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 .
Claim Rejections - 35 USC § 103
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.
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.
Claims 1,3, 5-6, 8-10, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kapoor et al (US 20150133784 A1, hereinafter referred to as Kapoor) in view of Ross et al (US 20190050991 A1, hereinafter referred to as Ross) and further in view of Lints et al (US20200160122A1; hereinafter referred to as Lints)
Regarding Claim 1, Kapoor discloses an ultrasound diagnostic apparatus ("the preferred embodiments described below include methods, computer readable media, and systems for 3D ultrasound imaging." [0004]) comprising:
an ultrasound probe (transducer) (“For freehand 3D ultrasound, a transducer for two-dimensional scanning is equipped with 6 degree-of-freedom tracking hardware to reconstruct a 3D volume of the target anatomy.” [0020], "The transducer 13 includes a probe housing in which the array is located. The probe housing is sized and shaped for handheld use by the clinician. The clinician grips the probe housing and moves the transducer 13 over the patient during scanning." [0101]);
a position sensor that is attached to the ultrasound probe and configured to acquire positional information of the ultrasound probe ("A sensor is attached to the transducer to track motion...The sensor indicates position and/or motion of the transducer over any number of degrees of freedom, such as over six degrees of freedom" [0035]);
a processor (“The acts are performed by a transducer, ultrasound imaging system, and/or processor.” [0029]) configured to
scan a region of interest of a subject with an ultrasound beam using the ultrasound probe to acquire B-mode images of a plurality of frames ("During real-time scanning, images using the gathered scan data may be generated to assist in gathering further information. Images are generated during the scanning (e.g., less than 3 second delay from acquisition to use of the data in an image) so that the clinician may identify regions needing additional focus or data. The transducer is moved to scan the regions." [0028], “A frame of data representing the plane is acquired. As the transducer is moved, other frames representing different planes are acquired. Due to the speed of movement and/or change in angle, some frames may represent one or more locations of other frames.” [0030], “FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [0046]);
calculate a presence probabilities (confidence values) of multiple types of findings related to the portion of the subject from each of the b-mode images of the plurality of frames by performing image recognition on the each of the B-mode images of the plurality of frames (“a machine learnt classifier or other image processing is applied to identify specific structures or artifacts or is applied to assign values based on structure or artifacts represented in the frame of data” [0044], "In act 22, confidence values are assigned to the data. The confidence values represent the quality of the ultrasound data. Each ultrasound value in a frame of data has an accuracy, quality, likelihood, or level of uncertainty regarding the accuracy of the representation of the acoustic response of the location. Confidence values are assigned to respective ultrasound values for each frame. Different or the same confidence values are assigned for the different locations or values in a frame of data. Due to various factors, different locations represented by a frame may have different confidence values. Accordingly, different locations within a volume have different confidence values. The same location in the volume may have the same or different confidence values in different frames." [0029], “FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [0046], it would be inherent to the device to use B-mode image data to generate the confidence map for multiple images taken);
PNG
media_image1.png
226
645
media_image1.png
Greyscale
and generate three-dimensional probability maps for the multiple types of findings based on the positional information of the ultrasound probe acquired by the position sensor and the presence probabilities of the multiple types of findings; and generate an integrated probability map by integrating the three- dimensional probability maps (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], “Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "The processor 12 is configured to render an image from the compounded ultrasound values and/or the confidence values. The processor 12 is configured to generate an image. The image is generated as a projection or surface rendering. The image is a volume or three-dimensional rendering or other reconstruction." [0110]).
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion, that the multiple types of findings include at least one of an edema, a necrosis, and an abscess; calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images, and in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information, and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013], “a single scan using a linear array probe (3-18 MHz frequency range) on a Noblus ultrasound device obtains B-mode ultrasound, TDI elastography and color Doppler imaging (CDI). If TDI image analysis is unreliable, B-mode ultrasound is analyzed” [0014])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Ross does not specifically teach that the multiple types of findings include at least one of an edema, a necrosis, and an abscess; to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images; in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information, and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information.
However, in a similar field of endeavor, Lints teaches A multi-label heat map display system is operable to receive a medical scan and a set of heat maps set of heat maps that each correspond to probability matrix data generated for each of a set of abnormality classes [Abstract].
