---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 .
Information Disclosure Statement
The IDS(s) dated 5/24/2023 and 10/9/2024 has/have been considered and placed in the application file.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are:
“image acquisition unit” in claim 19; and
“processing unit” in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
The following terms in the claims have been given the following interpretations in light of the specification:
“foreign object”: p. 4 lines 29-34 and p. 5 lines 1-6, “the term “foreign object” also explicitly covers in the context of the present invention a part of the patient's anatomy which is not expected in the intraoperative image in view of the present medical procedure and/or imaging procedure that the patient of the acquired intraoperative images is or was undergoing.”
Thus, a “foreign object” is patient anatomy detected at an unexpected position relative to expected/reference image content.
This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims. Should applicant wish different definitions, Applicant should point to the portions of the specification that clearly show a different definition.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim(s) 19 and 21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim(s) 19 recites the limitation "the at least one foreign object". There is insufficient antecedent basis for this limitation in the claim.
Claim(s) 21 depend either directly or indirectly from the rejection(s) of claim(s) 19, therefore they are also rejected.
Appropriate correction is required.
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.
Claim(s) 1-14, 17, 19, and 21 is/are rejected under 35 U.S.C. 103 as obvious over Haslam et al (US 20210346091 A1, hereafter referred to as Haslam) in view of Meissner et al (US 20090192385 A1, hereafter referred to as Meissner), further in view of Maurer et al (US 20100067660 A1, hereafter referred to as Maurer).
Claim 1
Regarding Claim 1, Haslam teaches a computer-implemented method of detecting, during a medical procedure, at least one foreign object in one or more intraoperative images, the method comprising the steps:
- acquiring, (Haslam in ¶8 discloses "generating a 3D physical model of a patient specific anatomic feature from 2D medical images"; ¶17 discloses "2D medical images may be images from the patient taken from a CT, MRI, PET and/or SPCET scanner." - intraoperative forms of the imaging modalities are well-known in the art);
- calculating or providing expected image content of the acquired intraoperative image based on data characterizing the patient and/or the medical procedure (Haslam in ¶292 discloses "a set of standard anatomical models are generated by retrieving all the available data within our data-store for a given anatomical feature in its healthy state (no pathology)"; ¶233 discloses "the automated pipeline will detect these anomalies by comparing the information provided against a simplified standard set of parameters expected within the 3D volumes"); and
- comparing, (Haslam in ¶233 discloses "the automated pipeline will detect these anomalies by comparing the information provided against a simplified standard set of parameters expected within the 3D volumes"; ¶314 discloses "The Anatomical classification described above provides the basis to detect any deviation from standard of a given scan").
Haslam does not explicitly teach all of acquiring, during the medical procedure, at least one intraoperative image of at least a part of a patient's body undergoing the medical procedure; comparing, during the medical procedure and in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content thereby automatically detecting, during the medical procedure, the at least one foreign object in the intraoperative image; wherein the method further comprises: automatically generating, during the medical procedure and based on the detection of the at least one foreign object, a control signal, and wherein the control signal is configured for: adjusting/suggesting a collimation of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting X-ray acquisition parameters, stopping the acquisition of intraoperative images, initiating a documentation of a detection result of the detection of the at least one foreign object, and/or adjusting/suggesting one or more parameters of a robotic arm used during the intraoperative imaging.
However, Meissner teaches acquiring at least one intraoperative image of at least a part of a patient's body undergoing a medical procedure (Meissner in Abstract discloses "the roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment"; ¶42 discloses "The live fluoroscopy image shown in FIG. 2G");
wherein the method further comprises:
- automatically generating, based on the detection of the at least one foreign object, a control signal, and wherein the control signal is configured for: adjusting/suggesting a collimation of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting X-ray acquisition parameters, stopping the acquisition of intraoperative images, initiating a documentation of a detection result of the detection of the at least one foreign object, and/or adjusting/suggesting one or more parameters of a robotic arm used during the intraoperative imaging (Examiner notes the use of "or" in the claim, which would require just one of the listed embodiments. Meissner in ¶9, 20-21, 44, 77 discloses "During the procedure, fluoroscopic images may be desirable from more than one vantage point (working view, orientation) … fluoroscopic images may be taken from other orientations, or where the patient has been moved … A new road mask may need to be produced, involving further administration of contrast agent and X-ray exposure." Discloses at least “adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haslam by incorporating the acquisition of at least one intraoperative image during a medical procedure that is taught by Meissner, since both reference are analogous art in the field of medical imaging for anomaly detection; thus, one of ordinary skilled in the art would be motivated to combine the references since Haslam’s automated pipeline for detecting anomalies by comparing acquired image content against a simplified standard set of parameters expected within the 3D volumes with Meissner’s real-time fluoroscopic X-ray imaging acquisition during treatment yields the predictable result of enabling the detection of deviations from standard anatomy in live intraoperative images to identify foreign objects.
