DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/25/26 has been entered.
Response to Arguments
Applicant’s arguments and amendments, filed 5/26/26, with respect to the rejection of claims 1, 17 and 21 under 35 U.S.C. 112(b) have been fully considered and are persuasive. Therefore the rejection has been withdrawn.
Applicant’s arguments and amendments, with respect to the rejection(s) of claim(s) 1, 11, 12, 21 and 31-33 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art.
Applicant's arguments with respect to the Nie et al. reference have been fully considered but they are not persuasive.
Applicant argued:
This claimed feature requires a specific, structured parameter: a model accuracy metric. This model accuracy is associated with a model, included as part of analytics information, and used in the discovery and selection process. In particular, the claimed model accuracy is part of the analytics information stored in a registration entry, which enables comparison across multiple analytics functions. Nie contains no teaching or suggestion of exposing any model- related metric in such a structured and discoverable form.
The Patent Office's interpretation conflates a general, qualitative statement about
accuracy (like "more accurately identified") with the specific, structured concept of "accuracy associated to a first service capability" as required by claim 1. The phrase "model accuracy of a model used by the ADF to provide the requested service" implies a measurable and defined relationship between the analytics accuracy and a discrete, named service offered by the system, which is a required feature of claim 1.
Moreover, a qualitative improvement in performance (e.g., "more accurate
identification") is fundamentally different from an explicit, structured parameter representing model accuracy. The present claims require that model accuracy be included as analytics information and therefore be accessible and usable by the ADF in a decision-making process, rather than merely being an inherent or implicit characteristic of model operation. As such, the combination of Hunt and Nie fails to teach all features of claim 1.
The Examiner respectfully disagrees and submits that Nie does indeed disclose exposing model-related metric in such a structured and discoverable form and does not just speak of updating a model but rather discloses model accuracy comprised in this accuracy. In paragraphs [0180, 0184, and 0252] Nie states:
[0180] There may be the following two implementations in which the data analytics network element obtains the service type that the terminal device allows or does not allow to be used in the period in which the hotspot switch is enabled: The UE sends an allowed or disallowed service type list to the data analytics network element, or the data analytics network element performs machine learning training, to obtain a service type identification model. The training data used for machine learning may include two types of data. One type is application data including a size, a start time and an end time, IP quintuplets, and a service type that are of a data packet, from an operator platform, an OTT (Over The Top) server, a vertical industry management and control center, or the like. Another type is network data including a UE identifier, a terminal type, an access point name (APN), a data network name (DNN), radio channel quality on a base station side, a cell ID, and other information from a network side. The data analytics network element may obtain the service type identification model based on the two types of information, and can classify a data packet of the UE that flows through the UPF network element, thereby obtaining the service type that the UE allows to be used in the period in which the Wi-Fi hotspot of the UE is enabled. (Emphasis added).
[0184] In this embodiment of this application, the data analytics network element may determine the service type of the first data packet through the service type identification model. For example, the data analytics network element may obtain a feature list of the service type identification model through feature learning, thereby obtaining a model parameter of the service type identification model through training, and performing calculation based on the characteristic parameter of the first data packet and the service identification model, so that the service type of the first data packet may be determined. (Emphasis added).
[0252] For each UE, the NWDAF network element performs big data analysis, assumes a behavior or a model of service type determination, obtains, by learning, a feature list of the service type determining model, and synchronizes the feature list corresponding to the model to feature engineering on the NWDAF network element and the UPF network element. The feature list in a feature vector may be understood as a parameter name. The feature vector reported to the NWDAF network element by the UPF network element is a parameter value. The NWDAF network element obtains, through training, the allowed (or unallowed) service type list of the UE in a specific state (the hotspot enabled state). (Emphasis added).
As described herein, Nie clearly discloses a measurable and defined relationship between the analytics accuracy and a discrete, named service offered by the system as required by claim 1. Thus the combination teaches the claimed limitations.
Applicant furthermore argued the grounds of rejection over Hunt et al. in view of Nie et al., however these arguments are no longer applicable in view of the new grounds of rejection as set forth below.
Applicant also argued:
Chan teaches various models that are used to predict if patents will be invalidated. There is no discussion of different models that have difference levels of accuracies. Chan does not teach "wherein the first characteristic indicates that a most accurate model is requested, or that an accuracy of the model must be over a certain threshold." As such, Chan does not remedy the deficiencies of Hunt and Nie. Therefore, claim 44 is allowable over the combination of Hunt, Nie, and Chan.
The Examiner submits that since in col. 33, ll. 43-col. 34, ll. 16, Chan states, “For example, if the trained model 310 fails to meet a particular threshold of reliability (e.g., based on the scored data), the prediction/recommendation engine 320 can give little to no consideration to that trained model in proposing one or more actionable tasks (e.g., while relying upon other trained models that meet such criteria in generating such tasks)”, Chan teaches the limitations, wherein the first characteristic indicates that a most accurate model is requested, or that an accuracy of the model must be over a certain threshold.
The Examiner points out that the pending claims must be "given the broadest reasonable interpretation consistent with the specification" [In re Prater, 162 USPQ 541 (CCPA 1969)] and "consistent with the interpretation that those skilled in the art would reach" [In re Cortright, 49 USPQ2d 1464 (Fed. Cir. 1999)]. In conclusion, upon taking the broadest reasonable interpretation of the currently amended claims, the cited references teach all of the claimed limitations and the rejections are maintained as below.
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 11-12, 31-32 and 44 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.
Claim 11 recites the limitation "the first characteristic " in line 1. There is insufficient antecedent basis for this limitation in the claim. Claims 12, 31-32 and 44 are rejected under the same rationale.