Lints also teaches that the multiple types of findings include at least one of an edema, a necrosis, and an abscess (“The medical scan image analysis system 112 can be operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans can be generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm can be performed on the plurality of three-dimensional subregions to generate a neural network. Inference data corresponding to a new medical scan received via the network can be generated by performing an inference algorithm on the new medical scan by utilizing the neural network. An inferred abnormality can be identified in the new medical scan based on the inference data” [0047], “A medical scan can include imaging data corresponding to a… Ultrasound” [0055], “the multi-label medical scan analysis system can be operable to train a computer vision based model on a plurality of medical scans. A medical scan training set can be received from the medical scan database 342 and/or from another subsystem 101. The medical scan training set can include a plurality of medical scans of the same or different modality and/or anatomical region. For example, the medical scan training set can include exclusively chest x-rays. The medical scan training set can include a plurality of medical labels assigned to the plurality of medical scans. The medical labels assigned to a medical scan can correspond to at least one of a set of abnormality classes, and each medical scan in the training set can be labeled with zero, one, or a plurality of labels of the set of abnormality classes that are present in the medical scan. For example, when the training set includes chest x-rays, the set of abnormality classes can include atelectasis, effusion, mass, pneumonia, consolidation, emphysema, pleural thickening, cardiomegaly, infiltration, nodule, pneumothorax, edema, fibrosis, and/or hernia. The abnormality classes can correspond to some or all of the abnormality classifier categories 444, and/or can correspond to any set of abnormality types or categories, diagnosis types or categories, or medical conditions.” [0251]).
to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images (“A medical scan can include imaging data corresponding to a CT scan, x-ray, MRI, PET scan, Ultrasound, EEG, mammogram, or other type of radiological scan or medical scan taken of an anatomical region of a human body, “ [0055], “A detection step 1372 can include determining if an abnormality is present in the medical scan based on the plurality of abnormality probabilities. Determining if an abnormality is present can include, for example, determining that a cluster of pixels in the same region of the medical scan correspond to high abnormality probabilities, for example, where a threshold proportion of abnormality probabilities must meet or exceed a threshold abnormality probability, where an average abnormality probability of pixels in the region must meet or exceed a threshold abnormality probability” [0137]],
in each of the probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information (“Determining if an abnormality is present can also include calculating a confidence score based on the abnormality probabilities and/or other data corresponding to the medical scan such as patient history data. The location of the detected abnormality can be determined in the detection step 1372 based on the location of the pixels with the high abnormality probabilities. The detection step can further include determining an abnormality region 1373, such as a two-dimensional subregion on one or more image slices that includes some or all of the abnormality.” [0137]) ,
and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information (“The abnormality region 1373 determined in the detection step 1372 can be mapped to the medical scan to populate some or all of the abnormality location data 443 for use by one or more other subsystems 101 and/or client devices 120. Furthermore, determining whether or not an abnormality exists in the detection step 1372 can be used to populate some or all of the diagnosis data 440 of the medical scan, for example, to indicate that the scan is normal or contains an abnormality in the diagnosis data 440.” [0137])].
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Kappor and Ross as outlined above with the multiple types of findings include at least one of an edema, a necrosis, and an abscess; to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images; in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information, and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information as taught by Lints, because it improves trust in and understanding of results when augmenting clinical workflow [0292].
Regarding Claim 3, Kapoor discloses the apparatus further comprising a monitor wherein the processor is further configured to display the integrated probability map on the monitor (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], "an image is generated from the confidence information. Using just the confidence values for each voxel or using the confidence values and ultrasound values, the image is generated and displayed." [0097] "The display 16 is a CRT, LCD, plasma, projector, printer, or other output device for showing an image." [0111]).
Regarding Claim 5, Kapoor discloses that the processor is further configured to: calculate finding information including at least one of a size, a depth, or a volume of the multiple types of findings based on the three-dimensional probability maps ("The processor 12 is configured to render an image from the compounded ultrasound values and/or the confidence values. The processor 12 is configured to generate an image. The image is generated as a projection or surface rendering. The image is a volume or three-dimensional rendering or other reconstruction." [0110]);
and display the finding information on the monitor ("The display 16 is a CRT, LCD, plasma, projector, printer, or other output device for showing an image. The display 16 displays an image of the volume of the patient. A measure based on the scanned volume, such as a distance, may be displayed in a chart, graph, and/or on an image. The image may include other information, such as an overlay or color coding from the indications." [0111]).
