DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the amendment filed on 11/13/2025. Claims 1, 3, 7, 8, 10, 14, 15, 17, and 20. Claims 1-20 are pending and have been examined.
Claim Rejections - 35 USC § 112
The rejection under 35 USC § 112 to Claims 3, 10, and 17 is WITHDRAWN in view of Applicant’s amendments to Claims 3, 10, and 17.
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.
Regarding Claim 1:
Step 1:
The claim recites a method, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generating… a plurality of perturbed seed data values by performing a plurality of perturbation operations amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea. Specifically, the limitation identifying, based on a selection, a reference value within a domain of the predictive model, wherein the reference value comprises a first output value for which a class probability for each class predicted by the predictive model is equal or a second output value for which a predicted probability by the predictive model is approximately fifty percent amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea. Specifically, the limitation performing a plurality of counterfactual operations to generate a plurality of background data values of a background data store is a mathematical concept.
The claim recites an additional abstract idea. Specifically, the limitation …generate a model analysis of the predictive model… wherein the model analysis relates the initial value to the reference value amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using a processing device is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of executing a model analysis engine… amounts to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of …utilizing the background data store and the initial value does not integrate the abstract idea into practical application because storing and retrieving data from memory is considered an insignificant extra solution activity of MPEP 2106.05(g). Therefore, the claim is directed to an abstract idea.
Step 2B:
The additional element of using a processing device is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of executing a model analysis engine… amounts to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of …utilizing the background data store and the initial value does not amount to significantly more because the additional element is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(iv), (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)).
Therefore, the claim is ineligible.
Regarding Claim 2:
Claim 2 which incorporates the rejection of Claim 1, recites a further abstract idea the model analysis engine generates the model analysis of the predictive model further utilizing a Shapley Additive exPlanations (SHAP) operation which is a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 3:
Claim 3 incorporates the rejection of Claim 1. This claim further recites a description of the
abstract idea of counterfactual operations in the performing a plurality of counterfactual operations step of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 4:
Claim 4 incorporates the rejection of Claim 1. This claim further recites a description of the
abstract idea of perturbation operations in the generating a plurality of perturbed seed values step of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 5:
Claim 5 incorporates the rejection of Claim 1. This claim further recites a description of the
abstract idea of the reference value in the performing a plurality of counterfactual operations step of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 6:
Claim 6 incorporates the rejection of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites a further additional element processing the initial value by the predictive model to generate an output value which amounts to mere instructions to apply the abstract idea MPEP 2106.05(f). The claim is ineligible.
Regarding Claim 7:
Claim 7 incorporates the rejection of Claim 1. This claim further recites a description of the
abstract idea of the reference value in the performing a plurality of counterfactual operations step of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 8:
Step 1:
The claim recites a system, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generate a plurality of perturbed seed data values by performing a plurality of perturbation operations amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea. Specifically, the limitation identify, based on a selection, a reference value within a domain of the predictive model, wherein the reference value comprises a first output value for which a class probability for each class predicted by the predictive model is equal or a second output value for which a predicted probability by the predictive model is approximately fifty percent amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea. Specifically, the limitation perform a plurality of counterfactual operations to generate a plurality of background data values of a background data store is a mathematical concept.
The claim recites an additional abstract idea. Specifically, the limitation …generate a model analysis of the predictive model… wherein the model analysis relates the initial value to the reference value amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using a memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a processing device is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of execute a model analysis engine… amounts to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of …utilizing the background data store and the initial value does not integrate the abstract idea into practical application because storing and retrieving data from memory is considered an insignificant extra solution activity of MPEP 2106.05(g). Therefore, the claim is directed to an abstract idea.
Step 2B:
The additional element of using a memory is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a processing device is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of execute a model analysis engine… amounts to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of …utilizing the background data store and the initial value does not amount to significantly more because the additional element is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(iv), (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)).
Therefore, the claim is ineligible.
