Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 09/04/2026 has been entered. Claims 1, 4, 7, 9, 12, & 15 are amended. Claims 1-16 are pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Claim 1
Step 1: The claim recites “An operating method of a provider providing a machine learning (ML) model, the operating method comprising:”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
after the providing of the first ML model, assessing the first ML model being used by the consumer and monitoring an accuracy of the first ML model based on the collected data: These limitations recite a mentally performable process with the aid of pen and paper of using observation and judgement to assess the first ML model by monitoring an accuracy of the first ML model based on collected data.
based on an assessment result of the first ML model indicating a performance degradation of the first ML model: These limitations recite a mentally performable process with the aid of pen and paper of using observation, judgement, and evaluation to generate an assessment result that indicates a performance degradation.
providing a first ML model to a consumer: These limitations recite methods of organizing human activity similar to providing information to a person without interfering with the person’s primary activity per MPEP 2106.04(a)(2)(II)(C).
providing a second ML model to the consumer to replace the first model: These limitations recite methods of organizing human activity similar to providing information to a person without interfering with the person’s primary activity per MPEP 2106.04(a)(2)(II)(C).
Thus, the claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
An operating method of a provider providing a machine learning (ML) model, the operating method comprising: These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. an operating method of a provider, to a particular technological environment or field of use, e.g. used to provide a machine learning (ML) model. See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
by collecting data from data sources: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment and include insignificant extra-solution activity of data gathering recited by “by collecting data from data sources” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the provider comprises a network data analytics function (NWDAF) comprising a model training logical function (MTLF), and the consumer comprises an NWDAF comprising an analytics logical function (AnLF): These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception similar to a commonplace business method or mathematical algorithm being applied on a general purpose computer per MPEP § 2106.05(f)(2)(i).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model: assessing the first ML model by collecting data from data sources: These additional elements amount to insignificant extra-solution activity in the form of selecting a particular data source or type of data to be manipulated per MPEP § 2106.05(g).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model: assessing the first ML model by collecting data from data sources” which are well-understood routine and conventional activities similar to receiving or transmitting data over a network per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the data sources comprise a data source network function (NF) and a data collection coordination function (DCCF): These additional elements are recited at a high level of generality and merely indicate a field of use or technological environment in which to apply a judicial exception, e.g. the data sources, to a particular technological environment or field of use, e.g. comprise a data source network function (NF) and a data collection coordination function (DCCF). See MPEP 2106.05(h). Elements that use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the assessing comprises monitoring accuracy of the first ML model by collecting data of an analytics data repository function (ADRF): These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g).
using at least one of an ADRF identifier (ID) and DataSetTag: These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception similar to a commonplace business method or mathematical algorithm being applied on a general purpose computer per MPEP § 2106.05(f)(2)(i).
Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception and insignificant extra-solution activity of data gathering recited by “wherein the assessing comprises monitoring accuracy of the first ML model by collecting data of an analytics data repository function (ADRF)” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the assessing comprises monitoring accuracy of the first ML model by collecting fault prediction analytics data from an MDAS (Management Data Analytics Service): These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein the assessing comprises monitoring accuracy of the first ML model by collecting fault prediction analytics data from an MDAS (Management Data Analytics Service)” which are well-understood routine and conventional activities similar to presenting offers and gathering statistics per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the assessing comprises subscribing to a unified data management (UDM) to receive a notification of modification in subscription data for a target of the first ML model, and wherein the UDM subscribes to a unified data repository (UDR) to receive a notification of user equipment (UE) subscription data: These additional elements amount to insignificant extra-solution activity in the form of mere data gathering per MPEP § 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by “wherein the assessing comprises subscribing to a unified data management (UDM) to receive a notification of modification in subscription data for a target of the first ML model, and wherein the UDM subscribes to a unified data repository (UDR) to receive a notification of user equipment (UE) subscription data” which are well-understood routine and conventional activities similar to receiving or transmitting data over a network per MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas as the judicial exception of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the second ML model comprises a newly generated ML model or a retrained first ML model based on the collected data from the data sources: These additional elements are recited at a high level of generality and amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception. See MPEP § 2106.05(f). Thus, the way in which the additional elements use or interact with the judicial exception do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include invoking generic computer components to apply the underlying judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP § 2106.05.
