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 .
Response to Amendments
2. The action is responsive to the Applicant’s Amendment filed on 1/21/2026. Claims 1-20 are pending in the application. Claims 1, 3-5, 8, 11, 13-15, and 18 are amended.
Response to Arguments
3. Applicant’s arguments with respect to the rejections of claims 1-20 have been fully considered. In view of the claim amendment filed, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Further, regarding the new limitations recited in claims 1, 3-5, 8, 11, 13-15, and 18, it is submitted that they are properly addressed by the new ground of rejection.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
Claim Rejections - 35 USC § 103
4. 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 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.
5. 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rossi (US 20200177466 A1) in view of Romero et al. (US 20250117256 A1), Murai et al. (“Selective Harvesting over Networks”, arXiv, published March 2017), and Srikanth et al. (US 20120198073 A1, published January 2012).
6. Regarding Claim 1, Rossi discloses a method ([0016]: Implementations of higher-order network embedding are described, and provide a system for learning higher-order network embeddings of entities represented as nodes in a graph based on subgraph patterns, which are also referred to as network motifs or graphlets) comprising:
decomposing a query graph into a set of graphlets (Fig. 4; [0088]-[0089]: At 402, interconnected data is received in the form of a graph that represents a network with network motifs representing subgraphs of the network… and the motifs, graphlets, or induced subgraphs that occur on every edge in the graph are counted),
wherein the query graph corresponds to tasks of a workload (Figs. 1-2; [0071]: A user of the computing device 202 may upload graph data 210 to the network system 204, where the graph data 210 may be the interconnected data 112, the graph 114, or any other type of graph data that represents a complex or dynamic network; [0082]: the higher-order network embeddings 130 can be used to model the roles of individual devices associated with users, as well as to determine the tasks performed by the devices);
building motifs based on the set of graphlets obtained from the query graph (Fig. 4; [Abstract]: The computing device includes a network embedding module that is implemented to… derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph; [0090]: At 408, motif-based matrices are derived from the frequency of each of the k-vertex motifs in the graph), by extracting semantic patterns from the set of graphlets (Figs. 4-5; [0004]: where the network motifs are recurrent and statistically significant subgraphs or patterns in the graph [i.e., subgraphs with semantic meaning]) and
building new graphs, as the motifs, that are clustered and filtered in terms of the patterns of the semantic patterns ([0016]: The computing device includes a network embedding module that is implemented to… derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph; Figs. 4-5; [0090]-[0098]: the motif-based matrices 128 that are derived from the frequency of the k-vertex motifs 126 are weighted motif graphs… where the joint higher-order network embedding represents subgraph patterns of the subgraphs of the network).
However, Rossi does not explicitly teach “wherein each node of the query graph includes one or more respective resource requirements of a respective task of the workload and the semantic patterns are built based on resource requirements of the tasks; performing, in parallel for each motif, a selective harvesting search in a target graph of a computing network using the motif to match nodes of the query graph to nodes of the target graph based on respective resource requirements of the motif, wherein each node of the target graph corresponds to a node in the computing network, and the selective harvesting search classifies nodes of the target graph based on whether they satisfy the semantic patterns of the motif; and placing the tasks of the workload at the matched nodes of the target graph for execution of the tasks by the computing network, and updating attributes of the target nodes based on the placed tasks.”
On the other hand, in the same field of endeavor, teaches Romero
wherein each node of the query graph includes one or more respective resource requirements of a respective task of the workload ([Abstract]: applying a selective harvesting (SH) algorithm to the infrastructure graph to identify the nodes that are able to meet the requirements of a workload; Fig. 6; [0068]: For example, Node A and Node B each carry information regarding: (i) computational resources such as CPU and GPU quantity and/or usage) and the semantic patterns are built based on resource requirements of the tasks ([0068]: The example of FIG. 6 thus depicts how, in an embodiment, the semantics in a heterogenous network can be useful during the search process of a workload placement scenario).
