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 .
Response to argument
Applicant's arguments filed 07/02/2026 ("Arguments/Remarks") have been fully considered but they are not persuasive.
Argument – 1: (pg. 6) Applicant contends: “Applicant respectfully disagrees for at least the following reasons. The claimed methods/systems provide means to obviate the need to classify data on an individual data point by data point basis, and are therefore directed to processes/systems that improve processing speeds, processing efficiencies, computational resource allocation, functionality, and accuracy of existing classification systems. See Applicant's published application, 1 [0003]. The PTO alleges that the functions recited by the claims are merely mental …”
Regarding the above argument, the Examiner respectfully disagrees with Applicant’s assertion that the claims are clearly directed to providing a technical solution to a technical problem because It lacks sufficient details required to support a conclusion that the claim recites a technological improvement. Although Applicant asserts improvements in processing speed, efficiency, computational resource allocation, the claims do not specify how these improvements are achieved through a particular technological implementation. Likewise, reciting that the received data in high dimensional, nonuniform or lacks a labeled corpus merely characterizes the type of data being processed and does not integrate the abstract idea into a practical application. The additional limitation of grouping data and labeling multiple data points simultaneously describes a data processing technique at a high level of abstraction without reciting a specific improvement to computer technology or another technical field. MPEP 2106.04(d)(1). “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.”
Applicant’s arguments with respect to independent claims 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. Examiner rely on reference Liu that disclose the argued limitations related to the ability to simultaneously classification and simultaneously labeling of data See at least Liu, “[0063] … An automated bulk labeling algorithm, as generally referred to herein, may comprise a set of computer instructions that, when executed, executes an automated sequence of tasks that may include automatically and/or simultaneously assigning at least one classification label to a volume of unlabeled digital event data samples included in a target digital event data corpus or a target cluster of digital event data.
As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1, 3 – 7, 9 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101 – analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the Examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components:
Regarding claim 1,
grouping each data point into one or more groups [ ]
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing characteristics of data points, evaluating similarities or criteria and deciding how to organize the data into groups. See (MPEP 2106.04)).
assigning each data point an index based one or more groups into which each data point is grouped;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating group membership and deciding an identifying reference or index for each data point. See (MPEP 2106.04)).
classifying each indexed-data points of a group simultaneously and labelling all of the classified indexed-data points of the group simultaneously with the same label.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing multiple indexed data points, evaluating them according to classification criteria, and deciding on a common label. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101 – analysis, set forth in MPEP 2106, the Examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
receiving complex high-dimensionality data including plural data points that are nonuniform in data schema and/or data structure;
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g)).
… via a clustering algorithm;
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Regarding limitation (II), recite mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 3, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
classifying each indexed-data points of a first group simultaneously and labelling all of the classified indexed-data points of the first group simultaneously with a first label; and classifying each indexed-data points of a second group simultaneously and labelling all of the classified indexed-data points of the second group simultaneously with a second label.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing different groups, evaluating their characteristics and deciding distinct labels for each group. See (MPEP 2106.04)).
Claim 9, recites similar subject matter as claim 3, so is rejected under the same rationale.
Regarding claim 4, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
encoding each data point before grouping each data point.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves converting information into a different representation before assigning them into groups. See (MPEP 2106.04)).
Claim 10, recites similar subject matter as claim 4, so is rejected under the same rationale.
Regarding claim 5, dependent upon claim 4, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein: encoding each data point involves one-hot encoding.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 11, recites similar subject matter as claim 5, so is rejected under the same rationale.
Regarding claim 6, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
performing dimensionality reduction of the encoded data points.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating multiple variables, deciding which aspect are relevant and reducing complexity by focusing on fewer dimensions. See (MPEP 2106.04)).
Claim 12, recites similar subject matter as claim 6, so is rejected under the same rationale.
