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 .
This communication is responsive to application filed on 06/02/2026.
Claims 1-15 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/02/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-13 are directed to a method.
Claim 14 is directed to a product.
Claim 15 is directed to a system.
Therefore, claims 1-15 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A method comprising:
receiving, at a computing device, a representation of an abnormal bone [insignificant extra solution, e.g. mere data-gathering];
inferring a representation of a normalized bone associated with the abnormal bone [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)] based on executing a machine learning (ML) model at the computing device with the representation of the abnormal bone as input to the ML model;
identifying a region of deformity on the abnormal bone based on the representation of the normalized bone [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)].; and
generating a surgical plan for altering the abnormal bone based on the region of deformity [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)] and/or [insignificant post solution, data output].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional element of “receiving”, “machine learning model” and “generating”. The additional element of “receiving” is insignificant pre-solution (i.e. data gathering). The additional element of “machine learning model” recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions and/or machine learning components) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). In addition, the additional element of “generating” is insignificant post solution, data output. Accordingly, the additional element(s) of each of this claim does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “receiving” is insignificant pre-solutions (i.e. data gathering). At most the additional element is not found to including anything more than data gathering or mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iv) - Obtaining information about transactions. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional element of “machine learning model” amount to no more than mere instructions to apply the judicial exception using generic computer component(s) such as machine learning component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In addition, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “generating” in insignificant post-solutions (i.e. mere data output). At most the additional element is not found to including anything more than mere data output. See MPEP 2106.04(d) referencing MPEP 2106.05(g), example (iii)- presenting offers to potential customers.
As per claim 2, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 3, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion)].
As per claim 4, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 5, the claim falls into [“mental process i.e. concepts performed with pen and paper (including an observation, evaluation judgement, opinion) and/or mathematical concepts].
As per claim 6, the claim falls into [a generic computer element for performing a generic computer function such as machine learning components].
As per claim 7, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 8, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 9, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 10, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 11, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 12, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per claim 13, the claim falls into [insignificant extra solution, e.g. mere data-gathering].
As per Claim 14, claim 14 recites limitations analogous in scope to those of claim 1, and as such are similar rejected.
As per claim 15, independent claim 15 recites limitations analogous in scope to those of independent claim 1, and as such are similar rejected. Further, claim 15 recites additional elements of “a surgical tool” and “a computing apparatus”. The components recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as discussed above with respect to the integration of the abstract into a practical application, the additional elements of “a surgical tool” and “a computing apparatus” amount to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8 and 10-15 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication No. 2014/0278322 A1 issued to Jaramaz et al in view of US Publication No. 2021/0015560 A1 issued to Boddington et al.
Claim 1. Jaramaz et al discloses a method comprising:
receiving, at a computing device, a representation of an abnormal bone (Abstract, An input interface can be configured to receive an abnormal bone representation including a data set representing the abnormal bone. A surgical planning module can include a registration module configured to register the generic normal bone model to the abnormal bone representation by creating a registered generic model; [0007] An input interface can be configured to receive an abnormal bone representation including a data set representing the abnormal bone. A surgical planning module can include a registration module configured to register the generic normal bone model to the abnormal bone representation by creating a registered generic model. A surgical plan formation module can be configured to identify one or more abnormal regions of the abnormal bone using the registered generic model; [0025] The input interface 120 can be configured to receive an abnormal bone representation including a data set representing the shape, appearance, or other morphological characteristics of the abnormal bone);
inferring a representation of a normalized bone associated with the abnormal bone (See: [0026] The registration module 131 can be configured to register the generic normal bone model to the abnormal bone representation. Due to the anatomical variations across subjects, and/or the extrinsic differences resulted from different data acquisition processes (e.g., the imaging system or the image acquisition processes), the generic normal bone model and the abnormal bone representation may have structural discrepancies resulting in reduced correspondence. The registration module 131 can transform the generic normal bone model into a registered generic model specific to the abnormal bone under analysis; [0034] the model transformation module 220 can employ both the rigid transformation to bring the generic normal bone model in global alignment with the size and orientation of the abnormal bone representation, and the non-rigid transformation to reduce the local geometric discrepancies by aligning the generic normal bone model with the abnormal bone representation);
