DETAILED ACTION
Notice of AIA Status
The present application is being examined under the AIA the first inventor to file provisions.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/22/2025 has been considered by the examiner and placed in Applicant 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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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.
Claims 1 and 12, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1; recites the limitation, “…captured via an image capture apparatus ….” [Line 3-4].
Claim 12; recites the limitation, “an image capture apparatus, capturing a ….” [Line 2].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claims 1 and 12:
(i) “image capture apparatus” (Fig. 2, Paragraph [0047-0048]- image capture apparatus is described as the image capture apparatus may be a 3D camera. The 3D camera may capture the whole body of the subject in real time, to generate a video stream, each frame in the video stream including the two- dimensional optical image and the depth image. The image capture apparatus being a 3D camera is used as an example, but the present application is not limited thereto. For example, the two-dimensional optical image data and the depth image data may also be obtained by a separate 2D optical camera and a separate depth camera, respectively. No further examples will be provided herein. (Wherein, the image capture apparatus have sufficient structure associated with it, a 3D camera.).).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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.
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 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.
Claims 1-3 and 8-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by KREUGER et al. (US 20240350010 A1), hereinafter referenced as KREUGER.
Regarding claim 1, KREUGER explicitly teaches a subject tracking method for medical imaging (Fig. 4. Paragraph [0106]-KRUEGER discloses FIG. 4 shows a flowchart which illustrates a method of operating the medical system 300 of FIG. 3 (wherein medical system 300 includes a magnetic resonance imaging system). In paragraph [0109]-KREUGER discloses a patient setup surveillance workflow-analyzing camera (camera system 324) is proposed that analyzes the position and orientation of the patient's limbs even when partly covered by blankets or coils. Such a camera can estimate the risk of looping or touching body parts that have highest probability of leading to safety-relevant body poses, e.g. like touching hands or looping arms or legs. The camera will run algorithms to detect position and orientation of the patient body joints and limbs. Please also see Fig. 1-3), characterized in that the method comprises:
acquiring a plurality of frames of image data (Fig. 5, #124 called a camera image. Paragraph [0106]. Further in paragraph [0116]-KRUEGER discloses the camera system 324 is streaming images to the processing unit (computational system 104) during patient setup. In case the camera is also providing 3D data (e.g. in form of a depth map), these additional data can be used to refine the computation of the body part presence regions. Please also see Fig. 2 and read paragraph [0096]) containing a subject (Fig. 5-9, #318 called a subject. Paragraph [0110]-KRUEGER discloses FIG. 5 shows an example of a camera image 502 that has the set of anatomical keypoint coordinates 126 superimposed upon it. Within the image the subject 318 reposing on the subject support 320 is visible), which are captured via an image capture apparatus (Fig. 3, #324 called a camera system. Paragraph [0097]-KRUEGER discloses the medical system 300 comprises a magnetic resonance imaging system 304 and a camera system 324. In paragraph [0106]-KRUEGER discloses in FIG. 4 the method begins with step 400. In step 400 the computational system 104 controls the camera system 324 to acquire the camera image 124);
detecting information of anatomical key points (Fig. 4, #502, #504, #506, and #508 called anatomical keypoints. Paragraph [0110]-KRUEGER discloses FIG. 5 shows an example of a camera image 502 that has the set of anatomical keypoint coordinates 126 superimposed upon it. Within the image the subject 318 reposing on the subject support 320 is visible. A number of the anatomical keypoints 502 of the set of anatomical keypoints is visible. Please also see Fig. 5-9 and read paragraph [0095]) in each of the plurality of frames of image data (Fig. 4. Paragraph [0110]-KRUEGER discloses after step 400 is performed steps 200, 202, and 204 as are illustrated in FIG. 2 are performed. In paragraph [0096]-KRUEGER discloses in step 200, the camera image 124 is received. Next, in step 202, the set of anatomical keypoint coordinates 126 are received from the anatomical keypoint locator module 122 in response to receiving the camera image 124 as input. Next, in step 204, a list of coordinates 128 is received. Please also see Fig. 2 and 5-9); and
determining a state of the subject based on the information of the anatomical key points (Fig. 4. Paragraph [0106]-KRUEGER discloses in step 402 a range of appendage coordinates are calculated from one or more appendage keypoint coordinates. In paragraph [0107]-KREUGER discloses after step 402 is performed, step 206 is performed. The list of coordinates is searched to determine if a particular body pose is detected. The particular body pose is defined by a relative position of various keypoint coordinates. Please also see Fig. 2 and read paragraph [0109 and 0116]).
