Detailed Action
Status of Claims
Claims 1-20 are currently pending and addressed below.
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 § 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 towards an abstract idea.
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, 8, and 15 are directed to a method (process) and systems (machine or manufacture), respectively. 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
The claim(s) recite(s) abstract limitations including:
Claim 1: storing…; splitting the database into a training dataset and an evaluation data set; evaluating the space time temperature probability models to ensure a performance level above a model accuracy threshold; generating, using the space time temperature probability models, a predicted temperature profile for a new well
Claim 8: storing…; splitting the database into a training dataset and an evaluation data set; evaluating the space time temperature probability models to ensure a performance level above a model accuracy threshold; generating, using the space time temperature probability models, a predicted temperature profile for a new well
Claim 15: storing…; splitting the database into a training dataset and an evaluation data set; evaluating the space time temperature probability models to ensure a performance level above a model accuracy threshold; generating, using the space time temperature probability models, a predicted temperature profile for a new well
These limitations, as drafted, are abstract mental processes that, under the broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of generic computing elements and/or sensors does not take the claim out of the mental process grouping. Thus the claim recites an abstract idea.
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.
Claims 1, 8, 15 recites the additional element of:
Claim 1:
Collecting…; temperature data corresponding to historical drilling operations of a well; training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well; training, using the evaluation dataset, the space time temperature probability models are considered an insignificant extra solution activity;
Database; generate a predicted temperature profile for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data; which is recited at a high level of generality and amounts to no more than mere instructions to apply the exception
Claim 8:
Collecting…; temperature data corresponding to historical drilling operations of a well; training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well; training, using the evaluation dataset, the space time temperature probability models are considered an insignificant extra solution activity;
Database; generate a predicted temperature profile for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data; which is recited at a high level of generality and amounts to no more than mere instructions to apply the exception
Claim 15:
Collecting…; temperature data corresponding to historical drilling operations of a well; training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well; training, using the evaluation dataset, the space time temperature probability models are considered an insignificant extra solution activity;
Database; generate a predicted temperature profile for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data; which is recited at a high level of generality and amounts to no more than mere instructions to apply the exception.
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).
Claim 1:
As discussed above, Collecting…; temperature data corresponding to historical drilling operations of a well; training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well; training, using the evaluation dataset, the space time temperature probability models; step is considered an insignificant extra-solution activity as the limitations do not amount to more than mere data gathering. Given the generality of the data acquisition (collecting and training), and the type of data collected (data sets and probability models), these limitations do not contain significantly more to provide a practical application (see MPEP 2106.05(g)) As noted in Electric Power Group, selecting information, based on types of information and availability of information for collection, analysis, and display is considered insignificant extra solution activity (see MPEP 2106.05(g)). Additionally, the Symantec, TLI, OIP Techs. And buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is considered insignificant extra solution activity
With respect to a database, this element is recited at a high level of generality such amounts to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, the specification fails to disclose that these elements are anything other than a generic database. (see MPEP2106.05(f)).
Furthermore, generate a predicted temperature profile for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data; merely amounts to “apply it”. The reciting of claim limitations that attempt to cover any solution (i.e. generate a predicted temperature profile) to an identified trend (i.e. temperature trends of the remaining portion of the temperature data) with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (i.e. how is the temperature profile being generated and how are the assumptions being applied) does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f)(1)
Claim 8
As discussed above, Collecting…; temperature data corresponding to historical drilling operations of a well; training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well; training, using the evaluation dataset, the space time temperature probability models; step is considered an insignificant extra-solution activity as the limitations do not amount to more than mere data gathering. Given the generality of the data acquisition (collecting and training), and the type of data collected (data sets and probability models), these limitations do not contain significantly more to provide a practical application (see MPEP 2106.05(g)) As noted in Electric Power Group, selecting information, based on types of information and availability of information for collection, analysis, and display is considered insignificant extra solution activity (see MPEP 2106.05(g)). Additionally, the Symantec, TLI, OIP Techs. And buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is considered insignificant extra solution activity
With respect to a database, this element is recited at a high level of generality such amounts to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, the specification fails to disclose that these elements are anything other than a generic database. (see MPEP2106.05(f)).
