S 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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 19, 2026 has been entered.
Claims 1-20 are pending. Claims 1, 2, 8, 9, 15 and 16 are amended.
Response to Arguments
Referring to the 35 USC 101 rejections of claims 1, 3, 7, 8, 10, 14, 15 and 17, Applicant’s amendments to the claims are acknowledged, however are not persuasive.
Referring to the 35 USC 101 rejection of the pending claims, Applicant argues that the recitation of the iterative denoising steps are not mental steps because they cannot be performed in the human mind or using pen and paper. However, Examiner respectfully disagrees. Examiner submits that the hierarchical clustering, classifying of the subclusters into a pure and impure subcluster can be done in the human mind based on a user’s chosen criterion for what constitutes a pure or impure subcluster. Furthermore, the outputting of the pure subclusters as denoised subclusters can also be done in the human mind or using pen and paper. The denoising of the impure subcluster is also a mental step because a user can decide what is not needed or impure in the data within a subcluster and correct that data e.g. a format or some other error in the data. Finally the repetition of all these steps making up the iterative denoising process can be repeated in the human mind. As such, Examiner maintains that the iterative denoising limitations are mental processes and recite a judicial exception.
Applicant furthermore argues that the added limitation of invoking an active learning engine to determine whether to merge the impure subcluster with a corresponding subcluster is also not a mental step and is tied to a practical application of network capacity planning via active learning. However Examiner respectfully disagrees.
The use of an active learning engine to determine whether to merge the impure subcluster with a corresponding subcluster is merely using a generic software component to implement the mental step of determining whether to merge the impure subcluster with a corresponding subcluster. Furthermore, with respect to Applicant’s argument that the claims are tied to a practical application and improvement to known technical problems, Examiner submits that the active learning engine merely used as a tool to implement the abstract idea. There are no specific details recited in the claims that describe the specific steps that how the active learning is used to achieve the determination step and denoising of data.
As, such, the 101 rejections of the pending claims is maintained for at least the reasons stated above and further in view of the new grounds of rejection necessitated by the amendments to claims 3, 7, 10, 14, and 17.
Applicant’s arguments with respect to claims 1-20 are moot in view of the new grounds of rejection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: ‘input data preprocessor’, ‘clustering engine’, ‘cluster denoising engine’ and ‘network capacity planning mechanism’ in claim 15.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 3, 7, 8, 10, 14, 15 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 8 recite: collecting first information representing characteristics and activities of a plurality of network users and second information representing characteristics and performance associated with a plurality of network elements; clustering the plurality of network users to obtain initial user clusters based on the first information, and the plurality of network elements to obtain initial network element clusters based on the second information; with respect to each of the initial user clusters and the initial network element clusters, deriving, via an iterative denoising process, at least one denoised subcluster that has no impure subcluster therein, wherein the iterative denoising process comprises: hierarchically clustering the initial cluster to obtain one or more subclusters, classifying each of the one or more subclusters into one of a pure and an impure subcluster according to a first criterion, wherein a pure subcluster has no impure subcluster therein, outputting the pure subclusters as denoised subclusters, denoising, with respect to each impure subcluster, to derive denoised subclusters, by invoking an active learning engine to determine whether to merge the impure subcluster with a corresponding pure subcluster, and repeating the steps of classifying, outputting and denoising until a denoising criterion is satisfied to obtain the denoised subclusters; determining correlations between denoised user subclusters and denoised network element subclusters; and performing network capacity planning based on the correlations identified between the denoised user subclusters and the denoised network element subclusters.
Step 1: The claims as a whole fall within one or more statutory categories.
Step 2A prong 1: At least claims 1 and 8 recite limitations that are abstract ideas.
The limitations “clustering the plurality of network users to obtain initial user clusters based on the first information, and the plurality of network elements to obtain initial network element clusters based on the second information; with respect to each of the initial user clusters and the initial network element clusters”. One can mentally cluster a plurality of users and network elements based on selected criteria. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “deriving, via an iterative denoising process, at least one denoised subcluster that has no impure subcluster therein, wherein the iterative denoising process comprises: hierarchically clustering the initial cluster to obtain one or more subclusters” are mental steps. One can mentally derive a denoised subcluster that has no impure subcluster within by clustering an initial cluster into subclusters. Thus, the claimed limitations can be performed by the human mind.
