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 Objections
Claim 10 is objected to under 37 CFR 1.75 as being a substantial duplicate of claim 6. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 10 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 10, which depends from claim 4 is the exactly the same as claim 6, which also depends from claim 4. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claim 1 recites accessing wellbore measurement data; identifying by a machine learning process data that corresponds to the wellbore measurement data; implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data; comparing the constrained machine learning process data to the wellbore measurement data; and identifying based on the comparison that the wellbore measurement data corresponds to the constrained machine learning process data based on an error being less than an error threshold which falls into the abstract idea groupings of mental concepts and mathematical concepts; as the claimed accessing measurement data, identifying process data, implementing a function, comparing data and identifying an error based on the comparison involves mental processes; as they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Although the claim recites a machine learning process and implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data, the examiner considers “machine learning” and the claimed “function” to rely on mathematics to identify patterns, optimize performance, and process data, as linear algebra represents data, calculus enables model learning through optimization, and statistics interprets, cleans, and analyzes data to make predictions. Therefore, the claim is directed towards abstract ideas.
This judicial exception is not integrated into a practical application because the claimed wellbore measurement data merely links the abstract ideas to a field of use; as neither the performance or result of the abstract ideas do not improve or better the wellbore or the measurement data. MPEP 2106.05(h)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of wellbore measurement data merely defines the intended environment the abstract ideas are linked. However, there is no improvement to the data or wellbore as a direct result of the abstract ideas, therefore the claim does not amount to significantly more.
Claims 2, 3, 4, 13, 14 and 16-18 further define the abstract idea falling into the abstract idea grouping of mathematical concepts without providing significantly more or integrating the abstract ideas into a practical application.
Claims 5, 9 and 19 further define the outputting steps by generating a map. The generated map further defines the abstract idea, as displaying the results of collecting data and analyzing it does not provide significantly more or integrate the abstract ideas into a practical application. At best, the generated map reads as an insignificant post-solution activity amounting to no more than an instruction to apply the exception, as the limitation generically recites an effect of the judicial exception, thereby amounting to a claim that is merely adding the words "apply it" to the judicial exception. MPEP 2106.05(f)
Claims 6 and 10 further define the abstract idea falling into the abstract idea grouping of mathematical concepts by reciting performing calculations to identify an error value by: subtracting values forecasted data identified by operation of a computer model from portions the wellbore measurement data to create a set of difference values; squaring the each of the set of difference values; and generating a sum of the set of squared difference values.
The claimed “computer model” merely reads as a tool for performing the abstract ideas in a computer environment, as the model is neither improved or bettered by the operation or result of the abstract ideas. MPEP 2106.05(a)
Therefore, the claim fails to integrate the abstract idea into a practical application or provide significantly more.
Claims 7 and 11 further define the abstract idea falling into the abstract idea grouping of mathematical concepts, as summing a set of weighted fitness estimates is simple enough to be performed in the human mind, with the aid of pen and paper.
Claims 8 and 12 further defines the abstract idea falling into the abstract idea grouping of mathematical concepts without providing significantly more or integrating the abstract idea into a practical application.
Claim 15 recites accessing wellbore measurement data; identifying by a machine learning process data that corresponds to the wellbore measurement data; implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data; comparing the constrained machine learning process data to the wellbore measurement data; and identifying based on the comparison that the wellbore measurement data corresponds to the constrained machine learning process data based on an error being less than an error threshold which falls into the abstract idea groupings of mental concepts and mathematical concepts; as the claimed accessing measurement data, identifying process data, implementing a function, comparing data and identifying an error based on the comparison involves mental processes; as they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Although the claim recites a machine learning process data and implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data, the examiner considers “machine learning” and the claimed “function” to rely on mathematics to identify patterns, optimize performance, and process data; as linear algebra represents data, calculus enables model learning through optimization, and statistics interprets, cleans, and analyzes data to make predictions. Therefore, the claim is directed towards abstract ideas.
This judicial exception is not integrated into a practical application because the claimed wellbore measurement data merely links the abstract ideas to a field of use; as neither the performance or result of the abstract ideas do not improve or better the wellbore or the measurement data. MPEP 2106.05(h)
The additional element of a processor merely reads as a tool for performing the abstract ideas, as neither the performance or result of the abstract idea improves the processor(s) themselves. These general-purpose computer elements are solely tasked to perform the abstract ideas and therefore fail to integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of wellbore measurement data merely defines the intended environment the abstract ideas are linked. However, there is no improvement to the data or wellbore as a direct result of the abstract ideas, therefore the claim does not amount to significantly more. Lastly, the processor merely acts as a tool to perform the abstract idea. Therefore the additional elements, neither alone or in combination, amount to significantly more.
