Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/18/2024 was filed and is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Status
Claims 1-16 are pending in the present application.
Claims 14-15 are objected to because of some informalities in need of correction..
Claim 8 and 15 are rejected under 35 USC 112(b).
Claims 1-8 and 12-16 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding claims 9-11, these claims contain eligible subject matter under 35 USC 101.
Claims 1, 6-9, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Prasad et al. (US 2022/0148157 A1) in view of Wang et al. (US 2020/0051226 A1).
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable over Prasad et al. (US 2022/0148157 A1) in view of Wang et al. (US 2020/0051226 A1) and Georgakis et al. (US 2021/0183097 A1).
Claims 11 are rejected under 35 U.S.C. 103 as being unpatentable over Prasad et al. (US 2022/0148157 A1) in view of Wang et al. (US 2020/0051226 A1) and Iqbal et al. (US 2019/0278983 A1).
No prior art rejection is currently applied to claims 2-5 and 12-13.
Claim Objections
Claim 14 is objected to because of the following informalities:
“the confidence level change information is determined according to the plurality of color images, and the depth change information is determined according to the plurality of depth images.” Since the previous limitation of claim 14 uses “or” and claim 1 uses “among one of”, it is not intuitive to the examiner when reading claim 14 that the “and” needs to be treated as an “or”. Appropriate correction is required.
Claim 15 is objected to because of the following informalities:
“a controller, configured to perform the method according to any claim 1 to determine an occlusion state of a keypoint”. Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 8 and 15 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 8 recites the limitation “…when a keypoint in the second image has a depth less than that of a keypoint of the same type in the first image by a second value, and when the second value is greater than a second threshold, said keypoint is an occluded keypoint.” In Ln. 2-4 of the claim. There is insufficient antecedent basis for this limitation in the claim as “second value” and “second threshold” implies there exists “first value” and “first threshold”, both of which are not present in claims 1 and 6, the parent claims of claim 8.
Examiner recommends changing the “second value” and “second threshold” to “first depth difference value” and “first depth threshold” or something similar to remove the implication of “first value” and “first threshold”.
Claim 15 recites …determining positioning information of an object according to the occlusion state of the keypoint… Lns. 3-4 of the claim. The use of “an object” can be interpreted as introducing a new object different from the object in claim 1 or refereeing to the previously introduced object in claim 1. As such, claim 15 is rejected under 112(b) as being indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 and 12-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental processes (concepts performed in a human mind, including as an observation, evaluation, judgment, opinion, organizing human activity and/or mathematical concepts and calculations). The independent claim 1 recites a method for determining the occlusion state of keypoints. This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory).
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the independent claim 1 is directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claim 1 is directed to a method for occlusion detection in medical images.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? YES, the claims are directed toward a mental processes and/or mathematical concepts (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claim 1comprises mental processes and/or mathematical concepts that can be practicably performed in the human mind (or generic computers or components configured to perform the method) and, therefore, an abstract idea.
Regarding independent claim 1, the limitations recite:
determining an occlusion state of the keypoint (The step of determining an occlusion state of the keypoint falls into the “mental processes” grouping of abstract ideas because determining an occlusion state of the keypoint can be performed in the human mind as an observation, evaluation, judgement or opinion. A person can determine if a person is occluded by someone or something else in a photo).
These limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could mentally determine the occlusion state of the keypoint. The mere nominal recitation that the various steps are being executed by a processor does not take the limitations out of the mental process and/or mathematical concepts groupings. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Independent claim 1 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Independent claim 1 discloses:
acquiring an image sequence, the image sequence comprising a plurality of images of an object in a time dimension; and
according to at least one among confidence level change information and depth change information of a keypoint of the object in the plurality of images, which are insignificant pre-solution extra activity that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea in a method.