Furthermore, the limitation “classifying each of the one or more subclusters into one of a pure and an impure subcluster according to a first criterion, wherein a pure subcluster has no impure subcluster therein” is also a mental step. A user can mentally determine which subcluster contains pure data and which one contains impure data. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “outputting the pure subclusters as denoised subclusters” is also a mental step. This limitation can be performed mentally on using pen and paper. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “denoising, with respect to each impure subcluster, to derive denoised subclusters, to determine whether to merge the impure subcluster with a corresponding pure subcluster” is also a mental step. A user can mentally denoise or remove data that is not desired to derive denoised subclusters. Furthermore the determining step as to whether to merge the impure subcluster with a corresponding pure subcluster is also a mental step. Thus, the claimed limitations can be performed by the human mind.
Furthermore, the limitation of ‘repeating the steps of classifying, outputting and denoising until a denoising criterion is satisfied to obtain the denoised subclusters’ is also a mental steps for the corresponding limitation addressed above.
Furthermore, the limitations “determining correlations between denoised user subclusters and denoised network element subclusters; and performing network capacity planning based on the correlations identified between the denoised user subclusters and the denoised network element subclusters” are also mental steps. A user can mentally determine how similar sets of data are to each other and mentally plan how to improve network capacity based on the determinations made. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2: Claims 1 and 8 recite the limitation “collecting first information representing characteristics and activities of a plurality of network users and second information representing characteristics and performance associated with a plurality of network elements”. This limitation is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, Claims 1 and 8 recite the following additional elements “a machine”, “storage medium” and “invoking an active learning engine”, note that these recited additional elements are a high-level recitation of generic computer components and software to perform the mental processes and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "collecting” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
Claim 15 recites: an input data preprocessor implemented by a processor and configured for collecting first information representing characteristics and activities of a plurality of network users and second information representing characteristics and performance associated with a plurality of network elements; a clustering engine implemented by a processor and configured for clustering the plurality of network users to obtain initial user clusters based on the first information, and the plurality of network elements to obtain initial network element clusters based on the second information; a cluster denoising engine implemented by a processor and configured for, with respect to each of the initial user clusters and the initial network element clusters, deriving, via an iterative denoising process, at least one denoised subcluster that has no impure subcluster therein, wherein the iterative denoising process comprises: hierarchically clustering the initial cluster to obtain one or more subclusters, classifying each of the one or more subclusters into one of a pure and an impure subcluster according to a first criterion, wherein a pure subcluster has no impure subcluster therein, outputting the pure subclusters as denoised subclusters, denoising, with respect to each impure subcluster, to derive denoised subclusters, by invoking an active learning engine to determine whether to merge the impure subcluster with a corresponding pure subcluster, and repeating the steps of classifying, outputting and denoising until a denoising criterion is satisfied to obtain the denoised subclusters; a network capacity planning mechanism implemented by a processor and configured for determining correlations between denoised user subclusters and denoised network element subclusters and performing network capacity planning based on the correlations identified between the denoised user subclusters and the denoised network element subclusters.
Step 1: The claim as a whole falls within one or more statutory categories.
Step 2A prong 1: At least claim 15 recites limitations that are abstract ideas.
The limitations “clustering the plurality of network users to obtain initial user clusters based on the first information, and the plurality of network elements to obtain initial network element clusters based on the second information; with respect to each of the initial user clusters and the initial network element clusters”. One can mentally cluster a plurality of users and network elements based on selected criteria. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “deriving, via an iterative denoising process, at least one denoised subcluster that has no impure subcluster therein, wherein the iterative denoising process comprises: hierarchically clustering the initial cluster to obtain one or more subclusters” are mental steps. One can mentally derive a denoised subcluster that has no impure subcluster within by clustering an initial cluster into subclusters. Thus, the claimed limitations can be performed by the human mind.
