DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments and amendments in the Amendment filed August 4, 2026 (herein “Amendment”) with respect to the objection to claims 8, 14, 23, 29 and claims depending therefrom have been fully considered and are persuasive. The objection to claims 8, 14, 23, 29 and claims depending therefrom has been withdrawn.
Applicant’s arguments and amendments in the Amendment, with respect to the rejection of claims 15 and 30 under 35 U.S.C. 112(b) have been fully considered and are persuasive. The rejection of claims 15 and 30 under 35 U.S.C. 112(b) has been withdrawn.
Applicant's arguments and amendments in the Amendment, with respect to the rejection of claim 1, and claims 6–7 which depend therefrom under 35 U.S.C. 101 for being directed to an abstract idea without a practical application or significantly more have been fully considered but they are not persuasive. Specifically, claim 1 was only amended to include the that detected features of interest are “from the simulated image,” and these additional limitations do not integrate the abstract idea into a practical application. Therefore, the rejection of claims 1 and claims 6–7 are maintained with slight updates to the rejection rationale to include the newly amended limitations.
Applicant’s arguments and amendments in the Amendment, with respect to the rejection of claims 16 and 21–22 under 35 U.S.C. 101 for being directed to an abstract idea without a practical application or significantly more have been fully considered and are persuasive. The rejection of claims 16 and 21–22 under 35 U.S.C. 101 has been withdrawn.
Applicant’s arguments and amendments in the Amendment, with respect to the rejections of independent claims 1 and 16, and claims depending therefrom under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection are made in view of Ho et al., US Patent No. 12,688,341 B1.
Applicant's arguments and amendments in the Amendment with respect to the rejections of independent claims 8 and 23 and claims depending therefrom under 35 U.S.C. 103 have been fully considered but they are not persuasive. Specifically, Applicant argues that primary reference Parekh does not teach or suggest the claimed “transmitting the first set of training simulated images to a labeling system; receiving a labeled first set of training simulated images from the labeling system,” in that Parekh’s classifier layer (mapped to the claimed labeling system) does not output “labeled first set of training simulated images” such that the “receiving labeled first set of training simulated images from the labeling system” is not met by Parekh. However, as detailed in the rejection rationale on page 12 of the Non-Final Action issued May 5, 2026, the stacked sparse autoencoder which outputs classifications, also outputs tumor maps including the determined classifications (labels) for tumors in an image, where cited fig. 3 of Parekh provides an example of an SAE tumor map with different colors in the image designating the classification labeling. Therefore, Parekh does teach the claimed “receiving labeled first set of training simulated images from the labeling system,” and therefore, while all of Applicants arguments and amendments have been fully considered, they are not persuasive and the rejection of claims 8 and 23, and claims depending therefrom under 35 U.S.C. 103 is maintained.
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, and 6–7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without a practical application or significantly more.
Regarding claim 1, this claim recites the following limitations which are found to be abstract ideas not reciting a practical application or significantly more:
receiving a source image; generating, using a first model, a simulated image based on the received source image, wherein the simulated image includes features of interest from the source image (abstract idea as a mental process as a human mind would practically perform generating a simulated image (such as an artist drawing a reproduction) from receiving (looking at) a source image, and where the artist would choose which visual elements of the source image to maintain (features of interest) in their rendering versus which ones to leave out); detecting, using a second model features of interest from the simulated image; annotating the source image based upon the detected features of interest from the simulated image (abstract idea as a mental process as a human mind would practically perform evaluating an artistic work for certain visual elements present (features of interest from the simulated image) and could indicate these certain visual elements by drawing labels on the source work to point out the specific visual features that were maintained (annotating)).
This judicial exception is not integrated into a practical application for the following reasons. Claim 1 recites the additional elements of the step of “displaying the annotated image,” which while not necessarily being an abstract idea, are insignificant extra solution activity since they are merely data gathering and data output (see MPEP §2106.05(g)). Moreover, these elements amount to receiving and outputting data in a computer based system and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Claim 1 further recites additional elements: both claims recite “a first model” and “a second model.” While the first and second models of claim 1 are additional elements, they are not sufficient to recite a practical application of the abstract ideas recited in claim 1 as they amount to mere generic computer elements and thus amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. see MPEP §2106.05(f).
Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claim 1 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Therefore, independent claim 1 is directed towards an abstract idea without a practical application or significantly more.
Regarding claim 6, the limitations are merely directed towards extending the abstract ideas recited in the independent claims, as they merely describe the data being processed. That is, the image processed in the independent claim is further narrowed by the limitations of claim 6 to be a patient image, and the simulated image masks identification of the patient—neither of which change the ability for the abstract ideas recited in the independent claim to be practically performed by the human mind. Therefore, the limitations in claim 6 do not integrate the abstract ideas in the independent claims into a practical application or recite significantly more.
Regarding claim 7, the limitations “wherein the second model is trained using a set of simulated images generated by the first model based upon a training set of source images,” are merely directed towards further abstract ideas as the human mind practically performs being trained using images, for example, an artist is trained to recognize “fake” paintings/reproductions that are based upon real paintings. As disclosed above in the rejections for independent claim 1, the first and second models do not recite a practical application or significantly more.
Finally, it is noted that as per MPEP 2106.04(d)(1), a practical application and thus subject matter eligibility can be found when the following two requirements are met per a two-step analysis: In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.
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.
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, 6–7, 16, and 21–22 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al., "Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images," in IEEE Transactions on Medical Imaging, vol. 40, no. 7, pp. 1737-1749, July 2021, doi: 10.1109/TMI.2021.3065727— cited by Applicant in the IDS filed 6/27/2024 (herein “Kim”) in view of Ho et al., US Patent No. 12,688,341 B1 (herein “Ho”) in view of Lee, US Patent Application Publication No. US 2017/0300621 A1 (herein “Lee”).
Regarding claims 1 and 16, with substantive differences between the claims noted in curly brackets {}, and deficiencies of Kim noted in square brackets [], Kim teaches {a method/system} for extracting features of interest from an image, comprising (Kim Abstract, network and operations thereof including the removal of specific features from an image, while keeping others (extracting features of interest)): { a memory configured to store instructions; and a controller configured to execute the instructions to: - claim 16 (Kim page 1747, disclosed network and operations run on an NVIDIA GTX 1080Ti graphics card which includes a processor and memory)}
receiving a source image (Kim page 1738, fig. 1, raw input images into the encoder);
generating, using a first model, a simulated image based on the received source image, wherein the simulated image includes features of interest from the source image (Kim page 1738, fig. 1, image encoder (first model) obfuscates the content of a raw input image while preserving enough information for downstream image analysis, with the output of the encoder being a feature (features of interest) map G(x) (simulated image), see also page 1740 left column, the feature map G(x) is an encoded (simulated) version of the input image);
detecting, using a second model, features of interest from the simulated image (Kim page 1738, fig. 1, segmentation network (second model) performing medical image analysis to analyze the content of the encoded image, and page 1740, left column, the segmentation network recovers (detects) the correct segmentation map given encoded image G(x), where fig. 4, page 1746 teaches that the segmentation map is a map of visual features distinguished by color);
[annotating the source image based upon the detected features of interest from the simulated image]; and
displaying the [annotated] image.
Kim does not explicitly teach, where Ho teaches annotating the source image based upon the detected features of interest from the simulated image (Ho col. 12, ll. 1–45, a perception model receives as input simulated images and outputs classification of objects in the image, bounding areas of the objects and center points of the objects (detected features of interest from the simulated image), where the single perception model is trained on the output for the simulated image (thus based upon the detected features of interest from the simulated image), and the single perception model processes real images (source image) to produce annotations assigned to the real image such as bounding areas of the objects in the real image (annotating the source image)).
Further, Kim does not explicitly teach where Lee teaches displaying the annotated image (Lee ¶34, fig. 3, the annotated image is visually presented (displayed)).
Therefore, taking the teachings of Kim and Ho together as a whole, it would have been obvious to a person having ordinary skill in the art (herein “PHOSITA”) before the effective filing date of the claimed invention to have modified the segmentation output of Kim’s network with annotations disclosed by Ho at least because doing so would mitigate the reality gap issue and produce computer vision systems with a greater accuracy of reflecting real word environments. See Ho col. 4, ll. 10–24 and col. 1, ll. 25–39.