These limitations are recited at a high level of generality (i.e. as a general action or change being taken based on the results of the acquiring step) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity. Further, the claims are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Independent claim 1 do not recite any additional elements that are not well-understood, routine or conventional. The use of a generic computer elements are routine, well-understood and conventional process that is performed by computers.
Thus, since independent claim 1 is: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that independent claim 1 is not eligible subject matter under 35 U.S.C 101.
Regarding claim 2, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitations:
wherein the confidence level change information is change information of confidence levels of keypoints of the same type in a first image and a second image among the plurality of images,
wherein the second image follows the first image in the time dimension, merely adds definition to previous limitation that do not add a meaningful limitation to the abstract idea.
Regarding claim 3, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation:
wherein the confidence level change information comprises at least one among the difference and the ratio between the confidence levels of the keypoints of the same type in the first image and the second image falls into the mathematical concepts grouping of abstract ideas.
Regarding claim 4, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation:
wherein when a keypoint in the second image has a confidence level less than that of a keypoint of the same type in the first image by a first value, and when the first value is greater than a first threshold, said keypoint is an occluded keypoint falls into the mental processes grouping of abstract ideas.
Regarding claim 5, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation:
the keypoints and the confidence levels are generated by performing keypoint recognition on the images is insignificant pre-solution activity of gathering data that does not add a meaningful limitation to the abstract idea; and
by means of a deep learning model is generic computer component.
Regarding claim 6, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitations:
wherein the depth change information is change information of depths of keypoints of the same type in a first image and a second image among the plurality of images,
wherein the second image follows the first image in the time dimension merely adds definition to previous limitation that do not add a meaningful limitation to the abstract idea.
Regarding claim 7, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation:
wherein the depth change information comprises at least one among the difference and the ratio between the depths of the keypoints of the same type in the first image and the second image falls into the mathematical concepts grouping of abstract ideas.
Regarding claim 8, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation:
wherein when a keypoint in the second image has a depth less than that of a keypoint of the same type in the first image by a second value, and when the second value is greater than a second threshold, said keypoint is an occluded keypoint falls into the mental processes grouping of abstract ideas.
Regarding claim 12, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitations:
the first image is an image adjacent to the second image in the time dimension merely adds definition to previous limitation that do not add a meaningful limitation to the abstract idea;
the first image is an image in which keypoints in at least a partial region of the object are not occluded falls into the mental processes grouping of abstract ideas.
Regarding claim 13, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation(s) selecting at least one among the confidence level change information and the depth change information according to the type of the keypoint, and determining the occlusion state of the keypoint according to the selected information falls into the mental processes grouping of abstract ideas.
Regarding claim 14, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitations:
wherein the image sequence comprises a color image sequence, a depth image sequence, or a color image sequence and a depth image sequence corresponding to each other, the color image sequence comprises a plurality of color images, and the depth image sequence comprises a plurality of depth images merely adds definition to previous limitation that do not add a meaningful limitation to the abstract idea,
the confidence level change information is determined according to the plurality of color images, and the depth change information is determined according to the plurality of depth images falls into the mental processes grouping of abstract ideas.
Regarding claim 15, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation:
determining an occlusion state of a keypoint on the basis of the method according to claim 1; determining positioning information of an object according to the occlusion state of the keypoint falls into the mental processes grouping of abstract ideas;
performing a scanning operation according to the determined positioning information is insignificant post-solution activity of generating data that does not add a meaningful limitation to the abstract idea.
Regarding claim 16, the additional limitations do not integrate the mental process into a practical application or add significantly more to the mental process. The limitation:
a controller, configured to perform the method according to any claim 1 to determine an occlusion state of a keypoint, and to determine positioning information of an object according to the occlusion state of the keypoint falls into the mental processes grouping of abstract ideas;
a scanning assembly, which performs a scanning operation according to the determined positioning information is insignificant post-solution activity of gathering data that does not add a meaningful limitation to the abstract idea;
the controller and scanning assembly are generic computer components.
