DETAILED ACTION
This office action is in response to the communication filed on January 30, 2026. Claims 1-7 and 9-15 are currently pending.
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 Arguments
Applicant's arguments filed on January 30, 2026 have been fully considered but they are not persuasive for the following reasons:
Applicant in Pages 9-11 of the Remarks argues that Abdelhamid and Nigam do not teach or even suggest the features "generation of a new node in the graph that represents a sub-set of a set of another node", “the sub-set inheriting properties from the set, the sub-set comprising at least one additional characteristic”, “identifying the nodes of the structural graph directly associated with the node capable of creating an overload and which share the at least one additional characteristic”, “associating the identified nodes with the indexing node”, “creating an indexing node as part of the graph model, where the indexing node represents a sub-set of a set-node”, and “inheritance of properties with the addition of a new characteristic, or associating structural graph nodes with such an indexing node”, as recited in the independent claims.
Examiner respectfully disagrees. The cited prior art alone and/or in combination discloses the argued features.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Abdelhamid in [0002], [0024], and [0028] discloses subgraph mining performed over a dynamic graph for indexing, classification and social network analysis, managing continuously changing social network graphs or web graphs of social networks or web that deal with frequent addition and removal of users as well as evolving relationships among users exhibiting rapid changes in size and structure, which is interpreted as processing a digital model of a system comprising entities.
Abdelhamid in [0005] and [0034] discloses maintaining matching embeddings of a given subgraph in an input graph, maintaining sets of subgraphs in the input graph based on a support threshold, checking a support of the subgraphs in the sets based on embeddings, find matches of a subgraph in the input graph, subgraphs part of a supergraph, which is interpreted as the digital model comprising a structural graph or subgraph and a super-graph.
Abdelhamid in Figure 1A discloses a graph comprising a structural graph and a super-graph representing entities of the social network system by interlinked nodes.
Abdelhamid in [0033], [0036], and [0091] and in Figure 1A discloses a dynamic input graph comprising a set of nodes and a set of edges where labels are assigned to the nodes and edge, the input graph comprising a subgraph and a super-graph representing entities of the social network system by interlinked nodes, subgraph containing node where at least one node of the subgraph is associated with at least one node of the super graph, a subgraph being a child or parent of another subgraph and can be a supergraph of another subgraph, which is interpreted as a supergraph corresponding to an abstraction of a structural graph and comprising nodes representing sets, and at least one of the nodes of the structural graph being associated with at least one of the nodes of the super-graph.
Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs.
Abdelhamid in [0034] and [0036] and in Figure 1A discloses finding matches of one graph in another graph, each match resulting from subgraph matching a graph is called an embedding of the subgraph in the graph, a subgraph containing nodes, a subgraph part of a supergraph.
Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph.
Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings.
Abdelhamid in [0091] discloses each subgraph can be either a child or a parent of one or more subgraphs, subgraph can be part of a supergraph.
Abdelhamid in [0096] and [0142] discloses subgraph mining for mining dynamic graphs
Abdelhamid in [0005] and [0007] discloses subgraph mining on dynamic graphs, maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph, the set of embeddings comprises a hash table of the embeddings indexed by a key.
Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph.
Abdelhamid in [0076] discloses index used to access embeddings given node IDs, each embedding has a unique key for quick lookup, keys created by concatenating embedding node IDs ordered by corresponding subgraph node IDs.
Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, some nodes can index more than one embedding, each node having an associated counter value representing the number of indexed embeddings.
Abdelhamid in [0091] discloses each subgraph can be either a child or a parent of one or more subgraphs, subgraph can be part of a supergraph.
Abdelhamid in [0096] and [0142] discloses subgraph mining for mining dynamic graphs.
Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph.
Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings.
Abdelhamid in [0005] and [0007] discloses subgraph mining on dynamic graphs, maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph, the set of embeddings comprises a hash table of the embeddings indexed by a key/
Abdelhamid in [0042] and [0057] discloses search space of subgraphs of an input graph divided into frequent subgraphs and infrequent subgraphs, mining graph by evaluating subgraphs based on one or more conditions, which is identifying nodes of structural graph that share an additional characteristic.
Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph.
Abdelhamid in [0076] discloses index used to access embeddings given node IDs, each embedding has a unique key for quick lookup, keys created by concatenating embedding node IDs ordered by corresponding subgraph node IDs.
Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, some nodes can index more than one embedding, each node having an associated counter value representing the number of indexed embeddings.
Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph.
Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings.
Therefore, Abdelhamid discloses "generation of a new node in the graph that represents a sub-set of a set of another node", “the sub-set inheriting properties from the set, the sub-set comprising at least one additional characteristic”, “identifying the nodes of the structural graph directly associated with the node…and which share the at least one additional characteristic”, “associating the identified nodes with the indexing node”, “creating an indexing node as part of the graph model, where the indexing node represents a sub-set of a set-node”, and “inheritance of properties with the addition of a new characteristic, or associating structural graph nodes with such an indexing node”.
Abdelhamid discloses determining a node of a super-graph, generating an indexing node representing a sub-set of a set represented by the node, associating the indexing node and the node, identifying nodes of a structural graph directly associated with and associated with the node, and associating the identified nodes with the indexing node, however, Abdelhamid does not explicitly disclose:
determining at least one node…capable of creating an overload…;
Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query.
Nigam in [0035] discloses a query that finds all possible paths between nodes in a network can require scanning all edges used to form a path connecting the nodes, query and/or complex queries of graph can require significantly more time and/or computational resources than simpler queries that contain two or fewer hops in graph, including a node with a large number of edges in a search space of a query can significantly increase the complexity of the query.
