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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 on 04 August 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 31 August 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
“determine . . . to perform a validation function before the payload is routed to the destination based on a detection of an outlier, 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 the 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 “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 “training the machine learning model to identify, for given types of payloads, 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 “training,” “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 “wherein the machine learning model is configured to classify, based on a value of the metric and the indicator, the metric as the outlier metric for a first sub-entity of the multiple sub-entities and as a non-outlier metric for a second sub-entity of the multiple sub-entities, different from the first sub-entity,” and accordingly, is merely more specific to the abstract idea.
The claim also recites “receive a request to route a payload to a destination, the payload comprising metric and type data and an indicator of a sub-entity of an entity,” and “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 activities of “receive” and “transmit” 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 “transmit the payload,” where “non-outlier payloads are not transmitted to the validation destination 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,” 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 “training the machine learning model to identify, for given types of payloads, 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 “training,” “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 “wherein the machine learning model is configured to classify, based on a value of the metric and the indicator, the metric as the outlier metric for a first sub-entity of the multiple sub-entities and as a non-outlier metric for a second sub-entity of the multiple sub-entities, different from the first sub-entity,” and accordingly, is merely more specific to the abstract idea.
The claim also recites “receive a request to route a payload to a destination, the payload comprising metric and type data and an indicator of a sub-entity of an entity,” and “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.” (MPEP § 2106.05(g)). These activities of “receive” and “transmit” 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 “transmit the payload,” where “non-outlier payloads are not transmitted to the validation destination 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,” 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 “determining . . . to perform a validation function before the payload is routed to the destination based on a detection of an outlier, 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 the 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 “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 “training the machine learning model to identify, for given types of payloads, 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 “training,” “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 “wherein the machine learning model is configured to classify, based on a value of the metric and the indicator, the metric as the outlier metric for a first sub-entity of the multiple sub-entities and as a non-outlier metric for a second sub-entity of the multiple sub-entities, different from the first sub-entity,” and accordingly, is merely more specific to the abstract idea.
The claim also recites “receiving a request to route a payload to a destination, the payload comprising metric and type data and an indicator of a sub-entity of an entity,” and “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 “transmitting the payload,” where “non-outlier payloads are not transmitted to the validation destination 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,” 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 “training the machine learning model to identify, for given types of payloads, 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 “training,” “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 “wherein the machine learning model is configured to classify, based on a value of the metric and the indicator, the metric as the outlier metric for a first sub-entity of the multiple sub-entities and as a non-outlier metric for a second sub-entity of the multiple sub-entities, different from the first sub-entity,” and accordingly, are merely more specific to the abstract idea.
The claim also recites “receiving a request to route a payload to a destination, the payload comprising metric and type data and an indicator of a sub-entity of an entity,” and “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 “transmitting the payload,” where “non-outlier payloads are not transmitted to the validation destination 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,” 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 “determining . . . to perform a validation function before the payload is routed to the destination based on a detection of an outlier, 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 the 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 “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 “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 “training,” “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 “wherein the machine learning model is configured to classify, based on a value of the metric and the indicator, the metric as the outlier metric for a first sub-entity of the multiple sub-entities and as a non-outlier metric for a second sub-entity of the multiple sub-entities, different from the first sub-entity:” and accordingly, is merely more specific to the abstract idea.
The claim also recites “receiving a request to route a payload to a destination, the payload comprising metric and type data and an indicator of a sub-entity of an entity,” and “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 “transmitting the payload,” where “non-outlier payloads are not transmitted to the validation destination 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,” 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 “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 “training,” “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 “wherein the machine learning model is configured to classify, based on a value of the metric and the indicator, the metric as the outlier metric for a first sub-entity of the multiple sub-entities and as a non-outlier metric for a second sub-entity of the multiple sub-entities, different from the first sub-entity: and accordingly, is merely more specific to the abstract idea.
The claim also recites “receiving a request to route a payload to a destination,” and “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 activities of “receiving” and transmitting” 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 “transmitting the payload,” where “non-outlier payloads are not transmitted to the validation destination en route to the destination,” and “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: receive feedback from the validation destination, the feedback indicative of an association between the payload and the validation function;” and “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 “receive” and “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: 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: “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: 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: “determine a plurality of payload validators;” claim 9: “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: “receive, from a first payload validator of the plurality of payload validators, a first validation resolution; and receive, from a second payload validator of the plurality of payload validators, a second validation resolution”; claim 9: transmit the recommended modification to a requestor device, 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: “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 “receive a request to route a payload,” (claims 11, 15, and 17: “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: 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: 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. Applicant submits that “Claims 1, 2, 6-13, 15, and 17-19 were rejected under 35 U.S.C. § 101 as directed to an abstract idea without significantly more.
