CTNF 18/840,596 CTNF 101541 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. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement (IDS) submitted on 11/26/2024 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 § 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 (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. 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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-11, 13, 16-22 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Zhang, Ling et al. “Spatial-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data.” ArXiv abs/1902.08716 (2019): n. pag .. Regarding claim 1, Zhang et. al. discloses a computer-implemented method for training a machine-learning model, wherein the method comprises (Zhang et. al., the training of the “ST-ConvLSTM network” disclosed in section III.B(1) on page 1119.): receiving training data: wherein the training data comprise representations T R 1 to T R n of an examination region for a multiplicity of examination objects, where n is an integer greater than two, wherein each representation T R, represents the examination region after administration of an amount a, of a contrast agent (Zhang et. al. III. Methods, A. Construction of 4D Longitudinal Tumor Dataset, As shown in Fig. 1, for each time point, the pre-and post-contrast (arterial phase) 3D CT images are first registered using the ITK implementation of mutation information-based B-spline registration), where i is an integer in the range from 1 to n, where the amounts a 1 to a n form a sequence of preferably increasing amounts, and wherein each examination object is preferably a human and the examination region is preferably part of the human (Zhang et. al. see “Our 4D longitudinal tumor imaging data set used in this study consists of dual-phase contrast-enhanced CT volumes at three time points for each patient” and “The dataset is prepared for every tumor volume at each time point, and imaging volumes at different times” in section III.A (pages 1116 and 1117), “Each patient has at least three time points (Eleven of these patients have the 4 th time points) of dual-phase contrast-enhanced CT imaging, with the time interval of 398 plus or minus 90 days” in the second sentence of section IV.A (page 1119) and “We train our ST-ConvLSTM models for 5 epochs with the batch size of 16. Each data point has 5 slices at three time points” in section IV.B(1)) training the machine-learning model; (Zhang et. al. the text from section III.B(1) on page 1119 discloses the training of the “ST-ConvLSTM network”.) wherein the model is trained to generate, starting from the representation T R 1 , representations T R 2 * to T R n * one after the other, wherein the representation T R 2 * is generated at least partly on the basis of the representation T R 1 and each further representation is generated at least partly on the basis of the respective previously generated representation T R k . 1 *, where k is an index passing through integers from 3 to n, where the representation represents the examination region after administration of the amount a k of the contrast agent and the representation T Rk1* represents the examination region after administration of the amount ak.1 of the contrast agent, wherein differences between the generated representations T R* and the representations T R; are quantified by means of a loss function, where j is an index passing through the numbers from 2 to n, and wherein the differences are minimized by modifying model parameters of the machine-learning mode; and (Zhang et. al. the text from section III.B(2) on pages 1117-1118 describes the function of the aforementioned “ST-ConvLSTM” network (see the left-hand column on page 1118, and figure 2 on page 1117) storing the trained machine-learning model and/or using the machine-learning model for prediction. PNG media_image1.png 666 1234 media_image1.png Greyscale PNG media_image2.png 345 618 media_image2.png Greyscale PNG media_image3.png 636 827 media_image3.png Greyscale It is clear from the aforementioned text and drawing of Zhang et. al. that a representation (an image of a layer) of a state of t+1 (future slice Ys, t+1) is predicted at least partially on the basis of a representation of a state of t (input slice Xs, t) by means of the “ST-ConvLSTM” network. The first input image in this time series can be regarded as the representation TR1 defined in the claimed invention. It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”) Regarding claim 2, Zhang et. al. discloses the method of claim 1, further comprising: for each generated representation T R f * : calculating a loss value for a pair composed of a representation T Rj and the generated representation T R f * with the aid of a loss function, where j is an index passing through the numbers from 2 to n; calculating a total loss value with the aid of a total loss function, where the total loss function is a function of the loss values; and minimizing the total loss value by modifying the model parameters (Zhang et. al. Section III.B(4) discloses the use of a total error function (objective function) for “modifying parameters of the machine learning model” on the basis of the calculated total error (see equation 3), the total error being a function of the error values calculated