Haslam in view of Meissner does not explicitly teach all of acquiring, during the medical procedure, at least one intraoperative image of at least a part of a patient’s body undergoing the medical procedure; comparing, during the medical procedure and in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content thereby automatically detecting, during the medical procedure, the at least one foreign object in the intraoperative image; wherein the method further comprises: automatically generating, during the medical procedure and based on the detection of the at least one foreign object, a control signal.
However, Maurer teaches acquiring, during the medical procedure (Maurer in ¶39 discloses “the diagnostic image data are used to track the location of the pathological anatomy during the course of delivering radiation”), at least one intraoperative image of at least a part of a patient’s body undergoing the medical procedure (Maurer in ¶53-55 discloses image guided radiation treatment and acquiring diagnostic X-ray images of the patient target during treatment);
- comparing, during the medical procedure and in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content (Maurer in ¶33 discloses comparing each diagnostic X-ray image to the corresponding DRR or previous diagnostic images to determine current target location and movement; ¶55-57 discloses sequential images are compared by feature recognition or pattern-intensity matching, and the detected movement is compared with predicted/expected movement) thereby automatically detecting, during the medical procedure, the at least one foreign object in the intraoperative image (Maurer in ¶56 discloses detecting whether target anatomy moved farther than expected between the images);
wherein the method further comprises:
- automatically generating, during the medical procedure and based on the detection of the at least one foreign object, a control signal (Maurer in ¶33, 56-57 discloses when the image comparison shows movement greater than expected, the X-ray imaging frequency is increased, and the same applies vice versa; ¶80, 83-84, 88 discloses digital processing systems control imaging operations and transfer data and commands to the X-ray source and detector).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haslam in view of Meissner by determining, during the medical procedure, whether a comparison of intraoperative X-ray images indicates patient or target anatomy movement outside an expected treatment tolerance and, based on that determination, automatically increasing or decreasing the X-ray imaging frequency that is taught by Maurer, since both reference are analogous art in the field of computer assisted medical image analysis and image guided treatment; thus, one of ordinary skilled in the art would be motivated to combine the references since Haslam in view of Meissner’s automated medical image anomaly detection and real-time fluoroscopic imaging with Maurer’s detection responsive imaging frequency control yields the predictable result of detecting unexpected anatomical displacement during a medical procedure and adjusting an X-ray acquisition parameter according to detected movement, thereby maintaining current target location information and treatment accuracy while reducing unnecessary X-ray acquisitions and radiation exposure when movement is below an expected tolerance.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 2
Regarding Claim 2, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 1, wherein the at least one intraoperative image is a live fluoroscopy video stream (Meissner in Abstract discloses "the roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment"; ¶42 discloses "The live fluoroscopy image shown in FIG. 2G").
Claim 3
Regarding Claim 3, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 1, wherein the data characterizing the patient and/or the medical procedure are embodied as at least one of: - imaging device parameters of an imaging device used for generating the acquired intraoperative image; - patient information; - medical procedure information describing a nature and/or application of the medical procedure, which the patient was undergoing when the at least one intraoperative image was acquired; and - one or more previous images of the patient (Examiner notes the use of "at least one of" in the claim, which would require just one of the listed embodiments. Haslam in ¶41 discloses "A threshold-based segmentation method may be used and the threshold value is a function of the 2D medical images scanning parameters"; ¶43 discloses "threshold-based segmentation method may be used and the threshold value is optimized based on one or more of the following parameters: scan type, bone type, tissue type, age, gender and weight of the patient"; ¶292 discloses "a set of standard anatomical models are generated by retrieving all the available data within our data-store for a given anatomical feature in its healthy state (no pathology) … models may also need to take into account some important aspects related to the patient … patient age, sex and clinical history."; See also ¶30 and 409-410).
