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 .
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Arguments
Applicant's arguments filed on 4 September 2026, with respect to the pending claims, have been fully considered but they are not persuasive.
Applicant’s Representative submits that the prior art (Yang, in view of Ebata), does not teach the limitations “process the user display …” and “present the user display image …” because the distance calculated in Yang is an internal computation intermediate and not the same as adding a two-dimensional shape centered over the centroid and a visual indication of a distance. Applicant’s Representative further submits that Yang’s directional readout external to the display image is not the same as presenting the user display image on the display.
The examiner respectfully disagrees. Yang teaches that an arrow indicating the distance from the center of the displayed image and the center of mass of the organ being imaged (Yang Fig. 30 & ¶¶0161: “the arrow feed back from the indicator 22 …how the program determines to show flashing arrow or solid arrow on the indicator panel …The procedure to determine the arrow displaying can be described as following: Compute the mass center of the C-mode shape, Calculate the direction based on the mass center location”).
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Yang Fig. 30 Excerpt
Note that the center of mass of the organ is indicated by a red dot, and an arrow “Mass center distance d” indicates the distance between the center of the displayed image and the center of the imaged organ.
Yang Fig. 31 further shows that an arrow indicator is displayed to the user so that the center of the displayed image moves closer to the center of mass of the imaged organ.
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Yang Fig. 31 Excerpt
Furthermore, the claim does not specify what type of “processing” is performed other than adding a 2D shape and a visual indication of a distance from the 2D shape to the center of the user display image. In addition, the “user display image” is generated by analyzing the ultrasound image, i.e., the user display image can be any image (synthetic, graphic, etc.) as long as it results from analyzing the ultrasound image and contains the anatomical structure of the patient.
Using the broadest reasonable interpretation of the claim, any type of processing (e.g., overlay, color change, highlight, etc.) that adds any 2D shape (e.g., arrow, dot, etc.) centered over the centroid (e.g., aligned with the centroid, on top of the centroid, etc.) and visually indicating the distance from the 2D shape to the center of the user display image (e.g., any image analyzed from the US image having the imaged organ) teaches the claimed limitation.
Applicant’s Representative further submits that the prior art does not teach a smartphone or a tablet because Ebata describes the substitution of a tablet for a display unit and an input device.
The examiner respectfully disagrees. Ebata teaches using a portable computer called a tablet (Ebata ¶¶0200). Ebata Fig. 32 further shows that the display unit is connected to the diagnostic apparatus body comprising a processor. Furthermore, one of ordinary skill in the art would understand that a tablet, as known in the art, is capable of performing various software processes using a processor.
Regarding claim 10, the term “Urabe” was a clerical error and has been deleted (Urabe was not used in the rejection).
Applicant’s Representative further submits that the prior art (Yang, in view of Ebata and Kadah) does not teach “wherein each pixel …has a value representing a probability that the pixel corresponds to a location within the anatomical structure” because Kadah’s probability classifies whether the result correctly characterizes a liver as normal or diseased, i.e., a probability associated with a diagnostic outcome.
The examiner respectfully disagrees. Kadah teaches that the pixel probability associated with various pathology is location-based and the statistical classification is estimated from the given samples (see Kadah Fig. 4, pg. 468 right column: “in addition not varying the size and location of the ROI … must be standardized for all scans to ensure the fidelity of the tissue characterization” & Kadah pg. 471 §B).
In view of this reasonable interpretation of the claims and the prior art, the examiner respectfully submits that the rejections set forth below remain proper.
Claim Rejections - 35 USC § 103
Claim(s) 1-6, 9-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 2009/0264757 A1), in view of Ebata et al. (US 2021/0353261 A1; EFD: 8 Mar, 2019), hereinafter referred to as Yang and Ebata, respectively.