Regarding Claim 6, Kapoor discloses that the processor is further configured to generate the integrated probability map in which a shade of a color is changed according to a value of the presence probabilities (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], "the confidence values may be used to modulate a color, brightness or other display characteristic" [0097])
Regarding Claim 8, Kapoor discloses that the processor is further configured to: perform a plurality of scanning operations on the portion of the subject to calculate the presence probabilities in each of the plurality of scanning operations; and generate each of the three-dimensional probability maps based on the presence probabilities which are calculated in the plurality of scanning operations (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], “Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "By displaying an image rendered from the maximum confidence values associated with each pixel and/or voxel, regions of the volume associated with lesser confidence may be identified by the user. The user then moves the transducer to acquire ultrasound values for that region to incrementally compound with the more uncertain values previously obtained. This confidence information is exposed to the clinician in order to assist the clinician in determining regions that need further examination for a complete reconstruction." [0097]).
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Regarding Claim 9, Kapoor discloses that the processor is further configured to: perform a plurality of scanning operations on the portion of the subject to calculate the presence probabilities in each of the plurality of scanning operations; and generate each of the three-dimensional probability maps based on the presence probabilities which are calculated in the plurality of scanning operations (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], “Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "By displaying an image rendered from the maximum confidence values associated with each pixel and/or voxel, regions of the volume associated with lesser confidence may be identified by the user. The user then moves the transducer to acquire ultrasound values for that region to incrementally compound with the more uncertain values previously obtained. This confidence information is exposed to the clinician in order to assist the clinician in determining regions that need further examination for a complete reconstruction." [0097]).
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Regarding Claim 10, Kapoor discloses that the processor is further configured to: perform a plurality of scanning operations on the portion of the subject to calculate the presence probabilities in each of the plurality of scanning operations; and generate each of the three-dimensional probability maps based on the presence probabilities which are calculated in the plurality of scanning operations (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], “Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "By displaying an image rendered from the maximum confidence values associated with each pixel and/or voxel, regions of the volume associated with lesser confidence may be identified by the user. The user then moves the transducer to acquire ultrasound values for that region to incrementally compound with the more uncertain values previously obtained. This confidence information is exposed to the clinician in order to assist the clinician in determining regions that need further examination for a complete reconstruction." [0097]).
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Regarding Claim 16, Kapoor discloses that the processor is further configured to detect that a region has already been scanned based on the positional information of the ultrasound probe acquired by the position sensor in a case in which scanning is performed on the portion of the subject, and notify a user that the region has already been scanned (" By displaying an image rendered from the maximum confidence values associated with each pixel and/or voxel, regions of the volume associated with lesser confidence may be identified by the user. The user then moves the transducer to acquire ultrasound values for that region to incrementally compound with the more uncertain values previously obtained. This confidence information is exposed to the clinician in order to assist the clinician in determining regions that need further examination for a complete reconstruction." [0097]).
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Regarding Claim 17, Kapoor discloses that the processor is further configured to display the integrated probability map that has already been generated on the monitor based on the positional information of the ultrasound probe acquired by the position sensor in a case in which scanning is performed on the portion of the subject (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], “Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "The registration uses calibration of the tracking sensor, patient registration, and/or transducer tracking registration. The calibration represents an adjustment or correction to relate signals from the tracking sensor to actual position. The patient registration represents motion of the patient altering the tracking. The tracking registration represents the position and orientation of the transducer at the time or period during which a given frame of data is acquired by scanning." [0049] "In act 24, the frames are spatially registered. The spatial registration is relative to the volume and/or other frames. The frames are spatially registered with each other so that the proper or actual position of the planes represented by the frames is located in the volume." [0048])
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Regarding Claim 18, Kapoor discloses that the processor is further configured to generate the integrated probability map in which a region, which has been repeatedly scanned a larger number of times, has a darker color or a higher density (“Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "the confidence values may be used to modulate a color, brightness or other display characteristic" [0097], see Fig. 3 for increased density due to confidence values).