Claim 9-16
Step 1: These claims are directed to “A server apparatus providing a machine learning (ML) model, the server apparatus comprising:”; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: Claims 9-16 recite the same judicial exception as Claims 1-8, respectively.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The analysis at this step for 9-16 mirrors that of Claims 1-8, respectively.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception for these claims. The analysis at this step for Claims 9-16 mirrors that of Claims 1-8, respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 8-10, 12, & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Papageorgiou et al. (US 2023/0060071 A1, filed 08/19/2022), hereinafter Papageorgiou, and in view of Wubbels et al. (US 10599984 B1, published 03/24/2020), hereafter Wubbels.
Regarding independent claim 1, Papageorgiou teaches a method comprising:
providing a first ML model to a consumer ([Fig. 4, 0089-0092] discusses providing a machine learning model to a consumer);
collecting data from data sources and monitoring an accuracy of the first ML model ([Fig. 5, 0010] discusses collecting data in order to gauge the accuracy of the ML model);
providing a second ML model to the consumer to replace the first model ([Fig. 5, 0089-0092] discusses providing a new ML model to be provisioned, triggering training of a new ML model).
Papageorgiou does not explicitly teach after the providing of the first ML model, assessing the first ML model being used by the consumer by collecting data from data sources and monitoring an accuracy of the first ML model based on the collected data; and based on an assessment result of the first ML model indicating a performance degradation of the first ML model, providing a second ML model to the consumer to replace the first model.
However, Wubbels teaches a method for monitoring a ML model provided to a consumer, wherein the accuracy and performance is assessed ([Col. 14, Lines 33-50] discusses monitoring the first ML model being used by a consumer by collecting data and monitoring the accuracy of the data and ML model’s performance); and upon assessment that the performance has degraded, a second ML model is provided to replace the first model ([Col. 15, Lines 4-22] discusses that if an assessment result of the first ML model indicates a substantial decrease in accuracy and performance, the first ML model will be decommissioned and a second ML model will be provided to replace the first).
Because Papageorgiou teaches providing a first ML model, collecting data and monitoring an accuracy of the first ML model, and providing a second ML model; and Wubbels teaches assessing the accuracy of the first ML model being used by a consumer by collecting data, and providing a second ML model given an assessment result of the first ML model that indicates performance degradation, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate assessing the accuracy of the first ML model being used by a consumer by collecting data, and providing a second ML model given an assessment result of the first ML model that indicates performance degradation as taught by Wubbels into Papageorgiou’s method, with a reasonable expectation of success, to teach providing a first ML model to a consumer; after the providing of the first ML model, assessing the first ML model being used by the consumer by collecting data from data sources and monitoring an accuracy of the first ML model based on the collected data; and based on an assessment result of the first ML model indicating a performance degradation of the first ML model, providing a second ML model to the consumer to replace the first model. This combination would have been motivated by the desire to help identify low-performing models that are already deployed, so that relevant parties can quickly take action to improve the machine learning model (Wubbels [Abstract]).
Regarding dependent claim 2, the combination of Papageorgiou & Wubbels teaches the claimed invention as claimed in claim 1, including wherein the provider comprises a network data analytics function (NWDAF) comprising a model training logical function (MTLF), and the consumer comprises an NWDAF comprising an analytics logical function (AnLF) (Papageorgiou [0004] discusses the provider comprising a NWDAF further comprising a MTLF; and the consumer comprises an NWDAF further comprising an AnLF).
Regarding dependent claim 4, the combination of Papageorgiou & Wubbels teaches the claimed invention as claimed in claim 1, including wherein the data sources comprise a data source network function (NF) and a data collection coordination function (DCCF) (Papageorgiou [Fig. 4, 0010] discusses collecting data from data sources such as a network function (NF), and a data collection coordination function (DCCF)).
Regarding dependent claim 8, the combination of Papageorgiou & Wubbels teaches the claimed invention as claimed in claim 1, including wherein the second ML model comprises a newly generated ML model or a retrained first ML model based on the collected data from the data sources (Papageorgiou [Fig. 5, 0089-0092] discusses generating a new ML model or retrained first ML model based on the collected data).
Regarding claims 9-10, 12, & 16, claims 9-10, 12, & 16 are machine claims that are substantially the same as the process of claims 1-2, 4, & 8, respectively. Therefore, claims 9-10, 12, & 16 are rejected for the same reasons as claims 1-2, 4, & 8, respectively.
Claims 3, 5-7, 11, & 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Papageorgiou, in view of Wubbels, as applied in claims 1 & 9, and further in view of 3GPP ("5G; Architecture enhancements for 5G System (5GS) to support network data analytics services", 3GPP TS 23.288 version 17.4.0 Release 17, ETSI) (Year: 2022), hereafter 3GPP.