Claim 20 recites accessing wellbore measurement data; identifying by a machine learning process data that corresponds to the wellbore measurement data; implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data; comparing the constrained machine learning process data to the wellbore measurement data; and identifying based on the comparison that the wellbore measurement data corresponds to the constrained machine learning process data based on an error being less than an error threshold which falls into the abstract idea groupings of mental concepts and mathematical concepts; as the claimed accessing measurement data, identifying process data, implementing a function, comparing data and identifying an error based on the comparison involves mental processes, as they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Although the claim recites a machine learning process data and implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data, the examiner considers “machine learning” and the claimed “function” to rely on mathematics to identify patterns, optimize performance, and process data as linear algebra represents data, calculus enables model learning through optimization, and statistics interprets, cleans, and analyzes data to make predictions. Therefore, the claim is directed towards abstract ideas.
This judicial exception is not integrated into a practical application because the claimed wellbore measurement data merely links the abstract ideas to a field of use; as neither the performance or result of the abstract ideas do not improve or better the wellbore or the measurement data. MPEP 2106.05(h)
The additional elements of a processor and memory merely read as tools for performing the abstract ideas, as neither the performance or result of the abstract idea improves the processor(s) or memory themselves. These general-purpose computer elements are solely tasked to perform the abstract ideas and therefore fail to integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of wellbore measurement data merely defines the intended environment the abstract ideas are linked. However, there is no improvement to the data or wellbore as a direct result of the abstract ideas, therefore the claim does not amount to significantly more. Lastly, the processor(s) and memory are recited generically and merely act as tools to perform the abstract idea. Therefore the additional elements, neither alone or in combination, amount to 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 13, 15, 18, 19 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (2021/0333428).
With respect to claim 1, Wang et al. teaches a method comprising: accessing wellbore measurement data (s602; waveform data is received [0038]); identifying by a machine learning process data that corresponds to the wellbore measurement data (as s608; receives the extracted features and determines a library or mapping function for producing data-driven scale factors; [0042]); implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data (as Wang et al. teaches using a supervised function to all borehole related data to access a quality the data via physics-based machine learning analysis; [0021]); comparing the constrained machine learning process data to the wellbore measurement data (as Wang et al. teaches in claim 7, comparing wave features generated by the model against the directly measured waves); and identifying based on the comparison that the wellbore measurement data corresponds to the constrained machine learning process data based on an error being less than an error threshold (Wang et al. teaches based on the comparison, identifying correspondence between the measured and modeled data being under a specific range defined by Sca; [0042]; essentially, Wang et al. teaches the method uses a blended approach, bounding the data-driven parameters within a specific range to minimize the impact of noise or errors in the data 0<Sca<1).
With respect to claims 2 and 16, Wang et al. teaches the method wherein the function that constrains the machine learning process data is implemented based on an assumption regarding the wellbore measurement data (as Wang et al. teaches extracting features from noisy synthetic waveform data, an assumption regarding the wellbore measurement data, and assumes a list of data driven scales used to make assumptions about the measured data; [t]he machine learning analysis may then extract wave features from the noisy synthetic waveform data (operation 906). A list of data-driven scales can be assumed (e.g., guessed) and used to perform one or more hybrid processes and inversion may be performed to derive body wave slownesses from the waveform data (operation 908). Each hybrid process may be associated with a respective data-driven scale value or values used for the process. Then the data-driven scale factor value that results in a hybrid process producing the most accurate body wave slowness result may be identified (e.g., by the association discussed above) by comparing outputs (e.g., body wave slowness) to the original synthetic waveform data used to produce the outputs and selecting the value with the smallest difference (e.g., error value) (operation 910); [0057]).
With respect to claims 3 and 17, Wang et al. teaches the method wherein the function that constrains the machine learning process data extracts features from the machine learning process data (as the process involves a hybrid process, which extracts features from the machine learning process using a synthetic waveform via the supervised constrain function).
With respect to claims 4 and 18, Wang et al. teaches the method further comprising classifying material properties (as the environment model 300 includes materials like mud, drilling fluid and other fluid materials; [0028]) of the machine learning process data by narrowing an output range associated with the machine learning process data (as Wang et al. teaches data-driven parameters, considered to include material properties, can be bounded by a range; [0042]).
With respect to claims 5 and 19, Wang et al. teaches the method further comprising: generating a mapping of a space that associates the constrained machine learning process data with known wellbore properties (as Wang et al. teaches the machine learning analysis can be performed across adjacent depths, or zones, in order to provide a more robust analysis of the borehole environment and the like, for example, mapping out different zones like a washout zone at different depths, mapping out a log of the scale factors to be displayed to a user in order to inform the user of which depths are of what data quality as well as which depths are associated with partial or total model-based processing. In some aspects, this may inform a user of where borehole complexity is impacting (e.g., decreasing) data quality; [0053]).
With respect to claim 13, Wang et al. teaches the method wherein the function that constrains the machine learning process data limits a bandwidth associated with a portion of the wellbore measurement data (as Wang et al. teaches limiting in [0045] limiting or selecting the data to remove unwanted noise, thereby providing a bandwidth associated with a nosey portion of the data).
