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 on 12 February 2026 [hereinafter Response] has been entered, where:
Claims 1, 12, and 17 have been amended.
Claims 3-5, 14, 16, and 20 have been cancelled.
Claims 1, 2, 6-13, 15, and 17-19 are pending.
Claims 1, 2, 6-13, 15, and 17-19 are rejected.
Information Disclosure Statement
3. An information disclosure statement was submitted on 13 April 2026. The submission complies with the provisions of 37 CFR 1.97. Accordingly, the Examiner considered the information disclosure statement.
Claim Rejections - 35 U.S.C. § 101
4. 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.
5. Claims 1, 2, 6-13, 15, and 17-19 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 “non-transitory computer-readable medium,” 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)] determine . . . to perform a validation function before the payload is routed to the destination based on a detection of an outlier, [(b.1)] wherein the supervised machine learning model identifies a characteristic of the payload as the outlier cluster.” In regards to training, the claim recites the limitations of “[(b.2.1)] clustering training examples from multiple sub-entities of an entity into two or more clusters of pairs of metric and type data, at least one of the two or more clusters identified as an outlier cluster,” and “[(b.2.2)] generating labeled training examples by applying a label to training examples.” These limitations of “determine,” “identifies,” “clustering,” and “applying,” are mental processes, (MPEP § 2106.04(a)(2) sub III), and is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of claim 1 is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “memory with instructions encoded thereon, the instructions, when executed, causing one or more processors to perform operations.” Instructions to apply the abstract idea on generic computer components (i.e., the memory, one or more processors) do not represent a practical application of the abstract idea. (MPEP § 2106.05(f)). The claim also recites “a supervised machine learning model,” which is an additional element that is a generic computer component used to implement the abstract idea and does not integrate the abstract idea into a practical application. (MPEP § 2016.05(f)). The claim recites the additional element of “[(b.2.3)] training the machine learning model to identify . . . whether the metric of the payload is an outlier metric,” which is the use of a generic computer component (machine learning model) to implement the abstract idea, and does not serve to implement the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim teaches more details or specifics to the additional element of “[(b.2.3)] training,” “[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity,” and accordingly, is merely more specific to the abstract idea. The claim also recites the additional elements of “[(a)] receive a request to route a payload to a destination, the payload comprising metric and type data,” and “[(c)] responsive to determining to perform the validation function based on the detection of the outlier, transmit the payload to a validation destination that performs the validation function prior to transmitting the payload to the destination following the validation function validating the payload.” These are insignificant extra-solution activities of mere data gathering that does not integrate the abstract idea into a practical application. (MPEP § 2106.05(g)). The claim also recites more details or specifics to the additional element of “[(c)] transmit the payload,” where “[(c.1)] non-outlier payloads are not transmitted to the validation destination for performance of the validation function en route to the destination,” and “[(c.2)] wherein outlier payloads are transmitted to the destination following the validation function validating the payload,” 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 claim recites a “memory with instructions encoded thereon, the instructions, when executed, causing one or more processors to perform operations.” Instructions to apply the abstract idea on generic computer components (i.e., the memory, one or more processors) do not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). The claim also recites “a supervised machine learning model,” which is an additional element that is a generic computer component used to implement the abstract idea and does not amount to significantly more than the abstract idea. (MPEP § 2016.05(f)). The claim recites the additional element of “[(b.2.3)] training the machine learning model to identify . . . whether the metric of the payload is an outlier metric,” which is the use of a generic computer component (machine learning model) to implement the abstract idea, and does not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). The claim teaches more details or specifics to the additional element of “[(b.2.3)] training,” “[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity,” and accordingly, is merely more specific to the abstract idea. The claim also recites the additional elements of “[(a)] receive a request to route a payload to a destination, the payload comprising metric and type data,” and “[(c)] responsive to determining to perform the validation function based on the detection of the outlier, transmit the payload to a validation destination for performance of the validation function the validation function prior to transmitting the payload to the destination following the validation function validating the payload.” These are insignificant extra-solution activities of mere data gathering that does not integrate the abstract idea into a practical application. (MPEP § 2106.05(g)). These are well-understood, routine, and conventional activities of receiving or transmitting data over a network, which does not amount to significantly more than the abstract idea. (MPEP § 2106.05(d) sub II.i). The claim also recites more details or specifics to the additional element of “[(c)] transmit the payload,” where “[(c.1)] non-outlier payloads are not transmitted to the validation destination for performance of the validation function en route to the destination,” and “[(c.2)] wherein outlier payloads are transmitted to the destination following the validation function validating the payload,” and accordingly, are merely more specific to the additional element. Therefore claim 1 is subject-matter ineligible.
