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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 7-8 and 13-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 7 and 13 recite the limitation "an inner representation of the 3D digital model". There is insufficient antecedent basis for this limitation in the claim.
NOTE: The second instance of “3D digital model” should be corrected to “the 3D digital model”.
Claim 2, 8 and 14 recite the limitation "the jaw". There is insufficient antecedent basis for this limitation in the claim.
Claim 2, 8 and 14 recite the limitation "the corresponding trimmed digital surface". There is insufficient antecedent basis for this limitation in the claim.
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.
Claim(s) 1, 7, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coulombe et al. (hereinafter Coulombe) (US 2023/0066220 A1) in view of Derzapf et al. (hereinafter Derzapf) (US 2022/0304782 A1).
Regarding claim 1: The structural elements of apparatus claim 7 perform all of the steps of method claim 1. Thus, claim 1 is rejected for the same reasons discussed in the rejection of claim 7.
Regarding claim 7: Coulombe discloses a processor (Fig. 4, processor 116); and a computer-readable storage medium (The software product may be stored in a non-volatile or non-transitory storage medium, which can be a compact disk read-only memory (CD-ROM), a USB flash disk, or a removable hard disk., par. 206) comprising instructions executable by the processor (Fig. 4, processor 116) to perform steps comprising: training a first neural network to determine an inner representation of a 3D digital model (FIG. 2 illustrates a selection of tooth descriptor surfaces described in relation to a common reference locus. This figure illustrates that with this encoding method, 3D mesh representations of the dental object can be segmented into different features with each view related by a common reference locus or centroid such that they can be overlapped in space and independently searched and optimized. The features of a bottom jaw dental location illustrated are from left to right, where: feature A is the surface of a preparation site (adjacent tooth walls); feature B is the surface of the preparation site (the post); feature C is the inside surface of a fabricated crown (inside surface); feature D is the outside surface of the fabricated crown (outside surface); and feature E is the occlusal surface of neighbouring top teeth in the top jaw. In one application of the present invention a crown can be designed by matching the inside of the crown (feature C) to the patient's post (feature B), and independently designing the outside of the crown (feature D) to match the preparation site (feature A) as well as the occlusal surface (feature E), each with its own descriptor matrix. Computer-aided design (CAD) can be done by matching similar descriptor matrixes to the one being designed to a machine learning trained model to provide a complete crown that fits into the patient's mouth, taking into consideration all aspects of the crown environment., par. 141); and training a second neural network (FIG. 43 is a schematic diagram of how the convolution Auto Encoder functions, in accordance with some embodiments. The first task is to make a database of good Gum Prognostic stacks 3902. The term “good” means that the dental anatomy whose Gum Prognostic stacks are stored in the database is in healthy condition, which serves as the training set. Now the set of Good Prognostic stacks 3906 will go through Convolutional Auto Encoder 3910, and each Reconstructed Good Gum Prognostic Stack 3908 is generated. The Reconstructed Good Gum Prognostic Stack 3908 is then stored in the Reconstructed Good Gum Prognostic database 3908. Similar to the structure of the combination of two Convolutional Neural Networks (CNN) as previously described for tooth alignment, a similar structure can be used for gum (periodontic) prognosis. In particular, two CNNs connected to one another can be used. The output of the second Convolutional Neural Network gives a reconstructed output which is a smaller dataset than the original input, and this reconstructed output can be used as a machine learning model for automated treatment. In this method for gum prognosis and treatment, Convolutional Neural Network 1 takes the original input data. The Convolutional Neural Network 1 has hidden layers which decrease in size as we go from the input layer to the output layer. The section of the model gives a compressed representation, meaning the lower dimensional latent representation and now, a compressed output is used as an input to another Convolutional Neural Network 2 in which allows the original file to be reconstructed and gives the reconstructed output of the original file. The output data, or reconstructed input, is a smaller dataset than the original file. The second Convolutional Neural Network has hidden layers that increase in size as you go from the input layer to the output layer and the last layer matches the input layer of the Convolutional Neural Network 1. From this output a comparison