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 .
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4, 5, 15-17, 22 and 23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Soulhi et al., US 10,616,257 B1 (hereinafter “Soulhi”).
Claim 1: Soulhi teaches a computer-implemented method performed by a network node to generate and return a configuration of a computing device in a network, the method comprising:
receiving data comprising (i) an observation for the computing device or the network for a second time interval, the observation comprising one or more of a plurality of configurations of the computing device, a plurality of performance metrics, and/or a plurality of key performance indicators, KPIs, and (ii) an intent comprising one or more of a performance metric, a KPI, and a configuration parameter (Soulhi, [Fig. 5], [Col. 13 Lines 9-11] note in block 505, n-dimensional network performance data X pertaining to a network device may be obtained, [Col. 5 Lines 49-52] note network performance data 207 may include performance indicator data (e.g., KPIs…), [Col. 5 Lines 63-67] note network performance data 207 may pertain to… time period, [Col. 6 Lines 14-19] note network performance data 207 may include characteristic data pertaining to… configuration data);
generating, with a first machine learning, ML, model, a plurality of configurations for the computing device for the second time interval based on a search on a plurality of latent variables in a latent space (Soulhi, [Col. 13 Lines 16-17] note In block 510, the n-dimensional network performance data X may be encoded in m-dimensions, [Col. 13 Lines 21-27] note In block 515, a latent space data G may be generated based on a generative model and the encoded network performance data. For example, network management device 130 may generate latent space data G that may have n dimensions based on the generative model and the encoding of the network performance data);
returning, from the search, a configuration for the computing device for the second time interval that best satisfies the performance metric and/or the KPI of the intent based on the search, wherein the configuration is constrained by at least one of the performance metric, the KPI, and the configuration parameter of the intent; and transmitting the configuration for the computing device (Soulhi, [Col. 13 Line 29] note In block 520, the latent space data G may be quantitatively assessed, [Col. 13 Lines 65-67] note When it is determined that the parameters may not be modified (block 525—NO), analysis of the quantitative results may be performed (block 530), [Col. 14 Lines 10-15] note In block 535, a network performance data report may be generated. For example, network management device 130 may generate the network performance data report based on the result of the quantitative analysis. By way of further example, the network performance data report may indicate network configurations for network devices, [Col. 14 Lines 24-37] note In block 540, the network performance data report may be used for new site development or remedial measures… Network management device 130 may transmit the network performance data report to network planning devices or other network management devices).
Claim 2: Soulhi teaches the method of Claim 1, wherein the search on the plurality of latent variables in the latent space comprises one or more of (1) encoding the plurality of configurations of the computing device, the plurality of performance metrics, and/or the plurality of KPIs of the data to a compressed representation in the latent space, (2) sampling a plurality of points in the latent space, (3) decoding respective points in the plurality of points in the latent space to a respective plurality of decoded points, and (4) generating the configuration for the computing device on the performance metric and/or the KPI while satisfying the constrained at least one of the performance metric, the configuration parameter, and the KPI (Soulhi, [Col. 13 Lines 16-17] note In block 510, the n-dimensional network performance data X may be encoded in m-dimensions).
Claim 4: Soulhi teaches the method of Claim 2, wherein the sampling comprises accessing an association of a plurality of constraints comprising one or more of the KPI, the performance metric, and the configuration parameter (Soulhi, [Col. 5 Lines 59-67]-[Col. 6 Lines 1-4] note network performance data 207 may pertain to a single access device 107, multiple access devices 107 (e.g., a subset of all access devices 107), or all access devices 107. Additionally, or alternatively, network performance data 207 may pertain to access devices 107 associated with other types of criteria, such as carrier (e.g., carrier frequency, sector of a cell, etc.), type of RAT, geographic location of access device 107, time period (e.g., day, day and time period, etc.), type of access device 107 (e.g., eNB versus gNB, etc.), and/or other types of factors (e.g., city versus rural, high versus low density, etc.) that may indicate a characteristic or an attribute pertaining to access device 107).
