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 .
DETAILED ACTION
The action is responsive to the Application filed on 01/30/2024
Claims 1-20 are pending in the case. Claims 1, 11 and 17 are independent.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claim(s) 1-10 are drawn to a computer-implemented method, claim(s) 11-16 are drawn to a computing system and claim(s) 17-20 are drawn to a computer-readable storage media, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1, 11 and 17 are nonverbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
Regarding claim 1:
Claim 1 recites: A computer-implemented method, the computer-implemented method comprising: generating, by one or more processors and using a metric-specific predictive model, a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group;
generating, by the one or more processors and using an action-specific causal inference model, a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action;
generating, by the one or more processors, a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold;
generating, by the one or more processors, a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics; and
initiating, by the one or more processors, a performance of the prediction-based action based on the categorical improvement prediction
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 1 is directed to an abstract idea, specifically, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III).
Independent claim 1 recites in part:
generating, […] a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, one can, with pen and paper, Identify an entity, consider a quality metric and determine or predict a quality score. See MPEP § 2106.04(a)(2)(III).
generating, […] a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, one can consider an evaluation entity, consider a quality metric, consider a proposed action and predict the impact of that action. This is essentially a person making a prediction or judgment. See MPEP § 2106.04(a)(2)(III).
generating, […] a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, comparing information (two predictive measures and a threshold), evaluating the comparison and determining or predicting a categorical outcome can be reasonably interpreted as a mental process—observation, evaluations, judgment and opinions. See MPEP § 2106.04(a)(2)(III).
generating, […] a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, weighting multiple prediction results, aggregating them and producing an overall prediction can be characterized as a mental process. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites in part:
A computer-implemented method, the computer-implemented method comprising: […] by one or more processors and using a metric-specific predictive model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
[…] by the one or more processors and using an action-specific causal inference model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
[…] by the one or more processors, […] recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
[…] by the one or more processors, […] recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
initiating, by the one or more processors, a performance of the prediction-based action based on the categorical improvement prediction, recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites in part:
A computer-implemented method, the computer-implemented method comprising: […] by one or more processors and using a metric-specific predictive model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
[…] by the one or more processors and using an action-specific causal inference model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
[…] by the one or more processors, […] recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
[…] by the one or more processors, […] recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
initiating, by the one or more processors, a performance of the prediction-based action based on the categorical improvement prediction, recites generic computing components . Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing data gathering and mathematical calculations) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 11
Claim 11 recites: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: generate, using a metric-specific predictive model, a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group;
generate, using an action-specific causal inference model, a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action;
generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold;
generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics; and initiate a performance of the prediction-based action based on the categorical improvement prediction
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 11 is directed to an abstract idea, specifically, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III).
generate, […] a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, one can, with pen and paper, Identify an entity, consider a quality metric and determine or predict a quality score. See MPEP § 2106.04(a)(2)(III).
generate, […] a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, one can consider an evaluation entity, consider a quality metric, consider a proposed action and predict the impact of that action. This is essentially a person making a prediction or judgment. See MPEP § 2106.04(a)(2)(III).
generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, comparing information (two predictive measures and a threshold), evaluating the comparison and determining or predicting a categorical outcome can be reasonably interpreted as a mental process—observation, evaluations, judgment and opinions. See MPEP § 2106.04(a)(2)(III).
generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, weighting multiple prediction results, aggregating them and producing an overall prediction can be characterized as a mental process. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 11 recites in part:
A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: […] using a metric-specific predictive model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
initiate a performance of the prediction-based action based on the categorical improvement prediction, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 11 recites in part:
A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: […] using a metric-specific predictive model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
initiate a performance of the prediction-based action based on the categorical improvement prediction, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 17
Claim 17 recites: One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: generate, using a metric-specific predictive model, a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group;
generate, using an action-specific causal inference model, a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action;
generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold;
generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics; and
initiate a performance of the prediction-based action based on the categorical improvement prediction
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 17 is directed to an abstract idea, specifically, a mental process – concepts performed in the human mind or by a human using a pen and paper" (including an observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2)(III).
generate, […] a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, one can, with pen and paper, Identify an entity, consider a quality metric and determine or predict a quality score. See MPEP § 2106.04(a)(2)(III).
