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 .
Priority
Priority is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/2/2026 has been entered.
The response filed on 4/6/2026 has been entered and made of record. Claims 1-2, 13, and 15-16 are amended. Claims 1-20 are pending.
The previous rejections of claims 1-20 under 35 USC 102 under Golden et al are withdrawn as necessitated by amendment. New rejection of claims 1-20 under 35 USC 103 under Golden et al in view of Yatziv et al have been made as necessitated by amendment.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
The drawings filed 7/5/2023 were accepted.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Golden et al (US20200380675A1; filed 11/15/2018) in view of Yatziv et al (US20070274582A1; filed 4/2/2007).
With regards to claim 1, Golden et al discloses A computer-implemented method for reading an imaging scan, the method comprising: accessing the imaging scan, the imaging scan comprising a stack of radiological images (Golden et al, paragraph 334: “Medical imaging, such as CT and MR, is frequently used to create a 3D image of anatomy from a stack of 2D images, where the 3D image then includes a three dimensional grid of voxels.” paragraph 339: “One or more implementations of the present disclosure are directed to systems, methods and articles that allow a user to interact with 3D imaging data”);
generating a plurality of two-dimensional images from cross-sectional data of the imaging scan…, the plurality of two-dimensional images comprising projected information from the stack of radiological images (Golden et al, paragraph 339: “One way to visualize a 3D volume is to produce a multiplanar reconstruction (MPR) of the volume, creating a 2D image representing a slice through the volume at some arbitrary position and orientation”), and the projected information comprising either a full imaged volume or an automatically-selected sub-volume (Golden et al, paragraph 339: “visualize a 3D volume”) and either a full range of image intensities or an automatically-selected sub-range of image intensities (Golden et al, paragraph 144: “If the image data are from CT scans, the data are clipped with a lower limit of −1000 Hounsfield units and an upper limit of 400 Hounsfield units before normalizing such that they have a mean of 0, though other clip values that contain the full range of lesion brightnesses would suffice”)…; and
displaying the plurality of two-dimensional images or a subset thereof in a user interface (UI) of an advanced interpretation environment (Golden et al, Fig. 36-37: Shows the GUI with a displayed image. Also described in paragraphs 339-348), the user interface providing access to the stack of radiological images or additional information derived from the stack of radiological images, by enabling interaction with the generated plurality of two-dimensional images (Golden et al, paragraph 339: “The action can be thought of as using the sphere to paint the voxels of interest. One way to visualize a 3D volume is to produce a multiplanar reconstruction (MPR) of the volume, creating a 2D image representing a slice through the volume at some arbitrary position and orientation. The placement and movement of the sphere may be controlled by the user clicking and dragging (e.g., via a mouse or other pointer) on such an MPR representation of the volume”).
However, Golden et al does not disclose and a profile data object corresponding to a patient of the imaging scan… wherein the profile data object maps at least one characteristic of the patient to a submodule configured to process the at least one characteristic and an instance of the cross-sectional data to generate a two-dimensional image of a particular feature of the patient.
Yatziv et al teaches and a profile data object corresponding to a patient of the imaging scan… wherein the profile data object maps at least one characteristic of the patient to a submodule configured to process the at least one characteristic and an instance of the cross-sectional data to generate a two-dimensional image of a particular feature of the patient (Yatziv et al, abstract: “The method uses a reference MPR to position subsequent MPRs in one or more other 3D digital medical images so their content matches the reference MPR;” The profile data object is interpreted as the position previously used in the reference MPR).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to have combined Golden et al and Yatziv et al such that some patient profile data is stored so it can be used for automatically adjusting MPR (multi-planar reformatting) positions to get desired 2D cross-sections. This would have enabled the invention to automatically find the 2D image with little or no user intervention (Yatziv et al, paragraph 6: “there is a need for a system and method that solves this problem for 3D digital medical images by automatically finding matching MPRs and allowing the medical staff/user to perform only the rather simple final tuning, if necessary”).