of both the original (Good Gum Prognostic Stack), which was an input to the Convolutional Autoencoder to the reconstructed output (Reconstructed Good Gum Prognostic Stack) and the reconstruction loss can be measured. Based on both databases the comparison threshold loss is identified. Now the model has been trained allowing it to be used for treatment. Once the model is trained the learning can be frozen. An Anonymous Gum Prognostic stack can then be fed to the trained Convolutional Autoencoder and a Reconstructed Anonymous Gum Prognostic Stack is obtained. Both the original and reconstructed stacks are compared to measure the reconstruction loss and then it is compared with threshold value as describe and based on the value comparison treatment can be suggested., par. 202) 24 illustrates an example of a dental quadrant 250 descriptor stack output of the present method. This example quadrant descriptor stack comprises five (5) descriptor matrices, a quadrant descriptor matrix 244 and four single tooth descriptor matrices 242a, 242b, 242c, 242d each individually describing teeth 1, 2, 3, 4 in the quadrant, respectively. Each of matrices are single tooth descriptor matrices 242a, 242b, 242c, 242d, with zero padding entries to ensure that they are the same size as quadrant descriptor matrix 244. The resulting quadrant descriptor stack provides a visual representation of a 2-dimensional descriptor matrix that represents the distance travelled by each indexing ray onto the surface of a dentist's preparation surgery scan. In each descriptor matrix each pixel corresponds to a single ray intersecting the exterior surface of the dental object that it describes, and the intensity of the pixel color corresponds to the distance the ray travelled from the common centroid before hitting or intersecting with the triangulated mesh surface. On this array we can see bold lines on the surface of the 2-dimensional matrix. The bolded or shaded areas in each single tooth descriptor matrices 242a, 242b, 242c, 242d are unique 2-dimensional descriptor matrices that each represent a specific feature on the surgical preparation or quadrant for each tooth. In each matrix it is noted that the bottom line represents the margin line, the middle line represents the location where the preparation shoulder meets the preparation wall, and the top line represents the transition to the occlusal surface on the preparation. By isolating each feature to its own matrix, data can be gathered from each dental object and an artificial intelligent/machine learning matching engine can be trained to make assessments about how the crown should be designed for any specific preparation., par. 179) and the inner representation of the 3D digital model (FIG. 2 illustrates a selection of tooth descriptor surfaces described in relation to a common reference locus. This figure illustrates that with this encoding method, 3D mesh representations of the dental object can be segmented into different features with each view related by a common reference locus or centroid such that they can be overlapped in space and independently searched and optimized. The features of a bottom jaw dental location illustrated are from left to right, where: feature A is the surface of a preparation site (adjacent tooth walls); feature B is the surface of the preparation site (the post); feature C is the inside surface of a fabricated crown (inside surface); feature D is the outside surface of the fabricated crown (outside surface); and feature E is the occlusal surface of neighbouring top teeth in the top jaw. In one application of the present invention a crown can be designed by matching the inside of the crown (feature C) to the patient's post (feature B), and independently designing the outside of the crown (feature D) to match the preparation site (feature A) as well as the occlusal surface (feature E), each with its own descriptor matrix. Computer-aided design (CAD) can be done by matching similar descriptor matrixes to the one being designed to a machine learning trained model to provide a complete crown that fits into the patient's mouth, taking into consideration all aspects of the crown environment., par. 141).
Coulombe fails to specifically address to determine a margin line proposal.
Derzapf discloses to determine a margin line proposal (The proposals for restoration recommendations 410 may include a CAD restoration geometry and restoration parameters such as a minimum wall thickness value, an edge thickness value, margin line proposal, an edge design (chamfer or rounded, chamfer length, edge radius), a spacer design etc., referred to herein as restoration parameter/geometry proposals 412., par. 56 and Fig. 4A).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include to determine a margin line proposal in order to provide restoration recommendations as taught by Derzapf (par. 56).
Regarding claim 13: Arguments analogous to those stated in the rejection of claim 7 are applicable. A non-transitory computer readable medium is inherently taught as evidenced by Coulombe (Fig. 4, processor 116) and various memories stored therein.
