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 .
Status of Claims
This action is in reply to the current action filed on 04/27/2026.
Claims 2 and 18-19 have been canceled.
Claims 21-23 have been added.
Claims 1, 3-17, and 20-23 are currently pending and have been examined.
This action is made FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-17, and 21-23 are rejected under 35 USC § 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claims 1 and 17 are within the four statutory categories. Claims 1 and 17 are directed to a method and a system, respectively. Dependent Claims 3-16 and 21-23 are directed to the method and Claim 20 is further directed to the system, and therefore the dependent claims also fall into one of the four statutory categories.
Step 2A Analysis – Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A computer-implemented method for training one or more neural networks to automatically validate digitally generated tooth segmentation data used in digital oral care, the method comprising:
receiving, by one or more computer processors, a first representation that is a digital 3D oral care representation of teeth of a patient,
wherein one or more aspects of the first representation have been assigned labels by one or more machine learning models having been trained to predict one more labels describing a segmentation of the first representation;
receiving, by the one or more computer processors, a second representation that is a 3D oral care digital representation of the patient's teeth,
wherein one or more aspects of the second representation having predefined labels assigned thereto;
validating, by the one or more computer processors, correctness of the segmentation of the first representation based on validation information associated with a comparison of the labels on the one or more aspects of the first with the labels on corresponding one or more aspects of the second representation,
wherein the correctness is validated when the validation information satisfies a predetermined threshold;
and automatically re-training, by the one or more computer processors, the one or more machine learning models based on the validation information,
wherein the retrained one or more machine learning models are used to generate updated segmentation labels for subsequent digital 3D oral care representations.
The limitations as shown in underline above, given the broadest reasonable interpretation, cover the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions, and/or mental process that a neurologist should follow when testing a patient for nervous system malfunctions – in this case, receiving a first 3D representation of a patient’s teeth which have been assigned labels, receiving a second representation having predefined labels, and validating the correctness of segmentation of the first representation by comparing it to the second representation) e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 3-12, 14-16, and 20-23 include other limitations directed toward the abstract idea. For example, Claim 3 recites generating one or more suggestions of how to correct the first representation when it is determined that the first representation is not correctly labelled, Claim 4 recites the first representation describes at least one of: one or more teeth of the patient, one or more non-organic structures, and one or more gums of the patient, Claim 5 recites the labels on the aspects of the first representation describe a boundary between one or more teeth of the patient and one or more gums of the patient, Claim 6 recites the labels on the one or more aspects of the first representation describe a boundary between one or more teeth of the patient and one or more non-organic structures, Claim 7 recites the labels on the one or more aspects of the first representation describe a boundary between one portion of the gums of the patient and another portion of the gums of the patient, Claim 8 recites the labels on the one or more aspects of the first representation describe a boundary between one portion of a tooth of the patient and another portion of that tooth, Claim 9 recites the labels on the one or more aspects of the first representation describe a boundary between the facial side of a tooth of the patient and the lingual side of that tooth, Claims 10 and 20 recite generating one or more two dimensional (2D) representations based on at least in part the first representation, Claim 11 recites classifying the one or more 2D representations, Claim 12 recites classifying one or more 3D oral care representations, Claim 14 recites generating output that specifies whether the aspects of the first representation has not been correctly labelled. Claim 15 recites determining that aspects of the first representation have not been correctly labeled, Claim 16 recites the validation information includes a loss value, accuracy score average boundary distance, boundary percentage, or am over-segmentation ratio. Claim 21 recites the first representation comprises mesh elements which each element comprising a vertex, edge, face, or voxel. Claim 22 recites encoding the first representation of the labels and providing one or more latent representations. Claim 23 recites computing mesh element feature vectors. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g., see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-16, and 18-20 not addressed above are deemed additional elements to the abstract idea and will be further addressed below. Hence dependent Claims 3-12, 14-16, and 20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 17.
Step 2A Analysis – Prong Two:
Claims 1 and 17 are not integrated into practical application because the additional elements (i.e., the non-underlined limitations above – in this case, the computer processors, machine learning model, and the digital [representation] of Claim 1, and the computer processors, non-transitory computer-readable storage, machine learning model, and the digital [representation] of Claim 17) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that processing unit includes processing circuitry that may include one or more processors 104 and memory 106 that, in some examples, provide a computer platform for executing an operating system 116,…Processors 104 are coupled to one or more I/O interfaces 114, which provide I/O interfaces for communicating with devices such as a keyboard, controllers, display devices, image capture devices, other computing systems, and the like…Additionally, processors 104 may be coupled to electronic display 108 (Applicant’s specification, ¶ 0036). In general, a machine learning model can be trained to validate datasets to be used for digital dentistry or digital orthodontics. In some implementations, a machine learning model, such as a neural network can be used to validate 2D raster image views of the 3D data [0113]. If a sufficient number of aspects do not receive a passing accuracy score, the system 100 can generate information as to why one or more aspects of the representation failed, and in some implementations automatically train the one or more neural networks based on the results and then perform method 1500 again leverage the additional training of the neural networks to see if a passing score can be achieved [0145]. Storage units 134 may include a computer-readable storage medium or computer-readable storage device [0039]. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claims 1 and 17 are directed to an abstract idea without practical application.