Regarding claim 7,
In step 2A prong 1:
group each data point into one or more groups [ ]
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing characteristics of data points, evaluating similarities or criteria and deciding how to organize the data into groups. See (MPEP 2106.04)).
assign each data point an index based one or more groups into which each data point is grouped;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating group membership and deciding an identifying reference or index for each data point. See (MPEP 2106.04)).
classify each indexed-data point of a group simultaneously and labelling all of the classified indexed-data points of the group simultaneously with the same label
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing multiple indexed data points, evaluating them according to classification criteria, and deciding on a common label. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
a processor; computer memory having instructions stored thereon that when executed will cause the processor to
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f).
receive complex high-dimensionality data including plural data points that are nonuniform in data schema and/or data structure;
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g)).
via a clustering algorithm;
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
store plural indexed data points in memory;
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to storing information, as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity. See MPEP (2106.05(g))).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I and III), recite mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (II), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Regarding limitation (IV), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 13,
In step 2A prong 1:
generating a corpus of labelled data points by: i) grouping two or more data points representative of the incoming data points into one or more groups; ii) assigning each grouped data point an index; iii) classifying each indexed-data point of a group simultaneously and labelling all of the classified indexed-data points of the group simultaneously with the same label;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves organizing information by grouping data points, assigning identifiers to the grouped data points, classifying the indexed data points and assigning labels to the classified data points based on the classification. See (MPEP 2106.04)).
comparing the incoming data points to a corpus of labelled data points,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves observing new data points, comparing it against known examples. See (MPEP 2106.04)).
labeling an incoming data point with a label based on the comparison.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves assigning a label to incoming data points based on comparison results. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
receiving incoming data points for which no labeled corpus of data exists;
Deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Claim 14, recites similar subject matter as claim 13, so is rejected under the same rationale.
Regarding claim 15, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
prior to assigning each data point an index, analyzing grouped data of the one or more groups using entropy processing to identify whether there are any anomalies in the one or more groups
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves analyzing grouped information to identify anomalies within the groups. See (MPEP 2106.04)).
wherein the labelling comprises adding at least one feature value indicating an anomaly status based on a result of the analyzing.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 16, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the anomalies correspond with cyber-attack vectors.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 17, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
prior to assigning each data point an index, analyzing grouped data of the one or more groups using entropy processing to identify feature values of each of the one or more groups,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves analyzing grouped information to determine feature values associated with each group. See (MPEP 2106.04)).
wherein features values comprise: a group identification and an anomaly status for each of the one or more groups.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 18, dependent upon claim 17, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
generating a reporting that comprises summary information for the one or more groups including summary information for the feature values;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves organizing analyzed information into a summary report describing the groups and their associated feature values. See (MPEP 2106.04)).
… outputting the reporting to aid the labelling.
The recitation in the additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Regarding claim 19, dependent upon claim 17, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
generating a report that comprises summary information for the one or more groups including summary information for the feature values; and utilizing the reporting to aid the labelling processing.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves summarizing information in a report and using that summarized information to assist in making labeling decisions. See (MPEP 2106.04)).
Regarding claim 20, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the plural data points are data points from a 5G Packet Forwarding Control Protocol.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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.
Claim(s) 1, 3, 7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Kanta, Pub. No.: US20220343115A1, in view of Bras, Pub. No.: US20040215428A1 and Liu et al., Pub. No.: US20220318381A1.
Regarding claim 1, Kanta teaches: A method for developing a model to classify data, the method comprising: receiving complex high-dimensionality data including plural data points that are nonuniform in data schema and/or data structure;
(Kanta, “[0020] Data store 140 can store multiple data records 145 and labels 147, and can be accessed by user devices 101A-101N and server device 105 over one or more networks 103. Data records 145 and/or labels 147 can be stored as one or more tables, spreadsheets, databases, distributed data stores, or other data structure [receiving complex high-dimensionality data including plural data points that are nonuniform in data schema and/or data structure].”)
grouping each data point into one or more groups via a clustering algorithm;
(Kanta, “[0012] The unsupervised classification model may implement a clustering algorithm to assign each data record of the dataset to one or more of the groups [grouping each data point into one or more groups] based on similarities between the data records that are assigned to the same group. The clustering algorithm [via a clustering algorithm] used may depend on the nature of the dataset and/or based on user preference. If the dataset is numerical, the unsupervised classification model may use a clustering algorithm that works better with numerical data (e.g., k-means clustering)...”)
classifying each indexed-data points of a group and
(Kanta, “[0025] The clustering module 114 can use a clustering algorithm to assign each data record (either of the entire dataset or of the subset identified by the data input module 112) to one or more groups [classifying each indexed-data points of a group ] based on the similarities between the data records that are assigned to the same group…”)
labelling all of the classified indexed-data points of the group with the same label.
