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 .
Response to Preliminary Amendment
This Office Action is responsive to communications filed on 08/15/2024 and a preliminary amendment filed on 03/27/2025. Claims 1-40 are pending in the instant application. Claims 8, 24-40 have been canceled. Claims 1, 12 and 18 are independent. The Examiner acknowledges the table of patents and patent applications for consideration of potential non-statutory obviousness type double patenting. An Office Action on the merits follows here below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/23/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 12-14, 18 and 20 are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as being anticipated by Junio (US 20210313062 A1).
Regarding Claim 1: (Currently Amended) Junio discloses a method (Refer to para [001]; “The present technology is related generally to spinal stenosis treatment and, more particularly, to spinal stenosis detection and generation of a spinal decompression plan based on image data.”) comprising: selecting a region of interest (ROI) in a three-dimensional (3D) volume of image data of a plurality of vertebra of a spine (Refer to para [044]; “In step 304, a spinal cord 104 and/or other anatomical elements may be identified in the image data 100. Such identification may be performed automatically using, for example, feature-based identification as described above. In some embodiments, the vertebrae locations may be determined based on the unique form of the spine in a 3D volume, e.g., by detecting a unique pattern that repeats itself along a prolonged line with anatomical distinct features, e.g. size, form, volume, etc. Once the vertebrae locations are determined, a spinal cord location may be determined.”) the ROI including image data of one or more vertebra from a plurality of vertebra and tissue structures surrounding the one or more vertebra (Refer to para [037]; “Each of these features in the spine region 102, including the spinal cord 104, the compression 106, the at least one nerve exit 108, and the plurality of vertebrae 110, may be identified using the image data 100. Identification of the spinal cord 104, the at least one nerve exit 108, and the plurality of vertebrae 110 may be accomplished using feature-based identification.”) identifying a spinal cord or thecal sac in the ROI (Refer to para [036 and 037]; “t least one compression 106 of the spinal cord 104, otherwise known as spinal stenosis, is visible in the image data 100. Spinal cord compressions may be caused by arthritis, bulging discs, a thickening of the ligaments in the back, injury, or other causes, and often result in patient pain. The at least one compression 106 may be alleviated by, for example, performing a decompression procedure to remove bone or disc material causing the compression. Each of these features in the spine region 102, including the spinal cord 104, the compression 106, the at least one nerve exit 108, and the plurality of vertebrae 110, may be identified using the image data 100.”) determining one or more parameters associated with the spinal cord or thecal sac in the ROI (Refer to para [037]; “Identification of the spinal cord 104, the at least one nerve exit 108, and the plurality of vertebrae 110 may be accomplished using feature-based identification. By way of example, feature-based identification may comprise pre-processing the image data 100 by applying one or more filters or other algorithms thereto. The pre-processing may comprise cleaning and/or aligning the image data 100, and/or conducting intensity mapping of the image data 100. The feature-based identification may further comprise applying one or more feature detection algorithms to the image data 100. These algorithms may be used to identify edges, corners or points of interest, blobs or regions of interest, ridges, and/or other defining elements in the image data 100. Identification of these elements may comprise comparing an intensity or other characteristic of one pixel in the image data 100 to the intensity or other corresponding characteristic of one or more adjacent pixels in the image data 100.”) and determining a severity of spinal stenosis in the ROI (Refer to para [079]; “The system 800 may be used to detect spinal stenosis, classify a compression, and/or generate a decompression plan, as described above.”) based on the one or more parameters associated with the spinal cord or thecal sac (Refer to para [048]; “Whether visual or not, the at least one marking can also include additional information about the at least one compression 106, such as, but not limited to, type, size, volume, severity or the like. Machine learning, deep learning, or artificial intelligence may be used to automatically mark the anatomical element.”).
