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 .
DETAILED ACTION
This action is in response to application filed 08/10/2026.
Claims 36-55 are pending in this application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/10/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 36, 47, 54, and 55 have been considered (see remarks 7/21/2026) 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. More specifically, the claims are now rejected with new reference Huang et al. (US 2023/0224752 A1).
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 36, 40-41, 44-45, 47, 51, 54-55 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 36, the claim recites the verification information comprises “configurations/instructions/semantics information for verifying the model” in lines 4-5. It is unclear what the slashed separated limitation “configurations/instructions/semantics” means. More specifically, it is unclear whether the verification information comprises: (1) configurations, instructions, or semantics information; or (2) configurations, instructions, and semantics information. Appropriate clarification and correction is required. For purposes of examination, the claim limitation is interpreted as the verification information comprises configurations, instructions, or semantics information.
Regarding claims 47, 54, and 55, the claims are interpreted and rejected for the same reason as set forth in claim 36.
Regarding claim 40, the claim recites the limitation "the THIRD MESSAGE" in line 2. There is insufficient antecedent basis for this limitation in the claim. Claim 40 depends on claim 36, and neither claim 36 nor claim 40 recite “a THIRD MESSAGE.” Appropriate correction is required.
Regarding claim 41, the claim recites “…the configuration for verifying the model…” line 2. There is insufficient antecedent basis for this limitation in the claim. The claims from which claim 41 depends introduces “a configuration for verifying the model.” Instead claim 36, recites “configurations/instructions/semantics information for verifying the model.” It is unclear whether “the configuration” refers to the previously recites “configurations” or another configuration. Appropriate correction and clarification is required.
Regarding claims 44, 45, and 51, the claims are interpreted and rejected for the same reason as set forth in claim 41.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 38 and 48 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 38, which depends on claim 36, recites receiving the SECOND MESSAGE “without prior transmission of the FIRST MESSAGE to the second network node.” The limitation excludes the prior transmission required by claim 36 rather than further limiting. In other words, claim 38 fails to include all the limitation of claim 36 upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Regarding claim 48, which depends on claim 47, the claim is interpreted and rejected for the same reason as set forth in claim 38.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 36-37, 44-45, 47, 54-55 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Huang et al. (US 2023/0224752 A1).
Regarding claim 36, Huang discloses a method performed by a first network node ([0116]: a data analytics network element may be the NWDAF network element. [0391]: training NWDAF (e.g. first network node), the method comprising:
transmitting a FIRST MESSAGE towards a second network node, the FIRST MESSAGE comprising verification information for verifying a model (Fig. 8, [0417]: Step 804 a″: The training NWDAF (e.g. first network node) sends, to the inference NWDAF (e.g. second network node), a model performance information request (e.g. FIRST MESSAGE) that carries the analytics ID, a model performance indicator, and a performance threshold), wherein the verification information comprises configurations/instructions/semantics information for verifying the model ([0417]: model performance indicator (e.g. configuration information) includes precision, accuracy, error rate, recall rate, F1 score, mean squared error, root mean squared error, root mean squared logarithmic error, mean absolute error, model inference duration, model robustness, model expandability, or model interpretability); and
receiving a SECOND MESSAGE transmitted by the second network node, the SECOND MESSAGE comprising a report associated with verifying the model, wherein the report is based on the verification information received with the FIRST MESSAGE ([0418]-[0419]: the inference NWDAF (e.g. second network node) receives the model performance information request (e.g. FIRST MESSAGE), the inference NWDAF performs model performance evaluation (e.g. verification) based on the model performance indicator, and sends the model performance information response (e.g. SECOND MESSAGE) to the training NWDAF (e.g. first network node. The model performance information response carries the analytics ID, the model performance indicator, and a value corresponding to the model performance indicator.).
Regarding claim 37, Huang discloses the method of claim 36 wherein the verification information provided to the second network node is intended for verifying that the model can perform as per tested and validated performance at the first network node or at least as per an acceptable performance level ([0412]: The training NWDAF sends, to the inference NWDAF, a model performance information subscription request that carries a performance threshold (e.g. acceptable performance level).[0414]: if determining that a value corresponding to the model performance indicator reaches the performance threshold, the inference NWDAF reports a model performance information notification to the training NWDAF. The model performance retraining notification is used to trigger the training NWDAF to perform model retraining).
