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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 recites the limitation "by the at least one hardware processor" in lines. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the examiner assumes the at least one hardware processor is the hardware processor recited in claim 1.
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. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines (“2019 PEG”).
Step 1: Independent claims 1 (A system comprising…), 11(A computer-implemented method, comprising…), and 20(A non-transitory, computer-readable medium storing computer-readable instructions, that upon execution by at least one hardware processor, cause performance of operations, comprising…) are directed towards a system, a method, and a manufacture respectively. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine (i.e. system), manufacture, or composition of matter).
Claim 1
Step 2A, Prong 1: The claim recites, inter alia:
computing one or more past residuals by subtracting at least a part of the first prediction from at least one actual observation value of the variable;
This limitation is a mathematical calculation being a computation recited using the mathematical operation of subtraction. See MPEP 2106.04(a)(2)(I)(C).
combining the first prediction and the second prediction to generate a combined prediction;
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to calculate a combination of predictions. See MPEP 2106.04(a)(2)(III).
[…] generating one or more action recommendations […] based on the combined prediction.
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to think of an action that should be taken based on the calculated combined prediction.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
A system comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
at least one memory storing instructions;
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
a network interface; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
at least one hardware processor interoperably coupled with the network interface and the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
obtaining, using a second machine learning model trained on the one or more past residuals, a second prediction predicting a residual of the first machine learning model for a second time period, the first time period longer than the second time period
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
automatically … by the at least one hardware processor…
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A system comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
at least one memory storing instructions;
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
a network interface; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
at least one hardware processor interoperably coupled with the network interface and the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
obtaining, using a second machine learning model trained on the one or more past residuals, a second prediction predicting a residual of the first machine learning model for a second time period, the first time period longer than the second time period
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
automatically … by the at least one hardware processor…
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 2
Step 2A, Prong 1: There are no further judicial exceptions in this claim.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
training the second machine learning model using auto-regressive features of the one or more past residuals
This limitation is recited at a high level of generality and recites use of generic features of residuals to perform training of a generic machine learning model. Mere instructions to apply a judicial exception while using generic features of residuals to train a generic machine learning model cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training the second machine learning model using auto-regressive features of the one or more past residuals
This limitation is recited at a high level of generality and recites use of generic features of residuals to perform training of a generic machine learning model. Mere instructions to apply a judicial exception while using generic features of residuals to train a generic machine learning model is not significantly more than the judicial exception. See MPEP 2106.05(f)
Claim 3
Step 2A, Prong 1: The claim recites, inter alia:
subtracting the at least the part of the first prediction from the at least one actual observation value to generate a past residual.
This limitation recites a mathematical calculation to subtract one value from another value. See MPEP 2106.04(a)(2)(I)
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
obtaining the at least the part of the first prediction, wherein the at least the part of the first prediction is associated with a third time period, and the first time period comprises the third time period;
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
obtaining the at least one actual observation value of the variable for the third time period; and
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining the at least the part of the first prediction, wherein the at least the part of the first prediction is associated with a third time period, and the first time period comprises the third time period;
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
obtaining the at least one actual observation value of the variable for the third time period; and
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
Claim 5
Step 2A, Prong 1: The claim recites, inter alia:
adding the second prediction to the at least the part of first prediction.
This limitation recites a mathematical calculation to add one value to another value. See MPEP 2106.04(a)(2)(I)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 6
Step 2A, Prong 1: The claim recites, inter alia:
determining one or more Shapley values associated with the combined prediction; and
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to calculate Shapley values. See MPEP 2106.04(a)(2)(III)
determining that the one or more Shapley values satisfy one or more conditions.
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to judge that the calculation of a Shapley value meets a condition.
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 7
Step 2A, Prong 1: The claim recites, inter alia:
in response to determining that the one or more Shapley values satisfy the one or more conditions, adding one or more actions to the one or more action recommendations.
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to judge that a Shapley value has met a condition and adding a recommendation of an action to take to a list of other recommendations.
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 8
Step 2A, Prong 1: The claim recites, inter alia:
wherein the one or more conditions comprise a condition that a sum of Shapley values associated with the first machine learning model are less than a predetermined ratio of a total sum of the Shapley values associated with the first machine learning model and Shapley values associated with the second machine learning model
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to evaluate whether a sum of values is less than a ratio. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the one or more actions comprise retraining the first machine learning model
This limitation is recited at a high level of generality and recites generic retraining of a generic machine learning model. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the one or more actions comprise retraining the first machine learning model
This limitation is recited at a high level of generality and recites generic retraining of a generic machine learning model. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model is not significantly more than the judicial exception. See MPEP 2106.05(f)
Claim 9
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
in response to determining that the sum of Shapley values associated with the first machine learning model are less than the predetermined ratio of the total sum of the Shapley values associated with the first machine learning model and the Shapley values associated with the second machine learning model, automatically triggering retraining of the first machine learning model.
This limitation is recited at a high level of generality and recites evaluating that a condition is met and triggering a generic retraining of a generic machine learning model in response. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
in response to determining that the sum of Shapley values associated with the first machine learning model are less than the predetermined ratio of the total sum of the Shapley values associated with the first machine learning model and the Shapley values associated with the second machine learning model, automatically triggering retraining of the first machine learning model.
This limitation is recited at a high level of generality and recites evaluating that a condition is met and triggering a generic retraining of a generic machine learning model in response. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model is not significantly more than a judicial exception. See MPEP 2106.05(f)
Claim 10
Step 2A, Prong 1: The claim recites, inter alia:
wherein the first machine learning model and the second machine learning model have at least one different feature
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to observe that the first and second machine learning models have at least one different feature.
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 11
Step 2A, Prong 1: The claim recites, inter alia:
computing one or more past residuals by subtracting at least a part of the first prediction from at least one actual observation value of the variable;
This limitation is a mathematical calculation being a computation recited using the mathematical operation of subtraction. See MPEP 2106.04(a)(2)(I)(C).
combining the first prediction and the second prediction to generate a combined prediction;
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to calculate a combination of predictions. See MPEP 2106.04(a)(2)(III).
[…] generating one or more action recommendations […] based on the combined prediction.
