DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Applicant’s submission filed 30 June 2026 [hereinafter Response] has been entered, where:
Claims 1, 2, 4, 8, 15, 16, and 20 have been amended.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 U.S.C. § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of
“storing, by the one or more processors, a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity.” The activity of “. . . to produce a utility classification” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental processes, (MPEP 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP 2106.04(a)(2)).
Also, the claim recites more details or specifics to the abstract idea of “. . . to produce a utility classification for the predictive entity,” “wherein producing the utility classification comprises: (i) determining a plurality of error measures that correspond to a plurality of timeseries processing machine learning models with respect to a historical utility timeseries data object associated with the predictive entity,” “(ii) generating a forecasted utility classification timeseries data object for the predictive entity based at least in part on an output of processing the historical utility timeseries data object using a timeseries processing machine learning model of the plurality of timeseries processing machine learning models that corresponds to a lowest error measure of the plurality of error measures,” “(iii) generating a utility classification score for the predictive entity based at least in part on the forecasted utility classification timeseries data object,” and “(iv) determining the utility classification is determined based at least in part on the utility classification score and a utility classification policy,” and accordingly, are merely more specific to the abstract idea. Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “computer-implemented method,” “one or more processors,” and “an electronic communication platform,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. The claim also recites limitations including “a temporal classification score generation machine learning model,” “a utility classification score generation machine learning model,” “a plurality of timeseries processing machine learning models,” and “an error-minimizing timeseries processing machine learning model.” These machine learning models are recited at a high-level of generality, and the claim does not provide any details about how the respective machine learning models operate or how the respective classifications are made. Accordingly, the respective “machine learning models” are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not integrate the abstract idea into a practical application.
The claim also recites the limitation “providing, by one or more processors, one or more temporal classification input features of a predictive entity to a temporal classification score generation machine learning model to produce a temporal classification for the predictive entity.” The activity of “providing” is a pre-processing insignificant extra-solution activity of data gathering for input, (MPEP §2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites more details or specifics of the additional element of “providing . . . one or more temporal classification input features,” wherein “(i) the temporal classification score generation machine learning model is configured to determining a temporal classification score based at least in part on one or more temporal classification input features for the predictive entity,” “(ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “(iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “(iv) the non-extremal prospective period is defined based at least in part on at least one of a lower extremal prospective period and an upper extremal prospective period,” “(v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “(vi) the one or more temporal classification input features include an entity cost density feature for the predictive entity,” and accordingly, are merely more specific to the additional element.
The claim further recites “storing . . . a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity, . . . .” The activity of “storing” is an insignificant extra-solution activity of mere data gathering, which does not integrate the abstract idea into a practical application.
Also, the claim recites “providing . . . the predictive entity to a decision tree model to perform one or more prediction-based actions based at least in part on a predicted level metric of the predictive entity,” in which the plain meaning of the term “providing ” includes sending a result of the claim, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and is further as post-processing insignificant extra-solution activity of sending a result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim recites more details or specifics of the additional element of “providing . . . to perform on ore more prediction-based actions,” “wherein (a) the predictive entity corresponds to a hybrid temporal-utility classification that is stored in association with a set of one or more decision features of the decision tree model based at least in part on the temporal classification and the utility classification,” “(b) the predicted level metric is based on the hybrid temporal-utility classification,” and “(c) the set of one or more decision features is stored in association with the one or more prediction-based actions,” and accordingly, are merely more specific to the additional element.
The claim also recites more details or specifics of the additional element of “the one or more prediction-based actions” that is “comprises (i) reducing a plurality of targeted communications, that correspond to the predictive entity, to a targeted communication that is prioritized for the predictive entity relative to another predictive entity,” “ (ii) generating the targeted communication in an electronic communication format,” and “ (iii) providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format,” and accordingly, are merely more specific to the additional element. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements recited in the claim beyond the identified judicial exception include a “computer-implemented method,” “one or more processors,” and “an electronic communication platform,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. The claim also recites limitations including “a temporal classification score generation machine learning model,” “a utility classification score generation machine learning model,” “a plurality of timeseries processing machine learning models,” and “an error-minimizing timeseries processing machine learning model.” These machine learning models are recited at a high-level of generality, and the claim does not provide any details about how the respective machine learning models operate or how the respective classifications are made. Accordingly, the respective “machine learning models” are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim also recites the limitation “providing, by one or more processors, one or more temporal classification input features of a predictive entity to a temporal classification score generation machine learning model to produce a temporal classification for the predictive entity.” The activity of “providing” is a well understood, routine, and conventional activity of retrieving information in memory, (MPEP 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea.