Claim 4
Regarding Claim 4, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 1,
wherein the intraoperative image was generated with an imaging device of a first imaging modality (Haslam in ¶8 discloses "generating a 3D physical model of a patient specific anatomic feature from 2D medical images"; ¶17 discloses "2D medical images may be images from the patient taken from a CT, MRI, PET and/or SPCET scanner." - intraoperative forms of the imaging modalities are well-known in the art),
wherein the step of calculating expected image content comprises:
- creating a synthetic image of the first imaging modality, the synthetic image representing the expected image content (Haslam in ¶292 discloses "A set of standard anatomical models are generated by retrieving all the available data within our data-store for a given anatomical feature in its healthy state (no pathology). The available segmentation data for a given anatomical feature (a bone, a specific organ) is registered and scaled in order to align the segmentation from the different data-sets corresponding to the various scans. The aligned and scaled segmentation data is then used to extract and compare the Hounsfield values as well as the CT scans and/or the MRI value; the similarity between each scan can then be used to generate the standard anatomical model of the feature and its expected appearance in a medical scan.").
Claim 5
Regarding Claim 5, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 4, wherein the step of creating the synthetic image further comprises:
- creating or acquiring a synthetic patient model (Haslam in ¶292 discloses "A set of standard anatomical models are generated by retrieving all the available data within our data-store for a given anatomical feature in its healthy state (no pathology). The available segmentation data for a given anatomical feature (a bone, a specific organ) is registered and scaled in order to align the segmentation from the different data-sets corresponding to the various scans. The aligned and scaled segmentation data is then used to extract and compare the Hounsfield values as well as the CT scans and/or the MRI value; the similarity between each scan can then be used to generate the standard anatomical model of the feature and its expected appearance in a medical scan."),
- adjusting the synthetic patient model based on patient information and/or based on intraoperative image data (Haslam in ¶292 discloses "A set of standard anatomical models … Such models may also need to take into account some important aspects related to the patient from which the data comes from"), and
- using the adjusted synthetic patient model in the creation of the synthetic image (Haslam in ¶292 discloses "A set of standard anatomical models are generated … The available segmentation data for a given anatomical feature (a bone, a specific organ) is registered and scaled in order to align the segmentation from the different data-sets corresponding to the various scans. … the similarity between each scan can then be used to generate the standard anatomical model of the feature and its expected appearance in a medical scan. Such models may also need to take into account some important aspects related to the patient from which the data comes from").
Claim 6
Regarding Claim 6, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 5,
wherein the at least one intraoperative image is a 2D image wherein the step of using the adjusted synthetic patient model in the creation of the synthetic image further comprises:
- deriving a 3D image from the synthetic patient model (Haslam in ¶293 discloses "The Standard anatomical model is stored as a reference within the ATLAS and ATLAS is also available in order to facilitate the retrieval of data. As new data is added to the Axial3D database, the standard models are updated to include the new data; the history of standard model generated for each anatomical feature is also preserved."), and
- deriving the synthetic image from the 3D image by calculating a Digitally Reconstructed Radiograph (DRR) thereby using imaging device parameters of the imaging device, which generated the 2D image (Meissner in Abstract discloses "The roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment.").
Claim 7
Regarding Claim 7, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 5,
wherein the step of creating the synthetic image further comprises:
- virtually placing and/or orienting the adjusted synthetic patient model relative to the imaging device of the first imaging modality based on medical procedure information (Meissner in Abstract discloses "The roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment … When the orientation of the fluoroscopic image is changed during the course of treatment, the roadmap mask image for the corresponding orientation is used").