Regarding Claim 9:
Claim 9 which incorporates the rejection of Claim 8, recites a further abstract idea the model analysis engine generates the model analysis of the predictive model further utilizing a Shapley Additive exPlanations (SHAP) operation which is a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 10:
Claim 10 incorporates the rejection of Claim 8. This claim further recites a description of the
abstract idea of counterfactual operations in the perform a plurality of counterfactual operations step of Claim 8. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 11:
Claim 11 incorporates the rejection of Claim 8. This claim further recites a description of the
abstract idea of perturbation operations in the generating a plurality of perturbed seed values step of Claim 8. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 12:
Claim 12 incorporates the rejection of Claim 8. This claim further recites a description of the
abstract idea of the reference value in the perform a plurality of counterfactual operations step of Claim 8. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 13:
Claim 13 incorporates the rejection of Claim 8. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites a further additional element process the initial value by the predictive model to generate an output value which amounts to mere instructions to apply the abstract idea MPEP 2106.05(f). The claim is ineligible.
Regarding Claim 14:
Claim 14 incorporates the rejection of Claim 8. This claim further recites a description of the
abstract idea of the reference value in the perform a plurality of counterfactual operations step of Claim 8. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 15:
Step 1:
The claim recites a non-transitory computer-readable storage medium, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generate a plurality of perturbed seed data values by performing a plurality of perturbation operations amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea. Specifically, the limitation identify, based on a selection, a reference value within a domain of the predictive model, wherein the reference value comprises a first output value for which a class probability for each class predicted by the predictive model is equal or a second output value for which a predicted probability by the predictive model is approximately fifty percent amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea. Specifically, the limitation perform a plurality of counterfactual operations to generate a plurality of background data values of a background data store is a mathematical concept.
The claim recites an additional abstract idea. Specifically, the limitation …generate a model analysis of the predictive model… wherein the model analysis relates the initial value to the reference value amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using a non-transitory computer-readable storage medium is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a processing device is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of execute a model analysis engine… amounts to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of …utilizing the background data store and the initial value does not integrate the abstract idea into practical application because storing and retrieving data from memory is considered an insignificant extra solution activity of MPEP 2106.05(g). Therefore, the claim is directed to an abstract idea.
Step 2B:
The additional element of using a non-transitory computer-readable storage medium is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a processing device is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of execute a model analysis engine… amounts to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of …utilizing the background data store and the initial value does not amount to significantly more because the additional element is an insignificant extra solution activity and further is a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(iv), (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)).
Therefore, the claim is ineligible.
Regarding Claim 16:
Claim 16 which incorporates the rejection of Claim 15, recites a further abstract idea the model analysis engine generates the model analysis of the predictive model further utilizing a Shapley Additive exPlanations (SHAP) operation which is a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 17:
Claim 17 incorporates the rejection of Claim 15. This claim further recites a description of the
abstract idea of counterfactual operations in the perform a plurality of counterfactual operations step of Claim 15. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 18:
Claim 18 incorporates the rejection of Claim 15. This claim further recites a description of the
abstract idea of perturbation operations in the generating a plurality of perturbed seed values step of Claim 15. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 19:
Claim 19 incorporates the rejection of Claim 15. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites a further additional element process the initial value by the predictive model to generate an output value which amounts to mere instructions to apply the abstract idea MPEP 2106.05(f). The claim is ineligible.
Regarding Claim 20:
Claim 20 incorporates the rejection of Claim 15. This claim further recites a description of the
abstract idea of the reference value in the perform a plurality of counterfactual operations step of Claim 15. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by White et al. “Measurable Counterfactual Local Explanations for Any Classifier”, hereinafter “White”.