Claim(s) 4, 6, 10, 12, 16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coulombe in view of Derzapf and further in view of Fabbri et al. (hereinafter Fabbri) (US 2023/0153476 A1).
Regarding claim 4: Coulombe in view of Derzapf satisfy all the elements of claim 1. The structural elements of apparatus claim 10 perform all of the steps of method claim 4. Thus, claim 4 is rejected for the same reasons discussed in the rejection of claim 10.
Regarding claim 6: Coulombe in view of Derzapf satisfy all the elements of claim 1. The structural elements of apparatus claim 12 perform all of the steps of method claim 6. Thus, claim 6 is rejected for the same reasons discussed in the rejection of claim 12.
Regarding claim 10: Coulombe in view of Derzapf satisfy all the elements of claim 7. Coulombe further discloses wherein the training the first neural network comprises training a neural network (FIG. 2 illustrates a selection of tooth descriptor surfaces described in relation to a common reference locus. This figure illustrates that with this encoding method, 3D mesh representations of the dental object can be segmented into different features with each view related by a common reference locus or centroid such that they can be overlapped in space and independently searched and optimized. The features of a bottom jaw dental location illustrated are from left to right, where: feature A is the surface of a preparation site (adjacent tooth walls); feature B is the surface of the preparation site (the post); feature C is the inside surface of a fabricated crown (inside surface); feature D is the outside surface of the fabricated crown (outside surface); and feature E is the occlusal surface of neighbouring top teeth in the top jaw. In one application of the present invention a crown can be designed by matching the inside of the crown (feature C) to the patient's post (feature B), and independently designing the outside of the crown (feature D) to match the preparation site (feature A) as well as the occlusal surface (feature E), each with its own descriptor matrix. Computer-aided design (CAD) can be done by matching similar descriptor matrixes to the one being designed to a machine learning trained model to provide a complete crown that fits into the patient's mouth, taking into consideration all aspects of the crown environment., par. 141)
Coulombe fails to specifically address for 3D point cloud analysis.
Fabbri discloses for 3D point cloud analysis (After completing training phase 201 of process 200, neural network engine 184 may commence execution phase 203. Execution phase 203 may begin when computing device 150 receives a digital 3D model of a proposed (e.g., post dental restoration therapy) dental anatomy for patient 102 (204). In one example, computing device 150 receives the digital 3D model of the proposed dental anatomy from another computing device, such as computing system 190 of clinic 104. The digital 3D model of the proposed dental anatomy for patient 102 may include a point cloud or 3D mesh of the proposed dental anatomy., par. 129).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Coulombe in view Derzapf to include for 3D point cloud analysis in order to utilize a point cloud to include a collection of points that represent or define an object in 3D space as taught by Fabbri (par. 130).
Regarding claim 12: Coulombe in view of Derzapf satisfy all the elements of claim 7. Coulombe further discloses wherein the second trained neural network (FIG. 43 is a schematic diagram of how the convolution Auto Encoder functions, in accordance with some embodiments. The first task is to make a database of good Gum Prognostic stacks 3902. The term “good” means that the dental anatomy whose Gum Prognostic stacks are stored in the database is in healthy condition, which serves as the training set. Now the set of Good Prognostic stacks 3906 will go through Convolutional Auto Encoder 3910, and each Reconstructed Good Gum Prognostic Stack 3908 is generated. The Reconstructed Good Gum Prognostic Stack 3908 is then stored in the Reconstructed Good Gum Prognostic database 3908. Similar to the structure of the combination of two Convolutional Neural Networks (CNN) as previously described for tooth alignment, a similar structure can be used for gum (periodontic) prognosis. In particular, two CNNs connected to one another can be used. The output of the second Convolutional Neural Network gives a reconstructed output which is a smaller dataset than the original input, and this reconstructed output can be used as a machine learning model for automated treatment. In this method for gum prognosis and treatment, Convolutional Neural Network 1 takes the original input data. The Convolutional Neural Network 1 has hidden layers which decrease in size as we go from the input layer to the output layer. The section of the model gives a compressed representation, meaning the lower dimensional latent representation and now, a compressed output is used as an input to another Convolutional Neural Network 2 in which allows the original file to be reconstructed and gives the reconstructed output of the original file. The output data, or reconstructed input, is a smaller dataset than the original file. The second Convolutional Neural Network has hidden layers that increase in size as you go from the input layer to the output layer and the last layer matches the input layer of the Convolutional Neural Network 1. From this output a comparison of both the original (Good Gum Prognostic Stack), which was an input to the Convolutional Autoencoder to the reconstructed output (Reconstructed Good Gum Prognostic Stack) and the reconstruction loss can be measured. Based on both databases the comparison threshold loss is identified. Now the model has been trained allowing it to be used for treatment. Once the model is trained the learning can be frozen. An Anonymous Gum Prognostic stack can then be fed to the trained Convolutional Autoencoder and a Reconstructed Anonymous Gum Prognostic Stack is obtained. Both the original and reconstructed stacks are compared to measure the reconstruction loss and then it is compared with threshold value as describe and based on the value comparison treatment can be suggested., par. 202).