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to think of an action that should be taken based on the calculated combined prediction.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
obtaining, using a second machine learning model trained on the one or more past residuals, a second prediction predicting a residual of the first machine learning model for a second time period, the first time period longer than the second time period
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
automatically … by the at least one hardware processor…
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
obtaining, using a second machine learning model trained on the one or more past residuals, a second prediction predicting a residual of the first machine learning model for a second time period, the first time period longer than the second time period
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
automatically … by the at least one hardware processor…
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 12
Step 2A, Prong 1: There are no further judicial exceptions in this claim.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
training the second machine learning model using auto-regressive features of the one or more past residuals
This limitation is recited at a high level of generality and recites use of generic features of residuals to perform training of a generic machine learning model. Mere instructions to apply a judicial exception while using generic features of residuals to train a generic machine learning model cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training the second machine learning model using auto-regressive features of the one or more past residuals
This limitation is recited at a high level of generality and recites use of generic features of residuals to perform training of a generic machine learning model. Mere instructions to apply a judicial exception while using generic features of residuals to train a generic machine learning model is not significantly more than the judicial exception. See MPEP 2106.05(f)
Claim 13
Step 2A, Prong 1: The claim recites, inter alia:
subtracting the at least the part of the first prediction from the at least one actual observation value to generate a past residual.
This limitation recites a mathematical calculation to subtract one value from another value. See MPEP 2106.04(a)(2)(I)
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
obtaining the at least the part of the first prediction, wherein the at least the part of the first prediction is associated with a third time period, and the first time period comprises the third time period;
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
obtaining the at least one actual observation value of the variable for the third time period; and
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining the at least the part of the first prediction, wherein the at least the part of the first prediction is associated with a third time period, and the first time period comprises the third time period;
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
obtaining the at least one actual observation value of the variable for the third time period; and
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
Claim 15
Step 2A, Prong 1: The claim recites, inter alia:
adding the second prediction to the at least the part of first prediction.
This limitation recites a mathematical calculation to add one value to another value. See MPEP 2106.04(a)(2)(I)
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 16
Step 2A, Prong 1: The claim recites, inter alia:
determining one or more Shapley values associated with the combined prediction; and
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to calculate Shapley values. See MPEP 2106.04(a)(2)(III)
determining that the one or more Shapley values satisfy one or more conditions.
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to judge that the calculation of a Shapley value meets a condition.
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 17
Step 2A, Prong 1: The claim recites, inter alia:
in response to determining that the one or more Shapley values satisfy the one or more conditions, adding one or more actions to the one or more action recommendations.
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to judge that a Shapley value has met a condition and adding a recommendation of an action to take to a list of other recommendations.
Step 2A, Prong 2: There are no further additional elements in this claim.
Step 2B: There are no further additional elements in this claim.
Claim 18
Step 2A, Prong 1: The claim recites, inter alia:
wherein the one or more conditions comprise a condition that a sum of Shapley values associated with the first machine learning model are less than a predetermined ratio of a total sum of the Shapley values associated with the first machine learning model and Shapley values associated with the second machine learning model
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to evaluate whether a sum of values is less than a ratio. See MPEP 2106.04(a)(2)(III)
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
wherein the one or more actions comprise retraining the first machine learning model
This limitation is recited at a high level of generality and recites generic retraining of a generic machine learning model. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the one or more actions comprise retraining the first machine learning model
This limitation is recited at a high level of generality and recites generic retraining of a generic machine learning model. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model is not significantly more than the judicial exception. See MPEP 2106.05(f)
Claim 19
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
in response to determining that the sum of Shapley values associated with the first machine learning model are less than the predetermined ratio of the total sum of the Shapley values associated with the first machine learning model and the Shapley values associated with the second machine learning model, automatically triggering retraining of the first machine learning model.
This limitation is recited at a high level of generality and recites evaluating that a condition is met and triggering a generic retraining of a generic machine learning model in response. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
in response to determining that the sum of Shapley values associated with the first machine learning model are less than the predetermined ratio of the total sum of the Shapley values associated with the first machine learning model and the Shapley values associated with the second machine learning model, automatically triggering retraining of the first machine learning model.
This limitation is recited at a high level of generality and recites evaluating that a condition is met and triggering a generic retraining of a generic machine learning model in response. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model is not significantly more than a judicial exception. See MPEP 2106.05(f)
Claim 20
Step 2A, Prong 1: The claim recites, inter alia:
computing one or more past residuals by subtracting at least a part of the first prediction from at least one actual observation value of the variable;
This limitation is a mathematical calculation being a computation recited using the mathematical operation of subtraction. See MPEP 2106.04(a)(2)(I)(C).
combining the first prediction and the second prediction to generate a combined prediction;
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to calculate a combination of predictions. See MPEP 2106.04(a)(2)(III).
[…] generating one or more action recommendations […] based on the combined prediction.
This limitation is a mental process using observation, evaluation, judgment, and opinion with aid of pen and paper to think of an action that should be taken based on the calculated combined prediction.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
A non-transitory, computer-readable medium storing computer- readable instructions, that upon execution by at least one hardware processor, cause performance of operations
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
obtaining, using a second machine learning model trained on the one or more past residuals, a second prediction predicting a residual of the first machine learning model for a second time period, the first time period longer than the second time period
This limitation represents an insignificant extra-solution activity of data gathering, being pre-solution activity, performed by a generic machine learning model. See MPEP 2106.05(g);
automatically … by the at least one hardware processor…
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory, computer-readable medium storing computer- readable instructions, that upon execution by at least one hardware processor, cause performance of operations
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
obtaining, using a first machine learning model, a first prediction predicting a variable for a first time period;
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
obtaining, using a second machine learning model trained on the one or more past residuals, a second prediction predicting a residual of the first machine learning model for a second time period, the first time period longer than the second time period
MPEP 2106.05(d)(II) indicates that storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), is a well-understood, routine, and conventional function when recited a merely generic manner or as insignificant extra-solution activity, as it is in this limitation.
automatically … by the at least one hardware processor…
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea. Mere instructions to apply a judicial exception using generic computer equipment in their ordinary capacity, are not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 22
Step 2A, Prong 1: There are no further abstract ideas in this claim.
Step 2A, Prong 2: The additional elements recited in this claim do not integrate the judicial exception into a practical application.
Additional elements:
iteratively retraining the second machine learning model based on the one or more past residuals.
This limitation is recited at a high level of generality and recites generic repeated retraining of a generic machine learning model using past residuals. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
iteratively retraining the second machine learning model based on the one or more past residuals.
This limitation is recited at a high level of generality and recites generic repeated retraining of a generic machine learning model using past residuals. Mere instructions to apply a judicial exception for generic retraining of a generic machine learning model is not significantly more than a judicial exception. See MPEP 2106.05(f)
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5,10-15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190188611 A1 by Wu et al., hereafter Wu, in view of US 20230061911 A1 by Elshocht et al., hereafter Elshocht, and in further view of US 20210089944 A1 by Zhou et al., hereafter Zhou.