Nigam in [0036] and [0045] discloses to improve processing of complex queries of graph, selectively index attributes for portions of graph that significantly add to the processing of the complex queries, selective indexing reduce the search space and associated processing of the complex queries while mitigating increases in write latency, overhead, and/or memory usage associated with building complete indexes of attributes in data sets, storing a graph in a number of logical partitions, with each partition containing a subset of nodes of graph and all edges belonging to the subset of nodes.
Nigam in [0040] discloses using index to scan only the edges that match the query predicate.
Nigam in [0046] and [0067] discloses selectively creating indexes of nodes with large number of edges.
Therefore, Nigam discloses determining at least one node capable of creating an overload.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Abdelhamid and Nigam, to have combined Abdelhamid and Nigam. The motivation to combine Abdelhamid and Nigam would be to improve performance of processing complex queries of social and/or online professional network data by forming a graph representing entities and relationships.
Applicant in Page 11 of the Remarks argues that the Office action provides no analysis supporting that each of the features of claim 1 are capable of being processed mentally or using a pen and paper, and further argues that the features cannot be considered to be inherently practically capable of being performed in the human mind.
Applicant in Pages 11-12 of the Remarks argues that the claimed features represent an improvement to a technical field, even if the improvement is not explicitly recited in the claims themselves, such that the claims as a whole should be considered to integrate the alleged judicial exception into a practical application.
Examiner respectfully disagrees. The 101 rejection in the last office action provided detailed argument, evidence, or statement for claims 1-7 and 9-15 reciting steps that are merely “mental processes”.
Independent claim 1 and similarly independent claims 10, 12, and 14 covers several steps, such as the processing, determining, generating, associating, and identifying steps, that recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps, which are again discussed in detail in the current 101 rejection below.
Independent claim 9 and similarly independent claims 11, 13, and 15 covers several steps, such as the searching and traversing steps, that recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps, which are again discussed in detail in the current 101 rejection below.
It is important to note that the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements (MPEP 2106.05(a)).
The remaining steps in the claims that are identified as reciting additional elements, are only adding insignificant extra-solution activity to the judicial exception, and are recognized as a well understood, routine, and conventional activity within the field of computer functions, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
For the above reasons, Examiner states that rejection of the current Office action is proper.
Claim Objections
Claims 5 and 10 are objected to because of the following informalities:
In claim 5 lines 2-3, the phrase “a semantic category of the super-graph called semantic category” should be “a semantic category of the super-graph”.
In claim 10 line 4, the semicolon after the phrase “representing sets” should be a comma.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation "the other of the hyper-structural graph" in lines 4-5. There is insufficient antecedent basis for these limitation in the claim.
In claim 6 line 7 the feature “the other node representing a second set” is indefinite because it is not clear which node the phrase “the other node” is referring to.
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 1-7 and 9-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Independent claims 1 and 9-15 respectively recite a method, an electronic device, a computer, and a non-transitory computer-readable recording medium, which are directed to a statutory category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Independent claim 1 and similarly independent claims 10, 12, and 14 recite the limitations:
“processing a digital model of a system comprising entities, the digital model comprising a structural graph and a super-graph, the structural graph representing the entities of the system by interlinked nodes”;
A person can mentally or using a pen and paper process a digital model of a system comprising entities, where the digital model comprises a structural graph and a super-graph, and where the structural graph represents the entities of the system by interlinked nodes.
“the super- graph corresponding to an abstraction of the structural graph and comprising nodes representing sets”;
A person can mentally or using a pen and paper process a digital model of a system comprising a super-graph corresponding to an abstraction of a structural graph and comprising nodes representing sets.
“at least one of the nodes of the structural graph being associated with at least one of the nodes of the super-graph”;
A person can mentally or using a pen and paper process a digital model of a system comprising entities, where the digital model comprises a structural graph representing the entities of the system by interlinked nodes, and where at least one of the nodes of the structural graph being associated with at least one of the nodes of the super-graph.
“determining at least one node of the super-graph capable of creating an overload during traversal of the digital model”;
A person can mentally or using a pen and paper analyze nodes of a super-graph of a digital model and the person can mentally or using a pen and paper determine that at least one node of a super-graph is capable of creating an overload during traversal of the digital model.
in response to this determination:
“generating an indexing node within the super-graph, the indexing node representing a sub-set of the set represented by the node capable of creating an overload, the sub-set inheriting properties from the set, the sub-set comprising at least one additional characteristic”;
A person can mentally or using a pen and paper make a determination that at least one node of a super-graph is capable of creating an overload during traversal of a digital model and in response to the determination the person can mentally or using a pen and paper generate an indexing node within the super-graph representing a sub-set of a set represented by the node capable of creating the overload, the sub-set inheriting properties from the set, and the sub-set comprising at least one additional characteristic.
“associating the indexing node and the node capable of creating an overload”;
A person can mentally or using a pen and paper associate an indexing node and a node capable of creating an overload.
“identifying the nodes of the structural graph directly associated with the node capable of creating an overload and which share the at least one additional characteristic”;
A person can mentally or using a pen and paper identify nodes of a structural graph directly associated with a node capable of creating an overload and which share at least one additional characteristic.
“associating the identified nodes with the indexing node”;
A person can mentally or using a pen and paper identify nodes and the person can mentally or using a pen and paper associate the identified nodes with an indexing node.
The limitations, as recited above in claim 1 and similarly in claims 10, 12, and 14, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 9 and similarly independent claims 11, 13, and 15 recite the limitations:
“searching for information represented by a pattern graph in the digital model of a system obtained by the method of claim 8”,
A person can mentally or using a pen and paper search for information represented by a pattern graph in a digital model of an obtained system.
“traversing the digital model by checking whether a portion of the digital model corresponds to the pattern graph”;
A person can mentally or using a pen and paper traverse a digital model by mentally or using a pen and paper checking whether a portion of the digital model corresponds to a pattern graph.
