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 Amendment
Applicant's arguments filed 3/19/26 have been fully considered but they are not persuasive.
Regarding comments pertaining to the interview, SPEs do not sit in on interviews conducted by primary examiners but I did a 101 consult to help the applicant, and only one interview per round of prosecution will be conducted.
Regarding the USC 112 rejection applicant did not better define what is meant by using labels or point to any specific part of the disclosure that defines it. The disclosure specifies “cluster-labeled training data” but it is not exactly clear what is meant by specifying a label to be used in labeling the cluster. How is the label used? What is it used for? Does it label complete or incomplete? Does it label the cluster or he training data or vectors? See rejection below.
In regards to the arguments pertaining to the art rejections, amended claims read on well-known clustering techniques referred to as e.g., reclustering, divisive clustering, or dynamic clustering as needed to reach convergence using divisive clustering.
Zhang teaches splitting a first cluster of the plurality of clusters into into multiple clusters (reclustering or dynamic clustering as needed to reach convergence using divisive clustering, “Points may be iteratively assigned, and updated until convergence (no point changes clusters), or equivalently, until the centroids remain the same”, 0033;
“Agglomerative and divisive. Agglomerative clustering merges close clusters in an initially high dimensionality space, while divisive splits large clusters”, 0038) in an unsupervised machine learning environment (“In unsupervised classification, called clustering”, 0021;
“There are two basic approaches for generating a hierarchical clustering: Agglomerative and divisive. Agglomerative clustering merges close clusters in an initially high dimensionality space, while divisive splits large clusters”, 0038-0039); using labels in the sense that centroids act as the labels (“An optimal clustering will be obtained as long as two initial centroids fall anywhere in a pair of clusters, since the centroids will redistribute themselves, one to each cluster. As the number of clusters increases, it is increasingly likely that at least one pair of clusters will have only one initial centroid”, 0035).
Regarding the arguments pertaining to the USC 101 rejections clustering, dynamic clustering, reclustering, divisive clustering are a mental process that can be performed with pen and paper. "The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea." MPEP § 2106.04(a)(2).III. "Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions." Id. For the purposes of this abstract idea, "[t]he courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation."
It is well-settled that collecting and analyzing information by steps people go through in their minds or by mathematical algorithms, without more, are mental processes in the abstract-idea category. Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016); see SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1167 (Fed. Cir. 2018) ("[S]electing certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis" is abstract); Intellectual Ventures I LLC v. Cap. One Fin. Corp., 850 F.3d 1332, 1341 (Fed. Cir. 2017) ("Organizing, displaying, and manipulating data of particular documents" is abstract.); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096-97 (Fed. Cir. 2016) (compiling and combining disparate data sources to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment to detect potential fraud does not differentiate a process from ordinary mental processes); In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022) ("These steps can be performed by a human, using 'observation, evaluation, judgment, [and] opinion,' because they involve making determinations and identifications, which are mental tasks humans routinely do").
The claims amount to data analysis/manipulation and using some form of AI as a tool. The transformation of data, or the mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic 'abstract idea,"' is not a transformation sufficient to integrate a judicial exception into a practical application. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2 (Fed. Cir. 2011) (quoting In re Warmerdam, 33 F.3d 1354, 1355, 1360 (Fed. Cir. 1994)).
Claiming AI/clustering on a high level can amount to using a black box without specifying any real details of how the AI operates or what’s in the black box. The claims need to specify the technical details of the AI.
See also USC 101 example 48, claim 3
A non-transitory computer-readable storage medium having computer-executable instructions stored thereon, which when executed by one or more processors, cause the one or more processors to perform operations comprising:
(a) receiving a mixed speech signal x comprising speech from multiple different sources sn, where n ∈ {1, . . . N}, at a deep neural network (DNN) trained on source separation;
(b) using the DNN to convert a time-frequency representation of the mixed speech signal x into embeddings in a feature space as a function of the mixed speech signal x;
(c) clustering the embeddings using a k-means clustering algorithm;
(d) applying binary masks to the clusters to obtain masked clusters;
(e) converting the masked clusters into a time domain to obtain N separated speech signals corresponding to the different sources sn; and
(f) extracting spectral features from a target source sd of the N separated speech signals and generating a sequence of words from the spectral features to produce a transcript of the speech signal corresponding to the target source sd.
Limitations b-d recite mathematical concepts and fall within the same grouping of abstract ideas (i.e., mathematical concepts), these limitations are considered together as a single abstract idea. The claim recites no details about a particular DNN. The DNN is used to generally apply the abstract idea (i.e., perform the mathematical calculation recited in step (b)) without placing any limitation on how the DNN operates to derive the embedding vectors. The claim omits any details as to how the DNN solves a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to implement the abstract idea recited in step (b), which is equivalent to adding the words “apply it” to the recited judicial exception. In addition, the claim confines the use of the judicial exception recited in step (b) to the technological environment of a DNN by generally linking the use of the judicial 27 exception to the recited DNN. Therefore, this general DNN recitation does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Therefore, it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to a particular field of use or a technological environment.