PNG
media_image2.png
211
316
media_image2.png
Greyscale
Regarding Claim 19, Kapoor discloses a method for controlling an ultrasound diagnostic apparatus ("the preferred embodiments described below include methods, computer readable media, and systems for 3D ultrasound imaging." [0004]), the method comprising:
acquiring positional information of an ultrasound probe (transducer) (“For freehand 3D ultrasound, a transducer for two-dimensional scanning is equipped with 6 degree-of-freedom tracking hardware to reconstruct a 3D volume of the target anatomy.” [0020], "The transducer 13 includes a probe housing in which the array is located. The probe housing is sized and shaped for handheld use by the clinician. The clinician grips the probe housing and moves the transducer 13 over the patient during scanning." [0101], "A sensor is attached to the transducer to track motion...The sensor indicates position and/or motion of the transducer over any number of degrees of freedom, such as over six degrees of freedom" [0035]);
scanning a region of interest of a subject with an ultrasound beam using the ultrasound probe to acquire b-mode images of a plurality of frames ("During real-time scanning, images using the gathered scan data may be generated to assist in gathering further information. Images are generated during the scanning (e.g., less than 3 second delay from acquisition to use of the data in an image) so that the clinician may identify regions needing additional focus or data. The transducer is moved to scan the regions." [0028], “A frame of data representing the plane is acquired. As the transducer is moved, other frames representing different planes are acquired. Due to the speed of movement and/or change in angle, some frames may represent one or more locations of other frames.” [0030], “FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [0046]);
calculating presence probabilities (confidence value) of multiple types of findings related to the portion of the subject from each of the b-mode images of the plurality of frames by performing image recognition on the each of the B-mode images of the plurality of frames (“a machine learnt classifier or other image processing is applied to identify specific structures or artifacts or is applied to assign values based on structure or artifacts represented in the frame of data” [0044], "In act 22, confidence values are assigned to the data. The confidence values represent the quality of the ultrasound data. Each ultrasound value in a frame of data has an accuracy, quality, likelihood, or level of uncertainty regarding the accuracy of the representation of the acoustic response of the location. Confidence values are assigned to respective ultrasound values for each frame. Different or the same confidence values are assigned for the different locations or values in a frame of data. Due to various factors, different locations represented by a frame may have different confidence values. Accordingly, different locations within a volume have different confidence values. The same location in the volume may have the same or different confidence values in different frames." [0029], “FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [0046], it would be inherent to the device to use B-mode image data to generate the confidence map for multiple images taken);
PNG
media_image1.png
226
645
media_image1.png
Greyscale
and generating three-dimensional probability maps for the multiple types of findings based on the acquired positional information of the ultrasound probe and the calculated presence probabilities of the multiple types of findings; and generating an integrated probability map by integrating the three-dimensional probability maps (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], “Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "The processor 12 is configured to render an image from the compounded ultrasound values and/or the confidence values. The processor 12 is configured to generate an image. The image is generated as a projection or surface rendering. The image is a volume or three-dimensional rendering or other reconstruction." [0110]).
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013], “a single scan using a linear array probe (3-18 MHz frequency range) on a Noblus ultrasound device obtains B-mode ultrasound, TDI elastography and color Doppler imaging (CDI). If TDI image analysis is unreliable, B-mode ultrasound is analyzed” [0014]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Ross does not specifically teach that the multiple types of findings include at least one of an edema, a necrosis, and an abscess; to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images; in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information, and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information.
However, in a similar field of endeavor, Lints teaches A multi-label heat map display system is operable to receive a medical scan and a set of heat maps set of heat maps that each correspond to probability matrix data generated for each of a set of abnormality classes [Abstract].