Claim 8
Regarding Claim 8, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 4,
the method further comprising the steps:
- segmenting anatomical structures in the synthetic image (Haslam in ¶8 discloses "the server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique"; ¶279 discloses "Both nodes and relationships contain additional information; in particular they contain a reference to a image containing such anatomical feature, its segmentation"),
- segmenting anatomical structures in the acquired intraoperative image (Haslam in ¶8 discloses "the server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique"), and
- comparing the segmented images for detecting the at least one foreign object (Haslam in ¶304-311 discloses "The general approach to perform such a task is the following: … derive accurate segmentation using the automated segmentation algorithms … compare to the existing data-set of interesting features and attempt to find a number of matches … the standard models are used to further refine the filtering and cross-checking by fitting a linear transform between the semi-classified segmented objects and what the standard model looks like". ).
Claim 9
Regarding Claim 9, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 4,
the method further comprising the steps:
- cropping the synthetic image based on positional information of the imaging device of the first imaging modality, a field of view of the imaging device of the first imaging modality, and/or medical procedure information (Meissner in Abstract discloses "The roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment.").
Claim 10
Regarding Claim 10, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 1,
wherein the step of providing the expected image content of the acquired intraoperative image comprises:
- providing a look up table, in which objects are stored as entries that are and/or are not expected to be present in images of the medical procedure (Haslam in ¶279-286 discloses "Axial3D uses a graph database in order to store such information in an ATLAS of human anatomy relevant to medical scanning techniques … Key features of ATLAS are: 1. to provide the Axial3D R&D and segmentation group a simple and fast information retrieval system (accessing the relevant scan and segmentation data); 2. provide a centralized area where specific information on anatomical features is stored (organ appearance and properties); 3. provide a simple method to access information of related organs; 4. provide a backbone of Ground Truth data for organ classification and automatic medical scan interpretation. Typical examples of ATLAS usage include, but are not limited to: 1. retrieve the reference of all scans in our data-store related to hips in CT scans". ), and
- comparing the automatically detected at least one foreign object of the intraoperative image with the entries in the look up table (Haslam in ¶292 discloses " A set of standard anatomical models are generated by retrieving all the available data within our data-store for a given anatomical feature in its healthy state (no pathology)"; ¶304-307 discloses "derive accurate segmentation using the automated segmentation algorithms … compare to the existing data-set of interesting features and attempt to find a number of matches").
Claim 11
Regarding Claim 11, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 1,
wherein the step of comparing, in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content comprises:
- using an image analysis algorithm and/or video analysis algorithm for analyzing the at least one acquired intraoperative image (Meissner in Abstract discloses "A system and method of producing virtual roadmap image of a patient … CT-like image data may be obtained for a patient … and differenced so as to form a roadmap mask. The roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment. The fluoroscopic image may be subtracted from the roadmap mask so as to more clearly visualize the position of a catheter introduced into the patient for treatment. When the orientation of the fluoroscopic image is changed during the course of treatment, the roadmap mask image for the corresponding orientation is used.").
Claim 12
Regarding Claim 12, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 11, wherein the video analysis algorithm uses machine learning, preferably a neural network (Haslam in ¶55 discloses "The segmentation technique may use a Neural Network method, in which the Neural Network is trained from a database of existing medical images.").
Claim 13
Regarding Claim 13, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 11,
wherein the video analysis algorithm uses a histogram analysis (Haslam in ¶71 discloses "The feature extraction algorithm may be used to extract one or more of the following: … histogram of the Hounsfield Units corresponding to the anatomic feature across an anatomical knowledge dataset". See also ¶44-46).
Claim 17
Regarding Claim 17, Haslam teaches a non-transitory computer-readable storage medium storing a program, that when executed on at least one processor of a computer or when loaded onto the at least one processor of the computer, causes the computer to perform a method to detect at least one foreign object in one or more intraoperative images, the method comprising:
acquiring at least one intraoperative image of at least a part of a patient's body undergoing a medical procedure (Haslam in ¶8 discloses "generating a 3D physical model of a patient specific anatomic feature from 2D medical images"; ¶17 discloses "2D medical images may be images from the patient taken from a CT, MRI, PET and/or SPCET scanner." - intraoperative forms of the imaging modalities are well-known in the art);
calculating or providing expected image content of the acquired intraoperative image based on data characterizing the patient and/or the medical procedure (Haslam in ¶292 discloses "a set of standard anatomical models are generated by retrieving all the available data within our data-store for a given anatomical feature in its healthy state (no pathology)"; ¶233 discloses "the automated pipeline will detect these anomalies by comparing the information provided against a simplified standard set of parameters expected within the 3D volumes"); and
comparing the acquired intraoperative image with the calculated/provided expected image content thereby automatically detecting the at least one foreign object in the intraoperative image (Haslam in ¶233 discloses "the automated pipeline will detect these anomalies by comparing the information provided against a simplified standard set of parameters expected within the 3D volumes"; ¶314 discloses "The Anatomical classification described above provides the basis to detect any deviation from standard of a given scan").