With regards to claim 2, which depends on claim 1, Golden et al discloses accessing the profile data object corresponding to the patient of the imaging scan, the profile data object comprising patient data (Golden et al, paragraph 35: “receives two sets of image data representative of the same anatomical structure; co-registers the image data; and aligns any potentially malignant anatomical structures across the two sets of image data. The two sets of image data may be from the same patient and may have been acquired at different times, or the two sets of image data may be from the same patient and may be from different scan sequences.” The patient data storage is also shown in Fig. 56 and 58 – load patient data 5616 and patient database 5804) from one or more of a Radiology Information system (RIS), an HL7 broker, an Electronic Medical Record (EMR), a Picture Archive and Communication system (PACS), or meta information in the imaging scan (Golden et al, paragraph 420: “The method described herein auto-triages disparate data streams (e.g., EMR data, imaging data, genotype data, phenotype data, etc.) and sends the data to the right algorithms and/or endpoints for processing and/or analysis;” paragraph 430: “Report sent to people (e.g., clinicians, patients, etc.) and/or archiving (e.g., EMR, PACS, RIS, etc.)”); and
determining the submodule to process the instance of the cross-sectional data based on the accessed profile data object (Golden et al, paragraph 35: “one processor communicably coupled to the at least one nontransitory processor-readable storage medium, in operation the at least one processor: receives two sets of image data representative of the same anatomical structure…” The submodule is interpreted as the part of the instructions that retrieve and/or use the patient data);
wherein generating the plurality of two-dimensional images further comprises processing, by the determined submodule, the instance of the cross-sectional data and the patient data, and the displayed plurality of two-dimensional images includes an interactable region of interest indicator which indicates a region of interest (Golden et al, paragraph 34: “The at least one processor may cause a display to present the segmentations to a user as a mask or contours; and implement a tool that is controllable via a cursor and at least one button, in operation, the tool edits the segmentations via addition or subtraction, and the tool continuously adds regions underneath the cursor to the segmentation, or continuously subtracts regions underneath the cursor from the segmentation, for as long as the at least one button is activated;” The segmentation and the interface are further described in paragraphs 343-345. The selections made by the user are highlighted (paragraph 345: “applying a color highlight”)).
With regards to claim 3, which depends on claim 2, Golden et al discloses wherein the region of interest indicator is configured to enable one or more of acceptance, rejection, or further inspection of the region of interest (Golden et al, paragraph 34: “The at least one processor may cause a display to present the segmentations to a user as a mask or contours; and implement a tool that is controllable via a cursor and at least one button…” The tools which enable the editing of the region (segmentation region) are further described in paragraphs 343-344: “controls are provided to easily manipulate the position and orientation of the MPRs so that the user can get the desired view… tool is then provided that allows the user to create a 3D segmentation by clicking and dragging on one of the displayed MPRs”), wherein further inspection of the region of interest results in generating a navigable subspace view through which a user may explore corresponding anatomical regions within the imaging scan in a magnified view compared to the generated plurality of two-dimensional images or subset thereof (Golden et al, paragraph 343: “The user is able to view either a single MPR of the volume or a collection of three orthogonal MPRs along with a 3D rendering of the volume. As with most medical image viewing software, controls are provided to easily manipulate the position and orientation of the MPRs so that the user can get the desired view of the anatomy feature of interest”).
With regards to claim 4, which depends on claim 3, Golden et al discloses wherein the user interface further comprises a list of one or more measurements, one of the one or more measurements flagging an incidental finding and linking to an image location corresponding to the region of interest indicator (Golden et al, paragraph 349: “FIG. 45 is a screenshot 4500 of the MPR that displays the regions covered by the individual segmentations shown in a list on the right hand side. The application further displays values associated with the physical extent of the segmentation, such as volume of the segmentation, the longest diameter of the segmentation, etc.” The “incidental finding” is not described in any way, so it is being interpreted as part of the selected region of the image (region of interest), and the measurements of the segmentations are interpreted as being linked to the image location because they are describing the segmentation area at the location).
With regards to claim 5, which depends on claim 1, Golden et al discloses wherein the user interface further comprises a semi-transparent rendering of an object of interest overlaid on the generated plurality of two-dimensional images or subset thereof, the object of interest comprising one of lungs, a vasculature tree, or a respiratory tree (Golden et al, paragraph 197: “It is important to display lung anatomy and lesions for doctor review in an easily accessible way. We allow the user to view the nodule annotations with the opacity of certain structures adjusted. FIG. 23 is an image 2300 that displays this effect from an axial top-down view, showing various lesions 2302;” Fig. 23 shows semi-transparent rendering of objects, including lungs).
With regards to claim 6, which depends on claim 1, Golden et al discloses wherein the imaging scan is one of a computed tomography (CT) scan, a low dose CT (LDCT) an ultra-low dose CT (ULDCT) scan, a spectral or dual energy CT scan, a photon counting CT scan, a magnetic resonance (MR) scan, or a positron emission tomography (PET or PET-CT) scan (Golden et al, paragraph 30: “The imaging system 170 may be a computed tomography imaging system, such as an ULDCT. However, the applications of the post-processing for radiological images described herein are not limited to ULDCT or even to CT generally.”).
