DETAILED ACTION
This communication is in response to applicant’s response filed under 37 C.F.R. §1.111 in response to a non-final office action. Claims 1, 2, 4, 6-9, 11-15, and 17-19 have been amended. Claims 1-19 are subject to examination.
Acknowledgement is made to the Applicant’s amendments to claims 6, 7, 17, and 18 to obviate the previous objections to claims 6, 7, 17, and 18. The previous objections to claims 6, 7, 17, and 18 are hereby withdrawn.
Acknowledgement is made to the Applicant’s amendments to claims 1 and 12 to obviate the previous 101 rejections to claims 1 and 12. The previous 101 rejections to claims 1 and 12 are hereby withdrawn.
Response to Arguments
Applicant’s arguments with respect to claims 1, 9, and 12 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 8-10 and 19 are objected to because of the following informalities:
Claims 8 and 19 each recite “retrieving data from analytics data repository ...”. For the sake of clarity, the examiner suggests amending these claims to instead recite “retrieving data from an analytics data repository ...”.
Claim 9 recites “requesting provisioning of the ML model to a network data analytics function ...”, however, it is believed that this should instead read “requesting provisioning of the ML mode from a network data analytics function ...” based on the subsequent context of the claim.
Claim 10 recites “requesting provisioning of the ML model to the NWDAF ...”, however, as with claim 9, it is believed this should instead read “requesting provisioning of the ML mode from the NWDAF”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11, 13, and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, claim 1 recites the limitation “provisioning a trained ML model to the NWDAF”. This limitation renders the claim indefinite because it is unclear whether the “a trained ML model” recited in this limitation corresponds to “the ML model” previously recited in line 3 of claim 1.
Regarding Claim 2, claim 2 recites the limitation “reselecting or retraining an ML model”. This limitation renders the claim indefinite because it is unclear whether the “ML model” recited in this limitation corresponds to “the trained ML model” previously recited in claim 1 or the “a machine learning (ML) model” in the preamble of claim 1.
Regarding Claim 3, claim 3 recites the limitation “receiving a provisioning request for the ML model”. This limitation renders the claim indefinite because it is unclear whether the “ML model” recited in this limitation corresponds to the “a trained ML model” previously recited in claim 1.
Regarding Claim 9, claim 9 recites the limitation “receiving a trained ML model from the NWDAF”. This limitation renders the claim indefinite because it is unclear whether the “a trained ML model” recited in this limitation corresponds to “the ML model” previously recited in line 3 of claim 9.
Additionally, claim 9 recites the limitation “receiving a reselected or retrained ML model”. This limitation renders the claim indefinite because it is unclear whether the “ML model” recited in this limitation corresponds to “the trained ML model” previously recited in claim 9 or the “a machine learning (ML) model” in the preamble of claim 9.
Regarding Claim 10, claim 10 recites the limitation “requesting provisioning of the ML model”. This limitation renders the claim indefinite because it is unclear whether the “ML model” recited in this limitation corresponds to “the trained ML model” previously recited in claim 9.
Regarding Claim 13, claim 13 recites the limitation “reselecting or retraining an ML model”. This limitation renders the claim indefinite because it is unclear whether the “ML model” recited in this limitation corresponds to “the trained ML model” previously recited in claim 12 or the “a machine learning (ML) model” in the preamble of claim 12.
Regarding Claim 14, claim 14 recites the limitation “receiving the provisioning request for the trained ML model”. There is insufficient antecedent basis for this limitation, as claim 12 only states “receiving a provisioning request for an ML model”.
Regarding Claims 4-8 and 11, claims 4-8 and 11 each depend on independent claims 1 or 9 and therefore inherit the 35 U.S.C. 112 issues of the independent claims.
Claim Rejections - 35 USC § 103
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 1-3, 6, 9-10, 12-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Papageorgiou et al. (US 2023/0060071 A1, hereinafter “Papageorgiou”) in view of CATT (3GPP S2-2102448, provided in IDS dated 1/22/2026).
Regarding Claim 1, Papageorgiou teaches a method for evaluating a machine learning (ML) model, comprising: receiving a provisioning request for the ML model from a network data analytics function (NWDAF) including an analytics logical function (AnLF) in a cellular system (Papageorgiou: the present disclosure relates to machine-learning (ML) based functions ... in a mobile/wireless communication system, see paragraph [0003]; the NWDAF(AnLF) provides ML model request information, and the NWDAF(MTLF) obtains the ML model request information. In this example ... the ML model request information is included in ... an ML model provisioning request, see paragraph [0126] and Fig. 5); and
provisioning a trained ML model to the NWDAF (Papageorgiou: In step 3, the NWDAF(MTLF) specifies an ML model to be provisioned ... In this example, the NWDAF(MTLF) selects an existing trained ML model ... In step 5, the NWDAF(MTLF) performs ML model provisioning in response to the request, see paragraphs [0127]-[0134]).