identifying a region of deformity on the abnormal bone based on the representation of the normalized bone (See: [0027] The surgical plan formation module 132 can be configured to identify one or more abnormal regions of the abnormal bone using comparison of the registered generic model and the abnormal bone representation. In an example, the surgical plan formation module 132 can calculate a level of disconformity between the registered generic model and the abnormal bone representation; [0038] The abnormity detection module 320 is configured to identify one or more abnormal regions of the abnormal bone using a comparison between the model features and the abnormal bone features. The comparison can be performed on all or selected segments from the registered generic model and from the abnormal bone representation. In an embodiment, the comparison can be performed only after the registration module 131 matches the segment of the registered generic model to the registration area of the abnormal bone; [0042] A comparison of the segments from the SS model 420 and the impinged proximal femur image 410 reveals a deformity region on the femur neck 412 of the impinged proximal femur image 410. The excess bone on the detected deformity region 412 can be defined as the volumetric difference between the detected deformity region 412 and the corresponding femur neck segment 422 on the SS model 420); and
generating a surgical plan for altering the abnormal bone based on the region of deformity (See: Abstract, Systems and methods for generating a surgical plan for altering an abnormal bone using a generic normal bone model are discussed; [0002] This document relates generally to computer-aided orthopedic surgery, and more specifically to systems and methods for generating surgical plan for altering an abnormal bone using generic normal bone models; [0029] The controller circuit is configured to execute the set of instructions to cause the surgical planning module 130 to generate the surgical plan for altering the portion of the abnormal bone from the one or more abnormal regions).
Jaramaz et al does not specify but Boddington et al discloses executing a machine learning (ML) model at the computing device with the representation of the abnormal bone as input to the ML model (See: [0070] Artificial Intelligence is the ability of machines to perform tasks that are characteristics of human intelligence. Machine learning is a way of achieving Artificial Intelligence. AI is the ability of machines to carry out tasks in an intelligent way. Machine learning is an application of Artificial Intelligence that involves a data analysis to automatically build analytical models. Machine learning operates on the premise that computers learn statistical and deterministic classification or prediction models from data; [0084] These automated artificial intelligence models include: Deep Learning, machine learning and reinforcement learning based techniques. For example, a Convolutional Neural Network (CNN) is trained using annotated/labeled images which include good and bad images to learn local image features linked to low-resolution, presence of noise/artifact, contrast/lighting conditions, etc. The CNN model uses the learning features to make predictions about a new image; [0102] Now referring to FIG. 2B, the computing platform 100, which includes one or more Artificial Intelligence (AI) Engines, including FIG. 4A, Modules 12, 13, and 15, and information from a series of datasets; [0138] More specifically, the CNN model is trained on datasets which include images with one or more fractures and other images without fractures. Then, the CNN model determines whether there is a fracture or not and also localizes the region of interest which contains the identified fracture and/or the abnormality).
It would have been obvious before the effective filing date to combine the artificial intelligence intra-operative surgical guidance by Boddington et al to systems and methods for using generic anatomy models in surgical planning of Jaramaz et al would be to identify and predict problems ahead of the user encountering them and avoid of complications and prevent errors (Boddington et al, [0079]).
Claim 2. Jaramaz et al discloses the method of claim 1, comprising: partitioning the abnormal bone into a plurality of segments (See: [0033] The segmentation module 210 can also partition the abnormal bone representation into a plurality of segments); identifying the region of deformity based on the plurality of segments of the abnormal bone (See: [0033] the segmentation module can differentiate the pathological portion from the normal portion on the abnormal bone representation, and identify from the segments of the abnormal bone representation a registration area free of anatomical abnormity).
Claim 3. Jaramaz et al discloses the method of claim 2, comprising: partitioning the normalized bone into a plurality of segments (See: [0033] The segmentation module 210 can be configured to partition the generic normal bone model into a plurality of segments); and identifying the region of deformity based on the plurality of segments of the abnormal bone and the plurality of segments of the normalized bone (See: [0033] the segmentation module can differentiate the pathological portion from the normal portion on the abnormal bone representation, and identify from the segments of the abnormal bone representation a registration area free of anatomical abnormity. In some embodiments, the segmentation module 210 can be optional. For example, the segmentation module 210 can be excluded from the registration module 131 when both the generic normal bone model and the abnormal bone representation, when received by the system 100, are segmented images with labels assigned according to the respective anatomical structures).
