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 .
Information Disclosure Statement
An Information Disclosure Statement (IDS) has not been submitted as of the mailing of the last Office Action dated 15 May 2026. Applicant is reminded of the continuing obligation under 37 CFR 1.56 to timely apprise the Office of any information which is material to patentability of the claims under consideration in this application.
Introductory Remarks
In response to communications filed on 5 August 2026, no claims were amended. No claims were cancelled. No new claims were added. Therefore, claims 1-17 are presently pending in the application, of which claims 1, 16 and 17 are presented in independent form.
The previously raised 103 rejection of the pending claims is maintained.
Response to Arguments
Applicant’s arguments filed 5 August 2026 with respect to the rejection of the claims under 35 U.S.C. 103 have been fully considered but are not persuasive.
Applicant’s argument that Lee does not teach or suggest “extending a K-th-order pattern and a K-th-order pattern instance of the to-be-mined full graph based on the DFS code”, emphasizing that Lee only discloses expanding edges and generating DFS code for each expanded edge (see Remarks, p. 4) is unpersuasive. Applicant’s arguments appear to be based on attempting to find a literal match of “K-th-order pattern” and “K-th-order pattern instance” in Lee, and makes no further remarks.
Applicant’s arguments rely on only one of the cited sections (Lee, [0042]). However, the first section cited as Lee, [0046], where DFS code of the generated subgraph is normalized to determine if the generated subgraph is a normalized graph with the minimum DFS code. Lee, [0046] further states that “If the generated subgraph is a normal graph, the edges in the subgraph are extended one by one to generate a new subgraph from the subgraph (S14)”. Thus, the extension occurs “based on the DFS code” from the generated subgraph that was normalized. Thus, Applicant’s arguments are directed to a subsection of the rejection, despite Lee, [0046] having been the first cited portion, followed by Lee, [0042], which provided further elaboration/context of Lee, [0042].
Additionally, based on broadest reasonable interpretation, e.g., relying on technical definitions of K-th-order pattern and K-order-pattern instance, Lee’s disclosed steps align with the characteristics of what is being claimed, despite not explicitly stating that this corresponds to a k-th-order pattern / pattern instance. Applicant’s arguments do not attempt to explain why the elements mentioned in the rejection do not correspond to the k-th-order pattern / pattern instance. Applicant makes similarly vague remarks with respect to (k+1)-th-order pattern / pattern instance (with respect to Sadredini), “determining, based on quantities of pattern instances corresponding to all orders of patterns of the to-be-mined full graph”, etc. (see Remarks, p. 5-8).
Applicant’s arguments rest solely on arguing that the prior art does not disclose the claimed subject matter with no further explanation as to why the elements disclosed in the prior art do not disclose the claimed subject matter. Instead, Applicant’s arguments rest on finding exact literal language matching the claimed elements. As one example, it is apparent, for example, that when Lee disclosed “frequency”, this is a type of “quantity” as claimed, yet Applicant solely argued that it was not disclosed by Lee without a substantive explanation (see, e.g., Applicant’s remarks on p. 7 with respect to the “determining, based on quantities of pattern instances corresponding to all orders of patterns of the to-be-mined full graph”).
Therefore, the prior art rejection suffices in its explanation as to why the prior art disclosed the claimed features. No modifications have been made to the 103 rejection, and the 103 rejection has been maintained.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (“Lee”) (KR2014/0130014A), in view of Sadredini et al. (“Sadredini”) (US 2019/0228012 A1).
Regarding claim 1: Lee teaches A graph mining method, comprising:
obtaining a depth first search (DFS) code corresponding to a to-be-mined full graph in a predetermined scenario; extending a K-th-order pattern and a K-th-order pattern instance of the to-be-mined full graph based on the DFS code … (Lee, [0046], where DFS code of the generated subgraph is normalized to determine if the generated subgraph is a normalized graph with the minimum DFS code (implying that the DFS code was previously “obtained” as claimed). Edges in the subgraph are extended one by one to generate a new subgraph from the subgraph S14. See also Lee, [0042], where the DFS code of the subgraph expands edges in the order of vertex identifiers, using a depth-first search method, and generates a DFS code for each expanded edge.
