228DETAILED 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
This Application is a continuation of Application 17/959,201 filed 3 October 2022 and has provisional Application 63/251,206 filed 1 October 2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 16 January 2025 and 5 September 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because while claim 13 is directed towards a system, it is noted that the use of the word “system” does not inherently mean that the claim is directed towards a machine or article of manufacture. The system comprises a processor and a memory. According to paragraph [0037], lines 5-6 of the Specification, the processor can be virtual which is software per se. The memory as defined in paragraph [0039] has an open-ended definition and therefore is not explicitly limited to hardware. Therefore, the claim language fails to provide the necessary hardware required for the claim to fall within the statutory category of a system.
According to MPEP 2106.03:
As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories. Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719. Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category. Digitech, 758 F.3d at 1350, 111 USPQ2d at 1720 (claimed "device profile" comprising two sets of data did not meet any of the categories because it was neither a process nor a tangible product). Similarly, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category. Another example of an intangible product that does not fall within a statutory category is a paradigm or business model for a marketing company. In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1039-40 (Fed. Cir. 2009).
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Even when a product has a physical or tangible form, it may not fall within a statutory category. For instance, a transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter. Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03. As such, a transitory, propagating signal does not fall within any statutory category. Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294, 112 USPQ2d 1120, 1133 (Fed. Cir. 2017); Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03.
Since claims 14-20 are dependent on claim 13 and fail to overcome the deficiencies of claim 13, the claims are rejected on the same grounds as claim 13.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 9, 11, 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2017/0323028 to Jonker et al (hereafter Jonker) in view of the English translation of CN112925952A to Wang et al (hereafter Wang).
Referring to claim 1, Jonker discloses a computer-implemented method comprising:
receiving a user interaction in a series of user interactions in association with rendering a visualization of hierarchical data (see [0024]; [0028] – The client application facilitates users to pan and zoom (via user interactions delivered via the queries) through increasingly detailed views of the source data.);
determining a query based on the user interaction (see [0020]; [0022]; [0028]; [0051] - The system 100 communicates via queries 212 over a network 214 (e.g. an extranet such as the Internet), for example, with a backend system 208 (e.g. web based application), which stores the original data set 200 in a server storage 209 that is processed into a series of data tiles 14 organized in a tile hierarchy 16, as further described below. Tiles can be served by the backend system and rendered by the client application on demand (via client queries requesting tiles by coordinate, level and information layer) as images or structured data objects for rendering in an interactive (e.g. web-based) client. The client application facilitates users to pan and zoom (via user interactions delivered via the queries) through increasingly detailed views of the source data.);
merging the plurality of trees into an aggregated tree (see [0028]-[0030] – clustering, aggregating of nodes); and
rendering the visualization of hierarchical data based on the aggregated tree (see [0051], lines 1-5 – These views of the visualization representation 10 containing the tiles are served as dynamically rendered image tiles sent to the client application on-demand based on the user’s query requests sent to the back end system.).
While Jonker discloses producing and retrieving tiles from a backend system (see [0027]) and using a cluster-computing and parallelization frameworks, such as Spark or Hadoop (see [0028]), Jonker fails to explicitly disclose the specific limitations of distributing the query to a cluster of computing devices, wherein each computing device in the cluster locally processes the query against one or more data segments of a database comprising the hierarchical data to iteratively build a tree in parallel and responsive to distributing the query, receiving a plurality of trees from the cluster of computing devices. Wang teaches the execution of a query on database partitions, including the limitations of
determining a query (see page 9, lines 34-35; Fig 6 – The data query unit 604 may be configured to perform a query in the graph database for a plurality of data in the plurality of partitions through a connection of each of the plurality of partitions to the graph database.);
distributing the query to a cluster of computing devices, wherein each computing device in the cluster locally processes the query against one or more data segments [partition] of a database comprising the hierarchical data to iteratively build a tree [visual sub-graph] in parallel [concurrent operations/parallel query] (see page 5, line 12; page 6, lines 3-4; page 6, last paragraph – page 7, second paragraph);
responsive to distributing the query, receiving a plurality of trees from the cluster of computing devices (see page 7, lines 3-8 - HBase returns the resulting data set from the analysis to the corresponding partition after the analysis mining process in the graph database is complete, as indicated by data flows S3-4-1 through S3-4-5. The resulting data may be referred to as subgraph data or subgraph snapshot data.);
merging the plurality of trees into an aggregated tree (see page 7, lines 12-13 – Data flow S3-6 indicates that the sub-graph data in different memory partitions of the distributed compute engine are aggregated.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the process of distributing the query as taught by Wang with the cluster-computing and parallelization framework of Jonker. One would have been motivated to do so since the data processing efficiency is improved by executing a parallel query for a plurality of data partitions (Wang: see page 5, 3rd paragraph).