Furthermore, the limitation “classifying each of the one or more subclusters into one of a pure and an impure subcluster according to a first criterion, wherein a pure subcluster has no impure subcluster therein” is also a mental step. A user can mentally determine which subcluster contains pure data and which one contains impure data. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “outputting the pure subclusters as denoised subclusters” is also a mental step. This limitation can be performed mentally on using pen and paper. Thus, the claimed limitation can be performed by the human mind.
Furthermore, the limitation “denoising, with respect to each impure subcluster, to derive denoised subclusters, to determine whether to merge the impure subcluster with a corresponding pure subcluster” is also a mental step. A user can mentally denoise or remove data that is not desired to derive denoised subclusters. Furthermore the determining step as to whether to merge the impure subcluster with a corresponding pure subcluster is also a mental step. Thus, the claimed limitations can be performed by the human mind.
Furthermore, the limitation of ‘repeating the steps of classifying, outputting and denoising until a denoising criterion is satisfied to obtain the denoised subclusters’ is also a mental steps for the corresponding limitation addressed above.
Furthermore, the limitations “determining correlations between denoised user subclusters and denoised network element subclusters; and performing network capacity planning based on the correlations identified between the denoised user subclusters and the denoised network element subclusters” are also mental steps. A user can mentally determine how similar sets of data are to each other and mentally plan how to improve network capacity based on the determinations made. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2: Claim 15 recites the limitation “collecting first information representing characteristics and activities of a plurality of network users and second information representing characteristics and activities of a plurality of network users and
second information representing characteristics and performance associated with a plurality of network elements”. This limitation is an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, Claim 15 recites the following additional elements “a system”, “a processor”, “an input data preprocessor”, “a clustering engine”, “a cluster denoising engine”, “a network capacity planning mechanism: and “invoking an active learning engine”, note that these recited additional elements are a high-level recitation of generic computer components and software to perform the mental processes and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
With respect to the "collecting” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. 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); … 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);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claim as a whole does not change this conclusion and the claim is ineligible.
Claims 3, 10 and 17 depend from claims 1, 8 and 15 and thus include all the limitations of claims 1, 8 and 15, therefore claims 3, 10 and 17 recite the same abstract ideas of "mental processes".
Claims 3, 10 and 17 furthermore recite that: the classifying comprises: determining a metric specified in the first criterion; and with respect to each of the one or more subclusters, computing the metric based on the subcluster, if the metric satisfies the first criterion, labeling the subcluster as a pure subcluster, and if the metric does not satisfy the first criterion, labeling the subcluster as an impure subcluster, wherein the metric is defined based on a size of a subcluster corresponding to a number of data samples included in the subcluster.
Step 1: Claims 3, 10 and 17 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 3, 10 and 17 recite limitations that are abstract ideas.
The limitations “determining a metric specified in the first criterion; and with respect to each of the one or more subclusters, computing the metric based on the subcluster, if the metric satisfies the first criterion, labeling the subcluster as a pure subcluster, and if the metric does not satisfy the first criterion, labeling the subcluster as an impure subcluster, wherein the metric is defined based on a size of a subcluster corresponding to a number of data samples included in the subcluster” are mental steps.
One can mentally determine a metric specified within a given criterion. A user can also mentally compute the metric depending on the criterion. A user can determine if the metric does or does not satisfy the criterion and mentally label the subcluster as a pure or impure subcluster based on whether the criterion is satisfied. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2: Claims 3, 10 and 17 do not recite any additional elements that would integrate the judicial exception into a practical application.
Step 2B: Claims 3, 10 and 17 do not recite any additional elements that would provide significantly more than the judicial exception.
Therefore, claims 3, 10 and 17 as a whole are ineligible.
Claims 7 and 14 depend from claims 1 and 8 and thus include all the limitations of claims 1 and 8, therefore claims 7 and 14 recite the same abstract idea of "mental process".
Claims 7 and 14 furthermore recite that: the denoising criterion is defined based on one or more of: a hierarchical cluster purity metric including at least one of a hierarchical purity factor, and a hierarchical impurity factor; and an active learning related metric including at least one of an active learning purity factor and an active learning impurity factor.
Step 1: Claims 7 and 14 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 7 and 14 recite limitations that are abstract ideas because they further define the denoising criterion recited in claims 1 and 8 which was considered a mental step under the “repeating” steps.