Regarding claim 1, Wu teaches:
A system (“Apparatus 1200”) comprising:
at least one memory storing instructions (“memory 1260”);
a network interface (“communication device 1220”); and
at least one hardware processor (“processor 1210”) interoperably coupled with the network interface and the at least one memory, wherein execution of the instructions by the at least one hardware processor causes performance of operations comprising: ((Wu) Paragraph [0087])
obtaining, using a first machine learning model (“first regression model’), a first prediction predicting a variable for a first time period (“obtain predicted values of the current future time point”); ((Wu) Paragraph [0043], Paragraph [0042], “For each future time point, … the time series of past time points … are used as input variables”)
computing (“calculated at 416”) one or more past residuals (“residual values”) by subtracting at least a part of the first prediction from at least one actual observation value of the variable (“by subtracting the predicted values from the actual/target values”); ((Wu) Paragraph [0043])
obtaining, using a second machine learning model trained on the one or more past residuals (“A second regression model (e.g., residual regression model)”), a second prediction predicting a residual (residual value as a new target variable) of the first machine learning model (“from 416”) for a second time period, […] ((Wu) Paragraph [0044], using a residual value as a target value means predicting a residual)
combining the first prediction and the second prediction to generate a combined prediction (“combines the forecasted results”); ((Wu) Paragraph [0026])
Wu does not explicitly disclose:
… the first time period longer than the second time period
and
automatically generating one or more action recommendations by the at least one hardware processor based on the combined prediction.
Elshocht teaches:
the first time period (“the time interval”) longer than the second time period (“first sub-interval”). ((Elshocht) Paragraph [0036], A sub-interval is shorter than the time interval.)
Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information that is gathered into time periods. In addition, Elshocht teaches that smaller sub-intervals enable a quick evaluation of a quality of the forecast and avoids accumulation of forecasting errors over a longer period of time, but larger intervals are often more precise, so it is important to use both types of time periods. ((Elshocht) Paragraph [0005], "...repeating the forecast for smaller and smaller time intervals until a desired time-granularity is reached, providing a concept for forecasting a trend of a numerical value which enables a quick evaluation of a quality of the forecast, while avoiding the accumulation of forecasting errors over a longer period of time...On the other hand, the forecasts for the smaller and smaller time intervals may use the forecasting result for the longer time intervals. This may save time and may yield more precise results, as forecasts for longer time intervals are often more precise than individual forecasts over shorter time intervals")
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined dividing a first time interval into sub-intervals, as Elshocht teaches, with the future time point with a time series interval of past time points to build regression models, as taught by Wu. The motivation for this, as Elshocht teaches, would have been to avoid accumulation of prediction errors that would occur over a longer period of time. This application of dividing the input intervals into sub-intervals, as Elshocht teaches, of the regression model time series intervals taught by Wu would yield the predictable result of regression models built for future time points with shorter time series inputs that avoid the accumulation of forecasting errors.
Wu, in view of Elshocht, still does not explicitly disclose:
automatically generating one or more action recommendations by the at least one hardware processor based on the combined prediction.
However, Zhou teaches:
automatically generating one or more action recommendations (determinations that, for instance, an ending balance is lower or higher than desired and needs a cash transfer) by the at least one hardware processor based on the combined prediction. (“based on the additional forecast”) ((Zhou) Paragraph [0045] " For instance, the forecast analysis platform may determine, based on the additional forecast, that an ending cash balance of the financial transaction account may be lower or higher than is desired on a particular date and may automatically schedule a transaction to transfer cash to or from the financial transaction account prior to the particular date." Determining that an ending cash balance may be lower or higher than a desired threshold that may prompt a scheduling of a transaction is a recommendation that the scheduling be performed.)
Zhou, Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information gathered into time periods. In addition, Zhou teaches that automatically generating action recommendations reduces the need for monitoring, which in turn reduces the resources needed. ((Zhou) Paragraph [0045], " This may allow for automatic management of the financial transaction account, which may reduce a need to use resources (e.g., processing resources, memory resources, power resources, networking resources, and/or the like) of one or more devices that would otherwise be needed to monitor and manage the financial transaction account.")
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined the automatic action recommendation generation as Zhou teaches with the predictions as taught by Wu, in view of Elshocht. The motivation for this would be to reduce a need for a manager to monitor the actions, as Zhou suggests. This combination of the automatic action recommendation Zhou teaches after a prediction as taught by Wu, in view of Elshocht, would result in the predictable combination that is the system disclosed in claim 1 of the instant application. The Examiner notes that these motivations apply to all dependent and/or otherwise subsequently addressed claims.
Regarding claim 2, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 1, and Wu additionally teaches:
training (building) the second machine learning model (second regression model (e.g. residual regression model) using auto-regressive features of the one or more past residuals (“residual value from 416”). ((Wu) Paragraph [0044], Paragraph [0042], “For each future time point … the time series of past time points … are used as input variables …” Using past time points means the future time points, which are used for residual calculation, have auto regressive features, so the past residuals would also have auto-regressive features.)
Regarding claim 3, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 1, and Wu additionally teaches:
subtracting the at least the part of the first prediction from the at least one actual observation value to generate a past residual (“Residual values are then calculated at 416 by subtracting the predicted values from the actual/target values”). ((Wu) Paragraph [0043])
Wu does not explicitly disclose:
obtaining the at least the part of the first prediction, wherein the at least the part of the first prediction is associated with a third time period, and the first time period comprises the third time period;
obtaining the at least one actual observation value of the variable for the third time period;
Elshocht teaches:
obtaining the at least the part of the first prediction (“estimate of the numerical value for the time interval(=(Wu)predicted values of the current future time point)”), wherein the at least the part of the first prediction is associated with a third time period (“determine an estimate of the numerical value for the second sub-interval based on the estimate of the numerical value for the time interval and based on the estimate of the numerical value for the first sub-interval.”), and the first time period (“the time interval”) comprises the third time period (the second); ((Elshocht) Paragraph [0036])
obtaining the at least one actual observation value of the variable for the third time period (“numerical value for a sub-interval”); ((Elshocht) Paragraph [0038])
Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information that is gathered into time periods. In addition, Elshocht teaches that smaller sub-intervals enable a quick evaluation of a quality of the forecast and avoids accumulation of forecasting errors over a longer period of time, but larger intervals are often more precise, so it is important to use both types of time periods. ((Elshocht) Paragraph [0005])
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined dividing a first time interval into sub-intervals, as Elshocht teaches, with the future time point with a time series interval of past time points to build regression models, as taught by Wu. The motivation for this, as Elshocht teaches, would have been to avoid accumulation of prediction errors that would occur over a longer period of time. This application of dividing the input intervals into sub-intervals, as Elshocht teaches, of the regression model time series intervals taught by Wu would yield the predictable result of regression models built for future time points with shorter time series inputs that avoid the accumulation of forecasting errors.