All arguments have been addressed above or in the body of the rejections below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims specify e.g., “determining, for each of the plurality of clusters and independent of user input specifying a label to be used in labeling the cluster” but it is not clear if the applicant is referring to labeled training data or e.g., a centroid that represents a cluster. The disclosure specifies “cluster-labeled training data” but it is not exactly clear what is meant by specifying a label to be used in labeling the cluster. How is the label used? What is it used for? Does it label complete or incomplete? Does it label the cluster or he training data or vectors?
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
With respect to claims 1, 14, 20:
2A Prong 1:
performing [unsupervised machine learning] to identify a plurality of clusters in training data describing utilization metrics for a plurality of computing devices (mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
determining, for each of the plurality of clusters and independent of user input specifying a label to be used in labeling the cluster, whether the cluster is complete or incomplete based on pairs of the utilization metrics (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data);
splitting a first cluster of the plurality of clusters into multiple clusters responsive to determining the first cluster is incomplete (mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation);
assigning a cluster label to a second cluster independent of user input or previously labeled data responsive to determining the selected cluster is complete (not required by claim but clustering is a math concept and a user can make determinations and selections and/or generate labels).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
training a machine learning classifier (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
Claims 14 and 20 computer readable storage medium (Adding insignificant extra- a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
Claim 1 and 14: Computing devices, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
unsupervised machine learning (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
training a machine learning classifier (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data);
Claims 14 and 20 computer readable storage medium (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
Claim 1 and 14: Computing devices, (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358);
unsupervised machine learning (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction).
The claim is not patent eligible.
2. The method of claim 1, wherein determining whether the cluster is complete or incomplete comprises comparing differences between the pairs of the utilization metrics included in the cluster to a median difference of the pairs of the utilization metrics included in the cluster(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
3. The method of claim 1, further comprising generating a completeness score for the selected cluster, wherein determining whether each cluster of the plurality of clusters is complete or incomplete is performed based, at least in part, on the completeness score(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
4. The method of claim 1, further comprising: determining whether each of the plurality of clusters have been assigned a cluster label; and responsive to determining that each of the plurality of clusters have been assigned a cluster label, merging at least two of the plurality of clusters into a single cluster(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
5. The method of claim 4, wherein assigning a cluster label to the selected cluster generates cluster-labeled training data, the method further comprising training the machine learning classifier by performing [supervised machine learning] on the cluster-labeled training data(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
6. The method of claim 5, further comprising assigning incident probability inferences to the plurality of clusters by performing Bayesian learning on the cluster-labeled training data(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
7. The method of claim 6, further comprising assigning a remedial action to be triggered to each of the plurality of clusters having an assigned incident probability inference that satisfies a threshold value(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
8. The method of claim 5, further comprising:
deploying the machine learning classifier (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)) to a production environment for use in identifying production machine metrics as indicating anomalies;
receiving data from the machine learning classifier indicating an instance of the production machine metrics indicates an anomaly(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g));
presenting data identifying the instance of the production machine metrics indicating an anomaly-to-incident likelihood in a user interface(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); and receiving an indication in the user interface that the instance of the production machine metrics indicates or does not indicate an incident(mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
The receiving/presenting steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/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). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer.
9. The method of claim 8, further comprising retraining the machine learning classifier based, at least in part, on the indication(the court finds that this training is generic and summarily states the process of the training a model is required for any e.g., machine learning model. Using a machine learning technique necessarily includes an iterative step training step; iterative training using selected training material and/or dynamic adjustments based on changes are incident to the very nature of machine learning).
10. The method of claim 1, wherein splitting the selected cluster into multiple clusters is performed using a plurality of computing devices operating in parallel(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
11. The method of claim 1, further comprising assigning a remedial action to at least one of the plurality of clusters, wherein the remedial action triggers a device restoration to a recent healthy state (further expand mental process).
12. The method of claim 1, further comprising assigning a remedial action to at least one of the plurality of clusters, wherein the remedial action triggers a device reboot(further expand mental process).
13. The method of claim 1, further comprising assigning a remedial action to at least one of the plurality of clusters, wherein the remedial action triggers a device reconfiguration(further expand mental process).