Papageorgiou does not explicitly teach collecting data for monitoring accuracy of the trained ML model provisioned to the NWDAF; and
evaluating the accuracy of the trained ML model based on the collected data.
However, in the same field of endeavor, CATT teaches collecting data for monitoring accuracy of the trained ML model provisioned to the NWDAF (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model), see pg. 4 highlights); and
evaluating the accuracy of the trained ML model based on the collected data (CATT: the model provider NWDAF determines that the previously provided trained ML Model required re-training, e.g. based on the subscribed ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou to include the features as taught by CATT above in order to meet the requirements of the consumer NWDAF (CATT: see pg. 1 highlights).
Regarding Claim 2, Papageorgiou-CATT teaches the method of claim 1.
CATT further teaches reselecting or retraining an ML model of which the accuracy is determined to be deteriorated according to the evaluation result of the trained ML model (CATT: the model provider NWDAF determines that the previously provided trained ML Model required re-training, e.g. based on the subscribed ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights); and
providing the reselected or retrained ML model to the NWDAF (CATT: The model provider NWDAF also invokes the Nnwdaf_MLModelProvision_Notify service operation to notify the available of a re-trained ML model, see pg. 4 highlights).
The rationale and motivation for adding the teaching of CATT is the same as the rationale and motivation for Claim 1.
Regarding Claim 3, Papageorgiou-CATT teaches the method of claim 1, wherein the receiving a provisioning request for the ML model from the NWDAF comprises: receiving, from the NWDAF, ML model accuracy level (Papageorgiou: depending on the information provided in the request (e.g. ... required accuracy of the requested ML model, see paragraph [0006]).
Regarding Claim 6, Papageorgiou-CATT teaches the method of claim 1.
CATT further teaches the collecting data for monitoring accuracy of the trained ML model comprises: subscribing to the NWDAF by invoking a service operation for accuracy provisioning (CATT: the model provider NWDAF notifies the model consumer NWDAF with the ML model information ... by invoking Nnwdaf_MLModelProvision_Notify service operation. The model provider NWDAF may also include an indication of subscribe ... to ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights); and
receiving accuracy information of the trained ML model from the NWDAF (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model), see pg. 4 highlights).
The rationale and motivation for adding the teaching of CATT is the same as the rationale and motivation for Claim 1.
Regarding Claim 9, Papageorgiou teaches a method for using a machine learning (ML) model, comprising: requesting provisioning of the ML model to a network data analytics function(NWDAF) including an ML model training logical function (MTLF) in a cellular system (Papageorgiou: the present disclosure relates to machine-learning (ML) based functions and/or operations and/or services in a mobile/wireless communication system, see paragraph [0003]; the NWDAF(AnLF) provides ML model request information, and the NWDAF(MTLF) obtains the ML model request information. In this example ... the ML model request information is included in ... an ML model provisioning request, see paragraph [0126] and Fig. 5); and
receiving a trained ML model from the NWDAF (Papageorgiou: In step 3, the NWDAF(MTLF) specifies an ML model to be provisioned ... In this example, the NWDAF(MTLF) selects an existing trained ML model ... In step 5, the NWDAF(MTLF) performs ML model provisioning in response to the request, see paragraphs [0127]-[0134]).
Papageorgiou does not explicitly teach transmitting accuracy information of the trained ML model received from the NWDAF to the NWDAF when a service operation for accuracy provisioning is invoked by the NWDAF; and
receiving a reselected or retrained ML model from the NWDAF after the trained ML model is evaluated based on the accuracy information by the NWDAF.