The claim also recites more details or specifics of the additional element of “providing . . . one or more temporal classification input features,” wherein “(i) the temporal classification score generation machine learning model is configured to determining a temporal classification score based at least in part on one or more temporal classification input features for the predictive entity,” “(ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “(iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “(iv) the non-extremal prospective period is defined based at least in part on at least one of a lower extremal prospective period and an upper extremal prospective period,” “(v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “(vi) the one or more temporal classification input features include an entity cost density feature for the predictive entity,” and accordingly, are merely more specific to the additional element.
The claim further recites “storing . . . a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity, . . . .” The activity of “storing” is an well-understood, routine, and conventional activity of storing information in memory, (MPEP 2106.05(d) sub II.iv), which does not amount to significantly more than the abstract idea.
Also, the claim recites “providing . . . the predictive entity to a decision tree model to perform one or more prediction-based actions based at least in part on a predicted level metric of the predictive entity,” in which the plain meaning of the term “providing” includes sending a result of the claim, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and that includes sending and/or outputs a result of the abstract idea, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP §2106.05(g)), and is as a well-understood, routine, and conventional activity of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim recites more details or specifics of the additional element of “providing . . . to perform on ore more prediction-based actions,” “wherein (a) the predictive entity corresponds to a hybrid temporal-utility classification that is stored in association with a set of one or more decision features of the decision tree model based at least in part on the temporal classification and the utility classification,” “(b) the predicted level metric is based on the hybrid temporal-utility classification,” and “(c) the set of one or more decision features is stored in association with the one or more prediction-based actions,” and accordingly, are merely more specific to the additional element.
The claim also recites more details or specifics of the additional element of “the one or more prediction-based actions” that is ”comprises (i) reducing a plurality of targeted communications, that correspond to the predictive entity, to a targeted communication that is prioritized for the predictive entity relative to another predictive entity,” “(ii) generating the targeted communication in an electronic communication format,” and “(iii) providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format,” and accordingly, is merely more specific to the additional element. Therefore, claim 1 is subject-matter ineligible.
Claim 15 recites an apparatus, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “storing, by the one or more processors, a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity.” The activity of “. . . to produce” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental processes, (MPEP 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP 2106.04(a)(2)).
The claim recites more details or specifics to the abstract idea of Also, the claim recites more details or specifics to the abstract idea of “. . . to produce a utility classification for the predictive entity,” “wherein producing the utility classification comprises: (i) determining a plurality of error measures that correspond to a plurality of timeseries processing machine learning models with respect to a historical utility timeseries data object associated with the predictive entity,” “(ii) generating a forecasted utility classification timeseries data object for the predictive entity based at least in part on an output of processing the historical utility timeseries data object using a timeseries processing machine learning model of the plurality of timeseries processing machine learning models that corresponds to a lowest error measure of the plurality of error measures,” “(iii) generating a utility classification score for the predictive entity based at least in part on the forecasted utility classification timeseries data object,” and “(iv) determining the utility classification is determined based at least in part on the utility classification score and a utility classification policy,” and accordingly, are merely more specific to the abstract idea. Thus, claim 15 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “one or more processors,” “one or more memories storing processor-executable instructions that, when executed by the one or more processors, causes . . .,” and “an electronic communication platform,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. The claim also recites limitations including “a temporal classification score generation machine learning model,” “a utility classification score generation machine learning model,” “a plurality of timeseries processing machine learning models,” and “an error-minimizing timeseries processing machine learning model.” These machine learning models are recited at a high-level of generality, and the claim does not provide any details about how the respective machine learning models operate or how the respective classifications are made. Accordingly, the respective “machine learning models” are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not integrate the abstract idea into a practical application.
The claim also recites the limitation “providing, by one or more processors, one or more temporal classification input features of a predictive entity to a temporal classification score generation machine learning model to produce a temporal classification for the predictive entity.” The activity of “providing” is a pre-processing insignificant extra-solution activity of data gathering for input, (MPEP §2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites more details or specifics of the additional element of “providing . . . one or more temporal classification input features,” wherein “(i) the temporal classification score generation machine learning model is configured to determining a temporal classification score based at least in part on one or more temporal classification input features for the predictive entity,” “(ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “(iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “(iv) the non-extremal prospective period is defined based at least in part on at least one of a lower extremal prospective period and an upper extremal prospective period,” “(v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “(vi) the one or more temporal classification input features include an entity cost density feature for the predictive entity,” and accordingly, are merely more specific to the additional element.
The claim further recites “storing . . . a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity, . . . .” The activity of “storing” is an insignificant extra-solution activity of mere data gathering, which does not integrate the abstract idea into a practical application.