Referring to claim 2, the combination of Jonker and Wang (hereafter Jonker/Wang) teaches the computer-implemented method of claim 1, wherein distributing the query to the cluster of computing devices further comprises:
determining a first set of criteria in association with the query (Wang: see page 6, last paragraph – page 7, line 2 - hash);
identifying the one or more data segments of the database storing the hierarchical data that match the first set of criteria (Wang: see page 6, last paragraph – page 7, line 2);
identifying the cluster of computing devices serving the one or more data segments for processing the query (Wang: see page 6, last paragraph – page 7, line 2 – The query statement may be mapped to the corresponding region using the automatic partition query mechanism of HBase.); and
distributing the query to the cluster of computing devices (Wang: see page 7, lines 39-45).
Referring to claim 9, Jonker/Wang teaches the computer-implemented method of claim 1, wherein receiving the user interaction in association with rendering the visualization of hierarchical data includes receiving, in the visualization, a selection of a graphical element corresponding to one of a node and a catchall bucket (Jonker: see [0028]).
Referring to claim 11, Jonker/Wang teaches the computer-implemented method of claim 1, wherein the aggregated tree is a multi-rooted tree (Jonker: see Fig 8).
Referring to claim 13, Jonker discloses a system comprising:
one or more processors (see [0025]); and
a memory, the memory storing instructions (see [0025]), which when executed cause the one or more processors to:
receive a user interaction in a series of user interactions in association with rendering a visualization of hierarchical data (see [0024]; [0028] – The client application facilitates users to pan and zoom (via user interactions delivered via the queries) through increasingly detailed views of the source data.);
determine a query based on the user interaction (see [0020]; [0022]; [0028]; [0051] - The system 100 communicates via queries 212 over a network 214 (e.g. an extranet such as the Internet), for example, with a backend system 208 (e.g. web based application), which stores the original data set 200 in a server storage 209 that is processed into a series of data tiles 14 organized in a tile hierarchy 16, as further described below. Tiles can be served by the backend system and rendered by the client application on demand (via client queries requesting tiles by coordinate, level and information layer) as images or structured data objects for rendering in an interactive (e.g. web-based) client. The client application facilitates users to pan and zoom (via user interactions delivered via the queries) through increasingly detailed views of the source data.);
merge the plurality of trees into an aggregated tree (see [0028]-[0030] – clustering, aggregating of nodes); and
render the visualization of hierarchical data based on the aggregated tree (see [0051], lines 1-5 – These views of the visualization representation 10 containing the tiles are served as dynamically rendered image tiles sent to the client application on-demand based on the user’s query requests sent to the back end system.).
While Jonker discloses producing and retrieving tiles from a backend system (see [0027]) and using a cluster-computing and parallelization frameworks, such as Spark or Hadoop (see [0028]), Jonker fails to explicitly disclose the specific limitations of distribute the query to a cluster of computing devices, wherein each computing device in the cluster locally processes the query against one or more data segments of a database comprising the hierarchical data to iteratively build a tree in parallel and receive a plurality of trees from the cluster of computing devices responsive to distributing the query. Wang teaches the execution of a query on database partitions, including the limitations of
determine a query (see page 9, lines 34-35; Fig 6 – The data query unit 604 may be configured to perform a query in the graph database for a plurality of data in the plurality of partitions through a connection of each of the plurality of partitions to the graph database.);
distribute the query to a cluster of computing devices, wherein each computing device in the cluster locally processes the query against one or more data segments [partition] of a database comprising the hierarchical data to iteratively build a tree [visual sub-graph] in parallel [concurrent operations/parallel query] (see page 5, line 12; page 6, lines 3-4; page 6, last paragraph – page 7, second paragraph);
receive a plurality of trees from the cluster of computing devices responsive to distributing the query (see page 7, lines 3-8 - HBase returns the resulting data set from the analysis to the corresponding partition after the analysis mining process in the graph database is complete, as indicated by data flows S3-4-1 through S3-4-5. The resulting data may be referred to as subgraph data or subgraph snapshot data.);
merging the plurality of trees into an aggregated tree (see page 7, lines 12-13 – Data flow S3-6 indicates that the sub-graph data in different memory partitions of the distributed compute engine are aggregated.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the process of distributing the query as taught by Wang with the cluster-computing and parallelization framework of Jonker. One would have been motivated to do so since the data processing efficiency is improved by executing a parallel query for a plurality of data partitions (Wang: see page 5, 3rd paragraph).