Regarding claim 5, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 1, and additionally Wu teaches:
adding the second prediction (“predicted residual value”) to the at least the part of first prediction (“predicted time series value”). ((Wu) Paragraph [0048] " The final predicted value (e.g., actual final prediction) is calculated at 512 by adding the predicted residual value to the predicted time series value.")
Regarding claim 10, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 1, and additionally Wu teaches:
wherein the first machine learning model (forecasting regression model) and the second machine learning model (residual regression model) have at least one different feature (residual value feature in the residual regression model is not in the forecasting regression model) ((Wu) Fig. 4, Paragraphs [0043]-[0044])
Regarding claim 11, Wu teaches:
A computer-implemented method, comprising:
obtaining, using a first machine learning model (“first regression model’), a first prediction predicting a variable for a first time period (“obtain predicted values of the current future time point”); ((Wu) Paragraph [0043], Paragraph [0042], “For each future time point, … the time series of past time points … are used as input variables”)
computing (“calculated at 416”) one or more past residuals (“residual values”) by subtracting at least a part of the first prediction from at least one actual observation value of the variable (“by subtracting the predicted values from the actual/target values”); ((Wu) Paragraph [0043])
obtaining, using a second machine learning model trained on the one or more past residuals (“A second regression model (e.g., residual regression model)”), a second prediction predicting a residual (residual value as a new target variable) of the first machine learning model (“from 416”) for a second time period, […] ((Wu) Paragraph [0044], using a residual value as a target value means predicting a residual)
combining the first prediction and the second prediction to generate a combined prediction (“combines the forecasted results”); ((Wu) Paragraph [0026])
Wu does not explicitly disclose:
… the first time period longer than the second time period
and
automatically generating one or more action recommendations by the at least one hardware processor based on the combined prediction.
Elshocht teaches:
the first time period (“the time interval”) longer than the second time period (“first sub-interval”). ((Elshocht) Paragraph [0036], A sub-interval is shorter than the time interval.)
Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information that is gathered into time periods. In addition, Elshocht teaches that smaller sub-intervals enable a quick evaluation of a quality of the forecast and avoids accumulation of forecasting errors over a longer period of time, but larger intervals are often more precise, so it is important to use both types of time periods. ((Elshocht) Paragraph [0005], "...repeating the forecast for smaller and smaller time intervals until a desired time-granularity is reached, providing a concept for forecasting a trend of a numerical value which enables a quick evaluation of a quality of the forecast, while avoiding the accumulation of forecasting errors over a longer period of time...On the other hand, the forecasts for the smaller and smaller time intervals may use the forecasting result for the longer time intervals. This may save time and may yield more precise results, as forecasts for longer time intervals are often more precise than individual forecasts over shorter time intervals")
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined dividing a first time interval into sub-intervals, as Elshocht teaches, with the future time point with a time series interval of past time points to build regression models, as taught by Wu. The motivation for this, as Elshocht teaches, would have been to avoid accumulation of prediction errors that would occur over a longer period of time. This application of dividing the input intervals into sub-intervals, as Elshocht teaches, of the regression model time series intervals taught by Wu would yield the predictable result of regression models built for future time points with shorter time series inputs that avoid the accumulation of forecasting errors.
Wu, in view of Elshocht, still does not explicitly disclose:
automatically generating one or more action recommendations by the at least one hardware processor based on the combined prediction.
However, Zhou teaches:
automatically generating one or more action recommendations (determinations that, for instance, an ending balance is lower or higher than desired and needs a cash transfer) by the at least one hardware processor based on the combined prediction. (“based on the additional forecast”) ((Zhou) Paragraph [0045] " For instance, the forecast analysis platform may determine, based on the additional forecast, that an ending cash balance of the financial transaction account may be lower or higher than is desired on a particular date and may automatically schedule a transaction to transfer cash to or from the financial transaction account prior to the particular date." Determining that an ending cash balance may be lower or higher than a desired threshold that may prompt a scheduling of a transaction is a recommendation that the scheduling be performed.)
Zhou, Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information gathered into time periods. In addition, Zhou teaches that automatically generating action recommendations reduces the need for monitoring, which in turn reduces the resources needed. ((Zhou) Paragraph [0045], " This may allow for automatic management of the financial transaction account, which may reduce a need to use resources (e.g., processing resources, memory resources, power resources, networking resources, and/or the like) of one or more devices that would otherwise be needed to monitor and manage the financial transaction account.")
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined the automatic action recommendation generation as Zhou teaches with the predictions as taught by Wu, in view of Elshocht. The motivation for this would be to reduce a need for a manager to monitor the actions, as Zhou suggests. This combination of the automatic action recommendation Zhou teaches after a prediction as taught by Wu, in view of Elshocht, would result in the predictable combination that is the system disclosed in claim 1 of the instant application. The Examiner notes that these motivations apply to all dependent and/or otherwise subsequently addressed claims.
Regarding claim 12, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 11, and Wu additionally teaches:
training (building) the second machine learning model (second regression model (e.g. residual regression model) using auto-regressive features of the one or more past residuals (“residual value from 416”). ((Wu) Paragraph [0044], Paragraph [0042], “For each future time point … the time series of past time points … are used as input variables …” Using past time points means the future time points, which are used for residual calculation, have auto regressive features, so the past residuals would also have auto-regressive features.)