Also, the claim recites “providing . . . the predictive entity to a decision tree model to perform one or more prediction-based actions based at least in part on a predicted level metric of the predictive entity,” in which the plain meaning of the term “providing” includes sending a result of the claim, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and is further as post-processing insignificant extra-solution activity of sending a result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim recites more details or specifics of the additional element of “providing . . . to perform on ore more prediction-based actions,” “wherein (a) the predictive entity corresponds to a hybrid temporal-utility classification that is stored in association with a set of one or more decision features of the decision tree model based at least in part on the temporal classification and the utility classification,” “(b) the predicted level metric is based on the hybrid temporal-utility classification,” and “(c) the set of one or more decision features is stored in association with the one or more prediction-based actions,” and accordingly, are merely more specific to the additional element.
The claim also recites more details or specifics of the additional element of “the one or more prediction-based actions” that is “comprises (i) reducing a plurality of targeted communications, that correspond to the predictive entity, to a targeted communication that is prioritized for the predictive entity relative to another predictive entity,” “ (ii) generating the targeted communication in an electronic communication format,” and “ (iii) providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format,” and accordingly, is merely more specific to the additional element. Therefore, claim 15 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements recited in the claim beyond the identified judicial exception include a “one or more processors,” “one or more memories storing processor-executable instructions that, when executed by the one or more processors, causes . . .,” and “an electronic communication platform,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. The claim also recites limitations including “a temporal classification score generation machine learning model,” “a utility classification score generation machine learning model,” “a plurality of timeseries processing machine learning models,” and “an error-minimizing timeseries processing machine learning model.” These machine learning models are recited at a high-level of generality, and the claim does not provide any details about how the respective machine learning models operate or how the respective classifications are made. Accordingly, the respective “machine learning models” are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim also recites the limitation “providing, by one or more processors, one or more temporal classification input features of a predictive entity to a temporal classification score generation machine learning model to produce a temporal classification for the predictive entity.” The activity of “providing” is a well understood, routine, and conventional activity of retrieving information in memory, (MPEP 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea.
The claim also recites more details or specifics of the additional element of “providing . . . one or more temporal classification input features,” wherein “(i) the temporal classification score generation machine learning model is configured to determining a temporal classification score based at least in part on one or more temporal classification input features for the predictive entity,” “(ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “(iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “(iv) the non-extremal prospective period is defined based at least in part on at least one of a lower extremal prospective period and an upper extremal prospective period,” “(v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “(vi) the one or more temporal classification input features include an entity cost density feature for the predictive entity,” and accordingly, are merely more specific to the additional element.
The claim further recites “storing . . . a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity, . . . .” The activity of “storing” is an well-understood, routine, and conventional activity of storing information in memory, (MPEP 2106.05(d) sub II.iv), which does not amount to significantly more than the abstract idea.
Also, the claim recites “providing . . . the predictive entity to a decision tree model to perform one or more prediction-based actions based at least in part on a predicted level metric of the predictive entity,” in which the plain meaning of the term “providing” includes sending a result of the claim, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and that includes sending and/or outputs a result of the abstract idea, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP §2106.05(g)), and is as a well-understood, routine, and conventional activity of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim recites more details or specifics of the additional element of “providing . . . to perform on ore more prediction-based actions,” “wherein (a) the predictive entity corresponds to a hybrid temporal-utility classification that is stored in association with a set of one or more decision features of the decision tree model based at least in part on the temporal classification and the utility classification,” “(b) the predicted level metric is based on the hybrid temporal-utility classification,” and “(c) the set of one or more decision features is stored in association with the one or more prediction-based actions,” and accordingly, are merely more specific to the additional element.
The claim also recites more details or specifics of the additional element of “the one or more prediction-based actions” that is “comprises (i) reducing a plurality of targeted communications, that correspond to the predictive entity, to a targeted communication that is prioritized for the predictive entity relative to another predictive entity,” “(ii) generating the targeted communication in an electronic communication format,” and “(iii) providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format,” and accordingly, is merely more specific to the additional element. Therefore, claim 15 is subject-matter ineligible.
Claim 20 recites one or more non-transitory computer-readable storage media, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “storing, by the one or more processors, a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity.” The activity of “. . . to produce” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental processes, (MPEP 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP 2106.04(a)(2)).
The claim recites more details or specifics to the abstract idea of Also, the claim recites more details or specifics to the abstract idea of “. . . to produce a utility classification for the predictive entity,” “wherein producing the utility classification comprises: (i) determining a plurality of error measures that correspond to a plurality of timeseries processing machine learning models with respect to a historical utility timeseries data object associated with the predictive entity,” “(ii) generating a forecasted utility classification timeseries data object for the predictive entity based at least in part on an output of processing the historical utility timeseries data object using a timeseries processing machine learning model of the plurality of timeseries processing machine learning models that corresponds to a lowest error measure of the plurality of error measures,” “(iii) generating a utility classification score for the predictive entity based at least in part on the forecasted utility classification timeseries data object,” and “(iv) determining the utility classification is determined based at least in part on the utility classification score and a utility classification policy,” and accordingly, are merely more specific to the abstract idea. Thus, claim 20 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “one or more processors,” “one or more non-transitory computer readable media storing processor-executable instructions that, when executed by any of the one or more processors, causes . . .,” and “an electronic communication platform,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. The claim also recites limitations including “a temporal classification score generation machine learning model,” “a utility classification score generation machine learning model,” “a plurality of timeseries processing machine learning models,” and “an error-minimizing timeseries processing machine learning model.” These machine learning models are recited at a high-level of generality, and the claim does not provide any details about how the respective machine learning models operate or how the respective classifications are made. Accordingly, the respective “machine learning models” are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not integrate the abstract idea into a practical application.
