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 .
Claims 1, 3-11 and 19-26 are pending. This Office Action is responsive to the amendment filed on 06/09/2026, which has been entered into the above identified application.
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.
Claims 1, 3-5, 7-11, 19-20, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (“Graph R-CNN for Scene Graph Generation”, published 9/10/2018), hereinafter Yang, in view of Hadar et al. (US 20220051111 A1, filed 08/17/2020), hereinafter Hadar; in further view of Liang et al. (“LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering”, published 11/21/2020). Yang and Hadar were cited in previous Office Actions.
Regarding Claim 1, Yang teaches a method, comprising:
receiving an input with content, the content comprising at least one or images, text documents, maps, audio, or speech (Yang: “we use the Faster R-CNN [32] framework to extract a set of n object proposals from an input image.” [Section 3.1 Object Proposals]);
predicting, by the machine learning model, possible combinations of nodes and edges for a predicted graph based on information inferred from the input and the structure of the content by the machine learning model (Yang: “Given an image (a), our proposed approach first extracts a set of objects visible in the scene and considers possible relationships between all nodes (b)” [Fig. 1]);
selecting, by the machine learning model, a subset of nodes and edges from the possible combinations of nodes and edges based on the information inferred from the input (Yang: “To model these regularities, we introduce a relation proposal network (RePN) which learns to efficiently estimate the relatedness of an object pair. By pruning edges corresponding to unlikely relations, the RePN can efficiently sparsify the candidate scene graph – retaining likely edges and suppressing noise introduced from unlikely ones.” [Section 3.2 Relation Proposal Network]; “The remaining m object pairs are considered as candidates having meaningful relationships E. With E, we obtain a graph G = (V , E), which
is much sparser than the original fully connected graph.” [Section 3.2 Relation Proposal Network]);
using the machine learning model to generate a predicted graph with predicted node labels for the subset of nodes and predicted edge labels for the subset of edges (Yang: “Then it prunes unlikely relationships using a learned measure of ‘relatedness’, producing a sparser candidate graph structure (c). Finally, an attentional graph convolution network is applied to integrate global context and update object node and relationship edge labels.” [Fig. 1]; “given the resulting sparsely connected scene graph candidate, we apply an attentional graph convolution network (aGCN) to propagate higher-order context throughout the graph – updating each object and relationship representation based on its neighbors.” [Section I. Introduction]);
providing an output of a complete representation of the predicted graph with predicted node labels for the subset of nodes indicating a type of node and the predicted edge labels for the subset of edges, wherein the machine learning model outputs different types of nodes based on information in the input (Yang: “we model scene graphs as graphs consisting of image regions, relationships, and their labellings.” [Section 3. Approach]; See “Scene Graph” in [Fig. 2]; “Recall that from the previous sections we have a set of N object regions and m relationships. From these, we construct a graph G with nodes corresponding to object and relationship proposals. We insert edges between relation nodes and their associated objects. We also add skip-connect edges directly between all object nodes. These connections allow information to flow directly between object nodes. Recent work has shown that reasoning about object correlation can improve detection performance [10]. We apply aGCN to this graph to update object and relationship representations based on global context. Note that our graph captures a number of different types of connections (i.e.object ↔ relationship, relationship ↔ subject and object ↔ object). In addition, the information flow across each connection may be asymmetric (the informativeness of subject on relationship might be quite different from relationship to subject). We learn different transformations for each type and ordering – denoting the linear transform from node type a to node type b as Wab with s=subjects, o=objects, and r=relationships.” [Section 3.3 Attentional GCN]); and
performing a task using information in the predicted graph (Yang: “These scene graphs form an interpretable structured representation of the image that can support higher-level visual intelligence tasks such as captioning [24, 39], visual question answering [1, 11, 35, 37–39], and image-grounded dialog [3].” [Section 1. Introduction]).
However, Yang fails to expressly disclose performing inference, by a machine learning model, for each content type inferring a structure of the content; wherein the machine learning model uses a parallel decoder that takes an input of node queries representing a set of the possible combination of nodes to select the subset of nodes and predict a node label for each node of the subset of nodes and edges for each node of the subset of nodes; and providing a predicted graph output in parallel.