(Kanta, “[0038] At block 220, the processing device may then assign a label to each of the data records based on the groups. The labels may be randomly generated, selected from an existing list of predefined labels, or may be sequential integer numbers, for example. For example, if the processing device divided the data set into C number of clusters, the processing device may assign the label “c1” to the data records belonging to the first cluster, the label “c2” to the data records belonging the second cluster, and so on [labelling all of the classified indexed-data points of the group with the same label] (i.e.: labeling all data records belonging to the same cluster with the same label). In embodiments, the processing device may add a column to the data set, wherein the column contains the dummy label of each corresponding data record.”)
Kanta does not teach:
assigning each data point an index based one or more groups into which each data point is grouped; and
classifying… … simultaneously and labelling … … simultaneously with the same label.
Bras teaches:
assigning each data point an index based one or more groups into which each data point is grouped; and classifying all indexed-data points of a group
(Bras, “[0011] According to another aspect of the invention, a computer-implemented method of generating a finite-element mesh incorporating model-specific response to produce variable resolution in the mesh comprises: assigning an index value to each of a group of original elements [assigning each data point an index based one or more groups into which each data point is grouped] based on an index function providing a heuristic measure of impact on a model to produce an indexed element for each original element; grouping the indexed elements into at least two groups based on the index value of each element [classifying all indexed-data points of a group]; selecting a subset of indexed elements from each of the groups based on a selection function; creating a finite-element mesh for each of the groups using the corresponding original elements of each of the subset of indexed elements selected by the selection function; and combining the finite-element mesh from each of the groups into a final finite-element mesh.”)
Bras and Kanta are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Bras with teachings of Kanta to add model specific mechanism (index function, selection function and variable resolution representations) to improve how data is grouped and selected. (Bras, Abstract).
Kanta in view of Bras do not teach:
classifying… … simultaneously and labelling … … simultaneously with the same label.
Liu teaches:
classifying… … simultaneously and labelling … … simultaneously with the same label.
(Liu, “[0063] … An automated bulk labeling algorithm, as generally referred to herein, may comprise a set of computer instructions that, when executed, executes an automated sequence of tasks that may include automatically and/or simultaneously assigning at least one classification label to a volume of unlabeled digital event data samples included in a target digital event data corpus or a target cluster of digital event data [classifying… … simultaneously and labelling … … simultaneously with the same label]. ”)
Liu, Kanta and Bras are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Liu with teachings of Kanta and Bras to add automatic bulk labeling into the unsupervised grouping and labeling system to improve efficiency and scalability. (Liu, Abstract).
Regarding claim 3, Kanta in view of Bras and Liu teach the method of claim 1.
Kanta further teaches: classifying each indexed-data points of a first group and labelling all of the classified indexed-data points of the first group a first label; and classifying each indexed-data points of a second group and labelling all of the classified indexed-data points of the second group a second label.
(Kanta, “[0038] At block 220, the processing device may then assign a label to each of the data records based on the groups [classifying each indexed-data points]. The labels may be randomly generated, selected from an existing list of predefined labels, or may be sequential integer numbers, for example. For example, if the processing device divided the data set into C number of clusters, the processing device may assign the label “c1” to the data records belonging to the first cluster [of a first group and labelling all of the classified indexed-data points of the first group a first label], the label “c2” to the data records belonging the second cluster [classifying each indexed-data points of a second group and labelling all of the classified indexed-data points of the second group a second label], and so on. In embodiments, the processing device may add a column to the data set, wherein the column contains the dummy label of each corresponding data record.”)
Liu further teaches: classifying [ ] a first group simultaneously, …, labelling [ ] the first group simultaneously with a first label, classifying [ ] a second group simultaneously … labelling [ ] the second group simultaneously with a second label,
(Liu, “[0063] … An automated bulk labeling algorithm, as generally referred to herein, may comprise a set of computer instructions that, when executed, executes an automated sequence of tasks that may include automatically and/or simultaneously assigning at least one classification label to a volume of unlabeled digital event data samples included in a target digital event data corpus or a target cluster of digital event data [classifying [ ] a first group simultaneously, …, labelling [ ] the first group simultaneously with a first label, classifying [ ] a second group simultaneously … labelling [ ] the second group simultaneously with a second label].”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Liu with teachings of Kanta and Bras for the same reasons disclosed for claim 1.
Claim 9, recites limitations analogous to claim 3, so is rejected under the same rationale.