Haslam does not explicitly teach all of acquiring at least one intraoperative image of at least a part of a patient's body undergoing a medical procedure; wherein the method further comprises:
- automatically generating, based on the detection of the at least one foreign object, a control signal, and wherein the control signal is configured for: adjusting/suggesting a collimation of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting X-ray acquisition parameters, stopping the acquisition of intraoperative images, initiating a documentation of a detection result of the detection of the at least one foreign object, and/or adjusting/suggesting one or more parameters of a robotic arm used during the intraoperative imaging.
However, Meissner teaches - acquiring at least one intraoperative image of at least a part of a patient's body undergoing a medical procedure (Meissner in Abstract discloses "the roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment"; ¶42 discloses "The live fluoroscopy image shown in FIG. 2G");
wherein the method further comprises:
- automatically generating, based on the detection of the at least one foreign object, a control signal, and wherein the control signal is configured for: adjusting/suggesting a collimation of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting X-ray acquisition parameters, stopping the acquisition of intraoperative images, initiating a documentation of a detection result of the detection of the at least one foreign object, and/or adjusting/suggesting one or more parameters of a robotic arm used during the intraoperative imaging (Examiner notes the use of "or" in the claim, which would require just one of the listed embodiments. Meissner in ¶9, 20-21, 44, 77 discloses "During the procedure, fluoroscopic images may be desirable from more than one vantage point (working view, orientation) … fluoroscopic images may be taken from other orientations, or where the patient has been moved … A new road mask may need to be produced, involving further administration of contrast agent and X-ray exposure." Discloses at least “adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haslam by incorporating the acquisition of at least one intraoperative image during a medical procedure that is taught by Meissner, since both reference are analogous art in the field of medical imaging for anomaly detection; thus, one of ordinary skilled in the art would be motivated to combine the references since Haslam’s automated pipeline for detecting anomalies by comparing acquired image content against a simplified standard set of parameters expected within the 3D volumes with Meissner’s real-time fluoroscopic X-ray imaging acquisition during treatment yields the predictable result of enabling the detection of deviations from standard anatomy in live intraoperative images to identify foreign objects.
Haslam in view of Meissner does not explicitly teach all of acquiring, during the medical procedure, at least one intraoperative image of at least a part of a patient’s body undergoing the medical procedure;
- comparing, during the medical procedure and in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content thereby automatically detecting, during the medical procedure, the at least one foreign object in the intraoperative image;
wherein the method further comprises:
- automatically generating, during the medical procedure and based on the detection of the at least one foreign object, a control signal.
