DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/23/2026 has been entered.
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.
Claim(s) 1, 2, 10, 11, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, and further in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva.
As to claim 1:
Harutyunyan discloses:
A system for detecting anomalies in metric data provided by one or more customers, the system comprising: at least one processor; and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to: receive metric data indicative of a plurality of time-series based observations for a particular customer metric [Paragraph 0003 teaches collect metric data within computing facilities, including large data centers and cloud-computing facilities… The compressed multidimensional metric data is subsequently decompressed for data analysis, including statistical analyses that detect significant changes in time series of metric data, unexpected events, anomalies, and other events and trends that may result in automated corrective actions, automated generation of alerts, automated or semi-automated system reconfiguration, and other such automated and semi-automated responses by monitoring, management, and administration facilities within computer systems.]; define, based on the metric data, a plurality of parameters to characterize one or more spheres each configured to capture a number of time-series based observations for the particular customer metric [Paragraph 0060 teaches clustering of multidimensional data points. As mentioned above, clustering of multidimensional data points provides for cluster-based data compression for efficient storage of multidimensional metric data sets. Paragraph 0061 teaches one or more multidimensional data points are selected as cluster centers and a maximum distance is selected as a radius of a spherical volume around each center that defines the cluster volume.]; and generate, based on the plurality of parameters, the one or more spheres to determine coverage of the metric data within the one or more spheres and detect one or more anomalies in the metric data [Figure 19C and Paragraph 0061 teaches one or more multidimensional data points are selected as cluster centers and a maximum distance is selected as a radius of a spherical volume around each center that defines the cluster volume. Multidimensional data points within a spherical volume about a cluster center are considered to belong to the cluster defined by the cluster center and cluster volume, or, equivalently, the corresponding cluster radius or diameter, while multidimensional data points outside of the spherical volume either belong to another cluster or are considered to be outliers that belong to no cluster.], wherein to generate the one or more spheres based on the plurality of parameters comprises to dynamically generate a plurality of spheres each having a radius that varies based on the plurality of time-series based observations for the particular customer metric [Paragraph 0060 teaches apparent clustering of the multidimensional data points into 3 distinct clusters 1616-1618. Paragraph 0062 teaches as shown in FIG. 16G, by slightly changing the value of the radius parameter d, the parameters K=3, S={(2, 3, 4), (8, 3, 2), (2, 8, 6)}, d=√{square root over (13)} do specify a covering subset of the multidimensional data point plotted in FIG. 16G. Multidimensional data point 1640, for example, lies at the farthest distance from any of the cluster centers but is within the specified radius from cluster center 1642. Finally, FIG. 16H shows that by changing the value of the parameter K rather than the parameter d, a different covering subset of multidimensional data points is found with parameter values K=4, S={(2, 3, 4), (8, 3, 2), (2, 8, 6)}, d=√{square root over (12)}. Thus, by varying the selected cluster centers, the number of cluster centers, and the maximum distance from a cluster center for a cluster member, various different covering sets of multidimensional data points are obtained for the multidimensional data points of the multidimensional metric-data set originally plotted in FIG. 16B. Note: Generating spherical volume clusters that are distinct based on changing the radius (dynamic generation) of spherical volume clusters reads on the claims (see also Figure 19C)].
Harutyunyan discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose including determination of a radius increment as a function of a determined average distance based on a distance matrix that defines distances between the time-series based observations and further including application of a density filter based on a determination of sphere density as a function of the distance matrix and a minimum radius.