Regarding Claim 1, White teaches:
A method comprising:
generating, by a processing device (The method of White uses software to train and test with datasets and models, demonstrating that White performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 5, col. 2, ¶2, “For reproducibility, the code for pre-processing the data is included with the CLEAR prototype on GitHub”, ¶3, “For the Iris dataset, a support vector machine (SVM) with RBF kernel was trained using the scikit-learn library”, p. 6, Figure 3 and Table 2 showing results), a plurality of perturbed seed data values by performing a plurality of perturbation operations on an initial value to be processed by a predictive model (actual b-perturbations are perturbed seed data values, p. 4, col. 1, ¶2, “generates an explanation of prediction y for observation x by the following steps: Determine x’s b-perturbations. For each feature f, a separate one-dimensional search is performed by querying m starting at x, and progressively moving away from x by changing the value of f by regular amounts, whilst keeping all other features constant”);
identifying, based on a selection, a reference value within a domain of the predictive model, wherein the reference value comprises a first output value for which a class probability for each class predicted by the predictive model is equal or a second output value for which a predicted probability by the predictive model is approximately fifty percent (The reference value is generated by identifying a feature value that causes the output value to be approximately 50%, p. 5, col. 1, paragraph 2, “Let the decision boundary be P(x ∈ class 1) = 0.5, p. 5, col. 1, paragraph 3, “the estimated b-perturbation is -0.512. The actual b-perturbation (step 1) for Glucose to achieve a probability of 0.5 of being in class 1”);
performing a plurality of counterfactual operations to generate a plurality of background data values of a background data store (p. 4, col. 1, Steps 1-6 are counterfactual operations, Step 2, “Generate labelled synthetic observations”, Step 3, “Create a balanced neighbourhood dataset”, Step 4, “Perform a step-wise regression on the neighbourhood dataset”, Step 5, “Estimate the b-perturbations by substituting x’s b-counterfactual values from minf (x)”, Step 6, fidelity errors are plurality of background data values based on steps 1-6, “Measure the fidelity of the regression coefficients”) based on respective ones of the plurality of perturbed seed data values (Step 6, “Fidelity errors are calculated by… actual b-perturbations determined in step 1”), the reference value, and the predictive model (Step 6, Reference values are estimated b-perturbations, “Fidelity errors are calculated by comparing the actual b-perturbations determined in step 1 with the estimates calculated in step 5”, Steps 1 and 2 use predictive model in operations, “one-dimensional search is performed by querying m starting at x” and “The synthetic observations are labelled by being passed through m”); and
executing a model analysis engine to generate a model analysis of the predictive model utilizing the background data store and the initial value, wherein the model analysis relates the initial value to the reference value (Model analysis is generated through steps 1-8, p. 4, col. 2, Step 7, “Figure 2 shows excerpts from a CLEAR report”, p. 5, col. 1, ¶5, “CLEAR’s HTML reports are interactive, providing an option to simplify the representation of its equations”, p. 4, Figure 2 shows report displaying model analysis, Figure 2 shows initial value of input value, actual b-counterfactual value, estimated b-counterfactual value, and the corresponding fidelity error relating all these values).
Regarding Claim 3, White teaches the method of Claim 1 as referenced above. White further teaches:
wherein the plurality of counterfactual operations comprise a non-diverse counterfactual operation that deterministically generates a same background data value based on a same one of the plurality of perturbed seed data values, a same reference value within the domain of the predictive model, and the predictive model (plurality of counterfactual operations are a non-diverse counterfactual operation, it is deterministic/non-diverse because for a given actual b-counterfactual(perturbed seed value), a given estimated b-counterfactual (reference value), and predictive model, the generated fidelity (background data value) is always the same, p. 4, col. 1, paragraph 2).
Regarding Claim 4, White teaches the method of Claim 1 as referenced above. White further teaches:
wherein generating the plurality of perturbed seed data values by performing the plurality of perturbation operations on the initial value comprises performing a first perturbation operation to alter a feature value of the initial value by less than 10% of a range of the feature value (Values can be perturbed by less than 10% of initial feature value, p. 3, col. 2, ¶5, “A b-perturbation is defined as the change in value of feature f for a target class y’, that is vf (minf (x)) − vf (x). For example, for the b-counterfactual that Mr Jones would have received his loan if his salary had been $35,000, a b-perturbation for salary is $3000”).
Regarding Claim 5, White teaches the method of Claim 1 as referenced above. White further teaches:
wherein the reference value comprises a null output value of the predictive model (The reference value is estimated b-perturbation which could be null when the feature is not found to be impactful based on model output, p. 3, col. 2, ¶5, “A b-perturbation is defined as the change in value of feature f for a target class y’… changes in a feature value may not always imply a change of classification”).
Regarding Claim 6, White teaches the method of Claim 1 as referenced above. White further teaches:
further comprising: processing the initial value by the predictive model to generate an output value (p. 2, col. 1, ¶5, “let m be a machine learning system mapping X → Y ; m is said to generate prediction y for observation x”), wherein the model analysis of the predictive model comprises respective contributions of feature values of the initial value to the output value (b-perturbations show how much respective initial feature values contribute to the output prediction, p .3, col. 2, ¶5, “Let vf (x) denote the value of feature f in x. A b-perturbation is defined as the change in value of feature f for a target class y’, that is vf (minf (x)) − vf (x)”, b-perturbations are part of model analysis report, p. 4, Figure 2 showing report with b-perturbations).
