Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in Chinese Patent Application No. 202111381794.7, filed on November 19, 2021
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/27/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 9, 13-16, is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Matlab. (2015, September 15). Transitive reduction of complete graph. MathWorks. https://www.mathworks.com/help/matlab/ref/digraph.transreduction.html, hereinafter “Matlab”.
Claim 1:
Matlab teaches a construction method for a bipartite graph, comprising: searching a computational graph for at least one cross-communication edge corresponding to a first communication node (i.e. pg. 1, Fig. 1, “H = transreduction(G) returns the transitive reduction of graph G as a new graph, H. The nodes in H are the same as those in G, but H has different edges. H contains the fewest number of edges such that if there is a path from node i to node j in G, then there is also a path from node i to node j in H”, wherein the BRI for a cross-communication edge encompasses an edge from noes 2 to 4, which bypasses a first communication node 3. Wherein it is noted on pg. 1 that an initial graph has many redundant edges.
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), wherein the first communication node is one of M communication nodes comprised in the computational graph, the first communication node corresponds to P predecessor nodes and Q successor nodes (i.e. pg. 1, “G = digraph([1 1 1 2 2 2 3 3 3 4 4 4],[2 3 4 1 3 4 1 2 4 1 2 3]); plot(G)”, wherein it is noted that the BRI for M communication node encompasses how node 3 has Predecessor node 2 and a Successor node 4 as it can be seen that node 3 flows into node 2 which flows into node 4), each of the at least one cross-communication edge indicates a communication path between one of the P predecessor nodes and one of the Q successor nodes, and no cross-communication edge passes through the M communication nodes (i.e. pg. 1, “Create and plot a complete graph of order four”, wherein it is noted that an edge from node 2 to node 4 does not pass through the communication node 3), wherein M, P, and Q are positive integers (i.e. pg. 1, “plot(g)”, wherein it is noted that there is at least one node for each of the above nodes thus the number of M, P, and Q nodes are positive integers); and
cutting cross-communication edges respectively corresponding to the M communication nodes (i.e. Fig. 2, “H = transreduction(G); plot(H)”, wherein it is noted that the transitive reduction cuts the redundant cross communication edge from node 2 to node 4 that bypassed node 3 and results in a simpler directed graph. Wherein it is noted on pg. 2 that a reduced graph has redundant edges cut.
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), and performing an aggregation operation to obtain the bipartite graph, wherein any two of the M communication nodes are not directly connected via an edge in the bipartite graph (i.e. pg. 2, “any cycle of four nodes produces the same transitive reduction as H”, wherein the BRI for performing an aggregation option encompasses the transitive reduction that may result in viewing nodes 3 and 1 as communication nodes and nodes 2 and 4 as processing nodes, the resulting graph could be considered a bipartite graph as nodes 1 and 3 are not directly connected via an edge).
Claim 2:
Matlab teaches the method according to claim 1,
wherein each cross-communication edge comprises at least one subedge, and each of the at least one subedge is directly connected to two computation nodes (i.e. pg. 2, “G=digraph”, wherein it is noted that each node in the digraph is a mathematical framework used to represent computational nodes and their connections. Wherein the BRI for a subedge being directly connected to two computation nodes encompasses how an edge from 2 to 4 is connected to computation nodes 2 and 4 of the digraph), and
each of the at least one subedge corresponds to one weight coefficient, and the weight coefficient corresponding to each subedge is determined by types of the two computation nodes directly connected to each subedge (i.e. pg. 2, “H contains the fewest number of edges such that if there is a path from node i to node j in G, then there is also a path from node i to node j in H”, wherein the BRI for a weight coefficient encompasses any sort of weighting that contributes the removal of an edge. Wherein it is noted that the Matlab transitive reduction algorithm effectively weights the edge by the topological reachability of each of the connected nodes and drops any edges based on the identification of redundant edges. Wherein the weight coefficient that represents that an edge is redundant would correspond to an edge determined to be redundant as it represents a connection to two nodes of a type that belong to a node path that already exists).