Dependent Claims 10-15, 20, and 23 recite additional elements. Claims 10 and 20 recite the previously recited computer processor and specifies the computer processor generates 2D representations based on the first representation. Claim 11 recites the previously recited machine learning models and specifies the machine learning models are trained to classify the one or more 2D representations. Claim 12 recites the previously recited machine learning models and specifies the machine learning models are trained to classify the one or more 3D oral care representations. Claim 13 recites the previously recited machine learning models and the new neural network and specifies the machine learning model is a neural network (new). Claims 14 recites the previously recited computer processor and specifies the computer processor generates output that specifies whether the aspects of the first representation has not been labeled correctly. Claims 15 recites the previously recited computer processor and specifies the computer processor determines that the aspects of the first representation has not been labeled correctly. Claim 23 recites the previously recite machine learning model and specifies providing mesh element feature vectors as input into the machine learning model or an encoder (new element). However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These additional elements amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims, whether considered individually or as an ordered combination, do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the computer processors, machine learning model, and the digital [representation] of Claim 1, and the computer processors, non-transitory computer-readable storage, machine learning model, and the digital [representation] of Claim 17 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”).
Dependent Claims 3-9, 16 and 21-22 do not recite any additional elements and only serve to narrow the abstract idea. Claim 3 recites generating one or more suggestions of how to correct the first representation when it is determined that the first representation is not correctly labelled, Claim 4 recites the first representation describes at least one of: one or more teeth of the patient, one or more non-organic structures, and one or more gums of the patient, Claim 5 recites the labels on the aspects of the first representation describe a boundary between one or more teeth of the patient and one or more gums of the patient. Claim 6 recites the labels on the one or more aspects of the first representation describe a boundary between one or more teeth of the patient and one or more non-organic structures. Claim 7 recites the labels on the one or more aspects of the first representation describe a boundary between one portion of the gums of the patient and another portion of the gums of the patient. Claim 8 recites the labels on the one or more aspects of the first representation describe a boundary between one portion of a tooth of the patient and another portion of that tooth. Claim 9 recites the labels on the one or more aspects of the first representation describe a boundary between the facial side of a tooth of the patient and the lingual side of that tooth. Claim 16 recites the validation information includes a loss value, accuracy score average boundary distance, boundary percentage, or am over-segmentation ratio. Claim 21 recites the first representation comprises mesh elements which each element comprising a vertex, edge, face, or voxel. Claim 22 recites encoding the first representation of the labels and providing one or more latent representations.
Dependent Claims 10-12, 14-15, and 20 recite previously recited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claims 10 and 20 recite the previously recited computer processor and specifies the computer processor generates 2D representations based on the first representation. Claim 11 recites the previously recited machine learning models and specifies the machine learning models are trained to classify the one or more 2D representations. Claim 12 recites the previously recited machine learning models and specifies the machine learning models are trained to classify the one or more 3D oral care representations. Claim 14 recites the previously recited computer processor and specifies the computer processor generates output that specifies whether the aspects of the first representation has not been labeled correctly. Claims 15 recites the previously recited computer processor and specifies the computer processor determines that the aspects of the first representation has not been labeled correctly.
Claims 13 and 23 recite new additional elements. Claim 13 recites the previously recited machine learning models and specifies the machine learning model is a neural network (new additional element). Claim 23 recites the previously recited machine learning model and specifies providing mesh element feature vectors as input into the machine learning model or an encoder (new element). Hence, Claims 2-16 and 20-23 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1, 3-17, and 20-23 are nonetheless rejected under 35 U.S.C 101 as being directed to non-statutory subject matter.
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, 4-6, 8-13, 16, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Pei et al. (CN 113139908 A1) in view of Brown et al. (US 20210073998 A1) and Sharma et al. (US 20170032222 A1).
Regarding Claim 1, Pei discloses the following:
A computer-implemented method for training one or more neural networks to automatically validate digitally generated tooth segmentation data used in digital oral care, the method comprising: (Pei discloses the invention claims a three-dimensional dentition segmentation and labeling method, automatic segmentation and labeling based on three-dimensional dentition grid model, which can effectively realize the automatic segmentation and labeling of the three-dimensional dentition grid model (p. 3, ¶ 0001, Fig. 1).)
receiving, by one or more computer processors, a first representation that is a digital 3D oral care representation of teeth of a patient, (Pei discloses the digital scanning model of the plaster model of human body dentition is used; the data form is a three-dimensional grid; the original model is down sampled by means of a secondary side shrinkage simplification algorithm to obtain a model containing about 15000 vertices (p. 4, ¶ 0009).)
receiving, by the one or more computer processors, a second representation that is a 3D oral care digital representation of the patient's teeth, wherein one or more aspects of the second representation… (Pei discloses the graph [uses] convolutional neural network module on characteristic guide provided by the method uses a supervised training method; the manual label of the three-dimensional dentition grid model vertex is used for optimizing the classification performance of the network (p. 5, ¶ 0007).)