However, in the same field of endeavor, CATT teaches teach transmitting accuracy information of the trained ML model received from the NWDAF to the NWDAF when a service operation for accuracy provisioning is invoked by the NWDAF (CATT: the model provider NWDAF notifies the model consumer NWDAF with the ML model information ... by invoking Nnwdaf_MLModelProvision_Notify service operation. The model provider NWDAF may also include an indication of subscribe ... to ML model performance feedback information from the model consumer NWDAF ... If the model provider NWDAF subscribes to ML model performance feedback information, the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model), see pg. 4 highlights); and
receiving a reselected or retrained ML model from the NWDAF after the trained ML model is evaluated based on the accuracy information by the NWDAF (CATT: The model provider NWDAF also invokes the Nnwdaf_MLModelProvision_Notify service operation to notify the available of a re-trained ML model, when the model provider NWDAF determines that the previously provided trained ML Model required re-training, e.g. based on the subscribed ML model performance feedback information from the model consumer NWDAF).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou to include the features as taught by CATT above in order to meet the requirements of the consumer NWDAF (CATT: see pg. 1 highlights).
Regarding Claim 10, Papageorgiou-CATT teaches the method of claim 9, wherein the requesting provisioning of the ML model to the NWDAF comprises: transmitting, to the NWDAF, ML model accuracy level (Papageorgiou: depending on the information provided in the request (e.g. ... required accuracy of the requested ML model, see paragraph [0006]).
Regarding Claim 12, Papageorgiou teaches a network data analytics function (NWDAF) including a machine learning (ML) model training logical function (MTLF) in a cellular system (Papageorgiou: the thus illustrated apparatus 600 may represent or realize/embody a (part of a) network entity in a mobile communication system, such as an NWDAF, NWDAF(MTLF) or MTLF entity, see paragraph [0161]), comprising:
a processor, a memory, and a communication device, wherein the processor executes a program stored in the memory (Papageorgiou: an apparatus 600 may comprise or realize at least one processor 610 and at least one memory 620 (and possibly also at least one interface 630) ... The processor 610 and/or the interface 630 of the apparatus 600 may also include a modem or the like to facilitate communication over a (hardwire or wireless) link, respectively... The memory 620 of the apparatus 600 may ... store respective software, programs ... that, when executed by the respective processor, enables the respective electronic device or apparatus to operate in accordance with the exemplifying embodiments of the present invention, see paragraphs [0155]-[0157]) to perform:
receiving a provisioning request for an ML model from a first NWDAF including an analytics logical function (AnLF) in the cellular system (Papageorgiou: the NWDAF(AnLF) provides ML model request information, and the NWDAF(MTLF) obtains the ML model request information. In this example ... the ML model request information is included in ... an ML model provisioning request, see paragraph [0126]); and
provisioning a trained ML model to the first NWDAF (Papageorgiou: In step 3, the NWDAF(MTLF) specifies an ML model to be provisioned ... In this example, the NWDAF(MTLF) selects an existing trained ML model ... In step 5, the NWDAF(MTLF) performs ML model provisioning in response to the request, see paragraphs [0127]-[0134]).
Papageorgiou does not explicitly teach collecting data for monitoring accuracy of the trained ML model provisioned to the first NWDAF; and
evaluating the accuracy of the trained ML model based on the collected data.
However, in the same field of endeavor, CATT teaches collecting data for monitoring accuracy of the trained ML model provisioned to the first NWDAF (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model), see pg. 4 highlights); and
evaluating the accuracy of the trained ML model based on the collected data (CATT: the model provider NWDAF determines that the previously provided trained ML Model required re-training, e.g. based on the subscribed ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou to include the features as taught by CATT above in order to meet the requirements of the consumer NWDAF (CATT: see pg. 1 highlights).
Regarding Claim 13, Papageorgiou-CATT teaches the NWDAF of claim 12.
CATT further teaches reselecting or retraining an ML model of which the accuracy is determined to be deteriorated according to the evaluation result of the trained ML model (CATT: the model provider NWDAF determines that the previously provided trained ML Model required re-training, e.g. based on the subscribed ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights); and
providing the reselected or retrained ML model to the first NWDAF (CATT: The model provider NWDAF also invokes the Nnwdaf_MLModelProvision_Notify service operation to notify the available of a re-trained ML model, see pg. 4 highlights).
The rationale and motivation for adding the teaching of CATT is the same as the rationale and motivation for Claim 12.
Regarding Claim 14, Papageorgiou-CATT teaches the NWDAF of claim 12, wherein the receiving a provisioning request for the ML model from the NWDAF comprises: receiving, from the NWDAF, ML model accuracy level (Papageorgiou: depending on the information provided in the request (e.g. ... required accuracy of the requested ML model, see paragraph [0006]).
Regarding Claim 17, Papageorgiou-CATT teaches NWDAF of claim 12.