Furthermore, generate a predicted temperature profile for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data; merely amounts to “apply it”. The reciting of claim limitations that attempt to cover any solution (i.e. generate a predicted temperature profile) to an identified trend (i.e. temperature trends of the remaining portion of the temperature data) with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (i.e. how is the temperature profile being generated and how are the assumptions being applied) does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f)(1)
Claim 15
As discussed above, Collecting…; temperature data corresponding to historical drilling operations of a well; training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well; training, using the evaluation dataset, the space time temperature probability models step is considered an insignificant extra-solution activity as the limitations do not amount to more than mere data gathering. Given the generality of the data acquisition (collecting and training), and the type of data collected (data sets and probability models), these limitations do not contain significantly more to provide a practical application (see MPEP 2106.05(g)) As noted in Electric Power Group, selecting information, based on types of information and availability of information for collection, analysis, and display is considered insignificant extra solution activity (see MPEP 2106.05(g)). Additionally, the Symantec, TLI, OIP Techs. And buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is considered insignificant extra solution activity
With respect to a database, this element is recited at a high level of generality such amounts to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additionally, the specification fails to disclose that these elements are anything other than a generic database. (see MPEP2106.05(f)).
Furthermore, generate a predicted temperature profile for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data; merely amounts to “apply it”. The reciting of claim limitations that attempt to cover any solution (i.e. generate a predicted temperature profile) to an identified trend (i.e. temperature trends of the remaining portion of the temperature data) with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (i.e. how is the temperature profile being generated and how are the assumptions being applied) does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f)(1)
Therefore, the claims does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
The various metrics of claims 2, 9, 16 merely narrow the recitation of the specific variables and data limitations are insufficient as “merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from §101 undergirds the information-based category of abstract ideas," (See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016)). Similar to claim 1, 8 and 15 this recitation does not provide a practical application of the abstract idea, and is not significantly more.
The various metrics of claims 3, 10, 17 merely amounts to “apply it”. The reciting of claim limitations that attempt to cover any solution (i.e. including various models) to an identified problem (i.e. statistical analysis) with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (i.e. how are the models included in the space time temperature probability models) does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f)(1)
The various metrics of claims 4, 7, 11, 14, and 18 merely narrow the recitation of the specific variables and data limitations are insufficient as “merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from §101 undergirds the information-based category of abstract ideas," (See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016)). Similar to claim 1, 8 and 15 this recitation does not provide a practical application of the abstract idea, and is not significantly more.
The various metrics of claims 5, 12, 19 merely amounts to “apply it”. The reciting of claim limitations that attempt to cover any solution (i.e. retraining) to an identified problem (i.e. Triggering) with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (i.e. what is the means for the triggering? Is it a processor, user, instructions?) does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See MPEP 2106.05(f)(1)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhardwaj (US Pub No 20210406792) in view of Molla (US Pub No 20210062650)
Bhardwaj discloses in claim 1. A computer-implemented method, comprising:
collecting temperature data (Bhardwaj [0048] [0070] historical data collection into a database [0127] distributed temperature sensing data ) corresponding to historical drilling operations of a well (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data and drilling operations), and storing the collected temperature data in a database (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data);
splitting the database into a training dataset and an evaluation dataset (Bhardwaj [0048] [0070] data set is split into a training data set and a validation data set) dataset. the training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data and drilling operations) and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well (Bhardwaj [0048] historical data collection into a database [0070] data set is split into a training data set and a validation data set [0127] distributed temperature sensing data and drilling operations);
generating, using the training dataset, space-time-temperature probability models (Bhardwaj [0048]-[0049] & [0070]-[0071] generating of a model utilizing the training data set);
training the space-time-temperature probability models (Bhardwaj [0048]-[0049] & [0070]-[0071] training the applied data to generate a model) to generate a predicted temperature profile (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets) for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets);
evaluating the space-time-temperature probability models to ensure a performance level above a model accuracy threshold (Bhardwaj [0048] [0070] validation data utilized to validate the training model);
and generating, using the space-time-temperature probability models, a predicted temperature profile for a new well (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets). Bhardwaj does not disclose using the evaluation dataset to further train the generated model.