…by the one or more computer processors, …correctness of the segmentation of the first representation based on… comparison of…the first representation… with the…the corresponding one or more aspects of the second representation; (Pei discloses the graph convolutional neural network module on characteristic guide provided by the method uses a supervised training method; the manual label of the three-dimensional dentition grid model vertex is used for optimizing the classification performance of the network. Lcls is cross entropy of network output label and manual label: (p. 5, ¶ 0007). The cross entropy of the network output and the manual label is interpreted as determining the similarity between the two outputs.)
and…training, by the one or more computer processors, the one or more machine learning model (Pei discloses the graph convolutional neural network module on characteristic guide provided by the method uses a supervised training method; the manual label of the three-dimensional dentition grid model vertex is used for optimizing the classification performance of the network (p. 5, ¶ 0007).)
Pei does not disclose automatically training the machine learning model based on the comparison which is met by Brown:
wherein one or more aspects of the first representation have been assigned labels… having predefined labels assigned thereto; (Brown teaches FIG. 4B shows the example projection of FIG. 4A after analyzing and labeling the 2D image of FIG. 4A (and others) and applying this analysis and labeling to a 3D model of the subject's dentition; in FIG. 4B just the segmented teeth are shown [0051].)
…one or more machine learning models having been trained to predict one more labels describing a segmentation of the first representation; (Brown teaches FIGS. 3A-3C illustrate training an agent, e.g., a machine learning agent, to recognize individual teeth from images of a subject's teeth. FIG. 3A illustrates mapping of height map inputs to manually identified segmented images (FIG. 3B), and using this information to predict labels from 2D height maps (FIG. 3C) [0049].)
…automatically training the one or more machine learning model (Brown teaches The 3D oral cavity modeling system 1910 may process the 2D images using manual, semi-manual, or automatic processing techniques…the processing may be driven, performed and/or guided by a machine learning agent. The machine learning agent may be trained on a variety of different datasets and may be adaptively trained, so that it may update/modify its behavior over time [0076].)
generate updated…labels for subsequent digital 3D oral care representations (Brown teaches the 3D oral cavity modeling system 1910 can accurately create and/or update a 3D dental model and the ability to predict multiple dental classes concurrently [0085].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate the machine learning model being automatically trained as a result of the comparison as taught by Brown. This modification would create a method capable of accurately segmenting, modifying, updating, and processing dental models (see Brown, ¶ 0004).
Pei and Brown do not teach the following limitations met by Sharma:
validating,…correctness of the …of the first representation based on validation information associated with a comparison… (Sharma teaches the classifier 220 may then compare the classified feature against a reference dataset to verify the feature validation accuracy using a variety of techniques, known in the art, related art, or developed later [0063].)
wherein the correctness is validated when the validation information satisfies a predetermined threshold; (Sharma teaches the classifier 220 may be configured to compute error over the expected classification based on the comparison and determine gradient descent of a corresponding error function…the classification module 322 may decrease the learning rate or increase the bias by a predefined number in the learning layers, such as the learning layers 102, of the depth-enhanced color CNN 214 if the error percentage is above a predefined threshold percentage [0063].)
wherein the retrained one or more machine learning models are used to generate updated…labels (Sharma teaches the pre-trained CNN 202 may be fine-tuned using the obtained color images 206 and the depth images 204 in multiple phases using a variety of methods known in the art, related art, or developed later including, but not limited to, mini-batch gradient descent with predetermined value of parameters (e.g., momentum of 0.9) and batch size [0043].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate validating the correctness of the segmentation based on validation information as taught by Sharma. This modification would create a method capable of improving object detection and recognition in image segmentation technology (see Sharma, ¶ 0002).
Regarding Claim 17, this claim recites limitations that are substantially similar to those recited in Claim 1 above; thus, the same rejection applies. Pei further discloses:
A system… (Pei discloses the system performs feature learning and training point classifier to the three-dimensional dentition surface grid model based on the feature-oriented graph convolutional neural network module on the basis of the multi-classification cross entropy loss function, providing shape consistency constraint, boundary consistency constraint and classification label smoothing constraint, the three-dimensional dentition grid model for efficient and accurate automatic segmentation and tooth position label.