Regarding claim 9, the additional limitation the depth of the keypoint is determined according to depths of pixels within a preset region in an image, and the keypoint is located within the preset region is NOT directed toward an abstract idea since it recites additional elements that integrate the judicial exception into a practical application and add significantly more that the judicial exception. Therefore, claim 9 is not directed to an abstract idea and therefore is/are not rejected under 35 USC 101.
Regarding claims 10 and 11, the claims are dependent upon claim 9. Therefore, claims 10 and 11 are not directed to an abstract idea and therefore is/are not rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 6-9, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Prasad et al. (US 2022/0148157 A1, hereinafter Prasad) in view of Wang et al. (US 2020/0051226 A1, hereinafter Wang).
In view of the current claim language and specification, the “at least one among…and” language in claim 1 is interpreted as “at least one among…or”. Such interpretation is also used of claim 14 for the section “the confidence level change information is determined according to the plurality of color images, and the depth change information is determined according to the plurality of depth images” since claim 14 depends upon claim 1 and claim 1’s above language allows for choosing one of the “change information” despite the “and”.
In addition, claim 14 also discloses “the image sequence comprises a color image sequence, a depth image sequence, or a color image sequence and a depth image sequence corresponding to each other, the color image sequence comprises a plurality of color images, and the depth image sequence comprises a plurality of depth images”. The options of “or” renders the later “and” to be interpreted as “or” as well.
Regarding claim 1, Prasad discloses
An occlusion detection method for medical imaging, characterized by comprising: acquiring an image sequence, the image sequence comprising a plurality of images of an object in a time dimension (Para [0040]: “The method further comprises obtaining a plurality of depth images, color images and infrared images of the subject using a three-dimensional (3D) depth camera”); and
according to at least one among confidence level change information and depth change information of a keypoint of the object in the plurality of images, determining an occlusion state of the keypoint (Para [0040]: “The method further comprises identifying the anatomical key points occluded by the coils of the MRI system to determine accurate positioning of the coils of the MRI system over the subject anatomy for imaging.”, Para [0041]: “The AI based deep learning module may be further configured to identify the anatomical key points occluded by the MRI coils to determine an accurate position of the MRI coil over the subject anatomy for imaging.”, Para [0063]: “The colorized depth input and depth frames (1040) are aligned to extract patient contour in occlusion and non-occlusion states. According to an aspect of the disclosure, as show in FIG. 10(b), the AI based deep learning module is used to identify whether the patient is present within the occluded coils. To perform the patient identification, the network of FIG. 10(b) computes the probable anatomical region co-ordinates and these co-ordinates may be further fed to a region proposal network (1050) in a fixed batch size.”, Para [0068]: “In accordance with an aspect of the disclosure, FIG. 11 shows a method (1100) for determining (1110) the key points (1111) that define the anatomical points of the subject before the placement of the RF coils… Identifying the key anatomical points and orientation of patient regions may depend on the regions occluded by blanket, hospital gown or any kind of cover over the subject body while determining the accurate positioning of the RF coils of the MRI system over the subject anatomy for imaging. Various type of RF imaging coils may be occluded by blanket, hospital gown and covers and identifying the RF coils is important for accurate localization and automated landmarking during MRI exams.”).
However, Prasad does not explicitly disclose
according to at least one among confidence level change information and depth change information of a keypoint of the object in the plurality of images, determining an occlusion state of the keypoint.
Wang discloses
according to at least one among confidence level change information and depth change information of a keypoint of the object in the plurality of images, determining an occlusion state of the keypoint (Fig. 2 Steps 203-204, Para [0076]: “203. Calculate a difference between first depth data and second depth data, where the first depth data is a distance between each of the plurality of sampling points and a plane on which a camera is located, and the second depth data is a distance between the projected point corresponding to each sampling point and the plane on which the camera is located.”, Para [0080]-[0081]: “204. Determine, based on the difference, whether an obstruction exists in the area of the quadrilateral. Optionally; whether the obstruction exists in the area of the quadrilateral may be determined in the following manner:”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Prasad with determining occlusion sate of an object in an area and other aspects of Wang as both Prasad and Wang handles determining the occlusion state of an object in an image with depth data of image (Prasad Para [0063] and Wang Para [0076]).