Leonard discloses:
including determination of a radius increment as a function of a determined average distance based on a distance matrix that defines distances between the time-series based observations and the distance matrix [Paragraph 0046 teaches the measurement 103 may be a time series. Paragraph 0054 teaches the estimated measurement hypersphere radius r.sub.i 202 is computed from a finite set on M measurement realizations. Paragraph 0090 teaches In the case of an Euclidian metric in custom-character.sup.NN with uniform averaging, the cross-measurement matrices to estimated covariance matrix distance. Paragraph 0100 teaches the radius (shell-to-signature domain average distance) r 504 must be corrected to take account of the part 514 of the measurement noise projected onto the signature domain 501. Note: Determining a radius measurement (radius increment) as part of averaging of distance based on cross-measurement matrix (distance matrix) for a matrix distance as part of clustering observed time series data reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan, by incorporating determining a radius measurement (radius increment) as part of averaging of distance based on cross-measurement matrix (distance matrix) for a matrix distance as part of clustering observed time series data, as taught by Leonard (see Paragraph 0046, 0054, 0090, and 0100), because both applications are directed to data analysis; determining a radius measurement (radius increment) as part of averaging of distance based on cross-measurement matrix (distance matrix) for a matrix distance as part of clustering observed time series data increase robustness and sensitivity (see Leonard Paragraph 0004).
Harutyunyan and Leonard discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose including determination of a radius increment as a function of a determined average distance based on a distance matrix that defines distances between the time-series based observations and further including application of a density filter based on a determination of sphere density as a function of the distance matrix and a minimum radius.
Bettencourt da Silva discloses:
and further including application of a density filter based on a determination of sphere density as a function of the matrix and a minimum radius [Paragraph 0015 teaches determine whether the characteristics of the subject are typical or atypical with respect to the other members of the classes. Paragraph 0037 teaches embodiments of the present invention further recognize that the degree of clustering (i.e., the feature vector density of a cluster) for any one concept or category is based, at least in part, on a number of feature vectors that are semantically related to the concept or category and the degree to which the feature vectors are semantically related (i.e., the proximity of the feature vectors within a cluster to one another). It is therefore advantageous, if possible, that the cellular presentation includes one or more cells that represent concepts and/or categories that encompass a plurality of respective feature vectors within the pre-trained model (i.e., multi-feature-vector cells). In some embodiments, visualization logic 116 uses a threshold feature vector density and/or a threshold radius from a point within a cluster (e.g., a centroid of a cluster) to identify feature vectors to represent as respective concepts and/or categories within the cellular presentation of the feature matrix. Similarly, various embodiments of visualization logic 116 utilize a threshold feature vector density and/or a threshold radius from a point within a cluster (e.g., a centroid of a cluster) and/or a threshold number of feature vectors, among various other factors, to identify the ranges of reduced multidimensional coordinates that define respective cells. Note: Recognizing and determining typical and atypical cluster features based on a threshold radius (minimum radius) and using a feature matrix (matrix) reads on the claims. The examiner further notes the Leonard reference reads on the claim language distance matrix.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan and Leonard, by incorporating recognizing clusters based on a threshold radius (minimum radius) and using a feature matrix (matrix), as taught by Bettencourt da Silva (see Paragraph 0015 and 0037), because the three applications are directed to data analysis; incorporating recognizing clusters based on a threshold radius (minimum radius) and using a feature matrix (matrix) is advantageous in identifying relationships within the data while preserving, at least in part, information with respect to the usage of entities in the data (see Bettencourt da Silva Paragraph 0027).
Claims 10 and 17 recite similar limitations as in claim 1. Therefore claim 10 and 17 are rejected for the same reasons as set forth above. See claim 1 for analysis.
As to claim 2:
Harutyunyan discloses:
The system of claim 1, wherein to define the plurality of parameters based on the metric data comprises to: define a coverage limit indicative of a maximum number of metric data points to be covered by the generated spheres [Paragraph 0073 teaches a cutoff value 1920 is determined for the number of members of a cluster, with multidimensional data points in any clusters with member numbers lower than the cutoff value considered to be outlier multidimensional data points.]
Claims 11 and 18 recite similar limitations as in claim 2. Therefore claim 11 and 18 are rejected for the same reasons as set forth above. See claim 2 for analysis.
Claim(s) 3, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva, and further in view of Nushi et al. (U.S. Publication No.: US 20200349395 A1) hereinafter Nushi.