Additionally, Murai teaches performing, in parallel for each motif, a selective harvesting search in a target graph ([Abstract]: Active search on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights… In this work we introduce selective harvesting, a variant of active search) using the motifs to match nodes of the query graph to nodes of the target graph ([Abstract]: the available training data for deciding which node to query is restricted to the subgraph induced by the queried set; [Pages 2-3, 6]: any node can be queried at any time… the use of multiple classifiers – helps improve accuracy… The algorithm’s output is the list of target nodes found in T steps).
wherein each node of the target graph corresponds to a node in the computing network ([Page 4]: In this section we formalize the selective harvesting problem… Let G = (V, E ) denote an undirected graph representing the network topology; Fig. 2; [Page 2]: i.e., nodes with a certain label – in a network; [Page 4]: Each node v ∈ V has M attributes (domain-related properties of the nodes).
Furthermore, Srikanth teaches and the selective harvesting search classifies nodes of the target graph based on whether they satisfy the semantic patterns of the motif ([Abstract]: A curating method is performed to enable semantic search including searching for cloud computing resources; Fig. 2; [0019]: Resource cataloging subsystem 210 stores away classified computing resources 208a-208c; Fig. 3R; [0060]: At block 3202, the method uses a graph-matching process which in turn uses a feature-matching process (attribute matching) to select a set of computing resources for each node in a graph); and
placing tasks of the workload at the matched nodes of the target graph for execution of the tasks by the computing network (Fig. 3R; [0060]-[0063]: the method 3000 proceeds to block 3200 where the method models a task or a workload as a graph of computing resources that depicts the order of resource provisioning and dependencies… Separately or combined presentation of such information enables consumers to visualize the provisioning and execution of their workloads and tasks), and
updating attributes of the target nodes based on the placed tasks ([0005]: The method further dynamically updates the catalog and the knowledge base to refresh pieces of information pertaining to the different computing resource providers… their attributes, and the taxonomy of their values; Fig. 3T; [0062]: At block 3226, the method also prepares to present attributes of matched computing resources in addition to the presentation of the ranked list).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rossi to incorporate the teachings of Romero, Murai, and Srikanth to perform a selective harvesting search in a target graph, using the motifs to match nodes of the query graph to nodes of the target graph, and place tasks of the workload at the matched nodes of the target graph.
The motivation for doing so would be to identify the nodes that are able to apply a workload placement optimization algorithm to the nodes of a subgraph, as recognized by Romero ([Abstract]: applying a workload placement optimization algorithm to the nodes of the subgraph), to collect a larger set of targets, as recognized by Murai ([Abstract]: Therefore, selective harvesting is a sequential decision problem… We demonstrate that it is possible to collect a much larger set of targets by using multiple classifiers), and to organize computing resources, as recognized by Srikanth ([Abstract] of Srikanth: Pieces of hardware on which pieces of software are executed are configured to organize computing resources from different computing resource providers so as to facilitate their discovery).
Regarding Claim 2, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 1.
Srikanth further teaches wherein the query graph comprises a workload graph ([0059]-[0060]: (FIG. 3Q), the method 3000 proceeds to decision block 3198 where the method performs a test to determine whether the query is specifying a task or workload… From Terminal E6 (FIG. 3R), the method 3000 proceeds to block 3200 where the method models a task or a workload as a graph of computing resources that depicts the order of resource provisioning and dependencies).
Regarding Claim 3, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 1.
Rossi further teaches wherein at least some of the graphlets in the set of graphlets share the patterns, wherein the motifs are built based on the semantic patterns (Fig. 4; [0016]: Implementations of higher-order network embedding are described, and provide a system for learning higher-order network embeddings of entities represented as nodes in a graph based on subgraph patterns, which are also referred to as network motifs or graphlets; Figs. 4-5; [0004]: where the network motifs are recurrent and statistically significant subgraphs or patterns in the graph [i.e., subgraphs with semantic meaning])).
Regarding Claim 4, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 3.