However, Molla teaches a method of predictive model generation, training and validation: training, using the evaluation dataset (Molla [0067]-[0068] Fig 11; 425 data partitioning, 430 training data, 435 validation data built into a classification model and further trained and optimized at elements 455 and 456 to develop a statistical prediction model)
It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to have modified the model of Bhardwaj to include using the validation data (evaluation data set) to further train the predictive model as taught by Molla for the purpose of training and optimizing a statistical prediction model (Molla [0067]-[0068] Fig 11; 425 data partitioning, 430 training data, 435 validation data built into a classification model and further trained and optimized at elements 455 and 456 to develop a statistical prediction model).
Bhardwaj et al discloses in claim 2. The computer-implemented method of claim 1, further comprising: generating, for display in a user interface, a plot of the predicted temperature profile for the new well (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets)
Bhardwaj et al discloses in claim 3. The computer-implemented method of claim 1, wherein the space-time-temperature probability models include a kriging spatial model (Bhardwaj [0070]-[0071] kriging technique), a time series forecasting model (Bhardwaj [0070]-[0071] various statistical models for prediction (forcasting) relying on historical data from a plurality of wells), and a probabilistic meta model (Bhardwaj [0070]-[0071] various statistical models built on similar parameters).
Bhardwaj et al discloses in claim 4. The computer-implemented method of claim 1, further comprising: cleaning the temperature data for validity before splitting the database into the training dataset and the evaluation dataset (Bhardwaj [0065] [0157] data processing and cleaning of data).
Bhardwaj et al discloses in claim 5. The computer-implemented method of claim 1 but does not disclose the use of retraining. However, Molla teaches that the work flow includes: triggering, based on triggering criteria, re-training of the space-time-temperature probability models (Molla [0070] retraining of the model based on specific fields);
and re-training the space-time-temperature probability models over time (Molla [0070] utilizing the built model to provide further training data to enrich subsequent iterations of the models)
It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to have modified Bhardwaj to include using the built model to further train the predictive model as taught by Molla for the purpose of enriching subsequent iterations of the models.
Bhardwaj et al discloses in claim 6. The computer-implemented method of claim 5, wherein the triggering criteria includes an occurrence of collecting new oil/gas surveys (Molla [0070] retraining of the model based on specific fields)
Bhardwaj et al discloses in claim 7. The computer-implemented method of claim 1, further comprising: cleaning the temperature data in the database for validity before splitting the database into the training dataset and the evaluation dataset (Bhardwaj [0065] [0157] data processing and cleaning of data).
Bhardwaj et al discloses in claim 8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
collecting temperature data (Bhardwaj [0048] [0070] historical data collection into a database [0127] distributed temperature sensing data ) corresponding to historical drilling operations of a well (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data and drilling operations), and storing the collected temperature data in a database (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data);
splitting the database into a training dataset and an evaluation dataset (Bhardwaj [0048] [0070] data set is split into a training data set and a validation data set) dataset. the training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data and drilling operations) and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well (Bhardwaj [0048] historical data collection into a database [0070] data set is split into a training data set and a validation data set [0127] distributed temperature sensing data and drilling operations);
generating, using the training dataset, space-time-temperature probability models (Bhardwaj [0048]-[0049] & [0070]-[0071] generating of a model utilizing the training data set);
training the space-time-temperature probability models (Bhardwaj [0048]-[0049] & [0070]-[0071] training the applied data to generate a model) to generate a predicted temperature profile (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets) for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets);
evaluating the space-time-temperature probability models to ensure a performance level above a model accuracy threshold (Bhardwaj [0048] [0070] validation data utilized to validate the training model);
and generating, using the space-time-temperature probability models, the predicted temperature profile for the new well (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets). Bhardwaj does not disclose using the evaluation dataset to further train the generated model.