Claim 5: Soulhi teaches the method of Claim 1, further comprising:
receiving a raw observation for the computing device and/or the network for a first time interval, the raw observation comprising one or more of a plurality of configurations of the computing device, a plurality of performance metrics, and/or a plurality of KPIs; and dividing the raw observation into categorical data and numerical data (Soulhi, [Fig. 5], [Col. 13 Lines 9-11] note in block 505, n-dimensional network performance data X pertaining to a network device may be obtained, [Col. 5 Lines 49-52] note network performance data 207 may include performance indicator data (e.g., KPIs…), [Col. 5 Lines 63-67] note network performance data 207 may pertain to… time period, [Col. 6 Lines 14-19] note network performance data 207 may include characteristic data pertaining to… configuration data).
Claim 11: Soulhi teaches the method of Claim 1, further comprising:
training the first ML model, wherein during the training a cross-entropy loss is calculated for a distribution over values for the configuration (Soulhi, [Col. 10 Lines 29-42] note the iterative process includes network management device 130 changing one or multiple parameters pertaining to the service, as described herein. According to various exemplary embodiments, the parameters may include… loss (e.g., cross entropy…)).
Claim 15: Soulhi teaches the method of Claim 1, further comprising:
generating, from a second ML model, a predicted observation for the computing device and/or the network for the second time interval, wherein the generating comprises propagating the data from the first time interval into the future for the second time interval, the propagating comprising predicting the observation and predicting a latent encoding (Soulhi, [Col. 7 Lines 33-45] note in the context of cell site planning or future provisioning of wireless service at a locale, network management device 130 may generate latent space data that indicates predictive network performance data within geogrid areas 337 of new cell area 335 and/or within geogrid area 340 at which a new access device 107 may be deployed. The reconstruction and prediction of geogrid radio maps may be generated to relate a geographic location, not only to a given system parameter (e.g., path loss, etc.), but to a generic and/or new latent dimension that captures a certain radio behavior, which may be difficult to identify using traditional statistical or machine-learning approaches on the raw data).
Claim 16: Soulhi teaches the method of Claim 15, wherein the second ML model comprises a long short term memory, LSTM, model and the predicting the predicted observation is performed with the LSTM model and wherein the generating comprises using the predicted observation to find the configuration (Soulhi, [Col. 10 Lines 34-37] note neural network connectivity pattern (e.g., shallow, fully connected, convolutional, attention, recursive, long short-term memory (LSTM), etc.)).
Claim 17: Soulhi teaches the method of Claim 1, wherein the computing device has a current configuration management setting, the performance metric comprises an energy performance metric, the KPI comprises a performance metric of the network, and the configuration of the computing device comprises another configuration management setting for the computing device (Soulhi, [Col. 5 Lines 49-58] note network performance data 207 may include performance indicator data (e.g., KPIs, network performance metrics that correlate to QOE, MOS, Quality of Service (QoS) values, etc.). For example, the performance indicator data may indicate values relating to the performance associated with user sessions, connections, channels, messaging (e.g., protocol level, etc.), bit rates, packet error loss rate, and other performance indicators of the wireless service in relation to access devices 107/RAN 105, [Col. 6 Lines 6-24] note network performance data 207 may pertain to core devices 117, external devices 122, or some combination of access devices 107, core devices, and/or external devices 122. Further, in similar fashion, other types of criteria, such as type of core device 117 (e.g., MME versus AMF, etc.), time period, type of RAT (e.g., 4G versus 5G, etc.), geographic location to which core device 117 serves, and so forth, may be used to further specify network performance data 207. Also, for example, network performance data 207 may include characteristic data pertaining to access device 107 (or core device 117, etc.), such as make/model information, specifications pertaining to various resources (e.g., processor, memory, type of antenna, etc.), configuration data (e.g., height of antenna from the ground, etc.), terrain of an area (e.g., buildings, open field, trees, houses, telephone poles, etc.), materials and dimensions of buildings (e.g., brick, 5 story building, etc.), season (e.g., fall, summer, etc.), and/or other applicable parameters that may pertain to a wireless service area).