See Lee, [0002], where graph classification classifies data with a graph structure, such as compounds, XML, web documents, and social networks (i.e., “graph in a predetermined scenario”));
determining, based on quantities of pattern instances corresponding to all orders of patterns of the to-be-mined full graph, support corresponding to all the orders of patterns; and determining a frequent subgraph of the to-be-mined full graph based on the support corresponding to all the orders of patterns (Lee, [0025], where the system generates a candidate frequent subgraph having a minimum DFS code through a normalization operation for each of a plurality of graphs and generates a candidate frequent subgraph that is expressed above the minimum support in the generated subgraph. The candidate frequent subgraphs are grouped into a number of similarity groups, and the candidate frequent subgraph with the highest classification power in each similarity group is selected as a feature frequent subgraph. The system then generates a frequent subgraph using the feature frequent subgraph).
Lee does not appear to explicitly teach [extending a K-th-order pattern and a K-th-order pattern instance of the to-be-mined full graph] to obtain a (K+1)-th-order pattern and a (K+1)-th-order pattern instance of the to-be-mined full graph, wherein K is an integer greater than or equal to 0.
Sadredini teaches [extending a K-th-order pattern and a K-th-order pattern instance of the to-be-mined full graph] to obtain a (K+1)-th-order pattern and a (K+1)-th-order pattern instance of the to-be-mined full graph, wherein K is an integer greater than or equal to 0 (Sadredini, [0046], where the system generates a (k+1) candidate, and DFS implementations use k-frequent candidates when looking at a (k+1)-candidate. See Sadredini, [0025] and [0027], with respect to k-th-order pattern and (K+1)-th-order pattern, e.g., where the (k+1)-candidates are generated from the known k-frequent-candidates within an equivalent class, e.g., the candidate generation step is based on an equivalent class right most extension approach.
See Sadredini, [0024], where k = 1, 2, …, m (i.e., “where K is an integer greater than or equal to 0”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee and Sadredini (hereinafter “Lee as modified”) with the motivation of enumerating all subgraphs, e.g., building a search tree where each level represents an extension to the pattern, with the goal of extending existing patterns to form larger patterns, which aids in graph mining operations.
Although Lee as modified does not appear to explicitly state that K can equal 0, one of ordinary skill in the art would have found it obvious to have modified Lee as modified such that K can equal 0 as well, with the motivation of including the entire graph, e.g., using the full graph as a starting point before progressively removing nodes of lower degree1, which helps analyze the graph’s structure from its sparsest, outermost layer to its densest core.2
Regarding claim 2: Lee as modified teaches The method according to claim 1, wherein the DFS code is in a form of a sextuple, and the sextuple comprises a start node identifier of an edge, an end node identifier of the edge, a start node label of the edge, an edge label of the edge, an end node label of the edge, and a direction of the edge (Lee, [0044] / [Math Formula 2], where the DFS code = {i, j, l(vi), l(e(vi, vj)), l(vj)}, where “i” and “j” are two vertex labels (i.e., “a start node identifier of an edge, an end node identifier of the edge”); l(vi) and l(vj) are edge labels that connect two vertices (i.e., “a start node label of the edge” and “an end node label of the edge”); and l(e(vi, vj)) is an edge label (i.e., “an edge label of the edge”)).
Although Lee does not appear to explicitly state that the DFS code contains “a direction of the edge” as claimed (thus resulting in a “sextuple” as claimed), one of ordinary skill in the art would have found it obvious to have modified Lee to have explicitly included such an element in the tuple with the motivation of enabling the disclosure to be applied to directed graphs, in which edges have a direction associated with them3, thereby increasing the types of applications that Lee’s disclosure can be applied to (e.g., instead of being limited to undirected graphs, the disclosure may more broadly apply to any structure requiring directed graphs as well).
