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
Election/Restrictions
Applicant’s election of Group I (Claims 1, 2, 5-7, 13, and 18-20), without traverse, in the reply filed on 8/20/2026 is acknowledged.
Claims 3, 4, 8-12, 14-17 (Groups II-IV) are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected invention groups, there being no allowable generic or linking claim.
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, 2, 5-7, 13, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A method comprising: receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; processing the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resampling the smoothed time series data; generating fluid and formation characteristics with respect to time based on the smoothed time series data; and outputting the fluid and formation characteristics with respect to time.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by 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. The above claim is considered to be in a statutory category (process).
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations
Similar limitations comprise the abstract ideas of Claims 19 and 20.
Next, under Step 2A, Prong Two, we consider whether the above claims that recite a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A method comprising: receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; outputting the fluid and formation characteristics with respect to time.
In Claim 19: A system comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; output the fluid and formation characteristics with respect to time.
In Claim 20: One or more computer-readable storage media comprising processor-executable instructions to instruct a computing system to: receive downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; output the fluid and formation characteristics with respect to time.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system (Claim 19), and One or more computer-readable storage media comprising processor-executable instructions to instruct a computing system (Claim 20) are examples of generic computer equipment (components) that are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application. The limitations that generically recite receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool (similar in Claims 19 and 20) represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity and does not integrate the judicial exception into a practical application”.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2, 5-7, 13, and 18 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Carlos TORRES-VERDIN et al. (US 20230273180), hereinafter ‘Torres’ in view of Bo Liang et al. (US 20210149386), hereinafter ‘Liang’.
With regard to Claim 1, Torres discloses
A method comprising:
receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool (a testing tool is lowered into the well bore to the depth of the oil-bearing formation, where it collects fluid samples for characterization during a formation test according to a pre-programmed routine [0005]; the fluid parameters may be determined as a function of time [0011]; using real-time downhole measurements [0060]):
processing the downhole formation testing time series data using a machine learning model to generate time series data (In some cases, the testing tool 130 determines one or more fluid parameters 236 from the data collected from the formation sampling 232 and/or pulse sequence 234 operations. The fluid parameters may include, but are not limited to, a mass density, a fluid viscosity, a fluid resistivity, a formation pressure, an estimated formation pressure, an optical density (“OD”), a level of contamination, or the like. In turn, the fluid parameters 236 may form part of the inputs to a numerical model 238 implemented by the testing tool 130. Input data 210 may also be provided to the numerical model to improve and/or refine model outputs including but not limited to simulation data 212 and measurement data 214. Simulation data 212 may include data generated by simulations for formation sampling and pulse sequence outputs based on analytical methods, physics-based models, or numerical methods (e.g., based on neural network models trained on empirical data collected from previous formation tests). In some cases, the numerical model 238 may generate one or more outputs, including but not limited to the formation condition 240, time values corresponding to one or more industrially relevant parameters, target testing values, and the like [0074]; the numerical model 238 may be implemented in a convolutional neural network as a machine learning algorithm [0076]).
resampling the smoothed time series data (However, the FCD may still exhibit a noisy response after employing the maximum smoothing factor. Noise filters may be applied to enable a reliable and accurate assessment of fluid contamination measurements and subsequent calculation of the contamination transients via the FCD method [0137]);
generating fluid and formation characteristics with respect to time based on the smoothed time series data; and outputting the fluid and formation characteristics with respect to time (The model output comprises a match for the complete curve of the contamination decay, independently of the transient trends changes, and the pump-out volume and time distributions, which are useful to recognize the trend changes in real time in order to estimate the volume and time required to achieve the required contamination target. In addition, these distributions may be useful to quantify diverse reservoir properties and flowrate conditions [0203].”
Torres also discloses smoothing time series data (employing the maximum smoothing factor [0137]; Smoothing factor case. Sensitivity analysis for noise reduction and over smoothing evaluation in the application of the FCD [0193]).
However, Torres does not specifically disclose using a machine learning model to generate smoothed time series data.
Liang discloses using a machine learning model to generate smoothed time series data (A sliding window may be used to smooth the output of the machine-learning model data and extract a metric [0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Torres in view of Liang to use a machine learning model to generate smoothed time series data to improve accuracy of a prediction model (For each time series record for a piece of equipment that is fed into the machine-learning model, the machine-learning model may output a prediction of the remaining useful life of the equipment of the piece of equipment, Liang [0034]).
With regards to Claims 19 and 20, Torres in view of Liang discloses the claim limitations as discussed above in regard to Claim 1.