Claim 12 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)] determining . . . to perform a validation function before the payload is routed to the destination based on a detection of an outlier, [(b.1)] wherein the supervised machine learning model identifies a characteristic of the payload as the outlier cluster.” In regards to training, the claim recites the limitations of “clustering training examples from multiple sub-entities of an entity into two or more clusters of pairs of metric and type data, at least one of the two or more clusters identified as an outlier cluster,” and “[(b.2.2)] generating labeled training examples by applying a label to training examples.” These limitations of “determine,” “identifies,” “clustering,” and “applying,” are mental processes, (MPEP § 2106.04(a)(2) sub III), and is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 12 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of claim 12 is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “a supervised machine learning model,” which is a generic computer component used to implement the abstract idea and does not integrate the abstract idea into a practical application. (MPEP § 2016.05(f)). The claim recites the additional element of “[(b.2.3)] training the machine learning model to identify . . . whether the metric of the payload is an outlier metric,” which is the use of a generic computer component (machine learning model) to implement the abstract idea, and does not serve to implement the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim teaches more details or specifics to the additional element of “[(b.2.3)] training,” “[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity,” and accordingly, is merely more specific to the abstract idea. The claim also recites the additional elements of “[(a)] receiving a request to route a payload to a destination, the payload comprising metric and type data,” and “[(c)] responsive to determining to perform the validation function based on the detection of the outlier, transmitting the payload to a validation destination that performs the validation function prior to transmitting the payload to the destination following the validation function validating the payload.” These are insignificant extra-solution activities of mere data gathering that does not integrate the abstract idea into a practical application. (MPEP § 2106.05(g)). The claim also recites more details or specifics to the additional element of “[(c)] transmitting the payload,” where “[(c.1)] non-outlier payloads are not transmitted to the validation destination for performance of the validation function en route to the destination,” and “[(c.2)] wherein outlier payloads are transmitted to the destination following the validation function validating the payload,” and accordingly, are merely more specific to the additional element. Therefore claim 12 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 claim recites “a supervised machine learning model,” which is an additional element that is a generic computer component used to implement the abstract idea and does not amount to significantly more than the abstract idea. (MPEP § 2016.05(f)). The claim recites the additional element of “[(b.2.3)] training the machine learning model to identify . . . whether the metric of the payload is an outlier metric,” which is the use of a generic computer component (machine learning model) to implement the abstract idea, and does not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). The claim teaches more details or specifics to the additional element of “[(b.2.3)] training,” “[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity,” and accordingly, is merely more specific to the abstract idea. The claim also recites the additional elements of “[(a)] receiving a request to route a payload to a destination, the payload comprising metric and type data,” and “[(c)] responsive to determining to perform the validation function based on the detection of the outlier, transmitting the payload to a validation destination that performs the validation function prior to transmitting the payload to the destination following the validation function validating the payload.” These are well-understood, routine, and conventional activities of receiving or transmitting data over a network, which does not amount to significantly more than the abstract idea. (MPEP § 2106.05(d) sub II.i). The claim also recites more details or specifics to the additional element of “[(c)] transmitting the payload,” where “[(c.1)] non-outlier payloads are not transmitted to the validation destination for performance of the validation function en route to the destination,” and “[(c.2)] wherein outlier payloads are transmitted to the destination following the validation function validating the payload,” and accordingly, are merely more specific to the additional element. Therefore claim 12 is subject-matter ineligible.