Regarding claim 3: Lee as modified teaches The method according to claim 1, wherein the extending the K-th-order pattern and the K-th-order pattern instance of the to-be-mined full graph based on the DFS code, to obtain the (K+1)-th-order pattern and the (K+1)-th-order pattern instance of the to-be-mined full graph comprises:
extending the K-th-order pattern through pattern extension based on a K-th-order pattern code corresponding to the K-th-order pattern of the to-be-mined full graph, to obtain (K+1)-th-order pattern codes of a plurality of (K+1)-th-order patterns of the to-be-mined full graph, wherein the (K+1)-th-order pattern corresponds to at least one (K+1)-th-order pattern code (Sadredini, [0046], where the system generates a (k+1) candidate, and DFS implementations use k-frequent candidates when looking at a (k+1)-candidate. See Sadredini, [0025] and [0027], with respect to k-th-order pattern and (K+1)-th-order pattern, e.g., where the (k+1)-candidates are generated from the known k-frequent-candidates within an equivalent class);
if a target (K+1)-th-order pattern code corresponding to the (K+1)-th-order pattern is a canonical pattern code, determining the (K+1)-th-order pattern instance from the to-be-mined full graph based on the target (K+1)-th-order pattern code and the K-th-order pattern instance (Lee, [0046], where the system normalizes the DFS code of the generated subgraph to determine if the generated subgraph is a normalized graph with the minimum DFS code (i.e., “if a target [pattern code] is a canonical pattern code”4). If the generated subgraph is a normal graph, the edges in the subgraph are extended one by one to generate a new subgraph from the subgraph S14. See Sadredini, [0025], [0027], and [0046] with respect to “K-th-order” pattern and “(K+1)-th-order” pattern as claimed), wherein the pattern code is at least one code generated based on a DFS code corresponding to a subgraph of the pattern in the to-be-mined full graph, and the canonical pattern code is a pattern code used to uniquely identify the pattern (See Lee, [0047], where the minimum DFS code among the subgraph’s DFS codes (i.e., “the pattern code is at least one code generated based on a DFS code corresponding to a subgraph of the pattern in the to-be-mined full graph”), is used in the form of the subgraph’s regular code to uniquely represent the subgraph).
Regarding claim 7: Lee as modified teaches The method according to claim 3, wherein before the determining the (K+1)-th-order pattern instance from the to-be-mined full graph, the method further comprises:
determining, based on a target (K+1)-th-order pattern code corresponding to a target (K+1)-th-order pattern obtained through pattern extension, a primal graph corresponding to the target (K+1)-th-order pattern code (Lee, [0047], where the process for finding the minimum DFS code of a subgraph (i.e., “canonical pattern code”) is as follows: represent the edges of the subgraph as DFS codes, and set the smallest value among the DFS codes of each edge as the starting point for the search (i.e., “a primal graph corresponding to the target [pattern] code”). Starting from the search start point, vertex identifiers are assigned sequentially according to the DFS order. If the same vertex has multiple edges, select the edge represented by the smallest DFS code and explore. If there are multiple edges that are the smallest possible, select one edge and explore all of them, then recursively explore all remaining edges as well. Find the minimum DFS code that is the smallest representation among the set of DFS codes obtained through the search. See Sadredini, [0025], [0027], and [0046] with respect to the “(K+1)-th-order” pattern);
extending the primal graph based on the target (K+1)-th-order pattern code, to obtain at least one (K+1)-th-order extended instance corresponding to the primal graph; and if a DFS code of each (K+1)-th-order extended instance is greater than or equal to a DFS code corresponding to the target (K+1)-th-order pattern code, determining that the target (K+1)-th-order pattern code of the target (K+1)-th-order pattern is the canonical pattern code (Lee, [0042], where the subgraph generating unit 111 generates a subgraph having a minimum DFS code, the DFS code of the subgraph expanding edges in the order of vertex identifiers using a DFS method and generates a DFS code for each expanded edge. Recall from Lee, [0047], where the subgraph is represented in the form of a canonized code (i.e., “canonical pattern code”). See Sadredini, [0025], [0027], and [0046] with respect to the “(K+1)-th-order” pattern).
