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 .
Continued Examination
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed 23 February 2026 [hereinafter Response] has been entered, where:
Claims 1, 15, 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 [(b)]1 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 “ [(b)] . . . 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)).
Also, the claim recites more details or specifics to the abstract idea of “[(b)] . . . to produce a utility classification for the predictive entity,” “wherein producing the utility classification comprises: [(b)](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,” “[(b)](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,” “[(b)](iii) generating a utility classification score for the predictive entity based at least in part on the forecasted utility classification timeseries data object,” and “[(b)](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 [(a)] 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 “[(a)] 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 “[(a)] providing . . . one or more temporal classification input features,” wherein “[(a)](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,” “[(a)](ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “[(a)](iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “[(a)](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,” “[(a)](v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “[(a)](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 “[(b)] 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 “[(b)] 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 “[(c)] performing . . . the one or more prediction-based actions based at least in part on a hybrid temporal-utility classification that corresponds to the temporal classification and the utility classification,” in which the plain meaning of the term “performing . . . actions” 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 also recites more details or specifics of the additional element of “[(c)] performing . . . actions,” of [(c)] (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,” “[(c)] (ii) generating the targeted communication in an electronic communication format,” and “[(c)] (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 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 [(a)] 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 “[(a)] 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 “[(a)] providing . . . one or more temporal classification input features,” wherein “[(a)](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,” “[(a)](ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “[(a)](iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “[(a)](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,” “[(a)](v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “[(a)](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 “[(b)] 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 “[(b)] 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 “[(c)] performing . . . the one or more prediction-based actions based at least in part on a hybrid temporal-utility classification that corresponds to the temporal classification and the utility classification,” in which the plain meaning of the term “performing . . . actions” 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 also recites more details or specifics of the additional element of “[(c)] performing . . . actions,” of [(c)] (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,” “[(c)] (ii) generating the targeted communication in an electronic communication format,” and “[(c)] (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 [(b)]2 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 “ [(b)] . . . 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 “[(b)] . . . to produce a utility classification for the predictive entity,” “wherein producing the utility classification comprises: [(b)](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,” “[(b)](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,” “[(b)](iii) generating a utility classification score for the predictive entity based at least in part on the forecasted utility classification timeseries data object,” and “[(b)](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 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 [(a)] 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 “[(a)] 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 “[(a)] providing . . . one or more temporal classification input features,” wherein “[(a)](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,” “[(a)](ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “[(a)](iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “[(a)](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,” “[(a)](v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “[(a)](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 “[(b)] 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 “[(b)] 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 “[(c)] performing . . . the one or more prediction-based actions based at least in part on a hybrid temporal-utility classification that corresponds to the temporal classification and the utility classification,” in which the plain meaning of the term “performing . . . actions” 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 also recites more details or specifics of the additional element of “[(c)] performing . . . actions,” of [(c)] (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,” “[(c)] (ii) generating the targeted communication in an electronic communication format,” and “[(c)] (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 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 [(a)] 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 “[(a)] 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 “[(a)] providing . . . one or more temporal classification input features,” wherein “[(a)](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,” “[(a)](ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “[(a)](iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “[(a)](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,” “[(a)](v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “[(a)](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 “[(b)] 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 “[(b)] 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 “[(c)] performing . . . the one or more prediction-based actions based at least in part on a hybrid temporal-utility classification that corresponds to the temporal classification and the utility classification,” in which the plain meaning of the term “performing . . . actions” 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 also recites more details or specifics of the additional element of “[(c)] performing . . . actions,” of [(c)] (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,” “[(c)] (ii) generating the targeted communication in an electronic communication format,” and “[(c)] (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 [(b)]3 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 “ [(b)] . . . 