Lints also teaches that the multiple types of findings include at least one of an edema, a necrosis, and an abscess (“The medical scan image analysis system 112 can be operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans can be generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm can be performed on the plurality of three-dimensional subregions to generate a neural network. Inference data corresponding to a new medical scan received via the network can be generated by performing an inference algorithm on the new medical scan by utilizing the neural network. An inferred abnormality can be identified in the new medical scan based on the inference data” [0047], “A medical scan can include imaging data corresponding to a… Ultrasound” [0055], “the multi-label medical scan analysis system can be operable to train a computer vision based model on a plurality of medical scans. A medical scan training set can be received from the medical scan database 342 and/or from another subsystem 101. The medical scan training set can include a plurality of medical scans of the same or different modality and/or anatomical region. For example, the medical scan training set can include exclusively chest x-rays. The medical scan training set can include a plurality of medical labels assigned to the plurality of medical scans. The medical labels assigned to a medical scan can correspond to at least one of a set of abnormality classes, and each medical scan in the training set can be labeled with zero, one, or a plurality of labels of the set of abnormality classes that are present in the medical scan. For example, when the training set includes chest x-rays, the set of abnormality classes can include atelectasis, effusion, mass, pneumonia, consolidation, emphysema, pleural thickening, cardiomegaly, infiltration, nodule, pneumothorax, edema, fibrosis, and/or hernia. The abnormality classes can correspond to some or all of the abnormality classifier categories 444, and/or can correspond to any set of abnormality types or categories, diagnosis types or categories, or medical conditions.” [0251]).
to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images (“A detection step 1372 can include determining if an abnormality is present in the medical scan based on the plurality of abnormality probabilities. Determining if an abnormality is present can include, for example, determining that a cluster of pixels in the same region of the medical scan correspond to high abnormality probabilities, for example, where a threshold proportion of abnormality probabilities must meet or exceed a threshold abnormality probability, where an average abnormality probability of pixels in the region must meet or exceed a threshold abnormality probability” [0137]],
in each of the probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information (“Determining if an abnormality is present can also include calculating a confidence score based on the abnormality probabilities and/or other data corresponding to the medical scan such as patient history data. The location of the detected abnormality can be determined in the detection step 1372 based on the location of the pixels with the high abnormality probabilities. The detection step can further include determining an abnormality region 1373, such as a two-dimensional subregion on one or more image slices that includes some or all of the abnormality.” [0137]) ,
and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information (“The abnormality region 1373 determined in the detection step 1372 can be mapped to the medical scan to populate some or all of the abnormality location data 443 for use by one or more other subsystems 101 and/or client devices 120. Furthermore, determining whether or not an abnormality exists in the detection step 1372 can be used to populate some or all of the diagnosis data 440 of the medical scan, for example, to indicate that the scan is normal or contains an abnormality in the diagnosis data 440.” [0137])].
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Kappor and Ross as outlined above with the multiple types of findings include at least one of an edema, a necrosis, and an abscess; to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images; in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information, and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information as taught by Lints, because it improves trust in and understanding of results when augmenting clinical workflow [0292].
Regarding Claim 20, Kapoor discloses A processor for an ultrasound diagnostic apparatus ("the preferred embodiments described below include methods, computer readable media, and systems for 3D ultrasound imaging." [0004]), the processor being configured to:
acquire positional information of an ultrasound probe (transducer) (“For freehand 3D ultrasound, a transducer for two-dimensional scanning is equipped with 6 degree-of-freedom tracking hardware to reconstruct a 3D volume of the target anatomy.” [0020], "The transducer 13 includes a probe housing in which the array is located. The probe housing is sized and shaped for handheld use by the clinician. The clinician grips the probe housing and moves the transducer 13 over the patient during scanning." [0101], "A sensor is attached to the transducer to track motion...The sensor indicates position and/or motion of the transducer over any number of degrees of freedom, such as over six degrees of freedom" [0035]);
scan a region of interest of a subject with an ultrasound beam using the ultrasound probe to acquire B-mode images of a plurality of frames ("During real-time scanning, images using the gathered scan data may be generated to assist in gathering further information. Images are generated during the scanning (e.g., less than 3 second delay from acquisition to use of the data in an image) so that the clinician may identify regions needing additional focus or data. The transducer is moved to scan the regions." [0028], “A frame of data representing the plane is acquired. As the transducer is moved, other frames representing different planes are acquired. Due to the speed of movement and/or change in angle, some frames may represent one or more locations of other frames.” [0030], “FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [0046]);
