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 .
Claims 1-20 are presented for examination.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim(s) 1-9 is/are method type claim. Claim(s) 10-18 is/are system type claim(s). Claim(s) 19, 20 is/are product type claim(s). Therefore, claims 1-20 is/are directed to either a process, machine, manufacture or composition of matter.
Independent claim(s):
Step 2A Prong 1:
Regarding claim(s) 1, 10 and 19, this/these claim(s) recite(s)
selecting a metric predictor model,
determining that a difference between the predicted value and the current value meets a reporting criterion.
The above limitations of selecting and determining appear to be practically implementable in the human mind and is understood to be a recitation of a mental process – a user can mentally select a model and determine whether a difference between two values exceeds a threshold.
Step 2A Prong 2:
Regarding claim(s) 1, 10 and 19, this judicial exception is not integrated into a practical application.
Additional elements:
Regarding claim(s) 10 and 19, this/these claim(s) recite(s) processor and medium to perform the step of selecting and determining (mere instructions stored in a generic memory component to apply the exception using a generic computer component);
Regarding claim(s) 1, 10 and 19, this/these claim(s) further recite(s)
a metric predictor model for predicting values of a data metric, the metric predictor model trained using ... data related to the data metric (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
obtaining a predicted value of the data metric using the metric predictor model (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction);
obtaining a .... value .... using data other than the historical data (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Examiner’s note: the obtaining is recited at a high level of generality and could constitute mere receiving of transmitted information);
historical data related to the data metric, current value of the data metric using data other than the historical data (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
in response to determining that the difference meets the reporting criterion, outputting a notification descriptive of the difference (Adding insignificant extra-solution activity to the judicial exception (mere data output of the abstract idea)- see MPEP 2106.05(g)).
The additional element(s) as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are directed to an abstract idea.
Step 2B:
Regarding claim(s) 1, 10 and 19, this/these claim(s) do/does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Regarding claim(s) 10 and 19, this/these claim(s) recite(s) processor and medium to perform the step of selecting and determining (mere instructions stored in a generic memory component to apply the exception using a generic computer component);
Regarding claim(s) 1, 10 and 19, this/these claim(s) further recite(s)
a metric predictor model for predicting values of a data metric, the metric predictor model trained using ... data related to the data metric (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
obtaining a predicted value of the data metric using the metric predictor model (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction);
obtaining a .... value .... using data other than the historical data (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Furthermore, MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data” buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer);
historical data related to the data metric, current value of the data metric using data other than the historical data (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
in response to determining that the difference meets the reporting criterion, outputting a notification descriptive of the difference ((These limitations appear to represents extrasolution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output, Furthermore, these limitations directed towards outputting information determined by the abstract idea, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed outputting step is well-understood, routine, conventional activity).
The additional element(s) as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are not patent eligible.
Step 2A Prong 1, Dependent claims:
Regarding claim(s) 2 and 11, this/these claim(s) recite(s) generating time series data of the data metric using the historical data;
Regarding claim(s) 3 and 12, this/these claim(s) recite(s) determining that none of the trained at least the subset of the one or more models provides an expected prediction of the second data metric;
Regarding claim(s) 4, 13 and 20, this/these claim(s) recite(s) determining that the dimension is invalid as a candidate contributor dimension;
Regarding claim(s) 5 and 14, this/these claim(s) recite(s) wherein the dimension is determined to be invalid based on a cardinality of the dimension;
Regarding claim(s) 6 and 15, this/these claim(s) recite(s) wherein the dimension is determined to be invalid based on a skewness of data that include the dimension;
Regarding claim(s) 7 and 16, this/these claim(s) recite(s) determining that at least one prediction value of the data metric obtained using the metric predictor model deviates from at least one corresponding current value of the data metric;
Regarding claim(s) 8 and 17, this/these claim(s) recite(s) determining a number of notifications descriptive of differences to be output by the metric predictor model using training data.
The above limitations appear to be practically implementable in the human mind and is understood to be a recitation of a mental process.