Regarding claim 13, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 11, and Wu additionally teaches:
subtracting the at least the part of the first prediction from the at least one actual observation value to generate a past residual (“Residual values are then calculated at 416 by subtracting the predicted values from the actual/target values”). ((Wu) Paragraph [0043])
Wu does not explicitly disclose:
obtaining the at least the part of the first prediction, wherein the at least the part of the first prediction is associated with a third time period, and the first time period comprises the third time period;
obtaining the at least one actual observation value of the variable for the third time period;
Elshocht teaches:
obtaining the at least the part of the first prediction (“estimate of the numerical value for the time interval(=(Wu)predicted values of the current future time point)”), wherein the at least the part of the first prediction is associated with a third time period (“determine an estimate of the numerical value for the second sub-interval based on the estimate of the numerical value for the time interval and based on the estimate of the numerical value for the first sub-interval.”), and the first time period (“the time interval”) comprises the third time period (the second); ((Elshocht) Paragraph [0036])
obtaining the at least one actual observation value of the variable for the third time period (“numerical value for a sub-interval”); ((Elshocht) Paragraph [0038])
Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information that is gathered into time periods. In addition, Elshocht teaches that smaller sub-intervals enable a quick evaluation of a quality of the forecast and avoids accumulation of forecasting errors over a longer period of time, but larger intervals are often more precise, so it is important to use both types of time periods. ((Elshocht) Paragraph [0005])
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined dividing a first time interval into sub-intervals, as Elshocht teaches, with the future time point with a time series interval of past time points to build regression models, as taught by Wu. The motivation for this, as Elshocht teaches, would have been to avoid accumulation of prediction errors that would occur over a longer period of time. This application of dividing the input intervals into sub-intervals, as Elshocht teaches, of the regression model time series intervals taught by Wu would yield the predictable result of regression models built for future time points with shorter time series inputs that avoid the accumulation of forecasting errors.
Regarding claim 15, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 11, and additionally Wu teaches:
adding the second prediction (“predicted residual value”) to the at least the part of first prediction (“predicted time series value”). ((Wu) Paragraph [0048] " The final predicted value (e.g., actual final prediction) is calculated at 512 by adding the predicted residual value to the predicted time series value.")
Regarding claim 20, Wu teaches:
A non-transitory, computer-readable medium storing computer- readable instructions(“memory 1260”), that upon execution by at least one hardware processor(“processor 1210”), cause performance of operations, comprising: ((Wu) Paragraph [0087])
obtaining, using a first machine learning model (“first regression model’), a first prediction predicting a variable for a first time period (“obtain predicted values of the current future time point”); ((Wu) Paragraph [0043], Paragraph [0042], “For each future time point, … the time series of past time points … are used as input variables”)
computing (“calculated at 416”) one or more past residuals (“residual values”) by subtracting at least a part of the first prediction from at least one actual observation value of the variable (“by subtracting the predicted values from the actual/target values”); ((Wu) Paragraph [0043])
obtaining, using a second machine learning model trained on the one or more past residuals (“A second regression model (e.g., residual regression model)”), a second prediction predicting a residual (residual value as a new target variable) of the first machine learning model (“from 416”) for a second time period, […] ((Wu) Paragraph [0044], using a residual value as a target value means predicting a residual)
combining the first prediction and the second prediction to generate a combined prediction (“combines the forecasted results”); ((Wu) Paragraph [0026])
Wu does not explicitly disclose:
… the first time period longer than the second time period
and
automatically generating one or more action recommendations by the at least one hardware processor based on the combined prediction.
Elshocht teaches:
the first time period (“the time interval”) longer than the second time period (“first sub-interval”). ((Elshocht) Paragraph [0036], A sub-interval is shorter than the time interval.)
Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information that is gathered into time periods. In addition, Elshocht teaches that smaller sub-intervals enable a quick evaluation of a quality of the forecast and avoids accumulation of forecasting errors over a longer period of time, but larger intervals are often more precise, so it is important to use both types of time periods. ((Elshocht) Paragraph [0005], "...repeating the forecast for smaller and smaller time intervals until a desired time-granularity is reached, providing a concept for forecasting a trend of a numerical value which enables a quick evaluation of a quality of the forecast, while avoiding the accumulation of forecasting errors over a longer period of time...On the other hand, the forecasts for the smaller and smaller time intervals may use the forecasting result for the longer time intervals. This may save time and may yield more precise results, as forecasts for longer time intervals are often more precise than individual forecasts over shorter time intervals")
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the references in front of them, to have combined dividing a first time interval into sub-intervals, as Elshocht teaches, with the future time point with a time series interval of past time points to build regression models, as taught by Wu. The motivation for this, as Elshocht teaches, would have been to avoid accumulation of prediction errors that would occur over a longer period of time. This application of dividing the input intervals into sub-intervals, as Elshocht teaches, of the regression model time series intervals taught by Wu would yield the predictable result of regression models built for future time points with shorter time series inputs that avoid the accumulation of forecasting errors.
Wu, in view of Elshocht, still does not explicitly disclose:
automatically generating one or more action recommendations by the at least one hardware processor based on the combined prediction.
However, Zhou teaches:
automatically generating one or more action recommendations (determinations that, for instance, an ending balance is lower or higher than desired and needs a cash transfer) by the at least one hardware processor based on the combined prediction. (“based on the additional forecast”) ((Zhou) Paragraph [0045] " For instance, the forecast analysis platform may determine, based on the additional forecast, that an ending cash balance of the financial transaction account may be lower or higher than is desired on a particular date and may automatically schedule a transaction to transfer cash to or from the financial transaction account prior to the particular date." Determining that an ending cash balance may be lower or higher than a desired threshold that may prompt a scheduling of a transaction is a recommendation that the scheduling be performed.)
Zhou, Wu and Elshocht are analogous art because they are in the same art: machine learning models that predict based on information gathered into time periods. In addition, Zhou teaches that automatically generating action recommendations reduces the need for monitoring, which in turn reduces the resources needed. ((Zhou) Paragraph [0045], " This may allow for automatic management of the financial transaction account, which may reduce a need to use resources (e.g., processing resources, memory resources, power resources, networking resources, and/or the like) of one or more devices that would otherwise be needed to monitor and manage the financial transaction account.")
Thus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined the automatic action recommendation generation as Zhou teaches with the predictions as taught by Wu, in view of Elshocht. The motivation for this would be to reduce a need for a manager to monitor the actions, as Zhou suggests. This combination of the automatic action recommendation Zhou teaches after a prediction as taught by Wu, in view of Elshocht, would result in the predictable combination that is the system disclosed in claim 1 of the instant application. The Examiner notes that these motivations apply to all dependent and/or otherwise subsequently addressed claims.
Regarding claim 21, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 1, and additionally Zhou teaches:
wherein the at least one hardware processor implements an autonomous agent (“forecast analysis platform”) that automatically executes one or more actions (“automatically schedule a transaction”) based on the combined prediction (“based on the additional forecast”). ((Zhou) Paragraph [0045])
Regarding claim 22, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 1, and additionally Wu teaches:
the operations further comprising iteratively retraining (‘The same training process is repeated on all future time points iteratively”) the second machine learning model based on the one or more past residuals (“actual residual value from 416 as a new target variable”). ((Wu) Paragraph [0044])
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, in view of Elshocht and Zhou, and in further view of US 20220114494 A1 by Sousa et al., hereafter Sousa.
Regarding claim 6, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 1.