With respect to claim 15, Wang et al. teaches in Fig. 11 a non-transitory computer-readable storage medium (1106) having embodied thereon instructions [0062] that when executed by one or more processor (1114) result in the one or more processors (1114): accessing wellbore measurement data (s602; waveform data is received [0038]); identifying by a machine learning process data that corresponds to the wellbore measurement data (as s608; receives the extracted features and determines a library or mapping function for producing data-driven scale factors; [0042]); implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data (as Wang et al. teaches using a supervised function to all borehole related data to access a quality the data via physics-based machine learning analysis; [0021]); comparing the constrained machine learning process data to the wellbore measurement data (as Wang et al. teaches in claim 7, comparing wave features generated by the model against the directly measured waves); and identifying based on the comparison that the wellbore measurement data corresponds to the constrained machine learning process data based on an error being less than an error threshold (Wang et al. teaches based on the comparison, identifying correspondence between the measured and modeled data being under a specific range defined by Sca; [0042]; essentially, Wang et al. teaches the method uses a blended approach, bounding the data-driven parameters within a specific range to minimize the impact of noise or errors in the data 0<Sca<1).
With respect to claim 20, Wang et al. teaches an apparatus (Fig. 11) comprising: a memory (1106); and one or more processors (1114) that execute instructions [0062]out of the memory (1106) to: accessing wellbore measurement data (s602; waveform data is received [0038]); identifying by a machine learning process data that corresponds to the wellbore measurement data (as s608; receives the extracted features and determines a library or mapping function for producing data-driven scale factors; [0042]); implementing a function that constrains the machine learning process data when mapping the constrained machine learning process data to the wellbore measurement data (as Wang et al. teaches using a supervised function to all borehole related data to access a quality the data via physics-based machine learning analysis; [0021]); comparing the constrained machine learning process data to the wellbore measurement data (as Wang et al. teaches in claim 7, comparing wave features generated by the model against the directly measured waves); and identifying based on the comparison that the wellbore measurement data corresponds to the constrained machine learning process data based on an error being less than an error threshold (Wang et al. teaches based on the comparison, identifying correspondence between the measured and modeled data being under a specific range defined by Sca; [0042]; essentially, Wang et al. teaches the method uses a blended approach, bounding the data-driven parameters within a specific range to minimize the impact of noise or errors in the data 0<Sca<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 (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 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(s) 6-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (2021/03334280 in view of Jain et al. (2016/0170065).
With respect to claims 6 and 10, Wang et al. teaches all that is claimed in the above rejection of claim 4, but remains silent regarding the method further comprising: performing calculations to identify an error value by: subtracting values forecasted data identified by operation of a computer model from portions the wellbore measurement data to create a set of difference values; squaring the each of the set of difference values; and generating a sum of the set of squared difference values.
Jain et al. teaches a machine learning process that includes the claimed sum of squared errors process between actual measurements and a theoretical model; [0026].
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the machine learning process to include the sum of squared error process, as taught by Jain et al., because Jain et al. teaches such a modification allows to for the machine learning process to evaluate how well the system model predictions match real-world measurements, thereby improving the accuracy of the machine learning process of Wang et al.
With respect to claims 7 and 11, Wang et al. as modified by Jain et al. teaches the method wherein the generated sum also includes a set of weighted fitness estimate values (as Jain et al. teaches each measurement is weight by squared noise, as Jain et al. teaches λ being a weighted sum; [0043]).
With respect to claims 8 and 12, Wang et al. as modified by Jain et al. teaches the method iteratively applying a data misfit gradient equation and a prior knowledge constraint equation to identify the values of the forecasted data (as Jain et al. teaches using iterative convex optimization algorithms, first and second example data sets, to identify the values of the forecasted data; [0049]).
With respect to claim 9, Wang et al. as modified by Jain et al. teaches the method Wang et al. teaches the method further comprising: generating a mapping of a space that associates the constrained machine learning process data with known wellbore properties (as Wang et al. teaches the machine learning analysis can be performed across adjacent depths, or zones, in order to provide a more robust analysis of the borehole environment and the like, for example, mapping out different zones like a washout zone at different depths, mapping out a log of the scale factors to be displayed to a user in order to inform the user of which depths are of what data quality as well as which depths are associated with partial or total model-based processing. In some aspects, this may inform a user of where borehole complexity is impacting (e.g., decreasing) data quality; [0053]).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (2021/03334280 in view of Wilson et al. (WO 2015/088563A1).
With respect to claim 14, Wang et al. teaches all that is claimed in the above rejection of claim 13, but remains silent regarding wherein the bandwidth is limited by applying a bandpass filter on the portion of the wellbore measurement data.
Wilson et al. teaches at block 106 applying a bandpass filter to reduce noise from sensor data from a wellbore.
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the data processing of Wang et al. to include the bandpass filter taught by Wilson because such a modification ensures noise does not negatively affect the accuracy of a model of the subsurface formation, thereby improving the overall accuracy of Wang et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Strack (2008/0071709A1) which teaches using a trained neural network for branch modeling.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Meier can be reached at 571-272-2149. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853