Regarding Claim 4, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei further discloses:
the first representation describes at least one of: one or more teeth of the patient, one or more non-organic structures, and one or more gums of the patient. (Pei discloses the network outputs the classification label of each kind of dental crown; dividing boundary between teeth and gum and dividing boundary between teeth; dividing the dental crown and boundary thereof for further enhancing feature extraction and vertex classification performance of the network model (p. 3, ¶ 0002). The Examiner interprets the dental crown as a non-organic structure.)
Regarding Claim 5, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei further discloses:
the labels on the one or more aspects of the first representation describe a boundary between one or more teeth of the patient and one or more gums of the patient. (Pei discloses the invention uses graph convolution neural network module on feature guide to perform feature learning and classification for three-dimensional dentition grid model vertex;… the network outputs the classification label… dividing boundary between teeth and gum and dividing boundary between teeth;…(p. 3, ¶ 0002).)
Regarding Claim 19, this claim recites limitations that are substantially similar to those recited in Claim 5 above; thus, the same rejection applies.
Regarding Claim 6, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei further discloses:
aspects of the first representation describe a boundary between… one or more non-organic structures. (Pei discloses the vertex in the model comprises a dental crown and a gum. considering the dentition arrangement and tooth shape change, the invention designs the dental crown shape distribution and dental crown boundary curvature constraint to improve the dental crown boundary segmentation confusion (p. 3, ¶ 0002).)
Pei does not disclose the following limitations met by Brown:
the labels… describe a boundary between one or more teeth of the patient (Brown teaches variations the segmentation agent may be a machine-learning agent that is trained on one or more datasets to recognize boundaries between teeth [0103].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate boundaries being between teeth as taught by Brown. This modification would create a method capable of accurately segmenting, modifying, updating, and processing dental models (see Brown, ¶ 0004).
Regarding Claim 8, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei further discloses:
the labels on the one or more aspects of the first representation describe a boundary between one portion of a tooth of the patient and another portion of that tooth. (Pei discloses the purpose of the method is to obtain the category label of each vertex on the grid model wherein N is three-dimensional dentition model vertex number; K is the number of category label…, comprising a gum; left side cutting tooth; left side cutting tooth; left side tip tooth; left side first front grinding tooth; left side second front grinding tooth; left side first grinding tooth; left side second grinding tooth; right side middle cutting tooth; right side cutting tooth; right side sharp tooth; right side first front grinding tooth; right side second front grinding tooth; right side first grinding tooth; right side second grinding tooth;…(p. 4, ¶ 0009).)
Regarding Claim 9, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 8 above. Pei further discloses:
the labels on the one or more aspects of the first representation… (Pei discloses the invention claims a three-dimensional dentition segmentation and labeling method, automatic segmentation and labeling based on three-dimensional dentition grid model, which can effectively realize the automatic segmentation and labeling of the three-dimensional dentition grid model (p. 3, ¶ 0001, Fig. 1).)
Pei does not disclose the following limitations met by Brown:
…describe a boundary between a facial side of a tooth of the patient and a lingual side of that tooth. (Brown teaches generating a plurality of interproximal separation planes between teeth of a digital three-dimensional (3D) model of a subject’s oral cavity; collecting a two-dimensional (2D) images corresponding to each of one or more of: buccal, lingual and occlusal views,…[0015, see Fig. 1A-D]. The Examiner interprets the buccal side of the tooth as the facial side.)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate labeling the facial and lingual side of the tooth as taught by Brown. This modification would create a method capable of accurately studying the dentitions of subjects (see Brown, ¶ 0004).
Regarding Claim 10, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei does not disclose the following limitations met by Brown:
generating, by the one or more computer processors, one or more two dimensional (2D) representations based on at least in part the first representation. (Brown teaches in some variations the apparatus may include identifying interproximals and calculating directions to view the 3D model in order to optimally see the interproximal space. The views that best (e.g., maximally) show the interproximal spacing between two or more teeth may be used to generate slices (e.g., 2D images, as described above) that may in turn be processed as described above…[0114].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate generating a 2D representation of the model based on the 3D model as taught by Brown. This modification would create a method capable of accurately segmenting, modifying, updating, and processing dental models (see Brown, ¶ 0004).
Regarding Claim 20, this claim recites limitations that are substantially similar to those recited in Claim 10 above; thus, the same rejection applies.
Regarding Claim 11, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 10 above. Pei does not disclose the following limitations met by Brown:
the one or more machine learning models are trained to classify the one or more 2D representations. (Brown teaches the 3D oral cavity modeling system 1910 may use a conditional Generative Adversarial Network (cGAN) and/or any other machine learning system to classify data from dental scans and/or dental images into dental classes. As noted herein, the 3D oral cavity modeling system 1910 may be trained with a library of labeled and/or accurately modeled 2D dental scans and/or dental images [0079].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate classifying 2D representations of the model as taught by Brown. This modification would create a method capable of accurately segmenting, modifying, updating, and processing dental models (see Brown, ¶ 0004).