Further, taking the teachings of Kim as modified by Ho and Lee together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the segmentation output of Kim’s network with annotations and displaying disclosed by Lee at least because doing so would reduce subjectivity in classification of visual features thereby ensuring that all users of the system have a common understanding of terminology applied to images that are classified. See Lee ¶¶6–7.
Regarding claims 6 and 21, Kim teaches wherein the source image is a patient image and wherein the simulated image masks identification of the patient associated with the patient image (Kim page 1738, the encoder converts patient-specific data in raw input images into an identity-obfuscated signal (masks identification) which obfuscates the identity of the patient while preserving task-specific information).
Regarding claims 7 and 22, Kim teaches wherein the second model is trained using a set of simulated images generated by the first model based upon a training set of source images (Kim page 1738, fig. 1, training configuration shows that the output of the encoder as G(xj) a set of feature maps (taught on page 1740 as being an encoded version of the input image, and thus simulated images) is input (trained using) to the segmentation network (second model), and where page 1742 section A, datasets, teaches that the Parkinson’s Progression Marker Initiative dataset was used for a training set as shown in table 1).
Claims 2–4 and 17–19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, Ho and Lee as set forth above regarding claims 1 and 16 from which claims 2–4 and 17–19 depend, and further in view of Parekh et al., “Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results,” Med. Phys. 47(1), January 2020 (herein “Parekh”).
Regarding claims 2–3 and 17–18, Kim does not explicitly teach where Parekh teaches wherein the first model is {an – claims 2 and 17 / a – claims 3 and 18} {autoencoder – claims 2 and 17 / sparse encoder – claims 3 and 18} (Parekh page 80, fig. 3, a sparse autoencoder (which is a type of sparse encoder) is used to extract from an input image a lesion tissue signature).
Therefore taking the teachings of Kim as modified above and Parekh together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the encoder of Kim to be a sparse autoencoder as disclosed by Parekh at least because doing so would allow for unsupervised segmentation (thus not needing specifically labeled data in training giving greater flexibility in training the network), that accurately defines lesions in images and is a robust model. See Parekh page 83.
Regarding claims 4 and 19, Kim does not explicitly teach where Parekh teaches the sparse encoder is a neural network (Parekh fig. 3, page 80, the sparse autoencoder (SAE) is comprised of nodes and layers including hidden layers, and thus is a neural network). The motivation to modify Kim with Parekh is the same as that given for claim 3 above from which claim 4 depends.
Claims 8–15, and 23–30 are rejected under 35 U.S.C. 103 as being unpatentable over Parekh in view of Kim.
Regarding claims 8 and 23, with substantive differences between the claims noted in curly brackets {}, and with deficiencies of Parekh noted in square brackets [], and with claim 8 as exemplary, Parekh teaches A {method – claim 8 / system – claim 23} for training an imaging system (Parekh Abstract, pages 86–87, operations of a multiparameter deep learning network, including training the network), comprising: {[a memory configured to store instructions; and a controller configured to execute the instructions to] – claim 23}
receiving a first set of training source images (Parekh page 80, fig. 3, tumor segmented multiparametric MRI are input to an unsupervised sparse autoencoder where page 81, section 3.B. teaches the training dataset to be mpMRIs (magnetic resonance images));
training a first model using the first set of training source images, wherein the first model is an autoencoder configured to generate simulated images based upon images input into the autoencoder, wherein the simulated images include features of interest from the source images (Parekh pages 86–87, Fig. A1(a), mpMRIs are input to an autoencoder which outputs reconstructed (simulated) mpMRI input tissue images, and where the output of the autoencoder is used to train a classifier to detect tissues as belonging into four categories, therefore the reconstructed images output from the autoencoder having at least the features (features of interest) directed towards classification into one of the four categories, also page 80 teaching that the output of the statistics layer of the autoencoder is a feature vector Z for processing by the classifier);
receiving a plurality of sets of training source images (Parekh page 81, 145 breast exam cases (sets of) with mpMRI images formed the training dataset, and page 80, fig. 3, tumor segmented multiparametric MRI are input (receiving) to an unsupervised sparse autoencoder);
inputting the plurality of sets of training source images into the first model to produce a first set of training simulated images (Parekh pages 86–87, Fig. A1(a), mpMRIs are input to an autoencoder (first model) which outputs (produces) reconstructed (simulated) mpMRI input tissue images (first set of training simulated images));