Regarding claim 14: Lee as modified teaches The method according to claim 1, wherein the predetermined scenario is a risk control scenario, and the support is a risk degree (Lee, [0002], where graph classification classifies data with a graph structure, such as compounds, XML, web documents, and social networks (i.e., “graph in a predetermined scenario”). Frequent subgraphs appear above a minimum support level in a graph database, where frequent subgraphs represent the unique characteristics of a graph and are used in graph classification, clustering, and indexing).
Although Lee does not appear to explicitly state that the type of information relates to a “risk control scenario” and “risk degree” as claimed, the claimed invention does not distinguish over the prior art because the differences in the claim limitations and the prior art’s disclosure are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The steps would have been performed the same regardless of the specific data involved (i.e., risk control/risk degree as claimed, or some other data). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 1385, 217 USPQ2d 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art to have referred to Lee’s teachings in making the claimed invention, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention over the prior art.
Regarding claim 15: Lee as modified teaches The method according to claim 1, wherein the to-be-mined full graph comprises a seed node, and the extending the K-th-order pattern and the K-th-order pattern instance of the to-be-mined full graph based on the DFS code comprises:
starting from the seed node, extending the K-th-order pattern and the K-th-order pattern instance of the to-be-mined full graph based on the DFS code (Lee, [0047], where the system finds the minimum DFS code of a subgraph for normalization operations by representing the edges of the subgraph as DFS codes, and sets the smallest value among the DFS codes of each edge as the starting point for the search. Starting from the search start point, vertex identifiers are assigned sequentially according to the DFS order. The minimum DFS code that is the smallest representation among the set of DFS codes is obtained through the search. See Sadredini, [0025], [0027], and [0046] with respect to k-th-order pattern and (K+1)-th-order pattern, e.g., where the (k+1)-candidates are generated from the known k-frequent-candidates within an equivalent class).
Regarding claim 16: Claim 16 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Sadredini teaches An electronic device, comprising: a processor; and a memory storing instructions executable by the processor; wherein the processor is configured to [implement the disclosed steps] (Sadredini, [0068] and [0071-0072], where the disclosed system may be implemented as a computer readable storage medium (a type of electronic device), which stores instructions provided to and executable by a processor for implementing the disclosed operations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee and Sadredini (with respect to the electronic device), as Lee discloses, e.g., devices for performing the disclosed steps. Therefore, one of ordinary skill in the art would have found it obvious to have incorporated Sadredini’s computer-readable media storing instructions that are executable by a processor with the motivation of enabling the steps disclosed by Lee to be carried out in an automated (and intended) manner.
Regarding claim 17: Claim 17 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Sadredini teaches A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform a graph mining method, the method comprising [the claimed steps] (Sadredini, [0010], [0068] and [0071-0072], where the disclosed system may be implemented as a non-transitory computer readable storage medium, which stores instructions provided to and executable by a processor for implementing the disclosed operations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee and Sadredini (with respect to the electronic device), as Lee discloses, e.g., devices for performing the disclosed steps. Therefore, one of ordinary skill in the art would have found it obvious to have incorporated Sadredini’s computer-readable media storing instructions that are executable by a processor with the motivation of enabling the steps disclosed by Lee to be carried out in an automated (and intended) manner, as well as persisting such code such that such operations can be repeatedly carried out without requiring re-uploading, re-downloading, re-installing, etc.
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (“Lee”) (KR2014/0130014A), in view of Sadredini et al. (“Sadredini”) (US 2019/0228012 A1), in further view of Zhou et al. (“Zhou”) (CN 111008196 A).
Regarding claim 4: Lee as modified teaches The method according to claim 3, but does not appear to explicitly teach wherein the extending the K-th-order pattern through pattern extension based on the K-th-order pattern code corresponding to the K-th-order pattern of the to-be-mined full graph comprises: extending the K-th-order pattern through forward extension based on the K-th-order pattern code corresponding to the K-th-order pattern of the to-be-mined full graph; and extending the K-th-order pattern through backward extension based on the K-th-order pattern code corresponding to the K-th-order pattern of the to-be-mined full graph.