Wu, in view of Elshocht and Zhou, does not expressly disclose:
determining one or more Shapley values associated with the combined prediction; and
determining that the one or more Shapley values satisfy one or more conditions.
Sousa teaches:
determining one or more Shapley values (“the relevance metric is a Shapely value”) associated with the combined prediction (“based at least in part on the plurality of perturbed prediction outputs”); ((Sousa) Paragraph [0036])and
determining that the one or more Shapley values (“relevance metric”) satisfy one or more conditions (“fall below a specified threshold”). ((Sousa) Paragraph [0041], "At 408, it is determined whether the relevance metric falls below a specified threshold. ")
Sousa, Wu, Elshocht, and Zhou are analogous art because all are in the same art: machine learning model predictions. In addition, Sousa teaches that using Shapley values in model interpretability ensures consistency, nullifies attribution from missing inputs, and ensures that the sum of each individual input attribution value is not different from the actual model reward value. ((Sousa) Paragraph [0022] "An advantage of bringing the Shapley values framework into model interpretability is inheriting Shapley properties for model explanations, these being: local accuracy ensuring that the sum of all individual input attribution values is equal to the model's score; missingness dictating that missing inputs should have no impact on the model's score, and therefore their attribution must be null; and consistency ensuring that if an input's contribution to the model increases, then its attributed importance should not decrease.")
Thus, it would be obvious to a person of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined including Shapley values as a metric to check if the invention meets a condition, as Sousa teaches, with the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou. The motivation for this would have been to take advantage of Shapley value properties of ensuring consistency, nullifying attribution from missing inputs, and ensuring that the sum of each individual input attribution value is not different from the actual model reward value. This application of Shapley values as a metric, as Sousa teaches, for the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou, would have produced the predictable result that is the invention claimed in claim 6 of this application.
Regarding claim 16, Wu, in view of Elshocht and Zhou, teaches the material disclosed in claim 11.
Wu, in view of Elshocht and Zhou, does not expressly disclose:
determining one or more Shapley values associated with the combined prediction; and
determining that the one or more Shapley values satisfy one or more conditions.
Sousa teaches:
determining one or more Shapley values (“the relevance metric is a Shapely value”) associated with the combined prediction (“based at least in part on the plurality of perturbed prediction outputs”); ((Sousa) Paragraph [0036])and
determining that the one or more Shapley values (“relevance metric”) satisfy one or more conditions (“fall below a specified threshold”). ((Sousa) Paragraph [0041], "At 408, it is determined whether the relevance metric falls below a specified threshold. ")
Sousa, Wu, Elshocht, and Zhou are analogous art because all are in the same art: machine learning model predictions. In addition, Sousa teaches that using Shapley values in model interpretability ensures consistency, nullifies attribution from missing inputs, and ensures that the sum of each individual input attribution value is not different from the actual model reward value. ((Sousa) Paragraph [0022] "An advantage of bringing the Shapley values framework into model interpretability is inheriting Shapley properties for model explanations, these being: local accuracy ensuring that the sum of all individual input attribution values is equal to the model's score; missingness dictating that missing inputs should have no impact on the model's score, and therefore their attribution must be null; and consistency ensuring that if an input's contribution to the model increases, then its attributed importance should not decrease.")
Thus, it would be obvious to a person of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined including Shapley values as a metric to check if the invention meets a condition, as Sousa teaches, with the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou. The motivation for this would have been to take advantage of Shapley value properties of ensuring consistency, nullifying attribution from missing inputs, and ensuring that the sum of each individual input attribution value is not different from the actual model reward value. This application of Shapley values as a metric, as Sousa teaches, for the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou, would have produced the predictable result that is the invention claimed in claim 16 of this application.
Claims 7-9, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu, in view of Elshocht, Zhou, and Sousa, and in further view of US 20220024032 A1 by Singh et al., hereafter Singh.
Regarding claim 7, Wu, in view of Sousa, Elshocht, and Zhou, teaches the material disclosed in claim 6, and additionally Sousa teaches:
in response to determining (“if it is determined…”) that the one or more Shapley values satisfy the one or more conditions… (“that the relevance metric falls below the specified threshold”) ((Sousa) Paragraph [0042])
Sousa, Wu, Elshocht, and Zhou are analogous art because all are in the same art: machine learning model predictions. In addition, Sousa teaches that using Shapley values in model interpretability ensures consistency, nullifies attribution from missing inputs, and ensures that the sum of each individual input attribution value is not different from the actual model reward value. ((Sousa) Paragraph [0022] "An advantage of bringing the Shapley values framework into model interpretability is inheriting Shapley properties for model explanations, these being: local accuracy ensuring that the sum of all individual input attribution values is equal to the model's score; missingness dictating that missing inputs should have no impact on the model's score, and therefore their attribution must be null; and consistency ensuring that if an input's contribution to the model increases, then its attributed importance should not decrease.")
Thus, it would be obvious to a person of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined including Shapley values as a metric to check if the invention meets a condition, as Sousa teaches, with the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou. The motivation for this would have been to take advantage of Shapley value properties of ensuring consistency, nullifying attribution from missing inputs, and ensuring that the sum of each individual input attribution value is not different from the actual model reward value. This application of Shapley values as a metric, as Sousa teaches, for the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou, would have produced the predictable result that is the condition disclosed in claim 7 on the instant application.
Wu, in view of Sousa, Elshocht, and Zhou, does not explicitly disclose:
in response to determining that the one or more Shapley values satisfy the one or more conditions, adding one or more actions to the one or more action recommendations.
Singh teaches:
in response to determining that the one or more conditions are satisfied (“When a change threshold is met”), adding one or more actions to the one or more action recommendations(“an alert or a retraining trigger may be generated). ((Singh) Paragraph [0016])
Singh, Wu, Sousa, Elshocht, and Zhou are analogous art because they in the same category of invention: machine learning model predictions. In addition, Singh teaches that predictions made by machine learning may change over time so it would be beneficial to give an alert or retrain the model when that happens. ((Singh) Paragraph [0002]," ...However, predictions made by AI/ML models may change, or drift, over time...Accordingly, improved techniques for detecting and/or correcting AI/ML model drift may be beneficial.”)
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have implemented an alert or a retraining trigger when a condition is met, as Singh teaches, into the trigger when Shapely values meet a condition as Sousa teaches, and the generating of action recommendations as taught by Wu, in view of Sousa, Elshocht, and Zhou. The motivation for this would have been to implement a detection and correction mechanism for when predictions made by the machine learning models change. This simple substitution would not change the functionality of the condition or the generating of action recommendations and would produce the predictable result that is claim 7 of the instant application.