generate, […] a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, one can consider an evaluation entity, consider a quality metric, consider a proposed action and predict the impact of that action. This is essentially a person making a prediction or judgment. See MPEP § 2106.04(a)(2)(III).
generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, comparing information (two predictive measures and a threshold), evaluating the comparison and determining or predicting a categorical outcome can be reasonably interpreted as a mental process—observation, evaluations, judgment and opinions. See MPEP § 2106.04(a)(2)(III).
generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
The limitation above is broadly and reasonably interpreted as a mental process, as a form of mental evaluation, judgment, comparison, or other mental process that could practically be performed in the human mind (or with pen and paper). For example, weighting multiple prediction results, aggregating them and producing an overall prediction can be characterized as a mental process. See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 17 recites in part:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: […]using a metric-specific predictive model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
initiate a performance of the prediction-based action based on the categorical improvement prediction, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 17 recites in part:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: […]using a metric-specific predictive model, […] amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “predictive model” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
initiate a performance of the prediction-based action based on the categorical improvement prediction, amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-10 are dependent on claim 1, claims 12-16 are dependent on claim 11 and claims 18-20 are dependent on claim 17, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claims 2 and 12 incorporates the rejection of independent claims 1 and 11 respectively, recites an evaluation, judgment, comparison, or other mental process that could practically be performed in the human mine (or with pen and paper). See MPEP § 2106.04(a)(2)(III).
Claims 3 and 13 incorporates the rejection of dependent claims 2 and 12 respectively, recites an evaluation, judgment, comparison, or other mental process that could practically be performed in the human mine (or with pen and paper). See MPEP § 2106.04(a)(2)(III).
Claims 4 and 14 incorporates the rejection of independent claims 1 and 11 respectively, and does not integrate the judicial exception into a practical application.
Claims 5 and 15 incorporates the rejection of independent claims 1 and 11 respectively, and does not integrate the judicial exception into a practical application.
Claims 6 and 16 incorporates the rejection of dependent claims 5 and 15 respectively, viewed as additional elements that merely refine the abstract idea. MPEP §§ 2106.04(d), 2106.05(g).
Claim 7 incorporates the rejection of dependent claim 5, viewed as additional elements that merely refine the abstract idea. MPEP §§ 2106.04(d), 2106.05(g).
Claims 8 and 18 incorporates the rejection of independent claims 1 and 17, and does not integrate the judicial exception into a practical application.
Claim 9 and 19 incorporates the rejection of dependent claims 8 and 18, adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “ML” is used nor the specification makes it clear how these actions are performed.
Claim 10 and 20 incorporates the rejection of dependent claims 9 and 19, recites an evaluation, judgment, comparison, or other mental process that could practically be performed in the human mine (or with pen and paper). See MPEP § 2106.04(a)(2)(III).
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.
Claim(s) 1-2, 4, 8-12, 14 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy et al. (Pub No.: 20240104407 A1), hereinafter referred to a McCarthy in view of Jimenez et al. (Pub No.: 20160140857 A1), hereinafter referred to a Jimenez and further in view of Song et al. (Pub No.: 20240320543 A1), hereinafter referred to as Song.
With respect to claim 1, McCarthy disclose:
A computer-implemented method, the computer-implemented method comprising: generating, by one or more processors and using a metric-specific predictive model, a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group (In paragraph [0032], McCarthy discloses that the predictive machine learning model may compute predictive risk score for each resource-requesting entity range of different relevant events. In Fig. 6 and paragraphs [0089-0090], McCarthy discloses the predictive analysis a computing entity generates, using a predictive machine learning model, one or more predictive risk scores associated with one or more events based at least in part on historical data.)
Generating, by the one or more processors and using an action-specific causal inference model, a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action (In Fig. 6 and paragraph [0092], the system uses a casual inference machine learning model to predict how much a proposed action will affect a particular entity's outcome. It computes numerical estimates of the action's effect using predictive risk scores, historical data, and casual graph data, then uses those estimates to determine which group is likely to benefit the most and prioritizes resources accordingly.)
Initiating, by the one or more processors, a performance of the prediction-based action based on the categorical improvement prediction (In paragraph [0085-0086], McCarthy discloses that the system first uses causal effect predictions to benefit from a proposed action. It then executes the prediction-based action, such as allocating resources for those prioritized groups based on the prediction results.)