Coulombe in view of Derzapf fails to specifically address comprises training a decoder neural network.
Fabbri discloses comprises training a decoder neural network (In some embodiments, neural network engine 184 implements the generator network of the 2D restoration image aspects of this disclosure as an encoder-decoder CNN. In these examples, the generator network reduces the dimensionality of the input image, and then expands the dimensionality back up to the original dimensionality (e.g, via a sequence of downsampling and upsampling, or in other ways). The generator network in these examples may also be referred to as a “U-Net.” As used herein, a “U-Net” refers to a type of encoder-decoder architecture in which the feature maps from the encoder are concatenated onto the respective feature maps in the decoder., par. 197).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Coulombe in view of Derzapf to include comprises a decoder neural network in order to improve process by reducing iterations caused by defective or suboptimal dental appliances supplied to a dental practitioner as taught by Fabbri (par. 24).
Regarding claim 16: Coulombe in view of Derzapf satisfy all the elements of claim 13. Arguments analogous to those stated in the rejection of claim 7 are applicable. A non-transitory computer readable medium is inherently taught as evidenced by Coulombe (Fig. 4, processor 116) and various memories stored therein.
Regarding claim 18: Coulombe in view of Derzapf satisfy all the elements of claim 13. Arguments analogous to those stated in the rejection of claim 12 are applicable. A non-transitory computer readable medium is inherently taught as evidenced by Coulombe (Fig. 4, processor 116) and various memories stored therein.
Claim(s) 5, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coulombe in view of Derzapf and further in view of Bhaskar et al. (hereinafter Bhaskar) (US 2020/0293878 A1).
Regarding claim 5: Coulombe in view of Derzapf satisfy all the elements of claim 1. The structural elements of apparatus claim 11 perform all of the steps of method claim 5. Thus, claim 5 is rejected for the same reasons discussed in the rejection of claim 11.
Regarding claim 11: Coulombe in view of Derzapf satisfy all the elements of claim 7. Coulombe further discloses wherein training the first neural network (FIG. 2 illustrates a selection of tooth descriptor surfaces described in relation to a common reference locus. This figure illustrates that with this encoding method, 3D mesh representations of the dental object can be segmented into different features with each view related by a common reference locus or centroid such that they can be overlapped in space and independently searched and optimized. The features of a bottom jaw dental location illustrated are from left to right, where: feature A is the surface of a preparation site (adjacent tooth walls); feature B is the surface of the preparation site (the post); feature C is the inside surface of a fabricated crown (inside surface); feature D is the outside surface of the fabricated crown (outside surface); and feature E is the occlusal surface of neighbouring top teeth in the top jaw. In one application of the present invention a crown can be designed by matching the inside of the crown (feature C) to the patient's post (feature B), and independently designing the outside of the crown (feature D) to match the preparation site (feature A) as well as the occlusal surface (feature E), each with its own descriptor matrix. Computer-aided design (CAD) can be done by matching similar descriptor matrixes to the one being designed to a machine learning trained model to provide a complete crown that fits into the patient's mouth, taking into consideration all aspects of the crown environment., par. 141)
Coulombe in view of Derzapf fail to specifically address comprises training a hierarchical neural network (“HNN”).
