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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/06/2026 has been entered.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Zeng et al. (US 20110150305 A1), hereinafter referred to as Zeng.
In regards to claim 1, Mailhe discloses A method for reconstruction of a medical image in a medical imaging system during a clinical operation, the method comprising: scanning, by the medical imaging system, a patient, the scanning resulting in measurements (Abstract and paragraphs 32, 35, 49, Mailhe discloses that the scans are created from a medical imaging system in the abstract and that the measurements come from the scans in paragraph 32 with paragraphs 35 and 49 explicitly disclosing that it occurs during a clinical operation); reconstructing, by an image processor in an online processing phase, the medical image from the measurements, the reconstructing including iterations, wherein a different mask of a plurality of different masks of the measurements is applied to each of the iterations (Paragraphs 80 and 121 and abstract, The abstract discloses that the reconstruction used is iterative and paragraph 80 discloses that multiple masks is applied to the iterations in the training phase, and 121 discloses that multiple masks are used the iterations in the reconstruction), wherein the plurality of different masks are created by sub-sampling a fully sampled portion of the measurements, each of the plurality of different masks configured to exclude different subsets of the fully sampled portion of the measurements (Paragraphs 67-68, Paragraph 67 establishes that the data is collected via under-sampling which is being used in an analogous way to sub-sampling in this context as it is being applied to fully-sampled data to create the masks used here with masks implicitly covering different areas of the data) and displaying the medical image (Paragraphs 5 and 7, Both paragraphs cover displaying the image in a display).
Mailhe does not explicitly disclose that it is to reduce banding artifacts.
However, Zeng does disclose that it is to reduce banding artifacts (Paragraphs 34 and 50, Paragraph 34 discloses that banding streaks are one of the problems that may need to be fixed. And paragraph 50 discloses the use of masks to fix similar problems which would align with the phrasing in Mailhe of paragraph 120).
It would be prima facie obvious to combine the teachings of Zeng and Mailhe as it would be simply substituting the known method of Mailhe to be used for the task required by Zeng. Mailhe discloses this known technique for the reduction of aliasing artifacts. However, paragraph 120 of the specification would allow for it to be potentially used for other corrective purposes. Zeng discloses that masks can be used to reduce banding artifacts, as such, a person of ordinary skill in the art could simply substitute the methods of Mailhe to perform the same task. As such, it would be prima facie obvious.
In regards to claim 2, Mailhe discloses wherein scanning comprises scanning with the medical imaging system being a magnetic resonance (MR) scanner using compressed sensing and the measurements being k-space measurements (Abstract and paragraph 31, The abstract discloses that compressed sensing and an MR scanner is used while paragraph 31 discloses the use of k-space measurements).
In regards to claim 8, Mailhe discloses wherein the plurality of different masks are based on different offsets, the different offsets based on an acceleration factor for the scanning, wherein a mask for each iteration is selected randomly from the plurality of different masks (Paragraph 67-68 and paragraph 5, Describes randomly selecting masks with an acceleration setting covering how the masks are selected).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Zeng et al. (US 20110150305 A1), hereinafter referred to as Zeng as applied to claims 1-2 and 8 above, and further in view of Seo et al. (“Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning”), hereinafter referred to as Seo.
In regards to claim 3, neither Mailhe nor Zeng explicitly disclose wherein the measurements include a first center line sampling, and wherein the mask sub-samples the first center line sampling, resulting in a second center line sampling, the measurements from the second center line sampling used in the one of the iterations.
However, Seo (which covers machine learning methods related to MRI imaging) does disclose wherein the measurements include a first center line sampling, and wherein the plurality of different masks are generated by sub-sampling the first center line sampling, to form a plurality of second center line samplings, wherein measurements from a respective one of the second center line samplings is used in one of the iterations (First new paragraph of page 5977, Discloses multiple types of sampling that are used in different times with a center sampling covering all center lines and one covering center-random sampling which includes the sampling again).
It would have been prima facie obvious to combine the teachings of Seo with the teachings of Mailhe and Zeng as it would have led to a predictable increase in the accuracy of analysis for the central region of the MRI image. Being able to determine the accuracy of the centerlines and to provide for a number of different ones would allow for greater accuracy in the k-space measurements overall. As such, it would have been prima facie obvious to combine the teachings of Seo and the teachings Mailhe and Zeng.
