Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The reply and amendment of 15 June 2026 has been received and entered. Claims 4-5 and 10 have been cancelled. Claims 1-3, 6-9, and 11-25 are pending. The response largely amends Claim 1, taking some aspects of now-cancelled Claims 4-6 and 10.
The response argues that the components of Harvey as described at paragraph [0052] are merely identified as present (Remarks at 10) and that Harvey does not identify attributes of the component. The attributes in the present specification are said to include “whether a fuse is open, closed, gang-operated, or line-disconnected and whether a distribution line is insulated.” (Remarks at 10).
The response also argues that the GIS system of Harvey is not updated with information on the components, but merely the location of the pole (Remarks at 11).
This is not persuasive. Harvey at [0069] elaborates that “The imaging module 325 may include artificial intelligence (e.g., a trained machine learning model, etc.) configured for vision recognition, object recognition, and/or the like that enables components of one or more constructs (e.g., utility constructs, utility assets, power poles, telephone poles, etc.) to be determined/identified, for example in real-time. Signs/tags of the components may be included with the image data/information. The image data/information may include date and time information that indicates when images/videos are captured/taken, as well as GPS coordinates associated with where the images/videos are captured/taken.”
The components are “determined/identified”, and not merely located. Read in context, “determined/identified” means more than a binary “yes/no”. This is consistent with Harvey’s usage in [0052]: ”For example, the UAV 202 may capture image data/information that may be analyzed to identify any adverse conditions such as such as wood rot, arc damage, flashing, tracking, floating conductors, loose tie-wires, loose hardware, and/or the like. The adverse conditions may be determined/identified via manual inspection of the image data/information, machine learning, computer vision, combinations thereof, and/or the like.” (emphasis added) It does not make sense in context that “determined/identified” is merely a binary state; it only makes sense that these diverse conditions are catalogued by type.
At [0070], Harvey further identified aspects of the components that are determined, including “arc damage, flashing, tracking, floating conductors, loose tie-wires, loose hardware, and/or the like”, which (at the level of specificity of claim 1) is an attribute like an open fuse or a line-disconnected component. Likewise, at [0071], if a component is overheating, that is identified.
Harvey does not specify in the text of the specification what types of components are on the constructs, but they are clearly the power transmission/control components as they have conditions including loose conductors and overheating. In context, “components” would be understood to include routine power transmission and control components like conductors, transformers, insulators, switches, fuses, etc.
This is consistent with Fig. 4 of Harvey and [0106]. The Examiner retrieved a higher quality image from the file of 17/335,995 (DRW.SUPP dated 6/1/2021) which is the application of the Harvey publication, and these are expanded segments of Fig. 4 so that the text is (hopefully…) legible:
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Harvey is cataloging what components are on each construct, not merely identifying that something is there.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-3, 12, 14-16, 19-20 and 23-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harvey et al. US Publication 2021/0374111 (previously cited) in in view of “Distribution System” (IEEEE Long Island, 2009, downloaded from https://www.ieee.li/pdf/viewgraphs/automating_power_distribution_system.pdf, also previously cited).
Regarding claim 1, Harvey describes a method of drone-based inspection of an overhead asset of an electrical grid (para. 0003, wherein the disclosure of Harvey is directed to a method of inspection of utility assets, such as power poles), the method comprising:
flying a drone toward the overhead asset, wherein the overhead asset comprises a distribution line device supported by a distribution pole of the electrical grid (paras. 0044-0046, disclosing the use of an unmanned aerial vehicle (UAV) to gather the images used for construct analysis, wherein the component is the utility asset, and the UAV may vary in its type of operation (either fully autonomous, partially autonomous, or fully teleoperated));
capturing, via the drone, a plurality of digital images of the overhead asset (above);
identifying, using computer vision with respect to the digital images, the overhead asset (paras. 0047-0049, disclosing the capture of multiple images, either in the form of static images or a video stream, and associated information about the respective images), comprising
[classifying the overhead asset as a type of distribution line device selected from the group consisting of a transformer, a regulator, a recloser, a capacitor, a line sensor, a primary meter, a fuse, a switch, and a sectionalizer;see below.
