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 .
Election/Restrictions
Applicant’s election of claims 1-12 in the reply filed on 18 March 2026 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Newly added claims 21-28 are found to be drawn to the elected invention and have been examined.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-9 and 21-28 are rejected under 35 U.S.C. 103 as being unpatentable over US 20240154912 to Naik et al. (“Naik”) in view of US 11689468 B2 to Koren et al. (“Koren”).
Regarding claim 1, Naik taught a system for controlling traffic within a communications network, the system comprising:
one or more circuits configured to perform operations comprising:
providing a first input based on a flow of data packets, the input comprising a first set of features of the flow of data packets; (consider paragraph 0092, specifically “the device may support a framework for determining whether a traffic class is known to a machine learning model. For example, according to techniques for traffic identification using machine learning, as described herein, the device may use a multi-step framework for determining whether a traffic class is known to the machine learning model used at the device” wherein “the set of features may include a quantity of packets, a statistic based on the quantity of packets, or a statistic based on an inter-arrival time, among other examples of features”)
providing a first set of results by evaluating a first model using the input, an element of the first set of results representing a possibility that the flow of data packets used to create the input is a member of a class of network traffic of a plurality of classes of network traffic; (consider paragraph 0093, specifically “In some examples, during a second step of the multi-step framework and based on determining that the traffic is periodic, the device may use a first machine learning model to determine whether the traffic class is known. For example, the device may use the first machine learning model to obtain a reconstruction of the set of features. In such an example, the device may determine a loss associated with the reconstruction (e.g., a difference between the reconstruction of the set of features and the set of features). In some examples, if the loss associated with the reconstruction satisfies a threshold, the device may determine that the traffic class is known to the first machine learning model”)
using the first set of results to determine a subset of a second set of models to evaluate; providing a second set of results by evaluating the subset using a second input, the second input comprising a second set of features of the flow of data packets; (consider paragraph 0093, specifically “In some examples, the second machine learning model may correspond to a multi-class classifier. Here, the device may input the set of features into the multi-class classifier to obtain the prediction of the application associated with the signaling. In some examples, identifying whether the traffic class is known to a machine learning model used at the device may increase an accuracy of predictions obtained using the machine learning model, among other possible benefits”) (consider further paragraph 0150, specifically “it may be desirable for the device to predict an application generating the data packets exchanged between the device and the application server during a time interval (e.g., the traffic). For example, in addition to predicting whether the traffic corresponds to a known traffic class (e.g., whether the traffic is of type ‘XR’), the device may determine to identify an application (e.g., an XR application) corresponding to the traffic. That is, subsequent to determining whether a data set (e.g., a set of features) is associated with a known traffic class (e.g., using an inference procedure as described with reference to FIG. 4) the device may use the data set for supervised learning. For example, the device may use the data set as input for a machine learning model (e.g., a multi-class classifier) to identify an application (e.g., obtain an application name) associated with the data set.”) (consider further paragraph 0204, specifically “In some examples, each autoencoder of the set of multiple autoencoders is associated with a respective traffic class of the set of known traffic classes”)
using the second set of results to determine a first class of the flow of data packets of the plurality of classes of network traffic; and controlling the flow of packets based on the first class (consider paragraph 0150, specifically “Based on the identified application, the device may perform additional determinations, such as predicting user hand movement or headset tracking, among other possible types of determinations”) (consider also paragraph 0231, specifically “the traffic class component 1225 may be configured as or otherwise support a means for mapping the data traffic to a QoS class based on the prediction of the traffic class. In some examples, the traffic class component 1225 may be configured as or otherwise support a means for outputting second signal to the second device, where the second signaling indicates one or more parameters associated with the QoS class, and where the one or more parameters are to be used for prioritization of uplink data traffic. In some examples, the traffic class component 1225 may be configured as or otherwise support a means for prioritizing the data traffic and other data traffic associated with other signaling from the second device based on the QoS class”)
Naik may be interpreted as not expressly teaching wherein each member of the second set of models is configured for a corresponding class of network traffic from the plurality of classes of network traffic.