In regards to claim 4, neither Mailhe nor Zeng explicitly discloses wherein the first center line sampling comprises a full sampling of center lines.
However, Seo does disclose wherein the first center line sampling comprises a full sampling of center lines (First new paragraph of page 5977, Sampling all center lines would be within the BRI of full sampling of center lines).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Zeng et al. (US 20110150305 A1), hereinafter referred to as Zeng, as applied to claims 1, 2, and 8 above, and further in view of Mostapha et al. (US 20220165002 A1), hereinafter referred to as Mostapha.
In regards to claim 5, neither Mailhe nor Zeng explicitly disclose the aspects of this claim.
However, Mostapha, which covers deep learning strategies for MRI reconstruction, does disclose wherein reconstructing comprises reconstructing a three-dimensional distribution of voxels representing a volume of the patient (Paragraphs 92, Describes that the voxels can be a three-dimensional distribution of the volume of the patient), and wherein displaying comprises volume or surface rendering from the voxels to a two-dimensional display (Paragraph 65, discloses that the two-dimensional image may be formatted to display as an MR image).
It would have been prima facie obvious to combine the teachings of Mailhe, Zeng, and Mostapha as allowing for a 3d representation of the volume of the patient would allow for a predictable increase in the modelling of the patient. A 3d model can cover more details and cover the entirety of a person’s body as such it would be able to show greater amounts of detail of how the condition affects the patient. As such, it would have been prima facie obvious to combine the teachings of Mailhe, Zeng, and Mostapha.
Claims 6 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Zeng et al. (US 20110150305 A1), hereinafter referred to as Zeng, as applied to claims 1, 2, and 8 above, and further in view of Raithel et al. (US 20230298230 A1), hereinafter referred to as Raithel.
In regards to claim 6, neither Zeng nor Mailhe disclose aspects of this claim.
Raithel does disclose comprises reconstructing as an unrolled iterative reconstruction, each iteration using a different machine-learned network (Paragraph 34, Discloses that the network used is an unrolled image reconstruction network which is analogous to the unrolled iterative reconstruction).
It would be prima facie obvious to combine Raithel with the teachings of Zeng and Mailhe. As utilizing unrolled iterative reconstruction would have led to a predictable increase in accuracy. As utilizing multiple different machine learned networks would allow for a greater number of results which would increase the overall accuracy of the cumulative result as the various methods would incorporate the most likely possible options. As such, it would be prima facie obvious to combine.
In regards to claim 10, Raithel discloses wherein all of the plurality of different masks are used for each iteration where the corresponding iteration is free of regularization (Paragraph 27, Discloses that some of the cascades can exclude the regularization function).
Claims 11-12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Raithel et al. (US 20230298230 A1), hereinafter referred to as Raithel, and in view of Seo et al. (“Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning”), hereinafter referred to as Seo.
In regards to claim 11, Mailhe discloses a method for reconstruction in magnetic resonance imaging during a clinical operation, the method comprising: scanning, by a magnetic resonance imaging system using parallel imaging with compressed sensing, a patient during a clinical operation, the scanning resulting in k-space measurements, reconstructing, by an image processor in an online processing phase, a magnetic resonance image from the measurements, (Abstract and paragraphs 31-32 and 35, Discloses that the reconstruction uses k-space data in paragraph 31 with the abstract disclosing the MRI machine along with the reconstruction with paragraph 35 saying that the method can be performed during a clinical operation) and displaying the magnetic resonance image (Paragraphs 5 and 7, Both paragraphs over displaying the image in a display).
However, Mailhe does not explicitly disclose having fully sampled center lines and sub-sampling of other lines; the reconstructing using an unrolled iterative sequence of machine-learned networks, each of the iterations of the sequency having an input for the k-space measurement, wherein different masks sub-sampling the fully sampled center lines of the k-space measurements are applied to each of the inputs of different iterations.
Raithel discloses the reconstructing using an unrolled iterative sequence of machine-learned networks, each of the iterations of the sequency having an input for the k-space measurement (Paragraph 34, Discloses that the network used is an unrolled image reconstruction network which is analogous to the unrolled iterative reconstruction).
It would be prima facie obvious to combine Raithel with the teachings of Mailhe. As utilizing unrolled iterative reconstruction would have led to a predictable increase in accuracy. As utilizing multiple different machine learned networks would allow for a greater number of results which would increase the overall accuracy of the cumulative result as the various methods would incorporate the most likely possible options. As such, it would be prima facie obvious to combine.