Regarding claim 44, Huang discloses the method of claim 36 wherein the configuration for verifying the model may comprise one or more information elements in the group of: an identity or an identifier of the model to which the configuration for verification is applicable to or associated to; an indication to verify the model; an instruction to verify the model; and a recommendation to verify the model ([0501]-[0502]: The training NWDAF sends, to the inference NWDAF, a model performance information subscription request that carries the analytics ID, a model performance indicator (e.g. instruction to verify the model) (for example, precision, accuracy, model inference duration, model robustness, model expandability, or model interpretability), a reporting periodicity, and the model identifier (e.g. identifier of model)).
Regarding claim 45, Huang discloses the method of claim 36 wherein the verification information for verifying the model ([0509]: the model performance information subscription request may carry a plurality of model identifiers and a model performance indicator and a performance threshold that correspond to each model identifier) comprises one or more information related to verifying the model in the group of: one or more conditions or events to be fulfilled for triggering the verification of the model indicated by the first network node ([0511]: if determining that a value corresponding to the model performance indicator of the submodel reaches the performance threshold, the inference NWDAF reports a model performance information notification corresponding to the submodel to the training NWDAF); one or more instructions or policies or recommendations related to verification of the model indicated by the first network node; a request to transmit to the first network node a report comprising information associated to the verification of the model indicated by the first network node ([0611]: a reporting periodicity and threshold information. The reporting periodicity indicates a time point at which the performance report of the model is reported); one or more conditions or events to be fulfilled for transmitting the report to the first network node comprising information associated to the verification of the model indicated by the first network node; one or more conditions or events to be fulfilled for transmitting/forwarding the configuration for verifying the model; weight factors for each input needed by the model, namely revealing the importance/priority of each input type with respect to the process of inference carried out by the model; frequency and/or frequency ranges and/or cumulative amount of samples in a given time window, with which each type of input is assumed to be received in order to allow the model to perform according to its tested performance or according to a sufficiently good performance level established by the first node; frequency and/or frequency ranges and/or cumulative number of samples in a given time window, with which each type of output is assumed to be generated in order to allow the model to perform according to its tested performance or according to a sufficiently good performance level established by the first node; and semantics of the inputs needed at the model and/or of the outputs generated by the model.
Regarding claim 47, Huang discloses a method performed by a second network node, the method comprising: receiving a FIRST MESSAGE transmitted by a first network node, the FIRST MESSAGE comprising verification information for verifying a model, wherein the verification information for verifying the model (Fig. 8, [0417]: Step 804 a″: The training NWDAF (e.g. first network node) sends, to the inference NWDAF (e.g. second network node), a model performance information request (e.g. FIRST MESSAGE) that carries the analytics ID, a model performance indicator, and a performance threshold) comprises configurations/instructions/semantics information for verifying the model ([0417]: model performance indicator (e.g. configuration information) includes precision, accuracy, error rate, recall rate, F1 score, mean squared error, root mean squared error, root mean squared logarithmic error, mean absolute error, model inference duration, model robustness, model expandability, or model interpretability); and
transmitting a SECOND MESSAGE towards the first network node, the SECOND MESSAGE comprising a report associated with verifying the model, wherein the report is based on the verification information received within the FIRST MESSAGE ([0418]-[0419]: the inference NWDAF (e.g. second network node) receives the model performance information request (e.g. FIRST MESSAGE), the inference NWDAF performs model performance evaluation (e.g. verification) based on the model performance indicator, and sends the model performance information response (e.g. SECOND MESSAGE) to the training NWDAF (e.g. first network node. The model performance information response carries the analytics ID, the model performance indicator, and a value corresponding to the model performance indicator.).
Regarding claim 54; the claim is interpreted and rejected for the same reason as set forth in claim 36.
Regarding claim 55; the claim is interpreted and rejected for the same reason as set forth in claim 47.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 39, 50-51 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Vasseur et al. (US 2015/0193697 A1).
Regarding claim 39, Huang discloses the method of claim 36.
However, Huang does not disclose further comprising: providing, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model to the second network node and the verification information for verifying the model associated to the model provided by the first network node.
In an analogous art, Vasseur discloses providing, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model to the second network node and the verification information for verifying the model associated to the model provided by the first network node ([0099],[0100]: a notification is received that the device has been selected as a validator to validate a machine learning model. The parameters for the machine learning model are sent to the selected validator. The machine learning model parameters correspond to the parameters that control how the model operates, such as the neurons, connections, and connection weights in an ANN).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang to comprise “providing, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model to the second network node and the verification information for verifying the model associated to the model provided by the first network node” taught by Vasseur.