Regarding Claim 12, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei further discloses:
the one or more machine learning models have been trained to classify one or more 3D oral care representations. (Pei discloses an automatic tooth segmentation and annotation is a challenging problem in a computer-assisted oral medical image processing. Existing geometry-based methods have a curvature threshold based approach and a motion profile tracking method. However, the method based on curvature threshold only obtains the initial estimation about the tooth boundary coarse, especially the tongue-side tooth-gingival boundary curvature change is not obvious in the local segmentation result noise (p. 2, ¶ 0004).)
Regarding Claim 13, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 12 above. Pei further discloses:
at least one of the one or more machine learning models is a neural network. (Pei discloses the invention uses graph convolutional neural network module on feature guide to perform feature learning and classification or three-dimensional dentition grid model vertex (p. 3, ¶ 0002).)
Regarding Claim 16, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei further discloses:
the validation information includes at least one of a loss value, an accuracy score, an intersection over union score, an average boundary distance, a boundary percentage, or an over-segmentation ratio, each of which quantifies one or more differences between the first representation and the second representation (Pei discloses the manual label of the three-dimensional dentition grid model vertex is used for optimizing the classification performance of the network. Lcls is cross entropy of network output label and manual label label…(p. 5, ¶ 0007).)
Claims 3, 14-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Pei, Brown, and Sharma in view of An et al. (US 20220300767 A1).
Regarding Claim 3, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei and Brown do not teach the following limitations met by Sharma:
…based on the validating,… (Sharma teaches the classifier 220 may then compare the classified feature against a reference dataset to verify the feature validation accuracy using a variety of techniques, known in the art, related art, or developed later [0063].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate validating the correctness of the segmentation based on validation information as taught by Sharma. This modification would create a method capable of improving object detection and recognition in image segmentation technology (see Sharma, ¶ 0002).
Pei, Brown, and Sharma do not teach the following limitations met by An:
generating, by the one or more computer processors, one or more suggestions of how to correct the first representation when the first representation is determined, …that the first representation is not correctly labelled. (An teaches the correcting the definition labels of the at least some images in the to-be-expanded images, includes: displaying a correction interface, wherein the correction interface includes a correction control, at least some images in the to-be-expanded images and corresponding definition labels;… [0012]. The correction interface displays at least some face images in the to-be-expanded images and corresponding definition labels obtained according to the extracted definition feature (i.e., face images with to-be-corrected definition labels and the to-be-corrected definition labels), and a correction control (for example, the correction control may include five selection controls representing definition levels 1-5 below the face image). In response to operations of the correction control by the annotator, a corrected definition label of the corresponding face image is obtained [0111].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate determining if the label is not correct and how to fix it as taught by An. This modification would create a method capable of determining whether images meet definition requirements and whether they can be used for subsequent applications (see An, ¶ 0003).
Regarding Claim 14, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei, Brown, and Sharma do not teach the following limitations met by An:
automatically generating, by the one or more computer processors, output… (An teaches the correcting the definition labels of the at least some images in the to-be-expanded images, includes: displaying a correction interface, wherein the correction interface includes a correction control, at least some images in the to-be-expanded images and corresponding definition labels; and in response to operation of the correction control, correcting the definition label of the corresponding image in the correction interface [0012].)
…that specifies whether the one or more aspects of the first representation has not been correctly labelled. (An teaches in response to operations of the correction control by the annotator, a corrected definition label of the corresponding face image is obtained [0111].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate outputting that the label is not correct as taught by An. This modification would create a method capable of determining whether images meet definition requirements and whether they can be used for subsequent applications (see An, ¶ 0003).
Regarding Claim 15, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei further discloses:
performing, by the one or more computer processors, the method of claim 1. (See the rejection of Claim 1 above.)
Pei and Brown do not teach the following limitations met by An:
when it is determined, based on analyzing, that one or more aspects of the first representation has not been correctly labeled, (An teaches the correcting the definition labels of the at least some images in the to-be-expanded images, includes: displaying a correction interface, wherein the correction interface includes a correction control, at least some images in the to-be-expanded images and corresponding definition labels;… [0012]. The correction interface displays at least some face images in the to-be-expanded images and corresponding definition labels obtained according to the extracted definition feature (i.e., face images with to-be-corrected definition labels and the to-be-corrected definition labels), and a correction control (for example, the correction control may include five selection controls representing definition levels 1-5 below the face image). In response to operations of the correction control by the annotator, a corrected definition label of the corresponding face image is obtained [0111].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate determining if the label is not correct as taught by An. This modification would create a method capable of determining whether images meet definition requirements and whether they can be used for subsequent applications (see An, ¶ 0003).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pei, Brown, and Sharma in view of Choi et al. (KR 20180049302 A).