However, Raithel does not explicitly disclose having fully sampled center lines and sub-sampling of other lines; each of the iterations of the sequency having an input for the k-space measurement, wherein different masks sub-sampling the fully sampled center lines of the k-space measurements are applied to each of the inputs of different iterations.
Seo does disclose having fully sampled center lines and sub-sampling of other lines; each of the iterations of the sequency having an input for the k-space measurement, wherein different masks sub-sampling the fully sampled center lines of the k-space measurements are applied to each of the inputs of different iterations (First new paragraph of page 5977, discloses multiple types of sampling that are used in different times with a center sampling covering all center lines and one covering center-random sampling which includes the sampling again).
It would have been prima facie obvious to combine the teachings of Seo, Mailhe, and Raithel as it would have led to a predictable increase in the accuracy of analysis for the central region of the MRI image. Being able to determine the accuracy of the centerlines and to provide for a number of different ones would allow for greater accuracy in the k-space measurements overall. As such, it would have been prima facie obvious to combine the teachings of Seo, Mailhe, and Raithel.
In regards to claim 12, Mailhe discloses wherein scanning comprises scanning with an acceleration factor; and further comprising: generating a set of the different masks with different offsets, a number of the different masks based on the acceleration factor; and randomly selecting one of the different masks for each of at least some of the iterations (Paragraph 67-68 and paragraph 5, Describes randomly selecting masks with an acceleration setting covering how the masks are selected).
In regards to claim 15, Raithel discloses wherein reconstructing comprises applying the different masks to the k-space measurements of the center lines for input to the iteration (Paragraph 34, Discloses that the network used is an unrolled image reconstruction network which is analogous to the unrolled iterative reconstruction which would imply the usage of differing inputs).
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Raithel et al. (US 20230298230 A1), hereinafter referred to as Raithel, and in view of Seo et al. (“Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning”), hereinafter referred to as Seo, as applied to claims 11-12 and 15 above, and further in view of Mostapha et al. (US 20220165002 A1), hereinafter referred to as Mostapha.
In regards to claim 13, Mostapha discloses wherein a first part the unrolled iterative sequence includes gradient update where each of the gradient updates of the first part have inputs of the k-space measurements masked by ones of the different masks and a second part of the unrolled iterative sequence does not include regularization where multiple of the masks are applied for one of the gradient updates of the second part (Paragraph 38, The paragraph describes the gradient update as occurring between two steps which includes regularization and data consistency. It also states that the regularization isn’t required for all the parts involved).
It would have been prima facie obvious to combine the teachings of these arts as it would lead to a predictable decrease in the amount of computing power used. Removing a process like regularization from the cascade would lead to a predictable decrease in the amount of computation required by the process as a whole which saves energy, saves time, and decreases strain on the processor. As such, it would be prima facie obvious to combine these teachings.
In regards to claim 14, Mailhe discloses wherein results derived from the gradient update in response to inputs of the k-space measurements from each of the multiple masks are averaged (Paragraph 76, Paragraph 76 discloses the use of averages to get an average loss in the training which is an average of the multiple masks).
It would have been prima facie obvious to combine the teaching of these arts. The disclosure of averaging provided by Mailhe would allow for a predictable increase in the ability to improve between cascades. The averages disclosed by Mailhe would allow for the results and failures of each cascade’s component parts to be quickly computed to get an overall ranking per cascade that could be used to improve any further cascades.
Claims 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Raithel et al. (US 20230298230 A1), hereinafter referred to as Raithel.
In regards to claim 16, Mailhe discloses a system for reconstruction in medical imaging, the system comprising: a magnetic resonance scanner configured to scan a region of a patient in a clinical operation, the scan providing sparsely sampled k-space data; an image processor configured to reconstruct a representation of the region from the sparsely sampled k-space data from the clinical operation in an online processing phase (Abstract and paragraphs 31-32 and 35, Discloses that the reconstruction uses k-space data in paragraph 31 with the abstract disclosing the MRI machine along with the reconstruction with paragraph 35 disclosing that it can be done in a clinical operation), and a display configured to display a magnetic resonance image of the region from the reconstructed representation for the clinical operation (Paragraphs 5, 7, and 35, Both paragraphs over displaying the image in a display with paragraph 35 disclosing that it can be performed during a clinical operation).