As to claim 3:
Harutyunyan, Leonard, and Bettencourt da Silva discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein to generate the one or more spheres based on the plurality of parameters comprises to determine a location corresponding to a maximum concentration of metric data based on the minimum radius.
Nushi discloses:
The system of claim 2, wherein to generate the one or more spheres based on the plurality of parameters comprises to determine a location corresponding to a maximum concentration of metric data based on the minimum radius [Paragraph 0014 teaches identifying clusters having a higher concentration of errors. Paragraph 0050 teaches the cluster manager 306 may identify or generate feature clusters in a variety of ways. As an example, the cluster manager 306 may identify any combination of one or two features (or other maximum number of combined features) and determine a correlation metric between test instances associated with each combination of one or two features. Paragraph 0051 teaches this may include generating clusters having approximate sizes (e.g., number of instances), ensuring that feature clusters have a minimum size (e.g., a minimum cluster constraint requiring 50 or more test instances), or other constraints or parameters. Note: Generating clusters by identifying features based on maximum number of features (maximum concentration of metric data) that includes a determination of minimum size (minimum radius) of cluster reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan, Leonard, and Bettencourt da Silva, by incorporating generating clusters by identifying features based on maximum number of features (maximum concentration of metric data) based that includes a determination of minimum size (minimum radius) of cluster, as taught by Nushi (see Paragraph 0014, 0050, and 0051), because the four applications are directed to data analysis; incorporating generating clusters by identifying features based on maximum number of features (maximum concentration of metric data) based that includes a determination of minimum size (minimum radius) of cluster provides benefits and/or solve problems associated with characterizing failures (see Nushi Paragraph 0013).
Claims 12 and 19 recite similar limitations as in claim 3. Therefore claim 12 and 19 are rejected for the same reasons as set forth above. See claim 3 for analysis.
Claim(s) 4, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva, in view of Nushi et al. (U.S. Publication No.: US 20200349395 A1) hereinafter Nushi, and further in view of Sapugay et al. (U.S. Publication No.: US 20190294676 A1) hereinafter Sapugay.
As to claim 4:
Harutyunyan, Leonard, Bettencourt da Silva, and Nushi discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein to generate the one or more spheres based on the plurality of parameters comprises to: determine whether the coverage limit has been reached; and increment a radius for generation of at least one new sphere based on the radius increment in response to a determination that the coverage limit has not been reached.
Sapugay discloses:
The system of claim 3, wherein to generate the one or more spheres based on the plurality of parameters comprises to: determine whether the coverage limit has been reached [Paragraph 0114 teaches wherein each intent vector represents a distinct cluster (e.g., maximum granularity). The processor 82 may then repeatedly increment the radius (e.g., up to a maximum radium value), enlarging the spheres, while determining the size of (e.g., the number of intent vectors contained within) each cluster, until all of the intent vectors and meaning clusters merge into a single cluster at a particular maximum radius value.]; and increment a radius for generation of at least one new sphere based on the radius increment in response to a determination that the coverage limit has not been reached [Paragraph 0114 teaches wherein each intent vector represents a distinct cluster (e.g., maximum granularity). The processor 82 may then repeatedly increment the radius (e.g., up to a maximum radium value), enlarging the spheres, while determining the size of (e.g., the number of intent vectors contained within) each cluster, until all of the intent vectors and meaning clusters merge into a single cluster at a particular maximum radius value.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan, Leonard, Bettencourt da Silva, and Nushi, by incorporating incrementing the radius up to a maximum radius value based on until a particular condition is met (coverage limit), as taught by Sapugay (see Paragraph 0114), because the five applications are directed to data analysis; incorporating incrementing the radius up to a maximum radius value based on until a particular condition is met improves learning models (see Sapugay Paragraph 0023).