Zhou teaches wherein the extending the K-th-order pattern through pattern extension based on the K-th-order pattern code corresponding to the K-th-order pattern of the to-be-mined full graph comprises: extending the K-th-order pattern through forward extension based on the K-th-order pattern code corresponding to the K-th-order pattern of the to-be-mined full graph; and extending the K-th-order pattern through backward extension based on the K-th-order pattern code corresponding to the K-th-order pattern of the to-be-mined full graph (Zhou, [0028-0029], where the frequent pattern mining method is based on depth-first search, where the DFS code sequence is gradually expanded from few to many until the traversal is finished. The pattern matching calculation is performed by gradually expanding downwards, where each expansion follows the rightmost path expansion principle. Given a graph G, a new edge e can be added between the rightmost node and another node on the rightmost path, which is called a backward expansion; or a new node can be introduced and connected to the rightmost node, which is called a forward expansion).
Although Zhou does not appear to explicitly state that these steps are actively performed (instead, utilizing the language “can”), one of ordinary skill in the art would have found it obvious to have modified Zhou to have performed both types of extensions with the motivation of mining frequent patterns for various types of node connections (see, e.g., Zhou, [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee as modified and Zhou with the motivation of reducing the generation of duplicate graphs.5
Regarding claim 5: Lee as modified teaches The method according to claim 3, wherein the K-th-order pattern corresponds to a plurality of K-th-order pattern instances, and
the determining the (K+1)-th-order pattern instance from the to-be-mined full graph based on the target (K+1)-th-order pattern code and the K-th-order pattern instance comprises:
grouping the plurality of K-th-order pattern instances corresponding to the K-th-order pattern into a plurality of groups, wherein each group comprises at least one K-th-order pattern instance (Lee, [0054-0057], where the initial group generation unit 121 generates an initial similar group and similar groups, where similar groups are generated by including candidate frequent subgraphs in the initial similar group having the highest similarity based on the calculated similarity. The generated similar group is determined as the final similar group if the initial and generated similar groups are the same) … .
Lee as modified does not appear to explicitly teach searching, based on the K-th-order pattern instance in each group and the target (K+1)-th-order pattern code, the to-be-mined full graph for a new edge corresponding to the target (K+1)-th-order pattern code, to generate a new (K+1)-th-order pattern instance.
Zhou teaches searching, based on the K-th-order pattern instance in each group and the target (K+1)-th-order pattern code, the to-be-mined full graph for a new edge corresponding to the target (K+1)-th-order pattern code, to generate a new (K+1)-th-order pattern instance (Zhou, [0029], where the graph-encoded linear sequence G_code sequence obtained through traversal is used as input to mine frequent patterns, where the pattern matching calculation is performed by gradually expanding downwards, each expansion following the rightmost path expansion principle. Given a graph G, a new edge e can be added between the rightmost node and another node on the rightmost path (a backward expansion). See Lee, [0001], where generated frequent subgraphs corresponding to the generated groups (Lee, [0068-0073]) can be used to accurately mine all graphs included in the graph database. See Sadredini, [0025], [0027], and [0046] with respect to the “K-th order” pattern and “(K+1)-th-order” pattern as claimed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee as modified and Zhou (hereinafter “Lee as modified”) with the motivation of increasing efficiency in finding connecting paths, e.g., improved efficiency in directional graphs when there are many outgoing edges.