In the same field of endeavor, Hadar teaches performing inference, by a machine learning model, for each content type inferring a structure of the content (Hadar: “Another example technique is a machine learning-based technique. In this approach, the knowledge graph generation engine 136 can use one or more machine learning models to generate the knowledge graph based on the data in the discovery database 134. This approach can be especially advantageous when the data includes unstructured or semi-structured text, images, and/or videos.” [0024]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated performing inference, by a machine learning model, for each content type inferring a structure of the content, as taught by Hadar to the method of Yang because both of these methods are directed towards utilizing machine learning to generate a predicted relationship graph to represent information in an input. In making this combination and determining the structure of the content before outputting a predicted graph in parallel, it would allow the method of Yang to interpret multiple types of inputs, allowing for wide applicability (Hadar: [0024]), as well as multitask, thereby speeding up computation while still achieving desirable results (Hadar: [0079]).
Yang and Hadar still fail to expressly disclose wherein the machine learning model uses a parallel decoder that takes an input of node queries representing a set of the possible combination of nodes to select the subset of nodes and predict a node label for each node of the subset of nodes and edges for each node of the subset of nodes; and providing a predicted graph output in parallel.
In the same field of endeavor, Liang teaches wherein the machine learning model uses a parallel decoder that takes an input of node queries representing a set of the possible combination of nodes to select the subset of nodes and predict a node label for each node of the subset of nodes and edges for each node of the subset of nodes (Liang: “Given an image I, its corresponding scene graph represents the objects in the image (e.g., girl, hamburger) as nodes and the objects’ pairwise relationships (e.g., holding) as edges. The first step of scene graph generation is object detection. We use DETR [7] as the object detection backbone since it removes the need for for hand-designed components like non-maximum suppression. DETR [7] feeds the image feature from ResNet50 [14] into a non-autoregressive transformer model, yielding an order-less set of N object vectors [o1, o2,..., oN], as in (1). Each object vector represents one detected object in the image. Then, for each object vector, DETR uses an object vector decoder (feed-forward network) to predict the corresponding object class (e.g., girl), and the bounding box in a multi-task manner.” [Section 2. LRTA: Look, Read, Think and Answer]; “We augment the object vector decoder with an additional object attributes predictor. For each attribute meta-concept (e.g., color), we create a classifier to predict the possible attribute values (e.g., red, pink). To predict the relationships, we consider all N(N − 1) possible pairs of object vectors, [e1, e2,..., eN(N−1)]. The relation encoder transforms each object vector pair to an edge vector through feed-forward and normalization layers as in (2). We then feed each edge vector to the relation decoder to classify its relationship label. Both object attributes and inter-object relationships are supervised in a multi-task manner.” [Section 2. LRTA: Look, Read, Think and Answer]; In light of the plain meaning of “non-autoregressive model” and paragraph [0039] of the specification, which states “the node queries are passed through the parallel decoder 14 and, unlike standard autoregressive approaches to graph generation, the final graph representation of the predicted graph 22 is emitted in parallel as the predicted set of nodes 24 and the set of edges 26”, BRI would support that a “non-autoregressive approach to graph generation” entails generating graphs in parallel, as opposed to autoregressive models which generate sequentially); and
providing a predicted graph output in parallel (Liang: “We use DETR [7] as the object detection backbone since it removes the need for for hand-designed components like non-maximum suppression. DETR [7] feeds the image feature from ResNet50 [14] into a non autoregressive transformer model, yielding an order-less set of N object vectors [o1, o2,..., oN], as in (1). Each object vector represents one detected object in the image. Then, for each object vector, DETR uses an object vector decoder (feed-forward network) to predict the corresponding object class (e.g., girl), and the bounding box in a multi-task manner.” [Section 2. LRTA: Look, Read, Think and Answer]; “We augment the object vector decoder with an additional object attributes predictor. For each attribute meta-concept (e.g., color), we create a classifier to predict the possible attribute values (e.g., red, pink). To predict the relationships, we consider all N(N − 1) possible pairs of object vectors, [e1, e2,..., eN(N−1)]. The relation encoder transforms each object vector pair to an edge vector through feed-forward and normalization layers as in (2). We then feed each edge vector to the relation decoder to classify its relationship label. Both object attributes and inter-object relationships are supervised in a multi-task manner… We construct the scene graph represented by N object vectors and N(N − 1) edge vectors instead of the symbolic outputs, and pass it to downstream modules.” [Section 2. LRTA: Look, Read, Think and Answer]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the machine learning model uses a parallel decoder that takes an input of node queries representing a set of the possible combination of nodes to select the subset of nodes and predict a node label for each node of the subset of nodes and edges for each node of the subset of nodes; and providing a predicted graph output in parallel, as taught by Liang to the method of Yang and Hadar because both of these methods are directed towards neural network-based scene graph generation. In making this combination and emitting graph elements in parallel with a similar non-autoregressive approach as Liang adopts with DETR, it would allow the method of Yang and Hadar to generate object vectors in an order-less manner (Liang: [Section 2. LRTA: Look, Read, Think and Answer], thereby helping the method to “streamline[[s]] the detection process” (Liang: [Appendix A: Related Work]).