However, Molla teaches a method of predictive model generation, training and validation: training, using the evaluation dataset (Molla [0067]-[0068] Fig 11; 425 data partitioning, 430 training data, 435 validation data built into a classification model and further trained and optimized at elements 455 and 456 to develop a statistical prediction model)
It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to have modified the model of Bhardwaj to include using the validation data (evaluation data set) to further train the predictive model as taught by Molla for the purpose of training and optimizing a statistical prediction model (Molla [0067]-[0068] Fig 11; 425 data partitioning, 430 training data, 435 validation data built into a classification model and further trained and optimized at elements 455 and 456 to develop a statistical prediction model).
Bhardwaj et al discloses in claim 9. The non-transitory, computer-readable medium of claim 8, the operations further comprising: generating, for display in a user interface, a plot of the predicted temperature profile for the new well (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets)
Bhardwaj et al discloses in claim 10. The non-transitory, computer-readable medium of claim 8, wherein the space-time-temperature probability models include a kriging spatial model (Bhardwaj [0070]-[0071] kriging technique), a time series forecasting model (Bhardwaj [0070]-[0071] various statistical models for prediction (forcasting) relying on historical data from a plurality of wells), and a probabilistic meta model (Bhardwaj [0070]-[0071] various statistical models built on similar parameters).
Bhardwaj et al discloses in claim 11. The non-transitory, computer-readable medium of claim 8, the operations further comprising: cleaning the temperature data for validity before splitting the database into the training dataset and the evaluation dataset (Bhardwaj [0065] [0157] data processing and cleaning of data).
Bhardwaj et al discloses in claim 12. The non-transitory, computer-readable medium of claim 8, but does not disclose the use of retraining. However, Molla teaches that the work flow includes: triggering, based on triggering criteria, re-training of the space-time-temperature probability models (Molla [0070] retraining of the model based on specific fields);
and re-training the space-time-temperature probability models over time (Molla [0070] utilizing the built model to provide further training data to enrich subsequent iterations of the models)
It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to have modified Bhardwaj to include using the built model to further train the predictive model as taught by Molla for the purpose of enriching subsequent iterations of the models.
Bhardwaj et al discloses in claim 13. The non-transitory, computer-readable medium of claim 12, wherein the triggering criteria includes an occurrence of collecting new oil/gas surveys (Molla [0070] retraining of the model based on specific fields)
Bhardwaj et al discloses in claim 14. The non-transitory, computer-readable medium of claim 8, the operations further comprising: cleaning the temperature data in the database for validity before splitting the database into the training dataset and the evaluation dataset (Bhardwaj [0065] [0157] data processing and cleaning of data).
Bhardwaj et al discloses in claim 15. A computer-implemented system, comprising:
one or more processors;
and a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising:
collecting temperature data (Bhardwaj [0048] [0070] historical data collection into a database [0127] distributed temperature sensing data ) corresponding to historical drilling operations of a well (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data and drilling operations), and storing the collected temperature data in a database (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data);
splitting the database into a training dataset and an evaluation dataset (Bhardwaj [0048] [0070] data set is split into a training data set and a validation data set) dataset. the training dataset comprising a portion of the temperature data corresponding to historical drilling operations of the well (Bhardwaj [0048] historical data collection into a database [0127] distributed temperature sensing data and drilling operations) and the evaluation dataset comprising a remaining portion of the temperature data corresponding to historical drilling operations of the well (Bhardwaj [0048] historical data collection into a database [0070] data set is split into a training data set and a validation data set [0127] distributed temperature sensing data and drilling operations);
generating, using the training dataset, space-time-temperature probability models (Bhardwaj [0048]-[0049] & [0070]-[0071] generating of a model utilizing the training data set);
training the space-time-temperature probability models (Bhardwaj [0048]-[0049] & [0070]-[0071] training the applied data to generate a model) to generate a predicted temperature profile (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets) for a new well based on assumptions comprising autocorrelation in temperature trends of the remaining portion of the temperature data (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets);
evaluating the space-time-temperature probability models to ensure a performance level above a model accuracy threshold (Bhardwaj [0048] [0070] validation data utilized to validate the training model);
and generating, using the space-time-temperature probability models, the predicted temperature profile for the new well (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets). Bhardwaj does not disclose using the evaluation dataset to further train the generated model.
