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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 27, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Objections
Claims 1 and 13 are objected to because of the following informalities: Failed to properly define what is B-mode and M-mode in the claim limitation. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 –
(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.
Claims 1 – 3 and 13 – 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kulhare et al. Patent Application Publication No. CN-113261066-A (hereinafter Kulhare).
Regarding claim 1, Kulhare discloses computing system for processing medical imagery of a lung, comprising (Kulhare in [0004] discloses, “To make his/her diagnosis of a pathology of a subject's lung, a pneumoconiosis physician looks for features in one or more ultrasound images, such features including pleural lines and the absence of lung sliding”): a memory storing instructions; and a processor coupled to the memory, the processor being configured to execute the instructions to (Kulhare in [0190] discloses, “non-volatile memory circuitry (e.g., EEPROM) configured to store software and configuration data, such as firmware, volatile or working memory circuitry, and other conventional circuitry and components”): automatically process a plurality of B-mode video frames from a video clip of the lung to generate a plurality of M-mode images associated with the video clip (Kulhare in [0067] discloses, “The presence or absence of lung sliding is determined based on classifying several reconstructed M-mode images 210 at different locations in the same ultrasound B-mode video [ B-mode video is a "normal" video, where the vertical dimension of the image is depth, while the horizontal dimension of the image has a spatial (e.g., width) dimension, rather than time for an M-mode image ]”); process the plurality of M-mode images using an image classifier to output a plurality of confidence values respectively corresponding to the plurality of M-mode images (Kulhare in [0065] discloses about any number of sequences of M-mode images implies to plurality of M-mode images, “the ultrasound system 130 may generate any number of sequences of M-mode images, up to one corresponding sequence per column of pixels in the ultrasound video image 140”. Furthermore, Kulhare in [0068] discloses, “the CNN lung sliding classifier determines a likelihood or confidence level that lung sliding is present. The confidence level is a number between 0 and 1. A confidence level threshold (e.g., 0.5) between whether a feature may or may not be present may be set by a user of the ultrasound system 130 (fig. 8). If the confidence is high (e.g., above a threshold), lung sliding may exist”. Several M-mode images are classified and each classification produces a confidence value equates to plurality of confidence value); and process the plurality of confidence values using a clip prediction module to output a binary class prediction (Kulhare in [0067] discloses about presence and or absence (binary class prediction) of lung sliding, “The presence or absence of lung sliding is determined based on classifying several reconstructed M-mode images 210, which indicates lung sliding is present or absent in the video clip (Kulhare in [0068] discloses, “If the confidence is high (e.g., above a threshold), lung sliding may exist; conversely, if the confidence is low (e.g., below a threshold), there may be no lung slip”).
Summary of Citations (Kulhare)
Paragraph [0004]; “To make his/her diagnosis of a pathology of a subject's lung, a pneumoconiosis physician looks for features in one or more ultrasound images, such features including pleural lines and the absence of lung sliding, a-lines, B-lines, pleural effusions, solid changes, and merged B-lines along the pleural line”.
Paragraph [0061]; “In step 210, the ultrasound machine 125 reconstructs the M-mode images and effectively provides these images to a lung slide classifier 220 to allow the classifier to classify lung slides”.
Paragraph [0065]; “the ultrasound system 130 may generate any number of sequences of M-mode images, up to one corresponding sequence per column of pixels in the ultrasound video image 140”.
Paragraph [0067]; “The presence or absence of lung sliding is determined based on classifying several reconstructed M-mode images 210 at different locations in the same ultrasound B-mode video [ B-mode video is a "normal" video, where the vertical dimension of the image is depth, while the horizontal dimension of the image has a spatial (e.g., width) dimension, rather than time for an M-mode image ]”.
Paragraph [0068]; “the CNN lung sliding classifier determines a likelihood or confidence level that lung sliding is present. The confidence level is a number between 0 and 1. A confidence level threshold (e.g., 0.5) between whether a feature may or may not be present may be set by a user of the ultrasound system 130 (fig. 8). If the confidence is high (e.g., above a threshold), lung sliding may exist; conversely, if the confidence is low (e.g., below a threshold), there may be no lung slip”.
Paragraph [0190]; “non-volatile memory circuitry (e.g., EEPROM) configured to store software and configuration data, such as firmware, volatile or working memory circuitry, and other conventional circuitry and components”.