calculate presence probabilities (confidence value) of multiple types of findings related to the portion of the subject from each of the b-mode images of the plurality of frames by performing image recognition on the each of the B-mode images of the plurality of frames (“a machine learnt classifier or other image processing is applied to identify specific structures or artifacts or is applied to assign values based on structure or artifacts represented in the frame of data” [0044], "In act 22, confidence values are assigned to the data. The confidence values represent the quality of the ultrasound data. Each ultrasound value in a frame of data has an accuracy, quality, likelihood, or level of uncertainty regarding the accuracy of the representation of the acoustic response of the location. Confidence values are assigned to respective ultrasound values for each frame. Different or the same confidence values are assigned for the different locations or values in a frame of data. Due to various factors, different locations represented by a frame may have different confidence values. Accordingly, different locations within a volume have different confidence values. The same location in the volume may have the same or different confidence values in different frames." [0029], “FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [0046], it would be inherent to the device to use B-mode image data to generate the confidence map for multiple images taken);
PNG
media_image1.png
226
645
media_image1.png
Greyscale
and generate three-dimensional probability maps for the multiple types of findings based on the positional information of the ultrasound probe acquired by the position sensor and the presence probabilities of the multiple types of findings; and generate an integrated probability map by integrating the three-dimensional probability maps (“In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [0080], “Where multiple sweeps occur, the volumes represented by each sweep are the same or overlap with other volumes. Any overlap may be compounded together. In act 38, the voxels from different sweeps are compounded together. When compounding multiple sweeps, the compounding used for combining the groups may be used (e.g., a weighted average where the weights are based on the confidence values).” [0083], "The processor 12 is configured to render an image from the compounded ultrasound values and/or the confidence values. The processor 12 is configured to generate an image. The image is generated as a projection or surface rendering. The image is a volume or three-dimensional rendering or other reconstruction." [0110]).
Kapoor does not specifically disclose that the portion of the subject being scanned is a wound portion.
However, in the similar field of diagnostic ultrasound imaging, Ross teaches a diagnostic ultrasound imaging system for determining a burn wound [Abstract].
Ross also teaches that the portion of the subject being scanned is a wound portion (“The imaging video which is evaluated by the classifier system is collected by an ultrasound transducer. Burn injury denatures proteins and ultrasound allows detection of changes in the elastic property of the subcutaneous tissue beneath the skin surface.” [0013], “a single scan using a linear array probe (3-18 MHz frequency range) on a Noblus ultrasound device obtains B-mode ultrasound, TDI elastography and color Doppler imaging (CDI). If TDI image analysis is unreliable, B-mode ultrasound is analyzed” [0014]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor as outlined above with the portion of the subject being scanned is a wound portion as taught by Ross, because a need exists for improved methods and systems for accurately assessing wounds in order to select the most appropriate treatment [0006].
Ross does not specifically teach that the multiple types of findings include at least one of an edema, a necrosis, and an abscess; to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images; in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information, and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information.
However, in a similar field of endeavor, Lints teaches A multi-label heat map display system is operable to receive a medical scan and a set of heat maps set of heat maps that each correspond to probability matrix data generated for each of a set of abnormality classes [Abstract].
Lints also teaches that the multiple types of findings include at least one of an edema, a necrosis, and an abscess (“The medical scan image analysis system 112 can be operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans can be generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm can be performed on the plurality of three-dimensional subregions to generate a neural network. Inference data corresponding to a new medical scan received via the network can be generated by performing an inference algorithm on the new medical scan by utilizing the neural network. An inferred abnormality can be identified in the new medical scan based on the inference data” [0047], “A medical scan can include imaging data corresponding to a… Ultrasound” [0055], “the multi-label medical scan analysis system can be operable to train a computer vision based model on a plurality of medical scans. A medical scan training set can be received from the medical scan database 342 and/or from another subsystem 101. The medical scan training set can include a plurality of medical scans of the same or different modality and/or anatomical region. For example, the medical scan training set can include exclusively chest x-rays. The medical scan training set can include a plurality of medical labels assigned to the plurality of medical scans. The medical labels assigned to a medical scan can correspond to at least one of a set of abnormality classes, and each medical scan in the training set can be labeled with zero, one, or a plurality of labels of the set of abnormality classes that are present in the medical scan. For example, when the training set includes chest x-rays, the set of abnormality classes can include atelectasis, effusion, mass, pneumonia, consolidation, emphysema, pleural thickening, cardiomegaly, infiltration, nodule, pneumothorax, edema, fibrosis, and/or hernia. The abnormality classes can correspond to some or all of the abnormality classifier categories 444, and/or can correspond to any set of abnormality types or categories, diagnosis types or categories, or medical conditions.” [0251]).