Claims 13 and 20 recite similar limitations as in claim 4. Therefore claim 13 and 20 are rejected for the same reasons as set forth above. See claim 4 for analysis.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva, in view of Nushi et al. (U.S. Publication No.: US 20200349395 A1) hereinafter Nushi, in view of Sapugay et al. (U.S. Publication No.: US 20190294676 A1) hereinafter Sapugay, and further in view of Roustant et al. (U.S. Publication No.: US 20040093321 A1) hereinafter Roustant.
As to claim 5:
Harutyunyan, Leonard, Bettencourt da Silva, Nushi, and Sapugay discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein to generate the one or more spheres based on the plurality of parameters comprises to: determine whether the at least one new sphere provides coverage of the metric data not previously provided and filter out metric data already covered by a previous sphere in response to a determination that the at least one new sphere does not provide coverage of the metric data not previously provided.
Roustant discloses:
The system of claim 4, wherein to generate the one or more spheres based on the plurality of parameters comprises to: determine whether the at least one new sphere provides coverage of the metric data not previously provided [Paragraph 0024 teaches the raw results may be filtered the following ways: (a) results with a score (e.g., provided by the information source with the raw data and/or computed or recomputed by the federated search engine based on user-selected keywords, using a relevancy ranking method) below a predefined threshold score are discarded; (b) results that do not satisfy the user-selected keywords (including subsumed queries) are discarded; and (c) results that are duplicates are discarded Paragraph 0044 teaches search results filtered by the filtering module 210 and received by the clustering module 212 are recorded in session result memory 214 (as shown in FIG. 2). When a user changes the clustering strategy, the filtered results that are stored in session memory 214 are re-clustered according to the new or modified clustering strategy, eliminating the need to re-query the information sources.]; and filter out metric data already covered by a previous sphere in response to a determination that the at least one new sphere does not provide coverage of the metric data not previously provided [Paragraph 0024 teaches the raw results may be filtered the following ways: (a) results with a score (e.g., provided by the information source with the raw data and/or computed or recomputed by the federated search engine based on user-selected keywords, using a relevancy ranking method) below a predefined threshold score are discarded; (b) results that do not satisfy the user-selected keywords (including subsumed queries) are discarded; and (c) results that are duplicates are discarded Paragraph 0044 teaches search results filtered by the filtering module 210 and received by the clustering module 212 are recorded in session result memory 214 (as shown in FIG. 2). When a user changes the clustering strategy, the filtered results that are stored in session memory 214 are re-clustered according to the new or modified clustering strategy, eliminating the need to re-query the information sources. Note: Filtering out results that are scored and duplicates to form clusters as part of the clustering algorithm reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan, Leonard, Bettencourt da Silva, Nushi, and Sapugay, by incorporating filtering out results that are scored and duplicates to form clusters as part of the clustering algorithm, as taught by Roustant (see Paragraph 0024 and 0044), because the six applications are directed to data analysis; incorporating filtering out results that are scored and duplicates to form clusters as part of the clustering algorithm provides improved methods for distilling and presenting search results to users so that they more readily locate the information they are searching for (see Roustant Paragraph 0004).
Claim 14 recites similar limitations as in claim 5. Therefore claim 14 is rejected for the same reasons as set forth above. See claim 5 for analysis.
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva, in view of Nushi et al. (U.S. Publication No.: US 20200349395 A1) hereinafter Nushi, and further in view of Sapugay et al. (U.S. Publication No.: US 20190294676 A1) hereinafter Sapugay, and further in view of Kim et al. (U.S. Publication No.: US 20080313252 A1) hereinafter Kim.
As to claim 6:
Harutyunyan, Leonard, Bettencourt da Silva, Nushi, and Sapugay discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein to generate the one or more spheres based on the plurality of parameters comprises to: update the minimum radius in response to a determination that the at least one new sphere provides coverage of the metric data not previously provided and update the location in response to the determination that the at least one new sphere provides coverage of the metric data not previously provided.