Regarding Claim 7, Pei, Brown, and Sharma teach the limitation as seen in the rejection of Claim 1 above. Pei further discloses:
the labels on the one or more aspects of the first representation describe a boundary between one portion of the gums of the patient… (Pei discloses dividing boundary between teeth and gum …(p. 3, ¶ 0002). [T]he boundary of the dental crown and the gum and the dental crown has a concave boundary;…(p. 6, ¶ 0004).)
Pei, Brown, and Sharma do not teach the following limitations met by Choi:
…and another portion of gums of the patient. (Choi teaches the tongue area dividing unit 305 can divide the gum area obtained by dividing the tongue area into three equal parts in the up and down directions. The tongue area dividing unit 305 can calculate the initial centerline of the tongue by applying linear interpolation to the center points of the trisected lines (p. 5, ¶ 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate the boundaries being between portions of gums as taught by Choi. This modification would create a method capable of effectively acquiring and analyzing images of the mouth (see Choi, p. 9, ¶ 0002).
Claims 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Pei, Brown, and Sharma in view of Claesson et al. (EP 3462373 A1).
Regarding Claim 21, Pei, Brown, and Sharma teach the limitations as seen in the rejection of Claim 1 above. Pei, Brown, and Sharma do not teach the following limitations met by Claesson:
the first representation comprises a plurality of mesh elements, each mesh element, of the plurality of mesh elements, comprising at least one of a vertex, edge, face, or voxel. (Claesson teaches the 3D image data may comprise voxels, i.e., 3D space elements associated with a voxel value, e.g. a grayscale value or a colour value, representing a radiation intensity or density value (p. 21, 0004). The shape of each crown is derived from the 3D scan and represented in form of a 3D mesh, including faces and vertices. These 3D meshes are subsequently used to determine aggregated features for each tooth (p. 3, ¶ 0003).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate the boundaries being between portions of gums as taught by Choi. This modification would create a method capable of effectively acquiring and analyzing images of the mouth (see Choi, p. 9, ¶ 0002).
Regarding Claim 23, Pei, Brown, Sharma, and Claesson teach the limitations as seen in the rejection of Claim 21 above. Pei and Sharma do not teach the following limitations met by Sharma:
computing, for one or more of the mesh elements, mesh element feature vectors, wherein each mesh element feature vector, of the mesh element feature vectors, comprises one or more spatial or structural features of a corresponding mesh element; (Sharma teaches the classification module 322 may concatenate a pair of the extracted depth feature and the color feature such as the RGB feature for each of the RGB-D images to generate a combined feature vector [0063]. The Examiner interprets the depth feature as a spatial feature.)
and providing the mesh element feature vectors as inputs to at least one of: the one or more machine learning models, or an encoder configured to generate one or more latent representations of the first representation. (Sharma teaches during a testing workflow 30, depth features (e.g., the depth features 210) from the depth CNN 208 and color features (e.g., the color features 216), such as the RGB features from the depth-enhanced color CNN 214, may be concatenated at a classification layer (not shown) to produce combined feature vectors 218, which may be used to train one of a variety of classifiers,…[0048].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate computing mesh element feature vectors as taught by Sharma. This modification would create a method capable of improving object detection and recognition in image segmentation technology (see Sharma, ¶ 0002).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Pei, Brown, Sharma, and Claesson in view of Wang et al. (US 10769848 B1).
Regarding Claim 22, Pei, Brown, Sharma, and Claesson teach the limitations as seen in the rejection of Claim 21 above. Pei further discloses:
…as input to the one or more machine learning models… (Pei discloses the graph convolutional neural network module on characteristic guide provided by the method uses a supervised training method; the manual label of the three-dimensional dentition grid model vertex is used for optimizing the classification performance of the network (p. 5, ¶ 0007).)
Pei, Brown, Sharma, and Claesson do not teach the following limitations met by Wang:
encoding at least one of the first representation or the labels assigned to the mesh elements into one or more latent representations; and providing the one or more latent representation… (Wang teaches given an image sequence or video of an object as an input in the form of RGB images paired with associated camera matrices, an image frame with the object centered is selected either manually or automatically. The selected frame is fed into an encoder to get an initial latent code vector as an output (col. 2, lines 29-37).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method and system for a first label of a dentition assigned by machine learning models and a second representation having predefined labels, determining that the labels are substantially similar, and training the machine learning model as disclosed by Pei to incorporate encoding the representation into a latent representation as taught by Wang. This modification would create a method capable of solving ill-posed 3D reconstruction problems (see Wang, col. 1, lines 36-45).
Response to Arguments
Regarding rejections under 35 USC 101 to Claims 1, 3-17, and 20-23, Applicant’s arguments have been considered but are not persuasive. The rejection has been updated in light of the amendments above.