The limitations, as recited above in claim 9 and similarly in claims 11, 13, and 15, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Independent claim 9 and similarly independent claims 11, 13, and 15 recite the limitations:
“upon a determination that a portion of the digital model corresponds to the pattern graph, obtaining information that makes it possible to identify the portion of the digital model”, which is a step of obtaining data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a method for processing a digital model of a system… the method implemented by an electronic device comprising a processor and a memory, the method comprising:” in the steps in claim 1 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a method for searching for information…of a system obtained by the method of claim 1, the method implemented by an electronic device, the method comprising:” in the steps in claim 9 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “an electronic device for processing a digital model of a system…the electronic device comprising a processor and a memory, the electronic device configured to:” in the steps in claim 10 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “an electronic device for searching for information…of a system obtained by the method of claim 1, the electronic device comprising a processor and a memory, the electronic device configured to:” in the steps in claim 11 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a computer comprising a processor and a memory, the memory having stored thereon instructions which, when executed by the processor, cause the computer to implement the method of claim 1” in the steps in claim 12 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a computer comprising a processor and a memory, the memory having stored thereon instructions which, when executed by the processor, cause the computer to implement the method of claim 9” in the steps in claim 13 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a non-transitory computer-readable recording medium having stored thereon instructions which, when executed by a processor, cause the processor to implement the method of claim 1” in the steps in claim 14 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a non-transitory computer-readable recording medium having stored thereon instructions which, when executed by a processor, cause the processor to implement the method of claim 9” in the steps in claim 15 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Independent claims 1 and 9-15 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Independent claim 9 and similarly independent claims 11, 13, and 15 recite the limitations:
“upon a determination that a portion of the digital model corresponds to the pattern graph, obtaining information that makes it possible to identify the portion of the digital model”, which is a step of obtaining data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 2 recites additional limitations, such as:
“wherein the determination of at least one node of the super-graph capable of creating an overload during traversal of the digital model comprises:
determining at least one indegree or outdegree of said node; and
comparing the determined at least one indegree or outdegree of said node with a predetermined value”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper determine that at least one node of the super-graph capable of creating an overload during traversal of the digital model by mentally or using a pen and paper determining at least one indegree or outdegree of said node, and by mentally or using a pen and paper comparing the determined at least one indegree or outdegree of said node with a predetermined value, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 recites additional limitations, such as:
“wherein the determination of at least one node of the super-graph capable of creating an overload during traversal of the digital model comprises analyzing requests previously received by said node”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper determine that at least one node of a super-graph is capable of creating an overload during traversal of a digital model by mentally or using a pen and paper analyzing requests previously received by said node, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 4 recites additional limitations, such as:
“determining that several of the entities represented by nodes of the structural graph associated with the node of the super-graph capable of creating an overload have a same location, in which the generated indexing node has said location as an additional characteristic”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper determine that several entities represented by nodes of a structural graph associated with a node of a super-graph capable of creating an overload have a same location, and the person can mentally or using a pen and paper determine that a generated indexing node has said location as an additional characteristic, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 recites additional limitation, such as:
“a semantic graph comprising nodes, each node representing a semantic category of the super-graph called semantic category, the nodes of the semantic graph being interlinked by arcs representing a subsumption relation or a semantic relation, at least one of the nodes of the structural graph being linked to at least one of the nodes of the semantic graph by an arc characterizing that the at least one of the nodes of the structural graph represents an instance of the semantic category represented by the at least one node of the semantic graph; and,
a hyper-structural graph comprising hyper-nodes, each hyper-node representing an extensional set of the super-graph, a hyper-node being associated with a plurality of nodes of the structural graph, and a hyper-node being linked to at least one other hyper-node or to at least one node of the semantic graph, in which the indexing node belongs to at least one of the semantic graph or the hyper-structural graph”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper process a super-graph which comprises a semantic graph and a hyper-structural graph, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 recites additional limitation, such as:
“the node capable of creating an overload belonging to one of the semantic graph or the hyper-structural graph, the method further comprising determining that several of the nodes of the structural graph associated with the node capable of creating an overload are also associated with another node of the other of hyper-structural graph or the semantic graph, the node capable of creating an overload representing a first set, the other node representing a second set, in which the generated indexing node represents a sub-set of the first and second sets”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper determine that a node capable of creating an overload belongs to a semantic graph or to a hyper- structural graph, and the person can mentally or using a pen and paper process a super-graph which comprises a semantic graph or a hyper-structural graph, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 recites additional limitation, such as:
“wherein the structural graph and at least a portion of the super-graph are stored within a distributed hardware architecture compliant with edge computing”, which is a step of storing data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
The additional elements “within a distributed hardware architecture compliant with edge computing” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 2-7 are also directed to abstract idea without significantly more and are not patent eligible.
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) 1-4 and 9-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abdelhamid (US Pub 2018/0032587) in view of Nigam (US Pub 2016/0092584).