Regarding claim 7, Kanta teaches A system for developing a model to classify data, the system
(Kanta, “[0010] Aspects of the present disclosure address the above-noted and other deficiencies by implementing an unsupervised classification model that uses unlabeled data as the input by converting the unlabeled data to labeled data [a model to classify data].”)
comprising: a processor; computer memory having instructions stored thereon that when executed will cause the processor to:
(Kanta, “[0065] The example computer system 500 may include a processing device 502, a main memory 504 (e.g., read-only memory (ROM) [a processor; computer memory having instructions stored thereon that when executed will cause the processor to], flash memory, dynamic random access memory (DRAM) (such as synchronous DRAM (SDRAM), etc.), a static memory 506 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 518, which communicate with each other via a bus 530.”)
receive complex high-dimensionality data including plural data points that are nonuniform in data schema and/or data structure;
(Kanta, “[0020] Data store 140 can store multiple data records 145 and labels 147, and can be accessed by user devices 101A-101N and server device 105 over one or more networks 103. Data records 145 and/or labels 147 can be stored as one or more tables, spreadsheets, databases, distributed data stores, or other data structure [receive complex high-dimensionality data including plural data points that are nonuniform in data schema and/or data structure].”)
group each data point into one or more groups via a clustering algorithm;
(Kanta, “[0012] The unsupervised classification model may implement a clustering algorithm to assign each data record of the dataset to one or more of the groups [group each data point into one or more groups] based on similarities between the data records that are assigned to the same group. The clustering algorithm [via a clustering algorithm] used may depend on the nature of the dataset and/or based on user preference. If the dataset is numerical, the unsupervised classification model may use a clustering algorithm that works better with numerical data (e.g., k-means clustering)...”)
classify each indexed-data point of a group
(Kanta, “[0025] The clustering module 114 can use a clustering algorithm to assign each data record (either of the entire dataset or of the subset identified by the data input module 112) to one or more groups [classify each indexed-data point of a group ] based on the similarities between the data records that are assigned to the same group…”)
labelling all of the classified indexed-data points of the group
(Kanta, “[0038] At block 220, the processing device may then assign a label to each of the data records based on the groups. The labels may be randomly generated, selected from an existing list of predefined labels, or may be sequential integer numbers, for example. For example, if the processing device divided the data set into C number of clusters, the processing device may assign the label “c1” to the data records belonging to the first cluster, the label “c2” to the data records belonging the second cluster, and so on [labelling all of the classified indexed-data points of the group ] (i.e.: labeling all data records belonging to the same cluster with the same label). In embodiments, the processing device may add a column to the data set, wherein the column contains the dummy label of each corresponding data record.”)
Kanta does not teach:
assign each data point an index based one or more groups into which each data point is grouped;
store plural indexed data points in memory; and
classifying… … simultaneously and labelling … … simultaneously with the same label.
Bras teaches:
assign each data point an index based one or more groups into which each data point is grouped;
(Bras, “[0011] According to another aspect of the invention, a computer-implemented method of generating a finite-element mesh incorporating model-specific response to produce variable resolution in the mesh comprises: assigning an index value to each of a group of original elements [assign each data point an index based one or more groups into which each data point is grouped] based on an index function providing a heuristic measure of impact on a model to produce an indexed element for each original element; grouping the indexed elements into at least two groups based on the index value of each element [classifying all indexed-data points of a group]; selecting a subset of indexed elements from each of the groups based on a selection function; creating a finite-element mesh for each of the groups using the corresponding original elements of each of the subset of indexed elements selected by the selection function; and combining the finite-element mesh from each of the groups into a final finite-element mesh.”)
store plural indexed data points in memory; and
(Bras, “[0046] Once index values have been calculated for each data point, the points are grouped (act 504) based on their respective index values. In the illustrated embodiment, the DEM data points are first sorted into groups and placed in “bins” in memory based on their respective index values [store plural indexed data points in memory], in a predetermined way.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Bras with teachings of Kanta for the same reasons disclosed for claim 1.
Kanta in view of Bras do not teach:
classifying… … simultaneously and labelling … … simultaneously with the same label.
Liu teaches:
classifying… … simultaneously and labelling … … simultaneously with the same label.