As such. the limitations “the denoising criterion is defined based on one or more of: a hierarchical cluster purity metric including at least one of a hierarchical purity factor, and a hierarchical impurity factor; and an active learning related metric including at least one of an active learning purity factor and an active learning impurity factor” are also mental steps.
Step 2A prong 2: Claims 7 and 14 do not recite any additional elements that would integrate the judicial exception into a practical application.
Step 2B: Claims 7 and 14 do not recite any additional elements that would provide significantly more than the judicial exception.
Therefore, claims 7 and 14 as a whole are ineligible.
To expedite a complete examination of the instant application, the claims rejected under 35 U.S.C. 101 (nonstatutory) above are further rejected as set forth below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 7, 8, 10, 14, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 2017/0290024 by Ouyang et al (hereafter Ouyang), in view of US 2019/0354810 by Samel et al (hereafter Samel), and further in view of US Patent 11,258,805 issued to Nguyen et al (hereafter Nguyen).
Referring to claim 1, Ouyang discloses a method for network planning [Abstract], comprising:
collecting first information representing characteristics and activities of a plurality of network users and second information representing characteristics and performance associated with a plurality of network elements [usage data is collected, Fig 5, element 510, para 51; usage data 102 associated with cells 120 includes messages exchanged between user device cells, performance attributes of messages, para 17-18; attributes may include information associated with equipment within each of the cells and attributes such as bandwidth capacity, processing capacity etc. (reads on: performance associated with network elements), para 38];
clustering the plurality of network users to obtain initial user clusters based on the first information, and the plurality of network elements to obtain initial network element clusters based on the second information [usage data 102 is grouped into one or more clusters 201, para 39; clustering of cells, Fig 5, element 520, para 52-53];
with respect to each of the initial user clusters and the initial network element clusters, deriving, via an iterative denoising process, at least one denoised subcluster that has no impure subcluster therein [usage data is iteratively clustered and redundant features are removed that would not be useful for predicting a selected KPI for the clusters, para 53-55; see Fig 6 and corresponding portions of specification];
determining correlations between denoised user subclusters and denoised network element subclusters [KPIs are estimated in cells based on the clustering, Fig 5, element 530, para 71]; and
performing network capacity planning based on the correlations identified between the denoised user subclusters and the denoised network element subclusters [network resources are allocated based on the estimates KPIs, Fig5, element 540, para 74].
Referring to claim 1, while Ouyang discloses all of the above subject matter and also discloses iterative denoising clustering [para 53-55], it remains silent as to: hierarchically clustering the initial cluster to obtain one or more subclusters, classifying each of the one or more subclusters into one of a pure and an impure subcluster according to a first criterion, wherein a pure subcluster has no impure subcluster therein, outputting the pure subclusters as denoised subclusters, denoising, with respect to each impure subcluster, to derive denoised subclusters by invoking an active learning engine to determine whether to merge the impure subcluster with a corresponding pure subcluster, and repeating the steps of classifying, outputting and denoising until a denoising criterion is satisfied to obtain the denoised subclusters.
Samel discloses hierarchically clustering an initial cluster to obtain one or more subclusters [training data is clustered to generate groupings 214 of features 210 and labels 232, para 32; balanced iterative reducing and clustering using hierarchies (BIRCH), para 32], classifying each of the one or more subclusters into one of a pure and an impure subcluster according to a first criterion, wherein a pure subcluster has no impure subcluster therein [labels 232 can have noise and/or inconsistency, para 32; labels 232 may be noisy, inaccurate and/or missing, para 29], outputting the pure subclusters as denoised subclusters [dimensionality of internal representations of training data is reduced, para 33], denoising, with respect to each impure subcluster, to derive denoised subclusters, by invoking an active learning engine [denoising engine 204 improves quality of clustered labels 232, para 30; updated labels are generated for training data in each grouping, para 34; active learning framework 120 used to perform denoising of labels, para 2, 21-44, Fig 2], and repeating the steps of classifying, outputting and denoising until a denoising criterion is satisfied to obtain the denoised subclusters [iterative training of machine learning model using features and updated labels from a previous iteration until accuracy of machine learning model and/or consistency of groupings and/or labels 232 converges, para 36].