Referring to claim 14, Jonker/Wang teaches the system of claim 13, wherein to distribute the query to the cluster of computing devices, the instructions further cause the one or more processors to:
determine a first set of criteria in association with the query (Wang: see page 6, last paragraph – page 7, line 2 - hash);
identify the one or more data segments of the database storing the hierarchical data that match the first set of criteria (Wang: see page 6, last paragraph – page 7, line 2);
identify the cluster of computing devices serving the one or more data segments for processing the query (Wang: see page 6, last paragraph – page 7, line 2 – The query statement may be mapped to the corresponding region using the automatic partition query mechanism of HBase.); and
distribute the query to the cluster of computing devices (Wang: see page 7, lines 39-45).
Claim(s) 3, 4, 10, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2017/0323028 to Jonker et al (hereafter Jonker) in view of the English translation of CN112925952A to Wang et al (hereafter Wang) as applied to claims 2 and 14 above, and further in view of US PGPub 2021/0005164 to Hayashi et al (hereafter Hayashi).
Referring to claims 3 and 15, while Jonker/Wang teaches determining a first criteria associated with a query, Jonker/Wang fails to explicitly teach the limitations associated with determining a second set of criteria associated with a query. Hayashi teaches wherein distributing the query to the cluster of computing devices further comprises: determining a first set of criteria in association with the query (Hayashi: see [0102] – determining the query search aggregate node group criterion); identifying one or more data segments in association with a database storing the hierarchical data that match the first set of criteria (Hayashi: see [0049]; [0102]; [0103] – identify component groups [data segments] in a database storing the hierarchical data selected according to the first criteria); identifying the cluster of computing devices serving the one or more data segments for processing the query (Hayashi: see [0064] and [0071] – identify the cluster providing the segments); and distributing the query to the identified cluster of computing devices (Hayashi: see [0079]; [0081]; and [0111] – provide the query to the identified cluster), including the further limitations of
wherein each computing device in the cluster locally processes the query by: determining a second set of criteria in association with the query (Hayashi: see [0086] and [0102] – the cluster processing the query by selecting a user instruction or prescribed size (second set of criteria) associated with searching aggregate node groups associated with the query); scanning data from the one or more data segments that match the second set of criteria (Hayashi: see [0049]; [0096]; [0102]; [0103] – identifying and obtaining component groups (data segments) selected according to the criteria); and adding a path to the tree based on the scanned data, the path having a level of depth and hierarchically linking a parent node to a child node in the tree (Hayashi: aggregating couplings (adding) and links (path) to the tree based on the scanned data having a tier above and below linking (level of depth and hierarchically linking) related nodes in the tree).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have the process of Jonker/Wang link a parent node to a child node using a second set of criteria as taught by Hayashi. One would have been motivated to do so since Jonker/Wang teaches the detection of communities (e.g., clustering, aggregating nodes) and this is merely a specific process for generating the communities (Jonker: see [0028]-[0030]).
Referring to claims 4 and 16, the combination of Jonker/Wang and Hayashi (hereafter Jonker/Wang/Hayashi) teaches the computer-implemented method of claim 3 and the system of claim 15, further comprising: identifying, from the second set of criteria, a first configuration parameter specifying a prune limit and a second configuration parameter specifying a prune target in association with pruning the tree (Hayashi: see [0086] and [0102] – the second set of criteria identifies a prescribed size (first configuration parameter specifying a limit) and a user selection criterion instruction target (second configuration parameter specifying a target) associated with the aggregated nodes); responsive to adding the path to the tree based on the scanned data, determining whether a current number of nodes in the tree satisfy the first configuration parameter specifying the prune limit (Hayashi: see [0084]; [0086]; [0102]; [0103] – based on aggregating couplings (adding) and links (path) to the tree based on the scanned data select a set of nodes (current number of nodes) configured by the limit); and responsive to determining that the current number of nodes in the tree satisfy the first configuration parameter specifying the prune limit, pruning down the current number of nodes in the tree to satisfy the second configuration parameter specifying the prune target (Hayashi: see [0084]; [0098]; [0102]; [0103] – aggregating and searching based on the number of nodes selected according to the first parameter limit and the second parameter target).
Referring to claim 10, Jonker/Wang fails to explicitly teach the further limitations of wherein rendering the visualization of hierarchical data based on the aggregated tree further comprises: automatically selecting a threshold number of nodes without user interaction; determining a query in association with automatically selecting each node in the threshold number of nodes; and distributing the query in parallel to the cluster of computing devices for processing.