Srikanth further teaches further comprising clustering and filtering the graphlets to identify the semantic patterns (FIG. 3R; [Abstract]: A curating method is performed to enable semantic search including searching for cloud computing resources that in combination cooperate to satisfy a workload or a task in addition to having a simple computational function. Semantic indexing is performed to facilitate the semantic search. [0060]: Progressing to block 3204, the method uses graph dependency to filter computing resources through logical Boolean operators. At block 3206, the filtering results in different sets of matched resource collections, which are ranked by the method using the graph-matching process).
Regarding Claim 5, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 4.
Rossi further teaches wherein the semantic patterns include one or more of network latency, task dependency relationship, frequency of execution, and/or computing requirements ([Abstract]: The computing device includes a network embedding module that is implemented to determine a frequency of k-vertex motifs for each of the edges in the graph, and derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph; [0004]: The graph can be representative of a complex or dynamic network, such as a social network, tech-based network, web-based network, or any other type of network data graph that can include a variety of subgraphs, where the network motifs are recurrent and statistically significant subgraphs or patterns in the graph; [0056]: This effectively allows the overall model to learn a combination of latent features using the local motif-based embeddings from different network motifs and from different steps (motif graph scales)).
Regarding Claim 6, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 1.
Murai further teaches further comprising performing the selective harvesting search in the target graph (FIG. 3R; [Page 2]: In this paper we introduce selective harvesting, where the goal is the same as in active search, but instead of assuming that the network topology is given, our node querying is subject to a partial and evolving understanding of the network; [Page 5]: Figure 2 illustrates a snapshot of the search process (see caption for details); [Page 8]: To demonstrate the tunnel vision effect and show how classifier diversity can mitigate it, we conduct a large set of simulations).
Regarding Claim 7, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 6.
Murai further teaches further comprising performing the selective harvesting search for all of the motifs in parallel ([Page 2]: In this paper we introduce selective harvesting, where the goal is the same as in active search, but instead of assuming that the network topology is given, our node querying is subject to a partial and evolving understanding of the network; [Page 5]: Figure 2 illustrates a snapshot of the search process (see caption for details); [Page 8]: To demonstrate the tunnel vision effect and show how classifier diversity can mitigate it, we conduct a large set of simulations).
Regarding Claim 8, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 6.
Srikanth further teaches further comprising matching the motifs to the target graph, wherein nodes in the computing network matching the motifs are capable of coping with requirements of tasks of the workload set forth in the motifs (Fig. 3R; [0060]: At block 3208, the method further weights each node, match, and graph edge (dependency) match using various factors. At block 3210, the method additionally weights each set of matched resource collection using global factors (such as resources within the same geographic location and so on)), and
wherein the matching is based on the classification of nodes of the target graph that satisfy the semantic patterns of the motif (Fig. 2; (Figs. 4-5; [0004]: where the network motifs are recurrent and statistically significant subgraphs or patterns in the graph [i.e., subgraphs with semantic meaning]; [0018]- [0019]: different classifications of the catalog facilitate the discovery of new resource types and matching of user computing resource requirements to computational offers… resource matching and ranking subsystem 222 searches a catalog maintained by the resource cataloging subsystem 210 to discover computing resources 208a-208c that satisfy the on-demand computing queries 202; [0031]: In the simple case, all of the mandatory attributes--as identified by (a.sub.1, a.sub.2, . . . , a.sub.m}--have to be satisfied by the resources; [0048] From Terminal E (FIG. 3M), the method proceeds to block 3136 where the method receives a query describing the computing resources desired and whose attributes would, if found, satisfied computation needs).
Regarding Claim 9, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 1.
Srikanth further teaches further comprising performing the tasks of one or more workloads at nodes in the computing network ([0014]: Various embodiments of the present subject matter provide… hardware/software mechanisms to dynamically select, allocate, and use resources across different providers to perform a workload or task; [0055]: The resources matched for a given query can come from multiple providers and from multiple regions. Computing resources are used to perform certain workloads or tasks).
Regarding Claim 10, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the method of claim 1.
