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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 & 7-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category: YES -The claims recite a computer-implemented method for detecting spinal fractures and an apparatus, and, therefore, are a method and an apparatus.
Step 2A, Prong 1, Judicial Exception: YES -Claims 1 & 15 recite the limitations “identifying the spine of the subject in the three-dimensional image data” and “identifying a fracture in at least one of the VOIs”. These limitations comprise method steps that, under their broadest reasonable interpretation, cover performance of the limitations in the mind, and nothing in the claim element precludes the step from practically being performed in the mind. Thus, the claims recite a mental process.
Step 2A, Prong 2, Integrated into Practical Application: No -Claims 1 & 15 recite additional elements such as “receiving three-dimensional image data including at least a portion of a spine of a subject”, “defining a spline approximating a local curvature along the spine of the subject”, and “defining multiple volumes of interest (VOIs), each VOI containing at least a portion of a vertebra of the spine of the subject, wherein each VOI is defined relative to an adjacent segment of the spline”. Claim 15 recites additional elements such as “a memory that stores a plurality of instructions” and “a processor that couples to the memory and is configured to execute the plurality of instructions”.
Claims 2 & 7 are drawn to a convolutional neural network which constitutes additional elements that are extra solution activity, and the functions of the CNN can be performed via mental steps. Claims 3-5, 9, & 13 are drawn to defining the position and size of the VOIs which can be performed via mental steps. Claims 8-11 & 14 are drawn to identifying fractures which can be performed via mental steps. Claims 10-11 & 14 are drawn to displaying data which does not add significantly more than the abstract idea because displaying the image and probability map amounts to outputting results. Claim 12 further defines the spline which constitutes additional elements that are extra solution activity and can be performed via mental steps. Because the claims are recited at a high level of generality, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea to integrate the judicial exception into a practical application. Thus, the claims are directed to an abstract idea.
Step 2B, Inventive Concept: No -The additional elements amount to no more than a means for gathering data. The gathering of data cannot integrate a judicial exception into a practical application or provide an inventive concept. Because the receiving and defining steps remain an insignificant pre-extra solution activity, the additional elements, either considered individually or as a whole, do not claim substantially more than the judicial exception and therefore does not confer an inventive concept. There is no inventive concept in the claim, and thus, it is ineligible.
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.
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 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hirakawa (US 2020/0058098) in view of Nicolaes (US 2020/0364856).
Regarding claim 1, Hirakawa teaches a computer-implemented method for detecting spinal fractures, comprising:
receiving three-dimensional image data including at least a portion of a spine of a subject ([0037]);
identifying the spine of the subject in the three-dimensional image data ([0037]);
defining a spline (spline interpolation, [0060]) approximating a local curvature along the spine of the subject ([0060]); and
defining multiple volumes of interest (VOIs) (vertebra regions VR, [0058]), each VOI containing at least a portion of a vertebra of the spine of the subject ([0058] & Figure 6), wherein each VOI is defined relative to an adjacent segment of the spline ([0060]).
However, Hirakawa fails to disclose identifying a fracture in at least one of the VOIs.
Nicolaes teaches identifying a fracture ([0040] & [0059]) in at least one of the VOIs (vertebra voxel, [0059]).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa to include identifying a fracture in at least one of the VOIs, as taught by Nicolaes. This provides a functional use to the steps of obtaining and segmenting images of the spine.
Regarding claim 2, Hirakawa in view of Nicolaes teach the method of claim 1, and Nicolaes further teaches that the spine is identified by a convolutional neural network (CNN) ([0051]) trained for segmenting the spine ([0051] & [0059]).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa such that the spine is identified by a convolutional neural network (CNN) trained for segmenting the spine. Convolutional neural networks are well-known in the art to be applied to imaging-related problems; per [0051] of Nicolaes, “CNNs have been applied successfully to classification, object detection and segmentation tasks”.
Regarding claim 3, Hirakawa in view of Nicolaes teach the method of claim 1, and Hirakawa further teaches that for each VOI a corresponding center point (middle point P3, [0059], Figure 7) is sampled at a location defined relative to the spline ([0060]), wherein the center points of the VOIs are sampled at regular intervals along the spline ([0059]).
Because each vertebra lies on the spline, the center points are sampled at a location defined relative to the spline. Additionally, sampling the center point at each vertebra constitutes a regular interval.
Regarding claim 4, Hirakawa in view of Nicolaes teach the method of claim 3, and Hirakawa further teaches that center points for adjacent VOIs are located so as to generate overlapping VOIs ([0059] & Figure 7).
As is detailed in [0059] and illustrated in Figure 7, the setting of the middle point P3 in each vertebra region VR is based on intersection points P1 and P2 in the intervertebral discs D both above and below each vertebral body C. Thus, each intervertebral disc belongs in the vertebra regions of the vertebra both above and below it, and each vertebra region overlaps in the discs.
Regarding claim 5, Hirakawa in view of Nicolaes teach the method of claim 3, and Hirakawa further teaches that each VOI is formed about the center point ([0059] & Figure 7) and is oriented based on a tangent of the spline adjacent the corresponding center point ([0060]).
Regarding claim 6, Hirakawa in view of Nicolaes teach the method of claim 5, and Hirakawa further teaches that after defining each VOI, each VOI is extracted ([0062]) and resampled from the three-dimensional image data to a target resolution ([0050]-[0051]).