(Liu, “[0063] … An automated bulk labeling algorithm, as generally referred to herein, may comprise a set of computer instructions that, when executed, executes an automated sequence of tasks that may include automatically and/or simultaneously assigning at least one classification label to a volume of unlabeled digital event data samples included in a target digital event data corpus or a target cluster of digital event data [classifying… … simultaneously and labelling … … simultaneously with the same label]. ”)
Liu, Kanta and Bras are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Liu with teachings of Kanta and Bras to add automatic bulk labeling into the unsupervised grouping and labeling system to improve efficiency and scalability. (Liu, Abstract).
Claim(s) 4 – 6 and 10 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kanta in view of Bras, Liu and in further view of Elkind et al., Pub. No.: US20190273510A1.
Regarding claim 4, Kanta in view of Bras and Liu teach the method of claim 1.
Kanta in view of Bras and Liu do not teach:
comprising: encoding each data point before grouping each data point
Elkind teaches:
comprising: encoding each data point before grouping each data point.
(Elkind, “[0127] The encoder RNN 725 takes as input a sequence of numbers or sequence of vectors of numbers that are generated from the input data [encoding each data point before grouping each data point] either through a vector space embedding, either learned or not, a one-hot or other encoding, or the sequence of single numbers, scaled, normalized, otherwise transformed, or not. Encoder RNN 725 may include any number of layers of one or more types of RNN cells, each layer including an LSTM, GRU, or other RNN cell type. Additionally, each layer may have multiple RNN cells, the output of which is combined in some way before being sent to the next layer up if present. The learned weights within each cell may vary depending on the cell type. Encoder RNN 725 may produce an output after each element of the input sequence is provided to it in addition to the internal states of the RNN cells, all of which can be sent to the decoder RNN during training or used for embedding or classification.”)
Elkind, Kanta, Bras and Liu are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Elkind with teachings of Kanta, Bras and Liu to add ability to classify variable length or complex data types, rather than just assigning labels to grouped data. (Elkind, Abstract).
Claim 10, recites limitations analogous to claim 4, so is rejected under the same rationale.
Regarding claim 5, Kanta in view of Bras, Liu and Elkind teach the method of claim 4.
Elkind further teaches: wherein: encoding each data point involves one-hot encoding.
(Elkind, “[0127] The encoder RNN 725 takes as input a sequence of numbers or sequence of vectors of numbers that are generated from the input data either through a vector space embedding, either learned or not, a one-hot or other encoding [encoding each data point involves one-hot encoding], or the sequence of single numbers, scaled, normalized, otherwise transformed, or not. Encoder RNN 725 may include any number of layers of one or more types of RNN cells, each layer including an LSTM, GRU, or other RNN cell type. Additionally, each layer may have multiple RNN cells, the output of which is combined in some way before being sent to the next layer up if present. The learned weights within each cell may vary depending on the cell type. Encoder RNN 725 may produce an output after each element of the input sequence is provided to it in addition to the internal states of the RNN cells, all of which can be sent to the decoder RNN during training or used for embedding or classification.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Elkind with teachings of Kanta, Bras and Liu for the same reasons disclosed for claim 4.
Claim 11, recites limitations analogous to claim 5, so is rejected under the same rationale.
Regarding claim 6, Kanta in view of Bras, Liu teach the method of claim 1.
Kanta in view of Bras and Liu do not teach:
comprising: performing dimensionality reduction of the encoded data points.
Elkind teaches:
comprising: performing dimensionality reduction of the encoded data points.
(Elkind, “[0121] This system characterizes its input by identifying a reduced number of features of the input. The system uses encoder RNN 725 to reduce (compress) the dimensionality of the input source data [performing dimensionality reduction of the encoded data points] so that a classifier can analyze this reduced vector space to determine the characteristics of the source data. An encoder RNN whose output includes less nodes than the sequential input data creates a compressed version of the input.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Elkind with teachings of Kanta, Bras and Liu for the same reasons disclosed for claim 4.
Claim 12, recites limitations analogous to claim 6, so is rejected under the same rationale.
Claim(s) 13 – 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kanta in view of Mars, Pub. No.: US20190130244A1.