Regarding claim 2, KREUGER explicitly teaches the method according to claim 1, KREUGER further teaches wherein the image data comprises at least one of two-dimensional optical image data and depth image data (Fig. 4. Paragraph [0106]-KRUEGER discloses the camera system 324 could for example be comprised of an optical camera, a color camera, an infra-red camera, and/or 3D camera. In paragraph [0116]-KRUEGER discloses in case the camera is also providing 3D data (e.g. in form of a depth map), these additional data can be used to refine the computation of the body part presence regions).
Regarding claim 3, KREUGER explicitly teaches the method according to claim 1, KREUGER further teaches wherein the information of the anatomical key points (Fig. 5, #502, #504, #506, and #508 called anatomical keypoints. Paragraph [0110]-KRUEGER discloses FIG. 5 shows an example of a camera image 502 that has the set of anatomical keypoint coordinates 126 superimposed upon it. Within the image the subject 318 reposing on the subject support 320 is visible. A number of the anatomical keypoints 502 of the set of anatomical keypoints is visible. Please also see Fig. 5-9 and read paragraph [0095]) comprises at least one of the following types of information:
anatomical types of the key points, positional coordinates of the key points (Fig. 4. Paragraph [0106]-KRUEGER discloses in step 402 a range of appendage coordinates are calculated from one or more appendage keypoint coordinates. An appendage as used herein is a hand or foot. So by determining one or more keypoint coordinates which indicate a coordinate on a hand or foot a range of coordinates for the hand or foot are calculated. This enables the prediction of the likely positions the subject will put her or his hand into during an examination.in step 402 a range of appendage coordinates are calculated from one or more appendage keypoint coordinates. Please also read paragraph [0095, 0110 and 0114]), confidence levels of the key points, and depth information of the key points (Fig. 4. Paragraph [0116]-KRUEGER discloses in case the camera is also providing 3D data (e.g. in form of a depth map), these additional data can be used to refine the computation of the body part presence regions. Please also read paragraph [0103]).
Regarding claim 8, KREUGER explicitly teaches the method according to claim 1, KREUGER further teaches wherein determining a state of the subject based on the information of the anatomical key points comprises:
determining a posture change of the subject (Fig. 5-9, #318 called a subject. Paragraph [0110]) based on at least one of a change in position of an anatomical key point (Fig. 5-9, #502, #504, #506, and #508 called anatomical keypoints. Paragraph [0110]. Please also read paragraph [0095]) in the plurality of frames of image data (Fig. 5, #124 called a camera image. Paragraph [0106]. Please also see Fig. 2 and read paragraph [0096 and 0116]), a change in a distance between anatomical key points, and a change in a depth of an anatomical key point (Fig. 4. Paragraph [0106]-KRUEGER discloses in step 402 a range of appendage coordinates are calculated from one or more appendage keypoint coordinates. In paragraph [0107]-KREUGER discloses after step 402 is performed, step 206 is performed. The list of coordinates is searched to determine if a particular body pose is detected. The particular body pose is defined by a relative position of various keypoint coordinates. In paragraph [0017]-KRUEGER discloses the list of coordinates may contain sets of coordinates against which the anatomical keypoints are compared. The list of coordinates may contain relative changes between different anatomical keypoint coordinates. The list of coordinates may also contain ratios of there relative changes. In paragraph [0019]-KRUEGER discloses the list of coordinates comprises body poses defined as relative changes between the sets of anatomical keypoint coordinates. Please also see Fig. 2 and 5-9, and read paragraph [0023, 0106-0107, 0109-0110, 0112, and 0116]).
Regarding claim 9, KREUGER explicitly teaches the method according to claim 1, KREUGER further teaches wherein the anatomical key points comprise:
at least two of the top of the head, a shoulder, the nose, an eye, an ear, an arm, an elbow, a wrist, a hip, a knee, the heart, a pelvic cavity, the abdomen, the chest, and an ankle (Fig. 5, #502, #504, #506 and #508 called anatomical keypoints. Paragraph [0110]-KRUEGER discloses FIG. 5 shows an example of a camera image 502 that has the set of anatomical keypoint coordinates 126 superimposed upon it. Within the image the subject 318 reposing on the subject support 320 is visible. A number of the anatomical keypoints 502 of the set of anatomical keypoints is visible. The set of anatomical keypoints also includes the wrist keypoint coordinates 504. The wrist keypoint coordinates are an example of an appendage keypoint coordinate or a hand keypoint coordinate. The wrist keypoint coordinate 504 defines the position of the subject's hands. From this a range of hand coordinates 506 has been calculated and is displayed. This shows the possible positions which the subject 318 could put her or his fingers into. The range of hand coordinates 506 overlap and touching fingers 508 are visible (wherein keypoints #502, #504, #506, and #508 correspond to upper head, arm, shoulder, elbow, wrist, hand, finger, etc.). Please also see Fig. 6-9 and read paragraph [0095]).