Claim 3:
Matlab teaches the method according to claim 2,
wherein the M communication nodes correspond to a total of N cross-communication edges, wherein N is a positive integer (i.e. pg. 1, the examiner notes that in a case where nodes 3 and 1 are viewed and intermediary communication nodes and thus M may be viewed as = 2, there are at least N=12 cross-communication edges, where N is a positive number); and the cutting of the cross-communication edges respectively corresponding to the M communication nodes comprises: cutting one subedge in each of the N cross-communication edges, wherein when E cross-communication edges in the N cross-communication edges comprise a common subedge (i.e. pg. 2, the examiner notes that at least E=8 of the 12 vertexes are cut that are redundant edges as part of the transreduction of (G) to obtain the fewest number of edges), a sum of weight coefficients respectively corresponding to all subedges cut in the E cross- communication edges is the largest or the smallest (i.e. pg. 2, the examiner notes that the BRI for a sum of weight coefficients encompasses the effective weighting of 1 that is given to shared edges that results in the removal of such redundant edges. Wherein the BRI of the largest or the smallest would encompasses how a binary weighting of 1 would be the largest effective weighting to identify a redundant edge), wherein E is a positive integer less than or equal to N (i.e. pg. 2, the examiner notes that a positive number of edges less than or equal to 12 are cut); or when an ith cross-communication edge and another cross-communication edge in the N cross- communication edges do not comprise a common subedge (i.e. pg. 2, the examiner notes an ith edge such as from [31] of graph (H1) has no common path among the other 12 edges as it would remain after a trans reduction), a subedge with a smallest weight coefficient or a largest weight coefficient among subedges comprised in the ith cross- communication edge is cut (i.e. pg. 2, the examiner notes that the BRI for a subedge with a smallest weight coefficient or a largest weight coefficient among subedges would encompasses the effective weighting of 1 that is given to shared edges that results in the removal of such redundant edges. Wherein the BRI of the largest or the smallest would encompasses how a binary weighting of 1 would be the largest effective weighting to identify a redundant edge), wherein i is a positive integer (i.e. pg. 2, the examiner notes that in graph (H1) there are at least 4 edges after a transitive reduction and thus i=4 which is a positive integer).
Claim 4:
Matlab teaches the method according to claim 1, wherein the computational graph after cutting comprises K connected blocks, wherein K is a positive integer (i.e. pg. 2, the examiner notes that the BRI for a K connected block encompasses a block of two nodes that are connected in a direction. Wherein it is noted that there are 12 connected blocks in graph G and that the reduced graph H that 4 connected blocks, where k=8 connected blocks have been cut)); and the performing an aggregation operation to obtain the bipartite graph comprises: separately aggregating the K connected blocks in the computational graph after the cutting to obtain the bipartite graph, wherein the K connected blocks are obtained by grouping computation nodes in the computational graph based on positions of the M communication nodes in the computational graph (i.e. pg. 2, “G1 = digraph([3 4 2 1]”, wherein it is noted that in a cutting and aggregation operation to display a diagraph with constraints G1 = digraph([3 4 1 2], the M communication nodes could be viewed as nodes 2 and 3 which are grouped as bipartite from nodes 1 and 4), the bipartite graph comprises K level-1 aggregation nodes and the M communication nodes, any two of the K level-1 aggregation nodes are not directly connected via an edge, and the K level-1 aggregation nodes respectively belong to K name scopes (i.e. pg. 2, “Create a directed graph that contains a different four node cycle: (1,3,4,2,1).”, wherein the BRI for K level-1 aggregation nodes encompasses nodes that either send or receive data from the nodes viewed as M communication nodes 2 and 3. Wherein it is noted that nodes 1 and 4 belong to a group in Graph G1 where nodes 1 and 4 are not directed connected via an edge and thus may be viewed as effectively belonging to the K name scope that is the other half of the nodes that are bipartite with nodes 2 and 3) .
Claim 9:
Claim 9 is the method claim reciting similar limitations to claim 1 and is rejected for similar reasons.
Claim 13:
Claim 13 is the apparatus claim reciting similar limitations to claim 1 and is rejected for similar reasons.
Claim 14:
Claim 14 is the apparatus claim reciting similar limitations to claim 2 and is rejected for similar reasons.
Claim 15:
Claim 15 is the apparatus claim reciting similar limitations to claim 3 and is rejected for similar reasons.
Claim 16:
Claim 16 is the apparatus claim reciting similar limitations to claim 4 and is rejected for similar reasons.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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) 5-6, 10, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matlab. (2015, September 15). Transitive reduction of complete graph. MathWorks. https://www.mathworks.com/help/matlab/ref/digraph.transreduction.html, hereinafter “Matlab” and further in light of U.S. Patent Application Publication NO. 20210286644 “Jingoi”.
Claim 5:
Matlab teaches the method according to claim 4.