The claim also recites the limitation “providing, by one or more processors, one or more temporal classification input features of a predictive entity to a temporal classification score generation machine learning model to produce a temporal classification for the predictive entity.” The activity of “providing” is a pre-processing insignificant extra-solution activity of data gathering for input, (MPEP §2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites more details or specifics of the additional element of “providing . . . one or more temporal classification input features,” wherein “(i) the temporal classification score generation machine learning model is configured to determining a temporal classification score based at least in part on one or more temporal classification input features for the predictive entity,” “(ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “(iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “(iv) the non-extremal prospective period is defined based at least in part on at least one of a lower extremal prospective period and an upper extremal prospective period,” “(v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “(vi) the one or more temporal classification input features include an entity cost density feature for the predictive entity,” and accordingly, are merely more specific to the additional element.
The claim further recites “storing . . . a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity, . . . .” The activity of “storing” is an insignificant extra-solution activity of mere data gathering, which does not integrate the abstract idea into a practical application.
Also, the claim recites “providing . . . the predictive entity to a decision tree model to perform one or more prediction-based actions based at least in part on a predicted level metric of the predictive entity,” in which the plain meaning of the term “providing” includes sending a result of the claim, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and is further as post-processing insignificant extra-solution activity of sending a result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim recites more details or specifics of the additional element of “providing . . . to perform on ore more prediction-based actions,” “wherein (a) the predictive entity corresponds to a hybrid temporal-utility classification that is stored in association with a set of one or more decision features of the decision tree model based at least in part on the temporal classification and the utility classification,” “(b) the predicted level metric is based on the hybrid temporal-utility classification,” and “(c) the set of one or more decision features is stored in association with the one or more prediction-based actions,” and accordingly, are merely more specific to the additional element.
The claim also recites more details or specifics of the additional element of “the one or more prediction-based actions” that is “comprises (i) reducing a plurality of targeted communications, that correspond to the predictive entity, to a targeted communication that is prioritized for the predictive entity relative to another predictive entity,” “(ii) generating the targeted communication in an electronic communication format,” and “(iii) providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format,” and accordingly, is merely more specific to the additional element. Therefore, claim 20 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements recited in the claim beyond the identified judicial exception include a “one or more processors,” “one or more non-transitory computer readable media storing processor-executable instructions that, when executed by any of the one or more processors, causes . . .,” and “an electronic communication platform,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. The claim also recites limitations including “a temporal classification score generation machine learning model,” “a utility classification score generation machine learning model,” “a plurality of timeseries processing machine learning models,” and “an error-minimizing timeseries processing machine learning model.” These machine learning models are recited at a high-level of generality, and the claim does not provide any details about how the respective machine learning models operate or how the respective classifications are made. Accordingly, the respective “machine learning models” are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim also recites the limitation “providing, by one or more processors, one or more temporal classification input features of a predictive entity to a temporal classification score generation machine learning model to produce a temporal classification for the predictive entity.” The activity of “providing” is a well understood, routine, and conventional activity of retrieving information in memory, (MPEP 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea.
The claim also recites more details or specifics of the additional element of “providing . . . one or more temporal classification input features,” wherein “(i) the temporal classification score generation machine learning model is configured to determining a temporal classification score based at least in part on one or more temporal classification input features for the predictive entity,” “(ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “(iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “(iv) the non-extremal prospective period is defined based at least in part on at least one of a lower extremal prospective period and an upper extremal prospective period,” “(v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “(vi) the one or more temporal classification input features include an entity cost density feature for the predictive entity,” and accordingly, are merely more specific to the additional element.
The claim further recites “storing . . . a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity, . . . .” The activity of “storing” is an well-understood, routine, and conventional activity of storing information in memory, (MPEP 2106.05(d) sub II.iv), which does not amount to significantly more than the abstract idea.