Regarding dependent claim 3, the combination of Papageorgiou & Wubbels teaches the claimed invention as claimed in claim 1, including generating analytics related to the accuracy of the first ML model (Papageorgiou [Fig. 5, 0010] discusses collecting data in order to gauge the accuracy of the ML model).
The combination of Papageorgiou & Wubbels does not explicitly teach registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model.
However, in the same field of endeavor, 3GPP’s publicly released specification teaches a method to register use of a ML model, wherein the registering indicates the capability to transmit a least one of analytics feedback information and information on analytics accuracy ([Figure 6.2A1-1] discusses registering a new ML model of the consumer; [7.2.2] discusses capabilities to transmit analytics information generated by the ML model and also the accuracy of the analytics).
Because the combination of Papageorgiou & Wubbels teaches generating analytics related to the accuracy of the first ML model; and 3GPP teaches registering a use of the ML model of the consumer, transmitting analytics feedback information generated by the first of second ML model, and information on analytics accuracy of the first or second ML model, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information and information on analytics accuracy as taught by 3GPP into the combination of Papageorgiou & Wubbels’ method, with a reasonable expectation of success, to teach registering a use of the ML model of the consumer, wherein the registering indicates a capability of transmitting at least one of analytics feedback information on analytics generated by the first or second ML model and information on analytics accuracy of the first or second ML model. This combination would have been motivated by the desire such that the ML Model Information is used by an NWDAF containing AnLF to derive analytics (3GPP [6.2A.1]).
Regarding dependent claim 5, the combination of Papageorgiou & Wubbels teaches the claimed invention as claimed in claim 1, including assessing the first ML model being used by the consumer by collecting data from data sources and monitoring an accuracy of the first ML model based on the collected data (Papageorgiou [Fig. 5, 0010] discusses collecting data in order to gauge the accuracy of the ML model; Wubbels [Col. 14, Lines 33-50] discusses monitoring the first ML model being used by a consumer by collecting data and monitoring the accuracy of the data and ML model’s performance);
The combination of Papageorgiou & Wubbels does not explicitly teach wherein the assessing comprises monitoring accuracy of the first ML model by collecting data of an analytics data repository function (ADRF) using at least one of an ADRF identifier (ID) and DataSetTag.
However, in the same field of endeavor, 3GPP’s publicly released specification teaches a method to collect data of an ADRF using a DataSetTag (3GPP [Figure 6.2B.2] discusses storing and retrieving data from an ADRF by invoking an identifier (ADRF ID); 3GPP [10.2.6] discusses the retrieval operation uses “Analytics Specification” and “Data Specification”, which operates as the DataSetTag parameter).
Because the combination of Papageorgiou & Wubbels teaches monitoring the accuracy of the first ML model by collecting data from data sources; and 3GPP teaches collecting data of an ADRF using a DataSetTag, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate collecting data of an ADRF using a DataSetTag as taught by 3GPP into the combination of Papageorgiou & Wubbels’ method, with a reasonable expectation of success, to teach wherein the assessing comprises monitoring accuracy of the first ML model by collecting data of an analytics data repository function (ADRF) using at least one of an ADRF identifier (ID) and DataSetTag. This combination would have been motivated by the desire that the ML Model Information is to be collected and used by common and well-known techniques to derive analytics (3GPP [6.2]).
Regarding dependent claim 6, the combination of Papageorgiou & Wubbels teaches the claimed invention as claimed in claim 1, including assessing the first ML model being used by the consumer by collecting data from data sources and monitoring an accuracy of the first ML model based on the collected data (Papageorgiou [Fig. 5, 0010] discusses collecting data in order to gauge the accuracy of the ML model; Wubbels [Col. 14, Lines 33-50] discusses monitoring the first ML model being used by a consumer by collecting data and monitoring the accuracy of the data and ML model’s performance);
The combination of Papageorgiou & Wubbels does not explicitly teach wherein the assessing comprises monitoring accuracy of the first ML model by collecting fault prediction analytics data from an MDAS (Management Data Analytics Service).
However, in the same field of endeavor, 3GPP’s publicly released specification teaches a method to collect fault prediction analytics data from a MDAS (3GPP [Figure 6.2.3] discusses collecting fault analytics using MDAS, and these fault analytics relate to performance assurance; 3GPP [Fig. 6.2.3.2-1] discusses the notification and subscription mechanism for collecting management data that includes fault and performance data).