15. The computer-readable storage medium of claim 14, wherein determining whether the cluster is complete or incomplete comprises comparing differences between the pairs of the utilization metrics included in the cluster to a median difference of the pairs of the utilization metrics included in the cluster(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
16. The computer-readable storage medium of claim 14, the operations further comprising generating a completeness score for the selected cluster, wherein determining whether each cluster of the plurality of clusters is complete or incomplete is performed based, at least in part, on the completeness score(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
17. The computer-readable storage medium of claim 14, the operations further comprising:
determining whether each of the plurality of clusters have been assigned a cluster label; and responsive to determining that each of the plurality of clusters have been assigned a cluster label, merging at least two of the plurality of clusters into a single cluster(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
18. The computer-readable storage medium of claim 17, wherein assigning a cluster label to the selected cluster generates cluster-labeled training data, the operations further comprising assigning incident probability inferences to the plurality of clusters by performing Bayesian learning on the cluster-labeled training data(additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
19. The computer-readable storage medium of claim 18, the operations further comprising assigning a remedial action to be triggered to each of the plurality of clusters having an assigned incident probability inference that satisfies a threshold value(mental process of modeling with assistance of pen and paper; and/or clustering is considered a mathematical concept user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
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) 1-5, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Shimanovsky (US 2014/0279757) in view of Galvin (US 2025/0363360) Zhang (US 2018/0165554) and Chudova (US 2016/0019338).
Shimanovsky discloses:
1, 14, 20. A method for training a machine learning classifier, the method comprising:
performing unsupervised machine learning (“an unsupervised learning technique. The SC module 114 can receive data records that have small differences between them. This allows the SC module 114 to unambiguously group the data records into the same cluster”, 0116, 0119) to identify a plurality of clusters in training data (training data records being clustered, “the training data records are known belong to the same cluster,”, 0025-6) describing utilization metrics (nonfunctional descriptive material, reads on any training data, 0025-0026 but not disclosed, see below) for a plurality of computing devices (Computing devices, Fig. 1);
determining, for each of the plurality of clusters and independent of user input specifying a label to be used in labeling the cluster (reads on using a label in some fashion e.g., labeling training data or clusters but Shimanovsky does but not disclose this, see below), whether the cluster is complete or incomplete based on pairs of the utilization metrics (“complete” “incomplete” is not further defined and is subjective and reads on e.g., reaching convergence, “The CC module 116 can use a graph clustering technique, such as a graph cut, to split clusters in the graph”, 0134; “CC module 118 can decide between adding the data record to an existing cluster and creating a new cluster for the data record. After multiple iterations of this approach, the CC module 118 can converge to a locally optimal set of clusters”, 0142;
pairs of training data records “merge pairs of data records into clusters”, 0043; “receive a plurality of pairs of data records, as illustrated in column 902, and perform a union-find operation to find clusters represented by the plurality of pairs of data records”, 0157);
splitting a first cluster of the plurality of clusters into multiple clusters responsive to determining the cluster is incomplete (“The CC module 116 can use a graph clustering technique, such as a graph cut, to split clusters in the graph”, 0134; “CC module 118 can decide between adding the data record to an existing cluster and creating a new cluster for the data record. After multiple iterations of this approach, the CC module 118 can converge to a locally optimal set of clusters”, 0142 and see below); and
assigning a cluster label to a second cluster of the plurality of clusters independent of user input or previously labeled data responsive to determining the selected cluster is complete (not required by claims 1, 14 or 20).
Shimanovsky fails to particularly call for training data describing utilization metrics; splitting clusters into multiple clusters; specifically using cluster labels.
Galvin teaches using utilization data as training data (“Training data may include, for example, records of past bundle formations, stability metrics, performance improvements, and resource utilization patterns” 0963).
Zhang more clearly teaches splitting a first cluster of the plurality of clusters into multiple clusters responsive to determining the cluster is incomplete (reclustering as needed to reach convergence using divisive clustering, “Points may be iteratively assigned, and updated until convergence (no point changes clusters), or equivalently, until the centroids remain the same”, 0033;
“Agglomerative and divisive. Agglomerative clustering merges close clusters in an initially high dimensionality space, while divisive splits large clusters”, 0038) in an unsupervised machine learning environment (“In unsupervised classification, called clustering”, 0021;
“There are two basic approaches for generating a hierarchical clustering: Agglomerative and divisive. Agglomerative clustering merges close clusters in an initially high dimensionality space, while divisive splits large clusters”, 0038-0039); using labels in the sense that centroids act as the labels (“An optimal clustering will be obtained as long as two initial centroids fall anywhere in a pair of clusters, since the centroids will redistribute themselves, one to each cluster. As the number of clusters increases, it is increasingly likely that at least one pair of clusters will have only one initial centroid”, 0035).