Also, the claim recites “providing . . . the predictive entity to a decision tree model to perform one or more prediction-based actions based at least in part on a predicted level metric of the predictive entity,” in which the plain meaning of the term “providing” includes sending a result of the claim, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP § 2111), and that includes sending and/or outputs a result of the abstract idea, which in the example is recommended programs to offer, which is not inconsistent with the Applicant’s disclosure, (MPEP §2106.05(g)), and is as a well-understood, routine, and conventional activity of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim recites more details or specifics of the additional element of “providing . . . to perform on ore more prediction-based actions,” “wherein (a) the predictive entity corresponds to a hybrid temporal-utility classification that is stored in association with a set of one or more decision features of the decision tree model based at least in part on the temporal classification and the utility classification,” “(b) the predicted level metric is based on the hybrid temporal-utility classification,” and “(c) the set of one or more decision features is stored in association with the one or more prediction-based actions,” and accordingly, are merely more specific to the additional element.
The claim also recites more details or specifics of the additional element of “the one or more prediction-based actions” that is “comprises (i) reducing a plurality of targeted communications, that correspond to the predictive entity, to a targeted communication that is prioritized for the predictive entity relative to another predictive entity,” “(ii) generating the targeted communication in an electronic communication format,” and “(iii) providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format,” and accordingly, is merely more specific to the additional element. Therefore, claim 20 is subject-matter ineligible.
Claim 2 depends from claim 1. Claim 16 depends from claim 15. The claims recite more details or specifics to the abstract idea of “determining the one or more prediction-based actions,” by (claims 2 and 16: “identifying a decision tree data object,” and “determining the one or more prediction-based actions based at least in part on the recommended engagement action of the leaf-level node of the decision tree data object that corresponds to the predictive entity”), and accordingly, are merely more specific to the abstract idea. The claim also recites the additional element of a “decision tree data object,” which is recited at a high-level of generality, and therefore is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application under Step 2A Prong Two, nor amount to significantly more than the abstract idea under Step 2B. The claim recites further details or specifics to the additional element of the “decision tree data object,” by (claims 2 and 16: “(i) a root-level node of the decision tree data object is associated with the hybrid temporal-utility classification,” “(ii) each decision tree segment of the decision tree data object is associated with a candidate hybrid temporal-utility classification of a plurality of hybrid temporal-utility classifications and comprises nodes corresponding to decision features associated with the candidate hybrid temporal-utility classification,” “(iii) each leaf-level node of the decision tree data object is associated with a recommended engagement action of a plurality of candidate engagement actions”), and accordingly, are merely more specific to the additional element. Therefore, claims 2 and 16 are subject-matter ineligible.
Claim 3 depends from claim 1. Claim 17 depends from claim 15. The claims recite more details or specifics to the abstract idea of “determining . . . a utility classification,” by “the utility score generation machine learning model” by (claims 3 and 17: “determining . . . a plurality of per-time-unit utility classification scores for the predictive entity, wherein each per-time-unit utility classification score is associated with a defined time unit of a plurality of defined time units of a prospective time period that is associated with the forecasted utility classification timeseries data object,” and “determining the utility classification score based at least in part on each per-time-unit utility classification score”), and accordingly, are merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claims 3 and 17 are subject-matter ineligible.
Claims 4 and 5 depend directly or indirectly from claim 1. The claims recite more details or specifics to the additional element of “the plurality of timeseries processing machine learning models,” which comprises (claim 4: “an autoregressive forecasting machine learning model and an Unobserved Components Model (UCM)),” and (claim 5: “wherein the autoregressive forecasting machine learning model comprises an Auto Regressive Integrated Moving Average (ARIMA) machine learning model”), and accordingly, are merely more specific to the additional element. Therefore, claims 4 and 5 are subject-matter ineligible.
Claim 6 depends from claim 1. Claim 18 depends from claim 15. The claims recite more details or specifics to the abstract idea of “determining . . . a temporal classification,” where (claims 6 and 15: “[(v) the temporal classification is determined based at least in part on . . . a temporal classification policy,] wherein the temporal classification policy defines, for each distribution threshold of a plurality of distribution thresholds that are determined based at least in part on a cross-entity temporal classification score distribution for a plurality of historical predictive entities, a selected temporal classification of a plurality of defined temporal classifications”), and accordingly, are merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claims 6 and 18 are subject-matter ineligible.
Claim 7 depends from claim 1. Claim 19 depends from claim 15. The claims provide more details or specifics to the abstract idea of “determining . . . a utility classification,” by (claims 7 and 19: “[. . . wherein: the utility score generation machine-learning model is configured to: . . . (iv) the utility classification is determined based at least in part on . . . a utility classification policy,] wherein the utility classification policy defines, for each distribution threshold of a plurality of distribution thresholds that are determined based at least in part on a cross-entity utility classification score distribution for a plurality of historical predictive entities, a selected utility classification of a plurality of defined utility classifications”), and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claims 7 and 19 are subject-matter ineligible.