identifying, using computer vision with respect to the digital images, an attribute of the identified overhead asset (para. 0052, “The UAV 202, or another data gathering system, may also be used to inspect the construct 205, identify components present on the construct, the attributes of these components (such as the location of a given component relative to another), and identify any adverse conditions that could affect the performance, integrity, and/or resiliency of the construct 205. For example, the UAV 202 may capture image data/information that may be analyzed to identify any adverse conditions such as such as wood rot, arc damage, flashing, tracking, floating conductors, loose tie-wires, loose hardware, and/or the like. The adverse conditions may be determined/identified via manual inspection of the image data/information, machine learning, computer vision, combinations thereof, and/or the like”);
and updating a geographic information system (GIS) database in response to identifying the overhead asset, comprising:
providing information regarding the overhead asset to the GIS database for a first time; or editing information in an existing entry in the GIS database regarding the overhead asset, (para. [0070], “Image data/information captured by the UAV 302, or another data gathering system, may be used to identify components present on the construct, the attributes of these components (such as the location of a given component relative to another), and/or to identify any adverse conditions that could affect the performance, integrity, and/or resiliency of the one or more constructs (e.g., utility constructs, utility assets, power poles, telephone poles, etc.). “ para. 0092, “In an embodiment, the methods and systems disclosed can thus determine which assets/constructs (e.g., utility poles) actually exist and what the actual location is for each asset/construct. A data record indicative of an asset/construct so determined may be created. One or more images collected of the asset/construct may be associated with the data record. To the extent a service provider and/or asset owner/operator provided a dataset comprising an asset/construct data record for one or more assets/constructs, an association (or link) between the data record created by the methods and systems disclosed and an asset/construct data record in the service provider and/or asset owner/operator dataset may be created. Such an association permits reconciliation with the asset/construct data records of the service provider and/or asset owner/operator with reality.”; paras. 0096-0097, disclosing the updating of a GIS with information on the construct upon recognition of the construct, its location, and particular information regarding its state) wherein the information indicates the type of distribution line device, a geographic position of the overhead asset, and the attribute of the overhead asset. Taken as a whole, Harvey is identifying utility poles (constructs), identifying the items on the poles (components) and storing the location of the poles and data about the poles in a data record. As shown in Fig. 4 this is data record understood to include an inventory of the components on the pole and a list of conditions of the pole which may include conditions of the components.
With respect to the step of “classifying…”, it appears that Harvey has classified one component as a transformer in Fig. 4 per the higher-quality images reprinted above under “conditions” and has classified an inventory of components including a fuse.
The list of elements of the group that are classified appears as a list of examples in the specification at [0030] and [0038]. There is no detail of these devices; it is understood that these are known components of an electrical distribution system.
“Distribution System” is cited as additional evidence that various components including capacitors, fuses, reclosers, regulators, switches, sectionalizers, and transformers are generally known and understood by those skilled in the art. (list at p. 5).
Harvey discloses the use of computer vision to classify components (i.e. system parts) for each construct (utility pole). It would have been obvious to one of ordinary skill in the art to classify all typical utility components in order to assist in analysis of the system. From Harvey at [0154]: “For example, the computing device may cause, based on an interaction with the one or more status interface elements, an image associated with the one or more status interface elements may be displayed. In some instances, based on an interaction with the one or more status interface elements, an inventory of equipment associated with a construct may be displayed, one or more indications of conditions affecting the construct may be displayed, and/or any data/information associated with a construct may be displayed.” and [0156] “The machine learning model may be and/or include a trained predictive model. The predictive model may be been trained with one or more labeled datasets to identify components present on the construct and/or adverse conditions affecting a construct and associate the conditions with priority rankings.”
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the application, to use the system of Harvey to gather information about all of the typical components of an electrical grid shown in “Distribution Systems” on a construct including their identity, operating status, and adverse conditions affecting their performance in order to “capture image data/information (e.g., static images, video, etc.) of any/all components of the one or more constructs from all angles” as described in Harvey.
Regarding claim 2, Harvey discloses all limitations of claim 1. Harvey further discloses wherein
the digital images comprise a first digital image of the overhead asset taken at a first shot angle; and a second digital image of the overhead asset taken at a second shot angle that is different from the first shot angle (para. 0047, wherein the capture of multiple digital images from multiple different angles using gimballed cameras and alternate UAV positions is disclosed, wherein a first and a second image could arbitrarily be determined from the image stream as any images captured from different angles).
Regarding claim 3, Harvey discloses all limitations of claim 2. Harvey further discloses identifying the first and second shot angles (paras. 0047 and 0173, wherein para. 0047 discloses UAV image captures at multiple different angles and para. 0173 discloses wherein all images captured as part of the collected construct data pre-identification include associated capture angle data).