With respect to claim 1, Abdelhamid discloses a method for processing a digital model of a system comprising entities (Abdelhamid in [0002], [0024], and [0028] discloses subgraph mining performed over a dynamic graph for indexing, classification and social network analysis, managing continuously changing social network graphs or web graphs of social networks or web that deal with frequent addition and removal of users as well as evolving relationships among users exhibiting rapid changes in size and structure, which is interpreted as processing a digital model of a system comprising entities), the digital model comprising a structural graph and a super-graph, the structural graph representing the entities of the system by interlinked nodes (Abdelhamid in [0005] and [0034] discloses maintaining matching embeddings of a given subgraph in an input graph, maintaining sets of subgraphs in the input graph based on a support threshold, checking a support of the subgraphs in the sets based on embeddings, find matches of a subgraph in the input graph, subgraphs part of a supergraph, which is interpreted as the digital model comprising a structural graph or subgraph and a super-graph; Abdelhamid in Figure 1A discloses a graph comprising a structural graph and a super-graph representing entities of the social network system by interlinked nodes), the super-graph corresponding to an abstraction of the structural graph and comprising nodes representing sets, at least one of the nodes of the structural graph being associated with at least one of the nodes of the super-graph (Abdelhamid in [0033], [0036], and [0091] and in Figure 1A discloses a dynamic input graph comprising a set of nodes and a set of edges where labels are assigned to the nodes and edge, the input graph comprising a subgraph and a super-graph representing entities of the social network system by interlinked nodes, subgraph containing node where at least one node of the subgraph is associated with at least one node of the super graph, a subgraph being a child or parent of another subgraph and can be a supergraph of another subgraph, which is interpreted as a supergraph corresponding to an abstraction of a structural graph and comprising nodes representing sets, and at least one of the nodes of the structural graph being associated with at least one of the nodes of the super-graph), the method implemented by an electronic device comprising a processor and a memory (Abdelhamid in [0100], [0101], and [0114] and in Figure 8 discloses method implemented by an electronic device comprising a processor and a memory having stored instructions executed by the processor to perform the method), the method comprising:
determining at least one node of the super-graph…during traversal of the digital model (Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Abdelhamid in [0034] and [0036] and in Figure 1A discloses finding matches of one graph in another graph, each match resulting from subgraph matching a graph is called an embedding of the subgraph in the graph, a subgraph containing nodes, a subgraph part of a supergraph; Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings; Abdelhamid in [0091] discloses each subgraph can be either a child or a parent of one or more subgraphs, subgraph can be part of a supergraph; Abdelhamid in [0096] and [0142] discloses subgraph mining for mining dynamic graphs; here Abdelhamid does not explicitly disclose determining a node capable of creating an overload, but the Nigam reference discloses the feature, as discussed below); and,
in response to this determination, generating an indexing node within the super-graph, the indexing node representing a sub-set of the set represented by the node…the sub-set inheriting properties from the set, the sub-set comprising at least one additional characteristic (Abdelhamid in [0005] and [0007] discloses subgraph mining on dynamic graphs, maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph, the set of embeddings comprises a hash table of the embeddings indexed by a key; Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0076] discloses index used to access embeddings given node IDs, each embedding has a unique key for quick lookup, keys created by concatenating embedding node IDs ordered by corresponding subgraph node IDs; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, some nodes can index more than one embedding, each node having an associated counter value representing the number of indexed embeddings; Abdelhamid in [0091] discloses each subgraph can be either a child or a parent of one or more subgraphs, subgraph can be part of a supergraph; Abdelhamid in [0096] and [0142] discloses subgraph mining for mining dynamic graphs; here Abdelhamid does not explicitly disclose a node capable of creating an overload, but the Nigam reference discloses the feature, as discussed below);
associating the indexing node and the node…(Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings);
identifying the nodes of the structural graph directly associated with the node…and which share the at least one additional characteristic (Abdelhamid in [0005] and [0007] discloses subgraph mining on dynamic graphs, maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph, the set of embeddings comprises a hash table of the embeddings indexed by a key; Abdelhamid in [0042] and [0057] discloses search space of subgraphs of an input graph divided into frequent subgraphs and infrequent subgraphs, mining graph by evaluating subgraphs based on one or more conditions, which is identifying nodes of structural graph that share an additional characteristic; Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0076] discloses index used to access embeddings given node IDs, each embedding has a unique key for quick lookup, keys created by concatenating embedding node IDs ordered by corresponding subgraph node IDs; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, some nodes can index more than one embedding, each node having an associated counter value representing the number of indexed embeddings; here Abdelhamid does not explicitly disclose a node capable of creating an overload, but the Nigam reference discloses the feature, as discussed below); and
associating the identified nodes with the indexing node (Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings).
Abdelhamid discloses determining a node of a super-graph, generating an indexing node representing a sub-set of a set represented by the node, associating the indexing node and the node, identifying nodes of a structural graph directly associated with and associated with the node, and associating the identified nodes with the indexing node, however, Abdelhamid does not explicitly disclose:
determining at least one node…capable of creating an overload…;
The Nigam reference discloses determining at least one node capable of creating an overload (Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0035] discloses a query that finds all possible paths between nodes in a network can require scanning all edges used to form a path connecting the nodes, query and/or complex queries of graph can require significantly more time and/or computational resources than simpler queries that contain two or fewer hops in graph, including a node with a large number of edges in a search space of a query can significantly increase the complexity of the query; Nigam in [0036] and [0045] discloses to improve processing of complex queries of graph, selectively index attributes for portions of graph that significantly add to the processing of the complex queries, selective indexing reduce the search space and associated processing of the complex queries while mitigating increases in write latency, overhead, and/or memory usage associated with building complete indexes of attributes in data sets, storing a graph in a number of logical partitions, with each partition containing a subset of nodes of graph and all edges belonging to the subset of nodes; Nigam in [0040] discloses using index to scan only the edges that match the query predicate; Nigam in [0046] and [0067] discloses selectively creating indexes of nodes with large number of edges).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Abdelhamid and Nigam, to have combined Abdelhamid and Nigam. The motivation to combine Abdelhamid and Nigam would be to improve performance of processing complex queries of social and/or online professional network data by forming a graph representing entities and relationships (Nigam: [0005] and [0027]).