With respect to claim 1, McCarthy does not explicitly disclose:
generating, by the one or more processors, a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold
generating, by the one or more processors, a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
However, it is known by Jimenez to disclose:
Generating, by the one or more processors, a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold (In paragraph [0036], Jimenez discloses how the system evaluates a user's performance by comparing individual performance metrics against predefined thresholds to identify areas needing improvement.)
McCarthy and Jimenez are analogous pieces of art because both references concern prediction model generating prediction-based actions. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify McCarthy by a predictive machine learning model is configured to generate one or more predictive risk scores associated with one or more events based at least in part on the historical data as taught by McCarthy, while a threshold set for each metric as taught by Jimenez. The motivation for doing so would have been to improve resource allocation by predicting causal effect of one or more actions (See [0020] of McCarthy.)
With respect to claim 1, McCarthy and Jimenez does not explicitly disclose:
generating, by the one or more processors, a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
However, it is known by Song to disclose:
Generating, by the one or more processors, a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics (In paragraph [0038], Song discloses the system assigning weights to each machine learning model in a cluster, combines the models' prediction outputs according to those weights, and generates one overall prediction for the cluster.)
McCarthy in view Jimenez and Song are analogous pieces of art because both references concern the trained machine learning model's predictive performance. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Song by combining a model performance score of each machine learning model in the cluster in accordance with a corresponding weight of each machine learning model as taught by Song. The motivation for doing so would have been to increase predictive accuracy as taught by Song (See [0035] of Song.)
Regarding claim 2, McCarthy in view Jimenez and Song disclose the elements of claim 1. In addition, McCarthy disclose:
The computer-implemented method of claim 1, further comprising: generating a metric-level impact score for the prediction-based action based on the metric-level categorical improvement prediction, the categorical improvement prediction, and a quality impact score corresponding to the categorical improvement prediction (In paragraph [0090], McCarthy discloses that the system uses a predictive machine learning model to analyze historical data and generates a predicted output (predictive risk scores).)
Generating a causal metric-level impact score for the prediction-based action based on the metric-level impact score and the metric-specific predictive impact measure (In paragraph [0090-0091], McCarthy discloses using historical data as input and producing outputs that feed into the causal inference model used in the next stage. The system generates, using the casual inference machine learning model, one or more casual effect prediction outcome of interest. )
Generating a causal quality-based impact score for the prediction-based action based on an aggregation of the causal metric-level impact score and a plurality of causal metric-level impact scores respectively corresponding to the plurality of quality metrics and the evaluation entity (In paragraph [0091], McCarthy discloses that the system passes the predictive risk scores generated by the predictive machine learning model, together with filtered historical data and expert causal graph information, to a causal inference machine learning model. The causal model uses this information to understand causal relationships, determine which variables should be controlled, and identify the effect of action on outcomes.)
Regarding claim 4, McCarthy in view Jimenez and Song disclose the elements of claim 1. In addition, Jimenez disclose:
The computer-implemented method of claim 1, wherein generating the metric-level categorical improvement prediction for the entity group comprises: generating a modified quality performance measure for the evaluation entity based on an aggregation of the predictive quality performance measure and the metric-specific predictive impact measure (In paragraph [0036], Jimenez discloses that the user performance report may include performance metrics (e.g., outcome and resource use measures, patient satisfaction measures, composite performance measures, electronic quality measures, and so forth). Based on the performance metrics in the user performance report, a user score for each of the performance metrics may be determined and compared to the predetermined threshold.)
generating a group modified quality performance measure for the entity group based on an aggregation of the modified quality performance measure and a plurality of modified quality performance measure respectively corresponding the plurality of evaluation entities within the entity group (In paragraph [0030], Jimenez discloses that the personalized and targeted training system can generate a completion model reflecting the predicted completion and completion timing of the training by a group of users.)
generating the metric-level categorical improvement prediction based on a comparison between the group modified quality performance measure and the metric-specific categorical threshold (In paragraph [0036], Jimenez discloses how the system evaluates a user's performance by comparing individual performance metrics against predefined thresholds to identify areas needing improvement.)