However, in an analogous art relating to the use of models to classify network traffic, Koren taught that a subset of a second set of models evaluate a set of results from a first model that represent a possibility that a flow of data packets used to create an input of features is a member of a class of network traffic of a plurality of classes of network traffic such that each member of the second set of models is configured for a corresponding class of network traffic from the plurality of classes of network traffic which enables finer classification of network traffic (consider further column 4, lines 14-29, “Embodiments can thus use multiple models to perform classifications at various or different levels. The classifications for each level may have different granularities. Serial classification decisions (e.g., layers, levels, stages, etc., of classification decisions, stage-wise classification, etc.) may be made thereby allowing better fidelity of classification and control of the classification process. This allows an improvement in classification over traditional classification which can result in lower confidence of classification at increasingly finer grained layers of classification. For example, embodiments may be able to classify an entity as a multimedia device, which can allow one or more polices to be applied, while a more traditional classification methodology may output a very low confidence classification below a threshold that the entity is a smart device (e.g., a smart speaker), which may then not be actionable.”). (consider column 3, lines 55-64, “A classification tree for function classification can have a top or higher level (e.g., layer, hierarchy, etc.) to represent that an entity can be classified using a first level classification (e.g., a classification associated with the first layer) with respect to function (e.g., entity or device function). A second level of the classification tree can have multiple models for further determining or classifying an entity or device. The models may classify the entity using one or more second level classifications (e.g., classifications associated with the second layer).”) (consider further column 5, lines 32-33, “A feature may be a set of distinguishing characteristics for each class of an entity” and also column 16, line 40-column 17, line 14 specifically regarding the “features” which “may include data or values extracted from network traffic (e.g., packets) transmitted by or sent to the entity” wherein “[t]he features 305 (e.g., the vector) may be provided to the machine learning model 311 as an input” wherein “[m]achine learning model 311 may be trained to determine a first classification (e.g., a first level classification) for an entity based on the features 305 (e.g., based on one or more properties) associated with the entity. The first classification may be one of classifications 321 through 328. The machine learning model 311 may take the features 305 as input and may generate an output indicating one of the classifications 321 through 328.”) (consider further column 18, lines 24-55 specifically wherein “the features 305 or a subset of the features 305 may be provided to one or more of the machine learning models 331 through 337 for further classification of a device or entity. For example, after the machine learning model 311 determines one or more of the classifications 321 through 327 for an entity, the features 305 or a subset of the features 305 may be provided to the machine learning models associated with the classifications (e.g., to machine learning model 331 for classification 321, to machine learning model 332 for classification 322, etc” and wherein “the tree 300 may include more than two classification levels. For example, the tree 300 may include three five, ten, or some other appropriate number of classification levels. Each of the additional classification levels may include or may be associated with a respective set of classifications and a respective set of machine learning models”) (consider further column 23, lines 12-26, “At block 425, a second set of machine learning models (e.g., one or more models) may be identified (e.g., determined, selected, accessed, etc.). For example, each classification that has a confidence level above the threshold confidence level may be associated with a machine learning model. One or more of those machine learning models (associated with classifications that had confidence levels above the threshold confidence level) may be selected. For example, the machine learning model associated with the classification that has the highest confidence level may be used or selected. In another example, the machine learning models associated with two classifications that had the two highest confidence levels may be used or selected. The second set of machine learning models may be at a next or higher classification level (e.g., may be finer grain models).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of these references such that their combination includes every element as claimed. One skilled in the art could have combined the teachings by known methods such as integration of software routines with no changes to the operation of either reference such that, in combination, each element merely performs the same function as it does separately. Additionally, the Examiner finds that, based on the references' analogous disclosure regarding the use of modeling to classify network traffic, further demonstrates that a combination of their features would have been known and obvious. Therefore, such a combination of the teachings of the references would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 2, the combined teachings of Naik and Koren taught the system of claim 1.