to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images (“A detection step 1372 can include determining if an abnormality is present in the medical scan based on the plurality of abnormality probabilities. Determining if an abnormality is present can include, for example, determining that a cluster of pixels in the same region of the medical scan correspond to high abnormality probabilities, for example, where a threshold proportion of abnormality probabilities must meet or exceed a threshold abnormality probability, where an average abnormality probability of pixels in the region must meet or exceed a threshold abnormality probability” [0137]],
in each of the probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information (“Determining if an abnormality is present can also include calculating a confidence score based on the abnormality probabilities and/or other data corresponding to the medical scan such as patient history data. The location of the detected abnormality can be determined in the detection step 1372 based on the location of the pixels with the high abnormality probabilities. The detection step can further include determining an abnormality region 1373, such as a two-dimensional subregion on one or more image slices that includes some or all of the abnormality.” [0137]) ,
and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information (“The abnormality region 1373 determined in the detection step 1372 can be mapped to the medical scan to populate some or all of the abnormality location data 443 for use by one or more other subsystems 101 and/or client devices 120. Furthermore, determining whether or not an abnormality exists in the detection step 1372 can be used to populate some or all of the diagnosis data 440 of the medical scan, for example, to indicate that the scan is normal or contains an abnormality in the diagnosis data 440.” [0137])].
It would have been obvious to an ordinary skilled person in the art before the effective filing
date of the claimed invention to modify the system of Kappor and Ross as outlined above with the multiple types of findings include at least one of an edema, a necrosis, and an abscess; to calculate presence probabilities of multiple types of findings related to background region different from the multiple types of findings for each pixel in the B-mode images; in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information, and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information as taught by Lints, because it improves trust in and understanding of results when augmenting clinical workflow [0292].
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kapoor in view of Ross further in view of Lints as applied to Claims 3 and 5 above, and further in view of Eggers et al (US 20180132722 A1, hereinafter referred to as Eggers)
Regarding Claim 14, Kapoor, Ross, and Lints disclose creating an integrated probability map of a B-mode image (“FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [Kapoor 0046]), “In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [Kapoor 0080], "The processor 12 is configured to render an image from the compounded ultrasound values and/or the confidence values. The processor 12 is configured to generate an image. The image is generated as a projection or surface rendering. The image is a volume or three-dimensional rendering or other reconstruction." [Kapoor 0110]).
Kapoor, Ross, and Lints do not specifically disclose that the processor is further configured to analyze the probability map or the image to detect a failure region in which the probability map is not normally generated or the ultrasound image is not normally acquired, and display the failure region on the monitor.
However, in the similar field of diagnostic ultrasound scanning, Eggers teaches scan completeness auditing system for screening a volume of tissue comprising a manual image scanning device having an imaging probe [Abstract]
Eggers also teaches that the processor is further configured to analyze the probability map or the ultrasound image to detect a failure region in which the probability map is not normally generated or the ultrasound image is not normally acquired, and display the failure region on the monitor (“using either of the above two algorithms (i.e., scan frame distance based computations or volumetric pixel density within unit volumes of the swept volume), if the predetermined requirement is not met (i.e., maximum allowed distance between scan frames is exceeded or the minimum required pixel density is achieved for all unit volumes), then block 3140 is reached via arrow 3138. As seen in block 3140, an audible alarm and visual error message is issued to instruct the operator that the scan failed to comply with the minimum user requirements for frame-to-frame resolution.” [0169]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor, Ross, and Lints as outlined above with the processor being further configured to analyze the probability map or the ultrasound image to detect a failure region in which the probability map is not normally generated or the ultrasound image is not normally acquired, and display the failure region on the monitor as taught by Eggers, because it improves the accuracy of the diagnostic and detection capabilities of the physician [0010].