One of ordinary skilled in the art would have been motivated because it would have enabled the receiving validator to verify the provided model using locally available data and determine performance metrics for the model parameters (Vasseur, [0077], [0100).
Regarding claim 50, Huang discloses the method of claim 47.
However, Huang does not disclose further comprising: receiving, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model from the first network node.
In an analogous art, Vasseur discloses receiving, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model from the first network node ([0100]: The parameters for the machine learning model are sent to the selected validator, the parameters correspond to the parameters that control how the model operates, such as the neurons, connections, and connection weights in an ANN).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang to comprise “receiving, either with the FIRST MESSAGE or with a THIRD MESSAGE, the model from the first network node” taught by Vasseur.
One of ordinary skilled in the art would have been motivated because it would have enabled the receiving validator to verify the provided model using locally available data and determine performance metrics for the model parameters (Vasseur, [0077], [0100).
Regarding claim 51, Huang-Vasseur discloses the method of claim 50 wherein the configuration for verifying the model provided with the FIRST MESSAGE is associated to the model provided by the first network node to the second network node (Vasseur, [0099]-[0100]: machine learning model parameters for an ANN may correspond to information regarding the number of neurons, weighted neuron connections, etc. to be used by the model). The same rationale applies as in claim 50.
Claims 38, 48 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Kar et al. (US 2021/0287050 A1).
Regarding claim 38, Huang discloses the method of claim 36.
However, Huang does not disclose wherein receiving the SECOND MESSAGE from the second network node comprises receiving the report associated with verifying the model without prior transmission of the FIRST MESSAGE to the second network node.
In an analogous art, Kar discloses wherein receiving the SECOND MESSAGE from the second network node comprises receiving the report associated with verifying the model without prior transmission of the FIRST MESSAGE to the second network node ([0053]: If the output produced by the ML model under test 150 in response to the test data is accurate, a successful test is recorded, else, if the output is inaccurate, a failed test is recorded at 714 and the model report 168 regarding the success or failure of the ML model under test 150 is produced at 716. The model report 168 including the test results may be conveyed to the users at 718. The results can convey if the ML model under test 150 is robust or if additional training or modifications are required).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang to comprise “wherein receiving the SECOND MESSAGE from the second network node comprises receiving the report associated with verifying the model without prior transmission of the FIRST MESSAGE to the second network node” taught by Kar.
One of ordinary skilled in the art would have been motivated because it would have enabled to test ML models for accuracy and robustness (Kar, [0017]).
Regarding claim 48, Huang discloses the method of claim 47.
However, Huang does not disclose wherein the SECOND MESSAGE is transmitted towards the first network node without previously receiving the FIRST MESSAGE from the first network node, wherein the second network node, without previous configurations/instructions/semantics from the first network node, verifies the model and notifies the first network node of inconsistencies between the instructions/configurations/semantics relative to the inputs and/or outputs, provided prior to using the model, and the actual availability of inputs and/or outputs over connected interfaces to the second network node.
In an analogous art, Kar discloses wherein the SECOND MESSAGE is transmitted towards the first network node without previously receiving the FIRST MESSAGE from the first network node, wherein the second network node, without previous configurations/instructions/semantics from the first network node, verifies the model and notifies the first network node of inconsistencies between the instructions/configurations/semantics relative to the inputs and/or outputs, provided prior to using the model, and the actual availability of inputs and/or outputs over connected interfaces to the second network node ([0053]: If the output produced by the ML model under test 150 in response to the test data is accurate, a successful test is recorded, else, if the output is inaccurate, a failed test is recorded at 714 and the model report 168 regarding the success or failure of the ML model under test 150 is produced at 716. The model report 168 including the test results may be conveyed to the users at 718. The results can convey if the ML model under test 150 is robust or if additional training or modifications are required).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang to comprise “wherein the SECOND MESSAGE is transmitted towards the first network node without previously receiving the FIRST MESSAGE from the first network node, wherein the second network node, without previous configurations/instructions/semantics from the first network node, verifies the model and notifies the first network node of inconsistencies between the instructions/configurations/semantics relative to the inputs and/or outputs, provided prior to using the model, and the actual availability of inputs and/or outputs over connected interfaces to the second network node” taught by Kar.
One of ordinary skilled in the art would have been motivated because it would have enabled to test ML models for accuracy and robustness (Kar, [0017]).
Claims 40-43 are rejected under 35 U.S.C. 103 as being unpatentable Huang in view of Chen et al. (US 2023/0127927 A1).
Regarding claim 40, Huang discloses the method of claim 36.
