system comprises at least one of: logistic regression models, tree-based models, neural networks or support vector machines ( different shallow ML models or DL models (820) may be used to perform the classification problem, such as random forest, decision trees, support vector machines, convolutional neural networks, including models for time series analysis (i.e., recurrent neural networks, long short term memory networks), deep neural networks; pp[0062]).
Regarding claim 10, Zhan teaches the method of claim 1, wherein the discriminative system comprises domain knowledge-informed rules-based logic (Data domain transformations (e.g., Fast Fourier Transform) are used to process data in order to discriminate the relevant signals from noises, and to identify relevant signals to increase ML model performance; pp[0020]).
Regarding claim 11, Zhan teaches the method of claim 1, further comprising converting the identified rig state to a time series representation (Specifically, different shallow ML models or DL models (820) may be used to perform the classification problem including models for time series analysis; pp[0062]).
Regarding claim 12, Zhan teaches the method of claim 1,further comprising:
evaluating the relevance of the identified detected rig state; selecting a preferred action based on the identified rig state; and providing information about the identified rig state to an operational model (Data domain transformations (e.g., Fast Fourier Transform) are used to process data in order to discriminate the relevant signals from noises, and to identify relevant signals to increase ML model performance. The characteristics of the PDC cutters are correlated with temperature, vibration, wear resistance, as well as with the surface drilling parameters (i.e., weight on bit—WOB, drilling mud rate, among others) to provide a complete set of information on the properties of the PDC cutter cutting element; pp[0020], [0060]).
Regarding claim 14, Zhan teaches the method of claim 12, wherein the preferred action is data filtering data during the identified rig state (Data domain transformations (e.g., Fast Fourier Transform) are used to process data in order to discriminate the relevant signals from noises, and to identify relevant signals to increase ML model performance; pp[0020]).
Regarding claim 15, Zhan teaches the method of claim 12, wherein the preferred action is configuring operational model parameters during the identified rig state (Data from the sensors are transferred to the data processing system for drilling optimization and drilling automation. Data domain transformations (e.g., Fast Fourier Transform) are used to process data in order to discriminate the relevant signals from noises, and to identify relevant signals to increase ML model performance. The characteristics of the PDC cutters are correlated with temperature, vibration, wear resistance, as well as with the surface drilling parameters; pp[0020]).
Regarding claim 16, Zhan teaches the method of claim 12, wherein the preferred action is informing the system's users of an event occurrence during the identified rig state (bit life (824) (e.g., a value between 0-100), which is the ability to reliably predict bit grade that informs geoscientists and drilling operators when it is more economic to simply trip the bit out of the hole and replace it with a new bit instead of continuing to drill with low ROP due to accumulated damage may also be predicted by the output ML models (822); pp[0065]).
Regarding claim 17, Zhan teaches the method of claim 1, further comprising recording the start and end times of the identified detected rig state ( the in-situ property information of the PDC cutter cutting elements and substrates is also used for optimizing the drilling parameters for specific intervals in current or future wells; pp[0067]).
Regarding claim 18, Zhan teaches a system for recognizing rig states in oilfield drilling and completion operations, the system consisting of a processor;
a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor (Sensors (121) may also be coupled to a processor assembly (122) that includes a processor, memory, and an analog-to-digital converter for processing sensor measurements; pp[0023]), cause the system to:
receive time series of individual sensor data from a real time data system located on a drilling rig (The one or more sensors may be embedded sensors or systems in the PDC drill bit cutter substrate with different types of sensors that are used to measure real-time wear and dimension changes of PDC cutters and related drill bits; pp[0032]; A telemetry transceiver (130B) may be installed in the BHA (123) of a drilling system (100) to transmit data and signals through a telemetry channel (132) from the BHA (123) to a telemetry transceiver (130A) located on the drilling rig (102). The telemetry channel (132) may use acoustic signals transmitted through the drilling fluid; pp[0024]);
transform the data series into a frequency domain representation using Fourier transforms (Processing the data may involve aggregating the data from different on-cutter PDC sensors, synchronizing the data, and transforming the data (e.g., using Fourier transforms) to remove noise; pp[0053]);
process the frequency domain representations by aggregating them into discrete frequency bins (Processing the data may involve aggregating the data from different on-cutter PDC sensors; pp[0053]);
apply a detection method to the processed frequency domain representations, wherein the detection method comprises a discriminative system based on processed spectral features (the wear of the cutting surface (634) of the instrumented PDC cutter (630) may be determined from the spectrum of the reflected ultrasonic wave (653); pp[0042]); and
identify a specific rig state selected from a group consisting of rig heave and downlinking from analysis of the frequency domain data representations (The data and signals transmitted through the telemetry channel (132) may be processed and analyzed to determine by a computer system (134). The computer system (134) may be located on the drilling rig (102) or at a remote location; pp[0024]. The data collected by the on-cutter sensors (802) includes measurement of drilling conditions such as real-time wear and dimension changes of PDC cutters and related drill bits; pp[0058]); and
a display device that presents output of the identified rig state to a user (the computer (902) may include a computer that includes an input device, such as a keypad, keyboard, touch screen, or other device that can accept user information, and an output device that conveys information associated with the operation of the computer (902), including digital data, visual, or audio information (or a combination of information), or a GUI; pp[0069]).
Allowable Subject Matter
Claims 3, 4 and 13 are not allowed due to the rejection above under 101; however, these claims have not been rejected under prior art.
Conclusion
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/LAMIA QUAIM/Examiner, Art Unit 3676
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