Claim 22: Soulhi teaches a network node configured to generate and return a configuration of a computing device in a network, the network node comprising: processing circuitry; memory coupled with the processing circuitry, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform operations comprising:
receive data comprising (i) an observation for the computing device or the network for a second time interval, the observation comprising one or more of a plurality of configurations of the computing device, a plurality of performance metrics, and/or a plurality of key performance indicators, KPIs, and (ii) an intent comprising one or more of a performance metric, a KPI, and a configuration parameter (Soulhi, [Fig. 5], [Col. 13 Lines 9-11] note in block 505, n-dimensional network performance data X pertaining to a network device may be obtained, [Col. 5 Lines 49-52] note network performance data 207 may include performance indicator data (e.g., KPIs…), [Col. 5 Lines 63-67] note network performance data 207 may pertain to… time period, [Col. 6 Lines 14-19] note network performance data 207 may include characteristic data pertaining to… configuration data);
generate, with a first machine learning, ML model, a plurality of configurations for the computing device for the second time interval based on a search on a plurality of latent variables in a latent space (Soulhi, [Col. 13 Lines 16-17] note In block 510, the n-dimensional network performance data X may be encoded in m-dimensions, [Col. 13 Lines 21-27] note In block 515, a latent space data G may be generated based on a generative model and the encoded network performance data. For example, network management device 130 may generate latent space data G that may have n dimensions based on the generative model and the encoding of the network performance data);
return, from the search, a configuration for the computing device for the second time interval that best satisfies the performance metric and/or the KPI of the intent based on a search on the plurality of latent variables in the latent space, wherein the configuration is constrained by at least one of the performance metric, the KPI, and the configuration parameter of the intent; and transmit the configuration for the computing device (Soulhi, [Col. 13 Line 29] note In block 520, the latent space data G may be quantitatively assessed, [Col. 13 Lines 65-67] note When it is determined that the parameters may not be modified (block 525—NO), analysis of the quantitative results may be performed (block 530), [Col. 14 Lines 10-15] note In block 535, a network performance data report may be generated. For example, network management device 130 may generate the network performance data report based on the result of the quantitative analysis. By way of further example, the network performance data report may indicate network configurations for network devices, [Col. 14 Lines 24-37] note In block 540, the network performance data report may be used for new site development or remedial measures… Network management device 130 may transmit the network performance data report to network planning devices or other network management devices).
Claim 23: Soulhi teaches the network node of Claim 22, wherein the memory includes instructions that when executed by the processing circuitry causes the network node to perform further operations comprising receiving data comprising an observation for the computing device or the network for a second time interval, the observation comprising one or more of a plurality of configurations of the computing device, a plurality of performance metrics, and/or a plurality of key performance indicators, KPIs, and an intent comprising one or more of a performance metric, a KPI, and a configuration parameter (Soulhi, [Fig. 5], [Col. 13 Lines 9-11] note in block 505, n-dimensional network performance data X pertaining to a network device may be obtained, [Col. 5 Lines 49-52] note network performance data 207 may include performance indicator data (e.g., KPIs…), [Col. 5 Lines 63-67] note network performance data 207 may pertain to… time period, [Col. 6 Lines 14-19] note network performance data 207 may include characteristic data pertaining to… configuration data);
generating, with a first machine learning, ML, model, a plurality of configurations for the computing device for the second time interval based on a search on a plurality of latent variables in a latent space (Soulhi, [Col. 13 Lines 16-17] note In block 510, the n-dimensional network performance data X may be encoded in m-dimensions, [Col. 13 Lines 21-27] note In block 515, a latent space data G may be generated based on a generative model and the encoded network performance data. For example, network management device 130 may generate latent space data G that may have n dimensions based on the generative model and the encoding of the network performance data);
returning, from the search, a configuration for the computing device for the second time interval that best satisfies the performance metric and/or the KPI of the intent based on the search, wherein the configuration is constrained by at least one of the performance metric, the KPI, and the configuration parameter of the intent; and transmitting the configuration for the computing device (Soulhi, [Col. 13 Line 29] note In block 520, the latent space data G may be quantitatively assessed, [Col. 13 Lines 65-67] note When it is determined that the parameters may not be modified (block 525—NO), analysis of the quantitative results may be performed (block 530), [Col. 14 Lines 10-15] note In block 535, a network performance data report may be generated. For example, network management device 130 may generate the network performance data report based on the result of the quantitative analysis. By way of further example, the network performance data report may indicate network configurations for network devices, [Col. 14 Lines 24-37] note In block 540, the network performance data report may be used for new site development or remedial measures… Network management device 130 may transmit the network performance data report to network planning devices or other network management devices),
