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 .
Status of the Claims
Claims 1-2, 5-6, 10-11, 14-15, and 18-20 have been amended. Claims 7-8 and 16-17 have been canceled. Claims 1-6, 9-15, and 18-20 are pending and have been fully considered by the Examiner.
Claim Objections
Claims 1, 9-10, and 18-20 are objected to because of the following informalities:
In claim 1, lines 21-22, both instances of the limitation “final set” should recite “a final set”. Claims 10 and 19 recite the same minor informalities as claim 1.
In claim 9, line 4, “edge node embeddings” should recite “edge embeddings”. Claim 18 recites the same minor informalities as claim 9.
In claim 20, the indentation of the final 3 lines should be corrected. 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 9 and 18 are 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 9 is rendered indefinite for multiple reasons. Claim 9 depends on canceled claim 7. It is unclear if the preamble of claim 9 should recite “The computer-implemented method of claim 1”. Both claim 1, in the final 5 lines, and claim 9 recite the limitations of a classifier head, a linear layer, a softmax layer, and a classification score. It is unclear if these limitations in claims 1 and 9 refer to the same or different classifier heads, linear layers, softmax layers, and classification scores. Examiner treats the classifier heads, linear layers, softmax layers, and classification scores in claims 1 and 9 as being the same.
Claim 18 is a product which recites the same indefinite limitations as the method of claim 9 and is therefore rejected for at least the same 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-3, 5, 9-12, 14, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lo et al. (“E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT”, cited in PTO-892 issued 04/03/2026) and Gui et al. (US 20210064959 A1, cited in PTO-892 issued 04/03/2026) Shi et al. (US 20140279306 A1), and Titov et al. (US 20210319323 A1, cited in PTO-892 issued 04/03/2026).
Regarding claim 1, Lo teaches: A computer-implemented method, the method comprising: accessing a neural network having K layers, where K is a natural number, K > 1; (Page 3, col. 2, from the third-to-last line to equation 2, Fig. 2 and its caption, and Algorithm 1, “input: depth K” and Line 2 discloses accessing a neural network having 2 layers.)
accessing a graph comprising a plurality of nodes and a plurality of edges linking the plurality of nodes; (On page 4, Algorithm 1 discloses an input is a graph G(V,E). On page 3, col. 2, § B, lines 7-8 discloses V is the set of nodes and E is the set of edges.)
determining a set of node features for each of the plurality of nodes based on information associated with the node; (On page 4, Algorithm 1 discloses an input is a set of node features xv for each node, which is further explained on page 4, col. 2, § A, lines 17-21. An identifier of a node “v” is information associated with the node.)
determining a set of edge features for each of the plurality of edges based on information associated with the edge; (On page 4, Algorithm 1 discloses an input is a set of edge features eruv for each edge, which is further explained on page 4, col. 2, § A, lines 13-17. An identifier of an edge “uv” is information associated with the edge.)
applying a first layer of the neural network to the node features and the edge features to output a first set of node embeddings 5, col. 1, lines 1-10 discloses calculating a first set of node embeddings hv1 by applying a first layer of a neural network. Based on Lines 4 and 5 of the algorithm, the “CONCAT” function applies both hv0 (node features) and euv0 (edge features).)
wherein an edge classifier predicts [network traffic] hv0 (node features) and euv0 (edge features) contribute to the edge embedding zuv which is then classified as benign or attack.)
applying a kth layer of the neural network to a (k-1)th set of node embeddings and a (k- 1)th set of edge embeddings to output a kth set of node embeddings and a kth set of edge embeddings, where k is a natural number, wherein the (k-1)th set of node embeddings
wherein the neural network includes a total of K layers, and the node embeddings and edge embeddings for the Kth layer are final set of node embeddings and final set of edge embeddings, (Page 5, col. 1, lines 11-17)
wherein the neural network further includes a classifier head,
receive the final set of [edge]
pass the final set of [edge]
Lo discloses that an output hvk of each layer k is a node v embedding. Thus, Lo does not explicitly teach: output a first set of node embeddings and a first set of edge embeddings, wherein a node classifier identifies whether a merchant represented by the node features is fraudulent, wherein an edge classifier predicts whether a transaction occurs between a consumer and the merchant based on the node features and the edge features;
wherein the (k-1)th set of node embeddings and the (k-1)th set of edge embeddings are output from a (k- 1)th layer of the neural network,
wherein the neural network further includes a classifier head, a linear layer, and a softmax layer, and the classifier head is configured to:
receive the final set of node embeddings as inputs; and
pass the final set of node embeddings through the linear layer and the softmax layer to output a classification score.