Regarding Claim 7, White teaches the method of Claim 1 as referenced above. White further teaches:
wherein the reference value comprises at least one of a minimum value of an output range of the predictive model or a maximum value of the output range of the predictive model (predicted b-perturbations are generated with all outputs of model which includes range of minimum and maximum values , p. 4, col. 1, Steps 2 and 3, “The synthetic observations are labelled by being passed through m… Create a balanced neighbourhood dataset… Synthetic observations that are near to x (Euclidean distance) are selected”, Steps 4 and 5, “Perform a step-wise regression on the neighbourhood dataset… Estimate the b-perturbations”), a first output value for which a class probability for each class predicted by the predictive model is equal, or a second output value for which a predicted probability by the predictive model is approximately fifty percent.
Regarding Claim 8, White teaches:
A system comprising:
A memory; and A processing device, operatively coupled to the memory(The method of White uses software to train and test with datasets and models, demonstrating that White performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 5, col. 2, ¶2, “For reproducibility, the code for pre-processing the data is included with the CLEAR prototype on GitHub”, ¶3, “For the Iris dataset, a support vector machine (SVM) with RBF kernel was trained using the scikit-learn library”, p. 6, Figure 3 and Table 2 showing results), to:
generate a plurality of perturbed seed data values by performing a plurality of perturbation operations on an initial value to be processed by a predictive model (b-perturbations are perturbed seed data values, p. 4, col. 1, ¶2, “generates an explanation of prediction y for observation x by the following steps: Determine x’s b-perturbations. For each feature f, a separate one-dimensional search is performed by querying m starting at x, and progressively moving away from x by changing the value of f by regular amounts, whilst keeping all other features constant”);
identify, based on a selection, a reference value within a domain of the predictive model, wherein the reference value comprises a first output value for which a class probability for each class predicted by the predictive model is equal or a second output value for which a predicted probability by the predictive model is approximately fifty percent (The reference value is generated by identifying a feature value that causes the output value to be approximately 50%, p. 5, col. 1, paragraph 2, “Let the decision boundary be P(x ∈ class 1) = 0.5, p. 5, col. 1, paragraph 3, “the estimated b-perturbation is -0.512. The actual b-perturbation (step 1) for Glucose to achieve a probability of 0.5 of being in class 1”);
perform a plurality of counterfactual operations to generate a plurality of background data values of a background data store (p. 4, col. 1, Steps 1-6 are counterfactual operations, Step 2, “Generate labelled synthetic observations”, Step 3, “Create a balanced neighbourhood dataset”, Step 4, “Perform a step-wise regression on the neighbourhood dataset”, Step 5, “Estimate the b-perturbations by substituting x’s b-counterfactual values from minf (x)”, Step 6, fidelity errors are plurality of background data values based on steps 1-6, “Measure the fidelity of the regression coefficients”) based on respective ones of the plurality of perturbed seed data values (Step 6, “Fidelity errors are calculated by… actual b-perturbations determined in step 1”), the reference value, and the predictive model (Step 6, Reference values are estimated b-perturbations, “Fidelity errors are calculated by comparing the actual b-perturbations determined in step 1 with the estimates calculated in step 5”, Steps 1 and 2 use predictive model in operations, “one-dimensional search is performed by querying m starting at x” and “The synthetic observations are labelled by being passed through m”); and
execute a model analysis engine to generate a model analysis of the predictive model utilizing the background data store and the initial value, wherein the model analysis relates the initial value to the reference value (Model analysis is generated through steps 1-8, p. 4, col. 2, Step 7, “Figure 2 shows excerpts from a CLEAR report”, p. 5, col. 1, ¶5, “CLEAR’s HTML reports are interactive, providing an option to simplify the representation of its equations”, p. 4, Figure 2 shows report displaying model analysis, Figure 2 shows initial value of input value, actual b-counterfactual value, estimated b-counterfactual value, and the corresponding fidelity error relating all these values).
Regarding Claim 10, the rejection of Claim 8 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 3.