Hayashi teaches wherein rendering the visualization of hierarchical data based on the aggregated tree further comprises: automatically selecting a threshold number of nodes without user interaction (Hayashi: see [0084]; [0086]; [0102] and [0110] – rendering the hierarchical data based on the aggregated tree using a computer software prescribed/criterion number (threshold number) of target tier nodes selected in advance of user selection); determining a query in association with automatically selecting each node in the threshold number of nodes (Hayashi: see [0084]; [0086]; [0102]; [0110] – determine a search associated with a prescribed selection of nodes in the threshold number); and distributing the query in parallel to the cluster of computing devices for processing (Hayashi: see [0079]; [0081]; [0111] – provide the query to the identified cluster; Roquet: see [0209]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to determine the query of Jonker/Wang in the manner taught by Hayashi. One would have been motivated to do so in order to automatically generate a visualization without use interaction (Jonker: see [0028]-[0030]).
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2017/0323028 to Jonker et al (hereafter Jonker) in view of the English translation of CN112925952A to Wang et al (hereafter Wang) in view of US PGPub 2021/0005164 to Hayashi et al (hereafter Hayashi) as applied to claims 4 and 16 above and further in view of US PGPub 2015/0363465 to Bordawekar et al (hereafter Bordawekar) in view of US PGPub 2012/0158933 to Shetty et al (hereafter Shetty).
Referring to claims 5 and 17, while Jonker/Wang/Hayashi teaches wherein pruning down the current number of nodes in the tree to satisfy the second configuration parameter specifying the prune target comprises: sorting the current number of nodes by their corresponding weight and level of depth in the tree (Hayashi: see [0079]; [0099]-[0100] – grouping and organizing the current number of nodes by their associated tier in the tree); identifying a node that is lowest and deepest in level of depth in the tree (Hayashi: see [0098]-[0100] – determine nodes relatively lower (lowest) and ordered in the lower (deepest) tier of the tree); pruning the child node from the tree by rolling a weight of the child node into a catchall bucket of its corresponding parent node (Hayashi: see [0079], [0081]); and repeating the sorting, the identifying, and the pruning until the current number of nodes in the tree satisfy the second configuration parameter specifying the prune target (Hayashi: see [0084], [0086], [0098]).
Jonker/Wang/Hayashi fails to disclose a weight, rolling a weight and a catchall bucket. Bordawekar discloses weight (see [0042], [0044], [0058]) and rolling a weight (see [0042], [0044], [0058]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date to modify Hitachi/Roquet to include weight and a rolling weight as taught by Bordawekar for the benefit of providing enhanced processing.
The combination of Jonker/Wang/Hayashi and Bordawekar fails to disclose a catchall bucket. Shetty discloses the catchall bucket (see [0058]; [0076]; [0078]). It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to modify the Hayashi/Roquet/Bordawekar to include the catchall bucket as taught by Shetty for the benefit of providing a more efficient network.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2017/0323028 to Jonker et al (hereafter Jonker) in view of the English translation of CN112925952A to Wang et al (hereafter Wang) as applied to claims 1 and 13 above and further in view of US PGPub 2021/0005164 to Hayashi et al (hereafter Hayashi) in view of US PGPub 2015/0363465 to Bordawekar et al (hereafter Bordawekar).
Referring to claims 6 and 18, Jonker/Wang teaches wherein merging the plurality of trees into the aggregated tree further comprises: receiving, from a first computing device of the cluster of computing devices, a first tree (see page 7, lines 3-8 - HBase returns the resulting data set from the analysis to the corresponding partition after the analysis mining process in the graph database is complete, as indicated by data flows S3-4-1 through S3-4-5. The resulting data may be referred to as subgraph data or subgraph snapshot data.) and receiving, from a second computing device of the cluster of computing devices, a second tree (see page 7, lines 3-8 - HBase returns the resulting data set from the analysis to the corresponding partition after the analysis mining process in the graph database is complete, as indicated by data flows S3-4-1 through S3-4-5. The resulting data may be referred to as subgraph data or subgraph snapshot data.). Jonker/Wang fails to explicitly teach the further limitations of wherein merging the plurality of trees into the aggregated tree further comprises: receiving, from a first computing device of the cluster of computing devices, a first tree; receiving, from a second computing device of the cluster of computing devices, a second tree; performing a traversal of the first tree and the second tree starting from a corresponding root node; responsive to performing the traversal of the first tree and the second tree from the corresponding root node, determining whether there are corresponding nodes in the first tree and the second tree having a same name; and responsive to determining that there are corresponding nodes in the first tree and the second tree having the same name, merging the corresponding nodes in the aggregated tree. Hayashi teaches
wherein merging the plurality of trees into the aggregated tree further comprises: receiving, from a first computing device of the cluster of computing devices, a first tree (Hayashi: see [0079]-[0081] – aggregating graph nodes received from a server of the cluster including node groups (first tree)); receiving, from a second computing device of the cluster of computing devices, a second tree (Hayashi: see [0079]-[0081]; [0086]; [0088] – aggregating other graph node groups (second tree) received from another server of the cluster); performing a traversal of the first tree and the second tree starting from a corresponding root node (Hayashi: see [0080]-[0081] – performing a search and check (traversal) of the first and second tree starting from indicated reference (corresponding) server cluster nodes (root node)); responsive to performing the traversal of the first tree and the second tree from the corresponding root node, determining whether there are corresponding nodes in the first tree and the second tree having a same name (Hayashi: see Figs 4, 6, 14; [0075]; [0083] – based on the traversal determine a corresponding overlapping node 1101 from the cluster devices having an overlapping relation and a corresponding node ID 202, 401, 402 (same name)); and responsive to determining that there are corresponding nodes in the first tree and the second tree having the same name, merging the corresponding nodes in the aggregated tree (Hayashi: see [0083]; [0096] – aggregate the overlapping nodes having the same name into the aggregate node groups).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have the aggregation of nodes of Jonker/Wang be based on the same name as taught by Hayashi. One would have been motivated to so to quickly detect communities of nodes (see [0029]).