Regarding claim 6: Lee as modified teaches The method according to claim 5, wherein each group corresponds to at least one task, each task corresponds to one thread, and the searching, based on the K-th-order pattern instance in each group and the target (K+1)-th-order pattern code, the to-be-mined full graph for a new edge corresponding to the target (K+1)-th-order pattern code, to generate a new (K+1)-th-order pattern instance comprises: searching, in a multithreaded manner based on the K-th-order pattern instance in each group and the target (K+1)-th-order pattern code, the to-be-mined full graph for a new edge corresponding to the target (K+1)-th-order pattern code, to generate a new (K+1)-th-order pattern instance (Sadredini, [0028] and [0041], where the disclosed system uses massive parallelism of the automata processor (AP), which allows a programmer to provide a stream of input symbols to be computed on the nondeterministic finite state automata (NFA) in parallel. See claim 5 above with respect to the rest of the “searching” limitation and “group” (e.g., Lee, [0054] and [0057]). Note that the existence of parallelism implies “wherein each group corresponds to at least one task [and] each task corresponds to one thread”6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee as modified and Sadredini with the motivation of performing high speed search and analysis on complex and unstructured data (Sadredini, [0007]).
Claims 8-9 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (“Lee”) (KR2014/0130014A), in view of Sadredini et al. (“Sadredini”) (US 2019/0228012 A1), in further view of Savkli (“Savkli”) (US 2016/0063037 A1).
Regarding claim 8: Lee as modified teaches The method according to claim 1, but does not appear to explicitly teach wherein the graph mining method is applied to a distributed system, the distributed system comprises a plurality of partitions, and the extending the K-th-order pattern and the K-th-order pattern instance of the to-be-mined full graph based on the DFS code, to obtain the (K+1)-th-order pattern and the (K+1)-th-order pattern instance of the to-be-mined full graph comprises: extending the K-th-order pattern and the K-th-order pattern instance of the to-be-mined full graph in all the partitions based on the DFS code, to obtain (K+1)-th-order patterns and (K+1)-th-order pattern instances of the to-be-mined full graph that correspond to all the partitions; and collecting statistics on the (K+1)-th-order pattern instances in all the partitions, to obtain sub-support information in all the partitions.
Savkli teaches wherein the graph mining method is applied to a distributed system, the distributed system comprises a plurality of partitions (Savkli, [0060] and [0065], where the graph may be partitioned into subgraphs which are stored on the nodes of the cluster (note that the subgraphs corresponding to “partitions” as claimed). See also, e.g., Savkli, [0080], with respect to the distributed nature of the underlying graph), and
the extending the K-th-order pattern and the K-th-order pattern instance of the to-be-mined full graph based on the DFS code, to obtain the (K+1)-th-order pattern and the (K+1)-th-order pattern instance of the to-be-mined full graph comprises:
extending the K-th-order pattern and the K-th-order pattern instance of the to-be-mined full graph in all the partitions based on the DFS code, to obtain (K+1)-th-order patterns and (K+1)-th-order pattern instances of the to-be-mined full graph that correspond to all the partitions (See Savkli above with respect to the graph partitions. See claim 1 above with respect to the “extending” step); and
collecting statistics on the (K+1)-th-order pattern instances in all the partitions, to obtain sub-support information in all the partitions (Savkli, [0072], where a query subgraph may be received and sent to each of the computing devices, where more than one computing device (which stores the subgraph, i.e., “partition”) returns a match or partial match, in which the results are unified. See Lee, [0049], where the support of each subgraph is calculated (i.e., “obtain sub-support information”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee as modified and Levin (hereinafter “Lee as modified”) with the motivation of enabling flexibility, parallelization, and scalability (Savkli, [0004]).
Regarding claim 9: Lee as modified teaches The method according to claim 8, further comprising:
sending the sub-support information in a current partition to a target partition in the distributed system based on a pattern extension manner of the (K+1)-th-order pattern in the current partition (Savkli, [0066], where joint neighbors of a pair of vertices may be accomplished by querying the edge tables of each node. See Lee, [0049], with respect to the support information being calculated for each subgraph, and Sadredini, [0025], [0027], and [0046] in claim 1 with respect to the “(K+1)-th-order” pattern).