But Gui teaches: applying a first layer of the neural network to the node features and the edge features to output a first set of node embeddings and a first set of edge embeddings, ([0004], lines 1-3, [0025], and [0094]-[0096] discloses applying an (l+1)th hidden layer to node features hvl to output a first set of node embeddings hvl+1. A first layer as claimed corresponds to Gui’s (l+1)th hidden layer, and thus node features hvl are initial node features. [0095], lines 6-9 and [0097]-[0098] discloses applying an (l+1)th hidden layer to edge features
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representing the aligned embedding of the edge Evv’ at layer l to output edge embeddings
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. Since a first layer as claimed is Gui’s (l+1)th hidden layer, edge features
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are initial edge features.)
wherein the (k-1)th set of node embeddings and (k-1)th set of edge embeddings are output from (k-1)th layer of the neural network, ([0025] and [0094]-[0098] discloses applying a an (l+1)th hidden layer to node features hvl to output node embeddings hvl+1 and applying the (l+1)th hidden layer to the edge features
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to output edge embeddings
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. A (k-1)th layer as claimed corresponds to Gui’s layer l, and a kth layer as claimed corresponds to Gui’s layer l+1.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Lo’s neural network to generate both node embeddings and edge embeddings as taught by Gui. A motivation for the combination is that node embedding and edge embedding can be enhanced by each other, where node embedding and edge embedding can be jointly modeled. (Gui, [0018], final 4 lines)
Gui teaches node-classification in the Abstract, lines 10-14. However, Lo and Gui do not explicitly teach: wherein a node classifier identifies whether a merchant represented by the node features is fraudulent, wherein an edge classifier predicts whether a transaction occurs between a consumer and the merchant based on the node features and the edge features;
…
wherein the neural network further includes a classifier head, a linear layer, and a softmax layer, and the classifier head is configured to:
receive the final set of node embeddings as inputs; and
pass the final set of node embeddings through the linear layer and the softmax layer to output a classification score.
But Shi teaches: wherein a node classifier identifies whether a merchant represented by the node features is fraudulent, wherein an edge classifier predicts whether a transaction occurs between a consumer and the merchant based on the node features and the edge features; ([0017], lines 1-4, [0018], lines 1-4 and 9-21, and all of [0032]-[0033] and [0037] discloses using a classification model to detect POC merchants at which a customer’s account is compromised. A “fraudulent” merchant is any compromised merchant, and both “a node classifier” and “an edge classifier” as recited in the claim correspond to the classification model, which determines whether a transaction occurs in order to identify POC merchants.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Shi’s classification model to Lo and Gui. A motivation for the combination is to extract features from a graph network in order to identify point of compromise merchants. (Shi, [0017])
However, Lo, Gui, and Shi do not explicitly teach: wherein the neural network further includes a classifier head, a linear layer, and a softmax layer, and the classifier head is configured to:
receive the final set of node embeddings as inputs; and
pass the final set of node embeddings through the linear layer and the softmax layer to output a classification score.
But Titov teaches: wherein the neural network further includes a classifier head, a linear layer, and a softmax layer, and ([0021] from line 1 to col. 2, line 5 discloses a multi-layer perceptron, and [0026], lines 4-5 clarify that layer 304 is a “fully connected layer”. A classifier head is the entire MLP, a linear layer is first fully-connected layer 304, and a softmax layer is softmax engine 310.)
the classifier head is configured to: receive the final set of node embeddings as inputs; and ([0021], lines 8-11)
pass the final set of node embeddings through the linear layer and the softmax layer to output a classification score. ([0021], line 8 to col. 2, line 5. The node embedding vectors are passed through both the first fully-connected layer 304 and softmax engine 310.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Lo and Gui’s final node embeddings to Titov’s multi-layer perceptron comprising both a fully-connected layer and a softmax layer for node classification. A motivation for the combination is that a fully-connected layer plus a softmax layer learns non-linear combinations of the node embedding features, which may improve node classification accuracy.