Naik taught wherein the subset of the second set of models to evaluate is determined by comparing the first set of results to a threshold. (consider paragraph 0145, specifically “In some examples, the device may use a trained autoencoder to select a threshold reconstruction loss for detecting a traffic class. That is, the device may use a trained autoencoder to determine whether signaling received at (or transmitted from) the device may correspond to a known traffic class. In some examples, the device may use the trained autoencoder to select a threshold reconstruction loss (Γ2) for detecting a traffic class (e.g., a desired traffic class, such as a real-time traffic class). The device may determine that an autoencoder is trained based on a reconstruction loss between a data set input in to the autoencoder and a reconstruction of the data set output from the autoencoder”) (consider further paragraph 0146, specifically “the device may select the threshold reconstruction loss (Γ2) based on a distribution of reconstruction loss across multiple data sets used to train the autoencoder. For example, the device may select a value for the threshold reconstruction loss that corresponds to a percentile (e.g., the 99th percentile or some other suitable percentile) of the reconstruction loss across the multiple data sets used to train the autoencoder. That is, the percent (e.g., 99 percent or some other suitable percent) of data sets used to train the autoencoder may be associated with a reconstruction loss smaller than the selected threshold. In some examples, the percent (e.g., the threshold) may be selected based on a performance of the autoencoder”) (consider further paragraph 0205, specifically “training the autoencoder using a set of multiple sets of features, where each set of features of the set of multiple sets of features is associated with a respective traffic class of the set of known traffic classes. In some examples, the threshold component 1280 may be configured as or otherwise support a means for selecting the threshold based on distribution of loss across the set of multiple sets of features”)
Regarding claim 3, the combined teachings of Naik and Koren taught the system of claim 2.
Naik taught wherein the threshold is dynamically updated based on the first set of results. (again, consider paragraph 0145, specifically “In some examples, the device may use a trained autoencoder to select a threshold reconstruction loss for detecting a traffic class. That is, the device may use a trained autoencoder to determine whether signaling received at (or transmitted from) the device may correspond to a known traffic class. In some examples, the device may use the trained autoencoder to select a threshold reconstruction loss (Γ2) for detecting a traffic class (e.g., a desired traffic class, such as a real-time traffic class). The device may determine that an autoencoder is trained based on a reconstruction loss between a data set input in to the autoencoder and a reconstruction of the data set output from the autoencoder”) (again, consider further paragraph 0146, specifically “the device may select the threshold reconstruction loss (Γ2) based on a distribution of reconstruction loss across multiple data sets used to train the autoencoder. For example, the device may select a value for the threshold reconstruction loss that corresponds to a percentile (e.g., the 99th percentile or some other suitable percentile) of the reconstruction loss across the multiple data sets used to train the autoencoder. That is, the percent (e.g., 99 percent or some other suitable percent) of data sets used to train the autoencoder may be associated with a reconstruction loss smaller than the selected threshold. In some examples, the percent (e.g., the threshold) may be selected based on a performance of the autoencoder”) (again, consider paragraph 0205, specifically “training the autoencoder using a set of multiple sets of features, where each set of features of the set of multiple sets of features is associated with a respective traffic class of the set of known traffic classes. In some examples, the threshold component 1280 may be configured as or otherwise support a means for selecting the threshold based on distribution of loss across the set of multiple sets of features”)
Regarding claim 4, the combined teachings of Naik and Koren taught the system of claim 1.