However, Huang does not disclose further comprising: providing to the second network node, either with the FIRST MESSAGE or with the THIRD MESSAGE, a set of reference data samples which can be used to verify the model.
In an analogous art, Chen discloses providing to the second network node, either with the FIRST MESSAGE or with the THIRD MESSAGE, a set of reference data samples which can be used to verify the model ([0030], [0077]: server 704 identifies distributional characteristics of the validation dataset (data with multiple samples) and generates a combined dataset. [0080]: server 704 sends (e.g. message) the combined dataset (e.g. data samples) to client 706 (e.g. second node). [0082]: the client trained AI model may generate output based on the combine dataset).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang to comprise “providing to the second network node, either with the FIRST MESSAGE or with the THIRD MESSAGE, a set of reference data samples which can be used to verify the model” taught by Chen.
One of ordinary skilled in the art would have been motivated because it would have enabled to transmit a validation dataset to the network node performing verification of the trainable model in order to evaluate and determine performance metrics for the model (Chen, [0081]).
Regarding claim 41, Huang-Chen discloses the method of claim 40 wherein the set of reference data samples is provided with the FIRST MESSAGE as part of the configuration for verifying the model (Chen, [0080]: server 704 sends (e.g. message) the combined dataset (e.g. data samples) to client 706 (e.g. second node). [0082]: the client trained AI model may generate output based on the combine dataset). The same rationale applies as in claim 40.
Regarding claim 42, Huang-Chen discloses the method of claim 40 wherein the provided reference data samples are explicitly associated to one or more models (Chen, [0012]: The system may transmit, over a network, the combined dataset as an input to a trained machine learning model. The client device may utilize the combined dataset to perform a validation on the trained model). The same rationale applies as in claim 40.
Regarding claim 43, Huang-Chen discloses the method of claim 40 wherein the provided reference data samples comprise one or more of: a set of reference input-output pairs, where each reference output value represents that output that is expected to obtain for the corresponding reference input data when provided to the model for verification (Chen, [0098]-[0099]: client 806 may comprise a trained model and may apply (e.g. input) the combined dataset through the trained model to generate output. Client 806 evaluates the output and detects cheating… determining whether the second portion of the output corresponds to the predetermined output (e.g. expected output); and reference state-action pairs, wherein the reference action represents either the expected output of the model or the decision of an AIML algorithm using the model, when feeding the model with the reference state. The same rationale applies as in claim 40.
Claims 46 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Kang et al. (US 2024/0205929 A1 – Priority Date 05/11/2021).
Regarding claim 46, Huang discloses the method of claim 36, while the second network node comprises one or more of: a Radio Access Network, RAN, node; a Next Generation Radio Access Network, NG-RAN, node; a function of the RAN node; a network node realizing at least in part a Non-Real Time Radio Intelligent Controller, RIC; a network node realizing at least in part a Near-Real Time RIC; a Core Network node ([0097]: the NWDAF is a standardized network element configured to collect core network data); and a Cloud-based centralized training node.
However, Huang does not disclose wherein the first network node comprises one or more of: an Operation and Management, OAM, node; and a Service and Management Orchestration, SMO, node.
In an analogous art, Kang discloses wherein the first network node comprises one or more of: an Operation and Management, OAM, node; and a Service and Management Orchestration, SMO, node (fig. 17, [0324]: Validation data: This refers to a data set for verifying a model for which learning has already been completed. Fig. 15, [0336]: FIG. 15 may be implemented through cooperation of two or more entities among a RAN, a network node, an OAM of network operator. For example, one entity may perform some of the functions of FIG. 15 and other entities may perform the remaining functions).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang a to comprise “wherein the first network node comprises one or more of: an Operation and Management, OAM, node; and a Service and Management Orchestration, SMO, node” taught by Kang.
One of ordinary skilled in the art would have been motivated because it would have enabled to perform the model training function at a network operator’s OAM and to transmit the trained model to another network node for model inference and model performance feedback a (Kang, [0317]-[0319]).
Claims 49 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Hyde et al. (US 2024/0298225 A1 – Priority Date 10/21/2021).
Regarding claim 49, Huang discloses the method of claim 47.
However, Huang does not disclose further comprising signaling the result of the verification/testing/validation process to any other external node or system in a network, to enable a system diagnostic and system optimization.