Kim discloses:
The system of claim 4, wherein to generate the one or more spheres based on the plurality of parameters comprises to: update the minimum radius in response to a determination that the at least one new sphere provides coverage of the metric data not previously provided [Paragraph 0063 teaches assuming that the estimates of the unselected dimensions are kept fixed and a minimum radius obtained in the selected dimension is smaller than a current initial radius. In this assumption, the current initial radius and the current lattice vector are updated by the new radius (the minimum radius) and the corresponding lattice vector, respectively.]; and update the location in response to the determination that the at least one new sphere provides coverage of the metric data not previously provided [Paragraph 0063 teaches assuming that the estimates of the unselected dimensions are kept fixed and a minimum radius obtained in the selected dimension is smaller than a current initial radius. In this assumption, the current initial radius and the current lattice vector are updated by the new radius (the minimum radius) and the corresponding lattice vector, respectively.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan, Leonard, Bettencourt da Silva, Nushi, and Sapugay, by incorporating using new unselected data to update radius associated with a sphere analysis, as taught by Kim (see Paragraph 0063), because the six applications are directed to data analysis; incorporating using new unselected data to update radius associated with a sphere analysis provides reduced computational complexity (see Kim Paragraph 0018).
Claim 15 recites similar limitations as in claim 6. Therefore claim 15 is rejected for the same reasons as set forth above. See claim 6 for analysis.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva, in view of Nushi et al. (U.S. Publication No.: US 20200349395 A1) hereinafter Nushi, and further in view of Novel et al. (U.S. Publication No.: US 20200371214 A1) hereinafter Novel.
As to claim 7:
Harutyunyan, Leonard, Bettencourt da Silva, and Nushi discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein to generate the one or more spheres based on the plurality of parameters comprises to determine a location corresponding to a maximum concentration of metric data based on the minimum radius.
Novel discloses:
The system of claim 3, wherein to generate the one or more spheres based on the plurality of parameters comprises to: determine another location corresponding to the maximum concentration of metric data based on the minimum radius of the at least one sphere and a distance between the at least one sphere and at least one nearest neighboring sphere [Paragraph 0034 teaches the scanner position process 118 may create additional clusters in a similar manner. Specifically, the scanner position process 118 may select a high density point not within an already created cluster and with a curvature value closest to 0 and that is less than 0.1, and then add different high density points when the three conditions are met. Paragraph 0035 teaches the scanner position process 118 may compute the mean distance from the centroid to its k nearest neighbors of the cluster. Paragraph 0038 teaches the scanner position process 118 may determine that a cluster is circular cluster if the density value of the centroid is 11 times greater than the density value of the point determined to have the maximum density value of the cluster. Paragraph 0039 teaches the scanner position process 118 may determine that a cluster is circular cluster if the density value of the centroid is 11 times greater than the density value of the point determined to have the maximum density value of the cluster.]; and generate at least one new sphere such that a center of the at least one new sphere is positioned at the another location [Paragraph 0034 teaches the scanner position process 118 may create additional clusters in a similar manner. Specifically, the scanner position process 118 may select a high density point not within an already created cluster and with a curvature value closest to 0 and that is less than 0.1, and then add different high density points when the three conditions are met. Note: Identifying another location to create a circular cluster based on an analysis of the nearest neighbors to the cluster reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Bettencourt da Silva, by incorporating identifying another location to create a circular cluster based on an analysis of the nearest neighbors to the cluster, as taught by Novel (see Paragraph 0034, 0035, 0038, and 0039), because the five applications are directed to data analysis; incorporating identifying another location to create a circular cluster based on an analysis of the nearest neighbors to the cluster provides an improvement in the existing technological field (see Novel Paragraph 0052).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva, in view of Nushi et al. (U.S. Publication No.: US 20200349395 A1) hereinafter Nushi, and further in view of Roustant et al. (U.S. Publication No.: US 20040093321 A1) hereinafter Roustant.
