CTNF 19/301,196 CTNF 94394 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim s 7-9, 13, and 15 are objected to because of the following informalities: In claims 7-8, the “BBB” should read “blood-brain barrier (BBB)”. In claim 9, “to a trace of the BBB openings” should read “to trace the BBB openings”. In claim 13, “times” should read “times.”. In claim 15, “claim 8” should read “claim 7”, and “the employing BBB openings patches” should read “the employing BBB opening patches” because the “employing BBB opening patches” is recited in claim 7 . Appropriate correction is required. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a processing unit configured to …” in claim 16, line 4. Interpreted as a computing system equipped with a multi-core processor [0084]; Fig. 6. Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 AIA Claim 17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claim 17 recites the “a Ktrans map” in line 2. This recitation is unclear because it is unclear whether this is a reference to the “map” in claim 1, para. “c”. For examination purposes, Examiner of record takes this to be “the map”. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-10, 13-17, and 19-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Lee et al (Deep Learning Enables Reduced Gadolinium Dose for Contrast-Enhanced Blood-Brain Barrier Opening, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. xx, NO. X, 1-10, January 2023)., hereinafter Lee . Regarding claim 1, Lee teaches a deep learning method ("STNet”) for reducing dosage of Gadolinium-Based Contrast Agents (GBCAs) in medical imaging ("STNet was shown to be a promising method of reducing the need of contrast agents for modeling BBB-opening K-trans maps from time-series Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans." Abstract), comprising: a. applying dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to a subject to obtain a plurality of DCE-MRI images (“We injected the contrast agent at two time points to obtain MRI scans with different volumes of contrast agent. We first injected 10 mmol/kg GBCAs, which is 3.3% of the full dosage of the GBCAs (low dose), then administered the remaining 97.7% GBCAs (full dose)." p. 3, right col.); b. analyzing the plurality of DCE-MRI images with a deep learning model ("STNet”) using a spatiotemporal network (“Then we used a Spatiotemporal Network (ST-Net), composed of a spatiotemporal convolutional neural network (CNN)-based deep learning architecture with the addition of a three-dimensional CNN encoder, to improve the model performance." Abstract) to obtain a corresponding plurality of volume transfer constants (Ktrans) ("This study is the first to propose the idea of modeling a volume transfer constant (Ktrans) through deep learning to reduce the dosage of contrast agents." Abstract); and c. forming a map using the plurality of Ktrans (“modeling BBB-opening K-trans maps from time-series Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans." Abstract; "eighty-four T1W DCE-MRI were acquired...We then generated the volume transfer constant (Ktrans) map through the general kinetic model (GKM) model. Finally, we extracted the whole brain Ktrans map with the manually labeled brain mask." Fig. 1). Regarding claim 2, Lee teaches the method of claim 1, wherein the analyzing the plurality of DCE-MRI images comprises: extracting spatial information using a three-dimensional convolutional neural network (CNN) encoder (“Fig. 2. Proposed ST-Net architecture. The four-dimensional dynamic contrast-enhanced (DCE)-Magnetic Resonance Imaging (MRI) scans were first cropped to 7x7x48 patches. We then extracted spatial information, using a three-dimensional convolutional neural network (CNN) encoder.”). Regarding claim 3, Lee teaches the method of claim 2 , wherein the analyzing the plurality of DCE-MRI images further comprises: concatenating the spatial information with two reference arrays, including average intensity of pre-contrast images and average DCE-MRI time series signal ("Concatenation of spatial features with two reference arrays: (1) The average of DCE-MRI signal before contrast agent injection for each voxel (2) contrast agent concentration in muscle tissues…The output from the proposed ST-Net is a volume transfer constant (Ktrans) value, which was reconstructed to acquire a whole-brain Ktrans map" Fig. 2. "Following the spatial network, we concatenated the output one-dimensional array (64x84) with the other two channels, which were used as reference: (1) Broadcast a single value, the average of the four pre-contrast images, to the same length of frames (48 acquisitions), and (2) Averaged Gd concentration changes in muscle tissue area with time."; 2) Temporal Network; p.4). Regarding claim 4, Lee teaches the method of claim 3, wherein the analyzing the plurality of DCE-MRI images further comprises: implementing a temporal network (“2) Temporal Network”; page 4), comprising a one-dimensional CNN layer to blend spatial and reference information, and two separate CNN pathways capturing long-term and short-term temporal characteristics ("We first performed a one-dimensional CNN layer in the temporal model to fuse the spatial information with reference information and extract low-level temporal features. The following two parallel CNN pathways were used to extract long-term (global pathway) and short-term (local pathway) features. Finally, two one dimensional CNN layers and a fully connected layer were used to fuse the long-term and short-term information and to predict the full dose Ktrans value for each pixel."; 2) Temporal Network; p.4). Regarding claim 5, Lee teaches the method of claim 4, wherein the analyzing the plurality of DCE-MRI images further comprises: fusing long-term and short-term temporal characteristics for outputting, using additional one-dimensional CNN layers and a fully connected layer (“Finally, two one dimensional CNN layers and a fully connected layer were used to fuse the long-term and short-term information and to predict the full dose Ktrans value for each pixel."; 2) Temporal Network; p.4). Regarding claim 6, Lee teaches the method of claim 1, wherein the analyzing the plurality of DCE-MRI images further comprises: applying a Leaky Rectified Linear Unit (ReLU) activation ("Each fully connected layer is followed by a Leaky ReLU activation." Fig. 2). Regarding claim 7, Lee teaches the method of claim 1, wherein the deep learning model is configured to be trained in a dataset