Applicant argues regarding Prong One, MPEP 2106.04(II)(A)(1) states (with emphasis added) that "examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim." MPEP 2106.07(a)(I) further states (with emphasis added) that "[w]here the claim describes, but does not expressly set forth, the judicial exception, the rejection must also explain what subject matter those limitations describe, and why the described subject matter is a judicial exception." Here, the Office Action identified "receiving a first 3D oral care representation of a patient's teeth" and "receiving a second 3D oral care representation of the patient's teeth." See Office Action, page 3. However, the claims specifically recite receiving digital 3D oral care representations. These are not abstract data or mere information, but are digital models generated by specialized dental imaging equipment (e.g., intraoral scanners, CT, or other digital modalities). Such digital 3D representations are fundamentally technological in nature and are not the type of information that can be received, manipulated, or even meaningfully perceived by a human mind without computer processing. The Office Action's analysis improperly ignores the "digital" nature of the claimed representations. The "digital" nature of the representations is not a mere field-of-use or insignificant extra-solution activity; it is integral to the invention. The claims do not cover receiving any information, but specifically recite receiving digital 3D models-a step that is inherently tied to computer technology and digital data processing. The claim elements are technological steps that require computer implementation and digital data processing (see Applicant’s Remarks, p. 9-11).
Regarding (a), Examiner respectfully disagrees. When analyzing the claims under Step 2A Prong One, the Examiner identifies whether any abstract ideas are present. If any of the identified limitations fall within one of the groupings of abstract ideas, the claim is then determined in Prong Two to determine whether any additional elements integrate the abstract idea into a practical application. The additional elements of the limitations are not considered in the claim until Prong Two, and, using broadest reasonable interpretation, the step of receiving a three-dimensional representation could be reasonably accomplished by a person using a generic computer (see the October 2019 Update: Subject Matter Eligibility on p. 5 which states certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping). Furthermore, the steps of validating the correctness of segmentations by analyzing representations can be done by a person following rules or instructions and does not require the use of a highly specialized computer. Hence, for the reasons disclosed above, the claimed invention is reasonably interpreted as reciting the abstract idea of a certain method of organizing human activities, specifically following rules or instructions in order to receive and validate three-dimensional representations.
Applicant argues regarding Prong Two of Step 2A of the subject matter eligibility test, MPEP 2106.04(d)(I) states that "if the claim as a whole integrates the recited judicial exception into a practical application . .. the claim is eligible" and "an improvement to other technology or technical field" is "indicative that an additional element (or combination of elements) may have integrated the exception into a practical application." Amended claim 1 integrates any alleged judicial exception into a practical application because it recites a specific, technical workflow implemented by a computer processing system for validating and improving digital dental segmentation. The claim requires not only the processing of digital three-dimensional oral care representations and the generation of segmentation labels using machine learning models, but also incorporates concrete, technical steps for validating the correctness of the segmentation based on quantitative validation information (such as an accuracy score, loss value, or intersection over union score) and automatically retraining the machine learning model based on this validation information. The retrained model is then used to generate updated segmentation labels for subsequent digital representations.
Specifically, amended claim 1 recites "validating, by the one or more computer processors, correctness of the segmentation of the first representation based on validation information associated with a comparison of the labels on the one or more aspects of the first representation with the labels on the corresponding one or more aspects of the second representation, wherein correctness is validated when the validation information satisfies a predetermined threshold," and "automatically retraining, by the one or more computer processors, the one or more machine learning models based on the validation information, wherein the retrained one or more machine learning models are used to generate updated segmentation labels for subsequent digital 3D oral care representations."
These steps are not generic computer functions, but rather define a specific technical solution for improving the accuracy and reliability of automated dental segmentation using machine learning. The claimed workflow provides a feedback loop in which the results of segmentation are quantitatively validated and used to automatically retrain the machine learning model, thereby continuously improving segmentation performance without requiring manual intervention. This technical improvement cannot be performed by the human mind, as it requires the system to process large volumes of digital 3D data, compute quantitative validation metrics, and update complex machine learning models in an automated manner (p. 12-13).
Regarding (b), Examiner respectfully disagrees. The claims do not provide any specific and technical steps to validating the correctness, as is argued above. The alleged technical steps of “an accuracy score, loss value, or intersection over union score” are not technical and are a part of the abstract idea. These can be determined manually and do not require a computer. Therefore, they cannot provide a technical solution or an improvement to technology.
The machine learning model is recited at a high level of generality, and there is no improvement to the functioning of a generic machine learning model provided. For example, Applicant’s specification states 2D data, such as photographs of dental or orthodontic appliances, can be directly validated using a machine learning model, such as a neural network [0114]. Therefore, the machine learning model claimed is merely a generic model which carries out a judicial exception with the words “apply it” (or an equivalent).
Furthermore, Examiner notes that the mere automation of a manual process is not enough to provide a practical application. Efficiency is not enough to amount to a practical application via an improvement to computer or technology under Step 2A Prong 2 (see MPEP § 2106.05(a)(I) examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)) (also see MPEP § 2106.05(f)(2) stating “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept (Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)”), and, thus, the combination of the generic computer components do not provide a non-conventional and non-generic arrangement of known, conventional pieces; note this is applied to Step 2B as well as Step 2A Prong 2).