Mailhe does not explicitly disclose the image processor configured to reconstruct by a sequence of cascades in an unrolled iterative reconstruction, wherein the image processor is configured to sub-sample the sparsely sampled k-space data from the scan differently for each cascade of the sequence of cascades.
However, Raithel does disclose the image processor configured to reconstruct by a sequence of cascades in an unrolled iterative reconstruction, wherein the image processor is configured to sub-sample the sparsely sampled k-space data from the scan differently for each cascade of the sequence of cascades (Paragraph 34, Discloses that the network used is an unrolled image reconstruction network which is analogous to the unrolled iterative reconstruction and due to this using different models with different inputs, it would read upon this claim).
It would have been prima facie obvious to combine the teachings of Mailhe and Raithel. As utilizing unrolled iterative reconstruction would have led to a predictable increase in accuracy. As utilizing multiple different machine learned networks would allow for a greater number of results which would increase the overall accuracy of the cumulative result as the various methods would incorporate the most likely possible options. As such, it would be prima facie obvious to combine.
In regards to claim 18, Mailhe discloses wherein the image processor is configured to generate different sub-sample patterns for the sub-sampling based on an acceleration factor of the scan and configured to randomly select one of the different sub-sample patterns for each of the cascades (Paragraph 67-68 and paragraph 5, Describes randomly selecting masks with an acceleration setting covering how the masks are selected).
In regards to claim 19, Raithel teaches wherein the sequence of cascades includes first cascades with data consistency and regularization functions and includes at least a second cascade with the data consistency function and no regularization function (Paragraph 27, discloses that some of the cascades can exclude the regularization function), wherein the image processor is configured to perform the second cascade multiple times with different ones of the sub-sampled sparsely sampled k-space data (Paragraph 27, Discloses that some of the cascades can exclude the regularization function and can be done multiple times).
Raithel does not disclose and average results from the performance of the second cascade.
However, Mailhe does disclose and average results from the performance of the second cascade (Paragraph 76, Paragraph 76 discloses the use of averages to get an average loss in the training).
It would have been prima facie obvious to combine the teaching of these arts. The disclosure of averaging provided by Mailhe would allow for a predictable increase in the ability to improve between cascades. The averages disclosed by Mailhe would allow for the results and failures of each cascade’s component parts to be quickly computed to get an overall ranking per cascade that could be used to improve any further cascades.
In regards to claim 20, Mailhe does disclose wherein each of the cascades includes an input for the sub-sampled sparsely sampled k-space data (Paragraph 31 discloses that the data is under-sampled k-space data and that it is utilized as input in the cascade process).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Mailhe et al. (US 20190172230 A1), hereinafter referred to as Mailhe, in view of Raithel et al. (US 20230298230 A1), hereinafter referred to as Raithel, as applied to claims 16 and 18-20 above, and further in view of Seo et al. (“Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning”), hereinafter referred to as Seo.
In regards to claim 17, Mailhe discloses wherein the magnetic resonance scanner is configured to scan combined with compressed sensing along a Cartesian format where the provided sparsely sampled k-space data (Paragraph 31, Paragraph 39, and the Abstract: Paragraph 31 discloses under-sampled k-space data which reads on the sparsely sampled k-space data with paragraph 39 disclosing that these components are acquired via a Cartesian acquisition strategy which would read upon the Cartesian format, along with the abstract providing for the use of an MRI machine along with compressed sensing).
Mailhe does not explicitly disclose with parallel imaging and includes fully sampled center lines, and wherein the sub-sampling is of the fully sampled center lines.
However, Seo does disclose with parallel imaging (Section 1.1.3, discloses that using parallel imaging and compressed sensing are conventional) and includes fully sampled center lines, and wherein the sub-sampling is of the fully sampled center lines (First new paragraph of page 5977, discloses multiple types of sampling that are used in different times with a center sampling covering all center lines and one covering center-random sampling which includes the sampling again).
It would have been prima facie obvious to combine the teachings of Seo, Mailhe, and Raithel as it would have led to a predictable increase in the accuracy of analysis for the central region of the MRI image. Being able to determine the accuracy of the centerlines and to provide for a number of different ones would allow for greater accuracy in the k-space measurements overall. As such, it would have been prima facie obvious to combine the teachings of Seo, Mailhe, and Raithel.