from the pairs of received representation Tri and a corresponding generated representation of Tri * (“loss between the predicted frames Y and the true future frames X at time 2 and time 3”), the individual error values all being weighted equally). Regarding claim 3, Zhang et. al. discloses the method of claim 2, wherein the total loss function has the following formula: L V = ∑ j = 2 n w j ∙ L F j wherein LV is the total loss value, where LFj are the loss functions for calculating the loss values for the differences between the representation T R f and the generated representation and wj are weight factors, where j is an index passing through the numbers from 2 to n (Zhang et. al. Section III.B(4) discloses the use of a total error function (objective function) for “modifying parameters of the machine learning model” on the basis of the calculated total error (see equation 3), the total error being a function of the error values calculated from the pairs of received representation Tri and a corresponding generated representation of Tri * (“loss between the predicted frames Y and the true future frames X at time 2 and time 3”), the individual error values all being weighted equally). Regarding claim 4, Zhang et. al. discloses a computer-implemented method for predicting a representation of an examination region of an examination object, comprising: receiving a representation R p of the examination region: wherein the representation R p represents the examination region without contrast agent or after administration of an amount a p of a contrast agent, where p is an integer less than n, where n is an integer greater than 2, where the examination object is preferably a human and the examination region is preferably part of the human; feeding the representation R p to a trained machine-learning model: wherein the machine-learning model has been trained on the basis of training data to generate, starting from a first representation T R 1 , a number n-1 of representations T R 2 * to T R n * one after the other, wherein the first representation T R 1 represents the examination region without contrast agent or after administration of a first amount ai of the contrast agent and each generated representation T R f * represents the examination region after an amount a; of the contrast agent, where j is an index passing through integers from 2 to n, where the amounts a 1 to a n form a sequence of preferably increasing amounts, and wherein the representation T R 2 * is generated at least partly on the basis of the representation T R 1 and each further representation T R k * is generated at least partly on the basis of the respective previously generated representation T R k . 1 *, where k is an index passing through integers from 3 to n; receiving one or more representations R pq * of the examination region from the machine-learning model; wherein each of the one or more representations R p4q * represents the examination region after administration of the amount a p + q of the contrast agent, where q is an index passing through the numbers from 1 to m, where m is an integer less than n-1 or equal to n-1 or greater than n-1; and outputting and/or storing and/or transmitting the one or more representations R, p+q *. (Zhang et. al. Last paragraph of section III: “In testing, each spatial sequence (at time 1 and time 2) is divided to several sub-sequences, and fed into our model to generate predictions for time 3.” See also section IV.A and IV.B.1, according to which contrast-enhanced CT images were predicted and used as training data). It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). Regarding claim 5, Zhang et. al. discloses the method of claim 4, wherein the method comprises: receiving the representation R, of the examination region, where the representation R p represents the examination region without contrast agent or after administration of the amount a p of the contrast agent, where p is an integer less than n, feeding the representation R p to the trained machine-learning model, receiving the one or more representations R pq * of the examination region from the machine-learning model, where each of the one or more representations R p4q * represents the examination region after administration of the amount a p + q of the contrast agent, where q is an index passing through the numbers from 1 to m, where m is an integer less than n-1 or equal to n- 1 or greater than n-1,and outputting and/or storing and/or transmitting the one or more representations R p4g *. (Zhang et. al. Last paragraph of section III: “In testing, each spatial sequence (at time 1 and time 2) is divided to several sub-sequences, and fed into our model to generate predictions for time 3.” See also section IV.A and IV.B.1, according to which contrast-enhanced CT images were predicted and used as training data). It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). Regarding claim 6, Zhang et. al. discloses the method according to claim 4, wherein each representation T R p + q * and R p + q * generated by the machine-learning model is returned to the machine-learning model in order to generate a representation T R,+ q +l* and R p + q +1*. (It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). Regarding claim 8, Zhang et. al. discloses the method according to claim 1, wherein