Bhaskar discloses comprises training a hierarchical neural network (“HNN”) (The memory 480 may further include a modeling unit 130 configured to generate and train a hierarchical neural network, par. 43).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Coulombe in view of Derzapf to include training a hierarchical neural network (“HNN”) in order to provide a choice of types of neural networks generated by the modeling unit 130 (Fig. 4) as taught by Bhashar.
Regarding claim 17: Coulombe in view of Derzapf satisfy all the elements of claim 13. Arguments analogous to those stated in the rejection of claim 11 are applicable. A non-transitory computer readable medium is inherently taught as evidenced by Coulombe (Fig. 4, processor 116) and various memories stored therein.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 4-7, 10-13 and 17-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-8 and 11-15 of U.S. Patent No. 12,210,802 (hereinafter ‘802). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Current Application ‘802
Claim 1:
training a first neural network to determine an inner representation of the 3D digital model; and
training a second neural network to determine a margin line proposal from a base margin line and the inner representation of a 3D digital model.
Claim 1:
receiving a 3D digital model of at least a portion of a jaw, the 3D digital model comprising a digital preparation tooth;
determining, using a first trained neural network, an inner representation of the 3D digital model; and
determining, using a second trained neural network, a margin line proposal from a base margin line and the inner representation of the 3D digital model, wherein the first trained neural network and the second trained neural network are trained using a training dataset comprising an untrimmed digital surface of the jaw and a target margin line on a surface of the corresponding trimmed digital surface.
Current Application ‘802
Claim 7:
a processor; and
a computer-readable storage medium comprising instructions executable by the processor to perform steps comprising:
training a first neural network to determine an inner representation of the 3D digital model; and
training a second neural network to determine a margin line proposal from a base margin line and the inner representation of a 3D digital model.
training a first neural network to determine an inner representation of the 3D digital model; and
training a second neural network to determine a margin line proposal from a base margin line and the inner representation of a 3D digital model.
Claim 7:
a processor; and
a computer-readable storage medium comprising instructions executable by the processor to perform steps comprising:
receiving a 3D digital model of at least a portion of a jaw, the 3D digital model comprising a digital preparation tooth;
determining, using a first trained neural network, an inner representation of the 3D digital model; and
determining, using a second trained neural network, a margin line proposal from a base margin line and the inner representation of the 3D digital model, wherein the first trained neural network and the second trained neural network are trained using a training dataset comprising an untrimmed digital surface of the jaw and a target margin line on a surface of the corresponding trimmed digital surface.
Current Application ‘802
Claim 13:
training a first neural network to determine an inner representation of the 3D digital model; and
training a second neural network to determine a margin line proposal from a base margin line and the inner representation of a 3D digital model.
Claim 13:
receiving a 3D digital model of at least a portion of a jaw, the 3D digital model comprising a digital preparation tooth; determining, using a first trained neural network, an inner representation of the 3D digital model; and
determining, using a second trained neural network, a margin line proposal from a base margin line and the inner representation of the 3D digital model, wherein the first trained neural network and the second trained neural network are trained using a training dataset comprising an untrimmed digital surface of the jaw and a target margin line on a surface of the corresponding trimmed digital surface.
Claim 4 of the current application corresponds to claim 5 of ‘802.
Claim 5 of the current application corresponds to claim 4 of ‘802.
Claim 6 of the current application corresponds to claim 6 of ‘802.
Claim 10 of the current application corresponds to claim 12 of ‘802.
Claim 11 of the current application corresponds to claim 11 of ‘802.
Claim 12 of the current application corresponds to claim 5 of ‘802.
Claim 17 of the current application corresponds to claim 14 of ‘802.
Claim 18 of the current application corresponds to claim 15 of ‘802.
Claims 2-3, 8-9 and 14-16 of the current application do not correspond to any claims of ‘802.
Allowable Subject Matter
Claims 2-3, 8-9 and 14-15 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 and provided the rejections in 35 USC 112(b) and Double Patenting are overcome.
Conclusion
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/CHARLOTTE M BAKER/Primary Examiner, Art Unit 2664
02 October 2026
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