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 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 1-3, 6-7, 9, 11-12, 17-19, 21-23, 26-27, 29 and 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (2020/0252813) in view of Nie et al. (2020/0244557).
As per claim 1, Li et al. teaches a method, in an analytics discovery function, ADF, in a service based architecture, SBA, network comprising a network function, the method comprising: receiving a discovery request from the network function for requested analytics information indicative of a requested service capability [see Li et al., paragraphs 0379-0387];
determining whether the requested analytics information matches first analytics information in a first registration entry [see Li et al., paragraphs 0346-0353]; and
responsive to the requested analytics information matching the first registration entry, selecting a first analytics function in the first registration entry [see Li et al., paragraph 0330];
wherein the first analytics information comprises a model used by the ADF to provide the requested service capability [see Li et al., paragraphs 0157, 0176 and 0180].
But Li et al. fails to explicitly teach, however, Nie et al. in the same field of endeavor teaches wherein the first analytics information comprises a model accuracy of a model [see Nie et al., paragraphs 0193-0194, 0180, 0184, 0252 and 0305].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li et al. with Nie et al. in order to accurately identify abnormal traffic and improve traffic management.
As per claim 2, Li-Nie teaches method as claimed in claim 1 further comprising: receiving a first registration request from the first analytics function in the SBA network, wherein the first registration request comprises the first analytics information indicative of a first service capability that the first analytics function is capable of providing to the network function, and storing the first registration entry comprising the first analytics information and a first identification of the first analytics function [see Li et al., paragraph 0097].
As per claim 3, Li-Nie teaches the method as claimed in claim 2 further comprising: receiving a second registration request from a second analytics functions in the SBA network, wherein the second registration request comprises second analytics information indicative of a second service capability that the second analytics function is capable of providing to the network function in the SBA network, and storing a second registration entry for the second analytics functions comprising the second analytics information and a second identification of the second analytics function [see Li et al., paragraphs 0136-0137].
As per claim 6, Li-Nie teaches the method as claimed in claim 2 wherein the step of determining whether the requested analytics information matches the first registration entry comprises determining whether a requested service capability indicated by the requested analytics information is the same as the first service capability [see Li et al., paragraph 0239].
As per claim 7, Li-Nie teaches the method as claimed in claim 6 wherein the first analytics information comprises a first filter value limiting circumstances in which the first service capability is available from the first analytics function [see Li et al., paragraph 0277].
As per claim 9, Li-Nie teaches the method as claimed in claim 7 wherein the first filter value comprises one or more of: at least one user identification value and at least one application identification value [see Li et al., paragraph 0347].
As per claim 11, Li-Nie teaches the method as claimed in claim 1 wherein the first characteristic further comprises one or more of: a location of the first analytics function, a time at which a model used provide the first service capability was trained [see Nie et al., paragraph 0112].
As per claim 12, Li-Nie teaches the method as claimed in claim 1 wherein the step of determining whether the requested analytics information matches the first registration entry comprises determining whether the first characteristic meets a requested criterion in the requested analytics information [see Nie et al., paragraph 0261].
As per claim 17, Li et al. teaches a method, in a network function in a service based architecture, SBA, network for discovery of an analytics function, the method comprising: transmitting a discovery request to an analytics discovery function, ADF, the discovery request comprising requested analytics information indicative of a requested service capability; and
receiving a response from the ADF comprising an identification of an analytics function capable of providing the requested service capability [see Li et al., paragraphs 0379-0387].
wherein the analytics information comprises a model used by the ADF to provide the requested service [see Li et al., paragraphs 0157, 0176 and 0180].
But Li et al. fails to explicitly teach, however, Nie et al. in the same field of endeavor teaches wherein the analytics information comprises a model accuracy of a model [see Nie et al., paragraphs 0193-0194, 0180, 0184, 0252 and 0305].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Li et al. with Nie et al. in order to accurately identify abnormal traffic and improve traffic management.
As per claim 18, Li-Nie teaches the method as claimed in claim 17 further comprising transmitting a service request to the analytics function [see Li et al., paragraph 0208].
As per claim 19, Li-Nie teaches the method as claimed in claim 17 wherein the response comprises a plurality of identifications of a plurality of analytics functions capable of providing the requested service capability, and wherein the method further comprises: selecting one of the plurality of analytics functions, and transmitting a service request to the selected one of the plurality of analytics functions [see Li et al., paragraph 0158].
As per claim 33, Li-Nie teaches the ADF as claimed in claim 32 wherein the requested criterion comprises one or more of: a location of the ADF with respect to the network function, a minimum accuracy, a maximum age [see Nie et al., paragraphs 0007-0010].
Claims 21-23, 26-27, 29, 31 and 32 have similar limitations as to the rejected claims above therefore, they are being rejected under the same rationale.
Claim(s) 44 is rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (2020/0252813) in view of Nie et al. (2020/0244557) as applied to claim 1 above, and further in view of Chan (10,133,791).
Hunt et al. and Nie et al. teach the limitations of claim 1 as above but fail to explicitly teach, however, Chan in the same field of endeavor teaches, wherein the first characteristic indicates that a most accurate model is requested, or that an accuracy of the model must be over a certain threshold [Chan, col. 33, ll. 43-col. 34, ll. 16].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Hunt et al. and Nie et al. with Chan in order to improve the accuracy of one or more trained analytical or statistical models.
There are prior art made of record not relied upon but is considered pertinent to applicant's disclosure. See attached.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RANODHI N SERRAO whose telephone number is (571)272-7967. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Follansbee can be reached on (571) 272-3964. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Ranodhi N. Serrao
/RANODHI SERRAO/Primary Examiner, Art Unit 2444
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