each generated representation is calculated according to the following formula: M q R 1 = R 1 + q * where M represents a transformation which is applied by the machine-learning model to input data of the machine-learning model, where M q means the q-times application of the transformation, where in a first step the transformation is applied to a first representation R 1 , in a second step the transformation is applied to the result of the application in the first step in that the result is passed back into the machine-learning model and the procedure is repeated with each further result of a transformation until the transformation has been applied a total of q times, where q is an integer which may assume the values of 1 to m, where m is an integer less than n-1 or equal to n-1 or greater than n-1 (Zhang et. al. Last paragraph of section III: “In testing, each spatial sequence (at time 1 and time 2) is divided to several sub-sequences, and fed into our model to generate predictions for time 3.” See also section IV.A and IV.B.1, according to which contrast-enhanced CT images were predicted and used as training data). It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). Regarding claim 10, Zhang et. al. discloses the method according to claim 4, wherein the received representation R p is a CT image, MRI image or ultrasound image (Zhang et. al. Figure 1, CT images are used). Regarding claim 13, which is a computer program product comprising a computer program that can be loaded into a working memory of a computer system, where it causes the computer system to execute the method of claim 1, which the rejection analysis is incorporated herein. Regarding claim 16, Zhang et. al. discloses a kit comprising a contrast agent and the computer program product of claim 13, which the rejection analysis is incorporated herein. Regarding claim 17, Zhang et. al. discloses the method of claim 4, where the trained machine-learning model has been trained, according to a computer-implemented method comprising: receiving training data: wherein the training data comprise representations T R 1 to T Rn of an examination region for a multiplicity of examination objects, where n is an integer greater than two, wherein each representation T R represents the examination region after administration of an amount a l of a contrast agent, where i is an integer in the range from 1 to n, where the amounts al to a n form a sequence of preferably increasing amounts, and wherein each examination object is preferably a human and the examination region is preferably part of the human; training the machine-learning model: wherein the model is trained to generate, starting from the representation T R 1 , representations T R 2 * to T Rn* one after the other, wherein the representation T R 2 * is generated at least partly on the basis of the representation T R 1 and each further representation T R k * is generated at least partly on the basis of the respective previously generated representation T R k . 1 *, where k is an index passing through integers from 3 to n, where the representation T R k * represents the examination region after administration of the amount a k of the contrast agent and the representation T Rk1* represents the examination region after administration of the amount ak.1 of the contrast agent, wherein differences between the generated representations T R f * and the representations T R; are quantified by means of a loss function, where j is an index passing through the numbers from 2 to n, and wherein the differences are minimized by modifying model parameters of the machine-learning model; and storing the trained machine-learning model and/or using the machine-learning model for prediction (Zhang et. al. Last paragraph of section III: “In testing, each spatial sequence (at time 1 and time 2) is divided to several sub-sequences, and fed into our model to generate predictions for time 3.” See also section IV.A and IV.B.1, according to which contrast-enhanced CT images were predicted and used as training data). It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). Regarding claim 18, Zhang et. al. discloses the method of claim 1, wherein each representation T R p+q * generated by the machine-learning model is returned to the machine-learning model in order to generate a representation T Rp+q+1* (Zhang et. al. Last paragraph of section III: “In testing, each spatial sequence (at time 1 and time 2) is divided to several sub-sequences, and fed into our model to generate predictions for time 3.” See also section IV.A and IV.B.1, according to which contrast-enhanced CT images were predicted and used as training data). It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). Regarding claim 19, Zhang et. al. discloses the method according to claim 1, wherein each generated representation is calculated according to the following formula: M q T R 1 = T R 1 + q * where M represents a transformation which is applied by the machine-learning model to input data of the machine-learning model, where M g means the q-times application of the transformation, where in a first step the transformation is applied to a first representation T R 1 , in a second step the transformation is applied to the result of the application in the first step in that the result is passed back into