While Jonker/Wang/Hayashi teaches merging the corresponding nodes in the aggregated tree, Jonker/Wang/Hayashi fails to explicitly teach adding up their corresponding weights. Bordawekar teaches adding up their corresponding weights (see [0042]; [0044]; [0058] – summing weights assigned to children and associated edges traversed to support aggregation).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the invention to have Jonker/Wang/Hayashi add the weights when merging the nodes as taught by Bordawekar. One would have been motivated to do so to have a more efficient way in which to aggregate nodes (Bordawekar: see [0044] and [0058]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2017/0323028 to Jonker et al (hereafter Jonker) in view of the English translation of CN112925952A to Wang et al (hereafter Wang) as applied to claim 1 above and further in view of US PGPub 2018/0129401 to Kim et al (hereafter Kim).
Referring to claim 12, while Jonker/Wang teaches visualization, Jonker/Wang fails to explicitly teach wherein the visualization is one from a group of a flame graph, a flame chart, an icicle chart, and a sunburst layout. Kim teaches visualization including the further claim wherein the visualization is one from a group of a flame graph, a flame chart, an icicle chart, and a sunburst layout (see [0060]; [0063]; and [0066] – hierarchical tree visualization using an icicle chart to provide a user specified representation).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the icicle chart of Kim to visualize the data of Hayashi/Roquet. One would have been motivated to do so since an icicle chart is merely a spec type of chart and tile-based visual analytics can support many different types of visualizations (Jonker: see [0028], lines 35-38).
Allowable Subject Matter
Claims 7, 8, 19 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Hayashi teaches the computer-implemented method of claim 6, further comprising: responsive to determining that there is an absence of corresponding nodes in the first tree and the second tree having the same name, determining whether there is a child node present in the first tree that is absent from a corresponding parent node in the second tree (Hayashi: see [0092]); responsive to determining that there is the node present in the first tree that is absent from the corresponding node in the second tree, determining the corresponding node in the second tree (Hayashi: see [0083] and [0092]); and responsive to determining that the corresponding node in the second tree, merging the corresponding parent node and rolling a node from the first tree into a group in the aggregated tree (Hayashi: see [0079] and [0095]).
The prior art of record fails to explicitly teach the limitations of responsive to determining that there is an absence of corresponding nodes in the first tree and the second tree having the same name, determining whether there is a child node present in the first tree that is absent from a corresponding parent node in the second tree; responsive to determining that there is the child node present in the first tree that is absent from the corresponding parent node in the second tree, determining whether a catchall bucket of the corresponding parent node in the second tree is non-empty; and responsive to determining that the catchall bucket of the corresponding parent node in the second tree is non-empty, merging the corresponding parent node and rolling a weight of the child node from the first tree into the catchall bucket of the corresponding parent node in the aggregated tree. These limitations are found in dependent claims 7 and 19. Dependent claims 8 and 20 are dependent on claims 7 and 19 and therefore incorporate the allowable subject matter.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent No 11,675,766 to Chen et al teaches scalable hierarchical clustering
US PGPub 2016/0359872 to Yadav et al teaches the monitoring of data centers
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY LOVEL WILSON whose telephone number is (571)272-2750. The examiner can normally be reached 8-4:30.
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/KIMBERLY L WILSON/Primary Examiner, Art Unit 2167