Regarding claim 13, Kanta teaches: A method for classifying data, the method comprising: receiving incoming data points for which no labeled corpus of data exists;
(Kanta, “[0038] … In implementations, a processing device executing an unsupervised classification model may receive an unlabeled dataset containing one or more data records. In embodiments, a processing device may reformat and/or clean the unlabeled dataset [receiving incoming data points for which no labeled corpus of data exists] prior to inputting the unlabeled dataset to the unsupervised classification model...”)
generating a corpus of labelled data points by: i) grouping two or more data points representative of the incoming data points into one or more groups;
(Kanta, “[0012] The unsupervised classification model may implement a clustering algorithm to assign each data record of the dataset to one or more of the groups [generating a corpus of labelled data points by: i) grouping two or more data points representative of the incoming data points into one or more groups] based on similarities between the data records that are assigned to the same group. The clustering algorithm used may depend on the nature of the dataset and/or based on user preference. If the dataset is numerical, the unsupervised classification model may use a clustering algorithm that works better with numerical data (e.g., k-means clustering)...”)
ii) assigning each grouped data point an index;
(Kanta, “[0025] The clustering module 114 can use a clustering algorithm to assign each data record (either of the entire dataset or of the subset identified by the data input module 112) [ii) assigning each grouped data point an index] to one or more groups based on the similarities between the data records that are assigned to the same group…”)
iii) classifying each indexed-data point of a group
(Kanta, “[0025] The clustering module 114 can use a clustering algorithm to assign each data record (either of the entire dataset or of the subset identified by the data input module 112) to one or more groups [iii) classifying each indexed-data point of a group ] based on the similarities between the data records that are assigned to the same group…”)
labelling all of the classified indexed-data points of the group
(Kanta, “[0038] At block 220, the processing device may then assign a label to each of the data records based on the groups. The labels may be randomly generated, selected from an existing list of predefined labels, or may be sequential integer numbers, for example. For example, if the processing device divided the data set into C number of clusters, the processing device may assign the label “c1” to the data records belonging to the first cluster, the label “c2” to the data records belonging the second cluster, and so on [labelling all of the classified indexed-data points of the group ] (i.e.: labeling all data records belonging to the same cluster with the same label). In embodiments, the processing device may add a column to the data set, wherein the column contains the dummy label of each corresponding data record.”)
Kanta does not teach:
iii) classifying [ ] simultaneously and labelling [ ] simultaneously with the same label;
comparing the incoming data points to a corpus of labelled data points,
labeling an incoming data point with a label based on the comparison
Liu teaches:
iii) classifying [ ] simultaneously and labelling [ ] simultaneously with the same label;
(Liu, “[0063] … An automated bulk labeling algorithm, as generally referred to herein, may comprise a set of computer instructions that, when executed, executes an automated sequence of tasks that may include automatically and/or simultaneously assigning at least one classification label to a volume of unlabeled digital event data samples included in a target digital event data corpus or a target cluster of digital event data [iii) classifying [ ] simultaneously and labelling [ ] simultaneously with the same label;]. ”)
Liu and Kanta are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Liu with teachings of Kanta to add automatic bulk labeling into the unsupervised grouping and labeling system to improve efficiency and scalability. (Liu, Abstract).
Kanta in view of Liu do not teach:
comparing the incoming data points to a corpus of labelled data points,
labeling an incoming data point with a label based on the comparison.
Mars teaches:
comparing the incoming data points to a corpus of labelled data points,
(Mars, “[0070] In a first implementation, S240 may function to implement or use a predetermined reference table to identify or determine a machine and/or program-comprehensible object or operation to map to each slot and associated one or more slot classification labels of the user input data. In such implementation, S240 implementing the slot extractor functions to match (or compare) [comparing] the slot (value or data) [the incoming data points] and the associated slot classification label(s) to the predetermined reference table [to a corpus of labelled data points] to identify the program-comprehensible object or operation that should be mapped to the slot and the associated slot classification label.”)
labeling an incoming data point with a label based on the comparison.
(Mars, “[0045] In operation, S220 implementing the competency classification deep machine learning algorithm may function to analyze the user input data and generate a classification label. Specifically, based on the features, meaning and semantics of the words and phrases in the user input data, the competency classification deep machine learning algorithm may function to calculate and output a competency classification label [labeling an incoming data point with a label based on the comparison] having a highest probability of matching an intent of the user input data. For example, the classification machine learning model generate, based on user input data, a classification label of “Income” having a probability of intent match of “89%” for a given query or command of the user input data, as shown by way of example in FIG. 3B.”)
Mars, Kanta and Liu are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Mars with teachings of Kanta and Liu to add using classification results, where generated labels are used to trigger further processing or actions. (Mars, Abstract).