Regarding claim 2, the combination of Lo, Gui, Shi, and Titov teaches: The computer-implemented method of claim 1,
Lo teaches: wherein applying the kth layer in the plurality of layers of the neural network to output a k-th set of node embeddings for a target node comprises: (Examiner treats “a kth layer” as a final layer K. Page 4, col. 1, line 4 to equation 2; Page 4, Algorithm 1, lines 1-8 and from page 4, col. 2, § A, line 22 to page 5, col. 1, lines 1-17 discloses applying (in Alg. 1, Line 5) a Kth layer of the neural network to (K-1)th set of node embeddings to output a Kth set of node embeddings. Alg. 1, Lines 6-8 discloses outputting a Kth set of edge embeddings based on the node embeddings output from the Kth layer.)
identifying a subset of nodes that are in a neighborhood of the target node, wherein the subset of nodes are within a number of edges from the target node; (Page 3, col. 2, § B, first sentence of the paragraph starting with “At each iteration” where identifying corresponds to sampling.)
obtaining node embeddings of the subset of nodes output from the (k-1)th layer; (Page 5, col. 1, lines 4-7 and Algorithm 1, Line 5 disclose applying hvk-1.)
aggregating the node embeddings of the subset of nodes output from the (k-1)th layer into an aggregated node vector; (Page 3, col. 2, § B, the entire paragraph starting with “At each iteration” and the entire paragraph below equation 1.)
identifying a subset of edges that are linking the subset of nodes in the neighborhood that are within the number of edges from the target node; (Page 4, col. 2, § A, entire paragraph starting with “In Line 4” where identifying corresponds to sampling.)
obtaining edge embeddings associated with the subset of edges in the (k-1)th layer; (Page 4, col. 2, § A, entire paragraph starting with “In Line 4”)
aggregating the edge embeddings of the subset of edges in the (k-1)th layer into an aggregated edge vector; and (Page 4, col. 2, § A, entire paragraph starting with “In Line 4” and page 5, col. 1, lines 1-4)
determining a set of node embeddings in the kth layer based in part on the hvk based in part on the node vector hvk-1 and the aggregated edge vector.)
Lo at page 4, § IV, first 2 paragraphs teaches that the GraphSAGE algorithm aggregates embeddings of all nodes u in the neighborhood of v of a kth layer to predict classification of a target node, and the E-GraphSAGE algorithm replaces the aggregated node vector with an aggregated edge vector in order to predict classification of a target edge. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied both the aggregated node vector and the aggregated edge vector to determine Lo’s node embeddings in the kth layer in Alg. 1, Line 5. A motivation for the combination is that by incorporate additional information about the target node’s neighborhood into the node embeddings, the model can predict classifications for both nodes and edges in a graph.
Regarding claim 3, the combination of Lo, Gui, Shi, and Titov teaches: The computer-implemented method of claim 2,
Lo teaches: wherein determining the set of node embeddings in kth layer based in part on the vk-1 and the aggregated edge vector.)
passing the concatenated vector through kth layer of the neural network with an activation function to generate a set of node embeddings. (Page 4, Alg. 1, Line 5, where the output of the “CONCAT” function is the kth layer input, and σ represents an activation function to generate a set of node embeddings hvk.)
Lo at page 4, § IV, first 2 paragraphs teaches that the GraphSAGE algorithm aggregates embeddings of all nodes u in the neighborhood of v of a kth layer to predict classification of a target node, and the E-GraphSAGE algorithm replaces the aggregated node vector with an aggregated edge vector in order to predict classification of a target edge. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied both the aggregated node vector and the aggregated edge vector to determine Lo’s node embeddings in the kth layer in Alg. 1, Line 5. A motivation for the combination is that by incorporate additional information about the target node’s neighborhood into the node embeddings, the model can predict classifications for both nodes and edges in a graph.