employing BBB-opening patches ("3) BBB-Opening Patches Steps The ST-Net tended to overfit due to the high overlap between BBB-opening patches in the training dataset."; p. 4). Regarding claim 8, Lee teaches the method of claim 1, wherein the applying DCE-MRI comprises: inducing focused ultrasound with administration of microbubbles to BBB-openings ("FUS with intravenous administration of microbubbles has been shown to open the BBB in small animals in-vivo and in clinical trials. This opening can be targeted and is transient. The FUS-enhanced BBB-opening can be validated with MRI"; IV. DISCUSSION; p. 7). Regarding claim 9, Lee teaches the method of claim 8, further comprising injecting contrast agents to trace the BBB openings ("A contrast agent was used as a tracer to depict the area of the BBB-opening. We injected the contrast agent at two time points to obtain MRI scans with different volumes of contrast agent. p. 3, right col., last para. "We not only successfully investigated the efficacy of detecting BBB-opening with low dosage contrast agent administration but also improved the model performance with an additional 3D CNN." IV. DISCUSSION, p. 7). Regarding claim 10, Lee teaches the method of claim 1, wherein the analyzing the plurality of DCE-MRI images comprises processing spatial and temporal information simultaneously by treating a three dimensional input as a single entity for a CNN encoder (“Fig. 2. Proposed ST-Net architecture. The four-dimensional dynamic contrast-enhanced (DCE)-Magnetic Resonance Imaging (MRI) scans were first cropped to 7x7x48 patches.”; p. 3. “One of our main contributions is the novelty of adding a spatial network to further enhance the performance of predicting Ktrans while retaining high fidelity. Instead of simply inputting data on the voxel level, we cropped the WB ROI to patches across time and extracts the spatial features for each patch through a three-dimensional CNN encoder.”; p. 7, right col.). Regarding claim 13, Lee teaches the method of claim 9, wherein the contrast agents are injected at two times ("We first injected 10 mmol/kg contrast agent (3.3% of the full dosage) gadodiamide and acquired eighty T1-weighted (T1W) dynamic contrast-enhanced (DCE)-MRI. Following the injection of the remaining 97.7% contrast agent (full dose), eighty-four T1W DCE-MRI were acquired" Fig. 1). Regarding claim 14, Lee teaches the method of claim 1, wherein the Ktrans map is formed through a general kinetic model (GKM) model ("We then generated the volume transfer constant (Ktrans) map through the general kinetic model (GKM) model" Fig. 1). Regarding claim 15, Lee teaches the method of claim 8, wherein the employing BBB openings patches comprises cropping each voxel of Whole Brain (WB) scan into patches for extracting spatial information ("Fig. 2. Proposed ST-Net architecture. The four-dimensional dynamic contrast-enhanced (DCE)-Magnetic Resonance Imaging (MRI) scans were first cropped to 7x7x48 patches. We then extracted spatial information, using a three-dimensional convolutional neural network (CNN) encoder."). Regarding claim 16, Lee teaches a medical imaging system ("a Magnetic Resonance Imaging (MRI) system" Fig. 1) integrating a deep learning method (“we used a Spatiotemporal Network (ST-Net), composed of a spatiotemporal convolutional neural network (CNN)-based deep learning architecture with the addition of a three-dimensional CNN encoder, to improve the model performance." Abstract), comprising: a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) apparatus (seen in the third image from the left in the top row in Fig. 1) configured to obtain a plurality of DCE-MRI images (“Following the injection of the remaining 97.7% contrast agent (full dose), eighty-four T1W DCE-MRI were acquired" Fig. 1); and a processing unit configured to implement the deep learning method of claim 1, for analyzing the plurality of DCE-MRI images (“All the models were trained on three 24 GB NVIDIA Quadro 6000 graphical processing units using PyTorch."; 3) Model Hyperparameters, p. 4). Regarding claim 17, Lee teaches the medical imaging system of claim 16, further comprising a display unit configured to present a Ktrans map on visual representations of the plurality of Ktrans (“Fig. 3. Full dose and low dose volume transfer constant (Ktrans) map obtained from the general kinetic model (GKM) model and the predicted Ktrans map from two neural networks (green box)”; "The following four columns displayed the residual differences between full dose and derived/predicted Ktrans images."; p. 7, left col., l. 1-4). Regarding claim 19, Lee teaches the medical imaging system of claim 16, wherein the processing unit is further configured to store the plurality of Ktrans in a storage device for subsequent analysis (“F. Statistical Analysis To evaluate the quality of the predicted K-trans map, we analyzed the similarity between the predicted K-trans map generated by deep learning and the ground truth K-trans map derived using experimental data from the DCE protocol. To investigate any advantage of adding a spatial network, we displayed the comparison between ST-Net and the modified fast-eTofts, a purely temporal network (T-Net). Additionally, we performed an evaluation on the GKM derived low dose image to show the improvement of detecting BBB-opening using deep learning."; p. 4, right col. The plurality of Ktrans have to be stored to evaluate the quality ). Regarding claim 20, Lee teaches the medical imaging system of claim 16, wherein a focused ultrasound apparatus (“FUS”) (seen in the first two images from the left in the top row in Fig. 1) is further integrated to the DCE-MRI apparatus for inducing a BBB-opening ("FUS with intravenous administration of microbubbles has been shown to open the BBB in small animals in-vivo and in clinical trials. This opening can be targeted and is transient. The FUS-enhanced BBB-opening can be validated with MRI"; IV. DISCUSSION; p. 7) . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 11-12 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXEI BYKHOVSKI whose telephone number is (571)270-1556. The examiner can normally be reached on Monday-Friday: 8:30am - 5:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pascal Bui Pho can be reached on 571-272-2714. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ALEXEI BYKHOVSKI/ Primary Examiner, Art Unit 3798 Application/Control Number: 19/301,196 Page 2 Art Unit: 3798
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