Claim 14, recites limitations analogous to claim 13, so is rejected under the same rationale.
Claim(s) 15 – 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kanta in view of Bras, Liu and in further view of MO et al., Pub. No.: US20200174867A1.
Regarding claim 15, Kanta in view of Bras and Liu teach the method of claim 1.
Kanta in view of Bras and Liu do not teach:
further comprising: prior to assigning each data point an index, analyzing grouped data of the one or more groups using entropy processing to identify whether there are any anomalies in the one or more groups, and wherein the labelling comprises adding at least one feature value indicating an anomaly status based on a result of the analyzing.
MO teaches:
further comprising: prior to assigning each data point an index, analyzing grouped data of the one or more groups using entropy processing to identify whether there are any anomalies in the one or more groups, and wherein the labelling comprises adding at least one feature value indicating an anomaly status based on a result of the analyzing.
(MO, “[0053] FIG. 8 illustrates adding the holo-entropy model and determining the model weight in step 750 in greater detail, according to an embodiment. As shown, at step 752, model creation engine 202 performs a modified holo-entropy. The modified holo-entropy algorithm at step 752 takes as inputs data points [analyzing grouped data of the one or more groups using entropy] in the normal data set and associated weights, as well as a test data set that is the same as the normal data set, and outputs a classification of data points in the normal data set as normal or abnormal, i.e., Dabnormal, Dnormal ←HL (Dt,Dtest) [to identify whether there are any anomalies in the one or more groups]. In one embodiment, the modified holo-entropy algorithm is a modification to the traditional holo-entropy algorithm that assigns weights to each data point, performs a statistical calculation for each feature by adding the weight of each data point and dividing by the total weight to calculate the probability of equation (1) [prior to assigning each data point an index] rather than counting the number of data points, and calculates an outlier factor for each data point according to equation (8) [wherein the labelling comprises adding at least one feature value indicating an anomaly status based on a result of the analyzing], in which n(yj) represents the weighted summation of yj in the jth feature rather than the number of times that yj appears in the jth feature.”)
MO, Kanta, Bras and Liu are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of MO with teachings of Kanta, Bras and Liu to add technique for analyzing the grouped data and determining whether the datapoint is normal or abnormal. (MO, Abstract).
Regarding claim 16, Kanta in view of Bras and Liu teach the method of claim 1.
Kanta in view of Bras and Liu do not teach:
wherein the anomalies correspond with cyber-attack vectors.
MO teaches:
wherein the anomalies correspond with cyber-attack vectors.
(MO, “[0074] As a result, use of HEAB models can reduce the risk of malware and attack vectors [wherein the anomalies correspond with cyber-attack vectors], provide greater security, and reduce the incidence of false positives in anomaly detection and alarm verification so that excessive user warning notifications are not generated and processes are not stopped that should continue running.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of MO with teachings of Kanta, Bras and Liu for the same reasons disclosed for claim 15.
Regarding claim 17, Kanta in view of Bras and Liu teach the method of claim 1.
Kanta in view of Bras and Liu do not teach:
further comprising: prior to assigning each data point an index, analyzing grouped data of the one or more groups using entropy processing to identify feature values of each of the one or more groups, wherein features values comprise: a group identification and an anomaly status for each of the one or more groups.
MO teaches:
further comprising: prior to assigning each data point an index, analyzing grouped data of the one or more groups using entropy processing to identify feature values of each of the one or more groups, wherein features values comprise: a group identification and an anomaly status for each of the one or more groups.
(MO, “[0074] Advantageously, embodiments disclosed herein provide an improved anomaly detection system that can be used to determine whether data, such as behavior data or alarms associated with a system process, is normal or abnormal relative to a normal data set [a group identification and an anomaly status for each of the one or more groups], such as intended state(s) associated with the system process, as well as calculate a score indicating how abnormal an alarm is. Experience has shown that HEAB models can perform better than traditional holo-entropy models at anomaly detection [prior to assigning each data point an index, analyzing grouped data of the one or more groups using entropy processing to identify feature values of each of the one or more groups, wherein features values comprise:] when data points in a baseline normal data set are from a multimodal distribution with more than one peak, in which case data points coming from minor modes in the baseline may be considered as anomalies by traditional holo-entropy models.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of MO with teachings of Kanta, Bras and Liu for the same reasons disclosed for claim 15.