However, Maurer teaches acquiring, during the medical procedure (Maurer in ¶39 discloses “the diagnostic image data are used to track the location of the pathological anatomy during the course of delivering radiation”), at least one intraoperative image of at least a part of a patient’s body undergoing the medical procedure (Maurer in ¶53-55 discloses image guided radiation treatment and acquiring diagnostic X-ray images of the patient target during treatment);
- comparing, during the medical procedure and in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content (Maurer in ¶33 discloses comparing each diagnostic X-ray image to the corresponding DRR or previous diagnostic images to determine current target location and movement; ¶55-57 discloses sequential images are compared by feature recognition or pattern-intensity matching, and the detected movement is compared with predicted/expected movement) thereby automatically detecting, during the medical procedure, the at least one foreign object in the intraoperative image (Maurer in ¶56 discloses detecting whether target anatomy moved farther than expected between the images);
wherein the method further comprises:
- automatically generating, during the medical procedure and based on the detection of the at least one foreign object, a control signal (Maurer in ¶33, 56-57 discloses when the image comparison shows movement greater than expected, the X-ray imaging frequency is increased, and the same applies vice versa; ¶80, 83-84, 88 discloses digital processing systems control imaging operations and transfer data and commands to the X-ray source and detector).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haslam in view of Meissner by determining, during the medical procedure, whether a comparison of intraoperative X-ray images indicates patient or target anatomy movement outside an expected treatment tolerance and, based on that determination, automatically increasing or decreasing the X-ray imaging frequency that is taught by Maurer, since both reference are analogous art in the field of computer assisted medical image analysis and image guided treatment; thus, one of ordinary skilled in the art would be motivated to combine the references since Haslam in view of Meissner’s automated medical image anomaly detection and real-time fluoroscopic imaging with Maurer’s detection responsive imaging frequency control yields the predictable result of detecting unexpected anatomical displacement during a medical procedure and adjusting an X-ray acquisition parameter according to detected movement, thereby maintaining current target location information and treatment accuracy while reducing unnecessary X-ray acquisitions and radiation exposure when movement is below an expected tolerance.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 19
Regarding Claim 19, Haslam teaches a medical image analysis system comprising:
- an image acquisition unit which is configured to acquire at least one intraoperative image of at least a part of a patient's body undergoing a medical procedure (Haslam in ¶8 discloses "generating a 3D physical model of a patient specific anatomic feature from 2D medical images"; ¶17 discloses "2D medical images may be images from the patient taken from a CT, MRI, PET and/or SPCET scanner." - intraoperative forms of the imaging modalities are well-known in the art);
a processing unit which is configured to (Haslam in Abstract discloses “the server processes the 2D medical images and automatically generates a 3D printable model of a patient specific anatomic feature from the 2D medical images using a segmentation technique.”):
- calculate or provide expected image content of the acquired intraoperative image based on data characterizing the patient and/or the medical procedure (Haslam in ¶292 discloses "a set of standard anatomical models are generated by retrieving all the available data within our data-store for a given anatomical feature in its healthy state (no pathology)"; ¶233 discloses "the automated pipeline will detect these anomalies by comparing the information provided against a simplified standard set of parameters expected within the 3D volumes"); and
- compare, in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content thereby automatically detecting the at least one foreign object in the intraoperative image (Haslam in ¶233 discloses "the automated pipeline will detect these anomalies by comparing the information provided against a simplified standard set of parameters expected within the 3D volumes"; ¶314 discloses "The Anatomical classification described above provides the basis to detect any deviation from standard of a given scan").
Haslam does not explicitly teach all of an image acquisition unit which is configured to acquire at least one intraoperative image of at least a part of a patient's body undergoing a medical procedure; and
- automatically generating, based on the detection of the at least one foreign object, a control signal, and wherein the control signal is configured for: adjusting/suggesting a collimation of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting X-ray acquisition parameters, stopping the acquisition of intraoperative images, initiating a documentation of a detection result of the detection of the at least one foreign object, and/or adjusting/suggesting one or more parameters of a robotic arm used during the intraoperative imaging.
However, Meissner teaches an image acquisition unit which is configured to acquire at least one intraoperative image of at least a part of a patient's body undergoing a medical procedure (Meissner in Abstract discloses "the roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment"; ¶42 discloses "The live fluoroscopy image shown in FIG. 2G");
wherein the method further comprises:
- automatically generating, based on the detection of the at least one foreign object, a control signal, and wherein the control signal is configured for: adjusting/suggesting a collimation of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image, adjusting/suggesting X-ray acquisition parameters, stopping the acquisition of intraoperative images, initiating a documentation of a detection result of the detection of the at least one foreign object, and/or adjusting/suggesting one or more parameters of a robotic arm used during the intraoperative imaging (Examiner notes the use of "or" in the claim, which would require just one of the listed embodiments. Meissner in ¶9, 20-21, 44, 77 discloses "During the procedure, fluoroscopic images may be desirable from more than one vantage point (working view, orientation) … fluoroscopic images may be taken from other orientations, or where the patient has been moved … A new road mask may need to be produced, involving further administration of contrast agent and X-ray exposure." Discloses at least “adjusting/suggesting a position and/or acquisition direction of an imaging device used for generating the acquired intraoperative image”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haslam by incorporating the acquisition of at least one intraoperative image during a medical procedure that is taught by Meissner, since both reference are analogous art in the field of medical imaging for anomaly detection; thus, one of ordinary skilled in the art would be motivated to combine the references since Haslam’s automated pipeline for detecting anomalies by comparing acquired image content against a simplified standard set of parameters expected within the 3D volumes with Meissner’s real-time fluoroscopic X-ray imaging acquisition during treatment yields the predictable result of enabling the detection of deviations from standard anatomy in live intraoperative images to identify foreign objects.