Regarding claim 8, Wu, in view of Sousa, Elshocht, and Zhou, teaches the material disclosed in claim 7, and additionally Sousa teaches:
a condition (“specified threshold”) that a sum of Shapley associated with the first machine learning model values (“an importance value”) are less than a predetermined ratio (“form of a ratio”) of a total sum of the Shapley values associated with the first machine learning model (“an importance value associated with the overall sequence of predictions”) and Shapley values associated with the second machine learning model (“an importance value associated with the second sub-sequence”) ((Sousa) Paragraph [0041])
Sousa, Wu, Elshocht, and Zhou are analogous art because all are in the same art: machine learning model predictions. In addition, Sousa teaches that using Shapley values in model interpretability ensures consistency, nullifies attribution from missing inputs, and ensures that the sum of each individual input attribution value is not different from the actual model reward value. ((Sousa) Paragraph [0022])
Thus, it would be obvious to a person of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined including Shapley values as a ratio metric to check if the invention meets a condition, as Sousa teaches, with the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou. The motivation for this would have been to take advantage of Shapley value properties of ensuring consistency, nullifying attribution from missing inputs, and ensuring that the sum of each individual input attribution value is not different from the actual model reward value. This application of Shapley values as a ratio metric, as Sousa teaches, for the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou, would have produced the predictable result that is the ratio condition of claim 8 of the instant application.
Wu, in view of Sousa, Elshocht, and Zhou, does not explicitly disclose:
wherein the one or more actions comprise retraining the first machine learning model.
Singh teaches:
wherein the one or more actions comprise retraining (“retraining trigger”) the first machine learning model. ((Singh) Paragraph [0016])
Singh, Wu, Sousa, Elshocht, and Zhou are analogous art because they in the same category of invention: machine learning model predictions. In addition, Singh teaches that predictions made by machine learning may change over time so it would be beneficial to give an alert or retrain the model when that happens. ((Singh) Paragraph [0002])
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have implemented an a retraining trigger when a condition is met, as Singh teaches, into the trigger when Shapely values meet a ratio condition as Sousa teaches, and the generating of action recommendations as taught by Wu, in view of Sousa, Elshocht, and Zhou. The motivation for this would have been to implement a detection and correction mechanism for when predictions made by the machine learning models change. This simple substitution would not change the functionality of the condition or the generating of action recommendations and would produce the predictable result that is claim 8 of the instant application.
Regarding claim 9, Wu, in view of Sousa, Elshocht, and Zhou, teaches the material disclosed in claim 8, and additionally Sousa teaches:
in response to determining (“it is determined whether the relevance metric falls below a specified threshold”) that the sum of Shapley values associated with the first machine learning model (“an importance value”) are less than the predetermined ratio (“form of a ratio”) of the total sum of the Shapley values associated with the first machine learning model (“an importance value associated with the overall sequence of predictions”) and the Shapley values associated with the second machine learning model (“an importance value associated with the second sub-sequence”) ((Soua) Paragraph [0041])
The rationale for combining Sousa and Wu, in view of Sousa, Elshocht, and Zhou, is the same as in claim 8.
Wu, in view of Sousa, Elshocht, and Zhou, does not explicitly disclose:
automatically triggering retraining of the first machine learning model.
Singh teaches:
automatically triggering retraining (“retraining trigger”) of the first machine learning model.
The rationale for combining Singh and Wu, in view of Sousa, Elshocht, and Zhou, is the same as in claim 8.
Regarding claim 17, Wu, in view of Sousa, Elshocht, and Zhou, teaches the material disclosed in claim 16, and additionally Sousa teaches:
in response to determining (“if it is determined…”) that the one or more Shapley values satisfy the one or more conditions… (“that the relevance metric falls below the specified threshold”) ((Sousa) Paragraph [0042])
Sousa, Wu, Elshocht, and Zhou are analogous art because all are in the same art: machine learning model predictions. In addition, Sousa teaches that using Shapley values in model interpretability ensures consistency, nullifies attribution from missing inputs, and ensures that the sum of each individual input attribution value is not different from the actual model reward value. ((Sousa) Paragraph [0022] "An advantage of bringing the Shapley values framework into model interpretability is inheriting Shapley properties for model explanations, these being: local accuracy ensuring that the sum of all individual input attribution values is equal to the model's score; missingness dictating that missing inputs should have no impact on the model's score, and therefore their attribution must be null; and consistency ensuring that if an input's contribution to the model increases, then its attributed importance should not decrease.")
Thus, it would be obvious to a person of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined including Shapley values as a metric to check if the invention meets a condition, as Sousa teaches, with the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou. The motivation for this would have been to take advantage of Shapley value properties of ensuring consistency, nullifying attribution from missing inputs, and ensuring that the sum of each individual input attribution value is not different from the actual model reward value. This application of Shapley values as a metric, as Sousa teaches, for the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou, would have produced the predictable result that is the condition disclosed in claim 7 on the instant application.
Wu, in view of Sousa, Elshocht, and Zhou, does not explicitly disclose:
in response to determining that the one or more Shapley values satisfy the one or more conditions, adding one or more actions to the one or more action recommendations.
Singh teaches:
in response to determining that the one or more conditions are satisfied (“When a change threshold is met”), adding one or more actions to the one or more action recommendations(“an alert or a retraining trigger may be generated). ((Singh) Paragraph [0016])
Singh, Wu, Sousa, Elshocht, and Zhou are analogous art because they in the same category of invention: machine learning model predictions. In addition, Singh teaches that predictions made by machine learning may change over time so it would be beneficial to give an alert or retrain the model when that happens. ((Singh) Paragraph [0002]," ...However, predictions made by AI/ML models may change, or drift, over time...Accordingly, improved techniques for detecting and/or correcting AI/ML model drift may be beneficial.”)
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have implemented an alert or a retraining trigger when a condition is met, as Singh teaches, into the trigger when Shapely values meet a condition as Sousa teaches, and the generating of action recommendations as taught by Wu, in view of Sousa, Elshocht, and Zhou. The motivation for this would have been to implement a detection and correction mechanism for when predictions made by the machine learning models change. This simple substitution would not change the functionality of the condition or the generating of action recommendations and would produce the predictable result that is claim 17 of the instant application.