Although Lee as modified does not appear to explicitly state that the sub-support information is “sent” between the partitions, because a subgraph whose support information is being calculated may theoretically exist on multiple partitions, therefore it would have been obvious to one of ordinary skill in the art to have sent the support information (e.g., the already-calculated information) to the next relevant partition so that the next relevant (target) partition may use that support information as the basis when calculating its own support information for the rest of the subgraph (e.g., if the score in the current partition was 3, instead of the target partition starting at 0, it would be better for it to start at 3, e.g., continuing where the current partition left off).
Regarding claim 11: Lee as modified teaches The method according to claim 8, wherein the determining, based on the quantities of pattern instances corresponding to all orders of patterns of the to-be-mined full graph, support corresponding to all the orders of patterns comprises:
aggregating the sub-support information of the (K+1)-th-order pattern in all the partitions, to obtain a support indicator of the (K+1)-th-order pattern, wherein the support indicator meets anti-monotonicity (Savkli, [0072], where a query subgraph may be received and sent to each of the computing devices, where more than one computing device (which stores the subgraph, i.e., “partition”) returns a match or partial match, in which the results are unified. See Lee, [0049], where the support of each subgraph is calculated (i.e., “obtain sub-support information”). Note that the pattern support implicitly includes anti-monotonic properties7).
Regarding claim 12: Lee as modified teaches The method according to claim 11, wherein the determining the frequent subgraph of the to-be-mined full graph based on the support corresponding to all the orders of patterns comprises:
if the support indicator of the (K+1)-th-order pattern is greater than a predetermined support threshold, determining the (K+1)-th-order pattern as a target-order pattern that meets the support indicator; and determining a subgraph corresponding to the target-order pattern as the frequent subgraph of the to-be-mined full graph (Lee, [0025], where the system generates a candidate frequent subgraph having a minimum DFS code through a normalization operation for each of a plurality of graphs and generates a candidate frequent subgraph that is expressed above the minimum support in the generated subgraph. The candidate frequent subgraphs are grouped into a number of similarity groups, and the candidate frequent subgraph with the highest classification power in each similarity group is selected as a feature frequent subgraph. The system then generates a frequent subgraph using the feature frequent subgraph. See, e.g., Sadredini, [0025], [0027] and [0046], with respect to (k+1)-th-order patterns).
Regarding claim 13: Lee as modified teaches The method according to claim 12, the method further comprising:
sending the target-order pattern to all the partitions in the distributed system through broadcasting (Savkli, [0057], where the graph processing module 44 causes transmission of the query to at least two nodes of the graph).
Although Lee as modified does not appear to explicitly teach that the transmission of the query is via “broadcasting” as claimed, one of ordinary skill in the art would have found it obvious to have modified Lee as modified to have explicitly performed the transmission via broadcasting as claimed, with the motivation of greater communication efficiencies, e.g., instead of having to assign target nodes specifically, it is faster to broadcast and allow receiving nodes tuned into the network to receive it.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (“Lee”) (KR2014/0130014A), in view of Sadredini et al. (“Sadredini”) (US 2019/0228012 A1), in further view of Savkli (“Savkli”) (US 2016/0063037 A1), in further view of Zhou et al. (“Zhou”) (CN 111008196 A).
Regarding claim 10: Lee as modified teaches The method according to claim 9, but does not appear to explicitly teach wherein the sending the sub-support information in the current partition to the target partition in the distributed system based on the pattern extension manner of the (K+1)-th-order pattern in the current partition comprises: if the pattern extension manner of the (K+1)-th-order pattern in the current partition is backward extension, sending a pattern instance quantity corresponding to the (K+1)-th-order pattern in the current partition to the target partition in the distributed system; or if the pattern extension manner of the (K+1)-th-order pattern in the current partition is forward extension, sending an instance identifier set of the pattern instances corresponding to the (K+1)-th-order pattern in the current partition to the target partition in the distributed system.