Claim(s) 18 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kanta in view of Bras, Liu, MO and in further view of Ben-Arie et al., Pub. No.: US20200184370A1.
Regarding claim 18, Kanta in view of Bras, Liu and MO teach the method of claim 17.
Kanta in view of Bras, Liu and MO do not teach:
further comprising: generating a reporting that comprises summary information for the one or more groups including summary information for the feature values; and outputting the reporting to aid the labelling.
Ben-Arie teaches:
further comprising: generating a reporting that comprises summary information for the one or more groups including summary information for the feature values; and outputting the reporting to aid the labelling.
(Ben-Arie, [0020] … “In addition, the frequency ratio and coverage previously computed are included. Also introduced is the feature value in this table 400. The feature value can be a label that now used to describe a feature associated with the cluster which can aid in understanding the data set grouping computed by the clustering model [generating a reporting that comprises summary information for the one or more groups including summary information for the feature values; and outputting the reporting to aid the labelling]. For example, consider cluster 1 of table 400.”)
Ben-Arie, Kanta, Bras, Liu and MO are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Ben-Arie with teachings of Kanta, Bras, Liu and MO to automatically and consistently determine labels for the generated groups. (Ben-Arie, Abstract).
Regarding claim 19, Kanta in view of Bras, Liu and MO teach the method of claim 17.
Kanta in view of Bras, Liu and MO do not teach:
further comprising: generating a report that comprises summary information for the one or more groups including summary information for the feature values; and utilizing the reporting to aid the labelling processing.
Ben-Arie teaches:
further comprising: generating a report that comprises summary information for the one or more groups including summary information for the feature values; and utilizing the reporting to aid the labelling processing
(Ben-Arie, “[0012] Aspects of the present disclosure involve systems, methods, devices, and the like for auto-labeling clusters generated by machine learning models. In one embodiment, a system is introduced that can perform a series of operations for determining comprehensive labels for clusters output from machine learning methods used to classify data sets. The auto-labeling system may include generating labels determined using a computation of a frequency count, ratio, and coverage. These computations may use feature-based dictionaries which aid in the determination, storage, and analysis of the relevant features useful in labeling the clusters [generating a report that comprises summary information for the one or more groups including summary information for the feature values; and utilizing the reporting to aid the labelling processing].”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Ben-Arie with teachings of Kanta, Bras, Liu and MO for the same reasons disclosed for claim 18.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Kanta in view of Bras, Liu and in further view of Rahman et al., Pub. No.: US20200374712A1.
Regarding claim 20, Kanta in view of Bras and Liu teach the method of claim 1.
Kanta in view of Bras and Liu do not teach:
wherein the plural data points are data points from a 5G Packet Forwarding Control Protocol.
Rahman teaches:
wherein the plural data points are data points from a 5G Packet Forwarding Control Protocol.
(Rahman, “… [0048] As illustrated in FIG. 1 and noted earlier, HFC device 102 can collect, collate, cluster, and/or classify (e.g., provide a classifying ranking or ordering, in response to for example, an importance value that can be determined based on one or more defined or definable threshold values being met and exceed or not being met and not surpassed) various data from each device included in collection of 5G small cell devices 104 and in response to, based on, and/or as a function of the received data can filter and/or detect (or determine) one or more threshold crossing alert/alarm (TCA) events”)
Rahman, Kanta, Bras and Liu are related to the same field of endeavor (i.e.: neural network optimization). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Rahman with teachings of Kanta, Bras and Liu to incorporate grouping of network devices and associated analytics module to enable the classification system to organize related data into groups based on common network characteristics to improve the efficiency and scalability of processing and classifying the data. (Rahman, Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al., Pub. No.: US11216491B2.
Li teaches an automatic data input and query system is controlled by well-defined control data. Certain control data may relate to data schemas and direct operations performed by the system to extract fields from machine data.
DUPLESSIS et al., Pub. No.: US12417407B2.
DUPLESSIS teaches monitoring and automated assessment is a computational approach that can be used to computationally monitor deployed models that are being used in production (e.g., client facing or in-use) systems as a warning mechanism tuned to issue alerts or cause downstream model changes upon detecting mismatches.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner
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Michelle Bechtold can be reached on (571)431-0762. The fax phone number for the organization were this application or proceeding is assigned is 571-273-8300.
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/M.T.M./Examiner, Art Unit 2148
/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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