Claim 8 depends from claim 1. The claim recites more details or specifics to the abstract idea of “determining . . . a temporal classification,” by “wherein: the temporal classification is selected from a plurality of defined temporal classifications, the plurality of defined temporal classifications comprise an upper temporal classification, a lower temporal classification, and a medial temporal classification,” and also to the abstract idea of “determining . . . a utility classification” by “the utility classification is selected from a plurality of defined utility classifications,” and “the plurality of defined utility classifications comprise an upper utility classification, a lower utility classification, and a medial utility classification,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 8 is subject-matter ineligible.
Claim 9 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “determining the hybrid temporal-utility classification,” where “in response to determining that the temporal classification is the upper temporal classification and the utility classification is the lower utility classification, determining that the hybrid temporal-utility classification is a high-tenure low-reward hybrid temporal-utility classification,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 9 is subject-matter ineligible.
Claim 10 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “determining the hybrid temporal-utility classification,” where “in response to determining that the temporal classification is the lower temporal classification and the utility classification is the lower utility classification, determining that the hybrid temporal-utility classification is a low-tenure low-reward hybrid temporal-utility classification,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 10 is subject-matter ineligible.
Claim 11 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “determining the hybrid temporal-utility classification,” where “in response to determining that the temporal classification is the lower temporal classification and the utility classification is the upper utility classification, determining that the hybrid temporal-utility classification is a low-tenure high-reward hybrid temporal-utility classification,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 11 is subject-matter ineligible.
Claim 12 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “determining the hybrid temporal-utility classification,” where “in response to determining that the temporal classification is the upper temporal classification and the utility classification is the upper utility classification, determining that the hybrid temporal-utility classification is a high-tenure high-reward hybrid temporal-utility classification,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 12 is subject-matter ineligible.
Claim 13 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “determining the hybrid temporal-utility classification,” where “in response to determining that the temporal classification is the medial temporal classification, determining that the hybrid temporal-utility classification is a default hybrid temporal-utility classification,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 13 is subject-matter ineligible.
Claim 14 depends directly or indirectly from claim 1. The claim recites more details or specifics to the abstract idea of “determining the hybrid temporal-utility classification,” where “in response to determining that the utility classification is the medial utility classification, determining that the hybrid temporal-utility classification is a default hybrid temporal-utility classification,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claim 14 is subject-matter ineligible.
Response to Argument
5. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
35 U.S.C. § 101
6. Applicant submits “[t]he Office Action rejects claims 1-20 under 35 U.S.C. § 101 for allegedly being directed to ‘an abstract idea without significantly more.’ Office Action, page 2. As discussed during the interview, the claims recite a process for improving a machine learning model by leveraging a hybrid temporal-utility classification to perform actions that reduce a plurality of targeted communications to a targeted communication that is prioritized for a predictive entity relative to another predictive entity. In this way, computer system performance is improved by reducing operational load on user engagement systems. See Specification, As such, the machine learning techniques recited by the claims conform with at least one of the examples provided by the Advance Notice of Change to the MPEP in light of Ex Parte Desjardins Memorandum, December 5, 2025 (hereinafter Desjardins Memorandum).” (Response at p. 13).
Applicant submits that under Step 2A “Prong One, the Office Action alleges that the step of,
storing .. a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity[.] .. can practically be performed in the human mind .. ,
and alleges that certain other steps recited by claim 1 "are merely more specific to the abstract idea." Office Action, pages 2-3.” (Response at p. 17).
Continuing, Applicant submits that “Claim 1 describes a machine learning model technique that coordinates multiple specialized machine learning models-including a temporal classification score generation model, a utility classification score generation model, and a plurality of timeseries processing models-to provide a multi-model architecture for managing electronic communications. . . . The techniques recited by claim 1 cannot be practically performed within the human mind. For example, a human mind is not equipped to (i) determine a plurality of error measures across multiple timeseries processing machine learning models, (ii) generate a forecasted utility classification timeseries data object using error-minimizing model selection, (iii) provide a predictive entity to a decision tree model to perform prediction-based actions, and (iv) reduce, generate, and provide targeted electronic communications, as recited by claim 1.” (Response at pp. 17-18).
Under Step 2A Prong Two, Applicant argues that “[the limited analysis by the Action] no longer aligns with the MPEP, which has been amended to read:
When evaluating a claim as a whole, examiners should not dismiss additional elements as mere "generic computer components" without considering whether such elements confer a technological improvement to a technical problem, especially as to improvements to computer components or the computer system. Desjardins Memorandum, pages 3-4 (with reference to MPEP § 2106.05(a)).