Regarding claim 2, Kulhare discloses the computing system of claim 1, wherein the processor is further configured to add a bounding box to the plurality of B-mode video frames wherein the bounding box encompasses a pleural line (Kulhare in [0072] discloses, “the first number of detections of output 233 is the probability or confidence level that the detected image feature is a pleural line. The remaining four numbers are the < x, y > coordinates of the upper left corner of the bounding box containing the detected pleural line, and the width of the bounding box < Δ x, Δ y >”), and the plurality of M-mode images intersect the bounding box (Kulhare in [0065] discloses, “the ultrasound system 130 to effectively perform lung slide detection at multiple locations of the pleural line simultaneously. In the case where the ultrasound transducer 120 (fig. 8) is curved to generate "radial" columns of pixels (straight lines in images 401, 402, and 403 of fig. 12), one way to generate the M-mode image 420 is to convert one or more corresponding ultrasound images (e.g., ultrasound images 401, 402, and 403) from polar coordinates to rectangular coordinate”).
Summary of Citations (Kulhare)
Paragraph [0065]; “the ultrasound system 130 to effectively perform lung slide detection at multiple locations of the pleural line simultaneously. In the case where the ultrasound transducer 120 (fig. 8) is curved to generate "radial" columns of pixels (straight lines in images 401, 402, and 403 of fig. 12), one way to generate the M-mode image 420 is to convert one or more corresponding ultrasound images (e.g., ultrasound images 401, 402, and 403) from polar coordinates to rectangular coordinate”.
Paragraph [0072]; “the first number of detections of output 233 is the probability or confidence level that the detected image feature is a pleural line. The remaining four numbers are the < x, y > coordinates of the upper left corner of the bounding box containing the detected pleural line, and the width of the bounding box < Δ x, Δ y >”.
Regarding claim 3, Kulhare discloses the computing system of claim 2, wherein the processor is further configured to execute a machine learning model to image process (Kulhare in [0067] discloses, “the ultrasound system 130 implements a Convolutional Neural Network (CNN) classifier”) one or more of the plurality of B-mode video frames to compute a location of the bounding box that encompasses the pleural line (Kulhare in [0053] discloses, “First, the machine 125 is configured to render an enhanced ultrasound video 140 (see fig. 13), such as a B-mode ultrasound video”. Kulhare in [0072] discloses, “the first number of detections of output 233 is the probability or confidence level that the detected image feature is a pleural line. The remaining four numbers are the < x, y > coordinates of the upper left corner of the bounding box containing the detected pleural line, and the width of the bounding box < Δ x, Δ y >”).
Summary of Citations (Kulhare)
Paragraph [0067]; “the ultrasound system 130 implements a Convolutional Neural Network (CNN) classifier”.
Paragraph [0053]; “First, the machine 125 is configured to render an enhanced ultrasound video 140 (see fig. 13), such as a B-mode ultrasound video”.
Paragraph [0072]; “the first number of detections of output 233 is the probability or confidence level that the detected image feature is a pleural line. The remaining four numbers are the < x, y > coordinates of the upper left corner of the bounding box containing the detected pleural line, and the width of the bounding box < Δ x, Δ y >”.
Regarding claim 13, method claim 13 corresponds to apparatus claim 1. Therefore, the rejection analysis of claim 1 is applicable to claim 13.
Regarding claim 14, method claim 14 corresponds to apparatus claim 2. Therefore, the rejection analysis of claim 2 is applicable to claim 14.
Regarding claim 15, method claim 15 corresponds to apparatus claim 3. Therefore, the rejection analysis of claim 3 is applicable to claim 15.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5 and 17 are rejected under 35 U.S.C 103 as being unpatentable over Kulhare in view of Jascur ‘Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning’ (hereinafter Jascur).
Regarding claim 5, Kulhare discloses the computing system of claim 2.
Kulhare doesn’t disclose about the following limitation as further recited in the claim.