Because the combination of Papageorgiou & Wubbels teaches monitoring the accuracy of the first ML model by collecting data from data sources; and 3GPP teaches collecting fault prediction analytics data from a MDAS, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate collecting fault prediction analytics data from a MDAS as taught by 3GPP into the combination of Papageorgiou & Wubbels’ method, with a reasonable expectation of success, to teach wherein the assessing comprises monitoring accuracy of the first ML model by collecting fault prediction analytics data from an MDAS (Management Data Analytics Service). This combination would have been motivated by the desire that the ML Model Information is to be collected and used by common and well-known techniques to derive analytics (3GPP [6.2]).
Regarding dependent claim 7, the combination of Papageorgiou & Wubbels teaches the claimed invention as claimed in claim 1, including assessing the first ML model being used by the consumer by collecting data from data sources and monitoring an accuracy of the first ML model based on the collected data (Papageorgiou [Fig. 5, 0010] discusses collecting data in order to gauge the accuracy of the ML model; Wubbels [Col. 14, Lines 33-50] discusses monitoring the first ML model being used by a consumer by collecting data and monitoring the accuracy of the data and ML model’s performance);
The combination of Papageorgiou & Wubbels does not explicitly teach wherein the assessing comprises subscribing to a unified data management (UDM) to receive a notification of modification in subscription data for a target of the first ML model; and wherein the UDM subscribes to a unified data repository (UDR) to receive a notification of user equipment (UE) subscription data.
However, in the same field of endeavor, 3GPP’s publicly released specification teaches a method wherein the assessing comprises subscribing to a UDM to receive a notification of modification in subscription data for a target of the first ML model ([Figure 6.2.9] discusses invoking a Nudm_SDM_Subscribe operation to subscribe to modification updates; [6.2.2.2] discusses the NWDAF subscribes to UDM to receive a notification of modification in subscription data); and the UDM subscribes to a UDR to receive notification of UE data ([Figure 6.1C.2] discusses the NWDAF registering into UDM to receive a notification of UE related data and does so by invoking indicator Nudm_UECM_Registration and thus, the subscription operates in a UDR to receive a notification of user equipment subscription data by invoking an Nudr_DM_Subscribe service operation).
Because the combination of Papageorgiou & Wubbels teaches monitoring the accuracy of the first ML model by collecting data from data sources; and 3GPP teaches subscribing to a UDM for notifications of subscription data modification, and the UDM subscribes to a UDR to receive notification of UE data, accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate subscribing to a UDM for notifications of subscription data modification, and the UDM subscribes to a UDR to receive notification of UE data as taught by 3GPP into the combination of Papageorgiou & Wubbels’ method, with a reasonable expectation of success, to teach wherein the assessing comprises subscribing to a unified data management (UDM) to receive a notification of modification in subscription data for a target of the first ML model; and wherein the UDM subscribes to a unified data repository (UDR) to receive a notification of user equipment (UE) subscription data. This combination would have been motivated by the desire that the ML Model Information is to be collected and used by common and well-known techniques to derive analytics (3GPP [6.2]).
Regarding claims 11, & 13-15, claims 11, & 13-15 are machine claims that are substantially the same as the process of claims 3 & 5-7, respectively. Therefore, claims 11, & 13-15 are rejected for the same reasons as claims 3 & 5-7, respectively.
Response to Arguments
Applicant’s amendments and remarks filed 09/04/2026, with respect to rejections under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (Remarks, pg. 8-9) that amended claim 1 cannot be considered an abstract idea specifically because a “human being cannot, using mental steps or pen and paper, ‘collect data from data sources’ within an active communication network, ‘monitor an accuracy’ of a machine-learning model concurrently processing live network traffic, determine that the computed accuracy metric reflects a ‘performance degradation,’ and automatically substitute the deployed model with a second model to maintain uninterrupted automated network analytics”.