wherein the search on the plurality of latent variables in the latent space comprises one or more of encoding the plurality of configurations of the computing device, the plurality of performance metrics, and/or the plurality of KPIs of the data to a compressed representation in the latent space, sampling a plurality of points in the latent space, decoding respective points in the plurality of points in the latent space to a respective plurality of decoded points, and generating the configuration for the computing device on the performance metric and/or the KPI while satisfying the constrained at least one of the performance metric, the configuration parameter, and the KPI (Soulhi, [Col. 6 Lines 56-58] note Generative model 305 may reduce the number of dimensions based on an encoding process, [Col. 5 Lines 59-67]-[Col. 6 Lines 1-4] note network performance data 207 may pertain to a single access device 107, multiple access devices 107 (e.g., a subset of all access devices 107), or all access devices 107. Additionally, or alternatively, network performance data 207 may pertain to access devices 107 associated with other types of criteria, such as carrier (e.g., carrier frequency, sector of a cell, etc.), type of RAT, geographic location of access device 107, time period (e.g., day, day and time period, etc.), type of access device 107 (e.g., eNB versus gNB, etc.), and/or other types of factors (e.g., city versus rural, high versus low density, etc.) that may indicate a characteristic or an attribute pertaining to access device 107, [Col. 13 Lines 34-36] note quantitative assessment may pertain to the latent space data and a decoded latent space data).
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 3, 6-9 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Soulhi in view of Lumezanu et al., US 12,242,542 B2 (hereinafter “Lumezanu”).
Claim 3: Soulhi does not explicitly teach the method of Claim 1, wherein the data comprises data for a first time interval, wherein a portion of the first time interval comprises missing data.
However, Lumezanu teaches this (Lumezanu, [Col. 3 Lines 42-43] note Ordinal retrieval-based classification module that enables imputation of missing training labels, [Col. 4 Lines 3-5] note computing device 100 is configured to perform ordinal time series classification with missing information, [Col. 6 Lines 28-30] note The input of the encoder is a slice through the time series (i.e., all values recorded during a limited continuous period of time…)).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the latent space data representative of network performance data of Soulhi with the ordinal time series classification with missing information of Lumezanu according to known methods (i.e. performing ordinal time series classification on network performance data with missing information). Motivation for doing so is that the classification accuracy can be improved (Lumezanu, [Col. 7 Line 64]).
Claim 6: Soulhi does not explicitly teach the method of Claim 5, wherein the categorical data comprises categorical data for the first time interval having a value and categorical data for the first time interval having a missing value, the method further comprising: ordinally-encoding the categorical data having the missing value with the categorical data having a value; scaling the numerical data; and joining the ordinally encoded categorical data and the numerical data.
However, Lumezanu teaches this (Lumezanu, [Col. 6 Lines 27-29] note temporal encoder based on LSTM is used to compute features for time series segments. The input of the encoder is a slice through the time series, [Col. 7 Lines 16-34] note Correlation-based retrieval is proposed to classify time series whose label was not present in the training data (missing labels). The method of the present invention can include the following steps: (a) Label distance computation: for each label (missing and non-missing) compute a label retrieval vector from the label distance to each non-missing label (this step could be done once, before training, as the label distance is known a priori and does not change) (b) For each incoming time series segment to be classified, compute a test retrieval vector from the Euclidean distance to the feature center of each non-missing label. The feature center of a label is defined as the average of the features of all the time series segments with that label. (c) Compare the test retrieval vector with all label retrieval vectors (using an existing rank correlation method) and choose the two most correlated label retrieval vectors using).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the latent space data representative of network performance data of Soulhi with the ordinal time series classification with missing information of Lumezanu according to known methods (i.e. performing ordinal time series classification on network performance data with missing information). Motivation for doing so is that the classification accuracy can be improved (Lumezanu, [Col. 7 Line 64]).