Claim 17 recites a “system,” 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)] determining . . . to perform a validation function before the payload is routed to the destination based on a detection of an outlier, [(b.1)] wherein the supervised machine learning model identifies a characteristic of the payload as the outlier cluster.” In regards to training, the claim recites the limitations of “[(b.2.1)] clustering training examples from multiple sub-entities of an entity into two or more clusters of pairs of metric and type data, at least one of the two or more clusters identified as an outlier cluster,” and “[(b.2.2)] generating labeled training examples by applying a label to training examples.” These limitations of “determine,” “identifies,” “clustering,” and “applying,” are mental processes, (MPEP § 2106.04(a)(2) sub III), and is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the abstract idea of claim 17 is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “memory with instructions encoded thereon; and one or more processors that, when executing the instructions, are caused to perform operations.” Instructions to apply the abstract idea on generic computer components (i.e., the memory, one or more processors) do not represent a practical application of the abstract idea. (MPEP § 2106.05(f)). The claim also recites “a supervised machine learning model,” which is an additional element that is a generic computer component used to implement the abstract idea and does not integrate the abstract idea into a practical application. (MPEP § 2016.05(f)). The claim recites the additional element of “[(b.2.3)] training the machine learning model to identify . . . whether the metric of the payload is an outlier metric,” which is the use of a generic computer component (machine learning model) to implement the abstract idea, and does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)). The claim teaches more details or specifics to the additional element of “[(b.2.3)] training,” “[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity,” and accordingly, is merely more specific to the abstract idea. The claim also recites the additional elements of “[(a)] receiving a request to route a payload to a destination,” and “[(c)] responsive to determining to perform the validation function based on the detection of the outlier, transmitting the payload to a validation destination that performs the validation function prior to transmitting the payload to the destination following the validation function validating the payload.” These are insignificant extra-solution activities of mere data gathering that does not integrate the abstract idea into a practical application. (MPEP § 2106.05(g)). The claim also recites more details or specifics to the additional element of “[(c)] transmitting the payload,” where “[(c.1)] non-outlier payloads are not transmitted to the validation destination for performance of the validation function en route to the destination,” and “[(c.2)] wherein outlier payloads are transmitted to the destination following the validation function validating the payload,” and accordingly, are merely more specific to the additional element. Therefore, claim 17 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 claim recites a “memory with instructions encoded thereon; and one or more processors that, when executing the instructions, are caused to perform operations.” Instructions to apply the abstract idea on generic computer components (i.e., the memory, one or more processors) do not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). The claim also recites “a supervised machine learning model,” which is an additional element that is a generic computer component used to implement the abstract idea and does not amount to significantly more than the abstract idea. (MPEP § 2016.05(f)). The claim recites the additional element of “[(b.2.3)] training the machine learning model to identify . . . whether the metric of the payload is an outlier metric,” which is the use of a generic computer component (machine learning model) to implement the abstract idea, and does not amount to significantly more than the abstract idea. (MPEP § 2106.05(f)). The claim teaches more details or specifics to the additional element of “[(b.2.3)] training,” “[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity,” and accordingly, is merely more specific to the abstract idea. The claim also recites the additional elements of “[(a)] receiving a request to route a payload to a destination,” and “[(a)] responsive to determining to perform the validation function, transmitting the payload to a validation destination associated with the validation function prior to transmitting the payload to the destination following the validation function validating the payload.” These are well-understood, routine, and conventional activities of receiving or transmitting data over a network, which does not amount to significantly more than the abstract idea. (MPEP § 2106.05(d) sub II.i). The claim also recites more details or specifics to the additional element of “[(c)] transmitting the payload,” where “[(c.1)] non-outlier payloads are not transmitted to the validation destination en route to the destination,” and “[(c.2)] wherein outlier payloads are transmitted to the destination following the validation function validating the payload,” and accordingly, are merely more specific to the additional element. Therefore claim 17 is subject-matter ineligible.
Claim 2 depends from claim 1. Claim 13 depends from claim 12. Claim 18 depends from claim 17. The claims recite limitations including additional elements. (claims 2, 13, and 18: [(d)] receive feedback from the validation destination, the feedback indicative of an association between the payload and the validation function;” and “[(e)] generate . . . a supplemental training set). These additional elements are directed to insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), which does not integrate the abstract idea into a practical application. Also, these additional elements of “[(d)] receive” and “[(e)] generate” are directed to well-understood, routine, and conventional activities of receiving data over a network, (MPEP § 2106.05(d) sub II.i), and/or storing and retrieving information in memory, (MPEP § 2106.05(d) sub II.iv), that do not provide significantly more than the abstract idea. Also, the claims recite a further additional element. (claims 2, 13, and 18: [(f)] train, using the supplemental training set, the supervised machine learning model). This additional element is merely applying or using the generic computer component of the supervised machine learning model, (MPEP § 2106.05(f)), which does not integrate the abstract idea into a practical application, nor does it amount to significantly more than the abstract idea. Accordingly, The abstract idea of these claim is not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05), because the claims recites no more than the abstract idea. Thus, claims 2, 13, and 18 are subject-matter ineligible.