Regarding claim 8, McCarthy in view Jimenez and Song disclose the elements of claim 1. In addition, Song disclose:
The computer-implemented method of claim 1, wherein the categorical improvement prediction for the entity group with respect to the categorical ranking scheme is based on a weighted aggregation of the metric-level categorical improvement prediction, the plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics, one or more operational quality measures, and one or more scheme-based quality improvement measures (In paragraph [0066-0067], Song discloses that the system combines weighted prediction results from multiple machine learning models to produce an overall cluster performance. If the score passes the threshold, it updates the cluster by adding a new model and recalculating model weights to improve predictive accuracy.)
Regarding claim 9, McCarthy in view Jimenez and Song disclose the elements of claim 8. In addition, McCarthy disclose:
The computer-implemented method of claim 8, further comprising: generating, using a machine learning operational forecasting model, the one or more operational quality measures for the entity group (In paragraph [0090], McCarthy discloses using a predictive machine learning model to generate one or more predictive risk scores for future events. )
Regarding claim 10, McCarthy in view Jimenez and Song disclose the elements of claim 8. In addition, McCarthy disclose:
The computer-implemented method of claim 8, further comprising: generating, using a rule-based model corresponding to the categorical ranking scheme, the one or more scheme-based quality improvement measures based on the plurality of metric-level categorical improvement predictions respectively corresponding to the plurality of quality metrics and the one or more operational quality measures (In paragraph [0032], McCarthy discloses that the predictive machine learning model may compute predictive risk scores for each resource-requesting entity for a range of different relevant events.)
With respect to claim 11, McCarthy disclose:
A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to: generate, using a metric-specific predictive model, a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group (In paragraph [0032], McCarthy discloses that the predictive machine learning model may compute predictive risk score for each resource-requesting entity range of different relevant events. In Fig. 6 and paragraphs [0089-0090], McCarthy discloses the predictive analysis a computing entity generates, using a predictive machine learning model, one or more predictive risk scores associated with one or more events based at least in part on historical data.)
Generate, using an action-specific causal inference model, a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action (In Fig. 6 and paragraph [0092], the system uses a casual inference machine learning model to predict how much a proposed action will affect a particular entity's outcome. It computes numerical estimates of the action's effect using predictive risk scores, historical data, and casual graph data, then uses those estimates to determine which group is likely to benefit the most and prioritizes resources accordingly.)
initiate a performance of the prediction-based action based on the categorical improvement prediction (In paragraph [0085-0086], McCarthy discloses that the system first uses causal effect predictions to benefit from a proposed action. It then executes the prediction-based action, such as allocating resources for those prioritized groups based on the prediction results.)
With respect to claim 11, McCarthy does not explicitly disclose:
Generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold
Generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
However, it is known by Jimenez to disclose:
Generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold (In paragraph [0036], Jimenez discloses how the system evaluates a user's performance by comparing individual performance metrics against predefined thresholds to identify areas needing improvement.)
McCarthy and Jimenez are analogous pieces of art because both references concern prediction model generating prediction-based actions. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify McCarthy by a predictive machine learning model is configured to generate one or more predictive risk scores associated with one or more events based at least in part on the historical data as taught by McCarthy, while a threshold set for each metric as taught by Jimenez. The motivation for doing so would have been to improve resource allocation by predicting causal effect of one or more actions (See [0020] of McCarthy.)
With respect to claim 11, McCarthy and Jimenez does not explicitly disclose:
Generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
However, it is known by Song to disclose:
Generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics (In paragraph [0038], Song discloses the system assigning weights to each machine learning model in a cluster, combines the models' prediction outputs according to those weights, and generates one overall prediction for the cluster.)
McCarthy in view Jimenez and Song are analogous pieces of art because both references concern the trained machine learning model's predictive performance. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Song by combining a model performance score of each machine learning model in the cluster in accordance with a corresponding weight of each machine learning model as taught by Song. The motivation for doing so would have been to increase predictive accuracy as taught by Song (See [0035] of Song.)
Regarding claim 12, McCarthy in view Jimenez and Song disclose the elements of claim 11. In addition, McCarthy disclose:
The computing system of claim 11, wherein the one or more processors are further configured to: generate a metric-level impact score for the prediction-based action based on the metric-level categorical improvement prediction, the categorical improvement prediction, and a quality impact score corresponding to the categorical improvement prediction (In paragraph [0090], McCarthy discloses that the system uses a predictive machine learning model to analyze historical data and generates a predicted output (predictive risk scores).)