Regarding claim 1, Yang teaches an apparatus for aiding a user with imaging an anatomical structure, comprising:
an ultrasound device configured to be moved across the body of a patient and having a transducer array capable of transmitting and receiving ultrasound data (Yang Abstract: “Systems, methods, and ultrasound transceivers equipped and configured to execute harmonic analysis”),
a device having a display and a processor (Yang Fig. 3 & ¶¶0074: “The system also includes a processor that may be configured to process reflected ultrasound energy that is received by the transceiver 10A to produce an image of the scanned anatomical region. Accordingly, the system generally includes a viewing device, such as a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display device, or other similar display devices”), the processor configured to:
receive ultrasound data from the ultrasound device (Yang ¶¶0074 discussed above; Yang Fig. 1 & ¶¶0060: “Ultrasound transceivers equipped with deliver and receive fundamental ultrasound energies utilize different signal processing algorithms than ultrasound transceivers equipped to probe cavity-containing organs with ultrasound harmonic energies. Algorithms described below are developed to optimally extract organ information from fundamental and/or harmonic ultrasound echoes delivered under A-mode, B-mode, and/or C-mode methodologies”);
analyze the ultrasound data and generate a user display image for presentation on the display, and having within the user display image, an image of an anatomical structure of the patient (Yang Figs. 2-3; Yang ¶¶0060 discussed above);
automatically determine an area of the anatomical structure using a statistical model trained from a plurality of training ultrasound images (Yang Abstract: “The neural network algorithms, refined for detection of the bladder and to ascertain the presence or absence of a uterus, is optimally applied to better segment and thus confer the capability to optimize measurement of bladder geometry, area, and volumes”; Yang ¶¶0116: “The Neural Network algorithm 224 (NNA 224) is parametric and is used to determine the statistical likelihood that a give scan line passing through a bladder or cavity appearing region does pass through a bladder or cavity … Then, a statistical likelihood expressed or termed as line grading represents the output of the NNA 224”);
automatically determine a location of a centroid of the image of the anatomical structure;
automatically determine a center of the user display image (Yang ¶¶0089: “After the front and back walls are found, a line passing through the center of the bladder is determined in the following process block Find Centroid 142. This center bladder line is used as a seed from which process block Fix Initial Walls 150 utilizes”; Yang Figs. 26-30);
process the user display image to add a two-dimensional shape centered over the centroid and a visual indication of a distance from the two-dimensional shape to the center of the user display image (Yang ¶¶0090-¶¶0094: “a local gradient 76 at process block 76 is computed for each sample point using a central difference formulation … The normalization factor is simply the distance between the two points”; Yang Fig. 2C, 27, & 30: see “Mass center distance d” and 2D crosshair); and
present the user display image on the display to visually present to the user a distance from the centroid of the anatomical structure to the center of the display image (Yang Fig. 2C, 27, & 30).
However, Yang does not appear to explicitly teach that the device is a smartphone or a tablet.
Pertaining to the same field of endeavor, Ebata teaches that the device is a smartphone or a tablet (Ebata ¶¶0200: “in a case where a portable thin computer, called a tablet, is used as the display unit 7 and the input device 17, it is possible to allow the user to more easily perform ultrasonography of the subject S, and to further improve convenience in ultrasonography”).
Yang and Ebata are considered to be analogous art because they are directed to ultrasound systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the ultrasound system and method for bladder detection (as taught by Yang) to use a tablet (as taught by Ebata) because the combination allows the user to more easily perform ultrasonography and further improves convenience (Ebata ¶¶0200).
Regarding claim 2, Yang, in view of Ebata, teaches the apparatus of claim 1, wherein the anatomical structure comprises a bladder (Yang Abstract: “The neural network algorithms, refined for detection of the bladder”).
Regarding claim 3, Yang, in view of Ebata, teaches the apparatus of claim 1, wherein the statistical model includes a trained model having been trained on input training data comprising ultrasound images of anatomical structures of the type being imaged by the user (Yang ¶¶0116: “The Neural Network algorithm 224 (NNA 224) is parametric and is used to determine the statistical likelihood that a give scan line passing through a bladder or cavity appearing region does pass through a bladder or cavity. Its weights are all the parameters which are calculated by pre-training. This training is to describe how a classifier learns to make correct classification using preclassified results and corresponding features, which include harmonic ratio at one important feature”).
Regarding claim 4, Yang, in view of Ebata, teaches the apparatus of claim 3, wherein the trained model automatically determines a centroid of an anatomical structure in an ultrasound image (Yang ¶¶0089 & Figs. 26-30 discussed above).
Regarding claim 5, Yang, in view of Ebata, teaches the apparatus of claim 3, wherein the trained model is trained to automatically generate a segmentation mask to refine a peripheral edge of a shape of an anatomical structure in an ultrasound image (Yang Figs. 2C, 14-15, 20-24, 28, 30; Yang ¶¶0152-¶¶153: “a series of bladder scan segmentations” – the segmentation shows the peripheral edge of the bladder).
Regarding claim 6, Yang, in view of Ebata, teaches the apparatus of claim 5, wherein the processor is further configured to process the user display image to add the peripheral edge within the user display image (Yang Figs. 2C, 14-15, 20-24, 28, 30; Yang ¶¶0152-¶¶153 discussed above).
Regarding claim 9, Yang, in view of Ebata, teaches the apparatus of claim 1, wherein the processor is further configured to generate a line extending vertically through the user display image and located within the user display image a known distance from the center of the user display image (Yang Fig. 2C, 27, & 30: see “Mass center distance d” and the 2D crosshair, the cross hair has a vertical line; also see Yang Fig. 11 & ¶¶0108: “The length of these projections can be found by calculating the Euclidean norm for each line. The Euclidean norm is more commonly known as the length or magnitude of the vector. To find the plane closest to the centroid, calculate the lengths for the perpendicular to the projection of the centroid on each plane, and take the plane with the shortest of these lengths”).