Claim 6 depends directly or indirectly from claim 1. The claim recites more details or specifics of the additional element of “receive a request to route a payload,” (claim 6: “[(a.1)] wherein the payload indicates a size corresponding to an amount of time needed for an entity to perform a function”), and accordingly, are merely more specific to the additional element. The abstract idea of these claims is not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05), because the claims recites no more than the abstract idea. Thus, claim 6 is subject-matter ineligible.
Claim 7 depends directly or indirectly from claim 1. The claim recites an additional element, (claim 7: [(b.3)] wherein the instructions further comprise instructions to perform the validation function), which is using the generic computer components to implement the abstract idea, (MPEP § 2106.05(f)), that does not integrate the abstract idea into a practical application, nor does it amount to significantly more than the abstract idea. Thus, claim 7 is subject-matter ineligible.
Claims 8 and 9 depend directly or indirectly from claim 1. The claims recite limitations that are directed to a mental process, (claim 8: “[(b.3.1)] determine a plurality of payload validators;” claim 9: “[(b.3.1)] reject the request to route the payload to the destination”), which are one of the groupings of abstract ideas, (MPEP § 2106.04(a)(2)), and thus, are directed to an abstract idea. The claims also recite additional elements, (claim 8: “[(b.3.2)] receive, from a first payload validator of the plurality of payload validators, a first validation resolution; and [(b.3.3)] receive, from a second payload validator of the plurality of payload validators, a second validation resolution”; claim 9: [(b.3.2)] transmit the recommended modification to a requestor device, [(b.3.2.1)] wherein the request is received from the requestor device), in which the “plurality of payload validators” are additional elements of generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), which does not integrate the abstract idea into a practical application, nor amount to significantly more than the abstract idea. Also, “receive” and “transmit” are insignificant extra-solution activities of mere data gathering, (MPEP § 2106.05(g)), that does not integrate the abstract idea into a practical application, and are well-understood, routine, and conventional activities of receiving or transmitting data over a network, (MPEP § 2016.05(d) sub II.i), that do not amount to significantly more than the abstract idea. Thus, claim 8 and 9 are subject-matter ineligible.
Claim 10 depends directly or indirectly from claim 1. The claims recite limitations directed to the additional element of the “supervised machine learning model,” (claim 10: “[(b.2.2.1)] the label corresponding to a first cluster of two or more clusters associated with respective levels of deviation from an expected size range”), where the limitations is using the generic computer component of the “supervised machine learning model” to implement the abstract idea, (MPEP § 2106.05(f)), which does not integrate the abstract idea into a practical application, nor does it amount to significantly more than the abstract idea. Thus, claim 10 is subject-matter ineligible.
Claim 11 depends directly or indirectly from claim 1. Claim 15 depends directly or indirectly from claim 12. Claim 19 depends directly or indirectly from claim 17. The claims recite more details or specifics of the additional element of “[(a)] receive a request to route a payload,” (claims 11, 15, and 17: “[(a.1)] wherein the payload is a first payload”), and accordingly, are merely more specific to the additional element. The claims also recite more details or specifics to the “supervised machine learning model,” (claims 11 and 15: [(a.2)] wherein a second cluster of the two or more clusters is a non-outlier cluster, a non-outlier label applied to the non-outlier cluster indicating that a request to route a second payload to the destination is authorized”; claim 19: [(a.2)] wherein the supervised machine learning model applies a non-outlier label to a second payload, indicating that a request to route a second payload to the destination is authorized), and accordingly, are merely more specific to the additional element. Also, the claims recite limitations directed to the use (that is, “applies a non-outlier label”) of the additional elements of generic computer components to implement the abstract idea, (MPEP § 2106.05(f)), which do not integrate the abstract idea into a practical application, nor do they amount to significantly more than the abstract idea. Thus, claims 11, 15, and 19 are subject-matter ineligible.
Response to Arguments
6. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
Claim Rejections – 35 U.S.C. § 101
7. Representative claim 1 recites:
1. (Currently amended) A non-transitory computer-readable medium comprising memory with instructions encoded thereon, the instructions, when executed, causing one or more processors to perform operations, the instructions comprising instructions to:
[(a)] receive a request to route a payload to a destination,
[(a.1)] the payload comprising metric and type data;
[(b)] determine, using a supervised machine learning model, to perform a validation function before the payload is routed to the destination based on a detection of an outlier,
[(b.1)] wherein the supervised machine learning model identifies a characteristic of the payload as the outlier, and
[(b.2)] wherein the supervised machine learning model is trained to identify outliers by:
[(b.2.1)] clustering training examples from multiple sub-entities of an entity into two or more clusters of pairs of metric and type data, at least one of the two or more clusters identified as an outlier cluster;
[(b.2.2)] generating labeled training examples by applying a label to training examples of the outlier cluster indicating that a validation function is to be performed before the payload is routed to the destination; and
[(b.2.3)] training the machine learning model to identify, for given types of payloads, whether the metric of the payload is an outlier metric based on the labeled training examples,
[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity; and
[(c)] responsive to determining to perform the validation function based on the detection of the outlier, transmit the payload to a validation destination that performs the validation function prior to transmitting the payload to the destination following the validation function validating the payload,
[(c.1)] wherein non-outlier payloads are not transmitted to the validation destination for performance of the validation function en route to the destination, and
[(c.2)] wherein outlier payloads are transmitted to the destination following the validation function validating the payload.