Regarding Claim 3, Yang, Hadar, and Liang teach the method of Claim 1, wherein the input includes one or more of an image, a document, a video, a scene, a map, audio, speech, or text (Yang: “Given an image, our model first uses RPN to propose object regions, and then prunes the connections between object regions through our relation proposal network (RePN).” [Fig. 2]; Hadar: “the data includes unstructured or semi-structured text, images, and/or videos.” [0024]).
Regarding Claims 19 and 20, they are system claims that correspond to Claims 1 and 3. Therefore, they are rejected for the same reasons as Claims 1 and 3 above.
Regarding Claim 4, Yang, Hadar, and Liang teach the method of Claim 3, wherein the machine learning model identifies the set of nodes and the set of edges based on relationships expressed in natural langue of the text or the document of the input (Hadar: “A knowledge graph can represent a real world system, such as a computer network, roadways in a geographic area, or a population of people during an epidemic outbreak. The nodes of the knowledge graph can represent the real world elements in the system, e.g., computing devices in a computer network, roads in the geographic area, or people in the population. The edges between the nodes can represent the relationships between the real world elements, e.g., pathways between pairs of elements and the characteristics of the pathways.” [0016]; “the data includes unstructured or semi-structured text, images, and/or videos.” [0024]).
Regarding Claim 5, Yang, Hadar, and Liang teach the method of Claim 3, wherein the machine learning model identifies the set of nodes based on identified entities in the image or the video of the input and the set of edges based on identified relationships between the identified entities (Yang: “We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images.” [Abstract]).
Regarding Claim 7, Yang, Hadar, and Liang teach the method of Claim 3, wherein the machine learning model identifies the set of nodes based on identified stops in the map of the input and the set of edges based on a travel mode between the identified stops (Hadar: “A knowledge graph can represent a real world system, such as a computer network, roadways in a geographic area, or a population of people during an epidemic outbreak. The nodes of the knowledge graph can represent the real world elements in the system, e.g., computing devices in a computer network, roads in the geographic area, or people in the population. The edges between the nodes can represent the relationships between the real world elements, e.g., pathways between pairs of elements and the characteristics of the pathways.” [0016]; “the data includes unstructured or semi-structured text, images, and/or videos.” [0024]; BRI of map is that a map is a type of image that shows geographic locations, including roads).
Regarding Claim 8, Yang, Hadar, and Liang teach the method of Claim 1, wherein the input includes any combination of documents, video, audio, speech, or text (Hadar: “the data includes unstructured or semi-structured text, images, and/or videos.” [0024]).
Regarding Claim 9, Yang, Hadar, and Liang teach the method of Claim 1, wherein the representation of the predicted graph includes different shapes and labels for the set of nodes and one or more of undirected edges, directed edges, or weighted edges in the set of edges (Hadar: “An example, knowledge graph 150 illustrated in FIG. 1. This knowledge graph 150 includes nodes 151 represented by circles and edges 152 represented by arrows. As described in more detail below, the knowledge graph 150 includes regular nodes (without shading), a cardinal node 153, and target nodes 154 and 155. The knowledge graph 150 will be used as an example for the remaining description of FIG. 1, although the techniques can be applied to knowledge graphs having different arrangements, sizes, numbers of nodes, different edges, etc.” [0026]; See [Figure 1]).
Regarding Claim 10, Yang, Hadar, and Liang teach the method of Claim 1, further comprising:
receiving a query (Hadar: “The analytical engine 142 can receive queries 117 from user terminals 116 (e.g., client computers)” [0038]); and
using the information in the set of nodes and the set of edges of the predicted graph to provide an answer to the query (Hadar: “the analytical engine 142 can evaluate the knowledge graph 150 to identify the nodes that are on a path to the specified targets and that have a high (e.g., greater than threshold or higher than other nodes) cardinal value. The analytical engine 142 can respond to each query 117 with node data 118 specifying the nodes that match the query 117.” [0038]).
Regarding Claim 11, Yang, Hadar, and Liang teach the method of Claim 1, further comprising:
presenting the representation of the predicted graph (Hadar: “the node prioritization engine 146 can generate a graph that plots the aggregate cardinal values over time so that a user can assess the effectiveness of the efforts and resource utilization to improve the system.” [0041]);
receiving a modification of the set of nodes or the set of edges in the predicted graph (Hadar: “The update may happen either as a next round of system scan, or, alternatively, as simulation run by a user.” [0041]); and
updating the representation of the predicted graph based on the modifications of the set of nodes or the set of edges (Hadar: “The knowledge graph generation engine 136 can update the knowledge graph 150 after elements corresponding to the nodes 151 in the knowledge graph 150 are improved, removed from the system, or the system is otherwise altered.” [0041]).
