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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e)
or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present
application is recognized as a continuation of parent Application No. 17/166,835 filed
on 2/03/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/30/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is/are being considered by the examiner.
Status of Claims
Claims 1-20 are currently pending in the present application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 8, 10, 15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bracholdt et al. (US PGPUB No. 2020/0012741; Pub. Date; Jan. 9, 2020) in view of ARONOVICH et al. (US PGPUB No. 2019/0310895; Pub. Date: Oct. 10, 2019).
Regarding independent claim 1,
Bracholdt discloses a method comprising: executing, by at least one processor, a clustering algorithm to generate one or more clusters of nodes within a set of nodes of a nodal network, each cluster having a subset of the set of nodes, wherein the set of nodes is parsed into a set of domain data tables and a set of dimension data tables, wherein the subset of the set of nodes in each cluster has at least one common attribute; See Paragraph [0119], (Disclosing a system for determining, evaluating and selecting data retrieval pathways for a plurality of database objects. The method includes retrieving a specification of database tables to be analyzed. A specification may comprise a name, reference or other table identifier that can be used to retrieve table data or metadata.) See Paragraph [0046], (In order to obtain data from multiple star schemas representing tabular arrangements of data, the method may use a dimension table common to both fact tables to bridge at least two dimension tables as long as at least one attribute is shared or conformed between the two star schemas. Note [0055] wherein tables may be represented as nodes in a graph, i.e. a clustering algorithm to generate one or more clusters of nodes within a set of nodes of a nodal network (e.g. tables may be represented/interpreted as nodes in a graph) , each cluster having a subset of the set of nodes (e.g. the process of bridging tables results in subsets of tables joined by shared attributes), wherein the set of nodes is parsed into a set of domain data tables and a set of dimension data tables, wherein the subset of the set of nodes in each cluster has at least one common attribute (e.g. the tables are bridged based on a common attribute );)
upon receiving a request from a user computing device, identifying, by the at least one processor, a cluster of nodes of the one or more clusters of nodes that correspond to at least one attribute of the request, See Paragraph [0098], (Path calculation engine 844 is configured to determine paths between database objects 814 of interest in response to a user request by traversing database objects 814 represented as a graph. Note [0048] wherein data may be obtained from data tables through the use of dimension tables having shared attributes, i.e. upon receiving a request from a user computing device, identifying, by the at least one processor, a cluster of nodes of the one or more clusters of nodes that correspond to at least one attribute of the request,)
Bracholdt does not disclose the step wherein identifying the cluster of nodes is based on the cluster of nodes comprising a defined percentage of nodes within the nodal network that contain one or more identifiers of at least one domain data table and at least one dimension data table that together contain a defined percentage of an entirety of data associated with the request represented by the nodal network, and wherein the defined percentage of nodes and the defined percentage of the entirety of the data associated with the request represented by the nodal network are defined by a user or a system administrator;
and presenting, by the at least one processor for display on a graphical user interface of the user computing device, an indication of data associated with nodes within the identified cluster of nodes.
ARONOVICH discloses the step wherein identifying the cluster of nodes is based on the cluster of nodes comprising a defined percentage of nodes within the nodal network that contain one or more identifiers of at least one domain data table and at least one dimension data table that together contain a defined percentage of an entirety of data associated with the request represented by the nodal network, See FIG. 7 & Paragraph [0077], (Disclosing a system for workload management by aggregating locality information for a set of files ina cluster of hosts. FIG. 7 illustrates the system identifying three hosts in a cluster based on a data locality proportions input 700 for a given set of files of a workload within the computing cluster, i.e. wherein identifying the cluster of nodes is based on the cluster of nodes comprising a defined percentage of nodes within the nodal network that contain one or more identifiers of at least one domain data table and at least one dimension data table that together contain a defined percentage of an entirety of data associated with the request represented by the nodal network (e.g. the grouping of cluster hosts identified in response to the data locality proportions input 700).)