(claim 1 (emphasis added by Examiner showing amended language)).
8. “Applicant respectfully traverses. Independent claims 1, 12, and 17 are recite that training examples used to train a supervised machine learning model to detect an outlier are from multiple sub-entities of an entity, thereby ensuring that the machine learning model is trained to detect outliers across each of the multiple sub-entities for payloads coming from the entity.
Similar to Ex Parte Desjardins, applicant's background recites a technical problem, noting that
[a]ddressing this problem in conventional systems may involve multiple machine learning models for clustering outliers, where each model can determine outlier clusters for different areas of an entity so that an outlier will eventually be detected (e.g., regardless of how outliers are defined across different areas). However, maintaining and applying multiple models for each area of an entity may consume excessive processing power, notwithstanding the processing needed to coordinate the results of the individual models to determine whether an outlier was detected within the entity as a whole.
See Specification, [0002]. Also like Desjardins, the features recited in the independent claims solve this problem by enabling one model to be used to detect outliers across multiple sub-entities, thereby resulting in a technological improvement of efficiency and consumption of processing power.
Finally, it is precisely the claimed training mechanism recited in the claims that results in this efficiency, and therefore, like Desjardins, the technical improvement is achieved through the recited functionality. Accordingly, independent claims 1, 12, and 17 are patent-eligible, as are the remaining claims at least by virtue of their dependencies. Reconsideration and withdrawal of the rejection is therefore respectfully requested.” (Response at pp. 9-10).
Examiner’s Response:
Under Step 2A Prong Two, 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)).
The instant claims recite, with respect to training of a machine learning model, that
* * *
[(b.2)] wherein the supervised machine learning model is trained to identify outliers by:
[(b.2.1)] clustering training examples from multiple sub-entities of an entity into two or more clusters of pairs of metric and type data, at least one of the two or more clusters identified as an outlier cluster;
[(b.2.2)] generating labeled training examples by applying a label to training examples of the outlier cluster indicating that a validation function is to be performed before the payload is routed to the destination; and
[(b.2.3)] training the machine learning model to identify, for given types of payloads, whether the metric of the payload is an outlier metric based on the labeled training examples,
[(b.2.3.1)] wherein the machine learning model is trained to detect outliers across each of the multiple sub-entities of the entity for payloads coming from the entity; and
* * *
The example of Desjardins, however, is directed to an improvement of the machine learning model which entails “training a machine learning model to learn new tasks while protecting knowledge about previous tasks.” Because the Desjardins claims reflect the disclosed improvement, the Appeal Review Board held that the claimed invention integrated the abstract idea into a practical application under Step 2A Prong Two.
The instant claims, however, recite the use of generic computer components (memory, one or more processors, supervised machine learning model) to implement the abstract idea of “[(b.2.1)] clustering training examples from multiple sub-entities of an entity into two or more clusters of pairs of metric and type data, at least one of the two or more clusters identified as an outlier cluster,” and “[(b.2.2)] generating labeled training examples by applying a label to training examples of the outlier cluster indicating that a validation function is to be performed before the payload is routed to the destination. Accordingly, the claims do not serve to integrate the abstract idea into a practical application, as set out above in detail.
With regard to “sub-entities” and “outliers,” the Specification recites multiple sub-entities may be input into a machine learning model for processing efficiency:
“In particular, a machine learning model clusters data from entity operations, which includes at least one cluster of outliers. This model does not need to be dedicated to any one area of an entity (i.e., a sub-entity). Rather, operations from multiple sub-entities may be input into the machine learning model, which outputs clusters that may represent outliers to any of the sub-entities. In this way, outliers for an entity as a whole may be detected without expending the processing power needed for multiple models or additional processing to coordinate the outputs of the individual models.”