Regarding Claim 11, the rejection of Claim 8 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 4.
Regarding Claim 12, the rejection of Claim 8 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 5.
Regarding Claim 13, the rejection of Claim 8 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 6.
Regarding Claim 14, the rejection of Claim 8 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 7.
Regarding Claim 15, White teaches:
A non-transitory computer-readable storage medium including instructions that, when executed by a processing device (The method of White uses software to train and test with datasets and models, demonstrating that White performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 5, col. 2, ¶2, “For reproducibility, the code for pre-processing the data is included with the CLEAR prototype on GitHub”, ¶3, “For the Iris dataset, a support vector machine (SVM) with RBF kernel was trained using the scikit-learn library”, p. 6, Figure 3 and Table 2 showing results), cause the processing device to:
generate a plurality of perturbed seed data values by performing a plurality of perturbation operations on an initial value to be processed by a predictive model (b-perturbations are perturbed seed data values, p. 4, col. 1, ¶2, “generates an explanation of prediction y for observation x by the following steps: Determine x’s b-perturbations. For each feature f, a separate one-dimensional search is performed by querying m starting at x, and progressively moving away from x by changing the value of f by regular amounts, whilst keeping all other features constant”);
identify, based on a selection, a reference value within a domain of the predictive model, wherein the reference value comprises a first output value for which a class probability for each class predicted by the predictive model is equal or a second output value for which a predicted probability by the predictive model is approximately fifty percent (The reference value is generated by identifying a feature value that causes the output value to be approximately 50%, p. 5, col. 1, paragraph 2, “Let the decision boundary be P(x ∈ class 1) = 0.5, p. 5, col. 1, paragraph 3, “the estimated b-perturbation is -0.512. The actual b-perturbation (step 1) for Glucose to achieve a probability of 0.5 of being in class 1”);
perform a plurality of counterfactual operations to generate a plurality of background data values of a background data store (p. 4, col. 1, Steps 1-6 are counterfactual operations, Step 2, “Generate labelled synthetic observations”, Step 3, “Create a balanced neighbourhood dataset”, Step 4, “Perform a step-wise regression on the neighbourhood dataset”, Step 5, “Estimate the b-perturbations by substituting x’s b-counterfactual values from minf (x)”, Step 6, fidelity errors are plurality of background data values based on steps 1-6, “Measure the fidelity of the regression coefficients”) based on respective ones of the plurality of perturbed seed data values (Step 6, “Fidelity errors are calculated by… actual b-perturbations determined in step 1”), the reference value, and the predictive model (Step 6, Reference values are estimated b-perturbations, “Fidelity errors are calculated by comparing the actual b-perturbations determined in step 1 with the estimates calculated in step 5”, Steps 1 and 2 use predictive model in operations, “one-dimensional search is performed by querying m starting at x” and “The synthetic observations are labelled by being passed through m”); and
execute a model analysis engine to generate a model analysis of the predictive model utilizing the background data store and the initial value (Model analysis is generated through steps 1-8, p. 4, col. 2, Step 7, “Figure 2 shows excerpts from a CLEAR report”, p. 5, col. 1, ¶5, “CLEAR’s HTML reports are interactive, providing an option to simplify the representation of its equations”, p. 4, Figure 2 shows report displaying model analysis, Figure 2 shows initial value of input value, actual b-counterfactual value, estimated b-counterfactual value, and the corresponding fidelity error relating all these values).
Regarding Claim 17, the rejection of Claim 15 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 3.
Regarding Claim 18, the rejection of Claim 15 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 4.
Regarding Claim 19, the rejection of Claim 15 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 6.
Regarding Claim 20, the rejection of Claim 15 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 7.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being obvious over White in view of Lundberg et al. “A Unified Approach to Interpreting Model Predictions”, hereinafter “Lundberg”.
Regarding Claim 2, White teaches the method of Claim 1 as referenced above. White does not teach, but Lundberg teaches:
wherein the model analysis engine generates the model analysis of the predictive model further utilizing a Shapley Additive exPlanations (SHAP) operation (Lundberg, p. 5, Figure 1 description “SHAP (SHapley Additive explanation) values attribute to each feature the change in the expected model prediction when conditioning on that feature”, p. 5, ¶1, Equation 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lundberg finding SHAP values and the model analysis report from White. The modification would have been motivated to show feature importance for a prediction in the model analysis (Lundberg, p. 1, Abstract, “Understanding why a model makes a certain prediction can be as crucial as the prediction’s accuracy in many applications… framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction”).