and wherein the defined percentage of nodes and the defined percentage of the entirety of the data associated with the request represented by the nodal network are defined by a user or a system administrator; See Paragraph [0074], (Data requirements evaluator 506 may receive a first input 504 indicating data locality proportions for a set of files associated with a workload which specify the proportion of total data of the set of files stored on each of the hosts in the cluster, i.e. wherein the defined percentage of nodes and the defined percentage of the entirety of the data associated with the request represented by the nodal network are defined by a user or a system administrator (e.g. data locality proportions is described in plural and may correspond multiple inputs relating to data locality proportions).)
and presenting, by the at least one processor for display on a graphical user interface of the user computing device, an indication of data associated with nodes within the identified cluster of nodes. See Paragraph [0085], (The system receives data locality proportions and data access costs as an input and generates an ordered list of preferred hosts optimized for workloads intensive in I/O when utilizing existing data stored in storage system 20, i.e. presenting, by the at least one processor for display on a graphical user interface of the user computing device, an indication of data associated with nodes within the identified cluster of nodes (e.g. the list of preferred hosts is associated with a cluster computing device storing a subset of files to be processed by a workload).)
Bracholdt and ARONOVICH are analogous art because they are in the same field of endeavor, data retrieval. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Bracholdt to include the method of processing data according to data locality proportions as disclosed by ARONOVICH. Paragraph [0008] of ARONOVICH discloses that the method of aggregating locality information facilitates workload scheduling in a cluster by allowing the system to select only a subset of the totality of files for use in a scheduled workload.
Regarding dependent claim 3,
As discussed above with claim 1, Bracholdt-ARONOVICH discloses all of the limitations.
Bracholdt further discloses the step wherein the at least one processor executes the clustering algorithm on a subset of the nodes within the nodal network. See Paragraph [0046], (In order to obtain data from multiple star schemas representing tabular arrangements of data, the method may use a dimension table common to both fact tables to bridge at least two dimension tables as long as at least one attribute is shared or conformed between the two star schemas. Note [0055] wherein tables may be represented as nodes in a graph, i.e. wherein the at least one processor executes the clustering algorithm on a subset of the nodes within the nodal network (e.g. the bridging process is performed over tables. Tables may be represented as nodes of a graph).)
Regarding independent claim 8,
The claim is analogous to the subject matter of independent claim 1 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 10,
The claim is analogous to the subject matter of dependent claim 3 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding independent claim 15,
The claim is analogous to the subject matter of independent claim 1 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 17,
The claim is analogous to the subject matter of dependent claim 3 directed to a computer system and is rejected under similar rationale.
Claim(s) 2, 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bracholdt in view of ARONOVICH as applied to claim 1 above, and further in view of HAKIM (US PGPUB No. 2015/0302303; Pub. Date: Oct. 22, 2015).
Regarding dependent claim 2,
As discussed above with claim 1, Bracholdt-ARONOVICH discloses all of the limitations.
Bracholdt-ARONOVICH does not disclose the step of executing, by the at least one processor, an analytical protocol using the data associated with nodes within the identified cluster of nodes, the analytical protocol selected from a group consisting of profit analysis, efficiency analysis, operational leakage analysis, net profit margins, monthly recurring revenue analysis, sales analysis, cybersecurity analysis, growth analysis, product quality analysis, or service quality analysis.
HAKIM discloses the step of executing, by the at least one processor, an analytical protocol using the data associated with nodes within the identified cluster of nodes, the analytical protocol selected from a group consisting of profit analysis, efficiency analysis, operational leakage analysis, net profit margins, monthly recurring revenue analysis, sales analysis, cybersecurity analysis, growth analysis, product quality analysis, or service quality analysis. See FIG. 7A & Paragraph [0081], (Disclosing a system for generating suggestions integrated into business applications. Big Data Cluster E-1 includes several clusters that manage a big data repository and analysis of said data.) See Paragraph [0084], (Server Software may include fetch agents that take requests from an external request handler for processing data with regards to various work nodes specific to businesses, industries and/or any type of AI processing. An example is provided where sales analysis may be performed for pharmaceuticals, inventory management for service industries, etc., i.e. executing an analytical protocol consisting of: sales analysis.)