Claim 4. Jaramaz et al discloses the method of claim 1, comprising: comparing a first plurality of anatomical features associated with the abnormal bone with a second plurality of anatomical features associated with the normalized bone (See: Abstract, The generic normal bone model, such as a parametric model derived from statistical shape data, can include a data set representing a normal bone having an anatomical origin comparable to the abnormal bone. An input interface can be configured to receive an abnormal bone representation including a data set representing the abnormal bone. A surgical planning module can include a registration module configured to register the generic normal bone model to the abnormal bone representation by creating a registered generic model. A surgical plan formation module can be configured to identify one or more abnormal regions of the abnormal bone using the registered generic model; [0008] A machine-readable storage medium embodiment can include instructions that, when executed by a machine, cause the machine to receive an abnormal bone representation and a generic normal bone model. The abnormal bone representation can include a data set representing an abnormal bone, while the generic normal bone model includes a data set representing a normal bone having an anatomical origin comparable to the abnormal bone. The machine can be caused to register the generic normal bone model to the abnormal bone representation to create a registered generic model. One or more abnormal regions of the abnormal bone can be identified using a comparison between the registered generic model and the abnormal bone representation; ; and identifying the region of deformity based on the comparison of the first plurality of anatomical features with the second plurality of anatomical features (See: [0038] The abnormity detection module 320 is configured to identify one or more abnormal regions of the abnormal bone using a comparison between the model features and the abnormal bone features. The comparison can be performed on all or selected segments from the registered generic model and from the abnormal bone representation. In an embodiment, the comparison can be performed only after the registration module 131 matches the segment of the registered generic model to the registration area of the abnormal bone).
Claim 5. Jaramaz et al discloses the method of claim 4, comprising: extracting the first plurality of anatomical features from the representation of the abnormal bone (See: [0039] The abnormity detection module 320 can also select similarity measure according to the data format (such as the imaging modality or image type) of the abnormal bone representation and the generic normal bone model. For example, if the extracted features from 310 are geometric features, the abnormity detection module 320 can calculate sum of squared distance between the model features and the abnormal bone features, where the distance can be computed as one of L1 norm, L2 norm (Euclidian distance), infinite norm, or other norm in the normed vector space. In another example, if the extracted features are intensity-based features, then the abnormity detection module 320 can calculate the similarity between the model features and the abnormal bone features using one of the measures such as correlation coefficient, mutual information, or ratio image uniformity); and extracting the second plurality of anatomical features from the representation of the normalized bone (See: [0039] The abnormity detection module 320 can also select similarity measure according to the data format (such as the imaging modality or image type) of the abnormal bone representation and the generic normal bone model. For example, if the extracted features from 310 are geometric features, the abnormity detection module 320 can calculate sum of squared distance between the model features and the abnormal bone features, where the distance can be computed as one of L1 norm, L2 norm (Euclidian distance), infinite norm, or other norm in the normed vector space. In another example, if the extracted features are intensity-based features, then the abnormity detection module 320 can calculate the similarity between the model features and the abnormal bone features using one of the measures such as correlation coefficient, mutual information, or ratio image uniformity).
Claim 6. Boddington et al discloses the method of claim 1, wherein the ML model comprises a convolutional neural network (CNN) (See: [0070] Artificial Intelligence is the ability of machines to perform tasks that are characteristics of human intelligence. Machine learning is a way of achieving Artificial Intelligence. AI is the ability of machines to carry out tasks in an intelligent way. Machine learning is an application of Artificial Intelligence that involves a data analysis to automatically build analytical models. Machine learning operates on the premise that computers learn statistical and deterministic classification or prediction models from data; [0084] These automated artificial intelligence models include: Deep Learning, machine learning and reinforcement learning based techniques. For example, a Convolutional Neural Network (CNN) is trained using annotated/labeled images which include good and bad images to learn local image features linked to low-resolution, presence of noise/artifact, contrast/lighting conditions, etc. The CNN model uses the learning features to make predictions about a new image).