Regarding claim 10, KREUGER explicitly teaches the method according to claim 1, KREUGER further teaches wherein the method further comprises:
after determining the state of the subject (Fig. 5-9, #318 called a subject. Paragraph [0110]. Further in paragraph [0106]-KRUEGER discloses in step 402 a range of appendage coordinates are calculated from one or more appendage keypoint coordinates. In paragraph [0107]-KREUGER discloses after step 402 is performed, step 206 is performed. The list of coordinates is searched to determine if a particular body pose is detected. The particular body pose is defined by a relative position of various keypoint coordinates. After step 206 is performed, step 208 is performed as was illustrated in FIG. 2. In paragraph [0096]-KRUEGER discloses in step 206, the list of coordinates 128 is searched to determine a match with the set of anatomical keypoint coordinates 126. Finally, in step 208, the warning signal 130 is provided if a match is determined. Please also see Fig. 2 and 4, and read paragraph [0095, 0109-0110 and 0116]), performing at least one of triggering a display change of a user interface of a medical imaging system, triggering automatic positioning processing, triggering processing for automatically identifying an orientation of the subject (Fig. 2. Paragraph [0116]-KREUGER discloses the camera system 324 is streaming images to the processing unit (computational system 104) during patient setup. The position of the body joints is detected automatically by a detection algorithm. This algorithm computes spatial probability maps of joint presence. The segments connecting two joints can be directly detected (the limbs thereafter). Based on the size and the orientation of the body segments a region representative of the likelihood of the presence is computed. In case the camera is also providing 3D data (e.g. in form of a depth map), these additional data can be used to refine the computation of the body part presence regions. If the computed regions overlap or are very close to each other, the pose configuration is flagged as potentially hazardous incl. localization info to guide the operator to solve the conflict. Please also see Fig. 4-5), triggering a blanket and coil occlusion algorithm (Fig. 2. Paragraph [0108]-KREUGER discloses the operator may place surface coils on or adjacent to the anatomy to be imaged. The patient setup tends to be complex and includes blankets, cushions, hearing protection, nurse call. In paragraph [0112]-KREUGER discloses FIG. 7 illustrates a further example of a camera image with the set of anatomical keypoints 502, 504 superimposed on the image 700. The subject is partially covered with a blanket. FIG. 7 shows a similar situation with a blanket obscuring the arms. In all 3 images, the color overlays represent the computed probability maps for the joints and limbs and the yellow half-circles show the estimated possible movement range. The camera will detect such situations with residual risk of unsafe situations. Please also see Fig. 4-5), or issuing an alert (Fig. 2. Paragraph [0096]-KREUGER discloses in step 206, the list of coordinates 128 is searched to determine a match with the set of anatomical keypoint coordinates 126. In step 208, the warning signal 130 is provided if a match is determined (wherein the warning may be audible or rendered on a display). Please also see Fig. 4-5, and read paragraph [0044-0045]).
Regarding claim 11, KREUGER explicitly teaches a computer-readable storage medium, KREUGER further teaches the computer-readable storage medium (Fig. 1, #110 called memory. Paragraph [0094]-KREUGER discloses the computational system 104 is further shown as being connected to a memory 110. The memory 110 represents the various types of memory such as a hard drive, RAM or non-transitory storage medium that could be in communication with the computational system 104) comprising a stored computer program (Fig. 1, #120 called executable instructions. Paragraph [0094]), wherein the subject tracking method for medical imaging according to claim 1 (Please see the rejection for claim 1 further above) is performed when the computer program is run (Fig. 1. Paragraph [0094]-KREUGER discloses the memory 110 is shown as containing machine-executable instructions 120. The machine-executable instructions 120 enable the computational system 104 to perform various computational and data processing tasks. The machine-executable instructions 120 may also enable the computational system 104 to control other components of the medical system 100. Please also see Fig. 2-4, and read paragraph [0067-0068]).