Regarding claims 12, 14-16 and 19-20, Harvey describes identifying electrical grid infrastructure elements but does not describe certain specific components of the electrical grid including fuses (claim 12) reclosers (Claim 14) regulators (Claim 15) or sectionalizers (Claim 16). Harvey does describe (para. 69-72) that the system takes images of components of the constructs to identify and analyze the components including adverse conditions, and that this includes labeling on the components. From [0069], “Signs/tags of the components may be included with the image data/information. “. “Distribution System” describes that fuses (page 5) sectionalizers (page 11) regulators (page 12) and reclosers (page 5 and 11) are known components of the electrical grid. Harvey also describes at [0069] that “The UAV 302, or another data gathering system, may capture image data/information (e.g., static images, video, etc.) of any/all components of the one or more constructs from all angles.”
With respect to Claims 19 and 20, Harvey captures data about all of the components on a construct and (as shown in the enhanced images above) has a count of the number of assets. When Harvey identifies elements, the list of elements itself is a total number of assets. Claims 19 and 20 are consistent with this, in that the total is a flexible number that includes the number of overhead assets in a specified list (19) but also later includes the distribution lines which are not on the specified list (20)..
Regarding claim 23, Harvey discloses all limitations of claim 1. Harvey further discloses wherein the digital images comprise respective still images that are captured by one or more cameras of the drone (para. 0047, wherein the capture of multiple digital images from multiple different angles using gimballed cameras and alternate UAV positions is disclosed, wherein the captured images may be static images captured at different points in time).
Regarding claim 24, Harvey discloses all limitations of claim 1. Harvey further discloses wherein the digital images comprise respective frames of a digital video that is captured by one or more cameras of the drone (para. 0047, wherein the capture of multiple digital images from multiple different angles using gimballed cameras and alternate UAV positions is disclosed, wherein the captured images may be adjacent or alternate frames of a video/image stream).
Regarding claim 25, Harvey discloses all limitations of claim 1. Harvey further discloses wherein the GIS database comprises GIS data about a distribution network of the electrical grid (para.
0097, wherein the GIS contains additional information and facilitated access).
Claim(s) 7-9, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Harvey in view of in view of “Distribution System” as applied to claim 1 above, and further in view of Nguyen et al. (“Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning”, hereinafter “Nguyen”).
Regarding claim 7, Harvey as modified discloses all limitations of claim 1. Harvey does not explicitly disclose wherein identifying the overhead asset comprises determining a likelihood that the overhead asset is a particular type of overhead asset. However, Nguyen discloses wherein identifying the overhead asset comprises determining a likelihood that the overhead asset is a particular type of overhead asset (pg. 14, section 4, para. 1, “YOLO(You Only Look Once) is a real-time object detection framework that directly predicts bounding boxes and class probabilities”; and pg. 16, section C and Algorithm 1, wherein the classification and defect detection are both probability-based determinations of class). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the likelihood classification method of Nguyen within the method and system of Harvey as modified by Nguyen according to the rationale of claim 4.
Regarding claim 8, Harvey as modified discloses all limitations of claim 1.Harvey further discloses the overhead asset comprises a first overhead asset of the electrical grid (para. 0051, containing the explicit disclosure of the determination of the construct and its location within images); and updating the GIS database in response to identifying an overhead asset (paras. 0096-0097, disclosing the updating of a GIS with information on the construct upon recognition of the construct, its location, and particular information regarding its state). Harvey does not explicitly disclose identifying, using computer vision with respect to the digital images, a second overhead asset of the electrical grid. However, Nguyen discloses identifying, using computer vision with respect to the digital images, a second overhead asset of the electrical grid (pg. 14 section 4 para. 1, 16, section C, and Algorithm 1, wherein the classification and defect detection are both probability-based determinations of class, and pg. 19 fig. 7 for the visualization of the algorithm, wherein the algorithm’s use of a YOLO-mediated bounding box method allows for multiple object detection and classification, as disclosed within the algorithm and visualized via fig. 7). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the multi-asset detection and inspection method of Nguyen within the method and system of Harvey as modified by Nguyen according to the rationale of
claim 4.
Regarding claim 9, Harvey and Nguyen disclose all limitations of claim 8. Nguyen further discloses wherein the first and second overhead assets are different types of overhead assets (pg. 14 section 4 para. 1, 16, section C, and Algorithm 1, wherein the classification and defect detection are both probability-based determinations of class, and pg. 19 fig. 7 for the visualization of the algorithm, wherein the algorithm’s use of a YOLO-mediated bounding box method allows for multiple object detection and classification, and wherein the detection of objects of different classes is evident from the experiment of fig. 7). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the multi-asset detection and classification method with different asset classes of Nguyen within the method and system of Harvey as modified by Nguyen according to the rationale of claim 4.