Claim 7. Jaramaz et al discloses the method of claim 1, comprising a plurality of images of pathological bones and for each one of the plurality of images of the pathological bones, an associated image of a non- pathological bone (See: [0021] The model receiver module 110 can be configured to receive a generic normal bone model. Examples of the normal bone can include a femur, an acetabulum, or any other bone in a body. The generic normal bone model can include a data set representing a normal bone which has an anatomical origin comparable to the abnormal bone to be altered by the system 100. In some examples, the generic normal bone model can represent the shape or appearance of the anatomical structure of the normal bone. The generic normal bone model can be in a form of a parametric model, a statistical model, a shape-based model, or a volumetric model. The generic normal bone model can also be based on physical properties of the normal bone, such as an elastic model, a geometric spine model, or a finite element model. In a particular example, the generic normal bone model may include a statistical shape (SS) model derived from a plurality of images of normal bones of comparable anatomical origin from a group of subjects known to have normal bone anatomy. The SS model comprises a statistical representation of the normal bone anatomy from the group of subjects. In some examples, the generic normal bone model can represent a desired postoperative shape or appearance of the normal bone. The desired postoperative shape or appearance of the normal bone can be obtained by modifying a normal bone model (such as a parametric model, a statistical model, a shape-based model, or a volumetric model) using a computer software configured for three-dimensional manipulation of the normal bone model).
Jaramaz et al does not specify but Boddington et al discloses wherein the ML model is trained with a data set (See: [0084] These automated artificial intelligence models include: Deep Learning, machine learning and reinforcement learning based techniques. For example, a Convolutional Neural Network (CNN) is trained using annotated/labeled images which include good and bad images to learn local image features linked to low-resolution, presence of noise/artifact, contrast/lighting conditions, etc. The CNN model uses the learning features to make predictions about a new image; [0102] Now referring to FIG. 2B, the computing platform 100, which includes one or more Artificial Intelligence (AI) Engines, including FIG. 4A, Modules 12, 13, and 15, and information from a series of datasets; [0138] More specifically, the CNN model is trained on datasets which include images with one or more fractures and other images without fractures. Then, the CNN model determines whether there is a fracture or not and also localizes the region of interest which contains the identified fracture and/or the abnormality).
It would have been obvious before the effective filing date to combine the artificial intelligence intra-operative surgical guidance by Boddington et al to systems and methods for using generic anatomy models in surgical planning of Jaramaz et al would be to identify and predict problems ahead of the user encountering them and avoid of complications and prevent errors (Boddington et al, [0079]).
Claim 8. Jaramaz et al discloses the method of claim 7, wherein at least one of the plurality of associated images of the non-pathological bone is of a post-operative pathological bone (See: [0021] The generic normal bone model can also be based on physical properties of the normal bone, such as an elastic model, a geometric spine model, or a finite element model. In a particular example, the generic normal bone model may include a statistical shape (SS) model derived from a plurality of images of normal bones of comparable anatomical origin from a group of subjects known to have normal bone anatomy. The SS model comprises a statistical representation of the normal bone anatomy from the group of subjects. In some examples, the generic normal bone model can represent a desired postoperative shape or appearance of the normal bone. The desired postoperative shape or appearance of the normal bone can be obtained by modifying a normal bone model (such as a parametric model, a statistical model, a shape-based model, or a volumetric model) using a computer software configured for three-dimensional manipulation of the normal bone model).
Claim 10. Jaramaz et al discloses the method of claim 7, wherein the plurality of images of the pathological bones are classified as having at least one of the same bone type, the same gender assigned at birth, the same ethnicity, or the same age range (See: [0047] the shape data or appearance data can be constructed from medical images or the point clouds of normal bones of comparable anatomical origin from a group of subjects with similar age, gender, ethnicity, size, or other physical or demographical data).