Regarding Claim 24, Yang and Hadar teach the method of Claim 1, wherein the machine learning model infers relationships between the entities that are both spatial and conceptual in predicting the possible combination of the nodes and the edges for the predicted graph (Yang: “The pipeline of our proposed Graph R-CNN framework. Given an image, our model first uses RPN to propose object regions, and then prunes the connections between object regions through our relation proposal network (RePN). Attentional GCN is then applied to integrate contextual information from neighboring nodes in the graph. Finally, the scene graph is obtained on the right side.” [Fig. 2]; See “Scene Graph” in [Fig. 2]).
Regarding Claim 25, Yang, Hadar, and Liang teach the method of Claim 1, wherein the machine learning infers relationships that are physical relationships in space between the entities in predicting the possible combination of the nodes and the edges for the predicted graph (Yang: “In our approach, we use the Faster R-CNN [32] framework to extract a set of n object proposals from an input image. Each object proposal i is associated with a spatial region roi = [xi, yi, wi, hi], a pooled feature vector xoi, and an initial estimated label distribution poi over classes C={1, . . . , k}. We denote the collection of these vectors for all n proposals as the matrices Ro∈ Rn×4, Xo∈ Rn×d, and Po∈ Rn×|C| respectively.” [Section 3.1 Object Proposals]).
Regarding Claim 26, Yang, Hadar, and Liang teach the method of Claim 1, wherein the machine learning models infers relationships between the entities based on actions occurring between the entities in predicting the possible combination of the nodes and the edges for the predicted graph (Yang: “Given the n proposed object nodes from the previous step, there are O(n2) possible connections between them; however, as previously discussed, most object pairs are unlikely to have relationships due to regularities in real-world object interactions. To model these regularities, we introduce a relation proposal network (RePN) which learns to efficiently estimate the relatedness of an object pair. By pruning edges corresponding to unlikely relations, the RePN can efficiently sparsify the candidate scene graph – retaining likely edges and suppressing noise introduced from unlikely ones.” [Section 3.2 Relation Proposal Network]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Hadar and Liang, as applied to Claim 3 above, in further view of Zhang et al. (US 20210104234 A1, filed 10/08/2020), hereinafter Zhang. Zhang was cited in a previous Office Action.
Regarding Claim 6, Yang, Hadar, and Liang teach the method of Claim 3. However, they fail to expressly disclose wherein the machine learning model identifies the set of nodes based on identified entities in the audio or the speech of the input and the set of edges based on actions performed between identified entities in the audio or the speech.
In the same field of endeavor, Zhang teaches wherein the machine learning model identifies the set of nodes based on identified entities in the audio or the speech of the input and the set of edges based on actions performed between identified entities in the audio or the speech (Zhang: “the knowledge graph is created by: receiving one or more phrases; for each of the one or more phrases: performing lemmatization on the respective phrase to reduce the phrase; tagging parts of speech in the reduced phrase; extracting action words and forming an action list; and extracting object words and forming an object list, wherein the one or more nodes added to the knowledge graph correspond to the extracted action words and the extracted object word” [0027]).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the machine learning model identifies the set of nodes based on identified entities in the audio or the speech of the input and the set of edges based on actions performed between identified entities in the audio or the speech, as taught by Zhang, to the method of Yang, Hadar, and Liang because both methods are directed towards using machine learning to generate graphical representations of inputs. Audio/speech is another form of language that can be interpreted through natural language processing, just like text. In making this combination and expanding the type of input to include relationships between actions and entities in audio/speech data, it would allow the method of Yang, Hadar, and Liang to expand its use to applications in “commercial and academic sectors… such as voice searching in mobile devices and meeting user summarization” (Zhang: [0010]).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Hadar and Liang, as applied to Claim 1 above, in further view of Chua et al. (US 20200410231 A1; filed 06/28/2019), hereinafter Chua. Chua was cited in a previous Office Action.
Regarding Claim 21, Yang, Hadar, and Liang teach the method of Claim 1. However, they fail to expressly disclose wherein the input is a PDF image of a table and the machine learning model extracts information from the table and provides the representation of the predicted graph of the table in the PDF image.