Applicant argues the specification explicitly describes both the technical problems addressed by the invention and the corresponding improvements provided by the claimed features. Specifically, the specification explains that existing segmentation techniques may be inaccurate or require manual correction, and that there is a need for improved, automated validation and retraining to enhance segmentation accuracy and efficiency. For example, paragraph [0002] of the specification notes that "Problems with past approaches included loss of accuracy in the mapping, and the inefficient processing of the data to generate a 2D to 3D conversion." Paragraph [0003] further notes that "[p]rojection operations performed by existing systems may cause a 3D mesh element to receive conflicting labels as the result of two or more projection operations. This can result in the need to perform additional machine learning models to disambiguate those conflicting labels, which adds to the complexity and error of the overall system." Paragraph [0070] explains that in this invention, "[t]he accuracy score (e.g., in normalized form) may be fed back into the neural network in the course of training the network, for example, through backpropagation. In the case of segmentation, an accuracy score may count matching labels between a predicted and a ground truth mesh (i.e., where each mesh element has an associated label). The higher the percentage of matching labels, the better the prediction (i.e., when comparing predicted labels to ground truth labels)." Paragraph [0151] then explains that "[i]n the event that a 3D validation check yields a failing output, then one or more instructions or feedback data may be communicated to the algorithm, process or model that created the 3D oral care representation, so that a further iteration of 3D oral care representation generation may improve the design and hopefully mitigate the conditions which led to the failure of the validation check." Thus, the claimed invention directly addresses the limitations of prior segmentation systems by providing a technical solution that enables automated, quantitative validation and continuous improvement of machine learning-based segmentation models, as described in the specification and reflected in the claims (p. 14-15).
Regarding (c), Examiner respectfully disagrees. The distributed nature of the claimed invention is not, in itself, dispositive in determining whether the claimed invention recites an abstract idea because the concept of validating and correcting image segmentation is not a technological solution to a technological problem – that is, image segmentation has existed since long before the advent of computer technology, and thus, cannot properly be considered a technological improvement and/or an improvement to the computer itself.
Furthermore, the specification does not provide any evidence as to how the computer analyzes the representation, validates the representation, or determines an accuracy score. MPEP 2106.05(f) states when determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Applicant argues regarding Step 2B, Claims 1 and 17 do not recite the use of a generic computer to perform routine data processing. Rather, the claims recite a specific and non-conventional arrangement of technical features that provide a concrete solution to a technical problem in the field of digital dental segmentation and machine learning model improvement. For example, the claims recite: 1) receiving digital three-dimensional oral care representations; (2) generating segmentation labels for these representations using one or more machine learning models; (3) validating the correctness of the segmentation based on validation information associated with a comparison of the predicted labels and reference labels, wherein correctness is validated when the validation information satisfies a predetermined threshold; and (4) automatically retraining the machine learning models based on the validation information, such that the retrained models are used to generate updated segmentation labels for subsequent digital 3D oral care representations.
These features, in combination, are not well-understood, routine, or conventional activities in the field. As described in the specification, prior systems for dental segmentation required manual review and correction, were computationally intensive, and often yielded inaccurate results. See, e.g., Specification, paragraph [0002] ("Problems with past approaches included loss of accuracy in the mapping, and the inefficient processing of the data to generate a 2D to 3D conversion.") and paragraph [0003] ("Projection operations performed by existing systems may cause a 3D mesh element to receive conflicting labels as the result of two or more projection operations. This can result in the need to perform additional machine learning models to disambiguate those conflicting labels, which adds to the complexity and error of the overall system."). The claimed invention, by contrast, enables automated, quantitative validation of segmentation results and continuous, feedback-driven retraining of machine learning models to
Regarding (d), Examiner respectfully disagrees. Examiner notes that, as stated in the rejection above, (1)-(3) listed can be carried out manually and do not require any specialized technology to carry out. Further, regarding (1), assuming, arguendo, that the claims do not recite a manual process, the courts have ruled that the following computer functions are well-understood, routine, and conventional functions when claimed in a generic manner:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Regarding (4), the machine learning model is recited at a high level of generality, and there is no improvement to the functioning of a generic machine learning model provided. Therefore, the machine learning model claimed is merely a generic model which carries out a judicial exception with the words “apply it” (or an equivalent).
Regarding rejections under 35 USC 103 to Claims 1, 3-17, and 20-23, Applicant’s arguments have been considered and are persuasive in light of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new rejection has been made rejecting the independent claims over Pei in view of Brown and Sharma.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLIVIA R GEDRA whose telephone number is (571)270-0944. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H Choi can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLIVIA R. GEDRA/Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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