Regarding Claim 9, the rejection of Claim 8 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 2.
Regarding Claim 16, the rejection of Claim 15 is incorporated and further, the claim is rejected for the same reasons as set forth in Claim 2.
Response to Arguments
35 U.S.C 101
Argument 1: The amended claims cannot be considered abstract as directed to a mental process, “executing a model analysis engine to generate a model analysis of the predictive model utilizing the background data store and the initial value, wherein the model analysis relates the initial value to the reference value” cannot be performed in a human mind.
Examiner Response: Examiner respectfully disagrees. As shown in the 103 rejection of Claim 1, the part of the limitation identified as an abstract idea is …generate a model analysis of the predictive model…wherein the model analysis relates the initial value to the reference value…. This is clearly an abstract idea as generating a model analysis could be performed in a human mind as it is not specified what the analysis is besides relating the initial value to the reference value which is able to be performed in a human mind. There is nothing in the claim language that would suggest the generation of model analysis could not be performed in a human mind.
Regarding the portion of the limitation executing a model analysis engine…, since the abstract idea is identified as generating the model analysis, executing a model analysis engine to perform this abstract idea amounts to mere instructions to apply the abstract idea. Regarding the portion of the limitation, …utilizing the background data store and the initial value… from this claim language utilizing the background data store and initial value is an extra solution activity of storing and retrieving data from memory and further is a well understood routine and conventional activity. Therefore the amended claims do recite an abstract idea that can be performed in a human mind.
Argument 2: Assuming arguendo the claims do recite a judicial process that can be performed in a human mind, the claims integrate any alleged abstract idea into a practical application that the improves the functioning of a computer.
Examiner Response: Examiner respectfully disagrees. Regarding applicant assertion that the disclosure provides “because [embodiments] allow for the background data store 150 to be generated with respect to a selected reference value 192, the model analysis 195 may be intuitively tailored to be easier to understand by humans”, the claim language does not reflect any technological improvement. Regarding model analysis, Claim 1 recites executing a model analysis engine to generate a model analysis of the predictive model utilizing the background data store and the initial value, wherein the model analysis relates the initial value to the reference value, there is no technological improvement shown, only an abstract idea with additional elements. The additional elements are not recited in a manner that amounts to improvement, integration to practical application or significantly more.
Regarding applicant assertion that the broadest reasonable interpretation of the claim must be limited to computer implementation and the claim scope cannot be performed mentally, the claim recites abstract ideas that can be performed mentally and every limitation that limits the computer implementation is an additional element and is addressed. For example, in Claim 1 the processing device and model analysis engine are mere instructions to apply the abstract idea and utilizing the background data store and initial value is an insignificant extra solution activity of storing and retrieving data from memory. Present claims from applicant do not show an improvement of function of a computer or technology.
35 U.S.C 102/103
Argument 1: Amended claim 1 requires identifying, based on a selection, a reference value within a domain of a predictive model and that comprises a first output value for which a class probability for each class predicted by the predictive model is equal or a second output value for which a predicted probability by the predictive model is approximately fifty percent. Applicant submits that white fails to disclose at least this element of claim 1.
Examiner Response: Examiner respectfully disagrees. In the amended Claim 1, White discloses identifying a reference value comprising a first output for which a class probability is equal or a second output value for which a predicted probability is approximately 50%. The reference value which is the estimated b-perturbations in White is generated from identifying a feature value that causes the output value to be approximately 50%, p. 5, col. 1, paragraph 2, “Let the decision boundary be P(x ∈ class 1) = 0.5, p. 5, col. 1, paragraph 3, “the estimated b-perturbation is -0.512. The actual b-perturbation (step 1) for Glucose to achieve a probability of 0.5 of being in class 1”. The reference value comprises the output value in that it was generated to correspond to an output value of 50%. An output value of 50% represents an equal class probability for each class, therefore the identifying of the b-perturbation value using a 50% output value covers both the first and second output value. There is nothing in the claim language recited that would cause the described process to differ from the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JESSE C COULSON/
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122