Regarding claim 6, dependent upon claim 1, Prasad in view of Wang teaches everything regarding claim 1.
Wang further teaches
the depth change information is change information of depths of keypoints of the same type in a first image and a second image among the plurality of images, wherein the second image follows the first image in the time dimension (Fig. 2 Steps 203-204, Para [0076]: “203. Calculate a difference between first depth data and second depth data, where the first depth data is a distance between each of the plurality of sampling points and a plane on which a camera is located, and the second depth data is a distance between the projected point corresponding to each sampling point and the plane on which the camera is located.”, Para [0080]: “204. Determine, based on the difference, whether an obstruction exists in the area of the quadrilateral”).
Regarding claim 7, dependent upon claim 6, Prasad in view of Wang teaches everything regarding claim 6.
Wang further teaches
the depth change information comprises at least one among the difference and the ratio between the depths of the keypoints of the same type in the first image and the second image (Fig. 2 Steps 203-204, Para [0076]: “203. Calculate a difference between first depth data and second depth data, where the first depth data is a distance between each of the plurality of sampling points and a plane on which a camera is located, and the second depth data is a distance between the projected point corresponding to each sampling point and the plane on which the camera is located.”, Para [0080]: “204. Determine, based on the difference, whether an obstruction exists in the area of the quadrilateral”).
Regarding claim 8, dependent upon claim 6, Prasad in view of Wang teaches everything regarding claim 6.
Wang further teaches
when a keypoint in the second image has a depth less than that of a keypoint of the same type in the first image by a second value, and when the second value is greater than a second threshold, said keypoint is an occluded keypoint (Para [0082]: “If a percentage of sampling points whose difference is greater than a first threshold in a total quantity of the plurality of sampling points is greater than a second thresh old, it is determined that the obstruction exists in the area of the quadrilateral;”).
Regarding claim 9, dependent upon claim 6, Prasad in view of Wang teaches everything regarding claim 6.
Wang further teaches
the depth of the keypoint is determined according to depths of pixels within a preset region in an image, and the keypoint is located within the preset region (Para [0076]: “203. Calculate a difference between first depth data and second depth data, where the first depth data is a distance between each of the plurality of sampling points and a plane on which a camera is located, and the second depth data is a distance between the projected point corresponding to each sampling point and the plane on which the camera is located.”, Para [0081]: “Optionally; whether the obstruction exists in the area of the quadrilateral may be determined in the following manner”).
Regarding claim 14, dependent upon claim 1, Prasad in view of Wang teaches everything regarding claim 1.
Prasad further discloses
the image sequence comprises a color image sequence, a depth image sequence, or a color image sequence and a depth image sequence corresponding to each other, the color image sequence comprises a plurality of color images, and the depth image sequence comprises a plurality of depth images (Para [0040]: “The method further comprises obtaining a plurality of depth images, color images and infrared images of the subject using a three-dimensional (3D) depth camera.”).
Wang further teaches
the confidence level change information is determined according to the plurality of color images, and the depth change information is determined according to the plurality of depth images (Para [0005]: “where the first depth data is a distance between each of the plurality of sampling points and a plane on which a camera is located, and the second depth data is a distance between the projected point corresponding to each sampling point and the plane on which the camera is located”).
Regarding claim 15, dependent upon claim 1, Prasad in view of Wang teaches everything regarding claim 1.