As amended, claim 1 recites a hybrid temporal-utility classification technique that is based on multiple machine learning models and improves machine learning technology and computing system performance for managing targeted communications. See Specification, paragraphs [0018], [0070], [0093 ], and [0101]. The elements of claim 1 constitute an improvement in a technical field (e.g., machine learning) and computing system performance such that the claim, as a whole, integrates any alleged abstract idea into a practical application. The MPEP states that "[l]imitations the courts have found indicative that an additional element ( or combination of elements) may have integrated the exception into a practical application include an improvement in the functioning of a computer, or an improvement to other technology or technical field." MPEP § 2106.04(d). The second paragraph of MPEP § 2106.05(a), subsection I, has been revised, following Ex Parte Desjardins, to add the following example xiv to the list of examples that show improvement in computer functionality:
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential). Desjardins Memorandum, page 4.
As explained above, the claims recite an improvement in system performance (e.g., enable selection of reliable and effective engagement actions and thus avoiding the need for repeated/revised engagement actions that would otherwise impose a substantial operational load on user engagement systems, see Specification, paragraphs [0018], [0070], [0093], and [0101]) based upon adjustment to parameters ( e.g., training of parameters, hyper-parameters, and/or stored operations of a machine learning model, Id. at paragraphs [0031], [0033], [0035], [0037], [0046], [0078], [0082], and [0084]) of a machine learning model (e.g., a temporal classification score generation machine learning model, a utility classification score generation machine learning model, a plurality of timeseries processing machine learning models, and a decision tree model, Id. at paragraphs [0031], [0033], [0035], and [0037]) associated with tasks or workstreams (e.g., performing a prediction-based action to reduce operational load on user engagement systems by selecting reliable and effective engagement actions, Id. at paragraphs [0098], [0099] and [0101]). The hybrid temporal-utility classification technique described by claim 1, therefore, integrates any abstract idea into a practical application.” (Response at pp. 19-20).
“Claim 1 recites an improvement to machine learning technology through a hybrid temporal-utility classification technique that combines multiple specialized machine learning models to generate reliable predictive classifications. Id. at paragraphs [0018]-[0019]. Specifically, claim 1 describes a temporal classification score generation machine learning model that determines temporal classification based on non-extremal periodicity likelihood measures defined by extremal prospective periods, a utility classification score generation machine learning model that selects from a plurality of timeseries processing machine learning models based on determining which model corresponds to a lowest error measure, and a decision tree model that performs prediction-based actions based on a hybrid temporal-utility classification corresponding to both the temporal and utility classifications. Id. at paragraphs [0031], [0034], and [0037].” (Response at p. 21).
Examiner Response:
Referring to Step 2A Prong One, Examiner respectfully submits that the claims are directed to an abstract idea, as set out above in detail. The rejection identifies the judicial exception (for example, an abstract idea) by referring to what is recited (that is, set forth or described) in the claim and explains why it is considered an exception. Specifically, where the claim is directed to an abstract idea, the rejection identifies the abstract idea as it is recited (i.e., set forth or described) in the claim and explains why it is an abstract idea. (see MPEP § 2106.07(a)).
Applicant points to the activity of “storing” as not being an abstract idea. Examiner identifies this activity above, where:
The claim further recites “storing . . . a historical utility timeseries data object in association with a utility classification score generation machine learning model, to produce a utility classification for the predictive entity, . . . .” The activity of “storing” is an insignificant extra-solution activity of mere data gathering, which does not integrate the abstract idea into a practical application.
The activity identified as an abstract idea “to produce a utility classification,” which can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental processes, (MPEP 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP 2106.04(a)(2)). In this manner, the claims are directed to an abstract idea.
Also, Applicant submits that the “techniques recited by claim 1 cannot be practically performed within the human mind. For example, a human mind is not equipped to (i) determine a plurality of error measures across multiple timeseries processing machine learning models, (ii) generate a forecasted utility classification timeseries data object using error-minimizing model selection, (iii) provide a predictive entity to a decision tree model to perform prediction-based actions, and (iv) reduce, generate, and provide targeted electronic communications, as recited by claim 1.” (Response at pp. 17-18).
Exemplar claim 1 recites, with regard to “storing . . . a historical utility timeseries data object . . . “ that:
* * *
wherein comprises:
(i) determining a plurality of error measures that correspond to a plurality of timeseries processing machine learning models by processing the historical utility timeseries data object using the plurality of timeseries processing machine learning models,
(ii) generating a forecasted utility classification timeseries data object for the predictive entity based at least in part on an output of processing the historical utility timeseries data object using a timeseries processing machine learning model of the plurality of timeseries processing machine learning models that corresponds to a lowest error measure of the plurality of error measures,
(iii) generating a utility classification score for the predictive entity based at least in part on the forecasted utility classification timeseries data object, and
(iv) determining the utility classification based at least in part on the utility classification score and a utility classification policy; and
providing, by the one or more processors, the predictive entity to a decision tree model to perform one or more prediction-based actions based at least in part on a predicted level metric of the predictive entity, wherein:
(a) the predictive entity corresponds to a hybrid temporal-utility classification that is stored in association with a set of one or more decision features of the decision tree model based at least in part on the temporal classification and the utility classification,
(b) the predicted level metric is based on the hybrid temporal-utility classification,
(c) the set of one or more decision features is stored in association with the one or more prediction-based actions, and
(d) the one or more prediction-based actions comprises
(i) reducing a plurality of targeted communications , that correspond to the predictive entity, to a targeted communication that is prioritized for the predictive entity relative to another predictive entity,
(ii) generating the targeted communication in an electronic communication format, and
(iii) providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format.
