CTFR 18/587,962 CTFR 95852 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. Response to Amendment Claims 1, and 19 has been amended. Claim 2 has been cancelled. Claims 3-20 are still pending for consideration. Response to Arguments Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. 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. 07-103 AIA The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 07-21-aia AIA Claim s 1, 3-7 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Igarashi et al. (US 20200334818 A1) in view of Vaz et al. (US 20210128092 A1) and further in view of Park et al. NPL “Recording of elapsed time and temporal information about biological events using Cas9” Regarding claim 1, Igarashi et al. teaches a medical image processing device comprising: one or more processors; and one or more memories that store a program to be executed by the one or more processors ( see para [0042]; “the processing circuitry 17 executing a program. Further, the processing circuitry 17 and the main memory 18 constitute a medical image processing apparatus M1”) , wherein the one or more processors execute commands of the program to receive an input of a plurality of images generated by performing contrast imaging ( see Abstract; “The processing circuitry is configured to acquire contrast image data generated by imaging a subject”, see also para [0004]; “the multiple contrast image data acquired in the respective multiple different time phases”) , to estimate an elapsed period from start of injection of a contrast agent for each of the plurality of images on the basis of image analysis of the plurality of images ( see para [0073]; “the multiple contrast image data acquired in the respective multiple different time phases”, see also para [0156]; “the medical image processing apparatus M4, not only the elapsed time from the start of contrast agent injection but also the time phase data based on the contrast image data can be estimated”, and Abstract; “a contrast agent included in the acquired contrast image data, the learned model being for generating the time phase data based on the acquired contrast image data”) , and to determine a contrast state of the plurality of images on the basis of the integrated elapsed period value (see para [0073]; “each contrast image data is able to be associated with information on a time phase classified by contrast state of a lesion area included in contrast image data by a contrast agent. This is because the time phase information is generally classified according to the elapsed time based on the start of injection of the contrast agent”). However, Igarashi et al. does not teach to obtain a plurality of elapsed period values, to perform a process of integrating the plurality of elapsed period values to determine an integrated elapsed period value. In the same field of endeavor, Vaz et al. teach to obtain a plurality of elapsed period values ( see para [0166]; “the machine learning model configured to output a plurality of estimated time points”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a method used learned model for generating the time phase data based on the acquired contrast image data of Igarashi et al. in view of a method upon an injection of a contrast agent, processing acquired projection data of an anatomical region of interest (ROI) of a subject of Vaz et al. in order to measure a contrast signal of the contrast agent ( see para [0161]). However, combination of Igarashi et al. and Vaz et al. as a whole does not teach to perform a process of integrating the plurality of elapsed period values to determine an integrated elapsed period value. In the same field of endeavor, Park et al. teaches to perform a process of integrating the plurality of elapsed period values to determine an integrated elapsed period value ( see page 1052, right col. 5 th para; “Elapsed time can be estimated by each target sequence; when multiple target sequences were used, multiple lengths of elapsed times were estimated at a time point… To deter mine the best method of obtaining one estimated length of elapsed time per time point from the remaining target sequences, we evaluated the mean, interquartile mean, median, and interquartile nps-weighted mean…. we used the interquartile nps-weighted mean to estimate elapsed time from multiple target sequences”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a method used learned model for generating the time phase data based on the acquired contrast image data of Igarashi et al. in view of a method upon an injection of a contrast agent, processing acquired projection data of an anatomical region of interest (ROI) of a subject of Vaz et al. and recording of elapsed time and temporal information about biological events using Cas9 of Park et al. in order to record biological information and to compute mathematical problems ( see page 1052, right col. 5 th para). Regarding claim 3, the rejection of claim 1 is incorporated herein. Igarashi et al. in the combination further teach wherein the one or more processors receive an input of at least one of a slice image, a partial image included in a three-dimensional image, a generated image generated on the basis of the partial image included in the three-dimensional image, or the three-dimensional image as the image ( see para [0052]; “he image generating circuit 14 performs coordinate conversion on the 3D B-mode data generated by the B-mode processing circuit 12, thereby generates 3D B-mode image data”, see also para [0053]; “. The image