Prasad further discloses
determining an occlusion state of a keypoint on the basis of the method according to claim 1 (Para [0040]: “The method further comprises identifying the anatomical key points occluded by the coils of the MRI system to determine accurate positioning of the coils of the MRI system over the subject anatomy for imaging.”, Para [0041]: “The AI based deep learning module may be further configured to identify the anatomical key points occluded by the MRI coils to determine an accurate position of the MRI coil over the subject anatomy for imaging.”, Para [0063]: “The colorized depth input and depth frames (1040) are aligned to extract patient contour in occlusion and non-occlusion states. According to an aspect of the disclosure, as show in FIG. 10(b), the AI based deep learning module is used to identify whether the patient is present within the occluded coils. To perform the patient identification, the network of FIG. 10(b) computes the probable anatomical region co-ordinates and these co-ordinates may be further fed to a region proposal network (1050) in a fixed batch size.”, Para [0068]: “In accordance with an aspect of the disclosure, FIG. 11 shows a method (1100) for determining (1110) the key points (1111) that define the anatomical points of the subject before the placement of the RF coils”);
determining positioning information of an object according to the occlusion state of the keypoint (Fig. 6 Step 630, Para [0069]: “The torso image (1113) of the subject defines anatomical shapes, their dimensions and identifies the location of the organs to determine accurate placement of the RF coils over the anatomy”); and
performing a scanning operation according to the determined positioning information (Fig. 6 Step 680).
Regarding claim 16, dependent upon claim 1, Prasad in view of Wang teaches everything regarding claim 1.
Prasad further discloses
a controller, configured to perform the method according to any claim 1 to determine an occlusion state of a keypoint (Para [0038]: “As used herein, the term "computer" and related terms, e.g., “computing device”, “computer system”, “processor”, “controller” are not limited to integrated circuits referred to in the art as a computer, but broadly refers to at least one microcontroller, microcomputer, programmable logic controller (PLC), application specific integrated circuit, and other programmable circuits, and these terms are used interchangeably herein”, Para [0040]: “The method further comprises identifying the anatomical key points occluded by the coils of the MRI system to determine accurate positioning of the coils of the MRI system over the subject anatomy for imaging.”, Para [0041]: “The AI based deep learning module may be further configured to identify the anatomical key points occluded by the MRI coils to determine an accurate position of the MRI coil over the subject anatomy for imaging.”, Para [0063]: “The colorized depth input and depth frames (1040) are aligned to extract patient contour in occlusion and non-occlusion states. According to an aspect of the disclosure, as show in FIG. 10(b), the AI based deep learning module is used to identify whether the patient is present within the occluded coils. To perform the patient identification, the network of FIG. 10(b) computes the probable anatomical region co-ordinates and these co-ordinates may be further fed to a region proposal network (1050) in a fixed batch size.”, Para [0068]: “In accordance with an aspect of the disclosure, FIG. 11 shows a method (1100) for determining (1110) the key points (1111) that define the anatomical points of the subject before the placement of the RF coils… Identifying the key anatomical points and orientation of patient regions may depend on the regions occluded by blanket, hospital gown or any kind of cover over the subject body while determining the accurate positioning of the RF coils of the MRI system over the subject anatomy for imaging. Various type of RF imaging coils may be occluded by blanket, hospital gown and covers and identifying the RF coils is important for accurate localization and automated landmarking during MRI exams.”), and to
determine positioning information of an object according to the occlusion state of the keypoint (Fig. 6 Step 630, Para [0069]: “The torso image (1113) of the subject defines anatomical shapes, their dimensions and identifies the location of the organs to determine accurate placement of the RF coils over the anatomy”); and
a scanning assembly, which performs a scanning operation according to the determined positioning information (Fig. 6 680, Para [0051]: “The 3D depth camera may be mounted at multiple different locations with respect to the MRI system (350) within the scanning room. Some non-limiting examples of the location of the 3D depth camera (310) include ceiling of the room, side walls, stand mount, scanner mount or lateral mount.”).