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 “[(b)] . . . to produce a utility classification for the predictive entity,” “wherein producing the utility classification comprises: [(b)](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,” “[(b)](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,” “[(b)](iii) generating a utility classification score for the predictive entity based at least in part on the forecasted utility classification timeseries data object,” and “[(b)](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 [(a)] 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 “[(a)] 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 “[(a)] providing . . . one or more temporal classification input features,” wherein “[(a)](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,” “[(a)](ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “[(a)](iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “[(a)](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,” “[(a)](v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “[(a)](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 “[(b)] 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 “[(b)] 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 “[(c)] performing . . . the one or more prediction-based actions based at least in part on a hybrid temporal-utility classification that corresponds to the temporal classification and the utility classification,” in which the plain meaning of the term “performing . . . actions” 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 also recites more details or specifics of the additional element of “[(c)] performing . . . actions,” of [(c)] (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,” “[(c)] (ii) generating the targeted communication in an electronic communication format,” and “[(c)] (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 [(a)] 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 “[(a)] 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 “[(a)] providing . . . one or more temporal classification input features,” wherein “[(a)](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,” “[(a)](ii) the temporal classification score describes a non-extremal periodicity likelihood measure for the predictive entity,” “[(a)](iii) the non-extremal periodicity likelihood measure is defined based at least in part on a non-extremal prospective period,” “[(a)](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,” “[(a)](v) the temporal classification is determined based at least in part on the temporal classification score and a temporal classification policy,” and “[(a)](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 “[(b)] 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 “[(b)] 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 “[(c)] performing . . . the one or more prediction-based actions based at least in part on a hybrid temporal-utility classification that corresponds to the temporal classification and the utility classification,” in which the plain meaning of the term “performing . . . actions” 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 also recites more details or specifics of the additional element of “[(c)] performing . . . actions,” of [(c)] (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,” “[(c)] (ii) generating the targeted communication in an electronic communication format,” and “[(c)] (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 “[(d)] determining the one or more prediction-based actions,” by (claims 2 and 16: “[(d)(1)] identifying a decision tree data object,” and “[(d)(2)] 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: “[(d)(1)](i) a root-level node of the decision tree data object is associated with the hybrid temporal-utility classification,” “[(d)(1)](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,” “[(d)(1)](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 “[(a)] determining . . . a temporal classification,” where (claims 6 and 15: “[(a)(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 “[(b)] determining . . . a utility classification,” by (claims 7 and 19: “[[(b)] . . . wherein: the utility score generation machine-learning model is configured to: . . . [(b)](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 “[(a)] 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 “[(b)] 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 “[(c)] 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 “[(c)] 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 “[(c)] 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 “[(c)] 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 “[(c)] 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 “[(c)] 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 that under Step 2A Prong Two, “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 Desardins Memorandum). Specifically, the claims recite an improvement in system performance (e.g., reducing a plurality of targeted communications to a targeted communication, which in turn, reduces operational load on user engagement systems) based upon adjustment to parameters (e.g., [(b)] storing a historical utility time series data object in association with a utility classification score generation machine learning model) of a machine learning model (e.g., a utility classification score generation machine learning model) associated with tasks or workstreams (e.g., [(c)] performing one or more prediction-based actions by generating the targeted communication in an electronic communication format and providing the targeted communication to the predictive entity via an electronic communication platform that corresponds to the electronic communication format). See Desardins Memorandum, p. 4. For at least this reason, the claims show an improvement in computer functionality that integrates any abstract idea into a practical application.” (Response at pp. 13-14 (emphasis added by Examiner)).
Claim 1, as amended, removes each of the steps allegedly encompassing mental processes. For example, claim 1, as amended recites:
* * *
[(a)] providing . . . 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 . . . :
* * *
[(b)] 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 . . . :
(emphasis added). As illustrated above, the operations recited by claim 1 are directed to nonabstract ideas, such as providing and storing data in a particular manner that improves the functionality of a machine learning model. Therefore, claim 1 cannot be not directed to a judicial exception under prong one of Step 2A.” (Response at p. 15).
Examiner Response:
Examiner respectfully disagrees because the rejection herein complies with the Office guidance. The rejection identifies any additional elements by specifically pointing to claim features/limitations/steps recited in the claim beyond the identified abstract idea; and evaluate the integration of the abstract idea 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 abstract idea into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)-(c) and (e)-(h). (see MPEP § 2106.07(a)).
With regard to the additional elements, the claims recite, inter alia, “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. These additional elements are used in the operating in its normal, expected manner.
With regard to the additional elements of “[(a)] providing . . . 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 . . .” and [(b)] 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 . . . ,” these activities of “[(a)] providing” and “[(b)] storing” are pre-processing, insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that do not integrate the abstract idea into a practical application.
With regard to the improvements pointed to by Applicant, these elements are not tethered to the instant claims. For example, per the emphasized language above, Applicant submits that the claims provide improvements pertaining to “associated with tasks or workstreams.” The claims, however, do not recite such elements.
With regard to “reducing a plurality of targeted communications to a targeted communication, which in turn, reduces operational load on user engagement systems,” the claims do recite “[(c)] performing . . . one or more prediction-based actions based at least in part on a hybrid temporal-utility classification that corresponds to the temporal classification and the utility classification by (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.” However, the claim does not reflect how such a “reduction” is accomplished.