[transmitting] the first set of training simulated images to a labeling system (Parekh page 87, and page 79, fig. 2, the output of the final sparse autoencoder is used as input for training a classifier layer (labeling system) of the stacked sparse autoencoder network, which outputs one of four classifications: background, fat, glandular or lesion (labels of the labeling system));
receiving a labeled first set of training simulated images from the labeling system (Parekh fig. 3, page 80, the SAE tumor maps output from the stacked sparse autoencoder network (therefore from the last classifier layer, and including the classification labeling) as a feature vector Z are concatenated with feature vectors to form an mpMRI classification feature vector W, and then input (receiving) to the final component of the SAE-SVM framework, the linear support vector machine SVM); and
training a second model using the labeled first set of training simulated images (Parekh page 80, fig. 3, the linear support vector machine SVM (second model) is trained on the feature vector W, which includes the classified/labeled output SAE tumor map feature vector Z, thus “using”), wherein the second model is configured to detect features of interest from images input into the first model (Parekh page 80, fig. 3, given that the linear SVM is trained on W to classify the benign and malignant tumors, the SVM would detect features from the feature vector W that distinguish (features of interest) between tumors that are benign versus those that are malignant, and where the breast cancer diagnosis classifier is analyzing the multiparametric MRI input upstream into the Unsupervised SAE (from images input into the first model)).
Parekh does not explicitly teach where Kim teaches a memory configured to store instructions; and a controller configured to execute the instructions to (Kim page 1747, disclosed network and operations run on an NVIDIA GTX 1080Ti graphics card which includes a processor and memory).
Parekh further does not explicitly teach transmitting the first set of training simulated images to a labeling system, although it does teach that the first set of training simulated images are input to a labeling layer of the stacked sparse autoencoder network as shown in fig. 2, page 79.
However, Kim teaches transmitting the first set of training simulated images to a labeling system (Kim page 1738, feature map G(x) converted from a raw image x on the client side is transferred (transmitting) to a cloud based server where a segmentation network is applied to return a segmentation map).
Therefore, taking the teachings of Parekh and Kim together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the image processing of Parekh to include the graphics card and transmitting taught in Kim at least because doing so would provide for segmenting medical images on a server without having to provide sensitive information about a subject patient and thus preserve privacy. See Kim page 1746.
Regarding claims 9 and 24, Parekh teaches wherein the plurality of sets of training sources images are received from different sources at different times (Parekh page 81, 145 different mpMRI cases were used for the training images, where figs. 4 and 5 illustrate results on 5 different patients, and where pages 76–77 teaching how the patients/people who were imaged consisted of 145 women imaged at the author’s institution, and 50 women imaged at a different institution, thus different sources at different times).
Regarding claims 10 and 25, Parekh teaches wherein the first model is a sparse encoder (Parekh page 80, fig. 3, a sparse autoencoder (which is a type of sparse encoder) is used to extract from an input image a lesion tissue signature).
Regarding claims 11 and 26, Parekh teaches the sparse encoder is a neural network (Parekh fig. 3, page 80, the sparse autoencoder (SAE) is comprised of nodes and layers including hidden layers, and thus is a neural network).
Regarding claims 12 and 27, Parekh does not explicitly teach where Kim teaches wherein the second model is a neural network (Kim page 1738, the medical image analysis network performing segmentation is a convolutional neural network (CNN)).
Therefore, taking the teachings of Parekh and Kim together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the image processing of Parekh to include the CNN taught in Kim at least because doing so would be employing a widely adopted structure U-Net, which is very effective at segmenting medical images. See Kim page 1740.
Regarding claims 13 and 28, with deficiencies of Parekh noted in square brackets [], Parekh teaches wherein the first set of training source images and the plurality of sets of training source images are patient images (Parekh pages 76–77, clinical subjects were women who were imaged for breast tumors) and wherein the simulated images associated with the first set of training source images and plurality of sets of training source images (Parekh pages 86–87, Fig. A1(a), autoencoder which outputs reconstructed (simulated) mpMRI input tissue images (first set of training simulated images, where Parekh page 81, 145 breast exam cases (sets of) with mpMRI images formed the training dataset (plurality of sets of training source images)) [masks identification of the patient associated with the patient images]. Parekh does not explicitly teach where Kim teaches masks identification of the patient associated with the patient images (Kim page 1738, the encoder converts patient-specific data in raw input images into an identity-obfuscated signal which obfuscates the identity of the patient (masks identification) while preserving task-specific information).