Regarding Claim 2: Junio discloses identifying the spinal cord or thecal sac includes: processing, using a convolutional neural network (CNN) trained to segment the plurality of vertebra and tissue structures surrounding the plurality of vertebra (Refer to para [069]; “In some examples, neural networks may apply algorithms based on historical or predetermined anatomical annotations and/or historical decompression surgery results to classify the at least one compression 106 and/or identify nearby bony anatomy/disc sections.”) the image data of the ROI to obtain segmentation data identifying the spinal cord or thecal sac (Refer to para [071]; “Thus, the machine learning engine may correlate documented clinical outcomes of procedures (e.g., whether a procedure has relieved the correct compression) and related information (e.g., information about patient characteristics, stenosis characteristics, type of procedure performed, a manner in which the procedure was performed, and so forth), combined with the annotations in the MM or other image (reflecting, e.g., information about the spinal cord 104 and/or other anatomical elements in the image) with annotated compression classifications. Alternatively, the machine learning engine may identify or determine one or more thresholds to use for classifying compressions and/or evaluating which procedures are most likely to be successful in correcting a compression based on information about the compression.”).
Regarding Claim 3: Junio discloses the one or more parameters includes a cross-sectional surface area of the spinal cord or thecal sac (Refer to para [061]; “In step 404, a spinal cord 104, as shown in FIG. 5B, at least one nerve exit 108, and/or other anatomical elements may be identified in the image data 100. In some embodiments, a cross-section of the spinal cord 104 along a plane extending in the superior-inferior (S-I) direction (which direction is shown by arrow 510 in FIG. 5B), as shown in FIGS. 5A and 5B, may be generated from the image data 100.”).
Regarding Claim 12 (Currently Amended): Junio discloses an apparatus (Refer to para [012]; “A system for detecting spinal stenosis”) comprising: a memory; and a processor operatively coupled to the memory (Refer to para [001, 012 and 013]; “The memory may further include instructions that, when executed, cause the processor to: classify a type for each of the at least one compression.”) the processor configured to: select a region of interest (ROI) in a three-dimensional (3D) volume of image data of a plurality of vertebra of a spine (Refer to para [044]; “In step 304, a spinal cord 104 and/or other anatomical elements may be identified in the image data 100. Such identification may be performed automatically using, for example, feature-based identification as described above. In some embodiments, the vertebrae locations may be determined based on the unique form of the spine in a 3D volume, e.g., by detecting a unique pattern that repeats itself along a prolonged line with anatomical distinct features, e.g. size, form, volume, etc. Once the vertebrae locations are determined, a spinal cord location may be determined.”) the ROI including image data of one or more vertebra from a plurality of vertebra and tissue structures surrounding the one or more vertebra (Refer to para [037]; “Each of these features in the spine region 102, including the spinal cord 104, the compression 106, the at least one nerve exit 108, and the plurality of vertebrae 110, may be identified using the image data 100. Identification of the spinal cord 104, the at least one nerve exit 108, and the plurality of vertebrae 110 may be accomplished using feature-based identification.”) identify a spinal cord or thecal sac in the ROI (Refer to para [036 and 037]; “t least one compression 106 of the spinal cord 104, otherwise known as spinal stenosis, is visible in the image data 100. Spinal cord compressions may be caused by arthritis, bulging discs, a thickening of the ligaments in the back, injury, or other causes, and often result in patient pain. The at least one compression 106 may be alleviated by, for example, performing a decompression procedure to remove bone or disc material causing the compression. Each of these features in the spine region 102, including the spinal cord 104, the compression 106, the at least one nerve exit 108, and the plurality of vertebrae 110, may be identified using the image data 100.”) determine one or more parameters associated with the spinal cord or thecal sac in the ROI (Refer to para [037]; “Identification of the spinal cord 104, the at least one nerve exit 108, and the plurality of vertebrae 110 may be accomplished using feature-based identification. By way of example, feature-based identification may comprise pre-processing the image data 100 by applying one or more filters or other algorithms thereto. The pre-processing may comprise cleaning and/or aligning the image data 100, and/or conducting intensity mapping of the image data 100. The feature-based identification may further comprise applying one or more feature detection algorithms to the image data 100. These algorithms may be used to identify edges, corners or points of interest, blobs or regions of interest, ridges, and/or other defining elements in the image data 100. Identification of these elements may comprise comparing an intensity or other characteristic of one pixel in the image data 100 to the intensity or other corresponding characteristic of one or more adjacent pixels in the image data 100.”) and determine a severity of spinal stenosis in the ROI (Refer to para [079]; “The system 800 may be used to detect spinal stenosis, classify a compression, and/or generate a decompression plan, as described above.”) based on the one or more parameters associated with the spinal cord or thecal sac (Refer to para [048]; “Whether visual or not, the at least one marking can also include additional information about the at least one compression 106, such as, but not limited to, type, size, volume, severity or the like. Machine learning, deep learning, or artificial intelligence may be used to automatically mark the anatomical element.”).