Ouyang and Samel are analogous art that are directed to the same field of endeavor- clustering of data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the iterative denoising clustering of Ouyang to include the hierarchical clustering and denoising of subclusters taught by Samel because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the iterative training of the machine learning model of Samel further refines the training of the data using the cell clustering module 320 of Ouyang.
Still referring to claim 1, while Ouyang/Samel discloses all of the above subject matter and also discloses denoising, with respect to each impure subcluster, to derive denoised subclusters, by invoking an active learning engine [denoising engine 204 improves quality of clustered labels 232, para 30; updated labels are generated for training data in each grouping, para 34; active learning framework 120 used to perform denoising of labels, para 2, 21-44, Fig 2], it remains silent as to: determining whether to merge the impure subcluster with a corresponding pure subcluster.
Nguyen discloses that determining that a first group 524 of events in a first cluster 520 includes dirty events 506 and a second group includes events 506 determined to be clean. Nguyen further discloses that the security subsystem can determine that the events in the second group 528 with clean data should be re-evaluated based on their membership in the first cluster 520 in which the majority of the events are dirty [col. 36, lines 3-10, Fig 5, element 532].
Ouyang, Samel and Nguyen are analogous art that are directed to the same field of endeavor- clustering of data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the denoising process of subclusters of Samel with the re-evaluation (i.e. merging) of clean and dirty event subclusters of Nguyen because it would achieve predictable results.
The ordinary skilled artisan would have been motivated to make this modification because the re-evaluation of whether the subclusters of data contain accurately classified event data in Nguyen allows for the reduction of false misses with respect to classification [Nguyen, col. 36, lines 3-10] and further enhances the denoising process of Samel.
Referring to claim 8, the limitations of the claim are similar to those of claim 1 in the form of a machine readable medium [Ouyang, memory 430, para 48]. As such, claim 8 is rejected for the same reasons as claim 1.
Referring to claim 15, the limitations of the claim are similar to those of claim 1 in the form of a system [Ouyang, computing device 400, para 48] and processor [Ouyang, processing unit 420, para 48]. Furthermore the input data preprocessor, a clustering engine, a cluster denoising engine, and network capacity planning mechanism [Ouyang, see software modules 310-340, Fig 3]. As such, claim 15 is rejected for the same reasons as claim 1.
Referring to claims 3, 10 and 17, Ouyang/Samel/Nguyen discloses that the classifying comprises: determining a metric specified in the first criterion; and with respect to each of the one or more subclusters, computing the metric based on the subcluster, if the metric satisfies the first criterion, labeling the subcluster as a pure subcluster, and if the metric does not satisfy the first criterion, labeling the subcluster as an impure subcluster, wherein the metric is defined based on a size of a subcluster corresponding to a number of data samples included in the subcluster [Samel, wherein a performance impact 226 is calculated based on the size of each grouping of training data with a larger grouping of training data (i.e. a grouping with more rows of training data) representing a larger impact on the performance of the machine learning model 208 than a smaller grouping of training data, para 41; the performance impact is used to target the generation of user-annotated labels 224 for groupings with the highest performance impact 226- Examiner submits that the highest performance impact groupings are the recited- impure subclusters because they are used as training data for the generation of user-annotated].
Referring to claims 7 and 14, Ouyang/Samel/Nguyen discloses that the denoising criterion is defined based on one or more of: a hierarchical cluster purity metric including at least one of a hierarchical purity factor [Samel, cluster purity evaluation measures, para 37], and a hierarchical impurity factor; and an active learning related metric including at least one of an active learning purity factor and an active learning impurity factor [Samel, proportions of mismatches between original labels 232 and updated labels 216 in the original groupings, para 36].
Novel/Non-Obvious/Allowable Subject Matter
Claims 2, 4-6, 9, 11-13, 16 and 18-20 were indicated in the Final Office action dated 12/2/2025 as containing novel and/or non-obvious subject matter and would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claims and to correct the noted 101 deficiencies.
Conclusion
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S//AMY NG/Supervisory Patent Examiner, Art Unit 2164