Paragraph [00260] of Applicant's Specification discloses that the analytical protocols refer to "one or more algorithms used by the analytics server to analyze the data".
The claim recites a listing of alternatives. The examiner notes that the step "the analytical protocol selected from a group consisting of profit analysis, efficiency analysis, operational leakage analysis, net profit margins, monthly recurring revenue analysis, sales analysis, cybersecurity analysis, growth analysis, product quality analysis, service quality analysis." is optional due to the use of the term "selected from a group", the claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art, see MPEP 2143.03.
Bracholdt, ARONOVICH and HAKIM are analogous art because they are in the same field of endeavor, data clustering. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Bracholdt-ARONOVICH to include the method of performing industry-specific analyses on clustered data as disclosed by HAKIM. Doing so would allow the system perform business and/or industry-specific tasks on a potentially large dataset managed via Big Data Clusters. The clustering of data facilitates performance of analysis. Paragraph [0136] of HAKIM discloses that the intelligent layer of the system adapts recommended business products for consumers which improves the user experience and/or reduce costs. These recommendations are determined based on data analysis.
Regarding independent claim 9,
The claim is analogous to the subject matter of dependent claim 2 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 16,
The claim is analogous to the subject matter of dependent claim 2 directed to a computer system and is rejected under similar rationale.
Claim(s) 4, 6-7, 11, 13-14, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bracholdt in view of ARONOVICH as applied to claim 3 above, and further in view of Smarda et al. (US PGPUB No. 2019/0303387; Pub. Date: Oct. 3, 2019).
Regarding dependent claim 4,
As discussed above with claim 3, Bracholdt-ARONOVICH discloses all of the limitations.
Bracholdt-ARONOVICH does not disclose the step wherein the subset of the nodes is selected based on one or more attributes received from the user computing device.
Smarda discloses the step wherein the subset of the nodes is selected based on one or more attributes received from the user computing device. See Paragraphs [0036]-[0038], (Disclosing a system for initializing centroids in large datasets before performing clustering operations. A setup initialization algorithm comprises receiving a k- parameter from a graphical user interface input.) See Paragraph [0091], (The second stage initialization method of the k-means++ process uses only a part of the input space whose size is derived from the algorithm's input parameters, i.e. wherein the subset of the nodes is selected based on one or more attributes received from the user computing device (e.g. the k-parameter is an attribute of the clustering operation).)
Bracholdt, ARONOVICH and Smarda are analogous art because they are in the same field of endeavor, data clustering. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Bracholdt-ARONOVICH to include the method of clustering data as disclosed by Smarda. Paragraph [0028] of Smarda discloses that the system may leverage multi-core machines to scale both processing power and total memory by adding more computing nodes 102 to a compute cluster 100 in order to facilitate computations.
Regarding dependent claim 6,
As discussed above with claim 1, Bracholdt-ARONOVICH discloses all of the limitations.
Bracholdt-ARONOVICH does not disclose the step wherein the clustering algorithm is a k-means clustering algorithm.
Smarda discloses the step wherein the clustering algorithm is a k-means clustering algorithm. See Paragraph [0026], (Disclosing a system for initializing centroids in large datasets before performing clustering operations. The disclosed method may be used to process high-dimensional datasets distributed among a plurality of nodes.) See FIG. 3 & Paragraph [0061], (FIG. 3 illustrates method 300 for executing a multi-node parallel k-means++ algorithm. Method 300 comprises step 316 wherein each thread of a plurality of CPU threads executes an iteration of an isolated k-means++ algorithm to produce a centroid of a cluster from its respective window, i.e. wherein the clustering algorithm is a k-means clustering algorithm (e.g. k-means++ is a variation of the k-means algorithm).)