However, Molla teaches a method of predictive model generation, training and validation: training, using the evaluation dataset (Molla [0067]-[0068] Fig 11; 425 data partitioning, 430 training data, 435 validation data built into a classification model and further trained and optimized at elements 455 and 456 to develop a statistical prediction model)
It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to have modified the model of Bhardwaj to include using the validation data (evaluation data set) to further train the predictive model as taught by Molla for the purpose of training and optimizing a statistical prediction model (Molla [0067]-[0068] Fig 11; 425 data partitioning, 430 training data, 435 validation data built into a classification model and further trained and optimized at elements 455 and 456 to develop a statistical prediction model).
Bhardwaj et al discloses in claim 16. The computer-implemented system of claim 15, the operations further comprising: generating, for display in a user interface, a plot of the predicted temperature profile for the new well (Bhardwaj [0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets)
Bhardwaj et al discloses in claim 17. The computer-implemented system of claim 15, wherein the space-time-temperature probability models include a kriging spatial model (Bhardwaj [0070]-[0071] kriging technique), a time series forecasting model (Bhardwaj [0070]-[0071] various statistical models for prediction (forcasting) relying on historical data from a plurality of wells), and a probabilistic meta model (Bhardwaj [0070]-[0071] various statistical models built on similar parameters).
Bhardwaj et al discloses in claim 18. The computer-implemented system of claim 15, the operations further comprising: cleaning the temperature data for validity before splitting the database into the training dataset and the evaluation dataset (Bhardwaj [0065] [0157] data processing and cleaning of data).
Bhardwaj et al discloses in claim 19. The computer-implemented system of claim 15, but does not disclose the use of retraining. However, Molla teaches that the work flow includes: triggering, based on triggering criteria, re-training of the space-time-temperature probability models (Molla [0070] retraining of the model based on specific fields);
and re-training the space-time-temperature probability models over time (Molla [0070] utilizing the built model to provide further training data to enrich subsequent iterations of the models)
It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to have modified Bhardwaj to include using the built model to further train the predictive model as taught by Molla for the purpose of enriching subsequent iterations of the models.
Bhardwaj et al discloses in claim 20. The computer-implemented system of claim 19, wherein the triggering criteria includes an occurrence of collecting new oil/gas surveys (Molla [0070] retraining of the model based on specific fields)
Response to Arguments
Applicants’ amendments and arguments filed 06/03/2026 have been fully considered but they are not persuasive.
In regards to the rejection under 35 USC 101 Examiner appreciates the amendments, however, the amendments do not overcome the current rejection as disclosed above. Examiner agrees with applicants arguments regarding the specification disclosing various algorithms (See section I of the arguments) that are not abstract i.e. kriging spatial model. However, the claim language fails to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., Kriging, LSTM, etc) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
For the same rationale as above, the arguments found in section II rely on information from the specification however, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In regards to the arguments regarding the rejection under 35 USC 103 Examiner respectfully disagrees Bhardwaj in view of Molla does not teach the newly amended limitations of claims 1, 8, and 15. As shown in the above rejection, Bhardwaj teaches building a 2d or 3d geospatial model ([0070] a 2d or 3d geospatial model is built utilizing the trained and validated data sets) based on the training and validated data sets comprising temperature data ([0048] historical data collection into a database [0070] data set is split into a training data set and a validation data set [0127] distributed temperature sensing data and drilling operations).
Therefore, for the reasons listed in the above rejection and the above response to arguments, Examiner does not find the amendments and arguments persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas D Wlodarski whose telephone number is (571)272-3970. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicole Coy can be reached at (571) 272-5405. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NICHOLAS D WLODARSKI/Examiner, Art Unit 3672
/Nicole Coy/Supervisory Patent Examiner, Art Unit 3672
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