Claims 10 are rejected under 35 U.S.C. 103 as being unpatentable over Prasad et al. (US 2022/0148157 A1, hereinafter Prasad) in view of Wang et al. (US 2020/0051226 A1, hereinafter Wang) and Georgakis et al. (US 2021/0183097 A1, hereinafter Georgakis).
Regarding claim 10, dependent upon claim 9, Prasad in view of Wang teaches everything regarding claim 9.
However, Prasad in view of Wang does not explicitly teach
the keypoint is located at the center of the preset region.
Georgakis teaches
the keypoint is located at the center of the preset region (Para [0020]: “Each proposed keypoint may be contained within a local patch of a training depth image 102. In example embodiments, a patch be an N pixel
×
M pixel portion of a training depth image 102 (where N and M may be the same value or different values), and each proposed keypoint may be a center pixel of a corresponding local patch.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Prasad in view of Wang with determining keypoint as the center point of a proposed region of Georgakis to effectively increase the accuracy when identifying keypoints.
Claims 11 are rejected under 35 U.S.C. 103 as being unpatentable over Prasad et al. (US 2022/0148157 A1, hereinafter Prasad) in view of Wang et al. (US 2020/0051226 A1, hereinafter Wang) and Iqbal et al. (US 2019/0278983 A1, hereinafter Iqbal).
Regarding claim 11, dependent upon claim 9, Prasad in view of Wang teaches everything regarding claim 9.
However, Prasad in view of Wang does not explicitly teach
the depth of the keypoint is at least one of the following: an average value of the depths of the pixels within the preset region; a weighted average value of the depths of the pixels within the preset region; or a depth of the keypoint obtained by performing convolutional processing on the depths of the pixels within the preset region.
Iqbal teaches
PNG
media_image1.png
38
157
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Greyscale
the depth of the keypoint is at least one of the following: an average value of the depths of the pixels within the preset region; a weighted average value of the depths of the pixels within the preset region; or a depth of the keypoint obtained by performing convolutional processing on the depths of the pixels within the preset region (Abstract: “A neural network architecture is configured to generate a depth value for each keypoint in the captured image, even when portions of the pose are occluded, or the orientation of the object is ambiguous. Generation of the depth values enables estimation of the 3D pose of the object.”, Para [0048]: “The neural network model 210 provides a K channel output for depth maps
H
Z
r
^
, where K is the number of keypoints.”, Para [0067]: “To this end, the 2K channel output of the neural network models 210 and 212 is considered as latent variables
H
k
*
2
D
and
H
k
*
Z
r
^
for 2D heatmaps and depth maps, respectively…Finally, in an embodiment, the 2D keypoint position of the
k
t
h
keypoint is computed by the soft-argmax 215 as the weighted average of the 2D pixel coordinates
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media_image2.png
34
200
media_image2.png
Greyscale
while, in an embodiment, the corresponding depth value is obtained by the per-keypoint depth computation unit 220 as the summation of the Hadamard product of
H
k
2
D
(
p
)
and
H
k
*
Z
r
^
(
p
)
as follows
”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Prasad in view of Wang with determining the depth value of keypoints using convolution on a preset region of Iqbal to effectively increasing the robustness when detecting keypoints and pose estimating.
Relevant Prior Art Directed to State of Art
Koivisto et al. (US 12,072,442 B2, hereinafter Koivisto) is prior art not applied in the rejection(s) above. Koivisto discloses a method comprising: determining a region corresponding to a first object depicted in a training image for one or more machine learning models (MLMs ); determining the first object is depicted as being closer than a second object in the training image; assigning coverage values to spatial element regions corresponding to the training image based at least on the spatial element regions at least partially falling within the region, wherein at least one coverage value of the coverage values is assigned to a spatial element region of the spatial element regions based at least on the first object being depicted as closer than the second object in the training image; and training the one or more MLMs to infer the coverage values in association with detecting the first object in the spatial element regions.
Conclusion
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/J. C./Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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