Regarding Claim 13: Junio discloses wherein the processor is configured to identify the spinal cord or thecal sac by: processing, using a convolutional neural network (CNN) trained to segment the plurality of vertebra and tissue structures surrounding the plurality of vertebra (Refer to para [069]; “In some examples, neural networks may apply algorithms based on historical or predetermined anatomical annotations and/or historical decompression surgery results to classify the at least one compression 106 and/or identify nearby bony anatomy/disc sections.”) the image data of the ROI to obtain segmentation data identifying the spinal cord or thecal sac (Refer to para [071]; “Thus, the machine learning engine may correlate documented clinical outcomes of procedures (e.g., whether a procedure has relieved the correct compression) and related information (e.g., information about patient characteristics, stenosis characteristics, type of procedure performed, a manner in which the procedure was performed, and so forth), combined with the annotations in the MM or other image (reflecting, e.g., information about the spinal cord 104 and/or other anatomical elements in the image) with annotated compression classifications. Alternatively, the machine learning engine may identify or determine one or more thresholds to use for classifying compressions and/or evaluating which procedures are most likely to be successful in correcting a compression based on information about the compression.”).
Regarding Claim 14: Junio discloses the one or more parameters includes a cross-sectional surface area of the spinal cord or thecal sac (Refer to para [061]; “In step 404, a spinal cord 104, as shown in FIG. 5B, at least one nerve exit 108, and/or other anatomical elements may be identified in the image data 100. In some embodiments, a cross-section of the spinal cord 104 along a plane extending in the superior-inferior (S-I) direction (which direction is shown by arrow 510 in FIG. 5B), as shown in FIGS. 5A and 5B, may be generated from the image data 100.”).
Regarding Claim 18 (Currently Amended): Junio discloses a non-transitory processor-readable medium storing code representing instructions to be executed by a processor (Refer to para [001 and 032]; “In one or more examples, the described methods, processes, and techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).”) the code comprising code to cause the processor to: select a region of interest (ROI) in a three-dimensional (3D) volume of image data of a plurality of vertebra of a spine (Refer to para [044]; “In step 304, a spinal cord 104 and/or other anatomical elements may be identified in the image data 100. Such identification may be performed automatically using, for example, feature-based identification as described above. In some embodiments, the vertebrae locations may be determined based on the unique form of the spine in a 3D volume, e.g., by detecting a unique pattern that repeats itself along a prolonged line with anatomical distinct features, e.g. size, form, volume, etc. Once the vertebrae locations are determined, a spinal cord location may be determined.”) the ROI including image data of one or more vertebra from a plurality of vertebra and tissue structures surrounding the one or more vertebra (Refer to para [037]; “Each of these features in the spine region 102, including the spinal cord 104, the compression 106, the at least one nerve exit 108, and the plurality of vertebrae 110, may be identified using the image data 100. Identification of the spinal cord 104, the at least one nerve exit 108, and the plurality of vertebrae 110 may be accomplished using feature-based identification.”) identify a spinal cord or thecal sac in the ROI (Refer to para [036 and 037]; “t least one compression 106 of the spinal cord 104, otherwise known as spinal stenosis, is visible in the image data 100. Spinal cord compressions may be caused by arthritis, bulging discs, a thickening of the ligaments in the back, injury, or other causes, and often result in patient pain. The at least one compression 106 may be alleviated by, for example, performing a decompression procedure to remove bone or disc material causing the compression. Each of these features in the spine region 102, including the spinal cord 104, the compression 106, the at least one nerve exit 108, and the plurality of vertebrae 110, may be identified using the image data 100.”) determine one or more parameters associated with the spinal cord or thecal sac in the ROI (Refer to para [037]; “Identification of the spinal cord 104, the at least one nerve exit 108, and the plurality of vertebrae 110 may be accomplished using feature-based identification. By way of example, feature-based identification may comprise pre-processing the image data 100 by applying one or more filters or other algorithms thereto. The pre-processing may comprise cleaning and/or aligning the image data 100, and/or conducting intensity mapping of the image data 100. The feature-based identification may further comprise applying one or more feature detection algorithms to the image data 100. These algorithms may be used to identify edges, corners or points of interest, blobs or regions of interest, ridges, and/or other defining elements in the image data 100. Identification of these elements may comprise comparing an intensity or other characteristic of one pixel in the image data 100 to the intensity or other corresponding characteristic of one or more adjacent pixels in the image data 100.”)