Response to Amendment
Amendment, entered on 4/06/2026, has been considered in full. The amendment includes the abstract that was previously submitted and entered into the record with the advisory action along with the additions of phrasing in the independent claims to indicate that the operation is a clinical one and during an online processing phase. Amended language does not overcome the rejections although the argument is more persuasive.
Response to Arguments
Applicant's arguments filed 4/06/2026 have been fully considered but they are not persuasive.
Applicant’s argument against Mailhe as a reference is not persuasive. Firstly, paragraph 121 allows for multiple different masks to be used. Furthermore, nothing within the specification language requires that the same mask be used or that it is a “fixed input”. Multiple different masks are utilized, and that is all that the claims require. The only reference to it being a “fixed input” is this line from paragraph 112 which simply states, “The architecture is inspired from optimization-based compressed sensing where the operators used for reconstruction are typically fixed, and a few free parameters may be adapted for each application.” Merely because the architecture that inspired this system typically has fixed operators does not mean that this architecture requires fixed operators. Furthermore, this line from the same paragraph, “The actor-critic architecture has an actor to perform each iteration of reconstruction and a critic to decide a change or action in one or more rendering parameters (e.g., scalar threshold and/or a step size) to be used for each iteration” shows that the critic explicitly changes the parameters. As such, Mailhe is found to teach the limitation in the claims. Mailhe further allows that reconstruction can occur during a clinical operation, paragraph 35, explicitly states, “These machine-learned computed parameters can be subsequently used during clinical operation to rapidly reconstruct images.” This is extremely analogous to the recited online phase of the claimed invention as it too relies on an offline training phase and using the parameters learned to reconstruct images in a clinical environment. The distinction of the two phases in this case is not persuasive due to the nature of the specification, paragraph 59 of the specification explicitly states, “Machine learning is an offline training phase where the goal is to identify an optimal set of values of learnable parameters of the model that can be applied to many different inputs (i.e., image domain data after gradient calculation in the optimization or minimization of the reconstruction). These machine-learned parameters can subsequently be used during clinical operation to rapidly reconstruct images. Once learned, the machine-learned model is used in an online processing phase in which MR scan data y (e.g., k-space measurements) for patients is input and the reconstructed representations for the patients are output based on the model values learned during the training phase.” This is a disclosure that training parameters and variable can be used in the online phase. Mailhe discloses the same concept in paragraph 35 with, “These machine-learned computed parameters can subsequently be used during clinical operation to rapidly reconstruct images.” As such, variables, parameters, and masks can be reused in both applications in an online phase as the online phases incorporate elements from the training phases. So, the argument is not persuasive.
Zeng is only being used to substitute the generic artifacts mentioned in Mailhe’s paragraph 60 with the banding artifacts disclosed in Zeng. Both arts are directed towards the reduction of artifacts in medical imaging equipment. Zeng is not being cited for a multi-mask reconstruction process. Mailhe is being cited for a multi-mask reconstruction process. So, the argument is not persuasive.
Similarly, neither Seo nor Raithel is being cited for the application of different masks during an iterative reconstruction. So, the arguments made against them are not persuasive.
Arguments made against the motivations are also not persuasive. Banding artifacts are common in any and all medical imaging modalities and plenty of non-medical imaging modalities. Mailhe discloses that their method is applicable to other forms of artifacts in paragraph 60 by stating, “The system 200 in FIG. 2 can be trained such that each layer L1, L2, . . . , LK learns to reduce the errors/artifacts of the previous layers.” As such, Zeng is merely being used to recite the term “banding artifacts” and not for using the method of Mailhe within a CT scanner.
Arguments against the combination of Mailhe, Seo, and Zeng similarly are not persuasive. The rationale is one of the rationales for obviousness that come from MPEP 2143. So, regardless of whether applicant believes that it is a “vague and conclusory goal that lacks any sound technical underpinning”, the rationale is still proper. The issues with Zeng have been addressed in the prior response.
Arguments against Mailhe and Rathiel are not persuasive. Mailhe does not explicitly disclose “unrolled iterative reconstruction” or use the term anywhere within the reference, hence the incorporation of Rathiel. Arguendo, even if Mailhe did disclose “unrolled iterative reconstruction”, the relevant claims would still be rejected instead as a combination of a 102 with Mailhe for the independent claim and the dependent claims in combination with Rathiel. As such, the arguments are not persuasive.
Conclusion
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CONOR AIDAN. O'MALLEY
Examiner
Art Unit 2675
/CONOR A O'MALLEY/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
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