Regarding claim 10, Yang, in view of Ebata, teaches the apparatus of claim 9, wherein the processor is configured to add the line to the user display image to provide a visual indication of the distance from the two-dimensional shape to the center of the user display image (Yang Fig. 2C, 27, & 30 discussed above – an arrow indicates the distance between the centroid and the center of the display image; also see Yang Fig. 31: a visual indicator is displayed over the centroid to guide the user to move in a downward direction).
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Yang Fig. 30 Excerpt
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Yang Fig. 31 Excerpt
Regarding claim 11, Yang, in view of Ebata, teaches that the device performs a method comprising the processes described in claim 1 (Yang Title: “System and method …”). Therefore claim 11 is rejected using the same rationale as applied to claim 1 discussed above.
Claims 12-16 and 19-20 are rejected using the same rationale as applied to claims 2-6 and 9-10 discussed above, respectively.
Claim(s) 7-8 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 2009/0264757 A1), in view of Ebata et al. (US 2021/0353261 A1; EFD: 8 Mar, 2019), and further in view of Kadah et al. (“Classification Algorithms for Quantitative Tissue Characterization of Diffuse Liver Disease from Ultrasound Images,” IEEE Transactions on Medical Imaging, Vol. 15, No. 4, August 1996), hereinafter referred to as Yang, Ebata, and Kadah, respectively.
Regarding claim 7, Yang, in view of Ebata, teaches the apparatus of claim 1, wherein the statistical model includes a statistical model trained from a plurality of training ultrasound images (Yang Abstract & ¶¶0116 discussed above).
However, Yang, in view of Ebata, does not appear to explicitly teach that the plurality of training ultrasound images include multiple pairs of input and output training data sets; each set of input training data includes an input training data ultrasound image depicting the anatomical structure; and each set of output training data includes an array of values equal in size to the input training data ultrasound image, wherein pixels in the array corresponding to locations within the anatomical structure in the input training data ultrasound image are set to a first value and other pixels are set to another value.
Pertaining to the same field of endeavor, Kadah teaches that the plurality of training ultrasound images include multiple pairs of input and output training data sets (Kadah Abstract: “diagnosing diffused liver diseases from ultrasound images”; Kadah pg. 473 left column: “Perform Steps 2) and 3) for all training pairs in the data sets”);
each set of input training data includes an input training data ultrasound image depicting the anatomical structure (Kadah Abstract & pg. 473 left column discussed above teaches that the ultrasound training images are for diagnosing liver diseases; Kadah Figs. 1-2; Kadah pg. 471 left column: “B. Statistical Classifiers … The probability that the classification result indicates a normal liver … The probability that the classification result indicates a liver disease while the true diagnosis is indeed a normal liver”); and
each set of output training data includes an array of values equal in size to the input training data ultrasound image, wherein pixels in the array corresponding to locations within the anatomical structure in the input training data ultrasound image are set to a first value and other pixels are set to another value (Kadah pg. 472: “The above approximation makes it simpler to estimate. Σi-1. The Bayes minimum error classifier (18) is implemented … Consider the subset of the first k distances in the sorted array; i.e., the k-NN. Knowing the class membership of each of these samples, assign the test sample to the majority class in this subset if it exists, otherwise the result is considered inconclusive”; also see Kadah Fig. 7).
Yang, in view of Ebata, and Kadah are considered to be analogous art because they are directed to ultrasound systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the portable ultrasound system and method for bladder detection (as taught by Yang, in view of Ebata) to use a training pair (as taught by Kadah) because the combination provides optimal classifiers with minimum classification error (Kadah Abstract).
Regarding claim 8, Yang, in view of Ebata and Kadah, teaches the apparatus of claim 7, wherein the statistical model is configured to output a segmentation mask having associated pixels, wherein each pixel of the segmentation mask has a value representing a probability that the pixel corresponds to a location with the anatomical structure (Yang Figs. 2C, 14-15, 20-24, 28, 30; Yang ¶¶0152-¶¶153 discussed above teaches segmentation masks; Kadah pg. 471 left column discussed above; also see Kadah Fig. 4: pathology #1, #2, #3, Kadah pg. 468 right column: “in addition to varying the size and location of the ROI … must be standardized for all scans to ensure the fidelity of the tissue characterization … ROI location … the selected region should be selected each time along the center line of the image”).
Claims 17-18 are rejected using the same rationale as applied to claims 7-8 discussed above, respectively.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent No. 11,631,172 B2 (S/N: 16/839,020). Although the claims at issue are not identical, they are not patentably distinct from each other because they are both directed to ultrasound guidance systems and methods by displaying a vertical graphical indicator based on the centroid of the imaged organ, e.g., bladder, and the center of the image.
Conclusion
THIS ACTION IS MADE FINAL. 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 SOO J SHIN whose telephone number is (571)272-9753. The examiner can normally be reached M-F; 10-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Soo Shin/Primary Examiner, Art Unit 2667 571-272-9753
soo.shin@uspto.gov
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