Applicant respectfully traverses. Independent claims 1, 12, and 17 are amended to clarify that the payload comprises, in addition to metric and type data, an indicator of a sub-entity of an entity, and that "the machine learning model is configured to classify, based on a value of the metric and the indicator, the metric as the outlier metric for a first sub-entity of the multiple sub-entities and as a non-outlier metric for a second sub-entity of the multiple sub-entities, different from the first sub-entity."
Under at least a Prong Two analysis, the claims are patent-eligible. Like Ex Parte Desjardins, the claims as amended describe improvements to the functioning of a machine learning system. As is claimed, one model, with one set of learned parameters, can substitute for what conventionally required separate models for various sub-entities.
For example, as described in the Specification, a single machine learning model is trained to handle various data values and types across different sub-entities to detect outliers, rather than maintaining separate machine learning models for each sub-entity.
See, e.g., Specification ¶ 0002 ("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");
see also Specification ¶ 0061 ("an automotive repair sub-entity may consider a payload having a size often auto parts to be an outlier . . . while the finance sub-entity considers this a non-outlier . . . 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.").
Condensing the machine learning capabilities into a single model provides comparable functionality of various models "without expending the processing power needed for multiple models or additional processing to coordinate the outputs of the individual models." Specification ¶ 0003.
In this way, the claimed machine learning model is able to produce different outputs for different sub-entities even when metrics are or appear identical.
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. 10-11).
Examiner 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 invention is generally directed to one clustering-based machine learning workflow to learn outlier behavior across multiple sub-entities, rather than relying on separate models for each sub-entity. (see Specification ¶¶ 0003, 0061). It clusters historical payload data, labels the outlier clusters, and trains a supervised model to recognize similar payloads later. (see Specification ¶¶ 0005, 0009-10). When a new request is classified as an outlier, the system routes it to a physical validation destination for review before it can proceed. (see Specification ¶¶ 0009-11, 0074-77). User feedback from that validation step is fed back into training so the model becomes more entity-specific over time (see Specification ¶¶ 0010, 0054, 0058).
More particularly, the Specification recites
For example, the user specifies that model output five clusters, which the user manually labels or engine 221 determines to label using a "non-outlier" label and four different labels for various degrees to which the data can be classified as an outlier.
In this way, a single model may be used across a whole entity to cluster outliers for a variety of classification models. For example, data from both an automotive repair sub-entity and a finance sub-entity within an automotive entity be input into model 222 for outlier detection. To enable this, the data from disparate sub-entities of an entity may be organized into a single format for outlier detection within the entity as a whole. The data input into the single, clustering unsupervised machine learning model 222 may characterize an entity function or a payload. In an example where entity function data is input into model 222, an automotive repair sub-entity may consider a periodic report generated on the last day of every month to be a non-outlier while a finance sub-entity considers this to be an outlier that should have been generated on the third week of the month. A combined input into model 222 from both sub-entities puts the automotive repair sub-entity's data into contrast with data from other sub-entities to detect outliers within the entities as a whole. In an example where payload data is input into model 222, the automotive repair sub-entity may consider a payload having a size of ten auto parts to be an outlier that is above an expected number of two auto parts, while the finance sub-entity considers this a non-outlier that is within expected range of one to twenty auto parts. 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 ¶ 0060).
With respect to the validation destination, the Specification discloses
Entity management system 140 determines 812 whether the user input affirms or rejects the classification. If the user input affirms the classification, entity management system 140 retrains 814 the supervised machine learning model by strengthening an association between the label and the entity function data. For example, system 140 includes the labeled data output by the supervised machine learning model into a supplemental training set used in the next iteration of training the model. If the user input rejects the classification, entity management system 140 retrains 816 the supervised machine learning model by weakening an association between the label and the entity function data.
(Specification ¶ 0098 (emphasis added by Examiner)).
In this regard, under Leg 1 of MPEP § 2106.05(d)(1), the disclosure may provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. (MPEP § 2106.05(d)(1)). The claims merely refer to “training the machine learning model.” (see, e.g., claim 1, lines 17-21).
The instant claims, however, appear directed to an improvement of the abstract idea without regard to aspects of the model structures affected by such feedback, which is simply “indicative of an association.” (see, e.g., claim 2, lines 3-4).
Accordingly, the instant claims are subject-matter ineligible, as set out above in detail.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20210089927 to Ryan et al.) teaches detecting patterns in data from a time-series and for detecting outliers in network data in an unsupervised manner are provided. The method includes the step of detecting outliers of the obtained data with respect to the window using an unsupervised deep learning process (e.g., using a Generalized Adversarial Network (GAN) learning technique and/or a Bidirectional GAN (BiGAN) learning technique) for enabling the learning of a data distribution. The unsupervised process, for example, does not require manual intervention.
(Aguinis et al., “Best-Practice Recommendations for Defining, Identifying, and Handline Outliers, Indiana University (2013)) teaches that the presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommendations can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research.
9. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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