Regarding claim 5, the combination of Lo, Gui, Shi, and Titov teaches: The computer-implemented method of claim 1,
Lo teaches: wherein applying the kth layer in the plurality of layers of the neural network to output a set of edge embeddings for an edge (u, v) linking a node u and a node v comprises: (Page 5, col. 1, lines 11-17)
outputting a first set of node embeddings for the node u; (Page 5, col. 1, lines 11-17 discloses outputting zuk)
outputting a second set of node embeddings for the node v; (Page 5, col. 1, lines 11-17 discloses outputting zvk)
…
outputting a set of edge embeddings for the edge (u, v) based in part on zuvK)
However, Lo, Shi, and Titov do not explicitly teach: obtaining a set of edge embeddings for the edge (u, v) output from the (k-1)th layer; and
outputting a set of edge embeddings for the edge (u, v) based in part on the set of edge embeddings for the edge output from the (k-1)th layer,
But Gui teaches: obtaining a set of edge embeddings for the edge (u, v) output from the (k-1)th layer; and ([0095], lines 6-9 and [0097]-[0098] discloses applying an (l+1)th hidden layer to edge features
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representing the aligned embedding of the edge Evv’ at layer l to output edge embeddings
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. A (k-1)th layer as claimed corresponds to Gui’s layer l. Applying edge embeddings to the (l+1)th hidden layer requires obtaining edge embeddings output from the l-th hidden layer.)
outputting a set of edge embeddings for the edge (u, v) based in part on the set of edge embeddings for the edge output from the (k-1)th layer, ([0095], lines 6-9 and [0097]-[0098] discloses applying an (l+1)th hidden layer to edge features
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representing the aligned embedding of the edge Evv’ at layer l to output edge embeddings
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.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Gui’s generation of edge embeddings based on edge embeddings output by a preceding layer into the combination of Lo, Gui, Shi, and Titov. A motivation for the combination is that node embedding and edge embedding can be enhanced by each other, where node embedding and edge embedding can be jointly modeled. (Gui, [0018], final 4 lines)
Regarding claim 9, the combination of Lo, Gui, Shi, and Titov teaches: The computer-implemented method of claim 7 [claim 1],
Lo teaches: wherein the neural network further includes a classifier head,
receive the final set of edge node embeddings as inputs; and (Page 6, col. 1, lines 11-19)
pass the final set of edge embeddings through
However, Lo, Gui, and Shi do not explicitly teach: wherein the neural network further includes a classifier head, a linear layer, and a softmax layer, and the classifier head is configured to: pass the final set of edge embeddings through the linear layer and the softmax layer
But Titov teaches: wherein the neural network further includes a classifier head, a linear layer, and a softmax layer, and ([0021] from line 1 to col. 2, line 5 discloses a multi-layer perceptron, and [0026], lines 4-5 clarify that layer 304 is a “fully connected layer”. A classifier head is the entire MLP, a linear layer is first fully-connected layer 304, and a softmax layer is softmax engine 310.)
the classifier head is configured to: pass the final set of [node]
Lo teaches passing a final set of edge embeddings through a softmax activation function, but not passing it through both a linear layer and a softmax layer. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have inputted Lo and Gui’s final edge embeddings into Titov’s multi-layer perceptron comprising a fully-connected layer and a softmax layer for node classification. A motivation for the combination is that a fully-connected layer plus softmax layer learns non-linear combinations of the edge embedding features, which may improve edge classification accuracy.
Claim 10 recites a product which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
However, Lo does not explicitly teach: A non-transitory computer-readable medium, stored thereon computer-executable instructions, that when executed by a processor of a computer system, cause the computer system to perform operations.
But Gui teaches: A non-transitory computer-readable medium, stored thereon computer-executable instructions, that when executed by a processor of a computer system, cause the computer system to perform operations. ([0106], lines 1-2 (processor) and Gui’s claim 8, lines 1-5 on page 9)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Gui’s processor and computer readable storage medium into the combination of Lo, Gui, Shi, and Titov. A motivation for the combination is to execute Gui, Shi, and Titov’s method on a real-world computer.
Claims 11-12, 14, and 18 each recites a product which implements the same features as the method of claims 2-3, 5, and 9 respectively, and are therefore rejected for at least the same reasons.
Claim 19 recites a system which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
However, Lo does not explicitly teach: A computer system comprising: a processor; and a non-transitory computer-readable storage medium, stored thereon computer- executable instructions, that when executed by the processor, cause the processor to perform operations.