Chudova (US 2016/0019338) teaches using cluster labels (“Alternatively, clusters to be collapsed may be identified by visual inspection of the ordered distance matrix. Collapsing is performed by giving the labels of one cluster to the other, so that the tree structure is preserved. The choice of labels can be arbitrary (e.g.: the right-most cluster's) or based on similarity of the old medoids to the new neighboring cluster or some other criteria”, 0541).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and labeling training data amounts to nonfunctional descriptive material but using utilization data as training data allows for resource data to be modeled/classified. Splitting or merging clusters or reclustering allows for more efficient classification (see e.g., Zhang:0032, 0038). Labeling clusters using centroids or other labels allows for the classified training data to be clustered according to the labels. As vectors get classified into one cluster or another there needs to be a cluster/centroid label that identifies each cluster.
2, 15. The method of claim 1, wherein determining whether the cluster is complete or incomplete comprises comparing differences between the pairs of the utilization metrics included in the cluster to a median difference of the pairs of the utilization metrics included in the cluster (reads on using the clustering algorithms, centroids which are a mean, comparing data to clusters “The cluster computation (CC) module 116 is configured to receive similarity values for pairs of data records, and determine, based on the similarity values, whether to place one or more pairs of data records in the same cluster. In some embodiments, the CC module 116 can use a graph clustering technique to cluster data records based on pairwise similarity values. In some cases, the CC module 116 can use a different clustering parameter for each tentative cluster based on the types of data records tentatively included in that cluster. In some cases, the CC module 116 can receive a clustering directive, requiring the CC module 116 to associate two or more data records with the same cluster”, 0067).
3, 16. The method of claim 1, further comprising generating a completeness score for the selected cluster, wherein determining whether each cluster of the plurality of clusters is complete or incomplete is performed based, at least in part, on the completeness score(reads on using the clustering algorithms, centroids which are a mean, comparing data to clusters “The cluster computation (CC) module 116 is configured to receive similarity values for pairs of data records, and determine, based on the similarity values, whether to place one or more pairs of data records in the same cluster. In some embodiments, the CC module 116 can use a graph clustering technique to cluster data records based on pairwise similarity values. In some cases, the CC module 116 can use a different clustering parameter for each tentative cluster based on the types of data records tentatively included in that cluster. In some cases, the CC module 116 can receive a clustering directive, requiring the CC module 116 to associate two or more data records with the same cluster”, 0067; see also reaching convergence, Zhang: “Points may be iteratively assigned, and updated until convergence (no point changes clusters), or equivalently, until the centroids remain the same”, 0033).
4, 17. The method of claim 1, further comprising: determining whether each of the plurality of clusters have been assigned a cluster label (see Chudova above); and responsive to determining that each of the plurality of clusters have been assigned a cluster label, merging at least two of the plurality of clusters into a single cluster (reads on each cluster having a centroid/mean and merging or splitting as needed Zhang: “Agglomerative and divisive. Agglomerative clustering merges close clusters in an initially high dimensionality space, while divisive splits large clusters. Agglomerative clustering relies upon a cluster distance, as opposed to an object distance. For example, the distance between centroids or medioids of the clusters, the closest points in two clusters, the further points in two clusters, or some average distance metric. Ward's method measures the proximity between two clusters in terms of the increase in the sum of the squares of the errors that results from merging the two clusters.”, 0038).
5. The method of claim 4, wherein assigning a cluster label to the selected cluster generates cluster-labeled training data, the method further comprising training the machine learning classifier by performing supervised (0062, 0086, 0110, 0114, 0125) machine learning on the cluster-labeled training data (reads on inherent labels of clusters, else there would be no distinction between any cluster and using training data, 0024-0026).
See rejection of claim 1, Chudova (US 2016/0019338) teaches using cluster labels (“Alternatively, clusters to be collapsed may be identified by visual inspection of the ordered distance matrix. Collapsing is performed by giving the labels of one cluster to the other, so that the tree structure is preserved. The choice of labels can be arbitrary (e.g.: the right-most cluster's) or based on similarity of the old medoids to the new neighboring cluster or some other criteria”, 0541).
Claim Rejections - 35 USC § 103
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Shimanovsky (US 2014/0279757) in view of Galvin (US 2025/0363360) and Zhang (US 2018/0165554) combined with Starosta (US 2020/0111030).
10. The method of claim 1, wherein splitting the selected cluster into multiple clusters is performed using a plurality of computing devices operating in parallel.
Starosta teaches performing computer operations in parallel is well-known (“The device applies a machine learning-based predictor to the determined data size and entropy measure of the training records to be used for the split operation, to predict its completion time. The device coordinates the workers of the computing cluster to perform the node split operations in parallel such that the node split operations in a given batch are grouped based on their predicted completion times.”, abstract).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and parallel processing can be more efficient that using only one processor.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R VINCENT whose telephone number is (571)272-3080. The examiner can normally be reached ~Mon-Fri 12-8:30.
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123