Regarding claim 7, Hirakawa in view of Nicolaes teach the method of claim 1, and Nicolaes further teaches that the fracture is identified by applying a convolutional neural network (CNN) ([0051]) to each VOI ([0059]).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa such that the fracture is identified by applying a convolutional neural network (CNN) to each VOI, as taught by Nicolaes. Convolutional neural networks are well-known in the art to be applied to imaging-related problems; per [0051] of Nicolaes, “CNNs have been applied successfully to classification, object detection and segmentation tasks”.
Regarding claim 8, Hirakawa in view of Nicolaes teach the method of claim 1, and Nicolaes further teaches that the output of the CNN, when applied to each VOI, is a probability map (classification layer, [0059]) identifying likely fractures within the corresponding VOI ([0059]-[0060]).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa such that the output of the CNN, when applied to each VOI, is a probability map identifying likely fractures within the corresponding VOI, as taught by Nicolaes. This allows an operator to assess both the likelihood that a fracture is present in a patient, but also the location of an expected fracture.
Regarding claim 9, Hirakawa in view of Nicolaes teach the method of claim 8, and Hirakawa further teaches that the VOIs are defined such that adjacent VOIs overlap ([0059] & Figure 7).
Nicolaes further teaches that all predictions corresponding to a particular location are aggregated into a final probability map ([0076] & [0105]; 1102, Figure 11; Figure 14).
Regarding the limitation that multiple predictions are generated for the overlapping voxels, [0079] of Nicolaes states that a centroid is located for each vertebra and the fracture probability is considered for each vertebra. When combining the teachings of Nicolaes with those of Hirakawa, which teach segmenting each vertebra region with overlapping components (the intervertebral discs), the overlapping voxels would be considered for multiple vertebrae when assessing each vertebra’s probability for a fracture.
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa to include generating multiple predictions for at least some equivalent voxels occurring in the multiple VOIs, wherein all predictions corresponding to a particular location are aggregated into a final probability map, as taught by Nicolaes. This increases the accuracy of fracture determination, and the final probability map allows the operator to visually understand where a potential fracture may be located on the spine.
Regarding claim 10, Hirakawa in view of Nicolaes teach the method of claim 8, and Nicolaes further teaches generating a final probability map from the probability maps associated with individual VOIs ([0076] & [0105]; 1102, Figure 11; Figure 14), the final probability map comprising a collation of the VOI probability maps into a coherent representation of the three-dimensional image data (Figure 14).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa to include generating a final probability map from the probability maps associated with individual VOIs, the final probability map comprising a collation of the VOI probability maps into a coherent representation of the three-dimensional image data, as taught by Nicolaes. Providing a visual representation of the fracture probability determination allows the operator to conceptually understand each patient’s condition.
Regarding claim 11, Hirakawa in view of Nicolaes teach the method of claim 10, and Nicolaes further teaches generating binary predictions based on the final probability map ([0059]; Figure 14) and filtering fracture candidates based on a relationship between a candidate location and the spine of the subject.
Paragraph [0059] teaches that each voxel is classified into one of three class probabilities: background, normal vertebra, or fractured vertebra. Because the background voxels do not correlate to a vertebra, there are two classes a vertebra voxel can be classified as. Thus, this is a binary prediction. Nevertheless, Figure 14 shows four classes: normal, mild, moderate, severe. Although there are four classes, they can be grouped as a binary prediction if so desired, in which “normal” constitutes a normal vertebra, and “mild”, “moderate”, and “severe” constitute a fractured vertebra. Regarding the filtering of the fracture candidates, the operator can mentally separate the normal vertebrae from the fractured vertebrae.
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa to include generating binary predictions based on the final probability map and filtering fracture candidates based on a relationship between a candidate location and the spine of the subject, as taught by Nicolaes. A binary prediction of normal and fractured vertebra allows the operator to understand the necessity of intervention, as a fractured vertebra requires intervention regardless of the level of severity.
Regarding claim 12, Hirakawa in view of Nicolaes teach the method of claim 1, and Hirakawa further teaches that the spline approximates a centerline (center line CL2, [0059], Figure 7) of a spinal canal for the spine ([0059]-[0060] & Figure 7).
Regarding claim 13, Hirakawa in view of Nicolaes teach the method of claim 1, and Hirakawa further teaches that a size for a first VOI is selected based on an adjacent first location along the spine, and wherein a size for a second VOI is selected based on an adjacent second location along the spine ([0059]).
Because each vertebra region VR is associated with an individual vertebra, and each vertebra has a different size, the sizes of the VOIs are based on the size of the vertebrae, which is a function of where along the spine they are located.
Regarding claim 14, Hirakawa in view of Nicolaes teach the method of claim 1, and Nicolaes further teaches locating fractures identified in a representation of the three-dimensional image data (Figure 14) and displaying identified fractures in the context of the spine of the subject (Figure 14).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the method of Hirakawa to include locating fractures identified in a representation of the three-dimensional image data and displaying identified fractures in the context of the spine of the subject, as taught by Nicolaes. This allows the operator to observe the results of the fracture determination performed by the CNN.
Claim 15 is rejected for similar reasons to claim 1. Hirakawa further teaches a memory (memory 12, [0040]) that stores a plurality of instructions (image processing program, [0042]); and a processor (CPU 11, [0040]) that couples to the memory ([0042]-[0043]) and is configured to execute the plurality of instructions ([0043]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM KOLKIN whose telephone number is (571)272-5480. The examiner can normally be reached Monday-Friday 1:00PM-10:00PM EDT.
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/ADAM D. KOLKIN/Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798