(claim 1, lines 21-50 (emphasis added by Examiner for amended language)).
As set out above in detail, the activity of “to produce a utility classification” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental processes, (MPEP 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP 2106.04(a)(2)).
Also, the activities of “determining a plurality of error measures,” “generating a forecasted utility classification timeseries data object,” “generating a utility classification score,” and “determining the utility classification,” provide more details or specifics to the abstract idea of “to produce a utility classification,” while further contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are also a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
With regard to Applicant’s argument that “a human mind is not equipped” to perform these limitations, Examiner notes that if a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another.” (see MPEP § 2106.04(a)(2) sub III.B). Notably, a claim that requires a computer may still recite a mental process. (MPEP § 2106.04(a)(2) sub III.C).
Accordingly, the claims are directed to an abstract idea.
With respect to Step 2A Prong Two, Applicant submits the disclosure, in view of the Desjardins guidance, is directed to “improving a machine learning model by leveraging a hybrid temporal-utility classification to perform actions that reduce a plurality of targeted communications to a targeted communication that is prioritized for a predictive entity relative to another predictive entity (Response at p. 13).
The rejection above identifies any additional elements recited in the claim beyond the identified judicial exception (i.e., abstract idea); and evaluate the integration of the judicial exception into a practical application by explaining that the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)-(c) and (e)-(h).
“Integration” may be based on the improvements in the functioning of a computer or an improvement to any other technology or technical field. (MPEP § 2106.04(d)(1)). The evaluation requires, [i]n sum, that (1) the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Next, (2) if the specification sets forth such an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.
By way of example to Desjardins, the MPEP provides under Step 2A Prong Two that “the [Desjardins] specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems. Importantly, the [appeals review panel (ARP)] evaluated the claims as a whole in discerning at least the limitation ‘adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task’ reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).” (MPEP § 2106.04(d) sub III; see “Advance Notice of Change to the MPEP in light of Ex Parte Desjardins” (05 December 2025) at p. 2)).
However, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more.
Generally, the claim is directed to a user-engagement system, which can be inefficient by repeatedly sending irrelevant or revised outreach. With respect to these inefficiencies, the Applicant’s disclosure addresses the need for more reliable targeting and better prioritization of engagement actions. (see Specification ¶¶ 0001, 0018-20). However, the claims do not recite an improvement in the functioning of a computer or an improvement to any other technology or technical field.
For example, the claims as a whole are not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “computer-implemented method,” “one or more processors,” and “an electronic communication platform,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application.
Moreover, the claim also recites limitations including “a temporal classification score generation machine learning model,” “a utility classification score generation machine learning model,” “a plurality of timeseries processing machine learning models,” and “an error-minimizing timeseries processing machine learning model.” These machine learning models are recited at a high-level of generality, and the claim does not provide any details about how the respective machine learning models operate or how the respective classifications are made. Accordingly, the respective “machine learning models” are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not integrate the abstract idea into a practical application.
In contrast, the Desjardins guidance emphasizes that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems, such as through model parameters.
Accordingly, for these reasons and in view of the rejection set out above in detail, the claims are subject-matter ineligible.
Conclusion
7. 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.
8. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20230137432 to Selim et al.) teaches performing predictive data analysis by using hybrid prediction scores that are determined based at least in part on co-occurrence-based prediction scores and temporal prediction scores, where the co-occurrence-based prediction scores are determined based at least in part on co-occurrence-based historical representation of a sequence of prediction input codes and temporal historical representation of the sequence of prediction input codes.
(US Published Application 20230094583 to Buckley et al.) teaches a method that includes generating, using one or more computer processors, by utilizing a similarity determination machine learning model and based at least in part on the primary audio data embedding data object, the predictive output for the primary audio data embedding data object; generating, by the one or more computer processors, a forwarding recommendation prediction based at least in part on the predictive output; and performing, by the one or more computer processors, one or more prediction-based actions based at least in part on the forwarding recommendation prediction.
(Zhang et al., Artificial intelligence in recommender systems,” Springer (2020)) teaches Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.
9. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