Regarding claim 12, KREUGER explicitly teaches a medical imaging system (Fig. 3, #300 called a medical system 300. Paragraph [0097]), KREUGER further teaches characterized in that the system comprises:
an image capture apparatus (Fig. 3, #324 called a camera system. Paragraph [0097]-KRUEGER discloses the medical system 300 comprises a magnetic resonance imaging system 304 and a camera system 324), capturing a plurality of frames of image data (Fig. 5, #124 called a camera image. Paragraph [0106]. Further in paragraph [0116]-KRUEGER discloses the camera system 324 is streaming images to the processing unit (computational system 104) during patient setup. In case the camera is also providing 3D data (e.g. in form of a depth map), these additional data can be used to refine the computation of the body part presence regions. Please also see Fig. 2 and read paragraph [0096]) containing a subject (Fig. 5, #318 called a subject. Paragraph [0110]-KRUEGER discloses FIG. 5 shows an example of a camera image 502 that has the set of anatomical keypoint coordinates 126 superimposed upon it. Within the image the subject 318 reposing on the subject support 320 is visible); and
a controller (Fig. 1, #104 called a processing unit. Paragraph [0033]-KRUEGER discloses the computer 102 is shown as comprising a computational system 104 which may represent one or more computational systems or processors located at one or more locations. The computational system 104 is connected to an optional hardware interface 106. If there are other components of the medical system 100 such as a medical imaging system, the hardware interface 106 may enable the computational system 104 to communicate with these other components. Please also see Fig. 3-5), connected to the image capture apparatus and used to execute the subject tracking method for medical imaging according to claim 1 (Fig. 1. Paragraph [0067]-KRUEGER discloses a ‘computational system’ as used herein encompasses an electronic component which is able to execute a program or machine executable instruction or computer executable code. In paragraph [0068]-KREUGER discloses machine executable instructions or computer executable code may comprise instructions or a program which causes a processor or other computational system to perform an aspect of the present invention. Please also see Fig. 2-4, and read paragraph [0094 and 0116]).
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 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 of this title, 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 4 is rejected under 35 U.S.C. 103 as being unpatentable over KREUGER et al. (US 20240350010 A1), hereinafter referenced as KREUGER in view of SOMMER et al. (US 20230414183 A1), hereinafter referenced as SOMMER.
Regarding claim 4, KREUGER explicitly teaches the method according to claim 1, KREUGER fails to explicitly teach wherein the method further comprises: selecting an anatomical key point having a confidence level higher than a threshold from the detected anatomical key points; and determining the state of the subject based on information of the selected anatomical key point.
However, SOMMER explicitly teaches wherein the method (Fig. 3. Paragraph [0037]-SOMMER discloses FIG. 3 is a schematic view of the method. The computer-implemented method is used for preparing a subject in medical imaging. The subject is in the present case patient) further comprises:
selecting an anatomical key point (Fig. 3. Paragraph [0038]-SOMMER discloses in step S20 a position of at least one landmark from the series of images, wherein the at least one landmark is anatomically related to a target anatomy is obtained. The position is obtained by an image analysis algorithm. In step S30 a confidence level assigned to the position of the at least one landmark is obtained. In paragraph [0039]-SOMMER discloses in step S40 the position of the target anatomy based on the position of the at least one landmark, and the confidence level is determined. In image 3 of FIG. 2 for example the position and confidence level of the foot ankle and the position and confidence level of the right hip are used to determine the position of right knee) having a confidence level higher than a threshold from the detected anatomical key points (Fig. 3. Paragraph [0039]-SOMMER discloses as the confidence level of the foot ankle is below a predetermined threshold with the value 0.85, the position of the foot ankle in this image is not used for determining the position of the right knee. Instead, the position of the foot ankle in image 2 of FIG. 2 is used and the position of the right hip in image 3 of FIG. 2, as the confidence level of the position the right hip in image 3 is above the predetermined threshold. Please also read paragraph [0036-0038]); and
determining the state of the subject based on information of the selected anatomical key point (Fig. 3. Paragraph [0039]-SOMMER discloses in step S40 the position of the target anatomy based on the position of the at least one landmark, and the confidence level is determined. In a step S50, the position of the target anatomy for preparing the subject in medical imaging is provided. The information of the position of target anatomy may be transmitted to a control of the imaging system or the imaging unit. The information of the position of the target anatomy may displayed on a screen to guide a medical assistant. Please also read paragraph [0036-0038]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KREUGER of having a subject tracking method for medical imaging, with the teachings of SOMMER of having wherein the method further comprises: selecting an anatomical key point having a confidence level higher than a threshold from the detected anatomical key points; and determining the state of the subject based on information of the selected anatomical key point.
Wherein KREUGER’s method having wherein the method further comprises: selecting an anatomical key point having a confidence level higher than a threshold from the detected anatomical key points; and determining the state of the subject based on information of the selected anatomical key point.
The motivation behind the modification would have been to obtain a method that improves the quality and safety of medical image scanning, since both KREUGER and SOMMER both concern medical imaging. Wherein KREUGER’s systems and methods improve the quality of medical imaging and patient safety by assessing and predicting patient movement during scanning, while SOMMER provides systems and methods that improves the patient imaging preparation and the accuracy of determining the position of target anatomy. Please see KREUGER et al. (US 20240350010 A1), Abstract and Paragraph [0041-0042] and SOMMER et al. (US 20260051035 A1), Abstract and Paragraph [0006, 0013 and 0020].