But Gui teaches: A computer system comprising: a processor; ([0106], lines 1-2)
and a non-transitory computer-readable storage medium, stored thereon computer- executable instructions, that when executed by the processor, cause the processor to perform operations. (Gui’s claim 8, lines 1-5 on page 9)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Gui’s processor and computer readable storage medium into the combination Lo, Gui, Shi, and Titov. A motivation for the combination is to execute Lo, Gui, Shi, and Titov’s method on a real-world computer.
Claim 20 recites a product which implements the same features as the method of claim 2 and is therefore rejected for at least the same reasons.
Claims 4, 6, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lo et al. (“E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT”, cited in PTO-892 issued 04/03/2026) and Gui et al. (US 20210064959 A1, cited in PTO-892 issued 04/03/2026) Shi et al. (US 20140279306 A1), Titov et al. (US 20210319323 A1, cited in PTO-892 issued 04/03/2026), and Hamilton et al. (“Inductive Representation Learning on Large Graphs”, cited in PTO-892 issued 04/03/2026).
Regarding claim 4, the combination of Lo, Gui, Shi, and Titov teaches: The computer-implemented method of claim 1,
However, Gui, Shi, and Titov do not explicitly teach: wherein determining a set of node embeddings for each of the plurality of nodes further comprises normalizing each set of node embeddings based on all sets of node embeddings in a same layer of the neural network.
But Hamilton teaches: wherein determining a set of node embeddings for each of the plurality of nodes further comprises normalizing each set of node embeddings based on all sets of node embeddings in a same layer of the neural network. (On page 4, Algorithm 1, Line 7 discloses replacing a set of node embeddings with a normalized set. The notation used by Hamilton is the same as Gui. This is taught by the “Inputs” of Algorithm 1 and the entire paragraph starting on page 4, line 4.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have normalized Lo and Gui’s set of node embeddings using Hamilton’s technique. A motivation for the combination is to reduce variance in node embeddings for each node at each layer.
Regarding claim 6, the combination of Lo, Gui, Shi, and Titov teaches: The computer-implemented method of claim 1,
Lo teaches: wherein determining a set of edge embeddings for each of the plurality of nodes further comprises [calculating]
However, Lo, Gui, Shi, and Titov do not explicitly teach: normalizing each set of edge embeddings based on all sets of edge embeddings in a same layer of the neural network.
But Hamilton teaches: normalizing each set of [node]
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have normalized Lo and Gui’s set of edge embeddings in layer K using Hamilton’s technique for normalizing node embeddings. A motivation for the combination is to reduce variance in edge embeddings for each edge at each layer.
Claims 13 and 15 each recites a product which implements the same features as the method of claims 4 and 6, respectively, and are therefore rejected for at least the same reasons.
Response to Arguments
The following is the Examiner’s response to the Applicant’s arguments filed on 6/9/2026.
Applicant’s Argument Under 35 U.S.C. 101: On page 13, the Applicant argues the features of claim 1 recite a practical application that would overcome the 101 rejection.
Examiner’s Response: Applicant’s arguments have been fully considered and are persuasive. The rejection of claim 1 has been withdrawn.
Applicant’s Argument Under 35 U.S.C. 103: On page 14, the Applicant argues the cited portions of Lo and Gui do not teach anything in relation to a node classifier identifying whether a merchant is fraudulent, and the cited portions do not teach or suggest anything in relation to an edge classifier making a prediction as to whether a transaction will occur.
Examiner’s Response: Applicant’s arguments have been fully considered. Lo teaches the limitation “wherein an edge classifier predicts [network traffic] based on the node features and the edge features”. Page 4, col. 1, § IV, lines 1-10 and page 6, col. 2, § A, lines 1-5 discloses an edge classifier classifies network flows as either a benign class or an attack class. Algorithm 1 on page 4 discloses that both hv0 (node features) and euv0 (edge features) contribute to the edge embedding zuv which is then classified as benign or attack.
With respect to the claim 1 limitations “wherein a node classifier identifies whether a merchant represented by the node features is fraudulent, wherein an edge classifier predicts whether a transaction occurs between a consumer and the merchant based on the node features and the edge features”, Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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