Examiner respectfully disagrees; the limitations identified as the abstract idea at Step 2A Prong One are monitoring an accuracy of the first ML model based on the collected data, based on an assessment result of the first ML model indicating a performance degradation of the first ML model, and providing a second ML model to the consumer to replace the first model; which as cited in the rejection above, are abstract ideas that characterize mental processes or methods of organizing human activity. In regard to the arguments, Claim 1 as written does not recite monitoring accuracy of a machine-learning model concurrently processing live network traffic, nor does it recite concurrently determining that the computer accuracy metric reflects a ‘performance degradation’, nor does it recite automatically substituting the deployed model with a second model to maintain uninterrupted automated network analytics. Further, Claim 1 does not recite any specific component that monitors an accuracy, determines a performance degradation, and substitutes a second model; therefore these are considered abstract ideas. Thus, Claim 1 continues to recite a judicial exception. Additionally, operations Applicant identifies as beyond the capacity of the mind, namely to ‘collect data from data sources’ are not relied upon as the abstract idea; they are additional elements evaluated at Step 2A Prong 2 and Step 2B, where, as explained above, collecting data from data sources amounts to well understood, routine and conventional activities. The arguments are therefore directed to limitations other than those identified as the mental process, and it does not undermine the Prong One finding. Claim 1 continues to recite a judicial exception.
Applicant argues (Remarks, pg. 9-10) that the claims, if seen as reciting an abstract idea at Prong One, are integrated into a practical application that imposes a meaningful limit. Applicant specifically cites that the claims are directed to an improvement of a technical problem (performance degradation over time resulting from data drift between live operational data and original training data). Applicant argues (Remarks, pg. 10) that the claims solve this problem by “continuously collecting operational data from network data sources while the first model remains in active use, computing an accuracy metric from that data, and automatically substituting the deployed model with a second model only when the computed metric indicates performance degradation to avoid arbitrary or continuous retraining”.
Examiner respectfully disagrees; while the application is tied to the improvement of the cited technical problem, the claims lack the necessary structure or recited steps that Applicant cites as the solution to the problem. For instance, in Claim 1, there is no recitation of continuously collecting operational data from network data sources while the first model remains in active use, nor is there recitation of automatically substituting the deployed model with a second model. Without the recitation of technical steps or elements that are responsible for achieving the technical solution, the claims do not actively integrate the exception into a practical application. Claims 2-8 and 9-16 further do not recite the technical solution and thus, also do not integrate the exception into a practical application.
Applicant argues (Remarks, pg. 11) that if Claim 1 is determined to recite a judicial exception not integrated into a practical application under Step 2A, a rejection must present factual evidence under Step 2B establishing a claim limitation as well-understood, routine, and conventional. Applicant cites the specific combination in Claim 1 of collecting operational data from network sources, computing an accuracy metric for an active in-service model, triggering replacement specifically upon detecting performance degradation, and executing these steps via specific 3GPP network functions and service-based interfaces (NF, DCCF, ADRF via ADRF ID/DataSetTag, MDAS, and a UDM/UDR notification chain)-cannot be dismissed by citing generic hypotheticals such as "gathering statistics" (MPEP §2106.05(d)(II)).
Examiner respectfully disagrees; with respect to Claim 1, the limitation in question is “collecting operational data from network sources”. This limitation recites gathering operational data from a source, which the courts have found to be well, understood, routine, and conventional activity when they are claimed in a generic manner similar to “presenting offers and gathering statistics”, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93. Thus, evidentiary support exists to establish the limitation of Claim 1 as well-understood, routine, and conventional. As a result, the rejection at Step 2B is sustained.
Applicant’s arguments filed 09/04/2026, on pages 11-14, with respect to claims 1-16 under 35 U.S.C. 102 & 103 have been considered but are moot because of the new ground of rejection (see rejection above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al. (US 20230254719 A1, filed 02/02/2023) ([Abstract] A method of performing federated learning by a first network data analytic function (NWDAF) is provided. The method includes determining whether to perform a federated learning based on an analytics request message from a network function (NF), receiving, from a network repository function (NRF), information regarding a second NWDAF capable of participating in the federated learning, generating a federated learning group, transmitting, to the second NWDAF, a federated learning join request message based on the information regarding the second NWDAF, and receiving, from the second NWDAF, a federated learning join response message including information regarding whether to participate in the federated learning group).
Khare et al. (US 12526669 B2, filed 02/08/2023) ([Abstract] Systems, methods, apparatuses, and computer program products for network data analytics function (NWDAF) accuracy enhancement. A method may include selecting a network function that can provide accuracy information. The method may also include sending a request to the network function for one or more accuracy reports for one or more analytics predictions. The method may further include receiving the one or more accuracy reports from the network function. In addition, the method may include managing the one or more analytics predictions based on the one or more accuracy reports).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RILEY S ACOSTA whose telephone number is (571)272-8714. The examiner can normally be reached Monday-Thursday 6am-4pm.
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/RILEY S ACOSTA/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