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over KREUGER et al. (US 20240350010 A1), hereinafter referenced as KREUGER in view of SOMMER et al. (US 20260137336 A1), hereinafter referenced as SOMMER (2026).
Regarding claim 5, KREUGER explicitly teaches the method according to claim 1, KRUEGER explicitly teaches wherein determining a state of the subject (Fig. 5-9, #318 called a subject. Paragraph [0110]) based on the information of the anatomical key points (Fig. 5, #502, #504, #506, and #508 called anatomical keypoints. Paragraph [0110]. Further in paragraph [0106]-KRUEGER discloses FIG. 4 shows a flowchart which illustrates a method of operating the medical system 300 of FIG. 3. Please also see Fig. 2 and 4, and read paragraph) comprises:
determining a reference position of the subject in each frame of image data (Fig. 5, #124 called a camera image. Paragraph [0106]-KRUEGER discloses in step 400 the computational system 104 controls the camera system 324 to acquire the camera image 124. After step 400 is performed steps 200, 202, and 204 as are illustrated in FIG. 2 are performed. Please also see Fig. 2 and read paragraph [0096 and 0116]) based on the information of the anatomical key points (Fig. 4. Paragraph [0110]. Further in paragraph [0106]-KRUEGER discloses in step 402 a range of appendage coordinates are calculated from one or more appendage keypoint coordinates. In paragraph [0107]-KREUGER discloses after step 402 is performed, step 206 is performed. The list of coordinates is searched to determine if a particular body pose is detected. The particular body pose is defined by a relative position of various keypoint coordinates. Please also see Fig. 2 and 5-9, and read paragraph [0095, 0109-0110 and 0116]); and
KREUGER fails to explicitly teach determining, based on the reference positions in the plurality of frames of image data, that the state of the subject is "no movement" or "moving".
However, SOMMER (2026) explicitly teaches determining, based on the reference positions in the plurality of frames of image data (Fig. 5. Paragraph [0108]-SOMMER discloses for each frame or image pair obtained from the two cameras, dedicated processing is applied, as shown in FIG. 2. As a first step, a pose detection neural network is used to localize anatomical landmarks of the patient (wherein a first millimetre wave and/or terahertz image of the patient on the patient support of the medical imaging unit is acquired at a first time frame and a second time frame after the first time frame, and the locations of landmarks are identified in both frames)), that the state of the subject is "no movement" or "moving" (Fig. 5. Paragraph [0109]-SOMMER discloses once an occlusion is placed onto the patient (blanket in FIG. 2, bottom row), the neural network is unable to detect all anatomical landmarks. In this case, the mm-wave data of the current frame is compared to the stored mm-wave data of the available un-occluded frames. If the mm-wave data is similar (up to a pre-defined threshold), patient motion between the two frames can be ruled out, and the stored coordinates of the occluded landmarks are transferred to the current frame. If the mm-wave data between the two frames shows marked deviations, the operator is informed that patient motion has occurred after placement of the occlusion. It is to be noted that a suitable threshold for millimetre-wave data similarity can be easily calibrated in a small volunteer study with instructed motion profiles, allowing to observe typical millimetre-wave data variations for the motion and no-motion cases).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KREUGER of having a subject tracking method for medical imaging, with the teachings of SOMMER (2026) of having determining, based on the reference positions in the plurality of frames of image data, that the state of the subject is "no movement" or "moving".
Wherein KREUGER’s method having determining, based on the reference positions in the plurality of frames of image data, that the state of the subject is "no movement" or "moving".
The motivation behind the modification would have been to obtain a method that improves the quality and safety of medical image scanning, since both KREUGER and SOMMER (2026) both concern medical imaging. Wherein KREUGER’s systems and methods improve the quality of medical imaging and patient safety by assessing and predicting patient movement during scanning, while SOMMER (2026) provides systems and methods that improves the ability to obtain the pose of patients prior to medical image scans with or without occlusions. Please see KREUGER et al. (US 20240350010 A1), Abstract and Paragraph [0041-0042] and SOMMER et al. (US 20260137336 A1), Abstract and paragraph [0002-0004, 0012 and 0014].