Jascur discloses the processor is configured to divide the video clip into a plurality of clip segments; and for each one of the plurality of clip segments, the processor is configured to (Jascur in [Page – 2, Paragraph – 2] discloses, “our architecture takes a sequence of frames from the source videos and segments the tissues from the first LUS image in the sequence. Second, based on these tissues—lung, pleura, and rib—we select M-mode slices and input them to the convolutional neural network (CNN), ResNet 18. Third, we aggregate slice prediction for a single sequence of frames”): a. perform binning for each clip segment, to divide x-coordinates of the pleural line into contiguous chunks with equal width (Jascur in [Section – 2.2.2, Paragraph – 2] discloses, “All the slices between the minimum and maximum of the lung mask x-coordinate are passed to the classification network”); b. obtain a predicted class for each clip segment (Jascur in [Page – 2, Paragraph – 2] discloses, “we aggregate slice prediction for a single sequence of frames”); and when at least one of the clip segments has the predicted class that indicates an absence of lung sliding, output a prediction for the video clip indicating the absence of lung sliding (Jascur in [Page – 2, Paragraph – 2] discloses, “the detection of the absence of lung sliding, automated motion mode (M-mode) classification. First, our architecture takes a sequence of frames from the source videos and segments the tissues from the first LUS image in the sequence ... the partial predictions are algorithmically aggregated, and it is decided whether lung sliding is present or absent”. Lastly, Jascur in [Abstract] discloses about partial prediction which equates to prediction for clip segments, “We aggregate the partial predictions over the entire video recording to determine whether the subject has developed post-surgery complications”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Jascur into the system of Kulhare because it would allow the system to improve the detection of absent lung sliding by analyzing the ultrasound vide across both time and spatial portions of the pleural line.
Summary of Citations (Jascur)
[Abstract]; “We aggregate the partial predictions over the entire video recording to determine whether the subject has developed post-surgery complications”.
[Page – 2, Paragraph – 2]; “the detection of the absence of lung sliding, automated motion mode (M-mode) classification. First, our architecture takes a sequence of frames from the source videos and segments the tissues from the first LUS image in the sequence. Second, based on these tissues—lung, pleura, and rib—we select M-mode slices and input them to the convolutional neural network (CNN), ResNet 18. Third, we aggregate slice prediction for a single sequence of frames ... the partial predictions are algorithmically aggregated, and it is decided whether lung sliding is present or absent”.
[Section – 2.2.2, Paragraph – 2]; “All the slices between the minimum and maximum of the lung mask x-coordinate are passed to the classification network”.
Regarding claim 17, method claim 17 corresponds to apparatus claim 5. Therefore, the rejection analysis of claim 5 is applicable to claim 17.
Claims 6 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Kulhare in view of Jascur and further in view of Moriguchi Patent Application Publication No. JP-6868852-B2 (hereinafter Moriguchi).
Regarding claim 6, Jascur discloses in the combination the computing system of claim 5.
Kulhare and Jascur doesn’t disclose about the following limitation as further recited in the claim.
Moriguchi discloses prior to obtaining the predicted class for each one of the plurality of clip segments, the processor is further configured to compute a moving average of a subset of the plurality of confidence values corresponding to each clip segment (Moriguchi in [0041] discloses, “as the correction amount of the previous frame, the correction amount of the previous frame may be used, or the moving average value of the previous multiple frames may be used. As the correction amount at the time of cutting out the current frame, the correction amount in one current frame may be used, or the moving average value including the current frame may be used”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Moriguchi into the system of Kulhare in view of Jascur because it would allow the system to improve the reliability of the clip segment classification by smoothing neighboring M-mode prediction confidence values.
Summary of Citations (Moriguchi)
Paragraph [0041]; “as the correction amount of the previous frame, the correction amount of the previous frame may be used, or the moving average value of the previous multiple frames may be used. As the correction amount at the time of cutting out the current frame, the correction amount in one current frame may be used, or the moving average value including the current frame may be used”.
Regarding claim 18, method claim 18 corresponds to apparatus claim 6. Therefore, the rejection analysis of claim 6 is applicable to claim 18.
Allowable Subject Matter
Claims 4, 7 – 12, 16, 19 – 24 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.
The following is a statement of reasons for the indication of allowable subject matter.