Haslam in view of Meissner does not explicitly teach all of acquire, during the medical procedure, at least one intraoperative image of at least a part of a patient’s body undergoing the medical procedure;
- compare, during the medical procedure and in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content thereby automatically detecting, during the medical procedure, the at least one foreign object in the intraoperative image; and
- automatically generating, during the medical procedure and based on the detection of the at least one foreign object, a control signal.
However, Maurer teaches acquire, during the medical procedure (Maurer in ¶39 discloses “the diagnostic image data are used to track the location of the pathological anatomy during the course of delivering radiation”), at least one intraoperative image of at least a part of a patient’s body undergoing the medical procedure (Maurer in ¶53-55 discloses image guided radiation treatment and acquiring diagnostic X-ray images of the patient target during treatment);
- compare, during the medical procedure and in a calculative manner, the acquired intraoperative image with the calculated/provided expected image content (Maurer in ¶33 discloses comparing each diagnostic X-ray image to the corresponding DRR or previous diagnostic images to determine current target location and movement; ¶55-57 discloses sequential images are compared by feature recognition or pattern-intensity matching, and the detected movement is compared with predicted/expected movement) thereby automatically detecting, during the medical procedure, the at least one foreign object in the intraoperative image (Maurer in ¶56 discloses detecting whether target anatomy moved farther than expected between the images); and
- automatically generating, during the medical procedure and based on the detection of the at least one foreign object, a control signal (Maurer in ¶33, 56-57 discloses when the image comparison shows movement greater than expected, the X-ray imaging frequency is increased, and the same applies vice versa; ¶80, 83-84, 88 discloses digital processing systems control imaging operations and transfer data and commands to the X-ray source and detector).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haslam in view of Meissner by determining, during the medical procedure, whether a comparison of intraoperative X-ray images indicates patient or target anatomy movement outside an expected treatment tolerance and, based on that determination, automatically increasing or decreasing the X-ray imaging frequency that is taught by Maurer, since both reference are analogous art in the field of computer assisted medical image analysis and image guided treatment; thus, one of ordinary skilled in the art would be motivated to combine the references since Haslam in view of Meissner’s automated medical image anomaly detection and real-time fluoroscopic imaging with Maurer’s detection responsive imaging frequency control yields the predictable result of detecting unexpected anatomical displacement during a medical procedure and adjusting an X-ray acquisition parameter according to detected movement, thereby maintaining current target location information and treatment accuracy while reducing unnecessary X-ray acquisitions and radiation exposure when movement is below an expected tolerance.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 21
Regarding Claim 21, Haslam in view of Meissner, further in view of Maurer teaches the medical image analysis system according to claim 19, wherein the at least one intraoperative image is a live fluoroscopy video stream (Meissner in Abstract discloses "the roadmap mask is in the form of a 2-dimensional digitally reconstructed radiograph from the same orientation as that of a fluoroscopic X-ray device which takes real-time images of the patient during treatment"; ¶42 discloses "The live fluoroscopy image shown in FIG. 2G").
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as obvious over Haslam et al (US 20210346091 A1, hereafter referred to as Haslam) in view of Meissner et al (US 20090192385 A1, hereafter referred to as Meissner), further in view of Maurer et al (US 20100067660 A1, hereafter referred to as Maurer), further in view of Falt et al (WO 2015054314 A1, hereafter referred to as Falt).
Claim 15
Regarding Claim 15, Haslam in view of Meissner, further in view of Maurer teaches the computer-implemented method according to claim 1.