Regarding claim 6, KREUGER in view of SOMMER (2026) explicitly teaches the method according to claim 5, KREUGER further teaches wherein the method further comprises:
determining, based on at least one of a result of comparison between the reference positions in the plurality of frames of image data and a standard position, or depth information of the reference positions in the plurality of frames of image data, a moving direction of the subject or a position of the subject when there is no movement (Fig. 4. Paragraph [0023]-KRUEGER discloses detection of the body pose is at least partially performed using the three-dimensional surface image to detect limb locations by matching the three-dimensional surface image to a set of predetermined surface images each depicting a different body pose. In paragraph [0109]-KREUGER discloses a patient setup surveillance workflow-analyzing camera (camera system 324) is proposed that analyzes the position and orientation of the patient's limbs even when partly covered by blankets or coils. Such a camera can estimate the risk of looping or touching body parts that have highest probability of leading to safety-relevant body poses, e.g. like touching hands or looping arms or legs. The camera will run algorithms to detect position and orientation of the patient body joints and limbs. From this the patient size is known and also the direction and (potentially touching) endpoints of the partly hidden limbs can be estimated. Please also see Fig. 2 and 5-9, and read paragraph [0017, 0019, and 0116]).
Regarding claim 7, KREUGER explicitly teaches the method according to claim 1,
KREUGER fails to explicitly teach wherein determining a state of the subject based on the information of the anatomical key points comprises: determining, based on a change in the quantity of anatomical keypoints and a change in positions of the anatomical key points in the plurality of frames of image data, whether the state of the subject is "partially occluded".
However, SOMMER explicitly teaches wherein determining a state of the subject (Fig. 1. Paragraph [0106]-SOMMER discloses FIG. 5 shows a workflow associated with a detailed embodiment of the apparatus, system and method, that utilizes millimetre wave imagery along with visible imagery in combination and acquired before and after a patient becomes partially occluded in order to determine a pose of the occluded patient. In paragraph [0107]-SOMMER discloses a RGB visible camera, for example mounted on the ceiling of the room within which a medical image acquisition unit is located acquires high resolution visible imagery of a patient on a table being prepared to be placed within the medical image acquisition unit for an image scan (wherein the medical image acquisition unit may be an MRI, PET, and/or CT scanner, for example)) based on the information of the anatomical key points (Fig. 5. Paragraph [0066]-SOMMER discloses the first mm wave and/or terahertz image and the associated visible or infrared image are used to determine locations of landmark features such as ankles, knees, hips, writs, elbows, shoulders and head of a patient, where the high resolution visible/infrared image helps locate the landmarks within the mm wave and/or terahertz image. Then in effect this combined mm Wave(Terahertz)/visible or infrared image and its landmarks can be used to aid location of the same landmarks of the patient in a subsequent mm wave image of the patient, where the patient is now partially occluded. This facilities the accurate determination of the pose of the patient when they are partially occluded) comprises:
determining, based on a change in the quantity of anatomical keypoints (Fig. 5. Paragraph [0108]-SOMMER discloses for each frame or image pair obtained from the two cameras, dedicated processing is applied, as shown in FIG. 2. As a first step, a pose detection neural network is used to localize anatomical landmarks of the patient (wherein a first millimetre wave and/or terahertz image of the patient on the patient support of the medical imaging unit is acquired at a first time frame and a second time frame after the first time frame, and the locations of landmarks are identified in both frames). If the patient is not occluded, all landmarks of the patient are detectable by the network (top row in FIG. 2). The acquired mm-wave data for this timepoint is stored along with the detected landmarks to be used for later frames. In paragraph [0109]-SOMMER discloses once an occlusion is placed onto the patient (blanket in FIG. 2, bottom row), the neural network is unable to detect all anatomical landmarks. In this case, the mm-wave data of the current frame is compared to the stored mm-wave data of the available un-occluded frames) and a change in positions of the anatomical key points in the plurality of frames of image data (Fig. 5. Paragraph [0109]-SOMMER discloses if the mm-wave data is similar (up to a pre-defined threshold), patient motion between the two frames can be ruled out, and the stored coordinates of the occluded landmarks are transferred to the current frame. If the mm-wave data between the two frames shows marked deviations, the operator is informed that patient motion has occurred after placement of the occlusion. It is to be noted that a suitable threshold for millimetre-wave data similarity can be easily calibrated in a small volunteer study with instructed motion profiles, allowing to observe typical millimetre-wave data variations for the motion and no-motion cases), whether the state of the subject is "partially occluded" (Fig. 5. Paragraph [0110]-SOMMER discloses thus, a pose detection neural network is used to localize anatomical landmarks in the RGB data while patient is not occluded. Both these landmarks as well as the corresponding millimetre wave sensor data is stored. Once an occlusion is placed on the patient, the current mm-wave sensor is compared to the stored data from the un-occluded frame. If no change is detected, the stored landmarks are transferred to the current frame. Therefore, it would have been obvious to a person of ordinary skill in the art to modify SOMMER to determine, based on a change in the quantity of anatomical keypoints and a change in positions of the anatomical key points in the plurality of frames of image data, whether the state of the subject is "partially occluded". SOMMER explicitly teaches determining a change in quantity and position of landmarks, determining whether a partial occlusion has occurred based on a change in the quantity of detectable landmarks and determining whether the motion occurred before or after an occlusion in order to update the positions of occluded/un-occluded landmarks. In addition, it is well-known that occlusions in this context can generally only result from either a change in position of the subject and/or a change in the presence of an occluding object, and both are represented by the position and/or visibility of keypoints. Thus, it would be obvious to a person of ordinary skill to determine whether the subject is partly occluded based on both a change in quantity and position of anatomical keypoints because this would further improve the efficiency and accuracy for identifying occlusions. Please also read paragraph [0111]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of KREUGER of having a subject tracking method for medical imaging, with the teachings of SOMMER (2026) of having wherein determining a state of the subject based on the information of the anatomical key points comprises: determining, based on a change in the quantity of anatomical keypoints and a change in positions of the anatomical key points in the plurality of frames of image data, whether the state of the subject is "partially occluded".
