DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7, 8-14, 15-20 are directed to a method, apparatus and system of training a ML model. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 8 and 15 these claims recite
obtaining, using at least one hardware processor, historical data associated with operational suspension events corresponding to respective locations; training, using at least one hardware processor, a machine learning model to predict a suspension time period using a training dataset comprising the historical data associated with operational suspension events; and predicting, using the at least one hardware processor, a suspension time period corresponding to a location using the trained machine learning model.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0032]-[0038], etc. Fig. 3. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 8 and 15 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 8 and 15: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). Further the claimed invention appears to be something that can be performed by head and hand (Gottschalk v. Benson). The claimed solution is not necessarily rooted in computer technology in order to overcome a problem (DDR v. Hotels.com). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of training, using at least one hardware processor, a machine learning model to predict a suspension time period using a training dataset comprising the historical data associated with operational suspension events; and predicting, using the at least one hardware processor, a suspension time period corresponding to a location using the trained machine learning model. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2-3, 9-10, 16-17
Claim 2-3, 9-10, 16-17 merely recite other additional elements that define the training dataset which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 11, 18
Claim 4, 11, 18 merely recite other additional elements that define the evaluating the ML model and retraining it which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 12, 19
Claim 5, 12, 19 merely recite other additional elements that define planning operation based on predicted time period which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 13, 20
Claim 6, 13, 20 merely recite other additional elements that define avoiding operation based on predicted time period which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 14
Claim 7, 14 merely recite other additional elements that define historical data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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.
Claims 1-3, 5-10, 12-17, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Karli et al. (Karli) US 11436777
In regard to claim 1, Karli disclose A computer-implemented method for predicting weather risk using artificial intelligence, the method comprising: (abstract, col. 1, line 42-col. 3, line 7, predicting hazard including weather using AI)
obtaining, using at least one hardware processor, historical data associated with operational suspension events corresponding to respective locations; (abstract, col. 1, line 42-col. 3, line 37, col. 5, line 20-col. 6, line 50, obtain, using the processor, hazard data related to hazard events corresponding to geographic locations)
training, using at least one hardware processor, a machine learning model to predict a suspension time period using a training dataset comprising the historical data associated with operational suspension events; (col. 1, line 42-col. 3, line 37, col. 5, line 20-col. 6, line 50, col. 9, line 57- col. 11, line 18, training a ML model to predict a hazard time period using a training dataset with obtained data related to the hazard events)
and
predicting, using the at least one hardware processor, a suspension time period corresponding to a location using the trained machine learning model. (col. 1, line 42-col. 3, line 37, col. 5, line 20-col. 6, line 50, col. 9, line 57- col. 11, line 18, predict a hazard time period corresponding to a location using the trained ML model)
In regard to claim 2, Karli disclose The computer implemented method of claim 1, Karli disclose wherein the training dataset comprises historical suspension time periods labeled by respective dates and respective locations. (col. 9, line 57- col. 11, line 18, the training dataset include historical hazard time periods annotated by the dates and corresponding locations)
In regard to claim 3, Karli disclose The computer implemented method of claim 1,
Karli disclose wherein the training dataset comprises historical suspension time periods and corresponding weather conditions. (col. 9, line 57- col. 11, line 18, the training dataset include historical hazard time periods annotated by the dates and corresponding weather conditions)
In regard to claim 5, Karli disclose The computer implemented method of claim 1,
Karli disclose comprising planning oil and gas operations based on the predicted suspension time period. (col. 11, line 19- col. 18, line 58, cause the operator to take appropriate action based on the predicted hazard time period related to the oil and gas)
In regard to claim 6, Karli disclose The computer implemented method of claim 1,
Karli disclose comprising avoiding lifting tasks in oil and gas operation planning during the predicted suspension time period. (col. 8, line 34-col. 56, col. 11, line 19- col. 18, line 58, cause the operator to take appropriate action based on the predicted hazard time period related to the oil and gas, “notify technicians of location(s) of existing equipment or other infrastructure that may pose a danger to the technicians”)
In regard to claim 7, Karli disclose The computer implemented method of claim 1,
Karli disclose wherein the historical data is pre-processed. (col. 9, line 57- col. 11, line 18, historical hazard data is corresponding to a particular geographic location and at times with certain conditions etc. therefore it is preprocessed)
In regard to claims 8-10, 12-14, claims 8-10, 12-14 are apparatus claims corresponding to the method claims 1-3, 5-7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 5-7.
In regard to claims 15-17, 19-20, claims 15-17, 19-20 are system claims corresponding to the method claims 1-3, 5-6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-3, 5-6.
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.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Karli et al. (Karli) US 11436777 in view of Nistala et al. (Nistala) US 2022/0214474
In regard to claim 4, Karli disclose The computer implemented method of claim 1,
But Karli fail to explicitly disclose “comprising evaluating the trained machine learning model by determining a mean absolute percentage error (MAPE) of the trained machine learning model, and re-training the trained machine learning model when the MAPE satisfies a predetermined threshold.”
Nistala disclose comprising evaluating the trained machine learning model by determining a mean absolute percentage error (MAPE) of the trained machine learning model, and re-training the trained machine learning model when the MAPE satisfies a predetermined threshold. ([0034]-[0037] determine the performance of the model based on MAPE and re-train the model when the MQI (MAPE) is below a threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Nistala‘s ML model into Karli’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Nistala‘s retraining the ML model based a condition would help to provide ML re-train trigger condition into Karli’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing ML retraining trigger condition would help to improve accuracy of prediction of the ML model.
In regard to claim 11, claim 11 is an apparatus claims corresponding to the method claim 4 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 4.
In regard to claim 18, claim 18 is a system claim corresponding to the method claim 4 above and, therefore, are rejected for the same reasons set forth in the rejections of claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20240078289 A1 2024-03-07 GOLLAPUDI et al.
TESTING AND BASELINING A MACHINE LEARNING MODEL AND TEST DATA
GOLLAPUDI et al. disclose A device may receive a machine learning model, training data, and test data, and may perform a unit test on the machine learning model to generate unit test results. The device may perform regression tests on the machine learning model, with the training data and the test data, to calculate model scores, create graphs, determine inference delays, and identify missing points for the machine learning model. The device may perform scale and longevity tests on the machine learning model, with the training data and the test data, to identify additional missing points and calculate a resource utilization for the machine learning model. The device may update the machine learning model, to generate an updated machine learning model, based on the unit test results, the model scores, the graphs, the inference delays, the missing points, the additional missing points, or the resource utilization… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143