Regarding claim 18, Wu, in view of Sousa, Elshocht, and Zhou, teaches the material disclosed in claim 17, and additionally Sousa teaches:
a condition (“specified threshold”) that a sum of Shapley associated with the first machine learning model values (“an importance value”) are less than a predetermined ratio (“form of a ratio”) of a total sum of the Shapley values associated with the first machine learning model (“an importance value associated with the overall sequence of predictions”) and Shapley values associated with the second machine learning model (“an importance value associated with the second sub-sequence”) ((Sousa) Paragraph [0041])
Sousa, Wu, Elshocht, and Zhou are analogous art because all are in the same art: machine learning model predictions. In addition, Sousa teaches that using Shapley values in model interpretability ensures consistency, nullifies attribution from missing inputs, and ensures that the sum of each individual input attribution value is not different from the actual model reward value. ((Sousa) Paragraph [0022])
Thus, it would be obvious to a person of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have combined including Shapley values as a ratio metric to check if the invention meets a condition, as Sousa teaches, with the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou. The motivation for this would have been to take advantage of Shapley value properties of ensuring consistency, nullifying attribution from missing inputs, and ensuring that the sum of each individual input attribution value is not different from the actual model reward value. This application of Shapley values as a ratio metric, as Sousa teaches, for the determination that an action should be taken as taught by Wu, in view of Elshocht and Zhou, would have produced the predictable result that is the ratio condition of claim 18 of the instant application.
Wu, in view of Sousa, Elshocht, and Zhou, does not explicitly disclose:
wherein the one or more actions comprise retraining the first machine learning model.
Singh teaches:
wherein the one or more actions comprise retraining (“retraining trigger”) the first machine learning model. ((Singh) Paragraph [0016])
Singh, Wu, Sousa, Elshocht, and Zhou are analogous art because they in the same category of invention: machine learning model predictions. In addition, Singh teaches that predictions made by machine learning may change over time so it would be beneficial to give an alert or retrain the model when that happens. ((Singh) Paragraph [0002])
Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, having the references in front of them, to have implemented an a retraining trigger when a condition is met, as Singh teaches, into the trigger when Shapely values meet a ratio condition as Sousa teaches, and the generating of action recommendations as taught by Wu, in view of Sousa, Elshocht, and Zhou. The motivation for this would have been to implement a detection and correction mechanism for when predictions made by the machine learning models change. This simple substitution would not change the functionality of the condition or the generation of action recommendations and would produce the predictable result that is claim 18 of the instant application.
Regarding claim 19, Wu, in view of Sousa, Elshocht, and Zhou, teaches the material disclosed in claim 18, and additionally Sousa teaches:
in response to determining (“it is determined whether the relevance metric falls below a specified threshold”) that the sum of Shapley values associated with the first machine learning model (“an importance value”) are less than the predetermined ratio (“form of a ratio”) of the total sum of the Shapley values associated with the first machine learning model (“an importance value associated with the overall sequence of predictions”) and the Shapley values associated with the second machine learning model (“an importance value associated with the second sub-sequence”) ((Soua) Paragraph [0041])
The rationale for combining Sousa and Wu, in view of Sousa, Elshocht, and Zhou, is the same as in claim 18.
Wu, in view of Sousa, Elshocht, and Zhou, does not explicitly disclose:
automatically triggering retraining of the first machine learning model.
Singh teaches:
automatically triggering retraining (“retraining trigger”) of the first machine learning model.
The rationale for combining Singh and Wu, in view of Sousa, Elshocht, and Zhou, is the same as in claim 18.
Response to Arguments
Applicant's arguments filed 06/26/2026 have been fully considered but they are not persuasive.
In response to applicant’s argument on pages 8 and 9 that the mental process characterization is improper based on that no human could practically perform certain operations with pen and paper, the Examiner notes that the first and second machine learning models are generic computer models performing generic computer calculations and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). In addition, “computing one or more past residuals by subtracting at least a part of the first prediction from at least one actual observation value of the variable” is a mathematical calculation of subtraction, “obtaining a second prediction predicting a residual of the first machine learning model for a second time period, the first time period longer than the second time period” is extra-solution activity of mere data gathering, “combining the first prediction and the second prediction to generate a combined prediction” can be done using simple mental calculation or by writing using pen and paper, and “generation one or more action recommendations based on the combined prediction” can be done mentally, while the limitation of “automatically… by the at least one hardware processor…” is simple instruction to apply that mental process automatically using a hardware processor which a generic computer component.
In response to applicant’s arguments on pages 9 and 10 concerning the statement that the applicant’s claimed subject matter integrates any recited exception into a practical application, the examiner notes that a simple combination of the first and second predictions for automatically generating an action recommendation does not necessarily constitute adjusting the first prediction with the second prediction for improving forecasting accuracy. if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., that the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See MPEP 2106.04(d)(1) paragraph 2. In addition, the comparison to the eligible claim in Example 47 of the PEG Subject Matter Eligibility Examples is inaccurate. Applicant compares how Example 47 improved a specific technical field by detecting anomalies and automatically taking remedial actions to their claim of recommending actions based on combining predictions. This comparison is inaccurate, simply recommending actions without executing them is not the same as automatically taking remedial actions.
In response to applicant's arguments on pages 11 and 12, “Thus, Wu uses…”, “Elshocht fails to remedy…”, “Zhao fails to remedy”, against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant’s argument on page 12 that there is no teaching, suggestion, or motivation to combine the references, ”The Examiner’s rejection also lacks…”, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, Elshocht paragraph [0005] recites benefits of splitting time intervals, giving motivation to combine, ((Elshocht) Paragraph [0005], "...repeating the forecast for smaller and smaller time intervals until a desired time-granularity is reached, providing a concept for forecasting a trend of a numerical value which enables a quick evaluation of a quality of the forecast, while avoiding the accumulation of forecasting errors over a longer period of time...On the other hand, the forecasts for the smaller and smaller time intervals may use the forecasting result for the longer time intervals. This may save time and may yield more precise results, as forecasts for longer time intervals are often more precise than individual forecasts over shorter time intervals"), and
Zhao paragraph [0045] recites benefits of automatic generation and execution of actions, which gives motivation to combine. ((Zhou) Paragraph [0045], " This may allow for automatic management of the financial transaction account, which may reduce a need to use resources (e.g., processing resources, memory resources, power resources, networking resources, and/or the like) of one or more devices that would otherwise be needed to monitor and manage the financial transaction account.")
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art with respect to machine learning, residual prediction learning, Shapley values, retraining machine learning models, and time series.
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 DYLAN H LAI whose telephone number is (571)272-8628. The examiner can normally be reached Monday - Friday 7:30am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 5712524241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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D. H. L.
Examiner
Art Unit 2144
/TAMARA T KYLE/ Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights