DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claims 1, 13, 19 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.
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 1-6, and 13-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Azernikov et al. (US 20190282344 A1 hereinafter “Azernikov”) in view of Saphier et al. (US 20210321872 A1, hereinafter “Saphier”).
Regarding claim 13,
Azernikov teaches:
An intraoral image processing device comprising: a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory (Azernikov: ¶48, “The entities shown in FIG. 1 are implemented using one or more computing devices. FIG. 2 is a high-level block diagram of a computing device 200 for acting as dental restoration server 101, design device 103, third party server 151, and/or client device 107, or a component of the above. Illustrated are at least one processor 202 coupled to a chipset 204. Also coupled to chipset 204 are a memory 206, a storage device 208, a graphics adapter 212, and a network adapter 216. A display 218 is coupled to the graphics adapter 212. In some embodiments, the functionality of the chipset 204 is provided by a memory controller hub 220 and an I/O controller hub 222. In another embodiment, memory 206 is coupled directly to processor 202 instead of chipset 204. Storage device 208 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. Memory 206 holds instructions and data used by processor 202. Graphics adapter 212 displays images and other information on the display 218. Network adapter 216 couples the computer system 200 to network 105”)
to obtain three-dimensional (3D) intraoral data on an oral cavity comprising teeth and a gingiva (Azernikov: ¶53, "In some embodiments, each dentition scan data set received may optionally be preprocessed before using the data set as input of the deep neural network. Dentition scan data are typically 3D digital image or file representing one or more portions of a patient's dentition. The 3D digital image (3D scan data) of a patient's dentition can be acquired by intraorally scanning the patient's mouth. Alternatively, a scan of an impression or of a physical model of the patient's teeth can be made to generate the 3D scan data of a patient's dentition. In some embodiments, the 3D scan data can be transformed into a 2D data format using, for example, 2D depth maps and/or snapshots", Azernikov: ¶62, "Referring again to FIG. 3, to generate a new 3D model of a dental prosthesis for a new patient, the new patient's dentition scan data (e.g., scanned dental impression, physical model, or intraoral scan) received and ingested at 315. In some embodiments, the new patient's dentition scan data can be preprocessed to transform 3D image data into 2D image data, which can make the dentition scan data easier to ingest by certain neural network algorithms. At 320, using the previously trained deep neural network, one or more dental features in the new patient's dentition scan data are identified. The identified features can be a preparation site, the corresponding margin line, adjacent teeth and corresponding features, and surrounding gingiva for example"),
and generate an outer surface of a target tooth that is a subject of a prosthesis from among the teeth included in the 3D intraoral data (Azernikov: ¶62, "Referring again to FIG. 3, to generate a new 3D model of a dental prosthesis for a new patient, the new patient's dentition scan data (e.g., scanned dental impression, physical model, or intraoral scan) received and ingested at 315"; Azernikov: ¶63, "At 325, using the trained deep neural network, a full 3D dental restoration model can be generated based on the identified features at 320. In some embodiments, the trained deep neural network can be tasked to generate the full 3D dental restoration model by: generating an occlusal portion of a dental prosthesis for the preparation site; obtaining the margin line data from the patient's dentition scan data; optionally optimizing the margin line; and generating a sidewall between the generated occlusal portion and the margin line. Generating an occlusal portion can include generating an occlusal surface having one or more of a mesiobuccal cusp, buccal grove, distobuccal cusp, distal cusp, distobuccal groove, distal pit, lingual groove, mesiolingual cusp, etc"; Azernikov: ¶51, "FIG. 3 illustrates a dental prosthesis generation process 300 using a deep neural network (DNN). Process 300 starts at 305 where a dentition scan data set is received or ingested into a database such as database 150. The dentition scan data set can include one or more scan data sets of real patient's dentitions with dental preparation sites and technician-generated (non-DNN generated) dental prostheses created for those preparation sites. A dental preparation site (also referred to as a tooth preparation or a prepared tooth) is a tooth (note: target tooth), a plurality of teeth, or an area on a tooth that has been prepared to receive a dental prosthesis (e.g., crown, bridge, inlay, etc.). A technician or a non-DNN generated dental prosthesis is a dental prosthesis mainly designed by a technician. Additionally, a technician-generated dental prosthesis can be designed based on a dental template library having a plurality of dental restoration templates. Each tooth in an adult mouth can have one or more dental restoration templates in the dental template library. More detail on the dental restoration library is provide below"),
wherein a portion corresponding to a void area between the target tooth and at least one adjacent tooth adjacent to the target tooth is automatically generated (Azernikov: ¶32, "In some embodiments, a plurality of scans (e.g., 3-5 scans per quadrant) is performed in order to obtain a suitable image of the patient's anatomy. For example, an occlusal, lingual, and buccal scan may be taken of both the preparation and the opposing jaws. Then, a single scan with the jaws in occlusion may be taken from the buccal perspective to establish the proper occlusion relationship between the preparation jaw and the opposing jaw. Additionally, in some embodiments, interproximal scans are added to capture the contact areas of neighboring teeth. Once the scanning process is completed, a scanning system (not shown in FIGS) will assemble the plurality of scans into a digital model (also referred to as a “dental model” or “digital dental model” herein) of the preparation tooth and its surrounding and opposing teeth. The dental model can be used to design a restoration to be used on the preparation tooth. For example, a dental restoration design program may process and display the dental model in a user interface on a user device. A user (e.g., a design technician) operating on the user device can view the dental model and design or refine a restoration model based on the dental model"; Azernikov: ¶45, "Design device 103 may be interacted by user 147 to design dental restoration requested by client 107. In some embodiments, design device 103 may be a smart phone, or a tablet, notebook, or desktop computer. User 147 may be a human operator, dental technician or designer, etc. Design device 103 may receive dental restoration design assignment from dental restoration server 101 and perform the design accordingly. A design software (not shown in FIG. 1) used for digital design of the dental restoration can be installed in design device 103. The design software may provide library-based automatic dental restoration proposal for the user 147 to accelerate and simplify the design process. In some embodiments, design device 103 may include a scan recognition module 125b for automatic recognition of dental information associated with the dental models. With the dental information (e.g., lower and upper jaws, prepared and opposing jaws, tooth numbers, restoration types such as crown, inlay, bridge and implant, buccal and lingual cusps, occlusal surface, buccal and lingual arcs, margin lines, etc.) being recognized, the design software can incorporate the dental information into the auto-proposal process to provide faster and better library-based restoration proposal"; Azernikov: ¶61, “Training module 120 can train a deep neural network to generate a 3D model of dental restoration using only the technician-designed dentition scan data set. In this way, the DNN generated 3D dental prosthesis will inherently include one or more features of dental prosthesis designed by a human technician using the library template. In some embodiments, training module 120 can train the deep neural network to output a probability vector that includes a probability of an occlusal surface of a technician-generated dental prosthesis representing the occlusal surface of a missing tooth (note: the portion corresponding the void area is the area where a tooth is missing) at the preparation site or margin. Additionally, training module 120 can train a deep neural network to generate a complete 3D dental restoration model by mapping the occlusal surface having the highest probability and margin line data from the scanned dentition data to a preparation site. Additionally, training module 120 can train the deep neural network to generate the sidewall of the 3D dental restoration model by mapping sidewalls data of technician-generated dental prostheses to a probability vector that includes a probability of that one of the sidewalls matches with the occlusal surface and the margin line data from the preparation site”; Azernikov: ¶69, "In some embodiments, training module 120 can simultaneously train two adversarial networks, generator 610 and discriminator 620. Training module 120 can train generator 610 using one or more of a patient's dentition scan data sets to generate a sample model of one or more dental features and/or restorations. For example, the patient's dentition scan data can be 3D scan data of a lower jaw including a prepared tooth/site and its neighboring teeth. Simultaneously, training module 120 can train discriminator 620 to distinguish a generated a 3D model of a crown for the prepared tooth (generated by generator 610) against a sample of a crown from a real data set (a collection of multiple scan data set having crown images). In some embodiments, GAN networks are designed for unsupervised learning, thus input 605 and real data 625 (e.g., the dentition training data sets) can be unlabeled"; Azernikov: ¶77, “In some embodiments, one of the useful applications for arch segmentation is to have the capability to generate an entirely new natural dentition data set (at 720) to train deep neural networks to generate naturally looking 3D model of dental prosthesis. A natural dentition scan data set as used herein has two main components. The first component is a data set that includes scanned dentition data of patients' natural teeth (ideally a full arch scan of top and bottom jaws). Data in the first component includes all of the patients' teeth in its natural and unmodified digital state. The second component of the natural dentition scan data is a missing-tooth data set with one or more teeth removed from the scanned data (note: a void area is created where one or more teeth is removed). In place of the missing-tooth, a DNN generated preparation site can be placed at the site of the missing-tooth. This process generates two sets of dentition data: a full and unmodified dentition scan data of patients' natural teeth; and a missing-tooth data set in which one or more teeth are digitally removed from the dentition scan data”).
However, Azernikov fails to teach wherein the void area comprises an unscanned area between the target tooth and the at least one adjacent tooth, where there is no scanned data.
The analogous art Saphier teaches:
wherein the void area comprises an unscanned area between the target tooth and the at least one adjacent tooth, where there is no scanned data (Saphier: ¶217, “. . . subject a patient to intraoral scanning. . . include a lower dental arch of the patient, an upper dental arch of the patient, one or more preparation teeth of the patient (e.g., teeth of the patient to which a dental device such as a crown or other dental prosthetic will be applied), one or more teeth which are contacts of preparation teeth. . .”; ¶ ¶296, “. . . intraoral scan application 115 may detect missing areas for which intraoral scan data was not generated. This can include . . . holes or voids in scanning information (e.g., voids above a threshold size), and so on. . .”; ¶272, “. . . intraoral scan application may identify an interproximal region between teeth (e.g., a tooth to tooth boundary) . . .”; NOTE: The target tooth is the preparation teeth of the patient, and the at least one adjacent tooth is the one or more teeth which are contacts of preparation teeth. The intraoral scan of Saphier can detect voids in scanning information. The unscanned area is the detected missing area, it is unscanned because scan data was not generated. If scan data was not generated, then there is no scanned data. Saphier’s system can also identify an interproximal region, which is an area between the target tooth and the at least one adjacent tooth. Intraoral scan of preparation tooth and teeth which are contacts of preparation teeth >> detects missing areas, if there is any unscanned area, which is the detected missing area for which scan data was not generated in the interproximal region, then Saphier’s system can detect it.)
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to combine Azernikov and Saphier and include: wherein the void area comprises an unscanned area between the target tooth and the at least one adjacent tooth, where there is no scanned data.
The reason for doing so is because the “surface quality (e.g., number of known points on a surface) may depend on a number of scans that have been received for that surface. With a few number of scans for a surface at a particular area, the area may be produced but with low certainty or low quality. Intraoral scan application 115 may flag such areas that have too few data points for further scanning” (Saphier: ¶296).
Regarding method claim 1,
method claim 1 is drawn to the method corresponding to the instructions of using same as claimed in apparatus claim 13. Therefore, method claim 1 corresponds to the instructions in the apparatus of claim 13, and is rejected for the same reasons of anticipation as used above.
Regarding CRM claim 19,
CRM claim 19 is drawn to the CRM corresponding to the instructions of using same as claimed in the apparatus of claim 13. Therefore, CRM claim 19 corresponds to the instructions in the apparatus of claim 15, and is rejected for the same reasons of anticipation as used above.
Regarding claim 14,
Azernikov teaches:
The intraoral image processing device of claim 13, wherein the processor is further configured to execute the one or more instructions stored in the memory to generate a portion corresponding to the void area, based on the 3D intraoral data (Azernikov: ¶62, "Referring again to FIG. 3, to generate a new 3D model of a dental prosthesis (note: such as a crown) for a new patient, the new patient's dentition scan data (e.g., scanned dental impression, physical model, or intraoral scan) received and ingested at 315. In some embodiments, the new patient's dentition scan data can be preprocessed to transform 3D image data into 2D image data, which can make the dentition scan data easier to ingest by certain neural network algorithms. . .).
Regarding claim 15,
Azernikov teaches:
The intraoral image processing device of claim 13, wherein the processor is further configured to execute the one or more instructions stored in the memory to identify a virtual margin line between the target tooth and the gingiva, based on the 3D intraoral data, and generate the outer surface, based on the target tooth and the identified virtual margin line (Azernikov: ¶63, "At 325, using the trained deep neural network, a full 3D dental restoration model (note: such as a crown which includes an outer surface) can be generated (note: a crown includes an outer surface) based on the identified features at 320. In some embodiments, the trained deep neural network can be tasked to generate the full 3D dental restoration model by: generating an occlusal portion of a dental prosthesis for the preparation site; obtaining the margin line data from the patient's dentition scan data; optionally optimizing the margin line; and generating a sidewall between the generated occlusal portion and the margin line. Generating an occlusal portion can include generating an occlusal surface having one or more of a mesiobuccal cusp, buccal grove, distobuccal cusp, distal cusp, distobuccal groove, distal pit, lingual groove, mesiolingual cusp, etc"; Azernikov: ¶64, "The trained deep neural network can obtain the margin line data from the patient's dentition scan data. In some embodiments, the trained deep neural network can optionally modify the contour of the obtained margin line by comparing and mapping it with thousands of other similar margin lines (e.g., margin lines of the same tooth preparation site) having similar adjacent teeth, surrounding gingiva, etc.").
Regarding method claim 2,
method claim 2 is drawn to the method corresponding to the instructions of using same as claimed in apparatus claim 14. Therefore, method claim 2 corresponds to the instructions in the apparatus of claim 14, and is rejected for the same reasons of anticipation as used above.
Regarding claim 3, depending on claim 2,
Azernikov teaches:
The intraoral image processing method of claim 2, wherein the generating of the outer surface comprises generating the portion corresponding to the void area, based on data adjacent to the void area in the 3D intraoral data (Azernikov: ¶62, "Referring again to FIG. 3, to generate a new 3D model of a dental prosthesis for a new patient, the new patient's dentition scan data (e.g., scanned dental impression, physical model, or intraoral scan) received and ingested at 315. In some embodiments, the new patient's dentition scan data can be preprocessed to transform 3D image data into 2D image data, which can make the dentition scan data easier to ingest by certain neural network algorithms. At 320, using the previously trained deep neural network, one or more dental features in the new patient's dentition scan data are identified. The identified features can be a preparation site, the corresponding margin line, adjacent teeth and corresponding features, and surrounding gingiva for example"; Azernikov: ¶63, "At 325, using the trained deep neural network, a full 3D dental restoration model can be generated based on the identified features at 320. In some embodiments, the trained deep neural network can be tasked to generate the full 3D dental restoration model by: generating an occlusal portion of a dental prosthesis for the preparation site; obtaining the margin line data from the patient's dentition scan data; optionally optimizing the margin line; and generating a sidewall between the generated occlusal portion and the margin line. Generating an occlusal portion can include generating an occlusal surface having one or more of a mesiobuccal cusp, buccal grove, distobuccal cusp, distal cusp, distobuccal groove, distal pit, lingual groove, mesiolingual cusp, etc."; " Azernikov: ¶64, "The trained deep neural network can obtain the margin line data from the patient's dentition scan data. In some embodiments, the trained deep neural network can optionally modify the contour of the obtained margin line by comparing and mapping it with thousands of other similar margin lines (e.g., margin lines of the same tooth preparation site) having similar adjacent teeth, surrounding gingiva, etc").
Regarding method claim 4, depending on claim 1,
method claim 4 is drawn to the method corresponding to the instructions of using same as claimed in apparatus claim 15. Therefore, method claim 4 corresponds to the instructions in the apparatus of claim 15, and is rejected for the same reasons of anticipation as used above.
Regarding claim 5, depending on 4,
Azernikov teaches:
The intraoral image processing method of claim 4, wherein the generating of the outer surface comprises generating a portion of the outer surface corresponding to the void area, based on a portion of the virtual margin line corresponding to the void area (Azernikov: ¶65, "To generate the full 3D model (note: a generated crown includes outer surfaces), the trained deep neural network can generate a sidewall to fit between the generated occlusal surface and the margin line. This can be done by mapping thousands of sidewalls of technician-generated dental prostheses to the generated occlusal portion and the margin line. In some embodiments, a sidewall having the highest probability value (in the probability vector) can be selected as a base model in which the final sidewall between occlusal surface and the margin line will be generated").
Regarding claim 16, depending on claim 15,
Azernikov teaches: The intraoral image processing device of claim 15, and generating the outer surface, based on the virtual target tooth and the virtual margin line.
However, Azernikov fails to teach the analogous art Saphier teaches.
Saphier teaches:
wherein the generating of the outer surface comprises: identifying a virtual target tooth by identifying a virtual bottom face corresponding to a bottom face of the target tooth based on the virtual margin line, and connecting the virtual bottom face to the target tooth (Saphier: ¶688, "FIG. 38 is a flow chart illustrating an embodiment for a method 3800 of determining which 3D surfaces to use to generate segments of a margin line in a 3D model of a preparation tooth. At block 3805 of method 3800, processing logic determines a conflicting surface for a pair of 3D surfaces, where a first one of the 3D surfaces was generated from scanning a preparation tooth and a second one of the 3D surfaces was generated from scanning an intaglio surface (note: virtual bottom face) of a dental prosthetic (e.g., a temporary crown) for the preparation tooth (target tooth) or an elastomeric impression of the preparation tooth. At block 3810, processing logic determines a first distance from a probe of an intraoral scanner (also referred to as a first depth and/or first height) for the conflicting surface for the first 3D surface. The first depth may be a combined depth value (e.g., an average depth or median depth) based on the depths of some or all pixels of the first 3D surface or a projection of the 3D surface onto a plane. At block 3815, processing logic determines a first mean curvature (or a first Gaussian curvature) for the conflicting surface for the first 3D surface"; Saphier: ¶702, "As shown, the 3D surface 4015 of the preparation tooth 4010 closely matches the 3D surface 4020 of the intaglio surface of the crown 4005. Accordingly, the 3D surface 4020 of the intaglio surface of the crown 4005 may be inverted and then registered to the 3D surface 4025 of the preparation tooth 4010. Once the two surfaces are registered (note: connecting the virtual bottom face to the target tooth), one or more of the aforementioned techniques for determining a margin line and/or generating a 3D model may be performed");
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to combine Azernikov and implement Saphier’s teachings wherein the generating of the outer surface comprises: identifying a virtual target tooth by identifying a virtual bottom face corresponding to a bottom face of the target tooth based on the virtual margin line, and connecting the virtual bottom face to the target tooth to generate a virtual 3D model of the one or more dental arch and to generate a treatment plan (Saphier: ¶3).
Regarding method claim 6,
method claim 6 is drawn to the method corresponding to the instructions of using same as claimed in apparatus claim 16. Therefore, method claim 6 corresponds to the instructions in the apparatus of claim 16, and is rejected for the same reasons of obviousness as used above.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Azernikov in view of Saphier further in view of Sirovskiy et al. (US 20200125069 A1, hereinafter “Sirovskiy”).
Regarding claim 7, depending on claim 4,
Azernikov teaches: The intraoral image processing method of claim 4, and identifying a virtual margin line.
However, the combination of Azernikov and Saphier fails to teach wherein the identifying of the virtual margin line comprises: for each of points of the target tooth, selecting at least one of the points of the target tooth, by identifying a point whose distance to at least one of points included in a boundary of the gingiva is equal to or less than a preset threshold value, based on data corresponding to the boundary of the gingiva and the target tooth in the 3D intraoral data; and identifying the virtual margin line by connecting the selected at least one point.
Sirovskiy teaches:
wherein the identifying of the virtual margin line comprises: for each of points of the target tooth, selecting at least one of the points of the target tooth, by identifying a point whose distance to at least one of points included in a boundary of the gingiva is equal to or less than a preset threshold value, based on data corresponding to the boundary of the gingiva and the target tooth in the 3D intraoral data; and identifying the virtual margin line by connecting the selected at least one point (Sirovskiy: Abstract, "Systems and methods of defining a trimline (virtual margin line) in relation to modeled teeth including modeled gingiva. The trimline is for use to manufacture an aligner. A margin point is placed proximate a gingival margin at each tooth on at least one jaw in the model. A trimline connects the plurality of margin points from which machine code is generated. The aligner manufactured includes an edge that correlates with the trimline according to the machine code. A margin point may be proximate a gingival zenith. At least one tooth cooperates with the modeled gingiva to define a line around the tooth. The trimline includes at least one tooth curve and at least one connector curve connected to the tooth curve at a transition point. At least one control point is on the trimline between two margin points. The trimline is defined by a spline that may be a Bèzier curve"; Sirovskiy: ¶61, "To that end, with reference to FIG. 1, the 3-D digital model 70 may represent an initial stage of treatment (i.e., stage zero), which often presents the most difficulties with valid trimline development. Referring to FIGS. 1 and 2, following creation of the 3-D digital model 70 at 100, the system 10 positions a plurality of margin points 104 proximate the gingival margin 96 for each of the teeth 74, 80 on a respective jaw 82, 84. As shown, the margin points 104 may be proximate a gingival zenith of each tooth. By proximate, the location of the margin point 104 may be at a gingival-occlusal height of the gingival margin (note: equal to or less than a preset threshold) 96 though it may be spaced labially or lingually apart from the gingival margin 96 by a predetermined distance (note: preset threshold). The system 10 may default to the gingival-most locations proximate a modeled gingival margin 96 for creation of the margin points 104. As is described below, the clinician may selectively or globally adjust the location of each margin point 104, and further may define additional margin points along the margin of the tooth to better control and define the cut line for that tooth")
It would have been obvious to a person having ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to combine Azernikov, Saphier and implement Sirovskiy’s teaching wherein the identifying of the virtual margin line comprises: for each of points of the target tooth, selecting at least one of the points of the target tooth, by identifying a point whose distance to at least one of points included in a boundary of the gingiva is equal to or less than a preset threshold value, based on data corresponding to the boundary of the gingiva and the target tooth in the 3D intraoral data; and identifying the virtual margin line by connecting the selected at least one point to reduce or eliminate any necessity for manual modification of the edges of one or more aligners in a series of aligners after those aligners are produced (Sirovskiy: ¶51).
Allowable Subject Matter
Claims 8-12, 17-18, and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
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/PATRICK P GALERA/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617