Srikanth further teaches further comprising updating the matching of the motifs by updating an availability of resources in the target graph ([0005]: The method further dynamically updates the catalog and the knowledge base to refresh pieces of information pertaining to the different computing resource providers, semantic descriptors of computing resources, types, their attributes, and the taxonomy of their values so as to inventory computing resources that are available on-demand over the Internet).
Regarding Claim 11, Rossi discloses a non-transitory storage medium having stored therein instructions (Fig. 1; [0032]: For example, the computing device 102 in this example includes memory 106 and a processor 108, as well as any type of data storage 110 that may be implemented as any suitable memory, memory device, or electronic data storage) that are executable by one or more hardware processors to perform operations comprising:
decomposing a query graph into a set of graphlets (Fig. 4; [0088]-[0089]: At 402, interconnected data is received in the form of a graph that represents a network with network motifs representing subgraphs of the network… and the motifs, graphlets, or induced subgraphs that occur on every edge in the graph are counted),
wherein the query graph corresponds to tasks of a workload (Figs. 1-2; [0071]: A user of the computing device 202 may upload graph data 210 to the network system 204, where the graph data 210 may be the interconnected data 112, the graph 114, or any other type of graph data that represents a complex or dynamic network; [0082]: the higher-order network embeddings 130 can be used to model the roles of individual devices associated with users, as well as to determine the tasks performed by the devices);
building motifs based on the set of graphlets obtained from the query graph (Fig. 4; [Abstract]: The computing device includes a network embedding module that is implemented to… derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph; [0090]: At 408, motif-based matrices are derived from the frequency of each of the k-vertex motifs in the graph), by extracting semantic patterns from the set of graphlets (Figs. 4-5; [0004]: where the network motifs are recurrent and statistically significant subgraphs or patterns in the graph [i.e., subgraphs with semantic meaning]) and
building new graphs, as the motifs, that are clustered and filtered in terms of the patterns of the semantic patterns ([0016]: The computing device includes a network embedding module that is implemented to… derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph; Figs. 4-5; [0090]-[0098]: the motif-based matrices 128 that are derived from the frequency of the k-vertex motifs 126 are weighted motif graphs… where the joint higher-order network embedding represents subgraph patterns of the subgraphs of the network).
However, Rossi does not explicitly teach “wherein each node of the query graph includes one or more respective resource requirements of a respective task of the workload and the semantic patterns are built based on resource requirements of the tasks; performing, in parallel for each motif, a selective harvesting search in a target graph of a computing network using the motif to match nodes of the query graph to nodes of the target graph based on respective resource requirements of the motif, wherein each node of the target graph corresponds to a node in the computing network, and the selective harvesting search classifies nodes of the target graph based on whether they satisfy the semantic patterns of the motif; and placing the tasks of the workload at the matched nodes of the target graph for execution of the tasks by the computing network, and updating attributes of the target nodes based on the placed tasks.”
On the other hand, in the same field of endeavor, teaches Romero
wherein each node of the query graph includes one or more respective resource requirements of a respective task of the workload ([Abstract]: applying a selective harvesting (SH) algorithm to the infrastructure graph to identify the nodes that are able to meet the requirements of a workload; Fig. 6; [0068]: For example, Node A and Node B each carry information regarding: (i) computational resources such as CPU and GPU quantity and/or usage) and the semantic patterns are built based on resource requirements of the tasks ([0068]: The example of FIG. 6 thus depicts how, in an embodiment, the semantics in a heterogenous network can be useful during the search process of a workload placement scenario).
Additionally, Murai teaches performing, in parallel for each motif, a selective harvesting search in a target graph ([Abstract]: Active search on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights… In this work we introduce selective harvesting, a variant of active search) using the motifs to match nodes of the query graph to nodes of the target graph ([Abstract]: the available training data for deciding which node to query is restricted to the subgraph induced by the queried set; [Pages 2-3, 6]: any node can be queried at any time… the use of multiple classifiers – helps improve accuracy… The algorithm’s output is the list of target nodes found in T steps).
wherein each node of the target graph corresponds to a node in the computing network ([Page 4]: In this section we formalize the selective harvesting problem… Let G = (V, E ) denote an undirected graph representing the network topology; Fig. 2; [Page 2]: i.e., nodes with a certain label – in a network; [Page 4]: Each node v ∈ V has M attributes (domain-related properties of the nodes).