Regarding claim 4, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Kulhare, Berlin (WO-2019104230-A1), Zhang (CN-113628156-A), XU (WO-2019034743-A1), Takeuchi (JP-2018193655-A), Jascur (Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning) and Vanberlo (Accurate assessment of the lung sliding artefact on lung ultrasonography) doesn’t disclose the limitation, applying a contour finding process to identify a plurality of contours that potentially bound the pleural line; identify a brightest contour from amongst the plurality of contours that comprises a sum of pixel intensities that is greatest, wherein the sum of pixel intensities are associated with coordinates which are below and within x-coordinate bounds of the brightest contour; and compute the bounding box around the brightest contour. Kulhare in [0072] discloses about determining the location of the bounding box. Furthermore, Berlin in [0053], [0081] and [0168] discloses about determining the video clip average of pixel intensities, recalling the pixel intensities in a range [0, 1] and dilation followed by erosion operation on an image. Xu in [0047] discloses about detecting the pleural line by determining the bright line. Lastly, Zhang in [Page – 9, Last Paragraph] discloses about plurality of contour line of different length. But none of the prior art in the combination discloses about computing sum of pixel intensities that is greatest wherein the sum of pixel intensities are associated with coordinates which are below and within x-coordinate bounds of the brightest contour and compute the bounding box around the brightest contour.
Regarding claim 7, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Kulhare, Berlin (WO-2019104230-A1), Zhang (CN-113628156-A), XU (WO-2019034743-A1), Takeuchi (JP-2018193655-A), Jascur (Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning) and Vanberlo (Accurate assessment of the lung sliding artefact on lung ultrasonography) doesn’t disclose the limitation, a. perform binning for each clip segment, to divide x-coordinates of the pleural line into contiguous chunks with equal width, resulting in a plurality of bins; b. identify a brightest M-mode image in each of the plurality of bins; C. apply a classification thresholding process to a prediction confidence for each one of the brightest M-mode images to obtain a class prediction for each of the plurality of bins. Jascur in [Section – 4, Paragraph – 3] discloses about clip segmenting and [Section – 2.2.2, Paragraph – 2] suggests binning clip segment. But none of the prior arts in combination discloses about binning clip segments based with equal width and applying a classification thresholding process to a prediction confidence for each one of the brightest M-mode images to obtain a class prediction for each of the plurality of bins.
Regarding claim 8, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Kulhare, Berlin (WO-2019104230-A1), Zhang (CN-113628156-A), XU (WO-2019034743-A1), Takeuchi (JP-2018193655-A), Jascur (Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning) and Vanberlo (Accurate assessment of the lung sliding artefact on lung ultrasonography) doesn’t disclose the limitation, a. perform binning for each clip segment, to divide x-coordinates of the pleural line into contiguous chunks with equal width, resulting in a plurality of bins; b. for each of the plurality of bins, determine a mean prediction confidence for its constituent M-mode images; C. apply a classification thresholding process to an averaged prediction confidence to compute a class prediction for each of the plurality of bins. Jascur in [Section – 4, Paragraph – 3] discloses about clip segmenting and [Section – 2.2.2, Paragraph – 2] suggests binning clip segment. But none of the prior arts in combination discloses about binning clip segments based with equal width and for each of the plurality of bins, determine a mean prediction confidence for its constituent M-mode images.
Regarding claim 9, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Kulhare, Berlin (WO-2019104230-A1), Zhang (CN-113628156-A), XU (WO-2019034743-A1), Takeuchi (JP-2018193655-A), Jascur (Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning) and Vanberlo (Accurate assessment of the lung sliding artefact on lung ultrasonography) doesn’t disclose the limitation, a. perform binning for each clip segment, to divide x-coordinates of the pleural line into contiguous chunks with equal width, resulting in a plurality of bins; replace a list of prediction confidences for a given clip segment with its moving average; C. compute a moving average of a brightness of each M-mode image at each x-coordinate of the pleural line; d. identify a M-mode image in each of the plurality of bins with the greatest brightness moving average. Jascur in [Section – 4, Paragraph – 3] discloses about clip segmenting and [Section – 2.2.2, Paragraph – 2] suggests binning clip segment. But none of the prior arts in combination discloses about binning clip segments based with equal width. Takeuchi in [Page – 3, Paragraph – 1] discloses a predetermined threshold for the change of the moving average value of each pixel which equates to replace a list of prediction confidences for a given clip segment with its moving average but it doesn’t disclose about moving average regarding ultrasound and computing moving average of a brightness of each M-mode image arrangement.