In the same field of endeavor, Chua teaches wherein the input is a PDF image of a table and the machine learning model extracts information from the table and provides the representation of the predicted graph of the table in the PDF image (Chua: “A method for extracting data from lineless tables includes storing an image including a table in a memory. A processor operably coupled to the memory identifies a plurality of text-based characters in the image, and defines multiple bounding boxes based on the characters. Each of the bounding boxes is uniquely associated with at least one of the text-based characters. A graph including multiple nodes and multiple edges is generated based on the bounding boxes, using a graph construction algorithm.” [Abstract]; “In some embodiments, a method for extracting data from lineless tables includes obtaining, at a processor, a portable document format (PDF) file including formatted data. The PDF file is converted to an image file, and OCR is performed on the image file to produce a scanned file.” [0005]).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the input is a PDF image of a table and the machine learning model extracts information from the table and provides the representation of the predicted graph of the table in the PDF image, as taught by Chua, to the method of Yang, Hadar, and Liang because both methods are directed towards using machine learning to generate graphical representations of inputs. In making this combination and expanding the type of input to include information extracted from PDF tables, it would allow the method of Yang, Hadar, and Liang to expand its use to address the issue that existing technologies have with OCR software, in that such software “typically cannot determine associations among the recovered characters/text or between the recovered characters/text and locations (e.g. cells) within a table” (Chua: [0037]).
Claims 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Hadar and Liang, as applied to Claim 1 above, in further view of Hamilton (“Graph Representation Learning”; published 2020). Hamilton was cited in the previous Office Action.
Regarding Claim 22, Yang, Hadar, and Liang teach the method of Claim 1. However, they fail to expressly disclose wherein the type of node is an atom, and the predicted graph is a molecule graph.
In the same field of endeavor, Hamilton teaches wherein the type of node is an atom, and the predicted graph is a molecule graph (Hamilton: “The same graph formalism can be used to represent social networks, interactions between drugs and proteins, the interactions between atoms in a molecule, or the connections between terminals in a telecommunications network—to name just a few examples.” [Chapter 1. Introduction]).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the type of node is an atom, and the predicted graph is a molecule graph, as taught by Hamilton, to the method of Yang, Hadar, and Liang because both methods are directed towards using machine learning to generate graphical representations of multiple types of inputs to represent relationships between multiple types entities. In making this combination and expanding the type of interpreted graphs to include molecular relationships between atoms, it would allow the method of Yang, Hadar, and Liang to “a mathematical foundation that we can build upon to analyze understand, and learning from real-world complex systems” (Hamilton: [Chapter 1. Introduction]).
Regarding Claim 23, Yang, Hadar, and Liang teach the method of Claim 1. However, they fail to expressly disclose wherein the type of node is a city, and the predicted graph is a transportation graph.
In the same field of endeavor, Hamilton teaches wherein the type of node is a city, and the predicted graph is a transportation graph (Hamilton: “For instance, in a multiplex transportation network, each node might represent a city and each layer might represent a different mode of transportation (e.g., air travel or train travel). Intra-layer edges would then represent cities that are connected by different modes of transportation, while inter-layer edges represent the possibility of switching modes of transportation within a particular city.” [Chapter 1.1.1 Multi-relational Graph]).
It would have been obvious to one or ordinary skill in the art before the effective filing date of the invention to have incorporated wherein the type of node is a city, and the predicted graph is a transportation graph, as taught by Hamilton, to the method of Yang, Hadar, and Liang because both methods are directed towards using machine learning to generate graphical representations of multiple types of inputs to represent relationships between multiple types entities. In making this combination and expanding the type of interpreted graphs to include transportation between cities, it would allow the method of Yang, Hadar, and Liang to “a mathematical foundation that we can build upon to analyze understand, and learning from real-world complex systems” (Hamilton: [Chapter 1. Introduction]).
Response to Arguments
Examiner acknowledges the Applicant’s amendments to Claims 1 and 19.
Applicant's arguments, filed 06/09/2026, regarding the rejection of Claims 1, 3-11 and 19-26 under 35 U.S.C. § 101 have been fully considered and are persuasive. The rejection has been withdrawn.
Applicant’s arguments, filed 06/09/2026, regarding the rejection of Claims 1, 3-11 and 19-26 under 35 U.S.C. § 103 have been fully considered and are found moot in light of the new grounds of rejection (see rejection above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tian et al. (“Mask and Predict: Multi-Step Reasoning for Scene Graph Generation”) discusses scene graph generation broken into two explicit learning stages including an intuitive training stage and a rational training stage to yield and refine an intuitive scene graph, in which the decoding manner of the transformer structure is changed for parallel output.
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.
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/M.E.H./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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