Matlab may not explicitly teach
wherein each of the K level-1 aggregation nodes is of a hierarchical structure, nodes at a jth layer in the hierarchical structure are obtained by expanding an aggregation node at a (j-1)th layer in the hierarchical structure, a first layer in the hierarchical structure is the level-1 aggregation node, and the nodes at the jth layer belong to different name scopes, wherein j is a positive integer; and the nodes at the jth layer comprise the aggregation node and/or the computation node, and the computation node is a node that cannot be expanded.
However, Jingoi teaches
wherein each of the K level-1 aggregation nodes is of a hierarchical structure (i.e. para. [0045], Fig. 5A, “In some embodiments after the selection of the operator group 502, the outgoing link from the operator group 502 to the leaf node 504 is highlighted in a different color as shown in FIG. 5A”, wherein the BRI for K Level-1 Aggregation nodes encompasses the operator groups 502 that contain a subset of operator nodes that cannot be expanded), nodes at a jth layer in the hierarchical structure are obtained by expanding an aggregation node at a (j-1)th layer in the hierarchical structure (i.e. para. [0045], “In some embodiments after the selection of the operator group 502, the operator group 502 will expand into the groups of vertices 508 of FIG. 5B”, wherein the BRI for expanding an aggregation node at a (j-1)th layer encompasses expanding an operator group to obtain operator nodes as seen in operator group 508 of Fig. 5B) , a first layer in the hierarchical structure is the level-1 aggregation node, and the nodes at the jth layer belong to different name scopes, wherein j is a positive integer (i.e. para. [0045], “This machine reasoning workflow includes vertices and operator groups, such as operator group 502 of FIG. 5A. The groups of vertices 508 of FIG. 5B have been grouped and contracted into the operator group 502 by using the methods described in FIG. 2 and FIG. 3”, wherein the BRI for a first layer encompasses the operator group structures 502 in Fig. 5A. Wherein the BRI for different name scopes encompasses how each operator group 502 represents a different aggregation of nodes and that there are a positive number of operator groups); and the nodes at the jth layer comprise the aggregation node and/or the computation node, and the computation node is a node that cannot be expanded (i.e. para. [0045], “the operator group 502 will expand into the groups of vertices 508 of FIG. 5B”, wherein it is noted that the expanded operator group contains computational vertices and their connected nodes that cannot be expanded again)
It would have been obvious to one of ordinary skill in the art at the time of filing to add using parameters trained using wherein each of the K level-1 aggregation nodes is of a hierarchical structure, nodes at a jth layer in the hierarchical structure are obtained by expanding an aggregation node at a (j-1)th layer in the hierarchical structure, a first layer in the hierarchical structure is the level-1 aggregation node, and the nodes at the jth layer belong to different name scopes, wherein j is a positive integer; and the nodes at the jth layer comprise the aggregation node and/or the computation node, and the computation node is a node that cannot be expanded, to Matlab’s graph reduction and grouping algorithm, with grouping and visualization of graph node groups, as taught by Jingoi. One would have been motivated to combine the graph visualization methos Jingoi and graph redundancy methos of Matlab is it can be advantageous as by grouping vertices into operator groups thus condensing the workflow graphs, the user can selectively expand the applicable operator groups that are of interest without having the complexity of the rest of the workflow graph interfere with understanding such a specific logical branch (Jingoi, para. [0021]).
Claim 6:
Matlab and Jingoi teach the method according to claim 5.
Matlab further comprising: updating a first name scope when a subedge between a first computation node and a second computation node in the first name scope is cut, wherein the first name scope is in the computational graph (i.e. pg. 2, “H1 = transreduction(G1); plot(H1)”, wherein it is noted that a first node [1] and second node [2] have redundant edges in Graph G1. Wheein after a trans reduction, a first name scope of nodes [13] of G1 are updated to remove such redundant subedges, which renders the transitive reduction H1 with an updated name scope in that the group of nodes [13] are now bipartite to nodes [24] due to the cutting of redundant edges); and constructing a name scope that comprises the first computation node, wherein the first computation node does not belong to an updated first name scope (i.e. pg. 2, “H1 = transreduction(G1); plot(H1)”, wherein it is noted that the BRI for construction a name scope that comprises the first computation node [1] now has no direct connection to a second node [2] and that node groups viewed as namescopes [13] and [24] are now bipartite).
Claim 10:
Claim 10 is the method claim reciting similar limitations to claim 5 and is rejected for similar reasons.
Claim 17:
Claim 17 is the apparatus claim reciting similar limitations to claim 5 and is rejected for similar reasons.
Claim 18:
Claim 18 is the apparatus claim reciting similar limitations to claim 6 and is rejected for similar reasons.