Regarding Claim 15, Kapoor, Ross, and Lints discloses creating an integrated probability map of a B-mode image (“FIG. 2 shows a B-mode image of a liver generated from a frame of ultrasound data. FIG. 3 shows the confidence map with confidence values assigned for the frame of data used for FIG. 2” [Kapoor 0046]), “In act 36, the groups are compounded. Within a sweep along the skin surface, the groups with different orientations and/or locations are combined. For each voxel in the 3D space or grid, the ultrasound values from different groups representing the voxel are weighted and compounded. In one embodiment, the weights are again a function of the confidence for the ultrasound value. Distance may or may not be used. The confidence value from the compounding within the group is the maximum of the confidences. These maximum confidences are used as weights for the respective ultrasound values of the groups. Each ultrasound value is weighted and the results summed. The compounding fuses multiple groups of frames into the reconstructed volume.” [Kapoor 0080], "The processor 12 is configured to render an image from the compounded ultrasound values and/or the confidence values. The processor 12 is configured to generate an image. The image is generated as a projection or surface rendering. The image is a volume or three-dimensional rendering or other reconstruction." [Kapoor 0110]).
Kapoor, Ross, and Lints do not specifically disclose that the processor is further configured to analyze the probability map or the ultrasound image to detect a failure region in which the probability map is not normally generated or the ultrasound image is not normally acquired, and display the failure region on the monitor.
However, in the similar field of diagnostic ultrasound scanning, Eggers teaches scan completeness auditing system for screening a volume of tissue comprising a manual image scanning device having an imaging probe [Abstract]
Eggers also teaches that the processor is further configured to analyze the probability map or the ultrasound image to detect a failure region in which the probability map is not normally generated or the ultrasound image is not normally acquired, and display the failure region on the monitor (“using either of the above two algorithms (i.e., scan frame distance based computations or volumetric pixel density within unit volumes of the swept volume), if the predetermined requirement is not met (i.e., maximum allowed distance between scan frames is exceeded or the minimum required pixel density is achieved for all unit volumes), then block 3140 is reached via arrow 3138. As seen in block 3140, an audible alarm and visual error message is issued to instruct the operator that the scan failed to comply with the minimum user requirements for frame-to-frame resolution.” [0169]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Kapoor, Ross, and Lints as outlined above with the processor being further configured to analyze the probability map or the ultrasound image to detect a failure region in which the probability map is not normally generated or the ultrasound image is not normally acquired, and display the failure region on the monitor as taught by Eggers, because it improves the accuracy of the diagnostic and detection capabilities of the physician [0010].
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3, 5-6, 8-10, & 14-20 have been considered but they are not persuasive.
Regarding the 35 U.S.C. 103 rejection of Claims 1,3, 5-6, 8-13, and 16-20 applicant argues the following:
Applicant contends that the cited references fail to disclose the features recited in the amended claims. Lints appears to describe a multi-label heat map display system. Paras. 0047, 0248, and 0251 of Lints appear to describe: estimating an abnormality in a medical scan by using neural network; that a set of abnormality classes can include atelectasis, effusion, mass, pneumonia, consolidation, emphysema, pleural thickening, cardiomegaly, infiltration, nodule, pneumothorax, edema, fibrosis, and/or hernia; and generating heatmaps for every abnormality classes based on a probability matrix and displaying them on a display device.
Furthermore, Applicant contends that Kapoor, Ross, and Lints do not disclose or suggest that:
in each of the three-dimensional probability maps, a presence probability of either one of the background region and each of the multiple types of findings is allotted to each of positions represented by the positional information; and in the integrated probability map, a presence probability of one of the background region and the multiple types of findings is allotted to each of the positions represented by the positional information.
Therefore, Applicant contends that even a person skilled in the art cannot reach the features of the amended claims by referring to Kapoor, Ross, and Lints.
However, it is noted that Lints does teach calculating a presence probability of an abnormality in each pixel of a medical scan and grouping the pixels together; which includes a background region not containing an abnormality and pixel clusters where the abnormality is likely to be found [0137]. Lints then proceeds to stack these probabilities through image slices to give the user a fully encompassed 3D view of regions likely to contain probabilities as well as regions where there are no probabilities [0137].
Lints in combination with Kapoor also teaches creating an integrated probability map wherein the findings are represented based on their positional information [Lints 0137], [Kapoor [0110].
When all 3 references Kapoor, Ross, & Lints are considered together, a system configured to analyze medical scans for abnormality probabilities and further configured to display these abnormality probabilities with respect to their positional information is taught.
Conclusion
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 STEVEN MALDONADO whose telephone number is 703-756-1421. The examiner can normally be reached 8:00 am-4:00 pm PST M-Th 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, Christopher Koharski can be reached on (571) 272-7230. 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.
/Steven Maldonado/
Patent Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
Prosecution Insights