As to claim 8:
Harutyunyan, Leonard, Bettencourt da Silva, and Nushi discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein to generate the one or more spheres based on the plurality of parameters comprises to: compare coverage of the metric data provided by the at least one sphere to coverage of the metric data provided by the at least one nearest neighboring sphere or the at least one new sphere; and select whichever sphere provides the greatest coverage of the metric data based on the comparison for generation of another new sphere.
Roustant discloses:
The system of claim 7, wherein to generate the one or more spheres based on the plurality of parameters comprises to: compare coverage of the metric data provided by the at least one sphere to coverage of the metric data provided by the at least one nearest neighboring sphere or the at least one new sphere [Paragraph 0067 teaches the clusters of stems grouped at 326 are ranked by importance by selecting the clusters that offer the best coverage of search results while: (a) the number of selected clusters does not exceed the maximum number defined by the grouping mode.]; and select whichever sphere provides the greatest coverage of the metric data based on the comparison for generation of another new sphere [Paragraph 0067 teaches the clusters of stems grouped at 326 are ranked by importance by selecting the clusters that offer the best coverage of search results while: (a) the number of selected clusters does not exceed the maximum number defined by the grouping mode.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan, Leonard, Bettencourt da Silva, and Nushi, by incorporating comparing clusters to determine which of the clusters provides the best of coverage of data, as taught by Roustant (see Paragraph 0067), because the five applications are directed to data analysis; incorporating comparing clusters to determine which of the clusters provides the best of coverage of data provides improved methods for distilling and presenting search results to users so that they more readily locate the information they are searching for (see Roustant Paragraph 0004).
Claim(s) 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harutyunyan et al. (U.S. Publication No.: US 201901384020 A1) hereinafter Harutyunyan, in view of Leonard (U.S. Publication No.: US 20160085725 A1) hereinafter Leonard, in view of Bettencourt da Silva (U.S. Publication No.: US 20190213167 A1) hereinafter Bettencourt da Silva, and in view of Mercado et al. (U.S. Publication No.: US 20180052903 A1) hereinafter Mercado.
As to claim 9:
Harutyunyan, Leonard, and Bettencourt da Silva discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein to define the plurality of parameters based on the metric data comprises to: filter out outliers from the metric data and define the coverage limit based at least partially on the filtered metric data.
Mercado discloses:
The system of claim 2, wherein to define the plurality of parameters based on the metric data comprises to: filter out outliers from the metric data [Paragraph 0044 teaches once outliers are removed, process 700 proceeds to step 720, which includes calculating clustering parameters for each of clusters 315.]; and define the coverage limit based at least partially on the filtered metric data [Paragraph 0064 teaches refining the normalized production data within each of the finalized clusters by removing outliers from each cluster according to a predetermined outlier tolerance range; calculating the clustering parameters for each cluster based on the refined production data; and scaling the refined production data within each cluster based on the corresponding clustering parameters. Note: Filtering out outliers and calculating the clustering parameters based on those filtered outliers (partially filtered metric data), wherein the clustering parameters includes refining the scale of data included in each cluster (coverage limit) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Harutyunyan, Leonard, and Bettencourt da Silva, by incorporating filtering out outliers and calculating the clustering parameters based on those filtered outliers (partially filtered metric data), wherein the clustering parameters includes refining the scale of data included in each cluster (coverage limit), as taught by Mercado (see Paragraph 0044 and 0064), because the four applications are directed to data analysis; incorporating filtering out outliers and calculating the clustering parameters based on those filtered outliers (partially filtered metric data), wherein the clustering parameters includes refining the scale of data included in each cluster (coverage limit) provides improved predictive modeling (see Mercado Paragraph 0014).
Claim 16 recites similar limitations as in claim 9. Therefore claim 16 is rejected for the same reasons as set forth above. See claim 9 for analysis.
Response to Arguments
Applicant’s arguments with respect to 35 USC § 103 rejections directed to claim 1 have been considered but are moot because the new ground of rejection does not rely on any combinations of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EARL LEVI ELIAS whose telephone number is (571)272-9762. The examiner can normally be reached Monday - Friday (IFP).
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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