Wherein KREUGER’s method having wherein determining a state of the subject based on the information of the anatomical key points comprises: determining, based on a change in the quantity of anatomical keypoints and a change in positions of the anatomical key points in the plurality of frames of image data, whether the state of the subject is "partially occluded".
The motivation behind the modification would have been to obtain a method that improves the quality and safety of medical image scanning, since both KREUGER and SOMMER (2026) both concern medical imaging. Wherein KREUGER’s systems and methods improve the quality of medical imaging and patient safety by assessing and predicting patient movement during scanning, while SOMMER (2026) provides systems and methods that improves the ability to obtain the pose of patients prior to medical image scans with or without occlusions. Please see KREUGER et al. (US 20240350010 A1), Abstract and Paragraph [0041-0042] and SOMMER et al. (US 20260137336 A1), Abstract and paragraph [0002-0004, 0012 and 0014].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant`s disclosure.
MIRI (US 20240328784 A1)- A tracking device and method of using the same includes a sensor generating inertial signals. The device further includes a controller in communication with the sensor. The controller determines a first position based on the inertial signals relative to a registration position........................... Please see Fig. 1A and para. [0029, 0032-0035]. Abstract.
XU et al. (US 20200402272 A1)- The present invention relates to a method and system for automatically setting a scan range. The method comprises: receiving an RGB image and a depth image of an object positioned on a scan table, respectively, by an RGB image prediction model and a depth image prediction model; generating an RGB prediction result based on the RGB image and a depth prediction result based on the depth image with respect to predetermined key points of the object, respectively, by the RGB image prediction model and the depth image prediction model; selecting a prediction result for setting the scan range from the RGB prediction result and the depth prediction result; and automatically setting the scan range based on the selected prediction result........................ Please see Fig. 1-5. Abstract.
PRASAD et al. (US 20220148157 A1)- Systems and methods for automated patient anatomy and orientation identification using an artificial intelligence (AI) based deep learning module are provided. The method comprises positioning a subject over a table of a magnetic resonance imaging (MRI) system and wrapping at least one radiofrequency (RF) imaging coil over the subject. The method comprises obtaining a plurality of depth images, color images and infrared images of the subject using a three-dimensional (3D) depth camera and identifying the table boundary of the MRI system using the images obtained by the 3D camera. The method further comprises identifying a location of the subject over the table to determine if the subject is positioned within the table boundary of the MRI system and identifying a plurality of key anatomical points or regions corresponding to a plurality of organs of the subject body. The method further comprises identifying all DICOM orientations of the subject over the table of the MRI system and identifying the coils of the MRI system wrapped around the subject body and determining the orientation of the subject with respect to the coils of the MRI system, hospital gown, and blankets. The method further comprises identifying the anatomical key points occluded by the coils of the MRI system, hospital gown, and blankets to determine accurate positioning of the coils of the MRI system over the subject anatomy for automated landmarking of anatomies and imaging......................... Please see Fig. 11-12. Abstract.
KRUEGER et al. (US 20230410346 A1)- A system for obtaining object keypoints for an object in a medical scanner, wherein the object keypoints are three dimensional, 3D, coordinates with respect to the medical scanner of pre-determined object parts. The system comprises a camera system for obtaining two dimensional, 2D, images of the object in the medical scanner, wherein the camera system comprises one or more cameras, and a processor. The processor is configured to obtain scanner variables from the medical scanner, wherein the scanner variables include the position of a part of the medical scanner which determines a relative position between the cameras in the camera system and the object. The processor determines object keypoint projections in 2D coordinates based on the 2D images from the camera system and determines the object keypoints in 3D coordinates by triangulating the object keypoint projections with respect to the camera system based on the scanner variables........................ Please see Fig. 1-2 and para. [0032 and 0067-0074]. Abstract.