Claim(s) 7, 12, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matlab. (2015, September 15). Transitive reduction of complete graph. MathWorks. https://www.mathworks.com/help/matlab/ref/digraph.transreduction.html, hereinafter “Matlab”, in light of U.S. Patent Application Publication NO. 20210286644 “Jingoi”, and further in light of
(2021). Opengenus.Org. https://iq.opengenus.org/merkle-tree/#gsc.tab=0, hereinafter “Opengenus”, and NetworkX. (2020, October 20). Weisfeiler_lehman_graph_hash — NetworkX 3.6.1 documentation. Networkx.Org. https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash.html, hereinafter “NetworkX”.
Claim 7:
Matlab and Jingoi teach the method according to claim 5.
While Matlab-Jingoi teach a bipartite graph, Matlab and Jingoi may not explicitly teach
further comprising: computing a hash value of the aggregation node and a hash value of the computation node in the bipartite graph
wherein when the node is the aggregation node, a hash value of the node is equal to a sum of hash values of all nodes obtained by expanding the aggregation node; or when the node is the computation node, a hash value of the node is determined by attributes of the computation node, wherein the attributes of the computation node comprise a type, an in-degree, an out-degree, a type of an auxiliary node, and a quantity of auxiliary nodes of the computation node.
However, Opengenus teaches
further comprising: computing a hash value of the aggregation node and a hash value of the computation node in the (i.e. pg. 3-4, “hash function is a function that takes a set of inputs and maps them into a table or data structure. The output generated by hash function is unique for every input. This helps in fingerprinting of data”, wherein is noted that the BRI for the aggregation node encompasses a top hash of a parent node represented by Hash( hash 0 + hash 1) and that the BRI for the computation node encompasses a child node, such as hash 0),
wherein when the node is the aggregation node, a hash value of the node is equal to a sum of hash values of all nodes obtained by expanding the aggregation node (i.e. pg. 4, “Hash 0-0 contains the hash of D1 and hash 0-1 contains the hash of D2. Hash 0 contains hash of the sum of its children( hash(hash 0-0 + hash 0-1)). Similarly, all non-leaf nodes are a hash of their child nodes and the root node contains the hash of all nodes below it”, wherein the examiner notes a top hash is equal to the sum of all the nodes below the parent node); or when the node is the computation node, a hash value of the node is determined by attributes of the computation node, wherein the attributes of the computation node comprise a type (i.e. pg. 4, “Hash 0-0 contains the hash of D1 and hash 0-1 contains the hash of D2”, wherein the computation nodes encompass child nodes which have has values according to their an attribute such as their hierarchy type), an in-degree, an out-degree, a type of an auxiliary node, and a quantity of auxiliary nodes of the computation node.
It would have been obvious to one of ordinary skill in the art at the time of filing to add computing a hash value of the aggregation node and a hash value of the computation node in the bipartite graph wherein when the node is the aggregation node, a hash value of the node is equal to a sum of hash values of all nodes obtained by expanding the aggregation node; or when the node is the computation node, a hash value of the node is determined by attributes of the computation node, wherein the attributes of the computation node comprise a type, to Matlab-Jingoi’s bipartite graph, with the specific hash value summations of individual node hashes, as taught by Opengenus. One would have been motivated to combine hashing of Opengenus and bipartite graph of Matlab-Jingoi as this type of hashing results in a Merkle tree that only needs small amounts of information to be transmitted across networks (Opengenus, pg. 7).
While Matlab-Jingoi-Opengenus teach hashing of graph nodes and that a hash depends on an attributes such as a node type, Matlab-Jingoi-Opengenus may not explicity teach that attributes comprise
an in-degree, an out-degree, a type of an auxiliary node, and a quantity of auxiliary nodes of the computation node
However, NetworkX teaches that attributes of the computation node comprise a type
an in-degree, an out-degree, a type of an auxiliary node, and a quantity of auxiliary nodes of the computation node (i.e. pg. 1-2, “The function iteratively aggregates and hashes neighborhoods of each node. After each node’s neighbors are hashed to obtain updated node labels, a hashed histogram of resulting labels is returned as the final hash …. If no node or edge attributes are provided, the degree of each node is used as its initial label. Otherwise, node and/or edge labels are used to compute the hash”, wherein it is noted that the hash value for a node within a hash table depends on the node attributes, wherein the neighboring node edges and number of neighbors as well as type of attributes would contribute to the computation of the hash).