Furthermore, Srikanth teaches and the selective harvesting search classifies nodes of the target graph based on whether they satisfy the semantic patterns of the motif ([Abstract]: A curating method is performed to enable semantic search including searching for cloud computing resources; Fig. 2; [0019]: Resource cataloging subsystem 210 stores away classified computing resources 208a-208c; Fig. 3R; [0060]: At block 3202, the method uses a graph-matching process which in turn uses a feature-matching process (attribute matching) to select a set of computing resources for each node in a graph); and
placing tasks of the workload at the matched nodes of the target graph for execution of the tasks by the computing network (Fig. 3R; [0060]-[0063]: the method 3000 proceeds to block 3200 where the method models a task or a workload as a graph of computing resources that depicts the order of resource provisioning and dependencies… Separately or combined presentation of such information enables consumers to visualize the provisioning and execution of their workloads and tasks), and
updating attributes of the target nodes based on the placed tasks ([0005]: The method further dynamically updates the catalog and the knowledge base to refresh pieces of information pertaining to the different computing resource providers… their attributes, and the taxonomy of their values; Fig. 3T; [0062]: At block 3226, the method also prepares to present attributes of matched computing resources in addition to the presentation of the ranked list).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rossi to incorporate the teachings of Romero, Murai, and Srikanth to perform a selective harvesting search in a target graph, using the motifs to match nodes of the query graph to nodes of the target graph, and place tasks of the workload at the matched nodes of the target graph.
The motivation for doing so would be to identify the nodes that are able to apply a workload placement optimization algorithm to the nodes of a subgraph, as recognized by Romero ([Abstract]: applying a workload placement optimization algorithm to the nodes of the subgraph), to collect a larger set of targets, as recognized by Murai ([Abstract]: Therefore, selective harvesting is a sequential decision problem… We demonstrate that it is possible to collect a much larger set of targets by using multiple classifiers), and to organize computing resources, as recognized by Srikanth ([Abstract] of Srikanth: Pieces of hardware on which pieces of software are executed are configured to organize computing resources from different computing resource providers so as to facilitate their discovery).
Regarding Claim 12, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 11.
Srikanth further teaches wherein the query graph comprises a workload graph ([0059]-[0060]: (FIG. 3Q), the method 3000 proceeds to decision block 3198 where the method performs a test to determine whether the query is specifying a task or workload… From Terminal E6 (FIG. 3R), the method 3000 proceeds to block 3200 where the method models a task or a workload as a graph of computing resources that depicts the order of resource provisioning and dependencies).
Regarding Claim 13, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 11.
Rossi further teaches wherein at least some of the graphlets in the set of graphlets share the patterns, wherein the motifs are built based on the semantic patterns (Fig. 3A; More specifically, the set of method steps 3002 gathers computing resource related information… by: crawling one or more data sources… identifying the different types of workloads and tasks that can be performed using one or more of the data sources; learning dependencies between computing resources and workload templates from one or more data sources; mapping and/or formatting resource information produced by previous steps into a structured knowledge base of computing resources; (Figs. 4-5; [0004]: where the network motifs are recurrent and statistically significant subgraphs or patterns in the graph [i.e., subgraphs with semantic meaning])).
Regarding Claim 14, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 13.
Srikanth further teaches further comprising clustering and filtering the graphlets to identify the semantic patterns (FIG. 3R; [Abstract]: A curating method is performed to enable semantic search including searching for cloud computing resources that in combination cooperate to satisfy a workload or a task in addition to having a simple computational function. Semantic indexing is performed to facilitate the semantic search; [0060]: Progressing to block 3204, the method uses graph dependency to filter computing resources through logical Boolean operators. At block 3206, the filtering results in different sets of matched resource collections, which are ranked by the method using the graph-matching process).