Therefore, taking the teachings of Parekh and Kim together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the image processing of Parekh to include the masking of identification in Kim at least because doing so would provide for being able to process medical patient images without having to provide sensitive information about a subject patient and thus preserve privacy. See Kim page 1746.
Regarding claims 14 and 29, Parekh teaches wherein the first model is trained using an unsupervised learning method (Parekh page 80, fig. 3, the sparse autoencoder network is an unsupervised neural network, which is able to be trained unsupervised by automatically extracting its own mpMRI intrinsic tissue signature labels to train on).
Regarding claims 15 and 30, with deficiencies of Parekh noted in square brackets [], and with claim 15 as exemplary, Parekh teaches receiving a second set of training source images (Parekh page 80, fig. 3, tumor segmented multiparametric MRI are input to an unsupervised sparse autoencoder where page 81, section 3.B. teaches the training dataset to be mpMRIs (magnetic resonance images));
inputting the second set of training source images into the first model to produce a second set of training simulated images (Parekh pages 86–87, Fig. A1(a), mpMRIs are input to an autoencoder (first model) which outputs (produces) reconstructed (simulated) mpMRI input tissue images (first set of training simulated images));
[transmitting] the second set of training simulated images to the labeling system (Parekh page 87, and page 79, fig. 2, the output of the final sparse autoencoder is used as input for training a classifier layer (labeling system) of the stacked sparse autoencoder network, which outputs one of four classifications: background, fat, glandular or lesion (labels of the labeling system));
receiving a labeled second set of training simulated images from the labeling system (Parekh fig. 3, page 80, the SAE tumor maps output from the stacked sparse autoencoder network (therefore from the last classifier layer, and including the classification labeling) as a feature vector Z are concatenated with feature vectors to form an mpMRI classification feature vector W, and then input (receiving) to the final component of the SAE-SVM framework, the linear support vector machine SVM); and
re-training the second model using the labeled first set of training simulated images and the labeled second set of training simulated images (Parekh page 80, fig. 3, the linear support vector machine SVM (second model) is trained on the feature vector W, which includes the classified/labeled output SAE tumor map feature vector Z, thus “using”, and where a re-training includes updated training using a second set of training simulated images that updates weights set prior using the labeled first set of training simulated images).
While Parekh gives an example of a first training of the second model and the upstream generated simulated image data from the sparse autoencoder network, Parekh does not explicitly teach repeating the training/simulated images training step so as to receive and process a second set of training source images and a second set of training simulated images. However, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified Parekh to repeat its training process at least because doing so would be a duplication of parts (processing steps) where mere duplication of parts has no patentable significance unless a new and unexpected result is produced. See MPEP §21440.4(VI)(B).
Further, Parekh further does not explicitly teach transmitting the second set of training simulated images to a labeling system, although it does teach that the second set of training simulated images are input to a labeling layer of the stacked sparse autoencoder network as shown in fig. 2, page 79.
However, Kim teaches transmitting the second set of training simulated images to a labeling system (Kim page 1738, feature map G(x) converted from a raw image x on the client side is transferred (transmitting) to a cloud based server where a segmentation network is applied to return a segmentation map).
Therefore, taking the teachings of Parekh and Kim together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the image processing of Parekh to include the transmitting taught in Kim at least because doing so would provide for segmenting medical images on a server without having to provide sensitive information about a subject patient and thus preserve privacy. See Kim page 1746.
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 MICHELLE M KOETH whose telephone number is (571)272-5908. The examiner can normally be reached Monday-Thursday, 09:00-17:00, Friday 09:00-13:00, EDT/EST.
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MICHELLE M. KOETH
Primary Examiner
Art Unit 2671
/MICHELLE M KOETH/Primary Examiner, Art Unit 2671
Prosecution Insights