Regarding claim 21, Harvey as modified discloses all limitations of claim 1. Harvey does not disclose classifying, for a first of the digital images, a background object and a foreground object, wherein the overhead asset comprises the foreground object and is on a utility pole, and wherein the background object is not on the utility pole.
However, Nguyen discloses classifying, for a first of the digital images, a background object and a foreground object, wherein the overhead asset comprises the foreground object and is on a utility pole, and wherein the background object is not on the utility pole (pg. 14 section 4 para. 1, 16, section C, and Algorithm 1, wherein the classification and defect detection are both probability-based determinations of class, and pg. 19 fig. 7 for the visualization of the algorithm, wherein the algorithm’s use of a YOLO-mediated bounding box method allows for multiple object detection and classification, and wherein the detection of objects of different classes is evident from the experiment of fig. 7; and further containing a plurality of both foreground objects and background objects (foreground being interpreted by the Examiner to mean objects nearest to the observer’s perspective), each of which is classified using YOLO, and wherein a utility pole in the background is classified separately from insulators and crossarms in the foreground) . Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the multi-asset detection and classification method in both the foreground and background of an image of Nguyen within the method and system of Harvey as modified by Nguyen according to the rationale of claim 4.
Regarding claim 22, Harvey and Nguyen disclose all limitations of claim 21. Harvey does not disclose wherein the background object is on another utility pole. However, Nguyen discloses wherein the background object is on another utility pole (pg. 14 section 4 para. 1, 16, section C, and Algorithm 1, wherein the classification and defect detection are both probability-based determinations of class, and pg. 19 fig. 7 for the visualization of the algorithm, wherein the objects classified within the background of the image not on the first utility pole are present on another utility pole). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the multi-asset detection and classification method in both the foreground and background of an image of Nguyen within the method and system of Harvey as modified by Nguyen according to the rationale of claim 4.
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Harvey in view of in view of “Distribution System” as applied to claim 1 above, and further in view of in view of Wong (WIPO PG Pub 2022101882).
Regarding claim 6, Harvey discloses all limitations of claim 1. Harvey does not disclose wherein the overhead asset comprises a transmission line or a distribution line. However, Wong discloses wherein the overhead asset comprises a transmission line or a distribution line (paras. 0002 and 0030, “Powerline inspection can be a very effective method for finding defects on powerline assets such as transmission and distribution lines”; and “the system can employ one or more drones that conduct aerial inspections to detect emerging faults on transmission and distribution lines using radio frequency
(RF) data collection devices, global positioning system (GPS) antennas, wireless and cellular communication systems, and high definition cameras with monocular or stereo vision”). Specifically, Wong discloses a multi-sensor method and system of drone-mediated powerline inspection through RF data measurement and image capture. Therefore, both Harvey and Wong disclose powerline inspection methods and system using at least image sensor data for defect detection and robust measurement. Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have applied the teachings of Wong with respect to transmission and distribution lines within the method of Harvey as a teaching or suggestion in the prior art which would have led one having ordinary skill to have modified the disclosure of Harvey to yield the predictable result of accurate inspection and defect detection on crucial powerline infrastructure.
Regarding claim 18, Harvey discloses all limitations of claim 10. Harvey does not disclose wherein classifying comprises determining that the overhead asset comprises a distribution line, and wherein identifying the attribute comprises determining one or more of the following: conductor wire code, conductor size, conductor material, and whether the distribution line is insulated or stranded.
However, Wong discloses wherein classifying comprises determining that the overhead asset comprises a distribution line (paras. 0002 and 0030, “Powerline inspection can be a very effective method for finding defects on powerline assets such as transmission and distribution lines”; and “the system can employ one or more drones that conduct aerial inspections to detect emerging faults on transmission and distribution lines using radio frequency (RF) data collection devices, global positioning system (GPS) antennas, wireless and cellular communication systems, and high definition cameras with monocular or stereo vision”), and wherein identifying the attribute comprises determining whether the distribution line is insulated or stranded (para. 0031, “Examples of RF events can include punctures in the insulation of wires, conductors with broken strands”). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have applied the teachings of Wong with respect to distribution lines, and the punctured insulation leading to stranded wire within the method of Harvey as a teaching or suggestion in the prior art which would have led one having ordinary skill to have modified the disclosure of Harvey to yield the predictable result of accurate inspection and defect detection on crucial powerline infrastructure.
Claims 11, 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Harvey in view of in view of “Distribution System” as applied to claim 1 above, and further in view of Nguyen and in further view of Galli et al. (“For the Grid and Through the Grid: The Role of Power Line Communications in the Smart Grid”, hereinafter “Galli”).