Claim 11. Jaramaz et al discloses the method of claim 7, wherein the plurality of images of the pathological bones are classified as having a surgical outcome (See: [0047] For example, if a CT scan of the pathological proximal femur from a patient is received at 510, then the SS model received at 520 can be constructed from the CT scans of proximal femurs with normal anatomy from a plurality of subjects. In another example, a CT scan of the pathological acetabulum from a patient can be received at 510, and the SS model received at 520 can be constructed from the CT scans of normal acetabula with normal anatomy from a plurality of subjects; [0048] At 530, the generic normal bone model can be registered to the abnormal bone representation. The generic normal bone model and the abnormal bone representation can each be partitioned into a plurality of segments representing various anatomical structures on the respective image. The segments can be labeled such that the segments with the same label share specified characteristics such as a shape, anatomical structure, or intensity. In partitioning the abnormal bone representation, the pathological portion can be differentiated from the normal portion of the abnormal bone representation, and a registration area free of anatomical abnormity can be identified from the abnormal bone representation).
Claim 12. Zaramaz et al discloses the method of claim 1, wherein the bone type is a femur (See: [0021] The model receiver module 110 can be configured to receive a generic normal bone model. Examples of the normal bone can include a femur, an acetabulum, or any other bone in a body).
Claim 13. Zaramaz et al discloses the method of claim 1, comprising generating control signals for a surgical tool of a surgical navigation system based on the surgical plan (See: [0029] The controller circuit 150 can be coupled to the surgical planning module 130 and the memory circuit 140. The controller circuit is configured to execute the set of instructions to cause the surgical planning module 130 to generate the surgical plan for altering the portion of the abnormal bone from the one or more abnormal regions).
As per Claims 14-15, claims 14-15 recite limitations analogous in scope to those of claim 1, and as such are similar rejected.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jaramaz et al and Boddington et al as applied to claim 1 above, and further in view of Xu et al (L. Xu, G. Tetteh, J. Lipkova, Y. Zhao, H. Li, P. Christ, M. Piraud, A. Buck, K. Shi, B. Menze, “Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods”, pgs. 1-11, 2018).
Claim 9. Jaramaz et al discloses the method of claim 7, wherein at least one of the plurality of associated images of the non-pathological bone is a one of the plurality of images of pathological bones (See: [0021] The generic normal bone model can also be based on physical properties of the normal bone, such as an elastic model, a geometric spine model, or a finite element model. In a particular example, the generic normal bone model may include a statistical shape (SS) model derived from a plurality of images of normal bones of comparable anatomical origin from a group of subjects known to have normal bone anatomy. The SS model comprises a statistical representation of the normal bone anatomy from the group of subjects. In some examples, the generic normal bone model can represent a desired postoperative shape or appearance of the normal bone. The desired postoperative shape or appearance of the normal bone can be obtained by modifying a normal bone model (such as a parametric model, a statistical model, a shape-based model, or a volumetric model) using a computer software configured for three-dimensional manipulation of the normal bone model).
None of the references discloses but Xu et al discloses at least one randomly generated anatomical feature (pg. 2 right side column, we compared the proposed approach with several traditional machine learning methods, including random forest classifier, 𝑘-Nearest Neighbor (k-NN) classifier, and support vector machine (SVM) algorithm, in which cases the advantages of deep learning methods are more evidently shown; pg. 5 left side column, 2.4. Comparison with Traditional Machine Learning Methods. Traditional machine learning methods [52] including random forest, 𝑘-NN, and SVM were employed in this study for the comparison with deep learning methods. The patch-based intensity information was extracted as features for different algorithmic implementation. Multimodality features were obtained by taking the PET and CT intensities patch wise with a size of 3 × 3 × 3 in order that neighbor and intensity information can be encoded. For training, a total of 2000lesion samples (patches) and 2000 non lesion samples for each data volume were randomly selected and normalized to form the feature space. Each sample in the training/test set was represented as an intensity-based feature vector of 54dimensions).
It would have been obvious before the effective filing date to combine imaging using deep learning methods by Xu et al to systems and methods for using generic anatomy models in surgical planning of Jaramaz et al would be to increase the amount of data may further enhance the performance of the proposed deep learning method (Xu et al, pg. 9 left side column).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIBROM K GEBRESILASSIE whose telephone number is (571)272-8571. The examiner can normally be reached M-F 9:00 AM-5:30 PM.
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/Primary Examiner, Art Unit 2189 09/21/2026
Prosecution Insights