generating circuit 14 performs the volume rendering (VR) processing for generating 2D image data reflecting 3D information, for example, as rendering processing. Further, the image generating circuit 14 performs the processing for generating multi-planer reconstruction (MPR) image data from the 3D image data by performing an MPR method”). Regarding claim 4, the rejection of claim 1 is incorporated herein. Vaz et al. in the combination further teach wherein the one or more processors estimate the elapsed period using a trained regression model ( see para [0161]; “a machine learning model configured to estimate the time when the VRTB is to occur based on the contrast signal. In a fourth example of the system, which optionally includes one or more or each of the first through third examples, the machine learning model is a regression model or a neural network”). Regarding claim 5, the rejection of claim 1 is incorporated herein. Igarashi et al. in the combination further teach wherein the one or more processors receive an input of a first image, receive an input of a second image that belongs to the same image series as the first image and that is captured at a position different from that of the first image ( see para [0072]; “the image generating circuit 14, and the like to execute ultrasonic imaging using the ultrasonic probe 20 and acquire multiple ultrasonic image data (e.g., contrast image data) in time series”, see also para [0073]; “each contrast image data is able to be associated with information (Hereinafter, referred to as “time phase” or “time phase data”) on a time phase classified by contrast state of a lesion area included in contrast image data by a contrast agent. This is because the time phase information is generally classified according to the elapsed time based on the start of injection of the contrast agent”), estimate a first elapsed period which is an elapsed period from the start of the injection of the contrast agent in the first image, estimate a second elapsed period which is an elapsed period from the start of the injection of the contrast agent in the second image, and estimate the elapsed period belonging to the image series on the basis of the first elapsed period and the second elapsed period ( see para [0079]; “each contrast image data is able to be associated with information (Hereinafter, referred to as “time phase” or “time phase data”) on a time phase classified by contrast state of a lesion area included in contrast image data by a contrast agent. This is because the time phase information is generally classified according to the elapsed time based on the start of injection of the contrast agent”, see also para [0082]; “When the needfulness data generating function 172 generates the needfulness data in substantially real time (or live) with respect to the multiple contrast image data sequentially acquired by the image acquiring function 171, the needfulness data generating function 172 can associate multiple time phase data with multiple contrast image data respectively”). Regarding claim 6, the rejection of claim 5 is incorporated herein. Vaz et al. in the combination further teach wherein the one or more processors integrate the first elapsed period and the second elapsed period to estimate the elapsed period ( see para [0028]; “The amount of time from an injection of contrast agent to the VRTB for the patient may be determined from the estimated AIF and VOF curves, and once this amount of time has elapsed since the administration of the first contrast bolus, the second contrast bolus may be administered ”, see also para [0066]; “that multiple arterial ROIs could be measured and combined (e.g., averaged) to measure the AIF curve. Further, the VOF curve could be measured for the same time period as the AIF curve (e.g., from time t1 until the respective time t2) by monitoring a venous ROI”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a method used learned model for generating the time phase data based on the acquired contrast image data of Igarashi et al. in view of a method upon an injection of a contrast agent, processing acquired projection data of an anatomical region of interest (ROI) of a subject of Vaz et al. in order to get a more robust estimation of the remaining portions of the AIF and VOF curves ( see para [0028]). Regarding claim 7, the rejection of claim 5 is incorporated herein. Vaz et al. in the combination further teach wherein the one or more processors estimate the first elapsed period and the second elapsed period using a trained regression model ( see para [0077]; “the model may output the estimated AIF curve and the estimated VOF curve. The model may be a suitable machine learning model, such as a decision tree, regression model”). Regarding claim 16, the rejection of claim 5 is incorporated herein. Igarashi et al. in the combination further teach wherein the first image and the second image include different partial images included in a three-dimensional image ( see para [0124]; “the contrast image data may be one dynamic image data including multiple continuous frames and is divided into multiple time phases by an index. In this case, partial image data of the dynamic image related to the first time phase divided by the index is used as the training input data related to the first time phase, and the partial image data Ua of the dynamic image of the subject in the first time phase is input to the learned model D21”, see also para [0048]; “thereby generating data (two-dimensional (2D) or three-dimensional (3D) data) acquired by extracting dynamic data of moving subject”). Regarding claim 17, the rejection of claim 5 is incorporated herein. Igarashi et al. in the combination further teach wherein the first image and the second image include generated images that are generated on the basis of different partial images included in a three-dimensional image ( see para [0124]; “the contrast image data may be one dynamic image data including multiple continuous frames and is divided into multiple time phases by an index. In this case, partial image data of the dynamic image related to the first time phase divided by the index is used as the training input data related to the first time phase, and the partial image data Ua of the dynamic image of the subject in the first time phase is input to the learned model D21”, see also para [0048]; “thereby generating data (two-dimensional (2D) or three-dimensional (3D) data) acquired by extracting dynamic data of moving subject”). Regarding claim 18, the rejection of claim 5 is incorporated herein. Igarashi et al. in the combination further teach wherein the first image and the second image include three-dimensional images ( see para [0053]; “the image generating circuit 14 performs the rendering processing on the 3D image data to generate various 2D image data for displaying the 3D image data on the display 40. The image generating circuit 14 performs the volume rendering (VR) processing for generating 2D image data reflecting 3D information”). Regarding claim 19, Igarashi et al. teaches a medical image processing method comprising: causing a computer to receive an input of an image generated by performing contrast imaging and to estimate an elapsed period from start of injection of a contrast agent in the image on the basis of image analysis of the image( see Abstract; “The processing circuitry is configured to acquire contrast image data generated by imaging a subject”, see also para [0156]; “According to the medical image processing apparatus M4, not only the elapsed time from the start of contrast agent injection but also the time phase data based on the contrast image data can be estimated” ). Regarding claim 20, the rejection of claim 19 is incorporated herein. Igarashi et al. in the combination further teach non-transitory, computer-readable tangible recording medium on which a program for causing, when read by a computer, the computer to execute the medical image processing method ( see para [0059]; “The processing circuitry 17 may refer to a dedicated or general-purpose central processing unit (CPU), an microprocessor unit (MPU), a graphics processing unit (GPU), or the like. The processing circuitry 17 may refers to an ASIC, a programmable logic device, or the like”) . 07-21-aia AIA Claim s 8-15 are rejected under 35 U.S.C. 103 as being unpatentable over Igarashi et al. and Vaz et al. in view of Park et al. as applied in claims 1, 5-7 above, and further in view of Marks et al. (US 20210104068 A1) . Regarding claim 8, the rejection of claim 5 is incorporated herein. Vaz et al. in the combination further teach wherein the one or more processors estimate an estimated value output from the regression model ( see para [0077]; “the model may output the estimated AIF curve and the estimated VOF curve. The model may be a suitable machine learning model, such as a decision tree, regression model”. However, the combination of Igarashi et al., Vaz et al. and Park et al. as a whole does not teach and a certainty of the output estimated value for each of the first image and the second image, and integrate estimation results for each of the first image and the second image on the basis of the estimated value and the certainty estimated for each of the first image and the second image using the regression model. In the same field of endeavor, Marks et al. teaches and a certainty of the output estimated value for each of the first image and the second image, and integrate estimation results for each of the first image and the second image on the basis of the estimated value and the certainty estimated for each of the first image and the second image using the regression model ( see para [0012]; “An example of a parametric probability distribution defined by values of parameters for a location of each landmark in each processed image is a Gaussian distribution, wherein the parameters determine a mean and a covariance matrix of the Gaussian distribution. In this example, the mean defines the point of location of the landmark, and the covariance matrix defines the uncertainty”, see also para [0014]; “the parametric probability distribution for each landmark includes an estimate of the location of the landmark, such that the location of the landmark and an uncertainty of the location estimation for each landmark are derived from parameters of the parametric probability distribution”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effecting filling date of the invention to modify a method used learned model for generating the time phase data based on the acquired contrast image data of Igarashi et al. in view of a method upon an injection of a contrast agent, processing acquired projection data of an anatomical region of interest (ROI) of a subject of Vaz et al. and recording of elapsed time and temporal information about biological events using Cas9 of Park et al. a task based on probabilistic image-based landmark localization, uses a neural network of Marks et al. in order to improve accuracy of execution of tasks dependent on