Zhou teaches if the pattern extension manner of the (K+1)-th-order pattern in the current partition is backward extension, sending a pattern instance quantity corresponding to the (K+1)-th-order pattern in the current partition to the target partition in the distributed system (Zhou, [0029], where given a graph G, a new edge can be added to the rightmost node and another node on the rightmost path, called a backward expansion. See Lee, [0049], with respect to the calculation of support for each subgraph (i.e., “pattern instance quantity”). See Sadredini, [0025], [0027], and [0046] in claim 1 above with respect to the “(K+1)-th-order” pattern. See Savkli above with respect to the partitions); or if the pattern extension manner of the (K+1)-th-order pattern in the current partition is forward extension, sending an instance identifier set of the pattern instances corresponding to the (K+1)-th-order pattern in the current partition to the target partition in the distributed system (Zhou, [0029], where given a graph G, a new node can be introduced and connected to the rightmost node, which is called a forward expansion. See Lee, [0049], with respect to the calculation of support for each subgraph (i.e., “pattern instance quantity”). See Sadredini, [0025], [0027], and [0046] in claim 1 above with respect to the “(K+1)-th-order” pattern. See Savkli above with respect to the partitions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Lee as modified and Zhou with the motivation of reducing the generation of duplicate graphs.8
Although Lee as modified and Zhou do not appear to explicitly state the type of information being sent between partitions, it would have been obvious to one of ordinary skill in the art to have sent pattern instance quantity (i.e., support information) in the case of backward expansion (e.g., where a new edge is added), as backward expansion is minimizes the generation of duplicate graphs (thus the count will be more accurate); and forward expansion introduces a new node, thus one of ordinary skill in the art would have found it obvious to have sent the instance identifier set of the pattern instances with respect to discovering structural patterns, e.g., which is enabled by forward expansion.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/IRENE BAKER/Primary Examiner, Art Unit 2154
27 August 2026
1 Cook (“Understanding a graph by peeling away nodes”) at [page 1] (“The k-core of a graph is the maximal subgraph such that every vertex has degree at least k. The k-shell is the set of vertices that are part of the k-core but not part of the (k+1)-core. For example, the 0-core of a graph is simply the entire graph since every vertex has at least zero edges; a vertex can’t have a negative number of edges. The 1-core of a graph contains the vertices that are connected to other vertices. You form the 1-core by throwing away the 0-shell, the set of isolated vertices. You move from the 1-core to the 2-core by removing nodes of degree 1 until everything that’s left has degree at least 2”).
2 Van Koevering et al. (“Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis”) [page 368, col. 1 under “The present work: A null model based on the k-core”] (“A long line of work in network analysis has shown that successive k-cores of G, for k = 0, 1, 2, ..., provides considerable information about the local structure of G, including the regions where it exhibits denser connectivity…”).
3 Anchuri et al. US 2016/0350443 A1 at [0002] (“A directed graph (or digraph) is a graph, or set of nodes connected by edges, where the edges have a direction associated with them”).
4 Bonchi et al. US 2011/0078189 A1 at [0070] (“One of the key elements in gSpan is the use of the minimum DFS code, which is a canonical form introduced to avoid multiple generations of the same pattern”).
5 University of Illinois Urbana-Champaign (“Graph Mining, Social Network Analysis, and Multirelational Data Mining”) at [page 8] (“Because many DFS trees/subscriptings may exist for the same graph, we choose one of them as the base subscripting and only conduct right-most extension on that DFS tree/subscripting. Otherwise, right-most extensions cannot reduce the generation of duplicate graphs because we would have to extend the same graph for every DFS subscripting”).
6 Ailamaki et al. US 2018/0246755 A1 at [0003] (“Task scheduling usually involves using a number of threads to process tasks. As a thread completes one task…it can move to processing the next task waiting to be processed. Such [task] scheduling systems are typically effective in leveraging the ability of modern multicore processors to process tasks in parallel”).
7 Barkol et al. US 2013/0097138 A1 at [0034] (“…pattern support, as defined for a database of many graphs, is anti-monotonic, i.e., a supergraph cannot have support more than any of its subgraphs. This property allows for fast pruning of candidate patterns during a pattern search, since a pattern (and all of its extensions) may be pruned, when its support falls below a user specified minimum support threshold, minsup”).
8 See footnote 5 above with respect to the University of Illinois Urbana-Champaign.
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