Claim 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Torres in view of Liang, in further view of Sarah Louise Rawlinson et al. (US 20210180439), hereinafter ‘Rawlinson’.
Torres in view of Liang are silent with regards to the receiving comprises receiving the downhole formation testing time series data by a computational framework implemented in the downhole tool.
Rawlinson discloses receiving the downhole formation testing time series data by a computational framework implemented in the downhole tool (a computational framework may be provided for handling of logging measurements and/or data derived from logging measurements [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Torres in view of Liang, in view of Rawlinson to receive the downhole formation testing time series data by a computational framework implemented in the downhole tool to be used in controlling and/or modeling logging operation (logging information may be provided to the seismic-to-simulation framework 302 and/or to the drilling framework 304. Such information may be utilized for model building (e.g., constructing a multidimensional model of a geologic environment), generating a trajectory for a well (e.g., or an extension thereof), generating a stimulation plan (e.g., fracturing, chemical treatment, etc.), controlling one or more drilling operations, etc., Rawlinson [0076]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Torres in view of Liang, in further view of Hamed Chok et al. (US 20130096835), hereinafter ‘Chok’.
With regards to Claim 5, Torres in view of Liang are silent about the machine learning model comprises a support vector machine.
Chok discloses a support vector machine (Many classifiers (e.g., support vector machines, neural networks, etc.) have been developed to achieve this purpose wherein training data is used to construct lithofacies classifiers [0192]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Torres in view of Liang, in view of Chok to use support vector machine in machine learning for training a machine learning model as known in the art (Chok).
Claims 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Torres in view of Liang, in further view of Matthew Chase Griffing et al. (US 20190049614), hereinafter ‘Griffing’.
Torres in view of Liang is silent about the machine learning model comprises a tunable parameter for identification of one or more of noise and outliers (Claim 6), wherein the noise comprises spikes (Claim 7).
Griffing discloses a tunable parameter for identification of one or more of noise and outliers, wherein the noise comprises spikes (embodiments can use a frequency resolution in the range of 10-100 Hz to accurately discern noise/interference peaks in the spectrum, which would give a sample period T=20.48 ms for a resolution of about 48.8 Hz and samples N=1024 (2.sup.10), although embodiments can increase or decrease resolution by increasing or decreasing the sample period of T, for enhanced or reduced accuracy with a tradeoff in computation speed and power [0052]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Torres in view of Liang, in view of Griffing to use a machine learning model that comprises a tunable parameter for identification of one or more of noise and outliers, wherein the noise comprises spikes, to accurately identify noise by changing (tuning) sampling frequency, frequency resolution, etc.
Claim 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Torres in view of Liang, in further view of Anders Eriksson et al. (US 6137882), hereinafter ‘Eriksson’.
Torres in view of Liang is silent about the processing processes the downhole formation testing time series data according to a first-time interval and wherein the resampling resamples at a second time interval that is at least an order of magnitude greater than the first time interval.
Eriksson discloses resampling at a second time interval that is at least an order of magnitude greater than the first sampling (at a first time interval) (R.sub.x.sup.lta represents the long time average, which typically is computed over a time interval that is at least an order of magnitude longer than the time period for calculating the short time average, for example of the order of 4 seconds, Col.4, Lines 17-21).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Torres in view of Liang, in view of Eriksson to processes (the downhole formation testing) time series data according to a first time interval and wherein the resampling resamples at a second time interval that is at least an order of magnitude greater than the first time interval for its advantages as known in the art (Thus, only the most recent samples are used for calculating the short time average, while a large number of samples are used for calculating the long time average. As can be seen from the figure the short time average at sample instant n exceeds the long time average at the same instant (the distance above the t-axis represents the corresponding average). Thus, in this case (curve 3, sample instant n) the present invention would allow filter updating, while the method in accordance with the prior art would inhibit filter updating, Eriksson, Col.21-30).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Torres in view of Liang, in further view of Youxiang Zuo et al. (US 12188919), hereinafter ‘Zuo’.
With regards to Claim 18, Torres in view of Liang is silent about performing machine learning model training using the fluid and formation characteristics.
Zou discloses performing machine learning model training using the fluid and formation characteristics (measuring composition and property of formation fluid according to claim 5, wherein the measuring model is obtained by training pre-created machine learning models based on big data about compositions and properties of various reservoir fluids and measurement signals of downhole sensors as sample data sets, Claim 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Torres in view of Liang, in view of Zou to perform machine learning model training using the fluid and formation characteristics as major characteristics affecting composition and formation property of fluids during logging.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2857
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