Karanam et al. (US 20240177326 A1)- A human model such as a 3D human mesh may be generated for a person in a medical environment based on one or more images of the person. The images may be captured using a sensing device that may be attached to an existing medical device such as a medical scanner in the medical environment. Such an arrangement may ensure that unblocked views of the person (e.g., body keypoints of the person) may be obtained and used to generate the human model. The position of the medical device in the medical environment may be determined and used to facilitate the human model construction such that the pose and body shape of the person in the medical environment may be accurately represented by the human model.......................... Please see Fig. 2. Abstract.
BYSTROV et al. (US 20260076630 A1)- A device (10), system, method and computer-program product are disclosed for detecting movement of a subject in a diagnostic imaging examination. The device comprises a camera/3D surface scanning system (11) and an input (14) for receiving a trigger to indicate that a current spatial configuration of the subject is to be maintained for the examination. The device comprises a processor (12) and an output (13), in which the processor (12) is adapted for acquiring reference data of the subject using the camera/3D system when said trigger is received, and for acquiring further data of the subject using the camera/3D system after acquiring said reference data. The processor is adapted to compare the reference data, which represents a reference state of the subject at substantially the time that the trigger was received, to the further data, which represents a more recent state of the subject. The processor is adapted to provide output data via said output (13), in which this output data is representative of the comparison of the further data to the reference data so as to indicate movement of the subject with respect to the reference state of the subject....................... Please see Fig. 1-3 and para. [0080, 0105, 0116 and 0121-0124]. Abstract.
GLIMBERG et al. (US 20250060443 A1)- A method of and an apparatus for motion tracking of a subject located in a scanner are presented. The method comprises generating a baseline 3D surface representation of a surface region of the subject at a first point of time (T(0)); generating a subsequent 3D surface representation of the surface region of the subject at a subsequent point of time (T(s)); determining a best-fit registration of the subsequent 3D surface representation with at least one constraint relative to the baseline 3D surface representation and determining at least one motion tracking parameter. The method may include selecting at least one virtual feature and associating the at least one virtual feature to the baseline 3D surface representation, wherein the constraint comprises a restriction of at least one parameter of the at least one virtual feature associated to the best-fit subsequent 3D surface representation relative to the at least one parameter of the at least one virtual feature associated to the baseline 3D surface representation........................ Please see Fig. 3 and para. [0091 and 0103-0105]. Abstract.
Yerushalmy et al. (US 20230419730 A1)- Described are systems and methods directed to the processing of two-dimensional (“2D”) images of a body to determine a physical activity performed by the body, repetitions of the physical activity, whether the body is performing the physical activity with proper form, and providing physical activity feedback. In addition, the disclosed implementations are able to determine the physical activity, repetitions, and/or form through the processing of 2D partial body images that include less than all of the body of the user......................... Please see Fig. 3, and para. [0025-0035]. Abstract.
CHANG et al. (US 20200258243 A1)- Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning........................ Please see Fig. 2 and 5-7, and para. [0025 and 0041-0043]. Abstract.
SOLUCH et al. (US 20220401039 A1)- Disclosed is a system (100, 200) for giving feedback based on motion before or during medical imaging, comprising: —an optical camera device (110, 210) that is configured to generate image data of at least two images of a subject (408) or of a part of a subject (408) which can be arranged or is arranged at a subject placing location (194) of a medical imaging device (192), and—a feedback signaling unit (120, 124) that is configured to generate based on movement data obtainable from the image data a feedback signal (Si1, Si2) that is perceptible by the subject (408) and/or by an operator of a medical imaging device or by MRI technician (192)......................... Please see Fig. 4 and 5A, and para. [0119, 0121 and 0178]. Abstract.
OKADA et al. (US 20210082128 A1)- An object tracking method includes: predicting a prediction distribution which is a distribution of each of prediction pose locations of first objects in a current frame by inputting pose time-sequence data to a pose predictor; estimating a set of locations of each of second objects in the current frame by inputting the current frame to a pose estimator; obtaining identification information indicating a one-to-one correspondence between the second objects and the first objects and location information of the second objects by matching the set of locations and the prediction distribution, to output the obtained identification information and location information; and updating the pose time-sequence data by adding, to the pose time-sequence data and based on the obtained identification information and location information, data which includes coordinates indicating a pose of each of the first objects and is obtained from the predicted prediction distribution....................... Please see Fig. 6-7 and para. [0062-0063 and 0089-0090]. Abstract.
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to Aaron Bonansinga whose telephone number is (703) 756-5380 The examiner can normally be reached on Monday-Friday, 9:00 a.m. - 6:00 p.m. ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, Chineyere Wills-Burns can be reached by phone at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/AARON TIMOTHY BONANSINGA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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