Claim 7: Soulhi and Lumezanu teach the method of Claim 6, further comprising:
performing an embedded lookup process on the categorical data (Lumezanu, [Col. 7 Lines 16-34] note Correlation-based retrieval is proposed to classify time series whose label was not present in the training data (missing labels). The method of the present invention can include the following steps: (a) Label distance computation: for each label (missing and non-missing) compute a label retrieval vector from the label distance to each non-missing label (this step could be done once, before training, as the label distance is known a priori and does not change) (b) For each incoming time series segment to be classified, compute a test retrieval vector from the Euclidean distance to the feature center of each non-missing label. The feature center of a label is defined as the average of the features of all the time series segments with that label. (c) Compare the test retrieval vector with all label retrieval vectors (using an existing rank correlation method) and choose the two most correlated label retrieval vectors using).
Claim 8: Soulhi and Lumezanu teach the method of Claim 7, wherein the embedded lookup process comprises one or more of (i) embedding the categorical data in an embedded association; and (ii) encoding the categorical data based on the embedded association (Lumezanu, [Col. 7 Lines 16-34] note Correlation-based retrieval is proposed to classify time series whose label was not present in the training data (missing labels). The method of the present invention can include the following steps: (a) Label distance computation: for each label (missing and non-missing) compute a label retrieval vector from the label distance to each non-missing label (this step could be done once, before training, as the label distance is known a priori and does not change) (b) For each incoming time series segment to be classified, compute a test retrieval vector from the Euclidean distance to the feature center of each non-missing label. The feature center of a label is defined as the average of the features of all the time series segments with that label. (c) Compare the test retrieval vector with all label retrieval vectors (using an existing rank correlation method) and choose the two most correlated label retrieval vectors using).
Claim 9: Soulhi and Lumezanu teach the method of Claim 7, further comprising:
performing a reverse lookup process on the embedded lookup process on the generation of a configuration from the first ML model (Soulhi, [Col. 8 Lines 4-11] note exemplary quantitative criteria 350 may include joint entropy, interaction information, total correlation, Jensen-Shannon divergence, and/or generalized Hausdorff distances. According to other exemplary implementations, other types of quantitative criteria may be used, such as Minimum Covariance Determinant (MCD)-based Mahalanobis distances).
Claim 13: Soulhi does not explicitly teach the method of Claim 1, when the data comprises one of a categorical data with a missing value or a numerical data with a missing value.
However, Lumezanu teaches this (Lumezanu, [Col. 3 Lines 42-43] note Ordinal retrieval-based classification module that enables imputation of missing training labels, [Col. 4 Lines 3-5] note computing device 100 is configured to perform ordinal time series classification with missing information, [Col. 6 Lines 28-30] note The input of the encoder is a slice through the time series (i.e., all values recorded during a limited continuous period of time…)).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the latent space data representative of network performance data of Soulhi with the ordinal time series classification with missing information of Lumezanu according to known methods (i.e. performing ordinal time series classification on network performance data with missing information). Motivation for doing so is that the classification accuracy can be improved (Lumezanu, [Col. 7 Line 64]).
Claims 12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Soulhi and Lumezanu in further view of PARK et al., US 2020/0388286 A1 (hereinafter “Park”).
Claim 12: Soulhi and Lumezanu do not explicitly teach the method of Claim 9, wherein the reverse lookup process comprises one or more of (i) dividing the generated configuration from the first ML model into a plurality of respective embedding vectors and a plurality of respective numerical vectors, wherein a respective embedding vector represents a single categorical feature of the categorical data; (ii) normalizing the respective embedding vectors and an association of a plurality of constraints comprising one or more of the KPI, the performance metric, and the configuration parameter; (iii) calculating a product of the normalized plurality of respective embedding vectors and the normalized embedded association data; (iv) applying a normalizing function to the product to obtain a distribution over values for the configuration.