Haslam in view of Meissner, further in view of Maurer does not explicitly teach all of wherein in case a body part of a medical practitioner is automatically detected in comparing the acquired intraoperative image with the calculated/provided expected image content as the at least one foreign object in the intraoperative image, the method comprises the step of:
- automatically calculating an X-ray dose, which the detected body part of the medical practitioner receives during the medical procedure.
However, Falt teaches wherein in case a body part of a medical practitioner is automatically detected in comparing the acquired intraoperative image with the calculated/provided expected image content as the at least one foreign object in the intraoperative image, the method comprises the step of:
- automatically calculating an X-ray dose, which the detected body part of the medical practitioner receives during the medical procedure (Falt in Abstract discloses "Systems and methods for determining radiation exposure during an x-ray guided medical procedure … x-ray equipment model that simulates the emission of radiation from x-ray equipment during the x-ray guided medical procedure, a human exposure model that simulates one or more human anatomies during the x-ray guided medical procedure, a radiation metric processor that calculates at least one radiation exposure metric, and a feedback system for outputting information based on the at least one radiation exposure metric. The radiation metric processor calculates radiation exposure metrics based on input parameters that correspond to operating settings as well as the location and structure of one or more human anatomies.").
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Haslam in view of Meissner, further in view of Maurer by incorporating the step of automatically calculating an X-ray dose that the medical practitioner receives during the medical procedure that is taught by Falt, since both reference are analogous art in the field of guided medical procedures involving intraoperative imaging; thus, one of ordinary skilled in the art would be motivated to combine the references since Haslam in view of Meissner, further in view of Maurer’s automated pipeline for detecting anomalies in intraoperative images by comparing against expected content with Falt’s radiation metric processor for calculating exposure metrics based on detected anatomical structures and procedure parameters yields the predictable result of enhancing procedural safety by quantifying radiation risks to medical personnel in real-time when anomalies like practitioner body parts are identified.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 16
Regarding Claim 16, Haslam in view of Meissner, further in view of Maurer, further in view of Falt teaches the computer-implemented method according to claim 15,
wherein the automatic calculation of the X-ray dose uses a power of the X-ray device (Falt in Abstract discloses "The radiation metric processor calculates radiation exposure metrics based on input parameters that correspond to operating settings as well as the location and structure of one or more human anatomies"; ¶9 discloses "can change the operating settings of the x-ray equipment during the course of the fluoroscopic procedure … There are a number of different input parameters that correspond to operating settings that may impact the level of radiation exposure delivered to the patient or the medical team … the radiation dose rate may be impacted by x-ray tube voltages and currents"),
a surface area of the detected body part of the medical practitioner (Falt in Abstract discloses "The radiation metric processor calculates radiation exposure metrics based on input parameters that correspond to operating settings as well as the location and structure of one or more human anatomies"; ¶9 discloses "can change the operating settings of the x-ray equipment during the course of the fluoroscopic procedure … There are a number of different input parameters that correspond to operating settings that may impact the level of radiation exposure delivered to the patient or the medical team"; ¶121 discloses "the surface area locations where radiation enters the patient may be calculated" - calculates surface area of detected body parts in an X-ray dose exposure method by using 3D human exposure models, which a POSITA would apply to a medical practitioner given the reference specifically aims to determine radiation exposure delivered to the medical team), and
an exposure time of the detected body part of the medical practitioner (Falt in Abstract discloses "The radiation metric processor calculates radiation exposure metrics based on input parameters that correspond to operating settings as well as the location and structure of one or more human anatomies"; ¶9 discloses "can change the operating settings of the x-ray equipment during the course of the fluoroscopic procedure … There are a number of different input parameters that correspond to operating settings that may impact the level of radiation exposure delivered to the patient or the medical team"; ¶142 discloses "Over the course of an entire procedure, the timeline will contain metrics at different points in time which can be used to visualize the relationship between a set of control input parameters and dose rate over time." - calculates dose as a function of time for detected body parts in an X-ray dose exposure method by using 3D human exposure models, which a POSITA would apply to a medical practitioner given the reference specifically aims to determine radiation exposure delivered to the medical team).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN P CASCAIS whose telephone number is (703) 756-5576. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/J.P.C./Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
Date: 8/17/2026
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