accuracy of the landmark localization ( see para [0012]). Regarding claim 9, the rejection of claim 8 is incorporated herein. Marks et al. in the combination further teach wherein the one or more processors estimate a probability distribution having the estimated value as a random variable for each of the first image and the second image on the basis of the estimated value and the certainty of the estimated value, integrate the probability distributions of the first image and the second image to generate an integrated distribution, and specify a final estimated value on the basis of the integrated distribution ( see para [0048]; “the neural network 112 is further trained to provide a mapping from the input images to the Gaussian probability distributions”, see also para [0019]; “the estimated landmark location is used as the mean of the Gaussian distribution, and the estimated covariance matrix is used as the covariance of the Gaussian distribution”). Regarding claim 10, the rejection of claim 9 is incorporated herein. Marks et al. in the combination further teach wherein the one or more processors perform variable conversion to convert the estimated value output from the regression model into a first parameter of a probability distribution model, and perform variable conversion to convert a value indicating the certainty output from the regression model into a second parameter of the probability distribution model (see para [0019]; “the estimated landmark location is used as the mean of the Gaussian distribution, and the estimated covariance matrix is used as the covariance of the Gaussian distribution”). Regarding claim 11, the rejection of claim 10 is incorporated herein. Marks et al. in the combination further teach wherein the probability distribution model is a Laplace distribution ( see para [0063]; “estimate the probability distribution for each landmark in an image, such as a Laplacian distribution”). Regarding claim 12, the rejection of claim 10 is incorporated herein. Marks et al. in the combination further teach wherein the probability distribution model is a Gaussian distribution ( see para [0012]; “An example of a parametric probability distribution defined by values of parameters for a location of each landmark in each processed image is a Gaussian distribution”). Regarding claim 13, the rejection of claim 9 is incorporated herein. Marks et al. in the combination further teach wherein the one or more processors perform logarithmic conversion to take a logarithm of the probability distribution, calculate a sum of logarithmic probability densities corresponding to the probability distributions of the first image and the second image during the integration, and calculate a value at which a simultaneous logarithmic probability density is maximized ( see para [0016]; “the neural network to provide a mapping from the input images to probability distributions that maximizes a likelihood of groundtruth landmark locations. For example, the neural network can be trained using negative log likelihood as a loss function”). Regarding claim 14, the rejection of claim 8 is incorporated herein. Marks et al. in the combination further teach wherein the regression model includes a trained model generated by performing machine learning using training data in which an image for input and a teaching signal are associated with each other ( see para [0016]; “train the neural network to provide a mapping from the input images to probability distributions that maximizes a likelihood of groundtruth landmark locations”, see also para [0062]; “training a neural network for estimation of landmark location and uncertainty of location estimation”). Regarding claim 15, the rejection of claim 8 is incorporated herein. Vaz et al. in the combination further teach wherein the regression model is configured using a convolutional neural network ( see para [0132]; “a machine learning model (e.g., convolutional neural network) to further improve the estimates of the AIF/VOF curves”, see also para [0144]; “the TUC signal may be input into a machine learning model (e.g., a regression model, a neural network, or other suitable model), which may output the estimated AIF and VOF curves”). Conclusion 07-40 AIA 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 WINTA GEBRESLASSIE whose telephone number is (571)272-3475. The examiner can normally be reached Monday-Friday9:00-5:00. 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, Andrew Bee can be reached at 571-270-5180. 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. /WINTA GEBRESLASSIE/ Examiner, Art Unit 2677 /ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677 Application/Control Number: 18/587,962 Page 2 Art Unit: 2677 Application/Control Number: 18/587,962 Page 3 Art Unit: 2677 Application/Control Number: 18/587,962 Page 4 Art Unit: 2677 Application/Control Number: 18/587,962 Page 5 Art Unit: 2677 Application/Control Number: 18/587,962 Page 6 Art Unit: 2677 Application/Control Number: 18/587,962 Page 7 Art Unit: 2677 Application/Control Number: 18/587,962 Page 8 Art Unit: 2677 Application/Control Number: 18/587,962 Page 9 Art Unit: 2677 Application/Control Number: 18/587,962 Page 10 Art Unit: 2677 Application/Control Number: 18/587,962 Page 11 Art Unit: 2677 Application/Control Number: 18/587,962 Page 12 Art Unit: 2677 Application/Control Number: 18/587,962 Page 13 Art Unit: 2677 Application/Control Number: 18/587,962 Page 14 Art Unit: 2677 Application/Control Number: 18/587,962 Page 15 Art Unit: 2677
Prosecution Insights