Naik taught wherein creating the first input or the second input based on the flow of data packets comprises: detecting an initiation of the flow of data packets; collecting an initial set of packets; and creating the first set of features or the second set of features based on packet control information and statistics of the data packets. (consider paragraphs 0113-0114, specifically “the device 205-a may receive signaling 210-a from the device 205-b during an observation window. In such an example, the device 205-a may use the multi-step framework to identify a traffic class associated with the signaling 210-a” and “the device 205-a may sample the signaling 210-a according to some sampling rate to obtain information regarding the traffic associated with the signaling 210-a. In some examples of the analysis 215, the information may correspond to a type of feature associated with data packets received at the device 205-a via the signaling 210-a. That is, the information may include a set of features that correspond to a type of feature, such as a quantity of data packets (e.g., received at the device 205-a via the signaling 210-a), a size of data packets, one or more statistics associated with the quantity of data packets, or one or more statistics associated with a packet inter-arrival time, or any combination thereof”) (consider further paragraph 0151, specifically “Example features may include a quantity of packets, one or more statistics (e.g., operations, such as such as sum, maximum, median, mean, minimum, Xth percentile) associated with a packet size, or one or more statistics (e.g., operations, such as such as sum, maximum, median, mean, minimum, Xth percentile) associated with a packet inter-arrival time, or any combination thereof. That is, example features may include a quantity of packets, an aggregate packet size, a maximum packet size, a median packet size, a mean packet size, a minimum inter-arrival time, a mean inter-arrival time, or a median inter-arrival time, among other examples”) (consider further paragraph 0179, specifically “The receiver 1010 may provide a means for receiving information such as packets, user data, control information, or any combination thereof associated with various information channels (e.g., control channels, data channels, information channels related to traffic identification using machine learning). Information may be passed on to other components of the device 1005”)
Regarding claim 5, the combined teachings of Naik and Koren taught the system of claim 1.
Naik taught wherein generating the first set of results is performed on an edge device of the communications network. (consider paragraph 0104, specifically “In some deployments, the wireless communications system 100, or devices of the wireless communications system 100, may support a framework for determining whether a traffic class is known to a machine learning model. In some examples, a first device (e.g., an AP 102, a STA 104) may receive signaling associated with a traffic class from a second device (e.g., another AP 102, another STA 104). In some examples, the first device may determine that the traffic class is included in a set of known traffic classes based on a set of features associated with the signaling”)
Regarding claim 6, the combined teachings of Naik and Koren taught the system of claim 5.
Naik taught wherein generating the second set of results is performed on a node in a cluster of computers (consider paragraph 0107, specifically “In some examples of unsupervised learning, such as clustering, a machine learning model may be used to identify (e.g., find, determine) a pattern and obtain insights (e.g., based on the identified pattern)”). (consider paragraph 0105, specifically “For example, the wireless communications system 200 may include a device 205-a, which may be an example of an AP 102 as described with reference to FIG. 1. The wireless communications system 200 may also include a device 205-b, a device 205-c, and a device 205-d, which may each be an example of a STA 104 (e.g., a non-AP STA) as described with reference to FIG. 1”) (consider further paragraph 0113, specifically “In such an example, the device 205-a may use the multi-step framework to identify a traffic class associated with the signaling 210-a”) (consider further paragraph 0124, specifically “The device 205-c may use the machine learning model information 235 to obtain (e.g., build, construct, updated) the machine learning model for classifying traffic. For example, the device 205-c may identify a traffic class associated with the signaling 210-b. In such an example, the device 205-c (e.g., a non-AP STA, such as a client device) may perform one or more operations in accordance with the identified traffic class”)
Regarding claim 7, the combined teachings of Naik and Koren taught the system of claim 1.
Naik taught wherein the first model comprises a plurality of dichotomizers, each dichotomizer of the plurality of dichotomizers trained to determine if the flow is part a class of the plurality of classes (“known”) or not part of the class (“unknown”). (consider paragraph 0116, specifically “In some examples, the multi-step framework may include a second step (e.g., Step-B) in which the device 205-a may perform the inference 220. For example, using the inference 220, the device 205-a may identify whether the information (e.g., the data sample) may be associated with a known traffic class (e.g., a traffic class that the machine learning model is trained, a traffic class the device 205-a may be interested in detecting) or an unknown traffic class (e.g., a traffic class the machine learning model is not trained on, a traffic class the device 205-a may be uninterested in detecting).”) (consider further paragraphs 0117-0119 regarding using the first model using a plurality of “traffic types” to determine if the flow is part of a class or not)
Regarding claim 8, the combined teachings of Naik and Koren taught the system of claim 1.
Naik taught wherein the second set of models comprises an autoencoder for each class of the plurality of classes. (again, consider paragraph 0204, specifically “In some examples, each autoencoder of the set of multiple autoencoders is associated with a respective traffic class of the set of known traffic classes”)
Regarding claim 9, the combined teachings of Naik and Koren taught the system of claim 8.