With respect to claim 2, Abdelhamid in view of Nigam discloses the method of claim 1, wherein the determination of at least one node of the super-graph capable of creating an overload during traversal of the digital model comprises:
determining at least one of indegree or outdegree of said node (Abdelhamid in [0005] and [0008] discloses determine if subgraphs in a graph satisfy a predefined support threshold, checking subgraphs and searching for new embeddings created by an edge addition to the graph, evaluating subgraph for multiple edge changes, evaluating nodes in a graph or subgraph by determining whether a given subgraph satisfies a predefined support threshold; Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0057] and [0067] discloses index created for nodes with large number of edges in a graph, such as greater than a threshold and omitted for nodes with smaller number of edges in the graph, such as less than the threshold, minimum connection strength in predicates used to identify paths that link entities through a series of strong connections); and
comparing the determined at least one of indegree or outdegree of said node with a predetermined value (Abdelhamid in [0005] and [0008] discloses determine if subgraphs in a graph satisfy a predefined support threshold, checking subgraphs and searching for new embeddings created by an edge addition to the graph, evaluating subgraph for multiple edge changes, evaluating nodes in a graph or subgraph by determining whether a given subgraph satisfies a predefined support threshold; Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0057] and [0067] discloses index created for nodes with large number of edges in a graph, such as greater than a threshold and omitted for nodes with smaller number of edges in the graph, such as less than the threshold, minimum connection strength in predicates used to identify paths that link entities through a series of strong connections).
With respect to claim 3, Abdelhamid in view of Nigam discloses the method of claim 1, wherein the determination of at least one node of the super-graph capable of creating an overload during traversal of the digital model comprises analyzing requests previously received by said node (Abdelhamid in [0032], [0051], and [0096] discloses dynamically updating index to reflect current status of system while consuming a minimal memory overhead, reordering execution based on information collected while processing past graph updates, utilizing a set of information collected during past iterations, utilizing information collected during past iterations to guide the processing in future iterations towards improved performance; Nigam in [0038] and [0046] discloses to identify complex query with a large search space for potential indexing of the query’s attributes obtaining statistics associated with previously run queries, using information associated with previously run queries to identify one or more queries associated with an overhead, create indexes of one or more attributes before, during, or after querying of graph using the attributes).
With respect to claim 4, Abdelhamid in view of Nigam discloses the method of claim 1, further comprisingcollected while processing past graph updates, utilizing a set of information collected during past iterations, utilizing information collected during past iterations to guide the processing in future iterations towards improved performance; Nigam in [0023] and [0034] discloses query may include a predicate referencing entities represented by nodes of a graph having same attributes or location; Nigam in [0038] and [0046] discloses to identify complex query with a large search space for potential indexing of the query’s attributes obtaining statistics associated with previously run queries, using information associated with previously run queries to identify one or more queries associated with an overhead, create indexes of one or more attributes before, during, or after querying of graph using the attributes).
With respect to claim 9, Abdelhamid in view of Nigam discloses a method for searching for information represented by a pattern graph in the digital model of a system obtained by the method of claim 1 (Abdelhamid in [0070] discloses subgraph and pattern mining in a graph), the method implemented by an electronic device, the method comprising:
traversing the digital model by checking whether a portion of the digital model corresponds to the pattern graph (Abdelhamid in [0005] and [0008] discloses determine if subgraphs in a graph satisfy a predefined support threshold, checking subgraphs and searching for new embeddings created by an edge addition to the graph, evaluating subgraph for multiple edge changes, evaluating nodes in a graph or subgraph by determining whether a given subgraph satisfies a predefined support threshold; Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Abdelhamid in [0034] and [0036] and in Figure 1A discloses finding matches of one graph in another graph, each match resulting from subgraph matching a graph is called an embedding of the subgraph in the graph, a subgraph containing nodes, a subgraph part of a supergraph; Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0038] and [0046] discloses to identify complex query with a large search space for potential indexing of the query’s attributes obtaining statistics associated with previously run queries, using information associated with previously run queries to identify one or more queries associated with an overhead, create indexes of one or more attributes before, during, or after querying of graph using the attributes); and,
upon a determination that a portion of the digital model corresponds to the pattern graph, obtaining information that makes it possible to identify the portion of the digital model (Abdelhamid in [0005] and [0008] discloses determine if subgraphs in a graph satisfy a predefined support threshold, checking subgraphs and searching for new embeddings created by an edge addition to the graph, evaluating subgraph for multiple edge changes, evaluating nodes in a graph or subgraph by determining whether a given subgraph satisfies a predefined support threshold; Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Abdelhamid in [0034] and [0036] and in Figure 1A discloses finding matches of one graph in another graph, each match resulting from subgraph matching a graph is called an embedding of the subgraph in the graph, a subgraph containing nodes, a subgraph part of a supergraph; Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0038] and [0046] discloses to identify complex query with a large search space for potential indexing of the query’s attributes obtaining statistics associated with previously run queries, using information associated with previously run queries to identify one or more queries associated with an overhead, create indexes of one or more attributes before, during, or after querying of graph using the attributes).