However, Park teaches this (Park, [0079] note a data recognition device extracts enrolled feature vectors 411, [0080] note the data recognition device normalizes the enrolled feature vectors 411, 412, and 413 by dividing the enrolled feature vectors 411, 412, and 413 by absolute values thereof. Thus, magnitudes of the normalized feature vectors 421, 422, and 423 may be limited to “1”, [0087] note a variance feature vector of the normalized feature vectors 421, 422, and 423 constituting the feature distribution 410, [0096] note Equation 8 is a vector product or matrix product operation, and thus is expressed by Equation 14).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the latent space data representative of network performance data of Soulhi and Lumezanu with the feature distribution based on normalized feature vectors of Park according to known methods (i.e. calculating a distribution based on normalized encoded network performance data). Motivation for doing so is that this may consider a distribution tendency of the enrolled feature vectors, and thus may provide a more accurate recognition than existing methods and devices that simply compare feature vectors (Park, [0110]).
Claim 14: Soulhi, Lumezanu and Park teach the method of Claim 12, wherein when a categorial data has a missing value, the method further comprises:
ordinally-encoding the categorical data having the missing value with categorical data having a value; and adding the ordinally-encoded data to an association of a plurality of constraints, wherein the association comprises one or more of a plurality of KPIs, a plurality of performance metrics, and a plurality of configuration parameters (Lumezanu, [Col. 3 Lines 42-43] note Ordinal retrieval-based classification module that enables imputation of missing training labels, [Col. 4 Lines 3-5] note computing device 100 is configured to perform ordinal time series classification with missing information, [Col. 6 Lines 28-30] note The input of the encoder is a slice through the time series (i.e., all values recorded during a limited continuous period of time…)).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Soulhi, Lumezanu and Park in further view of Li et al., US 2024/0069874 A1 (hereinafter “Li”).
Claim 13: Soulhi, Lumezanu and Park do not explicitly teach the method of Claim 12, wherein when the numerical data has a missing value, the method further comprises: imputing the missing value with zeroes; and joining the numerical data having values with the numerical data having zero-imputed values.
However, Li teaches this (Li, [0006] note a computerized method of intelligent generation of code for imputation of missing data in a machine learning dataset comprised of a plurality of features, [0035] note In order to ensure that the ML dataset is usable for model training, execution, analysis, etc. by ML production pipeline, the missing values must be filled in with replacement values generated using an imputation algorithm, [0045] note the values in some columns for each reference ML dataset were replaced with null or zero values).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the latent space data representative of network performance data of Soulhi, Lumezanu and Park with the replacement values of Li according to known methods (i.e. replacing missing values with null or zero values). Motivation for doing so is that this ensures that the ML dataset is usable for model training, execution and analysis (Li, [0035]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Soulhi in view of Wang et al., US 2023/0247094 A1 (hereinafter “Wang”).
Claim 20: Soulhi does not explicitly teach the method of Claim 1, wherein the network node comprises a node in a networks data analytics function, NWDAF.
However, Wang teaches this (Wang, [0151] note The 5GS may provide network analytics via a network data analytics function (NWDAF), [0152] note The NWDAF may collect relevant management data (e.g., any of NG RAN or 5GC performance measurements, 5G end-to-end KPIs) from services in the OAM as configured by the PLMN operator).
It would have been obvious to one of ordinary skill in the art at the effective filing date of the application to combine the network performance data of Soulhi with the NWDAF of Wang according to known methods (i.e. analyzing the network performance data based on a network data analytics function [NWDAF]) to yield predictable results; NWDAF provides a unified, open framework for network data collection and analytics, avoiding vendor lock-in and enabling multi-vendor interoperability.
Conclusion
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/GIUSEPPI GIULIANI/Primary Examiner, Art Unit 2153
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