(Specification ¶ 0003). The Specification notes that the effect is directed to “an entity as a whole:”
“Model 222 outputs clusters for outliers regardless of sub-entities' different outlier definitions, which avoids a need for multiple clustering models for each sub-entity, and thus saves processing power.”
(Specification ¶ 0061). The claims, however, are directed to training of the “supervised machine learning model” based on “clustering training examples from multiple sub-entities of an entity” and “generating labeled training examples.” The claims, however, do not indicate an interaction of distinct sub-entities in relation to an entity, and relation to the supervised machine learning model is not explained and/or reflected in the claims. (see, e.g., Specification, Figures 1 (“system environment”) & 2 (“entity management system of an entity”).
That is, the improvement appears to be directed to the abstract idea, which nevertheless remains an abstract idea.
Accordingly, the instant claims are subject-matter ineligible, as set out above in detail.
Claim Rejections – 35 U.S.C. § 103
9. “Claims 1, 2, 7, 9, 12, 13, 15, and 17-19 were rejected under 35 U.S.C. § 103 as unpatentable over Subbarayan et al. (US 2019/0114417) in view of Widmann et al. (US 2019/0378051) and further in view of Setteboun et al. (US 2020/0382524). Applicant respectfully traverses. Independent claims 1, 12, and 17 are amended to recite,
* * *
“[(c)] responsive to determining to perform the validation function based on the detection of the outlier, transmitting the payload to a validation destination that performs the validation function prior to transmitting the payload to the destination following the validation function validating the payload,
wherein non-outlier payloads are not transmitted to the validation destination for performance of the validation function for performance of the validation function en route to the destination, and
wherein outlier payloads are transmitted to the destination following the validation function validating the payload."
[(claim 1, lines 22-29; see also claims 12 & 17 (emphasis added by Applicant)]. For similar subject matter, the Office action concedes that Subbarayan and Widmann are silent, and relies on Settboun' s discussion of "a validation request." This discussion is deficient for several reasons.
First, Settboun' s "validation request" is not sent responsive to determining to perform a validation function based on detection of an outlier as claimed. Rather, Settboun simply "optionally" sends the request "subject to classifying the message as malformed." See e.g., Settboun, [0061]. Settboun' s determination of whether a message is malformed is based on a syntax test, rather than outlier determination. See e.g., Settboun, [0063].
Second, the Office action maps the clause, "wherein non-outlier payloads are not transmitted to the validation destination en route to the destination" to Settboun' s discussion in [0043] of "the message is not further processed subject to a validation value received from the remote server." However, this is an inconsistent mapping of the reference to what is claimed. The Office action's initial citation to [0061] maps the claimed "validation function" to a processing of Settboun' s "validation request." Message that obtained a "validation value" in Settboun have already been transmitted to the validation destination. Therefore, the mapping fails to show or render obvious what is claimed, because what is claimed has non-outlier payments entirely skip the validation destination (that is, Settboun's "validation value") en route to the destination.
Accordingly, independent claims 1, 12, and 17 are patentable, as are the remaining claims at least by virtue of their dependencies. Reconsideration and withdrawal of the rejection is therefore respectfully requested.” (Response at pp. 10-11).
Examiner’s Response:
Examiner finds Applicant’s arguments persuasive, and accordingly, WITHDRAWS the rejection under Section 103.
Conclusion
10. 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.
11. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20210150037 to Radhakrishnan et al.) teaches a participating entity may be comprised of a single sub-entity, or in one embodiment, a plurality of internal sub-entities. In one embodiment, each entity has a single set of security and configuration policies for a network domain. The entity manager (156) is configured to support and enable collaborative aggregation of weights based on a single sub-entity or a plurality of sub-entities. More specifically, the entity manager (156) conducts an intra-entity aggregation of weights representing a homogeneous data type from each internal sub-entity and subjects the intra-entity aggregation to encryption with the entity AHE public key. Accordingly, the intra-entity aggregation takes place before subjecting the aggregation to AHE encryption.
(Ghorbani et al., “Malchain: Virtual Application Behaviour Profiling by Aggregated Microservice Data Exchange Graph,” IEEE (2020)) teaches Malchain for profiling virtual applications based on using a new concept: microservice role. The roles are used to provide a consistent view of the virtual application addressing the mentioned new challenges. The microservice data exchange graph built using this consistent view is then used to extract features providing the appropriate measures to profile the aggregated behaviour of the microservices comprising a virtual application.
12. 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
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