With respect to claim 10, Abdelhamid discloses an electronic device (Abdelhamid in [0114] and in Figure 8 discloses an electronic device) for processing a digital model of a system comprising entities (Abdelhamid in [0002], [0024], and [0028] discloses subgraph mining performed over a dynamic graph for indexing, classification and social network analysis, managing continuously changing social network graphs or web graphs of social networks or web that deal with frequent addition and removal of users as well as evolving relationships among users exhibiting rapid changes in size and structure, which is interpreted as processing a digital model of a system comprising entities), the digital model comprising a structural graph and a super-graph, the structural graph representing the entities of the system by interlinked nodes (Abdelhamid in [0005] and [0034] discloses maintaining matching embeddings of a given subgraph in an input graph, maintaining sets of subgraphs in the input graph based on a support threshold, checking a support of the subgraphs in the sets based on embeddings, find matches of a subgraph in the input graph, subgraphs part of a supergraph, which is interpreted as the digital model comprising a structural graph or subgraph and a super-graph; Abdelhamid in Figure 1A discloses a graph comprising a structural graph and a super-graph representing entities of the social network system by interlinked nodes), the super-graph corresponding to an abstraction of the structural graph and comprising nodes representing sets; at least one of the nodes of the structural graph being associated with at least one of the nodes of the super-graph (Abdelhamid in [0033], [0036], and [0091] and in Figure 1A discloses a dynamic input graph comprising a set of nodes and a set of edges where labels are assigned to the nodes and edge, the input graph comprising a subgraph and a super-graph representing entities of the social network system by interlinked nodes, subgraph containing node where at least one node of the subgraph is associated with at least one node of the super graph, a subgraph being a child or parent of another subgraph and can be a supergraph of another subgraph, which is interpreted as a supergraph corresponding to an abstraction of a structural graph and comprising nodes representing sets, and at least one of the nodes of the structural graph being associated with at least one of the nodes of the super-graph), the electronic device comprising a processor and a memory, the electronic device configured (Abdelhamid in [0100], [0101], and [0114] and in Figure 8 discloses an electronic device comprising a processor and a memory having stored instructions executed by the processor to perform a method) to:
determine at least one node of the super-graph…during traversal of the digital model (Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Abdelhamid in [0034] and [0036] and in Figure 1A discloses finding matches of one graph in another graph, each match resulting from subgraph matching a graph is called an embedding of the subgraph in the graph, a subgraph containing nodes, a subgraph part of a supergraph; Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings; Abdelhamid in [0091] discloses each subgraph can be either a child or a parent of one or more subgraphs, subgraph can be part of a supergraph; Abdelhamid in [0096] and [0142] discloses subgraph mining for mining dynamic graphs; here Abdelhamid does not explicitly disclose determining a node capable of creating an overload, but the Nigam reference discloses the feature, as discussed below);
generate an indexing node within the super-graph, the indexing node representing a sub-set of the set represented by the node…the sub-set inheriting properties from the set, the sub-set comprising at least one additional characteristic (Abdelhamid in [0005] and [0007] discloses subgraph mining on dynamic graphs, maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph, the set of embeddings comprises a hash table of the embeddings indexed by a key; Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0076] discloses index used to access embeddings given node IDs, each embedding has a unique key for quick lookup, keys created by concatenating embedding node IDs ordered by corresponding subgraph node IDs; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, some nodes can index more than one embedding, each node having an associated counter value representing the number of indexed embeddings; Abdelhamid in [0091] discloses each subgraph can be either a child or a parent of one or more subgraphs, subgraph can be part of a supergraph; Abdelhamid in [0096] and [0142] discloses subgraph mining for mining dynamic graphs; here Abdelhamid does not explicitly disclose a node capable of creating an overload, but the Nigam reference discloses the feature, as discussed below);
associate the indexing node and the node…(Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings);
identify the nodes of the structural graph directly associated with the node…and which share the at least one additional characteristic (Abdelhamid in [0005] and [0007] discloses subgraph mining on dynamic graphs, maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph, the set of embeddings comprises a hash table of the embeddings indexed by a key; Abdelhamid in [0042] and [0057] discloses search space of subgraphs of an input graph divided into frequent subgraphs and infrequent subgraphs, mining graph by evaluating subgraphs based on one or more conditions, which is identifying nodes of structural graph that share an additional characteristic; Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0076] discloses index used to access embeddings given node IDs, each embedding has a unique key for quick lookup, keys created by concatenating embedding node IDs ordered by corresponding subgraph node IDs; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, some nodes can index more than one embedding, each node having an associated counter value representing the number of indexed embeddings; here Abdelhamid does not explicitly disclose a node capable of creating an overload, but the Nigam reference discloses the feature, as discussed below); and,
associate the identified nodes with the indexing node (Abdelhamid in [0068] and [0071] discloses when an edge is added to the graph searching the added edge for new embeddings and associating the newly found embeddings with a subgraph; Abdelhamid in [0077] and [0078] and in Figures 1 and 6C, discloses each node in a graph indexing the embeddings it is contained in, each node having an associated counter value representing the number of indexed embeddings).
Abdelhamid discloses determining a node of a super-graph, generating an indexing node representing a sub-set of a set represented by the node, associating the indexing node and the node, identifying nodes of a structural graph directly associated with and associated with the node, and associating the identified nodes with the indexing node, however, Abdelhamid does not explicitly disclose:
determining at least one node…capable of creating an overload…;
The Nigam reference discloses determining at least one node capable of creating an overload (Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0035] discloses a query that finds all possible paths between nodes in a network can require scanning all edges used to form a path connecting the nodes, query and/or complex queries of graph can require significantly more time and/or computational resources than simpler queries that contain two or fewer hops in graph, including a node with a large number of edges in a search space of a query can significantly increase the complexity of the query; Nigam in [0036] and [0045] discloses to improve processing of complex queries of graph, selectively index attributes for portions of graph that significantly add to the processing of the complex queries, selective indexing reduce the search space and associated processing of the complex queries while mitigating increases in write latency, overhead, and/or memory usage associated with building complete indexes of attributes in data sets, storing a graph in a number of logical partitions, with each partition containing a subset of nodes of graph and all edges belonging to the subset of nodes; Nigam in [0040] discloses using index to scan only the edges that match the query predicate; Nigam in [0046] and [0067] discloses selectively creating indexes of nodes with large number of edges).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Abdelhamid and Nigam, to have combined Abdelhamid and Nigam. The motivation to combine Abdelhamid and Nigam would be to improve performance of processing complex queries of social and/or online professional network data by forming a graph representing entities and relationships (Nigam: [0005] and [0027]).