With regards to claim 7, which depends on claim 1, Golden et al discloses wherein generating the plurality of two-dimensional images further comprises executing one or more artificial intelligence processes on the stack of radiological images (Golden et al, paragraph 28: “receives image data representative of anatomical structures; utilizes at least one CNN to both locate and segment lesion candidates represented in the received image data; classifies malignancy or other properties of the lesion candidates; post-processes the segmentations of the lesion candidates; computes lesion characteristics; stores the generated classifications in the at least one nontransitory processor-readable storage medium”).
With regards to claim 8, which depends on claim 7, Golden et al discloses wherein the one or more artificial intelligence processes are configured to detect anatomical characteristics in the stack of radiological images (Golden et al, paragraph 28: “utilizes at least one CNN to both locate and segment lesion candidates represented in the received image data; classifies malignancy or other properties of the lesion candidates”), and
wherein the projected information from the stack of radiological images is derived based on an anatomical characteristic detected from the one or more artificial intelligence processes (Golden et al, paragraph 28: “The processing by different sub-modules may result in presentation of variable types of two-dimensional images. Variations in two-dimensional images may include editable features such as those that highlight, accentuate, or suppress fat, bone and/or (soft) tissue that can be edited out of two-dimensional images depending on the data provided to the sub-module which is processing the cross-sectional data.”).
With regards to claim 9, which depends on claim 1, Golden et al discloses selectively editing an object captured in the stack of radiological images, wherein at least one of the plurality of two-dimensional images includes the selectively-edited object (Golden et al, paragraph 339: “The action can be thought of as using the sphere to paint the voxels of interest. One way to visualize a 3D volume is to produce a multiplanar reconstruction (MPR) of the volume, creating a 2D image representing a slice through the volume at some arbitrary position and orientation. The placement and movement of the sphere may be controlled by the user clicking and dragging (e.g., via a mouse or other pointer) on such an MPR representation of the volume”).
With regards to claim 10, which depends on claim 1, Golden et al discloses wherein the plurality of two-dimensional images are editable to selectively display fewer than all types of anatomy captured in the stack of radiological images (Golden et al, paragraph 34: “The at least one processor may cause a display to present the segmentations to a user as a mask or contours; and implement a tool that is controllable via a cursor and at least one button, in operation, the tool edits the segmentations via addition or subtraction, and the tool continuously adds regions underneath the cursor to the segmentation, or continuously subtracts regions underneath the cursor from the segmentation, for as long as the at least one button is activated.”).
With regards to claim 11, which depends on claim 1, Golden et al discloses automatically detecting an anatomical characteristic in the stack of radiological images, and
generating the plurality of two-dimensional images based on automatically detecting the detected anatomical characteristic in the stack of radiological images (Golden et al, paragraph 29: “The segmented lesion candidates may be predicted in 2D, and the at least one processor may stack the segmented lesion candidates to create a 3D prediction volume; and combine the segmented lesion candidates in 3D;” The lesions are interpreted as the anatomical characteristic; the automatic detection is described further in paragraphs 23-28).
With regards to claim 12, which depends on claim 11, Golden et al discloses wherein the plurality of two-dimensional images are reconstruction images generated synthetically from the stack of radiological images (Golden et al, paragraph 339: “One way to visualize a 3D volume is to produce a multiplanar reconstruction (MPR) of the volume, creating a 2D image representing a slice through the volume at some arbitrary position and orientation.”) and based on the detected anatomical characteristic (Golden et al, paragraph 29: “The segmented lesion candidates may be predicted in 2D, and the at least one processor may stack the segmented lesion candidates to create a 3D prediction volume; and combine the segmented lesion candidates in 3D”).
Claim 13 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
With regards to claim 14, which depends on claim 13, Golden et al discloses a display that provides the user interface for displaying the generated plurality of two-dimensional images or subset thereof (Golden et al, paragraph 36: “causes a display to present the set of image data comprising a plurality of anatomical structures”).
Claim 15 recites substantially similar limitations to claim 1 and is thus rejected along the same rationale.
Claim 16 recites substantially similar limitations to claim 2 and is thus rejected along the same rationale.
Claim 17 recites substantially similar limitations to claim 3 and is thus rejected along the same rationale.
Claim 18 recites substantially similar limitations to claim 8 and is thus rejected along the same rationale.
Claim 19 recites substantially similar limitations to claim 11 and is thus rejected along the same rationale.
Claim 20 recites substantially similar limitations to claim 12 and is thus rejected along the same rationale.
Response to Arguments
Applicant's arguments filed 3/2/2026 with respect to claim(s) 1, 13, and 15 have been considered but are moot because the new ground of rejection does not rely on only the same reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Applicant argues that Golden et al fails to disclose the amendments to claims 1, 13, and 15. Examiner agrees, but upon additional search has made a new rejection above over Golden et al in view of Yatziv et al. Thus the argument is moot.
Conclusion
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/B.C.A/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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