In an analogous art, Hyde discloses further comprising signaling the result of the verification/testing/validation process to any other external node or system in a network, to enable a system diagnostic and system optimization ([0088]: predicted information provided by various AI/ML models is accompanied with metrics to allow a network node, such as an OAM to know accuracy and error bounds for various training reports 414 or analytics reports 416. [0249] Example 6 may include NG-RAN that provides periodic feedback to OAM to improve the AI model training if model training occurs in OAM).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang to comprise “further comprising signaling the result of the verification/testing/validation process to any other external node or system in a network, to enable a system diagnostic and system optimization” taught by Hyde.
One of ordinary skilled in the art would have been motivated because it would have enabled to assist predicting network and service demand to enable the timely resource provisioning and deployment (Hyde, [0025]).
Claims 52-53 are rejected under 35 U.S.C. 103 as being unpatentable over Huang in view of Samdanis et al. (WO 2021/023388 A1).
Regarding claim 52, Huang discloses the method of claims 47.
However, Huang does not disclose further comprising one or more of: transmitting a FOURTH MESSAGE towards a third network node, the FOURTH MESSAGE comprising at least part of configurations/instructions/semantics information for verifying the model received from the first network node; and receiving a FIFTH MESSAGE transmitted by the third network node, the FIFTH MESSAGE comprising a report associated to verifying a model based on the configurations/instructions/semantics information received with the FOURTH MESSAGE.
In an analogous art, Samdanis discloses further comprising one or more of: transmitting a FOURTH MESSAGE towards a third network node (pg. 16, [0002]-[0003]: NWDAF), the FOURTH MESSAGE comprising at least part of configurations/instructions/semantics information for verifying the model received from the first network node (pg. 16, [0002]-[0003]: each of the messages between NEF (e.g. second network node) and NWDAF (e.g. third network node) for updating, validating and modifying (e.g. the lower portion of Fig. 2), the analytics model is identified by the unique analytics model ID. NEF maps the analytics model ID to an identifier of the AF (e.g. first network node). The AF may request (e.g. FOURTH MESSAGE) performance data for validation, i.e. to examine if the Analytics Model performs as expected (step 11 ). In addition such process can be triggered once certain conditions are met, e.g. periodic or upon a performance deviation observation at the NWDAF); and receiving a FIFTH MESSAGE transmitted by the third network node, the FIFTH MESSAGE comprising a report associated to verifying a model based on the configurations/instructions/semantics information received with the FOURTH MESSAGE (pg. 16, [0003]: the NWDAF prepares and formats the validation data (step 12) including optionally a root cause analysis to identify potential faulty data analytics sources. It may report (e.g. FIFTH MESSAGE) also the data sources of the analytics (e.g. the AMF instance in case of UE mobility analytics). Such validation reporting is forwarded to the AF via the NEF, which may provide an appropriate data abstraction, e.g. hiding network topology or other mobile network operator internal network or data details).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Huang to comprise “further comprising one or more of: transmitting a FOURTH MESSAGE towards a third network node, the FOURTH MESSAGE comprising at least part of configurations/instructions/semantics information for verifying the model received from the first network node; and receiving a FIFTH MESSAGE transmitted by the third network node, the FIFTH MESSAGE comprising a report associated to verifying a model based on the configurations/instructions/semantics information received with the FOURTH MESSAGE” taught by Samdanis.
One of ordinary skilled in the art would have been motivated because it would have enabled to expose model training and validation information to and from the AF, in order to be able to trigger model re-training and maintenance when needed (Samdanis, pg. 10, [0005]).
Regarding claim 53, Huang-Samdanis discloses the method of claim 52 further comprising: forwarding the report received from the third network node to the first network node via the SECOND MESSAGE (Samdanis, pg. 16, [0003]: It may report also the data sources of the analytics (e.g. the AMF instance in case of UE mobility analytics). Such validation reporting is forwarded to the AF via the NEF, which may provide an appropriate data abstraction, e.g. hiding network topology or other mobile network operator internal network or data details). The same rationale applies as in claim 52.
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Takasaki et al., US 2022/0343219: Parallel Cross Validation in Collaborative Machine Learning.
Yao et al., US 2023/0141237 A1: Techniques for Management Data Analytics (MDA) Process and Service.
Feki et al., US 2024/0152814 A1: Training Data Characterization and Optimization for a Positioning Task.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN C TURRIATE GASTULO whose telephone number is (571)272-6707. The examiner can normally be reached Monday - Friday 8 am-4 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Glenton B Burgess can be reached at (571)272-3949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.C.T/Examiner, Art Unit 2454
/OSCAR A LOUIE/Supervisory Patent Examiner, Art Unit 2445