Naik taught wherein using the second set of results to determine the first class comprises selecting the autoencoder that best fits the input or the flow of data packets according to a fit metric. (consider paragraph 0122, specifically “Additionally, or alternatively, a machine learning model may be trained at one of the devices 205 and used at another one of the devices 205 for identifying a traffic class. For example, the device 205-a may train one or more machine learning models to be used at the device 205-c for identifying a traffic class (e.g., classifying traffic). That is, the device 205-a may offer trained models for the device 205-c to be download and used at the device 205-c. The trained models may be used at the device 205-c for traffic type identification (e.g., autoencoders for detecting XR applications) or application identification.”) (consider further paragraph 0159, specifically “the device may determine whether the predictions output using the autoencoders are consistent with the prediction output using the multi-class classifier. For example, the device may apply combining logic to determine a confidence level associated with the output of the autoencoders (e.g., the first autoencoder trained using traffic class A and a second autoencoder trained using traffic class X) or the output of the multi-class classifier, or both. For example, the device may combine the output of the autoencoders with the output of the multi-class classifier (e.g., supervised learning) to determine whether the respective outputs are consistent. That is, the combining logic may analyze the predictions output using the autoencoders (e.g., at 670-a through 670-n) and the prediction output using the multi-class classifier (e.g., at 671) and determines whether the predictions are consistent”)
Claims 21-27 recite non-transitory computer-readable storage media that contain substantially the same limitations as recited in claims 1-6 and 8 respectively and are also rejected under 35 USC § 103 as being unpatentable over the same combined teachings of Naik and Koren and the same rationale supporting the conclusion of obviousness.
Claim 28 recites a system comprising one or more circuits of an edge device that contains substantially the same limitations as recited in claim 1 and is also rejected under 35 USC § 103 as being unpatentable over the same combined teachings of Naik and Koren and the same rationale supporting the conclusion of obviousness.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Naik and Koren as applied to claim 9 above, and in further view of US 20220129758 A1 to Sallee.
Regarding claim 10, the combined teachings of Naik and Koren taught the system of claim 9.
Naik and Koren may be interpreted as not expressly teaching wherein the fit metric comprises at least one of a median absolute deviation, mean absolute error, or a mean squared error.
However, in an analogous art relating to autoencoder failure detection through reconstruction modeling calculations (consider paragraphs 0037-0039), Sallee taught that a fit metric that may be used to determine whether an autoencoder best fits for determining a particular class comprises at least one of a median absolute deviation, mean absolute error, or a mean squared error. (consider paragraph 0026, “Alternative to the L2 norm the reconstruction loss can include a mean square error (MSE), a root MSE (RMSE), mean absolute error (MAE), R squared (e.g., 1−MSE(model)/MSE(baseline)) or adjusted R squared, mean square percentage error (MSPE), mean absolute percentage error (MAPE), root mean squared logarithmic error (RMSLE), or the like between the content 104 and the reconstructed content 114”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to simply substitute the fit metric taught in Naik and Koren with the fit metric taught in Sallee such that their combination includes every element as claimed. The Examiner finds that the teaching within Sallee demonstrates that the substituted elements and their functions were known in the art and one skilled in the art could have simply substituted one known element for another such that the substitution would have yielded nothing more than predictable results to one of ordinary skill in the art.
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Naik and Koren as applied to claim 1 above, and in further view of US 20230018960 A1 to Ardel.
Regarding claim 11, the combined teachings of Naik and Koren taught the system of claim 1.
Naik and Koren may be interpreted as not expressly teaching wherein the first set of results and the second set of results are combined using fuzzy set operations.