Regarding claim 10, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Kulhare, Berlin (WO-2019104230-A1), Zhang (CN-113628156-A), XU (WO-2019034743-A1), Takeuchi (JP-2018193655-A), Jascur (Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning) and Vanberlo (Accurate assessment of the lung sliding artefact on lung ultrasonography) doesn’t disclose the limitation, a. perform binning for each clip segment, to divide x-coordinates of the pleural line into contiguous chunks with equal width, resulting in a plurality of bins; b. replace a list of prediction confidences for a given clip segment with its moving average; C. identify a M-mode image corresponding to a midpoint of each bin from the plurality of bins; d. apply a classification thresholding process to a prediction confidence for each identified M-mode image to compute a class prediction for each of the plurality of bins. Jascur in [Section – 4, Paragraph – 3] discloses about clip segmenting and [Section – 2.2.2, Paragraph – 2] suggests binning clip segment. But none of the prior arts in combination discloses about binning clip segments based with equal width. Furthermore, Takeuchi in [Page – 3, Paragraph – 1] discloses a predetermined threshold for the change of the moving average value of each pixel which equates to replace a list of prediction confidences for a given clip segment with its moving average but it doesn’t disclose about moving average regarding ultrasound and computing moving average of a brightness of each M-mode image arrangement. Lastly, Takeuchi in combination with other prior arts doesn’t disclose about identifying a M-mode image corresponding to a midpoint of each bin from the plurality of bins.
Regarding claim 11, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Kulhare, Berlin (WO-2019104230-A1), Zhang (CN-113628156-A), XU (WO-2019034743-A1), Takeuchi (JP-2018193655-A), Jascur (Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning) and Vanberlo (Accurate assessment of the lung sliding artefact on lung ultrasonography) doesn’t disclose the limitation, a. perform binning for each clip segment, to divide x-coordinates of the pleural line into contiguous chunks with equal width, resulting in a plurality of bins; b. identify a M-mode image corresponding to a midpoint of a range of prediction confidences for each bin from amongst the plurality of bins; C. apply a classification thresholding process to a prediction confidence for each identified M-mode image to compute a class prediction for each of the plurality of bins. Jascur in [Section – 4, Paragraph – 3] discloses about clip segmenting and [Section – 2.2.2, Paragraph – 2] suggests binning clip segment. But none of the prior arts in combination discloses about binning clip segments based with equal width corresponding to a midpoint of a range of prediction confidences for each bin from amongst the plurality of bins.
Regarding claim 12, the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious the limitations as further recited. The applied prior arts Kulhare, Berlin (WO-2019104230-A1), Zhang (CN-113628156-A), XU (WO-2019034743-A1), Takeuchi (JP-2018193655-A), Jascur (Detecting the Absence of Lung Sliding in Lung Ultrasounds Using Deep Learning) and Vanberlo (Accurate assessment of the lung sliding artefact on lung ultrasonography) doesn’t disclose the limitation, a. perform binning for each clip segment, to divide x-coordinates of the pleural line into contiguous chunks with equal width, resulting in a plurality of bins; b. for each one of the plurality of bins, identify a M-mode image corresponding to a median of prediction confidences for that bin; C. apply a classification thresholding process to a prediction confidence for each identified M-mode image to compute a class prediction for each of the plurality of bins. Jascur in [Section – 4, Paragraph – 3] discloses about clip segmenting and [Section – 2.2.2, Paragraph – 2] suggests binning clip segment. But none of the prior arts in combination discloses about binning clip segments based with equal width and for each one of the plurality of bins, identify a M-mode image corresponding to a median of prediction confidences for that bin.
Regarding claim 16, method claim 16 corresponds to apparatus claim 4. Therefore, claim 16 is allowed for the same reason provided above claim 4.
Regarding claim 19, method claim 19 corresponds to apparatus claim 7. Therefore, claim 19 is allowed for the same reason provided above claim 7.
Regarding claim 20, method claim 20 corresponds to apparatus claim 8. Therefore, claim 20 is allowed for the same reason provided above claim 8.
Regarding claim 21, method claim 21 corresponds to apparatus claim 9. Therefore, claim 21 is allowed for the same reason provided above claim 9.
Regarding claim 22, method claim 22 corresponds to apparatus claim 10. Therefore, claim 22 is allowed for the same reason provided above claim 10.
Regarding claim 23, method claim 23 corresponds to apparatus claim 11. Therefore, claim 23 is allowed for the same reason provided above claim 11.
Regarding claim 24, method claim 24 corresponds to apparatus claim 12. Therefore, claim 24 is allowed for the same reason provided above claim 12.
Conclusion
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
07/27/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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