Generate a causal metric-level impact score for the prediction-based action based on the metric-level impact score and the metric-specific predictive impact measure (In paragraph [0090-0091], McCarthy discloses using historical data as input and producing outputs that feed into the causal inference model used in the next stage. The system generates, using the casual inference machine learning model, one or more casual effect prediction outcome of interest. )
Generate a causal quality-based impact score for the prediction-based action based on an aggregation of the causal metric-level impact score and a plurality of causal metric-level impact scores respectively corresponding to the plurality of quality metrics and the evaluation entity (In paragraph [0091], McCarthy discloses that the system passes the predictive risk scores generated by the predictive machine learning model, together with filtered historical data and expert causal graph information, to a causal inference machine learning model. The causal model uses this information to understand causal relationships, determine which variables should be controlled, and identify the effect of action on outcomes.)
Regarding claim 14, McCarthy in view Jimenez and Song disclose the elements of claim 11. In addition, Jimenez disclose:
The computing system of claim 11, wherein generating the metric-level categorical improvement prediction for the entity group comprises: generating a modified quality performance measure for the evaluation entity based on an aggregation of the predictive quality performance measure and the metric-specific predictive impact measure (In paragraph [0036], Jimenez discloses that the user performance report may include performance metrics (e.g., outcome and resource use measures, patient satisfaction measures, composite performance measures, electronic quality measures, and so forth). Based on the performance metrics in the user performance report, a user score for each of the performance metrics may be determined and compared to the predetermined threshold.)
generating a group modified quality performance measure for the entity group based on an aggregation of the modified quality performance measure and a plurality of modified quality performance measure respectively corresponding the plurality of evaluation entities within the entity group (In paragraph [0030], Jimenez discloses that the personalized and targeted training system can generate a completion model reflecting the predicted completion and completion timing of the training by a group of users.)
generating the metric-level categorical improvement prediction based on a comparison between the group modified quality performance measure and the metric-specific categorical threshold (In paragraph [0036], Jimenez discloses how the system evaluates a user's performance by comparing individual performance metrics against predefined thresholds to identify areas needing improvement.)
With respect to claim 17, McCarthy disclose:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: generate, using a metric-specific predictive model, a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group (In paragraph [0032], McCarthy discloses that the predictive machine learning model may compute predictive risk score for each resource-requesting entity range of different relevant events. In Fig. 6 and paragraphs [0089-0090], McCarthy discloses the predictive analysis a computing entity generates, using a predictive machine learning model, one or more predictive risk scores associated with one or more events based at least in part on historical data.)
Generate, using an action-specific causal inference model, a metric-specific predictive impact measure based on the quality metric, the evaluation entity, and a prediction-based action (In Fig. 6 and paragraph [0092], the system uses a casual inference machine learning model to predict how much a proposed action will affect a particular entity's outcome. It computes numerical estimates of the action's effect using predictive risk scores, historical data, and casual graph data, then uses those estimates to determine which group is likely to benefit the most and prioritizes resources accordingly.)
initiate a performance of the prediction-based action based on the categorical improvement prediction (In paragraph [0085-0086], McCarthy discloses that the system first uses causal effect predictions to benefit from a proposed action. It then executes the prediction-based action, such as allocating resources for those prioritized groups based on the prediction results.)
With respect to claim 17, McCarthy does not explicitly disclose:
Generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold
Generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
However, it is known by Jimenez to disclose:
Generate a metric-level categorical improvement prediction for the entity group with respect to the quality metric based on a comparison of the predictive quality performance measure, the metric-specific predictive impact measure, and a metric-specific categorical threshold (In paragraph [0036], Jimenez discloses how the system evaluates a user's performance by comparing individual performance metrics against predefined thresholds to identify areas needing improvement.)
McCarthy and Jimenez are analogous pieces of art because both references concern prediction model generating prediction-based actions. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify McCarthy by a predictive machine learning model is configured to generate one or more predictive risk scores associated with one or more events based at least in part on the historical data as taught by McCarthy, while a threshold set for each metric as taught by Jimenez. The motivation for doing so would have been to improve resource allocation by predicting causal effect of one or more actions (See [0020] of McCarthy.)
With respect to claim 17, McCarthy and Jimenez does not explicitly disclose:
Generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics
However, it is known by Song to disclose:
Generate a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics (In paragraph [0038], Song discloses the system assigning weights to each machine learning model in a cluster, combines the models' prediction outputs according to those weights, and generates one overall prediction for the cluster.)