However, in an analogous art relating to classification of results from models, Ardel taught that a first set of results from a model (“trained classifier”) and a second set of results from a subset of models (“adaptive neuro-fuzzy inference system”; consider paragraph 0039) are combined using fuzzy set operations. (consider paragraph 0003, “A particular aspect of the disclosure describes a method that includes obtaining data representative of a state or condition of an evaluation target. The method also includes providing first input based on the data to a trained classifier to generate a first result. The method further includes providing second input based on the data to an adaptive neuro-fuzzy inference system to generate a second result. The method also includes assigning a classification to the state or condition of the evaluation target based on the first result and the second result”) (consider further paragraphs 0053-0056 wherein “the data selection unit 132 is configured to provide the first input 150 to the trained classifier 134 to generate a first result 154 (e.g., a first classification of the state or condition of the evaluation target 102)” and “the data selection unit 132 is also configured to provide the second input 152 to the adaptive neuro-fuzzy inference system 136 to generate a second result 156 (e.g., a second classification of the state or condition of the evaluation target 102)” such that “the second result 156 can be used to select the trained classifier 134 from among a plurality of trained classifiers 140. For example, the second result 156 may indicate a classification of the state or condition of the evaluation target 102 based on the adaptive neuro-fuzzy inference system 136. Based on the classification generated by the adaptive neuro-fuzzy inference system 136 (e.g., the second result 156), the classification system 130 may select a particular trained classifier that is trained to classify objects having similar states or conditions” and that “the first result 154 is combined with data 106 selected by the data selection unit 132 such that the second input 152 includes the selected data 106 and the first result 154”) (consider also paragraph 0058 wherein “the first result 154 can be used to select the adaptive neuro-fuzzy inference system 136 from among a plurality of adaptive neuro-fuzzy inference systems 142”) (consider also paragraph 0065, specifically “decisions based on a combined output of the trained classifier 134 and the adaptive neuro-fuzzy inference system 136 can be used to replace or supplement SME analysis. Additionally, the trained classifier 134 and the adaptive neuro-fuzzy inference system 136 can be updated as additional data becomes available so that eventually the combined output is an improvement over decisions made by an SME”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of these references such that their combination includes every element as claimed. One skilled in the art could have combined the teachings by known methods such as integration of software routines with no changes to the operation of either reference such that, in combination, each element merely performs the same function as it does separately. Additionally, the Examiner finds that, based on the references' analogous disclosure regarding the use of modeling to classify results from input, further demonstrates that a combination of their features would have been known and obvious. Therefore, such a combination of the teachings of the references would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 12, the combined teachings of Naik, Koren and Ardel taught the system of claim 11.
Naik and Koren may be interpreted as not expressly teaching wherein the first model and the second set of models are trained together using the fuzzy set operations to calculate a classification metric during training, however, Ardel did teach these limitations. (consider paragraphs 0082-0083, specifically “For example, by using an output of the adaptive neuro-fuzzy inference system 136 to provide an input to the trained classifier 134, the use of both neural network architectures and fuzzy rules enables the classification system 130 to classify the state or condition of the evaluation target 102 using a relatively small amount of data 106. Thus, in scenarios where a relatively small amount of data 106 is available, the classification system 130 can accurately classify the state or condition of the evaluation target 102 using the data 106 as opposed to conventional classification systems that may require additional data for accurate classifications” such that “the trained classifier 134 and the adaptive neuro-fuzzy inference system 136 can be trained using a relatively small amount of data. The adaptive neuro-fuzzy inference system 136 captures rules used by SMEs to make decisions (e.g., predictions, classifications, etc.)”)
The motivations regarding the obviousness of claim 11 also apply to claim 12, therefore, claim 12 is rejected under 35 USC § 103 as being unpatentable over the combined teachings of Naik, Koren and Ardel and the same rationale supporting the conclusion of obviousness.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-12 and 21-28 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.
Conclusion
An updated search revealed additional prior art that is considered pertinent to the claimed invention and/or to the broader disclosure. Specifically, the cited prior art is directed to using modeling to classify network flows/traffic.
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 G. C. Neurauter, Jr. whose telephone number is (571)272-3918. The examiner can normally be reached Monday-Friday 9am-5pm Eastern Time.
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/G. C. Neurauter, Jr./Primary Examiner, Art Unit 2459
Prosecution Insights