With respect to claim 11, Abdelhamid in view of Nigam discloses an electronic device for searching for information represented by a pattern graph in the digital model of a system obtained by the method of claim 1 (Abdelhamid in [0070] discloses subgraph and pattern mining in a graph), the electronic device comprising a processor and a memory, the electronic device configured (Abdelhamid in [0100], [0101], and [0114] and in Figure 8 discloses an electronic device comprising a processor and a memory having stored instructions executed by the processor to perform a method) to:
traverse the digital model by checking whether a portion of the digital model corresponds to the pattern graph (Abdelhamid in [0005] and [0008] discloses determine if subgraphs in a graph satisfy a predefined support threshold, checking subgraphs and searching for new embeddings created by an edge addition to the graph, evaluating subgraph for multiple edge changes, evaluating nodes in a graph or subgraph by determining whether a given subgraph satisfies a predefined support threshold; Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Abdelhamid in [0034] and [0036] and in Figure 1A discloses finding matches of one graph in another graph, each match resulting from subgraph matching a graph is called an embedding of the subgraph in the graph, a subgraph containing nodes, a subgraph part of a supergraph; Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0038] and [0046] discloses to identify complex query with a large search space for potential indexing of the query’s attributes obtaining statistics associated with previously run queries, using information associated with previously run queries to identify one or more queries associated with an overhead, create indexes of one or more attributes before, during, or after querying of graph using the attributes); and,
obtain information that makes it possible to identify the portion of the digital model (Abdelhamid in [0005] and [0008] discloses determine if subgraphs in a graph satisfy a predefined support threshold, checking subgraphs and searching for new embeddings created by an edge addition to the graph, evaluating subgraph for multiple edge changes, evaluating nodes in a graph or subgraph by determining whether a given subgraph satisfies a predefined support threshold; Abdelhamid in [0031] discloses mining a dynamic graph by performing iterations on subgraphs; Abdelhamid in [0034] and [0036] and in Figure 1A discloses finding matches of one graph in another graph, each match resulting from subgraph matching a graph is called an embedding of the subgraph in the graph, a subgraph containing nodes, a subgraph part of a supergraph; Nigam in [0027] and [0032] discloses forming a graph of online professional network data representing entities and relationships, graph includes a set of nodes, a set of edges, and a set of attributes, obtain data from graph by submitting a query, processing the query by scanning graph for one or more nodes, edges, and attributes matching the query; Nigam in [0038] and [0046] discloses to identify complex query with a large search space for potential indexing of the query’s attributes obtaining statistics associated with previously run queries, using information associated with previously run queries to identify one or more queries associated with an overhead, create indexes of one or more attributes before, during, or after querying of graph using the attributes).
With respect to claim 12, Abdelhamid in view of Nigam discloses a computer comprising a processor and a memory, the memory having stored thereon instructions which, when executed by the processor, cause the computer to implement the method (Abdelhamid in [0100] and [0101] and in Figure 8 discloses a computer comprising a processor and a memory having stored instructions executed by the processor to perform the method) of claim 1 (see rejection of claim 1 above).
With respect to claim 13, Abdelhamid in view of Nigam discloses a computer comprising a processor and a memory, the memory having stored thereon instructions which, when executed by the processor, cause the computer to implement the method (Abdelhamid in [0100] and [0101] and in Figure 8 discloses a computer comprising a processor and a memory having stored instructions executed by the processor to perform the method) of claim 9 (see rejection of claim 9 above).
With respect to claim 14, Abdelhamid in view of Nigam discloses a non-transitory computer-readable recording medium having stored thereon instructions which, when executed by a processor, cause the processor to implement the method (Abdelhamid in [0097], [0100], and [0101] and in Figure 8 discloses a computer-readable recording medium having stored instructions executed by a processor to perform the method) of claim 1 (see rejection of claim 1 above).
With respect to claim 15, Abdelhamid in view of Nigam discloses a non-transitory computer-readable recording medium having stored thereon instructions which, when executed by a processor, cause the processor to implement the method (Abdelhamid in [0097], [0100], and [0101] and in Figure 8 discloses a computer-readable recording medium having stored instructions executed by a processor to perform the method) of claim 9 (see rejection of claim 9 above).
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abdelhamid (US Pub 2018/0032587) in view of Nigam (US Pub 2016/0092584) and in further view of Wical (US Pub 2014/0122228).
With respect to claim 5, Abdelhamid in view of Nigam discloses the method of claim 1, Abdelhamid further discloses the super-graph comprising subgraphs and indexing nodes belonging to the graph, and Nigam discloses subset of nodes in a graph and indexing nodes belonging to the graph, however, Abdelhamid and Nigam do not explicitly disclose:
a semantic graph comprising nodes, each node representing a semantic category of the…graph called semantic category, the nodes of the semantic graph being interlinked by arcs representing a subsumption relation or a semantic relation, at least one of the nodes of the structural graph being linked to at least one of the nodes of the semantic graph by an arc characterizing that the at least one of the nodes of the structural graph represents an instance of the semantic category represented by the at least one node of the semantic graph; and,
a hyper-structural graph comprising hyper-nodes, each hyper-node representing an extensional set of the…graph, a hyper-node being associated with a plurality of nodes of the structural graph, and a hyper-node being linked to at least one other hyper-node or to at least one node of the semantic graph, in which the…node belongs to at least one of the semantic graph or the hyper-structural graph.