Step 2A Prong 2, Dependent claims:
Regarding claim(s) 2 and 11, this/these claim(s) recite(s) wherein the metric predictor model is trained using the historical data related to the data metric by steps comprising: ... obtaining the metric predictor model using the time series data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data).
Regarding claim(s) 3 and 12, this/these claim(s) recite(s)
wherein the data metric is a first data metric (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
receiving a second data metric (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Examiner’s note: the obtaining is recited at a high level of generality and could constitute mere receiving of transmitted information);
training at least a subset of one or more models using historical data related to the second data metric (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data); and
outputting a notification indicating that predicting the second data metric using future data related to the second data metric will not be performed (Adding insignificant extra-solution activity to the judicial exception (mere data output of the abstract idea)- see MPEP 2106.05(g)).
Regarding claim(s) 4, 13 and 20, this/these claim(s) recite(s)
wherein the data metric is a first data metric, wherein the second data metric comprises a dimension (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
receiving a second data metric (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Examiner’s note: the obtaining is recited at a high level of generality and could constitute mere receiving of transmitted information);
outputting a notification indicating that the second data metric cannot be predicted based determining that the dimension is invalid (Adding insignificant extra-solution activity to the judicial exception (mere data output of the abstract idea)- see MPEP 2106.05(g)).
Regarding claim(s) 7 and 16, this/these claim(s) recite(s) re-training the metric predictor model using at least the data other than the historical data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data).
Regarding claim(s) 9, this/these claim(s) recite(s) receiving, from a user, an indication (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Examiner’s note: the obtaining is recited at a high level of generality and could constitute mere receiving of transmitted information),
indication whether to use the metric predictor model to predict the data metric (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)).
The additional element(s) as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are directed to an abstract idea.
Step 2B, Dependent claims:
Regarding claim(s) 2 and 11, this/these claim(s) recite(s) wherein the metric predictor model is trained using the historical data related to the data metric by steps comprising: ... obtaining the metric predictor model using the time series data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data).
Regarding claim(s) 3 and 12, this/these claim(s) recite(s)
wherein the data metric is a first data metric (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
receiving a second data metric (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Furthermore, MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data” buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer);
training at least a subset of one or more models using historical data related to the second data metric (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data); and
outputting a notification indicating that predicting the second data metric using future data related to the second data metric will not be performed (Adding insignificant extra-solution activity to the judicial exception (mere data output of the abstract idea)- see MPEP 2106.05(g). Furthermore, these limitations directed towards outputting information determined by the abstract idea, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed outputting step is well-understood, routine, conventional activity).
Regarding claim(s) 4, 13 and 20, this/these claim(s) recite(s)
wherein the data metric is a first data metric, wherein the second data metric comprises a dimension (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h));
receiving a second data metric (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Furthermore, MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data” buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer);
outputting a notification indicating that the second data metric cannot be predicted based determining that the dimension is invalid (Adding insignificant extra-solution activity to the judicial exception (mere data output of the abstract idea)- see MPEP 2106.05(g). Furthermore, these limitations directed towards outputting information determined by the abstract idea, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed outputting step is well-understood, routine, conventional activity).
Regarding claim(s) 7 and 16, this/these claim(s) recite(s) re-training the metric predictor model using at least the data other than the historical data (Adding the words “apply it” (or an equivalent) with the judicial exception, or 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 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data).
Regarding claim(s) 9, this/these claim(s) recite(s) receiving, from a user, an indication (Adding insignificant extra-solution activity (receiving information) to the judicial exception - see MPEP 2106.05(g). Furthermore, MPEP 2106.05(d)(II) indicate that merely “Receiving or transmitting data” buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer),
indication whether to use the metric predictor model to predict the data metric (These limitations appear to be directed to the specification of information to be used, and is understood to be generally linking the use of the judicial exception to a particular technological environment or field of use, which is not indicative of integration into a practical application. MPEP 2106.05(h)).
The additional element(s) as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Therefore, the claim(s) is/are not patent eligible.
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 1-6, 8-15, 17-20, are rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20230136094 A1), in view of Barkan (US 20230418909 A1).