It would have been obvious to one of ordinary skill in the art at the time of filing to add computing that attributes of the computation node comprise a type an in-degree, an out-degree, a type of an auxiliary node, and a quantity of auxiliary nodes of the computation node, to Matlab-Jingoi-Opengenus’ bipartite graph hashing, with how individual hash values may depend on the nodes attributes when using a weisfeiler Lehman (WL) graph hash, as taught by NetworkX. One would have been motivated to combine hashing of NetworkX and bipartite graph of Matlab-Jingoi-Opengenus as this type of hashing would help distinguish in- and outgoing edges.
Claim 12:
Claim 12 is the method claim reciting similar limitations to claim 7 and is rejected for similar reasons.
Claim 19:
Claim 10 is the apparatus claim reciting similar limitations to claim 7 and is rejected for similar reasons
Claim(s) 8, 11, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matlab, in light of U.S. Patent Application Publication NO. 20210286644 “Jingoi”, in light of Opengenus, in light of NetworkX, and further in light of Cintra, D., Valejo, A., Lopes, A., & Oliveira, M. (2020). Visualization to assist interpretation of the multilevel paradigm in bipartite graphs. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 3, 133–140. https://doi.org/10.5220/0008903501330140, hereinafter “Cintra”.
Claim 8:
Matlab, Jingoi, Opengenus, and NetworkX teach the method according to claim 7.
Matlab, Jingoi, Opengenus, and NetworkX may not explicitly teach
further comprising: displaying a plurality of nodes in the bipartite graph in a stacked manner, wherein the plurality of nodes are obtained by expanding the same aggregation node once, hash values of the plurality of nodes are the same, and the plurality of nodes are connected in serial or parallel.
However, Cintra teaches
displaying a plurality of nodes in the bipartite graph in a stacked manner, wherein the plurality of nodes are obtained by expanding the same aggregation node once, hash values of the plurality of nodes are the same, and the plurality of nodes are connected in serial or parallel (i.e. pg. 134, Section 2- Multilevel method on bipartite graph, “Figure 1 illustrates the stages of a multilevel method applied to a bipartite graph… a coarsening algorithm creates a sequence of simplified (coarsened) versions of the input graph at gradually increasing contraction levels. Coarsening comprises the steps of matching, which selects which vertices will be collapsed; and contraction, which builds the reduced representation, collapsing the matched vertices and their incident edges into so-called super vertices and super-edges, respectively”, wherein it is noted in Fig. 1 a coarsened network has nodes that may be expanded or ‘uncoarsened’ to represent the collapsed stack of nodes and vertices. Wherein the graph is a bipartite graph)
It would have been obvious to one of ordinary skill in the art at the time of filing to add displaying a plurality of nodes in the bipartite graph in a stacked manner, wherein the plurality of nodes are obtained by expanding the same aggregation node once, hash values of the plurality of nodes are the same, and the plurality of nodes are connected in serial or parallel, to Matlab-Jingoi-Opengenus-NetworkX’s bipartite graph hashing, with a graph hierarchy for a bipartite graph may be simplified visually, as taught by Cintra. One would have been motivated to combine visualization of a bipartite graph of Cintra and bipartite hashing of Matlab-Jingoi-Opengenus-NetworkX as the combination results in a visual solution that is computed at a reduced cost.
Claim 11:
Claim 11 is the method claim reciting similar limitations to claim 8 and is rejected for similar reasons.
Claim 20:
Claim 20 is the apparatus claim reciting similar limitations to claim 8 and is rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication NO. 20160125289 “Amir”, which teaches in para. [0070], Initialize an empty source set S.sub.i, a target set T.sub.i, an empty edges sets B.sub.i and C.sub.i 3. Select a node v from V\(S.sub.i + T.sub.i) which has an outgoing edge in E\C.sub.i % ‘\’ denotes the set difference. 4. Insert v to S.sub.i %Each node v appears in at most one set S.sub.i, wherein i = 1 . . .K 5. For each edge e=v−>u in E\C.sub.i, insert u into T.sub.i, insert e to B.sub.i, insert all edges u−>* and *−>v to C.sub.i. 6. If any nodes left in V\( S.sub.i + T.sub.i), return to 3 7. Output the bipartite sub-graph G.sub.i =(S.sub.i, T.sub.i, B.sub.i). 8. Let E=E\B.sub.i % hereby removing all edges which are in G.sub.i 9. If E is not empty, then i=i+1 and return to 2 10. Output K=i % the number of sub-graphs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID H TAN whose telephone number is (571)272-7433. The examiner can normally be reached M-F 7:30-4:30.
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/D.T./Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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