and determine a severity of spinal stenosis in the ROI (Refer to para [079]; “The system 800 may be used to detect spinal stenosis, classify a compression, and/or generate a decompression plan, as described above.”) based on the one or more parameters associated with the spinal cord or thecal sac (Refer to para [048]; “Whether visual or not, the at least one marking can also include additional information about the at least one compression 106, such as, but not limited to, type, size, volume, severity or the like. Machine learning, deep learning, or artificial intelligence may be used to automatically mark the anatomical element.”).
Regarding Claim 19: Junio discloses wherein the code to cause the processor to identify the spinal cord or thecal sac includes code to cause the processor to: process, using a convolutional neural network (CNN) trained to segment the plurality of vertebra and tissue structures surrounding the plurality of vertebra (Refer to para [069]; “In some examples, neural networks may apply algorithms based on historical or predetermined anatomical annotations and/or historical decompression surgery results to classify the at least one compression 106 and/or identify nearby bony anatomy/disc sections.”) the image data of the ROI to obtain segmentation data identifying the spinal cord or thecal sac (Refer to para [071]; “Thus, the machine learning engine may correlate documented clinical outcomes of procedures (e.g., whether a procedure has relieved the correct compression) and related information (e.g., information about patient characteristics, stenosis characteristics, type of procedure performed, a manner in which the procedure was performed, and so forth), combined with the annotations in the MM or other image (reflecting, e.g., information about the spinal cord 104 and/or other anatomical elements in the image) with annotated compression classifications. Alternatively, the machine learning engine may identify or determine one or more thresholds to use for classifying compressions and/or evaluating which procedures are most likely to be successful in correcting a compression based on information about the compression.”).
Regarding Claim 20: Junio discloses the one or more parameters includes a cross-sectional surface area of the spinal cord or thecal sac (Refer to para [061]; “In step 404, a spinal cord 104, as shown in FIG. 5B, at least one nerve exit 108, and/or other anatomical elements may be identified in the image data 100. In some embodiments, a cross-section of the spinal cord 104 along a plane extending in the superior-inferior (S-I) direction (which direction is shown by arrow 510 in FIG. 5B), as shown in FIGS. 5A and 5B, may be generated from the image data 100.”).
Allowable Subject Matter
Claims 4-7, 9-11, 15-17, 21-23 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 prior art either singly or in combination does not teach, disclose or suggest at least the following claim limitation(s): “… determining an average surface area for the spinal cord or thecal sac based on (1) the surface area of the cross-section determined for a first axial scan from the plurality of axial scans corresponding to a most superior scan of the vertebrae and (2) the surface area of the cross-section determined for a second axial scan from the plurality of axial scans corresponding to a most inferior scan of the vertebrae; identifying one or more surface areas of the cross-sections determined for the plurality of axial scans that are lower than the remaining surface areas of the cross-sections determined for the plurality of axial scans; and determining a compression factor based on the one or more surface areas that are lower than the remaining surface areas and the average surface area.”
Conclusion
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MIA M. THOMAS
Primary Examiner
Art Unit 2665
/MIA M THOMAS/Primary Examiner
Art Unit 2665
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