Regarding claim 1, Du teaches a method, comprising (Du [9, 82-86] method to perform processes, processor executes instructions stored in non-transitory memory):
selecting a metric predictor model for predicting values of a data metric, the metric predictor model trained using historical data related to the data metric (Du [10, 41, 79] models are used for metric service, model for determining data metrics for stored (historical) datasets may be used at appropriate time (selected), model may be trained using stored datasets);
obtaining a predicted value of the data metric using the metric predictor model; obtaining a current value of the data metric using data other than the historical data; determining that a ...score based on... the predicted value and the current value meets a reporting criterion (Du [3, 6, 9, 10, 51, 79] trained model predicts value(s) of data metric(s) and ground truth for new (current) data is obtained, score of prediction performance is determined and determination is made whether the score meets metric threshold (reporting criterion); and
in response to determining that the ...score...meets the reporting criterion, outputting a notification descriptive of the difference (Du [43] based on whether or not score(s) meets metric threshold(s)- notification(s) (alerts) are displayed to use).
Du does not specifically teach a difference between the predicted value and the current value.
However Barkan teaches obtaining a predicted value of the data metric using the metric predictor model; obtaining a current value of the data metric using data other than the historical data (Barkan [25-27, 34, 35] model predictions for data metric value(s) (number of instances of dog) are made, predictions are compared to actual data metric values(s) (actual instances of dog), Barkan [21] predictions may be for real-time data) ;
determining that a difference between the predicted value and the current value meets a reporting criterion (Barkan [27, 28, 34, 35, 17] difference between actual and predicted metric (recall) is determined, difference is compared to metric threshold (reporting criterion), Barkan [28] using recall to determine quality of predictions provides higher sensitivity by reducing the false negatives).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Barkan of obtaining a predicted value of the data metric using the metric predictor model; obtaining a current value of the data metric using data other than the historical data; determining that a difference between the predicted value and the current value meets a reporting criterion, into the invention suggested by Du,
so that determining that the ...score...meets the reporting criterion, comprises
determining that a difference between the predicted value and the current value meets a reporting criterion, as taught by Barkan;
since both inventions are directed towards analyzing predicted and current values of data metric(s) against reporting criterion, and incorporating the teaching of Barkan into the invention suggested by Du would provide the added advantage of providing higher sensitivity by reducing the false negatives, and the combination would perform with a reasonable expectation of success (Barkan [25-28, 34, 35, 21, 17]).
Regarding claim 2, Du and Barkan teach the invention as claimed in claim 1 above.
Du further teaches wherein the metric predictor model is trained using the historical data related to the data metric by steps comprising: generating time series data of the data metric using the historical data; and obtaining the metric predictor model using the time series data (Du [3, 47, 78-80] data quality metric(s) for stored data may be converted to time series and monitored, based on monitoring adjustments (obtaining model predictor model) may be made to how model decodes new data).
Regarding claim 3, Du and Barkan teach the invention as claimed in claim 1 above.
Du further teaches wherein the data metric is a first data metric, further comprising: receiving a second data metric (Du [46, 52] multiple metrics are calculated for data);
training at least a subset of one or more models using historical data related to the second data metric(Du [10] model(s) is/are trained using stored data) ;
determining that none of the trained at least the subset of the one or more models provides an expected prediction of the second data metric; and outputting a notification indicating that predicting the second data metric using future data related to the second data metric will not be performed (Du [43, 47, 48] Figs. 4A, 4B determination is made whether models are good for each (second) metric based on threshold, user may be notified (dashboard alert(s)) that threshold is not met, user can use notification to specify whether or not to use model for metric associated with alert).
Regarding claim 4, Du and Barkan teach the invention as claimed in claim 1 above.
Du further teaches wherein the data metric is a first data metric, further comprising: receiving a second data metric, wherein the second data metric comprises a dimension (Du [10, 46, 52, 55] multiple metrics are calculated for data as a data quality metric matrix “Q” (dimension(s)));
determining that the dimension is invalid as a candidate contributor dimension; and
outputting a notification indicating that the second data metric cannot be predicted based determining that the dimension is invalid (Du [43, 47, 48] Figs. 4A, 4B determination is made whether models are good for each (second) metric (dimension) based on threshold, user may be notified (dashboard alert(s)) that threshold is not met, user can use notification to specify whether or not to use model for metric associated with alert).