Regarding claim 11, Harvey as modified discloses all limitations of claim 1. As mentioned above, Harvey does not explicitly disclose wherein the detected and classified components of the power line are distribution line devices. However, Nguyen discloses a fine-grained classification mechanism, allowing for more granular detection and identification of powerline components of different types (pg. 14, section III subsection C, “The detected components are then classified into more fine-grained power components classes using our component classification models”). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the classification model of Nguyen within the method and system of Harvey according to the rationale of claim 4.
The combination of Harvey and Nguyen fails, however, to disclose wherein the classifying comprises determining that the overhead asset comprises a switch, and wherein identifying the attribute comprises determining one or more of the following: switch normal status, switch size, switch operating mechanism, whether switch is gang-operated.
However, Galli discloses the presence, utility, and operating mechanism of switches within a
power grid network (pg. 14 section B para. 1, “In the case of fault location, fault isolation and service restoration, substation IEDs must communicate with external IEDs such as switches, reclosers, or sectionalizers”; and pg. 21 section B para. 7, “points of connection are normally open but allow various configurations by the operating utility by closing and opening switches. Operation of these switches may be by remote control from a control center or by a lineman”.). Specifically, Galli analyzes the effectiveness of power line communications as a method of interconnection within smart utility grids, specifically power distribution networks. To accomplish this, Galli describes identification of a variety of power line components crucial to the functioning of a power distribution system including their electrical and topological positions within a sample powerline framework. One having ordinary skill in the art would have found it obvious that the components such as switches (and their respective topological and electrical information) could easily be integrated as classes for object detection within the method of Nguyen as integrated within the system of Harvey. Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the disclosure of Galli with respect to switches of a power line network within the method and system of Harvey as modified by Nguyen as a teaching in the prior art which would led one of ordinary skill in the art to have augmented Nguyen’s classification model (within the method of Harvey as modified by Nguyen) to include an additional class for switches and an associated operating mechanism attribute.
Regarding claim 13, Harvey as modified discloses all limitations of claim 1. As mentioned above, Harvey does not explicitly disclose wherein the detected and classified components of the power line are distribution line devices. However, Nguyen discloses a fine-grained classification mechanism, allowing for more granular detection and identification of powerline components of different types (pg. 14, section III subsection C, “The detected components are then classified into more fine-grained power components classes using our component classification models”). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the classification model of Nguyen within the method and system of Harvey according to the rationale of claim 4.
The combination of Harvey and Nguyen fails, however, to disclose wherein classifying comprises determining that the overhead asset comprises a capacitor, and wherein identifying the attribute comprises determining one or more of the following: capacitor switch type, capacitor SCADA type, capacitor control type, capacitor sensing phase and sensor location.
However, Galli discloses wherein an overhead asset comprises a capacitor, and wherein
identifiable attribute of the capacitor is capacitor SCADA type, capacitor control type (pg. 9, section IV A, paras. 1-2, “A system conforming to the SCADA model usually comprises the following components… sets of Intelligent Electronic Devices (IEDs), and the supporting communications infrastructure that furnishes the communications between the supervisory Master and the RTUs and between the RTUs and IEDs. The IEDs usually include various types of microprocessor-based controllers of power system equipment, such as circuit breakers, transformers, and capacitor banks”). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the disclosure of Galli with respect to capacitor identification and SCADA type determination within the method and system of Harvey as modified by Nguyen according to the rationale of claim 11.
Regarding claim 17, Harvey as modified discloses all limitations of claim 1. Harvey does not explicitly disclose wherein classifying comprises determining that the overhead asset comprises a transformer. However, Nguyen discloses wherein classifying a captured overhead asset comprises determining that the overhead asset comprises a transformer (pg. 15, para. 1, “The selected component classes include… a class for transformers”). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have integrated Nguyen’s disclosure of the determination of a transformer into the method and system of Harvey as modified by Nguyen according to the rationale of claim 4. The combination of Harvey and Nguyen fails to disclose wherein identifying the attribute comprises determining one of more of the following: transformer size, transformer KVA, and transformer secondary voltage. However, Galli discloses wherein the identifiable attribute of a transformer includes transformer secondary voltage (pg. 14, section B, para. 4, “voltage measurement on the secondary winding”, which would be observable and determinable). Thus, it would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have utilized the disclosure of Galli with respect to transformer secondary voltage within the method and system of Harvey as modified by Nguyen according to the rationale of claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Jianlong et al. describes a computer vision system that recognizes electrical components including transformers, breakers and insulators.
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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/GREGORY A MORSE/ Supervisory Patent Examiner, Art Unit 2698
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