The claim term “targeted” is referenced in the Applicant’s disclosure, where:
[m]embers who are part of a system, such as an insurance system, may consistently be targeted by different programs offered within the network based at least in part on a member’s propensity to respond or engage. However, overwhelming members with numerous and/or irrelevant campaigns for such programs may cause member abrasion.
(Specification ¶ 0020 (emphasis added by Examiner)). Also, the Applicant’s disclosure refers to categorization of Consumer Activation Measure (CAM) scores, which are a predicted metric to better predict health outcomes, reduce healthcare costs, and improve overall member experience. (Specification ¶ 0020). The disclosure provides for CAM segments A, B, C, and D, where::
a CAM score may be categorized into one of two or more segments. . . . [M]embers corresponding to segment C should be targeted through other interventions such as awareness and education programs, plan change promotions, and the like because they might be potentially underutilizing their current plan; and . . . members corresponding to segment D should not be targeted with retention-related outbound call campaigns programs and should be offered member experience campaigns programs or clinical programs, plan change promotions, promotions related to updating their RAF scores by offering house call visits or wellness visits, and/or the like.
(Specification ¶ 0026 (emphasis added by Examiner)). The claims, however, do not reflect such discernment between members in relation to “targeted communications” resulting in an improvement of (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.” (see, e.g., claim 1, lines 47-49).
With regard to the Desjardins Memorandum, the update to the Office guidance provides further explanation and examples relating to MPEP § 2106.04(d)(1). In particular, the Desardins Memorandum notes that the claimed Desjardins invention was a method of training a machine learning model on a series of tasks.
Under the first leg of MPEP § 2106.04(d)(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 in the functioning of a computer, or an improvement to other technology or a technical field.” (MPEP § 2106.04(d)(1)). The Desjardins ARP “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.” (Desjardins Memorandum at p. 2).
Under the second leg of MPEP § 2106.04(d)(1), “if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.” (MPEP § 2106.04(d)(1)). “Importantly, the [Desjardins] 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.’” (Desardins Memorandum at p. 2 (emphasis added by Examiner)).
As shown above, the Applicant’s improvement appears to relate to the CAM metric and CAM segments in relation to reducing “targeted” communications; however, the claims do not reflect the improvement disclosed in the Applicant’s specification.
Accordingly, for these reasons and in view of the rejection set out above in detail, the claims are subject-matter ineligible.
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20200349610 to Publicover et al.) teaches Targeted Content can be provided in place of generic advertisements on a first device or on personal computing devices. Targeted Content can be presented during, or in place of, generic advertisements in Content (e.g., television content, streaming content, etc.). Targeted Content can be provided in individual and/or group environments. In a group environment, Users and/or Devices can be grouped into a shared advertising group and Targeted Content can be selected based on Profiles of one or more members of the group. Feedback can be received regarding Targeted Content and payout amount can be determined.
(US Published Application 20190303947 to Cristofalo et al.) teaches targeted assets may include any type of asset that is desired to be targeted to network users. It is noted that such targeted assets may include, without limitation, advertisements, internal marketing (e.g., information about network promotions, scheduling or upcoming events), public service announcements, weather or emergency information, or programming. The targeted assets may be independent or included in a content stream with other assets such as untargeted network programming. In the latter case, the targeted assets may be interspersed/interleaved with untargeted programming (e.g., provided during programming breaks) or may otherwise be combined with the programming. In the description below, specific examples are provided in the context of targeted assets provided during breaks in television programming.
(Muench et al., "A Randomized Controlled Pilot Trial of Different Mobile Messaging Interventions for Problem Drinking Compared to Weekly Drink Tracking," PLoS (2017)) teaches exploratory, single-blind randomized controlled pilot study comparing four different types of alcohol reduction-themed text messages sent daily to weekly drink self-tracking texts in order to determine their impact on drinking outcomes over a 12-week period in 152 participants ( 30 per group) seeking to reduce their drinking on the internet. Messaging interventions included: weekly drink self-tracking mobile assessment texts (MA), loss framed texts (LF), gain-framed texts (GF), static tailored texts (ST), and adaptive tailored texts (TA). Poisson and least squares regressions were used to compare differences between each active messaging group and the MA control.
8. 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.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
1 Examiner has added markers to the claim elements to aid in discussion of the evaluation (that is, (a), (b), (c), etc.).
2 Examiner has added markers to the claim elements to aid in discussion of the evaluation (that is, (a), (b), (c), etc.).
3 Examiner has added markers to the claim elements to aid in discussion of the evaluation (that is, (a), (b), (c), etc.).
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