Regarding claim 5, Du and Barkan teach the invention as claimed in claim 4 above.
Du further teaches wherein the dimension is determined to be invalid based on a cardinality of the dimension (Du [48] invalidity may be based on cardinality).
Regarding claim 6, Du and Barkan teach the invention as claimed in claim 4 above.
Du further teaches wherein the dimension is determined to be invalid based on a skewness of data that include the dimension (Du [48] invalidity may be based on skewness).
Regarding claim 8, Du and Barkan teach the invention as claimed in claim 1 above.
Du further teaches determining a number of notifications descriptive of differences to be output by the metric predictor model using training data (Du [10, 37, 43] alerts to display may based on metrics not meeting their respective thresholds).
Regarding claim 9, Du and Barkan teach the invention as claimed in claim 1 above.
Du further teaches receiving, from a user, an indication of whether to use the metric predictor model to predict the data metric (Du [43, 44, 47, 79], user can accept/ adjust which metrics to use for model(s)).
Claim 10 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale.
Du further teaches a device, comprising: a memory; and a processor, the processor configured to execute instructions stored in the memory (Du [9, 82-86] method to perform processes, processor executes instructions stored in non-transitory memory).
Claim(s) 11-15, 17, 18, is/are dependent on claim 10 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 2-6, 8,9, respectively, and is/are rejected under the same rationale.
Claim 19 is directed towards a medium storing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale. Du further teaches a non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations (Du [9, 82-86] method to perform processes, processor executes instructions stored in non-transitory memory).
Claim(s) 20 is/are dependent on claim 19 above, is/are directed towards a medium storing instructions similar in scope to the instructions performed by the method of claim(s) 4 respectively, and is/are rejected under the same rationale.
Claims 7, 16, are rejected under 35 U.S.C. 103 as being unpatentable over Du (US 20230136094 A1) in view of Barkan (US 20230418909 A1), and further in view of Ghanta (US 20200034665 A1).
Regarding claim 7, Du and Barkan teach the invention as claimed in claim 1 above.
Du does not specifically teach determining that at least one prediction value of the data metric obtained using the metric predictor model deviates from at least one corresponding current value of the data metric; and re-training the metric predictor model using at least the data other than the historical data.
However Barkan teaches determining that at least one prediction value of the data metric obtained using the metric predictor model deviates from at least one corresponding current value of the data metric (Barkan [25-27, 34, 35] model predictions for data metric value(s) (number of instances of dog) are made, predictions are compared to actual data metric values(s) (actual instances of dog), Barkan [21] predictions may be for real-time data, Barkan [27, 28, 34, 35, 17] difference between actual and predicted metric (recall) is determined, difference is compared to metric threshold (reporting criterion)).
Du and Barkan do not specifically teach re-training the metric predictor model using at least the data other than the historical data
However Ghanta teaches determining that at least one prediction value of ...a... data metric obtained using ...a... metric predictor model deviates from at least one corresponding current value of the data metric; re-training the metric predictor model using at least the data other than the historical data (Ghanta [91, 92, 109] difference between predicted value and actual value of data metric(s) for inference data (other data) is determined (deviation), if difference does not satisfy threshold then model may be retrained using other data, Ghanta [38] retraining a model when it is not a good fit for a data set, results in generating a more accurate machine learning model).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Ghanta of determining that at least one prediction value of ...a... data metric obtained using ...a... metric predictor model deviates from at least one corresponding current value of the data metric; re-training the metric predictor model using at least the data other than the historical data, into the invention suggested by Du and Barkan; since both inventions are directed towards analyzing predicted versus actual data metric values provided by a model, and incorporating the teaching of Ghanta into the invention suggested by Du and Barkan would provide the added advantage of generating a more accurate machine learning model when a model it is not a good fit for a data set, and the combination would perform with a reasonable expectation of success (Ghanta [91, 92, 109, 38]).
Claim(s) 16 is/are dependent on claim 10 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 7 respectively, and is/are rejected under the same rationale.
Conclusion
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SANCHITA ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146
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