The Wical reference discloses a semantic graph comprising nodes, each node representing a semantic category of a graph called semantic category, the nodes of the semantic graph being interlinked by arcs representing a subsumption relation or a semantic relation, at least one of the nodes of a structural graph being linked to at least one of the nodes of the semantic graph by an arc characterizing that the at least one of the nodes of the structural graph represents an instance of the semantic category represented by the at least one node of the semantic graph (Wical in [0035], [0036], and [0048] discloses computing a large connection of sub-graphs that compare to other graphs and provide many metrics that show the semantics of how the sub-graphs compare, comparison evaluates both the intersection of one artifact’s detailed sub-graph against all others that it connects to, and also compares one artifact’s sub-graph to a sub-graph of associations that extend each node of the artifact’s sub-graph, and then compares the associations against any target sub-graphs, creating graph connections and creating semantic dimensions that measure assortments of items with specific levels of similarity across sources by comparing any collection of items to any and all other collections of items in the graph, representing different types of semantic structures in hyper graph, interpreted as a semantic graph comprising nodes liked to a structural graph); and
a hyper-structural graph comprising hyper-nodes, each hyper-node representing an extensional set of a graph, a hyper-node being associated with a plurality of nodes of a structural graph, and a hyper-node being linked to at least one other hyper-node or to at least one node of the semantic graph, in which the node belongs to at least one of the semantic graph or the hyper-structural graph (Wical in [0003] and [0005] discloses data stored into a structure able to dynamically connect at various levels of granularity and interpretation, such as a hyper-graph structure, a portion or a sub-graph of the hyper-graph can be selected based on a defined combination of data, content in the selected portion can be compared to other structures or used to find similar sub-graphs within the hyper-graph; Wical in [0035] and [0069] discloses expanding or extending data and data dimension associations in a hyper-graph through various sub-graphs; [0048] and [0099] discloses representing different types of semantic structures in hyper-graph, traversing a network and measuring semantics and distances from start points, placing data or content into a graph, such as a hyper-graph type structure, dimensions of content stored in the graph with semantic tags that define what is known about them; Wical in [0102] and [0105] discloses for semantic relativity measuring relative positions in the hyper-graph structure of a sub-graph that represents some object, the object can include any collection of nodes and edges in the graph as well as being a single node in the graph, any node can be connected to any other node via an edge, and the edges are defined semantically, interpreted as hyper-nodes of a hyper-graph linked to other nodes in a semantic graph or hyper-graph structure).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Abdelhamid, Nigam, and Wical, to have combined Abdelhamid, Nigam, and Wical. The motivation to combine Abdelhamid, Nigam, and Wical would be to generate an organized structure of personalized content for a customer by storing and selecting content in a hyper-graph structure (Wical: [0004]).
With respect to claim 6, Abdelhamid in view of Nigam and in further view of Wical discloses the method of claim 5, the node capable of creating an overload belonging to one of the semantic graph or the hyper-structural graph, the method further comprising determining that several of the nodes of the structural graph associated with the node capable of creating an overload are also associated with another node of the other of the hyper-structural graph or the semantic graph, the node capable of creating an overload representing a first set, the other node representing a second set, in which the generated indexing node represents a sub-set of the first and second sets (Abdelhamid in [0032], [0051], and [0096] discloses dynamically updating index to reflect current status of system while consuming a minimal memory overhead, reordering execution based on information collected while processing past graph updates; Nigam in [0036] and [0045] discloses to improve processing of complex queries of graph, selectively index attributes for portions of graph that significantly add to the processing of the complex queries, selective indexing reduce the search space and associated processing of the complex queries while mitigating increases in write latency, overhead, and/or memory usage associated with building complete indexes of attributes in data sets, storing a graph in a number of logical partitions, with each partition containing a subset of nodes of graph and all edges belonging to the subset of nodes; Wical in [0035] and [0069] discloses expanding or extending data and data dimension associations in a hyper-graph through various sub-graphs; [0048] and [0099] discloses representing different types of semantic structures in hyper-graph; Wical in [0102] and [0105] discloses for semantic relativity measuring relative positions in the hyper-graph structure of a sub-graph that represents some object, the object can include any collection of nodes and edges in the graph as well as being a single node in the graph, any node can be connected to any other node via an edge, and the edges are defined semantically, interpreted as hyper-nodes of a hyper-graph linked to other nodes in a semantic graph or hyper-graph structure).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abdelhamid (US Pub 2018/0032587) in view of Nigam (US Pub 2016/0092584) and in further view of Charry (US Pub 2021/0027091).
With respect to claim 7, Abdelhamid in view of Nigam discloses the method of claim 1, wherein the structural graph and at least a portion of the super-graph are stored within a distributed hardware architecture… (Abdelhamid in [0028], [0036] and [0071] discloses embeddings for a structural graph and at least a portion or subgraph of a supergraph being stored; Abdelhamid in [0107] and [0108] and in Figure 9 discloses storage in a distributed hardware architecture; Nigam in [0027], [0072], and [0074] and in Figure 2 discloses storing graph within a distributed hardware architecture).
Abdelhamid and Nigam discloses storing graph with a distributed hardware architecture, however, Abdelhamid and Nigam do not explicitly disclose:
…a distributed hardware architecture compliant with edge computing.
The Charry reference discloses a distributed hardware architecture compliant with edge computing (Charry in [0019], [0035], and [0041] discloses maintaining a super-graph composed of individual sub-graphs, generating a graph from infrastructure monitoring data, within graph each component are represented by a vertex wherein two vertices are interconnected by an edge, obtaining data from each graph to generate and maintain a super-graph that is a composition of individual data center sub-graphs; Charry in [0055] and [0062] and in Figure 4 discloses a distributed hardware architecture compliant with one or more edge computing networks).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Abdelhamid, Nigam, and Charry, to have combined Abdelhamid, Nigam, and Charry. The motivation to combine Abdelhamid, Nigam, and Charry would be to manage infrastructure in a computing environment by graphically representing information using graphs (Charry: [0004] and [0005]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.M/Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159
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