Regarding Claim 15, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 14.
Rossi further teaches wherein the semantic patterns include one or more of network latency, task dependency relationship, frequency of execution, and/or computing requirements ([Abstract]: The computing device includes a network embedding module that is implemented to determine a frequency of k-vertex motifs for each of the edges in the graph, and derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph; [0004]: The graph can be representative of a complex or dynamic network, such as a social network, tech-based network, web-based network, or any other type of network data graph that can include a variety of subgraphs, where the network motifs are recurrent and statistically significant subgraphs or patterns in the graph; [0056]: This effectively allows the overall model to learn a combination of latent features using the local motif-based embeddings from different network motifs and from different steps (motif graph scales)).
Regarding Claim 16, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 11.
Murai further teaches further comprising performing the selective harvesting search in the target graph (FIG. 3R; [Page 2]: In this paper we introduce selective harvesting, where the goal is the same as in active search, but instead of assuming that the network topology is given, our node querying is subject to a partial and evolving understanding of the network; [Page 5]: Figure 2 illustrates a snapshot of the search process (see caption for details); [Page 8]: To demonstrate the tunnel vision effect and show how classifier diversity can mitigate it, we conduct a large set of simulations).
Regarding Claim 17, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 16.
Murai further teaches further comprising performing the selective harvesting search for all of the motifs in parallel ([Page 2]: In this paper we introduce selective harvesting, where the goal is the same as in active search, but instead of assuming that the network topology is given, our node querying is subject to a partial and evolving understanding of the network; [Page 5]: Figure 2 illustrates a snapshot of the search process (see caption for details); [Page 8]: To demonstrate the tunnel vision effect and show how classifier diversity can mitigate it, we conduct a large set of simulations).
Regarding Claim 18, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 16.
Srikanth further teaches further comprising matching the motifs to the target graph, wherein nodes in the computing network matching the motifs are capable of coping with requirements of tasks of the workload set forth in the motifs (Fig. 3R; [0060]: At block 3208, the method further weights each node, match, and graph edge (dependency) match using various factors. At block 3210, the method additionally weights each set of matched resource collection using global factors (such as resources within the same geographic location and so on)), and
wherein the matching is based on the classification of nodes of the target graph that satisfy the patterns of the motif (Fig. 2; [0018]- [0019]: different classifications of the catalog facilitate the discovery of new resource types and matching of user computing resource requirements to computational offers… resource matching and ranking subsystem 222 searches a catalog maintained by the resource cataloging subsystem 210 to discover computing resources 208a-208c that satisfy the on-demand computing queries 202; [0031]: In the simple case, all of the mandatory attributes--as identified by (a.sub.1, a.sub.2, . . . , a.sub.m}--have to be satisfied by the resources; [0048] From Terminal E (FIG. 3M), the method proceeds to block 3136 where the method receives a query describing the computing resources desired and whose attributes would, if found, satisfied computation needs).
Regarding Claim 19, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 11.
Srikanth further teaches further comprising performing the tasks of one or more workloads at nodes in the computing network ([0014]: Various embodiments of the present subject matter provide… hardware/software mechanisms to dynamically select, allocate, and use resources across different providers to perform a workload or task; [0055]: The resources matched for a given query can come from multiple providers and from multiple regions. Computing resources are used to perform certain workloads or tasks).
Regarding Claim 20, the combined teachings of Rossi, Romero, Murai, and Srikanth disclose the non-transitory storage medium of claim 11.
Srikanth further teaches further comprising updating the matching of the motifs by updating an availability of resources in the target graph ([0005]: The method further dynamically updates the catalog and the knowledge base to refresh pieces of information pertaining to the different computing resource providers, semantic descriptors of computing resources, types, their attributes, and the taxonomy of their values so as to inventory computing resources that are available on-demand over the Internet).
Conclusion
26. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S D H/Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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