the machine-learning model and the procedure is repeated with each further result of a transformation until the transformation has been applied a total of q times, where q is an integer which may assume the values of 1 to m, where m is an integer less than n-1 or equal to n-1 or greater than n-1 (It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). Regarding claim 21, Zhang et. al. discloses the method of claim 1, wherein the received representations are CT images, MRI images or ultrasound images (Zhang et. al. Figure 1, CT images are used) . Claim Rejections - 35 USC § 103 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 (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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-27-aia AIA Claim(s) 7, 9, 11, 20, 22 is/are rejected under 35 U.S.C. 102( (a)(1) ) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Zhang, Ling et al. “Spatial-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data.” ArXiv abs/1902.08716 (2019): n. pag .. Regarding claim 7, Zhang et. al. discloses the method according to claim 4, wherein m is in the range from n to n+10 (It should also be noted that the penultimate paragraph in section IV on page 1125 of Zhang et. al. (see the last sentence) discloses a possibility for recursively predicting later points in time, i.e. on the basis of predictions of earlier points in time (“Another solution for predicting further into the future is to recursively apply the two-time-input model as in [27], i.e. predicting the outcome of time 4 based on the time 2 and the predicted time 3 results.”). It is expected that in the recursive application of the model, it would have been obvious for a person skilled in the art to predict the same or a larger number of points in time than were used in training. The recursive model can be substituted as a solution of the claimed invention for the specified range to predict these points in time, on the basis of predictions of earlier points in time (KSR rationale B). Regarding claim 9, Zhang et. al. discloses the method according to claim 4, wherein the examination region is the human liver or is part of the human liver (Zhang et. al. discloses that the examination region is the human pancreas. However, it would be obvious for a person skilled in the art to apply the method of Zhang et. al. to the human liver. An application of hepatobiliary contrast agent at a later point in time (even in the very far future) after the capturing of one or more CT images of a patient in Zhang et. al. is a simple substitution of regions of interest). Regarding claim 11, Zhang et. al. discloses the method of claim 4, wherein the received representation R p is a CT image and wherein the contrast agent is an MRI contrast agent (Zhang et. al. Figure 1, CT images are used; and the contrast agent is a CT contrast agent because CT contrast agents can only be used for CT imaging. It would have been obvious to one skilled in the art to use MRI contrast agent if the imaging modality is to produce an MRI image. The contrast agent is well known in the art). Regarding claim 20, Zhang et. al. discloses the method of claim 1, wherein the examination region is the human liver or is part of the human liver (Zhang et. al. discloses that the examination region is the human pancreas. However, it would be obvious for a person skilled in the art to apply the method of Zhang et. al. to the human liver. An application of hepatobiliary contrast agent at a later point in time (even in the very far future) after the capturing of one or more CT images of a patient in Zhang et. al. is a simple substitution of regions of interest). Regarding claim 22, Zhang et. al. discloses the method of claim 1, wherein the received representations are CT images and wherein the contrast agent is an MRI contrast agent (Zhang et. al. Figure 1, CT images are used; and the contrast agent is a CT contrast agent because CT contrast agents can only be used for CT imaging. It would have been obvious to one skilled in the art to use MRI contrast agent if the imaging modality is to produce an MRI image. The contrast agent is well known in the art). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off. 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, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JESSICA YIFANG LIN/Examiner, Art Unit 2668 June 2, 2026 /VU LE/Supervisory Patent Examiner, Art Unit 2668 Application/Control Number: 18/840,596 Page 2 Art Unit: 2668 Application/Control Number: 18/840,596 Page 3 Art Unit: 2668 Application/Control Number: 18/840,596 Page 4 Art Unit: 2668 Application/Control Number: 18/840,596 Page 5 Art Unit: 2668 Application/Control Number: 18/840,596 Page 6 Art Unit: 2668 Application/Control Number: 18/840,596 Page 7 Art Unit: 2668 Application/Control Number: 18/840,596 Page 8 Art Unit: 2668 Application/Control Number: 18/840,596 Page 9 Art Unit: 2668 Application/Control Number: 18/840,596 Page 10 Art Unit: 2668 Application/Control Number: 18/840,596 Page 11 Art Unit: 2668 Application/Control Number: 18/840,596 Page 12 Art Unit: 2668 Application/Control Number: 18/840,596 Page 13 Art Unit: 2668 Application/Control Number: 18/840,596 Page 14 Art Unit: 2668 Application/Control Number: 18/840,596 Page 15 Art Unit: 2668
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