Bracholdt, ARONOVICH and Smarda are analogous art because they are in the same field of endeavor, data clustering. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Bracholdt-ARONOVICH to include the method of clustering data as disclosed by Smarda. Paragraph [0028] of Smarda discloses that the system may leverage multi-core machines to scale both processing power and total memory by adding more computing nodes 102 to a compute cluster 100 in order to facilitate computations.
Regarding dependent claim 7,
As discussed above with claim 1, Bracholdt-ARONOVICH discloses all of the limitations.
Bracholdt-ARONOVICH does not disclose the step wherein the clustering algorithm is configured to cluster the set of nodes based on a multidimensional distance between attributes of one or more nodes.
Smarda discloses the step wherein the clustering algorithm is configured to cluster the set of nodes based on a multidimensional distance between attributes of one or more nodes. See Paragraph [0026], (Disclosing a system for initializing centroids in large datasets before performing clustering operations. The disclosed method may be used to process high-dimensional datasets distributed among a plurality of nodes.) See FIG. 3 & Paragraph [0061], (FIG. 3 illustrates method 300 for executing a multi-node parallel k-means++ algorithm. Method 300 comprises step 316 wherein each thread of a plurality of CPU threads executes an iteration of an isolated k-means++ algorithm to produce a centroid of a cluster from its respective window, i.e. wherein the clustering algorithm is configured to cluster the set of nodes based on a multidimensional distance between attributes of one or more nodes (e.g. Note [0077] wherein k-means++ requires that for all datapoints, the nearest centroid and distance from it are determined).)
Bracholdt, ARONOVICH and Smarda are analogous art because they are in the same field of endeavor, data clustering. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Bracholdt-ARONOVICH to include the method of clustering data as disclosed by Smarda. Paragraph [0028] of Smarda discloses that the system may leverage multi-core machines to scale both processing power and total memory by adding more computing nodes 102 to a compute cluster 100 in order to facilitate computations.
Regarding dependent claim 11,
The claim is analogous to the subject matter of dependent claim 4 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 13,
The claim is analogous to the subject matter of dependent claim 6 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 7 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 4 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 20,
The claim is analogous to the subject matter of dependent claim 6 directed to a computer system and is rejected under similar rationale.
Claim(s) 5, 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bracholdt in view of ARONOVICH as applied to claim 3 above, and further in view of Parandehgheibi et al. (US PGPUB No. 2017/0034018; Pub. Date: Feb. 2, 2017).
Regarding dependent claim 5,
As discussed above with claim 3, Bracholdt-ARONOVICH discloses all of the limitations.
Bracholdt-ARONOVICH does not disclose the step wherein the at least one processor selects the subset of the nodes based on a score associated with each node.
Parandehgheibi discloses the step wherein the at least one processor selects the subset of the nodes based on a score associated with each node. See Paragraph [0043], (Disclosing a system relating to generating a communication graph of a network using an ADM pipeline. Analytics module 124 can determine similarity scores for nodes and generate node clusters based on the similarity levels of a node. Presentation module 128 may display similarity scores on a user interface, i.e. wherein the at least one processor selects the subset of the nodes based on a score associated with each node.)
Bracholdt, ARONOVICH and Parandehgheibi are analogous art because they are in the same field of endeavor, data clustering. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Bracholdt-ARONOVICH to include the method of clustering data as disclosed by Parandehgheibi. Paragraph [0046] of Parandehgheibi discloses that the system may present data in a UI 130 that allows users to drill down on information sets to obtain a filtered data representation specific to the user's wishes. This represents an improvement in the user experience by providing a user interface that allows users to explore data at varying degrees of granularity.
Regarding dependent claim 12,
The claim is analogous to the subject matter of dependent claim 5 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Regarding dependent claim 19,
The claim is analogous to the subject matter of dependent claim 5 directed to a computer system and is rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FMMV/Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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