CATT further teaches when collecting data for monitoring accuracy of the trained ML model, the processor performs: subscribing to the first NWDAF by invoking a service operation for accuracy provisioning (CATT: the model provider NWDAF notifies the model consumer NWDAF with the ML model information ... by invoking Nnwdaf_MLModelProvision_Notify service operation. The model provider NWDAF may also include an indication of subscribe ... to ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights); and
receiving accuracy information of the trained ML model from the first NWDAF (CATT: If the model provider NWDAF subscribes to ML model performance feedback information, the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model), see pg. 4 highlights).
The rationale and motivation for adding the teaching of CATT is the same as the rationale and motivation for Claim 12.
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Papageorgiou-CATT in view of Liu et al. (US 2024/0414063 A1, hereinafter “Liu”).
Regarding Claim 4, Papageorgiou-CATT teaches the method of claim 1.
CATT further teaches determining to check the accuracy of the trained ML model (CATT: The model provider NWDAF may also include an indication of subscribe ... to ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou to include the features as taught by CATT above in order to meet the requirements of the consumer NWDAF (CATT: see pg. 1 highlights).
Papageorgiou-CATT does not explicitly teach, based on a notification received from a policy control function (PCF) in the cellular system.
However, in the same field of endeavor, Liu teaches, based on a notification received from a policy control function (PCF) in the cellular system (Liu: By receiving a notification sent by the PCF ... adjustment of the information of the application layer model based on an analytics result from the NWDAF can be achieved, see paragraph [0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou-CATT to include the features as taught by Liu above in order to improve network performance (Liu: see paragraph [0116]).
Regarding Claim 15, Papageorgiou-CATT teaches the NWDAF of claim 12.
CATT further teaches determining to check the accuracy of the trained ML model (CATT: The model provider NWDAF may also include an indication of subscribe ... to ML model performance feedback information from the model consumer NWDAF, see pg. 4 highlights).
The rationale and motivation for adding the teaching of CATT is the same as the rationale and motivation for Claim 4.
Papageorgiou-CATT does not explicitly teach, based on a notification received from a policy control function (PCF) in the cellular system.
However, in the same field of endeavor, Liu teaches, based on a notification received from a policy control function (PCF) in the cellular system (Liu: By receiving a notification sent by the PCF ... adjustment of the information of the application layer model based on an analytics result from the NWDAF can be achieved, see paragraph [0059]).
The rationale and motivation for adding the teaching of Liu is the same as the rationale and motivation for Claim 4.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Papageorgiou-CATT-Liu in view of Garcia Azorero et al. (US 2022/0191052 A1, hereinafter “GA”).
Regarding Claim 5, Papageorgiou-CATT-Liu teaches the method of claim 4, but does not explicitly teach, wherein the notification includes a notification about a change in policy for user equipment (UE).
However, in the same field of endeavor, GA teaches, the notification includes a notification about a change in policy for user equipment (UE) (GA: The PCF then, identifies the UE Policy associations stored in the PCF for the UEs (roamers) ... calculates the new UE Policies to deliver to each roamer due to the changes notified by the UDR, and notifies every roamer, see paragraph [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou-CATT-Liu to include the features as taught by GA above in order to reduce unnecessary signaling (GA: see paragraph [0079]).
Regarding Claim 16, Papageorgiou-CATT-Liu teaches the NWDAF of claim 15, but does not explicitly teach, wherein the notification includes a notification about a change in policy for user equipment (UE).
However, in the same field of endeavor, GA teaches, the notification includes a notification about a change in policy for user equipment (UE) (GA: The PCF then, identifies the UE Policy associations stored in the PCF for the UEs (roamers) ... calculates the new UE Policies to deliver to each roamer due to the changes notified by the UDR, and notifies every roamer, see paragraph [0076]).
The rationale and motivation for adding the teaching of GA is the same as the rationale and motivation for claim 5.
Claims 7-8, 11, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Papageorgiou-CATT in view of 3GPP TS 23.288 v17.3.0 (hereinafter “3GPP”).
Regarding Claim 7, Papageorgiou-CATT teaches the method of claim 6.
CATT further teaches receiving accuracy information of the trained ML model from the NWDAF further comprises: receiving data or analytics from the NWDAF (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model) ... The model consumer NWDAF may also include the data collected for the output analytics using the ML model, see pg. 4 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou to include the features as taught by CATT above in order to meet the requirements of the consumer NWDAF (CATT: see pg. 1 highlights).