McCarthy in view Jimenez and Song are analogous pieces of art because both references concern the trained machine learning model's predictive performance. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Song by combining a model performance score of each machine learning model in the cluster in accordance with a corresponding weight of each machine learning model as taught by Song. The motivation for doing so would have been to increase predictive accuracy as taught by Song (See [0035] of Song.)
Regarding claim 18, McCarthy in view Jimenez and Song disclose the elements of claim 17. In addition, Song disclose:
The one or more non-transitory computer-readable storage media of claim 17, wherein the categorical improvement prediction for the entity group with respect to the categorical ranking scheme is based on a weighted aggregation of the metric-level categorical improvement prediction, the plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics, one or more operational quality measures, and one or more scheme-based quality improvement measures (In paragraph [0066-0067], Song discloses that the system combines weighted prediction results from multiple machine learning models to produce an overall cluster performance. If the score passes the threshold, it updates the cluster by adding a new model and recalculating model weights to improve predictive accuracy. )
Regarding claim 19, McCarthy in view Jimenez and Song disclose the elements of claim 18. In addition, McCarthy disclose:
The one or more non-transitory computer-readable storage media of claim 18, wherein the one or more processors are further caused to: generating, using a machine learning operational forecasting model, the one or more operational quality measures for the entity group (In paragraph [0090], McCarthy discloses using a predictive machine learning model to generate one or more predictive risk scores for future events. )
Regarding claim 20, McCarthy in view Jimenez and Song disclose the elements of claim 8. In addition, McCarthy disclose:
The one or more non-transitory computer-readable storage media of claim 18, wherein the one or more processors are further caused to: generating, using a rule-based model corresponding to the categorical ranking scheme, the one or more scheme-based quality improvement measures based on the plurality of metric-level categorical improvement predictions respectively corresponding to the plurality of quality metrics and the one or more operational quality measures (In paragraph [0032], McCarthy discloses that the predictive machine learning model may compute predictive risk scores for each resource-requesting entity for a range of different relevant events.)
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy in view of Jimenez, Song and further in view of Brooks et al. (Pub No.: 20220187774 A1), hereinafter referred to as Brooks.
Regarding claim 3, McCarthy in view Jimenez and Song disclose the elements of claim 2. McCarthy in view Jimenez and Song do not explicitly disclose:
The computer-implemented method of claim 2, wherein the performance of the prediction-based action is based on a direct comparison between the causal quality-based impact score of the prediction-based action and a plurality of causal quality-based impact scores respectively corresponding to a plurality of candidate prediction-based actions for the entity group
However, Brooks disclose the limitation (In paragraph [0212], Brooks compares different possible parameter values using a casual model, assigns probabilities to those values based on their expected merit, and selects a value to use.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of McCarthy in view Jimenez and Song before them to include Brooks with determining causal models for controlling environments as taught by Brooks. The motivation for doing so would have been to increase the precision of the causal model (See [0208] of Brooks.)
Regarding claim 13, McCarthy in view Jimenez and Song disclose the elements of claim 12. McCarthy in view Jimenez and Song do not explicitly disclose:
The computing system of claim 12, wherein the performance of the prediction-based action is based on a direct comparison between the causal quality-based impact score of the prediction-based action and a plurality of causal quality-based impact score respectively corresponding to a plurality of candidate prediction-based actions for the entity group
However, Brooks disclose the limitation (In paragraph [0212], Brooks compares different possible parameter values using a casual model, assigns probabilities to those values based on their expected merit, and selects a value to use.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of McCarthy in view Jimenez and Song before them to include Brooks with determining causal models for controlling environments as taught by Brooks. The motivation for doing so would have been to increase the precision of the causal model (See [0208] of Brooks.)
Claims 5-7 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over McCarthy in view of Jimenez, Song and further in view of Nichols et al. (Pub No.: 20140330621 A1), hereinafter referred to as Nichols.