Papageorgiou-CATT does not explicitly teach using a storage transaction identifier for data or analytics.
However, in the same field of endeavor, 3GPP teaches using a storage transaction identifier for data or analytics (3GPP: The consumer NF [NWDAF] uses this service operation ... to retrieve stored data or analytics from the ADRF ... Inputs: ... Storage Transaction Identifier, see p. 196 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou-CATT to include the features as taught by 3GPP above in order to support network data analytics services in 5G Core network (3GPP: see p. 9 highlights).
Regarding Claim 8, Papageorgiou-CATT-3GPP teaches the method of claim 7.
CATT further teaches collecting data for monitoring accuracy of the trained ML model (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model) ... The model consumer NWDAF may also include the data collected for the output analytics using the ML model, see pg. 4 highlights).
The rationale and motivation for adding the teaching of CATT is the same as the rationale and motivation for claim 7.
3GPP further teaches, retrieving data from analytics data repository (ADRF) using the storage transaction identifier (3GPP: The consumer NF [NWDAF] uses this service operation to retrieve stored data or analytics from the ADRF ... Inputs: ... Storage Transaction Identifier ... Outputs: ... Data, see p. 196 highlights).
The rationale and motivation for adding the teaching of 3GPP is the same as the rationale and motivation for Claim 7.
Regarding Claim 11, Papageorgiou-CATT teaches the method of claim 9.
CATT further teaches transmitting accuracy information of the trained ML model to the NWDAF further comprises: sending data or analytics to the NWDAF (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model) ... The model consumer NWDAF may also include the data collected for the output analytics using the ML model, see pg. 4 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou to include the features as taught by CATT above in order to meet the requirements of the consumer NWDAF (CATT: see pg. 1 highlights).
Papageorgiou-CATT does not explicitly teach using a storage transaction identifier for data or analytics.
However, in the same field of endeavor, 3GPP teaches using a storage transaction identifier for data or analytics (3GPP: The consumer NF [NWDAF] uses this service operation ... to retrieve stored data or analytics from the ADRF ... Inputs: ... Storage Transaction Identifier, see p. 196 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou-CATT to include the features as taught by 3GPP above in order to support network data analytics services in 5G Core network (3GPP: see p. 9 highlights).
Regarding Claim 18, Papageorgiou-CATT teaches the NWDAF of claim 17.
CATT further teaches when performing the receiving the accuracy information of the trained ML model from the first NWDAF, the processor performs: receiving data or analytics from the NWDAF (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model) ... The model consumer NWDAF may also include the data collected for the output analytics using the ML model, see pg. 4 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou to include the features as taught by CATT above in order to meet the requirements of the consumer NWDAF (CATT: see pg. 1 highlights).
Papageorgiou-CATT does not explicitly teach using a storage transaction identifier for data or analytics.
However, in the same field of endeavor, 3GPP teaches using a storage transaction identifier for data or analytics (3GPP: The consumer NF [NWDAF] uses this service operation ... to retrieve stored data or analytics from the ADRF ... Inputs: ... Storage Transaction Identifier, see p. 196 highlights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Papageorgiou-CATT to include the features as taught by 3GPP above in order to support network data analytics services in 5G Core network (3GPP: see p. 9 highlights).
Regarding Claim 19, Papageorgiou-CATT-3GPP teaches the NWDAF of claim 18.
CATT further teaches collecting data for monitoring accuracy of the trained ML model (CATT: the model consumer NWDAF notifies the model provider NWDAF with the ML model performance feedback information (e.g. accuracy level of the output analytics using the ML model) ... The model consumer NWDAF may also include the data collected for the output analytics using the ML model, see pg. 4 highlights).
The rationale and motivation for adding the teaching of CATT is the same as the rationale and motivation for claim 18.
3GPP further teaches, retrieving data from analytics data repository (ADRF) using the storage transaction identifier (3GPP: The consumer NF [NWDAF] uses this service operation to retrieve stored data or analytics from the ADRF ... Inputs: ... Storage Transaction Identifier ... Outputs: ... Data, see p. 196 highlights).
The rationale and motivation for adding the teaching of 3GPP is the same as the rationale and motivation for Claim 18.
Conclusion
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 PHILLIP J EGAN KEARNS whose telephone number is 571-272-4869. The examiner can normally be reached M-F 10-6 EST.
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/P.K./Examiner, Art Unit 2416
/NOEL R BEHARRY/Supervisory Patent Examiner, Art Unit 2416