Regarding claim 5, McCarthy in view Jimenez and Song disclose the elements of claim 1. McCarthy in view Jimenez and Song do not explicitly disclose:
The computer-implemented method of claim 1, wherein the plurality of quality metrics comprises: (i) one or more member-based quality metrics that respectively define an effectiveness of the evaluation entity with respect to a service for a plurality of predictive entities associated with the evaluation entity
(ii) one or more survey-based quality metrics that respectively define a performance of the evaluation entity with respect to a survey performed by the plurality of predictive entities
However, Nichols disclose the limitation:
The computer-implemented method of claim 1, wherein the plurality of quality metrics comprises: (i) one or more member-based quality metrics that respectively define an effectiveness of the evaluation entity with respect to a service for a plurality of predictive entities associated with the evaluation entity (In paragraph [0097], Nichols discloses performance metrics such as outcome measures, resource-use measures, and composite performance measures.)
(ii) one or more survey-based quality metrics that respectively define a performance of the evaluation entity with respect to a survey performed by the plurality of predictive entities (In paragraph [0097], Nichols discloses electronic quality measures, while also using survey-derived information in the evaluation process.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of McCarthy in view Jimenez and Song before them to include Nichols with prioritizing opportunities for improved performance across the different modeled Centers for Medicare and Medicaid Services (CMS) measures as taught by Nichols. The motivation for doing so would have been to select improved performance goals (See [0122] of Nichols.)
Regarding claim 6, McCarthy in view Jimenez, Song and Nichols disclose the elements of claim 5. In addition, Jimenez disclose:
The computer-implemented method of claim 5, wherein the metric-specific predictive model for a member-based quality metric comprises a metric-specific performance forecasting model that is previously trained to generate the predictive quality performance measure for the evaluation entity based on a plurality of historical data objects associated with the evaluation entity (In paragraph [0032], Jimenez discloses a predictive machine learning model that analyzes historical data to generate predictive risk scores. The model may be implemented as a RETAIN neural network that emphasizes recent events and important historical factors.)
Regarding claim 7, McCarthy in view Jimenez, Song and Nichols disclose the elements of claim 5. In addition, Nichols disclose:
The computer-implemented method of claim 5, wherein the metric-specific predictive model for a survey-based quality metric comprises a metric-specific performance simulation model that is configured to simulate a performance of a respective survey corresponding to the survey-based quality metric (In Fig. 10 and paragraph [0125], Nichols discloses creating a simulation model, modeling different operational scenarios, simulating performance under various conditions and evaluating predicted outcomes before implementation.)
Regarding claim 15, McCarthy in view Jimenez and Song disclose the elements of claim 11. McCarthy in view Jimenez and Song do not explicitly disclose:
The computing system of claim 11, wherein the plurality of quality metrics comprises: (i) one or more member-based quality metrics that respectively define an effectiveness of the evaluation entity with respect to a service for a plurality of predictive entities associated with the evaluation entity
(ii) one or more survey-based quality metrics that respectively define a performance of the evaluation entity with respect to a survey performed by the plurality of predictive entities
However, Nichols disclose the limitation:
The computing system of claim 11, wherein the plurality of quality metrics comprises: (i) one or more member-based quality metrics that respectively define an effectiveness of the evaluation entity with respect to a service for a plurality of predictive entities associated with the evaluation entity (In paragraph [0097], Nichols discloses performance metrics such as outcome measures, resource-use measures, and composite performance measures.)
(ii) one or more survey-based quality metrics that respectively define a performance of the evaluation entity with respect to a survey performed by the plurality of predictive entities (In paragraph [0097], Nichols discloses electronic quality measures, while also using survey-derived information in the evaluation process.)
Accordingly, it would have been obvious to a person having ordinary skills in the art before the effective filling date of the claimed invention, having the teaching of McCarthy in view Jimenez and Song before them to include Nichols with prioritizing opportunities for improved performance across the different modeled Centers for Medicare and Medicaid Services (CMS) measures as taught by Nichols. The motivation for doing so would have been to select improved performance goals (See [0122] of Nichols.)
Regarding claim 16, McCarthy in view Jimenez, Song and Nichols disclose the elements of claim 15. In addition, Jimenez disclose:
The computing system of claim 15, wherein the metric-specific predictive model for a member-based quality metric comprises a metric-specific performance forecasting model that is previously trained to generate the predictive quality performance measure for the evaluation entity based on a plurality of historical data objects associated with the evaluation entity (In paragraph [0032], Jimenez discloses a predictive machine learning model that analyzes historical data to generate predictive risk scores. The model may be implemented as a RETAIN neural network that emphasizes recent events and important historical factors.)
Conclusion
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights